MX2022013358A

Method and system for using sensor data from rehabilitation or exercise equipment to treat patients via telemedicine.

Abstract

One method includes receiving treatment data related to a user using a treatment device to carry out a treatment plan. The treatment data includes at least one of characteristics of the user, measurement information related to the user while the user uses the treatment device, characteristics of the treatment device, and the treatment plan. The method also includes generating treatment information using the treatment data and storing it, to access the treatment information on a computing device of a healthcare provider. The method also includes communicating with an interface on the healthcare provider's computing device, wherein the interface is configured to receive input about the treatment plan and modify the treatment plan in response to receipt of the information. entry of data about the treatment plan that includes at least one modification made to the treatment plan.

MX2022013358A, drawing sheet 1 — Sheet 1 of 47

Term

14.6 yearsleft in the term

Expires 22 April 2041.

30 claims: 4 independent, 26 dependent

1
Un sistema implementado por computadora, el cual comprende:un dispositivo de tratamiento configurado para ser manipulado por un usuario mientras el usuario realiza un plan de tratamiento, en donde el dispositivo de tratamiento incluye por lo menos un pedal;una interfaz de paciente asociada con el dispositivo de tratamiento, la interfaz de paciente comprende una salida configurada para presentar información de telemedicina asociada con una sesión de telemedicina;y un dispositivo de cómputo configurado para: recibir datos de tratamiento relacionados con el usuario que utiliza el dispositivo de tratamiento para realizar el plan de tratamiento, en donde los datos de tratamiento comprenden por lo menos uno de características del usuario, información de medición relacionada con el usuario mientras el usuario utiliza el dispositivo de tratamiento, características del dispositivo de tratamiento, y por lo menos un aspecto del plan de tratamiento;generar información de tratamiento con el uso de los datos de tratamiento;escribir en una memoria asociada, para acceder a la información de tratamiento en un dispositivo de cómputo de un proveedor de atención médica;comunicarse con una interfaz, en el dispositivo de cómputo del proveedor de atención médica, en donde la interfaz se configura para recibir una entrada de datos sobre el plan de tratamiento;y modificar el o los aspectos de el o los aspectos y cualquier otro aspecto del plan de tratamiento en respuesta a la recepción de una entrada de datos sobre el plan de tratamiento que incluye por lo menos una modificación hecha a el o los aspectos de el o los aspectos y cualquier otro aspecto del plan de tratamiento.
2
El sistema implementado por computadora de conformidad con la reivindicación 1, en donde el dispositivo de cómputo también se configura para controlar, mientras el usuario utiliza el dispositivo de tratamiento, y con base en el o los aspectos modificados de el o los aspectos y cualquier otro aspecto del plan de tratamiento, el dispositivo de tratamiento. QCCP Ln/Zznz/E/YIAI 254
3
El sistema implementado por computadora de conformidad con la reivindicación 1, en donde el dispositivo de cómputo también se configura para controlar, mientras el usuario utiliza el dispositivo de tratamiento durante la sesión de telemedicina, y con base en el o los aspectos modificados de el o los aspectos y cualquier otro aspecto del plan de tratamiento, el dispositivo de tratamiento.
4
El sistema implementado por computadora de conformidad con la reivindicación 1, en donde la información de medición incluye por lo menos uno de un signo vital del usuario, una frecuencia respiratoria del usuario, un ritmo cardíaco del usuario, una temperatura del usuario y una presión arterial del usuario.
5
El sistema implementado por computadora de conformidad con la reivindicación 1, en donde por lo menos parte de los datos de tratamiento corresponde con por lo menos parte de los datos de sensor de un sensor asociado con el dispositivo de tratamiento.
6
El sistema implementado por computadora de conformidad con la reivindicación 1, en donde por lo menos parte de los datos de tratamiento corresponde con por lo menos parte de los datos de sensor de un sensor asociado con un dispositivo portátil utilizado por el usuario mientras utiliza el dispositivo de tratamiento.
7
Un método que comprende:recibir datos de tratamiento relacionados con un usuario que utiliza un dispositivo de tratamiento para realizar un plan de tratamiento, en donde los datos de tratamiento comprenden por lo menos uno de características del usuario, información de medición relacionada con el usuario mientras el usuario utiliza el dispositivo de tratamiento, características del dispositivo de tratamiento, y por lo menos un aspecto del plan de tratamiento;generar información de tratamiento con el uso de los datos de tratamiento, en donde el dispositivo de tratamiento incluye una máquina de ciclismo estática;QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ 255 escribir en una memoria asociada, para acceder a la información de tratamiento en un dispositivo de cómputo de un proveedor de atención médica;comunicarse con una interfaz, en el dispositivo de cómputo del proveedor de atención médica, en donde la interfaz se configura para recibir una entrada de datos sobre el plan de tratamiento;y modificar el o los aspectos de el o los aspectos y cualquier otro aspecto del plan de tratamiento en respuesta a la recepción de la entrada de datos sobre el plan de tratamiento que incluye por lo menos una modificación hecha a el o los aspectos de el o los aspectos y cualquier otro aspecto del plan de tratamiento.
8
El método de conformidad con la reivindicación 7, también comprende controlar, mientras el usuario utiliza el dispositivo de tratamiento, y con base en el o los aspectos modificados de el o los aspectos y cualquier otro aspecto del plan de tratamiento, el dispositivo de tratamiento.
9
El método de conformidad con la reivindicación 7, también comprende controlar, mientras el usuario utiliza el dispositivo de tratamiento durante la sesión de telemedicina, y con base en el o los aspectos modificados de el o los aspectos y cualquier otro aspecto del plan de tratamiento, el dispositivo de tratamiento.
10
El método de conformidad con la reivindicación 7, en donde la información de medición incluye por lo menos uno de un signo vital del usuario, una frecuencia respiratoria del usuario, un ritmo cardíaco del usuario, una temperatura del usuario y una presión arterial del usuario.
11
El método de conformidad con la reivindicación 7, en donde por lo menos parte de los datos de tratamiento corresponde con por lo menos parte de los datos de sensor de un sensor asociado con el dispositivo de tratamiento.
12
El método de conformidad con la reivindicación 7, en donde por lo menos parte de los datos de tratamiento corresponde con por lo menos parte de los datos de sensor de un sensor asociado con un dispositivo portátil utilizado por el usuario mientras utiliza el dispositivo de tratamiento. QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ 256
13
El método de conformidad con la reivindicación 7, también comprende, mientras el usuario utiliza el dispositivo de tratamiento para realizar el plan de tratamiento, recibir datos de tratamiento posteriores relacionados con el usuario.
14
El método de conformidad con la reivindicación 13, también comprende modificar el o los aspectos modificados de el o los aspectos y cualquier otro aspecto del plan de tratamiento en respuesta a la recepción de una entrada de datos posterior sobre el plan de tratamiento que incluye por lo menos una modificación adicional hecha a el o los aspectos modificados de el o los aspectos o cualquier otro aspecto del plan tratamiento, en donde la entrada de datos posterior sobre el plan de tratamiento se basa por lo menos en uno de los datos de tratamiento y los datos de tratamiento posteriores.
15
Un medio legible por computadora, no transitorio y tangible que almacena instrucciones que, cuando se ejecutan, hacen que un dispositivo de procesamiento:reciba datos de tratamiento relacionados con un usuario que utiliza un dispositivo de tratamiento para realizar un plan de tratamiento, en donde los datos de tratamiento comprenden por lo menos uno de características del usuario, información de medición relacionada con el usuario mientras el usuario utiliza el dispositivo de tratamiento, características del dispositivo de tratamiento, y el o los aspectos de el o los aspectos y cualquier otro aspecto del plan de tratamiento, en donde el dispositivo de tratamiento incluye por lo menos un pedal;genere información de tratamiento con el uso de los datos de tratamiento;escriba en una memoria asociada, para acceder a la información de tratamiento en un dispositivo de cómputo de un proveedor de atención médica;se comunique con una interfaz, en el dispositivo de cómputo del proveedor de atención médica, en donde la interfaz se configura para recibir entradas de datos sobre el plan de tratamiento;y modifique el o los aspectos de el o los aspectos y cualquier otro aspecto del plan de tratamiento en respuesta a la recepción de una entrada de datos sobre el plan de tratamiento que incluye por lo menos una modificación hecha a el o los aspectos de el o los aspectos y cualquier otro aspecto del plan de QCCP Ln/Zznz/E/YIAI 257 tratamiento.
16
El medio legible por computadora de conformidad con la reivindicación 15, en donde el dispositivo de procesamiento también se configura para controlar, mientras el usuario utiliza el dispositivo de tratamiento, y con base en el o los aspectos modificados de el o los aspectos y cualquier otro aspecto del plan de tratamiento, el dispositivo de tratamiento.
17
El medio legible por computadora de conformidad con la reivindicación 15, en donde el dispositivo de procesamiento también se configura para controlar, mientras el usuario utiliza el dispositivo de tratamiento durante la sesión de telemedicina, y con base en el o los aspectos modificados de el o los aspectos y cualquier otro aspecto del plan de tratamiento, el dispositivo de tratamiento.
18
El medio legible por computadora de conformidad con la reivindicación 15, en donde la información de medición incluye por lo menos uno de un signo vital del usuario, una frecuencia respiratoria del usuario, un ritmo cardíaco del usuario, una temperatura del usuario y una presión arterial del usuario.
19
El medio legible por computadora de conformidad con la reivindicación 15, en donde por lo menos parte de los datos de tratamiento corresponde con por lo menos parte de los datos de sensor de un sensor asociado con el dispositivo de tratamiento.
20
El medio legible por computadora de conformidad con la reivindicación 15, en donde por lo menos parte de los datos de tratamiento corresponde con por lo menos parte de los datos de sensor de un sensor asociado con un dispositivo portátil utilizado por el usuario mientras utiliza el dispositivo de tratamiento.
21
El medio legible por computadora de conformidad con la reivindicación 15, en donde el dispositivo de procesamiento también se configura para, mientras el usuario utiliza el dispositivo de tratamiento para realizar el plan de tratamiento, recibir datos de tratamiento posteriores relacionados con el QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ 258 usuario.
22
El medio legible por computadora de conformidad con la reivindicación 21, en donde el dispositivo de procesamiento también se configura para modificar el o los aspectos modificados de el o los aspectos y cualquier otro aspecto del plan de tratamiento en respuesta a la recepción de una entrada de datos posterior sobre el plan de tratamiento que incluye por lo menos una modificación adicional hecha a el o los aspectos modificados de el o los aspectos o cualquier otro aspecto del plan tratamiento, en donde la entrada de datos posterior sobre el plan de tratamiento se basa por lo menos en uno de los datos de tratamiento y los datos de tratamiento posteriores.
23
Un sistema que comprende:un dispositivo de memoria que almacena instrucciones;y un dispositivo de procesamiento acoplado de forma comunicativa al dispositivo de memoria, el dispositivo de procesamiento ejecuta las instrucciones para: recibir datos de tratamiento relacionados con un usuario que utiliza un dispositivo de tratamiento para realizar un plan de tratamiento, en donde los datos de tratamiento comprenden por lo menos uno de características del usuario, información de medición relacionada con el usuario mientras el usuario utiliza el dispositivo de tratamiento, características del dispositivo de tratamiento, y el o los aspectos de el o los aspectos y cualquier otro aspecto del plan de tratamiento, en donde las características del dispositivo de tratamiento incluyen por lo menos un ajuste de la resistencia de por lo menos un componente del dispositivo de tratamiento;generar información de tratamiento con el uso de los datos de tratamiento;escribir en por lo menos un dispositivo de memoria y otro dispositivo de memoria asociado, para acceder a la información de tratamiento en un dispositivo de cómputo de un proveedor de atención médica;comunicarse con una interfaz, en el dispositivo de cómputo del proveedor de atención médica, en donde la interfaz se configura para recibir una entrada de datos sobre el plan de tratamiento;y modificar el o los aspectos de el o los aspectos y cualquier otro aspecto del plan de QCCP ίη/77Π7/Ε/ΥΙΛΙ 259 tratamiento en respuesta a la recepción de la entrada de datos sobre el plan de tratamiento que incluye por lo menos una modificación hecha a el o los aspectos de el o los aspectos y cualquier otro aspecto del plan de tratamiento.
24
El sistema de conformidad con la reivindicación 23, en donde el dispositivo de procesamiento también se configura para controlar, mientras el usuario utiliza el dispositivo de tratamiento, y con base en el o los aspectos modificados de el o los aspectos y cualquier otro aspecto del plan de tratamiento, el dispositivo de tratamiento.
25
El sistema de conformidad con la reivindicación 23, en donde el dispositivo de procesamiento también se configura para controlar, mientras el usuario utiliza el dispositivo de tratamiento durante la sesión de telemedicina, y con base en el o los aspectos modificados de el o los aspectos y cualquier otro aspecto del plan de tratamiento, el dispositivo de tratamiento.
26
El sistema de conformidad con la reivindicación 23, en donde la información de medición incluye por lo menos uno de un signo vital del usuario, una frecuencia respiratoria del usuario, un ritmo cardíaco del usuario, una temperatura del usuario y una presión arterial del usuario.
27
El sistema de conformidad con la reivindicación 23, en donde por lo menos parte de los datos de tratamiento corresponde con por lo menos parte de los datos de sensor de un sensor asociado con el dispositivo de tratamiento.
28
El sistema de conformidad con la reivindicación 23, en donde por lo menos parte de los datos de tratamiento corresponde con por lo menos parte de los datos de sensor de un sensor asociado con un dispositivo portátil utilizado por el usuario mientras utiliza el dispositivo de tratamiento.
29
El sistema de conformidad con la reivindicación 23, en donde el dispositivo de procesamiento también se configura para, mientras el usuario utiliza el dispositivo de tratamiento para realizar el plan de QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ 260 tratamiento, recibir datos de tratamiento posteriores relacionados con el usuario.
30
El sistema de conformidad con la reivindicación 29, en donde el dispositivo de procesamiento también se configura para modificar el o los aspectos modificados de el o los aspectos y cualquier otro 5 aspecto del plan de tratamiento en respuesta a la recepción de una entrada de datos posterior sobre el plan de tratamiento que incluye por lo menos una modificación adicional hecha a el o los aspectos modificados de el o los aspectos o cualquier otro aspecto del plan tratamiento, en donde la entrada de datos posterior sobre el plan de tratamiento se basa por lo menos en uno de los datos de tratamiento y los datos de tratamiento posteriores.

Independent claims30

1,326 paragraphs in 5 sections, as filed

METHOD AND SYSTEM FOR USING SENSOR DATA FROM REHABILITATION OR EXERCISE EQUIPMENT TO TREAT PATIENTS VIA TELEMEDICINE

BACKGROUND OF THE INVENTION

[0005] Remote medical assistance, or telemedicine, can help a patient perform various aspects of a rehabilitation regimen for a body part. The patient may use a patient interface in communication with an assistant interface to receive remote medical assistance via audio and/or audiovisual communications.

SUMMARY OF THE INVENTION

[0006] One aspect of the described modalities includes a method that includes receiving treatment data related to a user using a treatment device to carry out a treatment plan. The treatment data includes at least one of characteristics of the user, measurement information related to the user while the user uses the treatment devices, characteristics of the treatment device, and the treatment plan. The method also includes generating treatment information using the treatment data and writing it to an associated memory, to access the treatment information on a computing device of a healthcare provider. The method also includes communicating with an interface, on the computing device of the healthcare provider, wherein the interface is configured to receive input of data about the treatment plan, and modifying the treatment plan in response to receipt of data. the entry of data about the treatment plan that includes at least one modification made to the treatment plan.

[0007] One aspect of the described modalities includes a computer-implemented system that includes a treatment apparatus configured to be manipulated by a patient while performing an exercise session, a patient interface configured to receive a virtual avatar. The patient interface comprises an output device configured to display the virtual avatar. The virtual avatar uses a virtual representation of the treatment device to guide the patient through an exercise session. The virtual avatar is associated with a medical professional. The system

Computer-implemented QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ includes a server computing device configured to provide the patient's virtual avatar to the patient interface, receive, from the patient interface, a message related to a trigger event and wherein The message comprises an intensity level of the triggering event, determining whether an intensity level of the triggering event exceeds a threshold intensity level and, in response to the determination that the intensity level of the triggering event exceeds the threshold intensity level, replacing in the patient interface the presentation of the virtual avatar with a presentation of a multimedia stream from a computing device of the medical professional.

[0008] One aspect of the described modalities includes a method for providing, through an artificial intelligence engine, an optimal treatment plan for use with a treatment apparatus. The method includes receiving, from a data source, clinical information relating to the results of using the treatment device to make specific treatment plans for people with certain characteristics, wherein the clinical information has a first data format, translating a portion from the clinical information of the first data format to a descriptive medical language used by the artificial intelligence engine, determine, based on the portion of clinical information described using descriptive medical language and a plurality of characteristics related to a patient, the optimal treatment plan for the patient to follow with the use of the treatment device to achieve a desired result; and provide the optimal treatment plan for presentation on a medical professional's computing device.

[0009] One aspect of the described modalities includes a method for providing, through an artificial intelligence engine, an optimal treatment plan for use with a treatment apparatus. The method includes receiving, from a data source, clinical information related to the results of having used the treatment device to make specific treatment plans for people who have certain characteristics, wherein the clinical information has a first data format, translating a portion of the clinical information from the first data format to a descriptive medical language used by the artificial intelligence engine, determine, based on the portion of clinical information described using descriptive medical language and a plurality of characteristics related to a patient, the optimal treatment plan for the patient to follow ocher ίη/ζζηζ/Ε/γίΛΐ with the use of the treatment to achieve a desired outcome, and provide the optimal treatment plan for presentation on a computing device to a medical professional.

[0010] Another aspect of the described embodiments includes a system that includes a processing device and a memory communicatively coupled to the processing device and capable of storing instructions. The processing device executes instructions to perform any of the methods, operations or steps described herein.

[0011] Another aspect of the described embodiments includes a tangible, non-transitory computer-readable medium that stores instructions that, when executed, cause a processing device to perform any of the methods, operations or steps described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The disclosure is better understood from the following detailed description, when read in conjunction with the accompanying drawings. It is important to note that, in accordance with common practice, the various attributes of the drawings are not to scale. Rather, the dimensions of the various attributes are arbitrarily expanded or reduced for clarity.

[0013] Figure 1 generally illustrates a block diagram of one embodiment of a computer-implemented system for managing a treatment plan in accordance with the principles of the present disclosure.

[0014] Figure 2 generally illustrates a perspective view of one embodiment of a treatment device in accordance with the principles of the present description.

[0015] Figure 3 generally illustrates a perspective view of a pedal of the treatment device of Figure 2 in accordance with the principles of the present description.

[0016] Figure 4 generally illustrates a perspective view of a person using the treatment device of Figure 2 in accordance with the principles of the present description.

[0017] Figure 5 generally illustrates an exemplary embodiment of a visual presentation of general information of a wizard interface in accordance with the principles of the present disclosure.

[0018] Figure 6 generally illustrates an example of a block diagram of training a machine learning model to generate, based on data related to the

QCCP Ln/Zznz/E/YIAI patient, a treatment plan for the patient in accordance with the principles of this description. [0019] Figure 7 generally illustrates an embodiment of a visual presentation of the overview of the assistant interface that presents in real time the recommended treatment plans and the excluded treatment plans during a telemedicine session according to the principles of this description.

[0020] Figure 8 generally illustrates an embodiment of the visual presentation of the general information of the assistant interface that presents in real time, during a telemedicine session, the recommended treatment plans that have been modified as a result of the modification made to patient data in accordance with the principles of the present description.

[0021] Figure 9 is a flow chart generally illustrating a method for modifying, based on treatment data received while a user uses the treatment device of Figure 2, a treatment plan for the patient, and control, based on the modification, at least one treatment device in accordance with the principles of the present description.

[0022] Figure 10 is a flow chart generally illustrating an alternative method for modifying, based on treatment data received while a user uses the treatment device of Figure 2, a treatment plan for the patient, and controlling, based on the modification, at least one treatment device in accordance with the principles of the present description.

[0023] Figure 11 is a flow chart that generally illustrates an alternative method for modifying, based on treatment data received while a user uses the treatment device of Figure 2, a treatment plan for the patient , and control, based on the modification, at least one treatment device in accordance with the principles of the present description.

[0024] Figure 12 generally illustrates a computing system in accordance with the principles of the present description.

[0025] Figure 13 shows a block diagram of one embodiment of a computer-implemented system for managing a treatment plan in accordance with the present description.

ocher Ln/zznz/E/YiAi

[0026] Figure 14 illustrates a perspective view of one embodiment of a treatment apparatus according to the present description.

[0027] Figure 15 shows a perspective view of a pedal of the treatment apparatus of Figure 14 according to the present description.

[0028] Figure 16 shows a perspective view of a person using the treatment apparatus of Figure 14 in accordance with the present description.

[0029] Figure 17 shows an exemplary embodiment of a visual presentation of general information of a wizard interface according to the present description.

[0030] Figure 18 shows an exemplary embodiment of a visual presentation of the assistant interface overview that presents in real time the recommended optimal treatment plans and the excluded treatment plans during a telemedicine session according to the present description.

[0031] Figure 19 shows an exemplary embodiment of a server that translates clinical information into a descriptive medical language for processing by an artificial intelligence engine in accordance with the present description.

[0032] Figure 20 shows an exemplary embodiment of a method for recommending an optimal treatment plan in accordance with the present description.

[0033] Figure 21 shows an exemplary embodiment of a method for translating clinical information into descriptive medical language in accordance with the present description.

[0034] Figure 22 shows an example of a computing system according to the present description.

[0035] Figure 23 generally illustrates a block diagram of one embodiment of a computer-implemented system for managing a treatment plan in accordance with the principles of the present disclosure.

[0036] Figure 24 generally illustrates a perspective view of one embodiment of a treatment device in accordance with the principles of the present description.

[0037] Figure 25 generally illustrates a perspective view of a pedal of the treatment device of Figure 24 in accordance with the principles of the present description.

QCCP Ln/Zznz/E/YIAI

[0038] Figure 26 generally illustrates a perspective view of a person using the treatment device of Figure 24 in accordance with the principles of the present description.

[0039] Figure 27 generally illustrates an exemplary embodiment of a visual presentation of general information of a wizard interface in accordance with the principles of the present disclosure.

[0040] Figure 28 generally illustrates an example of a block diagram of training a machine learning model to generate, based on patient-related data, a treatment plan for the patient in accordance with the principles of the present description.

[0041] Figure 29 generally illustrates one embodiment of a visual presentation of the general information of the assistant interface that presents in real time the recommended treatment plans and the excluded treatment plans during a telemedicine session according to the principles of this description.

[0042] Figure 30 generally illustrates an embodiment of the visual presentation of the general information of the assistant interface that presents in real time, during a telemedicine session, the recommended treatment plans that have been modified as a result of the modification made to the patient's data in accordance with the principles of the present description.

[0043] Figure 31 is a flow chart generally illustrating a method for monitoring, based on treatment data received while a user uses the treatment device of Figure 24, the characteristics of the user while the user uses the treatment device in accordance with the principles of the present description.

[0044] Figure 32 is a flow chart that generally illustrates an alternative method for monitoring, based on treatment data received while a user uses the treatment device of Figure 24, the characteristics of the user while the user Use the treatment device in accordance with the principles of the present description.

[0045] Figure 33 is a flow chart generally illustrating an alternative method for monitoring, based on treatment data received while a user uses the treatment device of Figure 24, the characteristics of the user while the user uses the treatment device in accordance with the principles of the present description.

[0046] Figure 34 is a flow chart generally illustrating a method of receiving a

QCCP Ln/Zznz/E/YIAI selecting an optimal treatment plan and controlling, based on the optimal treatment plan, a treatment device while the patient uses the treatment device in accordance with the present description.

[0047] Figure 35 generally illustrates a computing system in accordance with the principles of the present description.

[0048] Figure 36 shows a block diagram of one embodiment of a computer-implemented system for managing a treatment plan in accordance with the present description.

[0049] Figure 37 shows a perspective view of one embodiment of a treatment apparatus according to the present description.

[0050] Figure 38 shows a perspective view of a pedal of the treatment apparatus of Figure 37 according to the present description.

[0051] Figure 39 shows a perspective view of a person using the treatment apparatus of Figure 37 in accordance with the present description.

[0052] Figure 40 shows an exemplary embodiment of a visual presentation of general information of a wizard interface according to the present description.

[0053] Figure 41 shows an example of a block diagram of training a machine learning model to generate, based on data related to the patient, a treatment plan for the patient in accordance with the present description.

[0054] Figure 42 shows an embodiment of a visual presentation of general information of the patient interface that presents a virtual avatar that guides the patient through an exercise session in accordance with the present description.

[0055] Figure 43 shows an embodiment of the assistant interface's overview information display that receives a patient-related notification and allows the assistant to initiate a real-time telemedicine session in accordance with the present description. .

[0056] Figure 44 shows a modality of the visual presentation of the general information of the patient interface that presents in real time, during a telemedicine session, a transmission of the medical professional who replaced the virtual avatar in accordance with the present description .

QCCP Ln/Zznz/E/YIAI

[0057] Figure 45 shows an exemplary embodiment of a method for replacing a virtual avatar, based on a triggering event that occurs, by transmission from a medical professional in accordance with the present description.

[0058] Figure 46 shows an exemplary embodiment of a method for providing a virtual avatar in accordance with the present description.

[0059] Figure 47 shows an example of a computer system according to the present description.

NOTATION AND NOMENCLATURE

[0060] Various terms are used to refer to specific components of the system. Different companies may refer to a component by different names; This document is not intended to make any distinction between components that differ in name but not in function. In the following description and claims, the terms “including” and “comprising” are used openly, meaning that they are to be interpreted as “including, but not limited to…”. Furthermore, the term “couples” or “couples” refers to a direct or indirect connection. Therefore, if a first device is coupled to a second device, that connection can be through a direct connection or through an indirect connection through other devices and connections.

[0061] The terminology used herein is intended only to describe specific exemplary embodiments and not to limit such embodiments. As used herein, the singular forms “a” and “the” may also include the plural forms, unless the context clearly indicates otherwise. The steps, processes and operations of the method described herein should not be construed as necessarily requiring their performance to be in the specific order described or illustrated, unless specifically indicated as an order of performance. It should also be understood that it is possible to employ additional or alternative steps.

[0062] The terms first, second, third, etc., may be used herein to describe various elements, components, regions, layers and/or sections; however, these elements, components, regions, layers and/or sections shall not be limited by these terms.

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ

These terms can only be used to distinguish between an element, component, region, layer or section from another region, layer or section. Terms such as “first,” “second,” and other numerical terms, when used herein, do not imply sequence or order unless the context clearly indicates so. Thus, a first element, component, region, layer or section described below could be referred to as a second element, component, region, layer or section without departing from the teachings of the exemplary embodiments. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more list items may be used and that only one list item may be necessary. For example, “at least one of: A, B and C” includes any of the following combinations: A, B, C, A and B, A and C, B and C, A and B and C. In another example , the phrase “one or more,” when used with a list of elements, means that there may be one element or an appropriate number of elements exceeding one.

[0063] Spatially relative terms, such as “internal”, “external”, “below”, “below”, “bottom”, “above”, “top”, “top”, “bottom” and the like, may used in this document. These spatially relative terms can be used to facilitate description and describe the relationship of one element or attribute with respect to another element or attribute, as illustrated in the figures. The spatially relative terms may also encompass different orientations of the device in use or operation, in addition to the orientation depicted in the figures. For example, if the figure device is rotated, the orientation of elements described as “below” or “below” other elements or attributes would change to “above” other elements or attributes. Therefore, the exemplary term “bottom” may encompass an up and down orientation. The device may be oriented in some other way (90 degrees of rotation or in other orientations) and the spatially relative descriptions used in this document may be interpreted accordingly.

[0064] A “treatment plan” may include one or more treatment protocols, and each treatment protocol includes one or more treatment sessions. Each treatment session comprises several session periods, where each session period includes a specific exercise to treat the patient's body part. For example, a treatment plan for rehabilitation

Postoperative QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ after knee surgery may include an initial treatment protocol with twice-daily stretching sessions for the first 3 days after surgery and a more intensive treatment protocol with exercise sessions active performed 4 times a day starting on day 4 after surgery. A treatment plan may also include information related to a medical procedure to be performed on the patient, a treatment protocol for the patient using a treatment device, a dietary regimen for the patient, a medication regimen for the patient, a sleep regimen for the patient, additional regimens, or some combination thereof. The treatment plan may also include one or more training protocols, such as strength training protocols, movement training protocols, cardiovascular training protocols, resistance training protocols, and the like. Each training protocol may include one or more training sessions comprising multiple training session periods, wherein each session period comprises a specific exercise targeting one or more of strength training, movement interval training, cardiovascular training , resistance training and the like.

[0065] The terms telemedicine, telehealth, teletherapy, telemedicine, remote medicine, etc., may be used interchangeably in this document.

[0066] The term “enhanced reality” may include a user experience, which comprises one or more of augmented reality, virtual reality, mixed reality, immersive reality, or a combination of the above (e.g., immersive augmented reality, immersive reality). mixed augmented reality, augmented and immersive reality, etc.).

[0067] The term “augmented reality” can refer, but is not limited to, an interactive user experience that provides an enhanced environment that combines elements of a real environment with computer-generated components that are perceptible to the user.

[0068] The term "virtual reality" may refer, but is not limited to, an interactive and simulated user experience that provides an enhanced environment perceptible to the user and where that enhanced environment may be similar or different to a virtual environment. from the real world.

[0069] The term “mixed reality” can refer to an interactive user experience that

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ combines aspects of augmented reality with aspects of virtual reality to offer a mixed reality environment perceptible to the user.

[0070] The term “immersive reality” can refer to an interactive and simulated user experience that uses virtual and/or augmented reality images, sounds and other stimuli to immerse the user, to the specific extent possible (e.g., partial immersion or total immersion), in the simulated interactive experience. For example, in some embodiments, to the specific extent possible, the user experiences one or more aspects of immersive reality quite naturally, as the user typically experiences corresponding aspects of the real world. Additionally, or alternatively, an immersive reality experience may include actors, a narrative component, a theme (e.g., an entertainment theme or other suitable theme), and/or other suitable features of the components.

[0071] The term “body halo” may refer to one or more hardware components, where the component(s) may include one or more platforms, one or more body supports or frames, one or more chairs or seats, one or more more back supports or back support mechanisms, one or more leg or foot support mechanisms, one or more arm or hand support mechanisms, one or more head support mechanisms, other suitable hardware components, or a combination thereof.

[0072] As used herein, the term “improved environment” may refer to an improved environment as a whole, at least one aspect of the improved environment, more than one aspect of the improved environment, or any suitable amount of aspects of the improved environment.

[0073] As used herein, the term “threshold” and/or the term “interval” may include one or more values expressed as a percentage, absolute value, unit of measurement, difference value, numerical quantity or other expression. appropriate of one or more values.

[0074] The term “optimal treatment plan” may refer to the optimization of a treatment plan based on a certain parameter or combinations of more than one parameter, such as, but not limited to, an amount of monetary value generated by a plan. of treatment or a billing sequence, where the amount of monetary value is measured by an absolute amount in dollars or other currency, a Net Present Value (NPV) or any other measure, a clinical consequence of the patient

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ as a result of the treatment plan and/or billing sequence, a fee paid to a medical professional, a payment plan for the patient to pay a sum of money owed or a portion thereof, a reimbursement plan, an amount of income, earnings or other amount of monetary value to be paid to insurance or a third-party provider, or some combination thereof.

[0075] Real time may refer to less than or equal to 2 seconds. Near real-time can refer to any interaction of a time short enough to allow two people to engage in a conversation through that user interface, and will typically be less than 10 seconds, but greater than 2 seconds.

[0076] Any of the systems and methods described in this disclosure can be used in connection with rehabilitation. Rehabilitation may be directed at cardiac rehabilitation, stroke rehabilitation, multiple sclerosis, Parkinson's disease, myasthenia gravis, Alzheimer's disease, any other neurodegenerative or neuromuscular disease, a brain injury, a spinal cord injury, a disease of the spinal cord, a joint injury, osteoarthritis or similar. Rehabilitation may also include muscle contraction to improve blood flow and lymphatic flow, activate the brain and nervous system to control and affect a traumatized area to increase the speed of healing, reverse or reduce pain (including arthralgias and myalgias) , reverse or reduce stiffness, regain range of motion, encourage cardiovascular activation to stimulate the release of pain-blocking hormones or to stimulate highly oxygenated blood flow to help support an overall sense of well-being. Rehabilitation can be provided to people of average height in reasonably good physical condition and without significant deformities, as well as people who normally need rehabilitation, such as the elderly, people with obesity, people subject to disease processes, injured people, or people who They have severely limited range of motion. Unless expressly stated otherwise, rehabilitation should be understood to include prehabilitation (also referred to as “pre-habilitation” or “prehab”). Prehabilitation can be used as a preventative procedure or as a procedure prior to surgery or treatment. Prehabilitation may include any action carried out by a patient or on a patient (or indicated to be carried out by a patient or on a patient, including, but not limited to, remotely

QCCP ίη/77Π7/Ε/ΥΙΛΙ or remotely via telemedicine) to, among other things, prevent or reduce the likelihood of injury (for example, before the injury occurs); improve recovery time after surgery; improve post-surgery strength; or any of the above with respect to any non-surgical clinical treatment plan to be undertaken for the purpose of alleviating or mitigating injury, dysfunction or other negative consequences of surgical or non-surgical treatment to any external or internal part of the body of the patient. For example, in a mastectomy, prehabilitation may be necessary to strengthen the muscles or muscle groups affected directly or indirectly by the mastectomy. In another non-limiting example, the removal of an intestinal tumor, hernia repair, open heart surgery or other procedures performed on internal organs or structures, whether to repair those organs or structures, to remove them in their entirety or a part, to treat them, etc., it may be necessary to cut, dissect and/or damage numerous muscles and muscle groups in or around, but not limited to, the skull or face, abdomen, the ribs and/or the thoracic cavity, as well as in or around all joints and appendages. Prehabilitation can improve the speed of patient recovery, measure quality of life, pain level, etc., in all of the above procedures. In one embodiment of prehabilitation, a presurgical procedure or non-presurgical treatment may include one or more sets of exercises for a patient to perform prior to such procedure or treatment. You may need to perform one or more sets of exercises to qualify for elective surgery, such as knee replacement. The patient can prepare an area of his body for the surgical procedure by performing one or more sets of exercises, thereby strengthening muscle groups, improving existing muscle memory, reducing pain, reducing stiffness, establishing new muscle memory, improving mobility (i.e., improving range of motion), improving blood flow and/or the like.

DETAILED DESCRIPTION OF THE INVENTION

[0077] The following description is directed to various embodiments of the present disclosure. Although one or more of these embodiments may be preferred, the embodiments described should not be construed or otherwise used to limit the scope of the description.

QCCP Ln/Zznz/E/YIAI including claims. Furthermore, one skilled in the art will understand that the following description has broad application, and that the description of any embodiment is only for purposes of exemplifying that embodiment and not to suggest that the scope of the description, including the claims, is limited to that embodiment. modality.

[0078] Determine a treatment plan for a patient who presents certain characteristics (for example, vital signs or other types of measurements; performance; demographic, geographic, diagnostic, measurement or test-based, medically historical, etiological, associative to cohorts, differentially diagnostic, surgical, therapeutic from a physical aspect, pharmacological and other recommended treatments, etc.) can constitute a technically difficult problem. For example, establishing a treatment plan may involve a large amount of information, which can lead to inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitation setting, some of that wealth of information considered may include patient characteristics, such as personal information, performance information, and measurement information. Personal information may include, for example, demographic, psychographic or other information, such as age, weight, gender, height, body mass index, medical condition, family medication history, injury, a medical procedure, a prescription medication, or some combination thereof. Performance information may include, for example, an elapsed time of use of a treatment device, a degree of force exerted on a portion of the treatment device, a range of motion achieved on the treatment device, a speed of movement of a part of the treatment device, an indication of a plurality of pain levels with the use of the treatment device or some combination thereof. The measurement information may include, for example, a vital sign, a respiratory rate, a heart rate, a temperature, a blood pressure, or some combination thereof. It may be desirable to process the characteristics of a large number of patients, the treatment plans made for those patients, and the results of the treatment plans for those patients.

[0079] Furthermore, another technical problem may consist of the remote treatment, through a computing device during a telemedicine or telehealth session, of a patient from a

QCCP Ln/Zznz/E/YIAI location other than the location where the patient is located. Another technical problem is controlling or enabling, from a different location, the control of a treatment device used by the patient at the location where the patient is located. Often, when a patient undergoes rehabilitation surgery (for example, knee surgery), a healthcare provider may prescribe a treatment device for the patient to use to perform a treatment protocol at home or at any mobile location or temporary home. A healthcare provider may refer a patient to a physician, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, fitness trainer, instructor, personal trainer, or the like. A healthcare provider may refer a patient to anyone who has a credential, license, degree, or similar in the field of medicine, physical therapy, rehabilitation, or the like.

[0080] When the healthcare provider is in a location other than the patient and the treatment device, it may be technically difficult for the healthcare provider to monitor the patient's actual progress (rather than relying on the patient's word). patient about his or her progress) using the treatment device, modify the treatment plan based on the patient's progress, adapt the treatment device to the personal characteristics of the patient as the patient carries out the treatment plan, and the like.

[0081] Accordingly, it may be desirable to use systems and methods, such as those described herein, that use sensor data to modify a treatment plan and/or adapt the treatment device while a patient performs the treatment plan. treatment with the use of the treatment device.

[0082] In some embodiments, the systems and methods described herein may be configured to receive treatment data related to a user while the user uses the treatment device to perform the treatment plan. The user may include a patient, user, or a person who uses the treatment device to perform various exercises. The treatment plan may be a rehabilitation treatment plan, a prehabilitation treatment plan, an exercise-based treatment plan, or another appropriate treatment plan. The treatment data may include various characteristics of the user, various measurement information related to the user while the user uses the treatment device

QCCP Ln/Zznz/E/YIAI treatment, various characteristics of the treatment device, the treatment plan, other appropriate data or a combination thereof.

[0083] In some embodiments, while the user uses the treatment device to carry out the treatment plan, at least part of the treatment data may correspond to sensor data from a sensor configured to detect various characteristics of the treatment device and /or the user's measurement information. Additionally, or alternatively, while the user uses the treatment device to carry out the treatment plan, at least some of the treatment data may correspond to sensor data from a sensor associated with a wearable device configured to detect the information. user measurement.

[0084] The various features of the treatment device may include one or more settings of the treatment device, current revolutions per time period (e.g., one minute) of a rotating member (e.g., a wheel) of the treatment device, a resistance setting of the treatment device, other suitable characteristics of the treatment device or a combination thereof. The measurement information may include one or more vital signs of the user, a respiratory rate of the user, a heart rate of the user, a temperature of the user, a blood pressure of the user, other suitable measurement information of the user, or a combination thereof. .

[0085] In some embodiments, the systems and methods described herein may be configured to generate treatment information using treatment data. The treatment information may include a summary of the user's performance of the treatment plan while using the formatted treatment device, such that the treatment data can be presented on a computing device of a healthcare provider or professional. responsible for the user's performance of the treatment plan. The terms “healthcare provider” and “healthcare professional” may be used interchangeably in this document. The health care provider or health professional may include a medical professional (for example, a doctor, a nurse, a therapist or the like), an exercise professional (for example, an instructor, a trainer, a nutritionist and the like) or another professional who possesses at least one of the medical and exercise attributes (for example, an exercise physiologist, a

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ physiotherapist, an occupational therapist and similar). As used herein, and without limiting the foregoing, a health care provider or health professional may be a human, a robot, a virtual assistant, a virtual assistant in a virtual and/or augmented reality, or an artificially intelligent entity, including a software program, integrated software and hardware, or hardware alone.

[0086] The systems and methods described herein may be configured to write the treatment information to an associated memory, to access the treatment information on the healthcare provider's computing device, and/or provide the information treatment on the healthcare provider's computing device. For example, the systems and methods described herein may be configured to provide treatment information to an interface configured to present the treatment information to the healthcare provider. The interface may include a graphical user interface configured to provide treatment information and receive data input from the healthcare provider. The interface may include one or more input fields, such as text input fields, drop-down selection input fields, radio button input fields, virtual switch input fields, virtual toggle input fields, input powered by audio, haptics, touch, biometrics and/or otherwise, other suitable input fields or a combination thereof.

[0087] In some embodiments, the healthcare provider may review the treatment information and determine whether the treatment plan and/or one or more features of the treatment device should be modified. For example, the healthcare provider may review the treatment information and compare the treatment information to the treatment plan the user is undertaking.

[0088] The healthcare provider may compare the following (i) the expected information, which is related to the user while the user uses the treatment device to perform the treatment plan, with (i¡) the measurement information (for example, as indicated by treatment information), which is related to the user while the user uses the treatment device to carry out the treatment plan. The expected information may include one or more

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ vital signs of the user, a respiratory rate of the user, a heart rate of the user, a temperature of the user, a blood pressure of the user, other appropriate information of the user or a combination thereof. The health care provider may determine that the treatment plan is having the desired effect if one or more parts or portions of the measurement information fall within an acceptable range associated with one or more corresponding parts or portions of the expected information. Conversely, the health care provider may determine that the treatment plan is not having the desired effect if one or more parts or portions of the measurement information fall outside the range associated with one or more corresponding parts or portions of the measurement information. expected information.

[0089] For example, the healthcare provider may determine whether a blood pressure value (e.g., systolic pressure, diastolic pressure, and/or differential blood pressure) corresponding to the user while the user uses the treatment device (e.g. , that indicated by the measurement information) is within an acceptable range (for example, plus or minus 1%, plus or minus 5% or any appropriate interval) of an expected blood pressure value indicated by the expected information. The healthcare provider can determine that the treatment plan is having the desired effect if the user's corresponding blood pressure value while the user uses the treatment device is within the range of the expected blood pressure value. Conversely, the healthcare provider may determine that the treatment plan is not having the desired effect if the user's corresponding blood pressure value while the user uses the treatment device is outside the range of the expected blood pressure value. .

[0090] In some embodiments, the healthcare provider may compare the expected characteristics of the treatment device while the user uses the treatment device to perform the treatment plan with the characteristics of the treatment device indicated by the treatment information. For example, the healthcare provider may compare an expected resistance setting of the treatment device to an actual resistance setting of the treatment device indicated by the treatment information. The health care provider can determine that the user is following the treatment plan correctly if the

QCCP ίη/77Π7/Ε/ΥΙΛΙ actual characteristics of the treatment device indicated by the treatment information are within a range of characteristics corresponding to the expected characteristics of the treatment device. Conversely, the healthcare provider may determine that the user is not performing the treatment plan correctly if the actual characteristics of the treatment device indicated by the treatment information are outside the corresponding characteristic range of the expected characteristics of the device. of treatment.

[0091] If the healthcare provider determines that the treatment information indicates that the user is performing the treatment plan correctly and/or that the treatment plan is having the desired effect, the healthcare provider may determine not to modify the treatment plan. treatment plan or the characteristics of the treatment device. Conversely, while the user is using the treatment device to perform the treatment plan, if the healthcare provider determines that the treatment information indicates that the user is not or has not performed the treatment plan correctly and/or that the treatment plan is not having or has not had the desired effect, The healthcare provider may determine to modify the treatment plan and/or the feature(s) of the treatment device.

[0092] In some embodiments, the healthcare provider may interact with the interface to provide input of data about the treatment plan indicating one or more modifications made to the treatment plan and/or to one or more features of the treatment device. if the healthcare provider determines to modify the treatment plan and/or the characteristic(s) of the treatment device. For example, the healthcare provider may use the interface to provide data input indicating an increase or decrease in the resistance setting of the treatment device or other appropriate modification made to the characteristic(s) of the treatment device. Additionally, or alternatively, the healthcare provider may use the interface to provide a data entry indicating a modification made to the treatment plan. For example, the healthcare provider may use the interface to provide a data entry indicating an increase or decrease in an amount of time that the user should use the treatment device in accordance with the treatment plan or other appropriate modifications made. to the treatment plan.

ocher Ln/zznz/E/YiAi

[0093] In some embodiments, the systems and methods described herein may be configured to modify the treatment plan based on one or more modifications indicated by input of data about the treatment plan. Additionally, or alternatively, the systems and methods described herein may be configured to modify the characteristic(s) of the treatment device based on the modified aspect(s) of the treatment plan and/or input of data about the treatment device. treatment plan. For example, data entry about the treatment plan may indicate that the characteristic(s) of the treatment device be modified and/or the treatment plan may require or indicate adjustments to the treatment device for the user to obtain the desired results. of the modified treatment plan.

[0094] In some embodiments, the systems and methods described herein may be configured to receive subsequent treatment data related to the user while the user uses the treatment device to perform the treatment plan. For example, after the healthcare provider provides input that modifies the treatment plan and/or controls the characteristic(s) of the treatment device, the user may continue to use the treatment device to perform the modified treatment plan. . Subsequent treatment data may correspond to treatment data generated while the user uses the treatment device to perform the modified treatment plan. In some embodiments, subsequent treatment data may correspond to treatment data generated while the user continues to use the treatment device to carry out the treatment plan, after the healthcare provider has received the treatment information and determined not to. modify the treatment plan and/or control the characteristics of the treatment device.

[0095] Based on subsequent data input about the treatment plan received from the healthcare provider's computing device, the systems and methods described herein may be configured to further modify the treatment plan and/or control the characteristic(s) of the treatment device. Subsequent data entry about the treatment plan may correspond to data entry provided by the healthcare provider, at the interface, in response to receipt and/or review of the subsequent treatment information.

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ corresponding to subsequent processing data. It should be understood that the systems and methods described herein may be configured to continuously and/or periodically provide treatment information to the healthcare provider's computing device based on treatment data received continuously and/or periodically from sensors or other suitable sources described in this document.

[0096] The healthcare provider may receive and/or review treatment information continuously or periodically while the user uses the treatment device to carry out the treatment plan. Based on one or more trends indicated by treatment information received on a continuous and/or periodic basis, the healthcare provider may determine whether to modify the treatment plan and/or monitor the characteristic(s) of the treatment device. For example, the trend(s) may indicate an increase in heart rate or other appropriate trends that indicate that the user is not performing the treatment plan correctly and/or that the user's performance of the treatment plan is not being met. desired effect.

[0097] In some embodiments, the systems and methods described herein may be configured to use artificial intelligence and/or machine learning to assign patients to cohorts and to dynamically control a treatment device based on the assignment during a adaptive telemedicine session. In some embodiments, a large number of treatment devices may be provided to patients. Patients can use the treatment devices to carry out treatment plans at home, at a gym, at a rehabilitation center, at a hospital, or at any suitable location, including permanent or temporary homes.

[0098] In some embodiments, the processing devices may be communicatively coupled to a server. Patient characteristics, including treatment data, may be collected before, during, or after patients make treatment plans. For example, personal information, performance information, and measurement information may be collected before, during, and/or after the individual makes treatment plans. The results (for example, improved performance or reduced performance) of performing each exercise can be collected from the treatment device throughout the treatment plan and afterward.

QCCP Ln/Zznz/E/YIAI for the treatment plan to be carried out. The parameters, settings, configurations, etc. (e.g., pedal position, degree of resistance, etc.) of the treatment device may be collected before, during, or after performing the treatment plan.

[0099] Each patient characteristic, each result and each parameter, setting, configuration, etc., can be time stamped and can be correlated to a specific stage of the treatment plan. This technique can allow you to determine which stages of the treatment plan produce the desired results (e.g., improvement in muscle strength, range of motion, etc.) and which stages produce diminishing returns (e.g., continuing to exercise after 3 minutes in actually delays or damages recovery).

[0100] Over time, data may be collected from treatment devices and/or any suitable computing devices (e.g., computing devices where personal information is entered, such as the computing device interface described herein). document, a clinician interface, a patient interface and the like) as patients use the treatment devices to carry out the various treatment plans. Data that may be collected may include patient characteristics, treatment plans made by patients, results of treatment plans, any of the data described herein, any other suitable data, or a combination thereof.

[0101] In some embodiments, data may be processed to group certain individuals into cohorts. People can be grouped by people who have certain similar or selected characteristics, treatment plans, and results of having made the treatment plans. For example, athletic people who have no medical conditions and who follow a treatment plan (for example, use the treatment device for 30 minutes a day, 5 times a week, for 3 weeks) and make a full recovery may group into a first cohort. Seniors who are classified as obese and who complete a treatment plan (for example, use the treatment plan for 10 minutes a day, 3 times a week, for 4 weeks) and who improve their range of motion by 75 % can be grouped into a second cohort.

[0102] In some embodiments, an artificial intelligence engine may include one or more machine learning models that are trained using the cohorts. For example, the

QCCP Ln/Zznz/E/YIAI machine learning models can be trained to receive an input of data about the characteristics of a new patient and to generate a treatment plan for the patient that produces the desired outcome. Machine learning models can match a pattern between the characteristics of the new patient and at least one patient of the patients included in a specific cohort. When searching for pattern matches, machine learning models can assign the new patient to the specific cohort and select the treatment plan associated with at least one patient. The artificial intelligence engine can be configured to remotely control the treatment device based on the treatment plan while the new patient uses the treatment device to perform the treatment plan.

[0103] As can be appreciated, the characteristics of the new patient (e.g., a new user) may change as the new patient uses the treatment device to carry out the treatment plan. For example, patient performance may improve faster than expected for people in the cohort to which the new patient is currently assigned. Accordingly, machine learning models can be trained to dynamically reassign the new patient, based on the modified characteristics, to a different cohort that includes people with characteristics similar to the currently modified characteristics, as the new patient. For example, a clinically obese patient may lose weight and no longer meet the weight criterion for the initial cohort, which may result in the patient being reassigned to a different cohort with a different weight criterion.

[0104] A different treatment plan may be selected for the new patient, and the treatment device may be controlled remotely (for example, which may be referred to as remotely) and based on the different treatment plan, the treatment device while the new patient uses the treatment device to carry out the treatment plan. Such techniques may provide the technical solution to remotely control a treatment device.

[0105] Additionally, the systems and methods described herein may result in faster recovery times and/or better outcomes for patients because the treatment plan that most closely matches their characteristics is selected and implemented. , in real time, at any given moment. “Real time” can also refer to near real time, which ocher ίη/ζζηζ/Ε/γίΛΐ can be less than 10 seconds. As described herein, the term “outcomes” may refer to medical outcomes or medical consequences. Clinical outcomes and consequences may refer to responses to medical actions.

[0106] Depending on the desired outcome, the artificial intelligence engine can be trained to generate various treatment plans. For example, one outcome may include recovering to a threshold level (e.g., 75% range of motion) in a faster amount of time, while another outcome may include full recovery (e.g., 75% range of motion). 100%) regardless of the amount of time. Data collected from patients and classified into cohorts may indicate that a first treatment plan provides the first outcome for people with similar characteristics to the patient, and that a second treatment plan provides the second outcome for people with similar characteristics to the patient. of the patient.

[0107] Additionally, the artificial intelligence engine may be trained to generate treatment plans that are suboptimal, that is, suboptimal, non-standard, or otherwise excluded (all of which are referred to, but not limited to, as “treatment plans.” “excluded treatment options”) for the patient. For example, if a patient has high blood pressure, a specific exercise may not be approved or may not be suitable for the patient, as it may pose an unnecessary risk to the patient or even induce a hypertensive crisis and, therefore, that Exercise may be marked on the excluded treatment plan for the patient. In some embodiments, the artificial intelligence engine may monitor the treatment data received while the patient (e.g., the user) with, for example, high blood pressure, uses the treatment device to make an appropriate treatment plan and may modify the appropriate treatment plan to include features of an excluded treatment plan that may provide favorable outcomes for the patient if the treatment data indicates that the patient is adhering to the treatment plan. appropriate treatment plan without aggravating, for example, the patient's high blood pressure condition.

[0108] In some embodiments, treatment plans and/or excluded treatment plans may be presented, during a telemedicine or telehealth session, to a healthcare provider. The healthcare provider can select a specific treatment plan

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ for the patient to have that treatment plan transmitted to the patient and/or to control the treatment device based on the treatment plan. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, establishment of treatment plans and pharmacological and/or rehabilitation prescriptions, the artificial intelligence engine may receive and/or operate remotely from the patient and of the treatment device.

[0109] In such cases, the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in real time or near real time during a telemedicine or telehealth session in a user interface of a computing device from a healthcare provider. Video may also include audio, text, and other multimedia information. Real time may refer to less than or equal to 2 seconds. Near real-time can refer to any interaction of a time short enough to allow two people to engage in a conversation through that user interface, and will typically be less than 10 seconds, but greater than 2 seconds.

[0110] Presenting the treatment plans generated by the artificial intelligence engine at the same time as a presentation of the patient's video can provide an improved user interface, as the healthcare provider can continue to communicate visually or otherwise. mode with the patient while reviewing treatment plans in the same user interface. The improved user interface may improve the experience of the healthcare provider using the computing device and may encourage the healthcare provider to use the user interface again. This technique can also reduce computing resources (e.g., processing, memory, network) because the healthcare provider does not have to switch to another user interface screen to enter a query about a treatment plan to recommend with care. based on patient characteristics. The AI engine can be configured to dynamically provide treatment plans and excluded treatment plans on the fly.

[0111] In some embodiments, the treatment device may be adaptive and/or personalized because its properties, configurations, and positions may be tailored to the needs of a specific patient. For example, the pedals can be adjusted dynamically and

QCCP Ln/Zznz/E/YIAI on-the-fly (e.g., via a telemedicine session or based on programmed settings in response to certain detected measurements) in order to increase or decrease a range of motion to meet a treatment plan designed for the user. In some embodiments, during a telemedicine session, a healthcare provider can remotely adapt the treatment device to the patient's needs by causing a control instruction to be transmitted from a server to the treatment device. Such adaptive nature can improve a patient's recovery outcomes, promoting the goals of personalized medicine, and allowing separate treatment plan personalization.

[0112] Figure 1 generally illustrates a block diagram of a computer-implemented system 10, hereinafter referred to as “the system” for managing a treatment plan. Treatment plan management may include using an artificial intelligence engine to recommend treatment plans and/or providing excluded treatment plans that should not be recommended to a patient.

[0113] System 10 also includes a server 30 configured to store (e.g., write to an associated memory) and provide data related to treatment plan management. Server 30 may include one or more computers and may take the form of one or more distributed and/or virtualized computers. The server 30 also includes a first communication interface 32 configured to communicate with the clinician interface 20 over a first network 34. In some embodiments, the first network 34 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communications (NFC), cellular data network, among others. The server 30 includes a first processor 36 and a first machine-readable storage memory 38, which may be referred to as "memory" for short, which contains first instructions 40 for performing the various actions of the server 30 for execution by the first processor 36.

[0114] Server 30 is configured to store data related to the treatment plan. For example, memory 38 includes system data storage 42 configured to contain system data, such as data related to treatment plans for treating one or more patients. The server 30 is also configured to store data related to the

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ performance of a patient following a treatment plan. For example, memory 38 includes patient data storage 44 configured to retain patient data, such as data related to the patient(s), including data representing the performance of each patient within the treatment plan.

[0115] In addition, or alternatively, the characteristics (e.g., personal, performance, measurement, etc.) of the individuals, the treatment plans followed by the individuals, the level of compliance with the treatment plans, as well as the results of treatment plans, They may use correlations and other statistical or probabilistic measures to allow splitting or dividing treatment plans into different databases equivalent to patient cohorts in patient data storage 44. For example, data from a first cohort of first patients who have a similar first injury, a similar first medical condition, a similar first medical procedure performed, a first treatment plan followed by the first patient, as well as a first outcome of the plan of treatment, can be stored in a database of first patients. Data from a second cohort of second patients who have a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment plan followed by the second patient, as well as a second outcome of the treatment plan, can be stored in a second patient database. Any individual characteristic or combination of characteristics can be used to separate patient cohorts. In some embodiments, different patient cohorts may be stored in different partitions or volumes of the same database. There is no specific limit for the number of different patient cohorts allowed, other than the limitation by mathematical combinatorial and/or partition theory.

[0116] These characteristic data, treatment plan data and outcome data may be obtained from a large number of treatment devices and/or computing devices over time and may be stored in the database 44. The characteristics, treatment plan data, and outcome data can be correlated across patient cohort databases in the patient data warehouse 44. Characteristics of individuals may include personal information, performance information, and/or measurement information.

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ

[0117] In addition to historical information about other individuals stored in patient cohort-equivalent databases, real-time or near real-time information based on current patient characteristics about a current patient being treated may be stored. in an appropriate patient cohort equivalent database. It may be determined that the patient's characteristics match or are similar to those of another person in a specific cohort (e.g., Cohort A) and the patient may be assigned to that cohort.

[0118] In some embodiments, the server 30 may run an artificial intelligence (AI) engine 11 that uses one or more machine learning models 13 to perform at least one of the embodiments described herein. The server 30 may include a training engine 9 capable of generating the machine learning model(s) 13. Machine learning models 13 can be trained to assign people to certain cohorts based on their characteristics, select treatment plans using real-time and historical data correlations that include patient cohort equivalents, and control a treatment device 70 , among other things.

[0119] The training engine 9 may generate the machine learning model(s) 13 and may be implemented into executable computer instructions by one or more processing devices of the training engine 9 and/or the servers 30. To generate the one(s) machine learning models 13, the training engine 9 can train the machine learning model(s) 13. The artificial intelligence engine 11 may use the machine learning model(s) 13.

[01 20] The training engine 9 may be a rack-mount server, a router computer, a personal computer, a personal digital assistant, a smartphone, a laptop, a tablet, an ultraportable (netbook), a desktop computer, an Internet of Things (IoT) device, any other suitable computing device or a combination thereof. The training engine 9 may be cloud-based or a real-time software platform, and may include privacy software or protocols, and/or security software or protocols.

[0121] To train the machine learning model(s) 13, the training engine

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ may use a training data set of a corpus of the characteristics of people who used the treatment device 70 to make treatment plans, the details (for example, the treatment protocol that includes the exercises, the amount of time to do the exercises, how often the exercises are done, an exercise schedule, the parameters/settings/adjustments of the treatment device 70 throughout each stage of the treatment plan, etc.) of the treatment plans made by the persons using the treatment device 70, as well as the results of the treatment plans treatment carried out by people. The machine learning model(s) 13 can be trained to match patterns of characteristics of a patient to the characteristics of other people assigned to a specific cohort. The term “match” may refer to an exact match, a correlative match, a substantial match, etc. The machine learning model(s) 13 may be trained to receive the characteristics of a patient as a data input, map the characteristics to the characteristics of individuals assigned to a cohort, and select a treatment plan from that cohort. The machine learning model(s) 13 may also be trained to control, based on the treatment plan, the machine learning apparatus 70.

[0122] Different machine learning models 13 can be trained to recommend different treatment plans for different desired outcomes. For example, one machine learning model can be trained to recommend treatment plans for more effective recovery, while another machine learning model can be trained to recommend treatment plans based on speed of recovery.

[0123] Using training data that includes training data inputs and corresponding target outputs, the machine learning model(s) 13 may refer to model artifacts created by the training engine 9. The training engine 9 may find patterns in the training data where such patterns map the training data input to the target output, and generate machine learning models 13 that capture these patterns. In some embodiments, the artificial intelligence engine 11, database 33, and/or training engine 9 may reside in another component (e.g., assistant interface 94, clinician interface 20, etc.) depicted. in Figure 1.

QCCP Ln/Zznz/E/YIAI

[0124] The machine learning model(s) 13 may comprise, for example, a single level of linear or non-linear operations (e.g., a support vector machine [SVM]) or the machine learning model(s) 13 may be a deep learning network, that is, a machine learning model that comprises multiple levels of nonlinear operations. Examples of deep learning networks are neural networks which include generative adversarial networks, convolutional neural networks, recurrent neural networks with one or more hidden layers, and fully connected neural networks (for example, each neuron can transmit its output signal to the input of data from the remaining neurons, as well as itself). For example, the machine learning model may include a large number of layers and/or hidden layers that perform calculations (e.g., dot products) using multiple neurons.

[0125] System 10 also includes a patient interface 50 configured to communicate information to a patient and to receive feedback from the patient. In particular, the patient interface includes an input device 52 and an output device 54, which may be collectively referred to as a patient and user interface 52, 54. Input device 52 may include one or more devices, such as a keyboard, a mouse, a touch screen data input, a gesture sensor, and/or a microphone and processor configured for speech recognition. The output device 54 may take one or more different forms, such as a computer monitor or a display screen on a tablet, smartphone, or smart watch. The output device 54 may include other hardware and/or software components, such as a projector, virtual reality capability, augmented reality capability, among others. The output device 54 may incorporate various visual, audio, or other presentation technologies. For example, the output device 54 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds, such as tones, bells and/or melodies, which may indicate different conditions. and/or instructions. The output device 54 may comprise one or more different displays that present various data and/or interfaces or controls for use by the patient. The output device 54 may include graphics, which may be presented through a web-based interface and/or through a computer program or application (App.).

[0126] As generally illustrated in Figure 1, patient interface 50 includes a

QCCP Ln/Zznz/E/YIAI second communication interface 56, which may also be referred to as a remote communication interface configured to communicate with the server 30 and/or the clinician interface 20 through a second network 58. In some In embodiments, the second network 58 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the second network 58 may include the Internet, and security of communications between the patient interface 50 and the server 30 and/or the clinician interface 20 may be established through encryption, such as, for example, by the use of a virtual private network (VPN). In some embodiments, the second network 58 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communications (NFC), cellular data network, among others. In some embodiments, the second network 58 may be the same as the first network 34 and/or operatively coupled thereto.

[0127] The patient interface 50 includes a second processor 60 and a second machine-readable storage memory 62 containing second instructions 64 for execution by the second processor 60 to perform various actions of the patient interface 50. The second machine-readable storage memory 62 also includes a local data store 66 configured to contain data, such as data related to a treatment plan and/or patient data, such as data representing the performance of a patient within a treatment plan. The patient interface 50 also includes a local communication interface 68 configured to communicate with various devices for use by the patient near the patient interface 50. The local communication interface 68 may include wired and/or wireless communications. In some embodiments, the local communication interface 68 may include a local wireless network, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communications (NFC), cellular data network, among others.

[0128] System 10 also includes a treatment device 70 configured to be manipulated by the patient and/or to manipulate a part of the patient's body to perform activities in accordance with the treatment plan. In some embodiments, the treatment device 70 may take the form of an exercise and rehabilitation apparatus configured to perform and/or assist in performing a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, and the treatment includes rehabilitation of a part of the patient's body, such as a joint, bone, or

QCCP ίη/77Π7/Ε/ΥΙΛΙ muscle group. The treatment device 70 may be any suitable medical, rehabilitation, therapeutic, etc. device configured to be controlled remotely via another computing device to treat a patient and/or exercise the patient. The treatment device 70 may be an electromechanical machine that includes one or more weights, an electromechanical bicycle, an electromechanical spinning wheel, a smart mirror, a treadmill, or the like. The body part may include, for example, a spine, a hand, a foot, a knee, or a shoulder. The body part may include a part of a joint, bone, or muscle group, such as one or more vertebrae, a tendon, or a ligament. As generally illustrated in Figure 1, the treatment device 70 includes a controller 72, which may include one or more processors, computer memory and/or other components. The treatment device 70 also includes a fourth communication interface 74 configured to communicate with the patient interface 50 through the local communication interface 68. The treatment device 70 also includes one or more internal sensors 76 and an actuator 78, such as a motor. The actuator 78 may be used, for example, to move the patient's body part or to resist forces by the patient.

[0129] The internal sensors 76 may measure one or more operating characteristics of the treatment device 70, such as a force, a position, a speed and/or a speed. In some embodiments, the internal sensors 76 may include a position sensor configured to measure at least one of a linear motion or an angular motion of a part of the patient's body. For example, an internal sensor 76 in the form of a position sensor can measure a distance up to which the patient is able to move a part of the treatment device 70, where the distance can correspond to a range of motion that the body part of the patient can achieve. In some embodiments, the internal sensors 76 may include a force sensor configured to measure a force applied by the patient. For example, an internal sensor 76 in the form of a force sensor may measure a force or weight that the patient is able to apply, with a specific part of the body, to the treatment device 70.

[0130] The system 10 generally illustrated in Figure 1 also includes a ambulation sensor 82, which communicates with the server 30 through the local communication interface 68 of the patient interface 50. The ambulation sensor 82 wandering can track and store

QCCP Ln/Zznz/E/YIAI a series of steps taken by the patient. In some embodiments, the ambulation sensor 82 may take the form of a bracelet, wristwatch, or smartwatch. In some embodiments, the ambulation sensor 82 may be integrated into a telephone, such as a smartphone.

[0131] The system 10 generally illustrated in Figure 1 also includes a goniometer 84, which communicates with the server 30 through the local communication interface 68 of the patient interface 50. The goniometer 84 measures a angle of the patient's body part. For example, goniometer 84 can measure the angle of flexion of the patient's knee, elbow, or shoulder.

[0132] The system 10 generally illustrated in Figure 1 also includes a pressure sensor 86, which communicates with the server 30 through the local communication interface 68 of the patient interface 50. The pressure sensor pressure 86 measures a degree of pressure or weight applied by a part of the patient's body. For example, pressure sensor 86 may measure a degree of force applied by a patient's foot when pedaling a stationary bicycle.

[0133] The system 10 generally illustrated in Figure 1 also includes a monitoring interface 90 that may be similar or identical to the clinician interface 20. In some embodiments, the monitoring interface 90 may have further enhanced functionality. beyond what is provided in the clinician interface 20. The monitoring interface 90 may be configured for use by a person responsible for the treatment plan, such as an orthopedic surgeon.

[0134] The system 10 generally illustrated in Figure 1 also includes a reporting interface 92 that may be similar or identical to the clinician interface 20. In some embodiments, the reporting interface 92 may have less functionality than that provided in the clinician interface 20. For example, the reporting interface 92 may not have the ability to modify a treatment plan. Such reporting interface 92 may be used, for example, by a biller to determine the use of system 10 for billing purposes. In another example, the reporting interface 92 may not have the ability to display patient identifying information and may only display pseudonymous patient data and/or anonymous patient data for certain data fields related to a registered person and/or for certain data fields related to a quasi-identifier of the registered person. For example, a researcher

QCCP Ln/Zznz/E/YIAI may use the reporting interface 92 to determine the various effects of a treatment plan on different patients.

[0135] System 10 includes an assistant interface 94 for a healthcare provider, such as those described herein, to remotely communicate with the patient interface 50 and/or the treatment device 70. These remote communications may allow the healthcare provider to provide assistance or advice to a patient using system 10. More specifically, the assistant interface 94 is configured to communicate a telemedicine signal 96, 97, 98a, 98b, 99a, 99b to the patient interface 50 through a network connection, such as through the first network 34 and/or the second network 58. The telemedicine signal 96, 97, 98a, 98b, 99a, 99b comprises one of an audio signal 96, an audiovisual signal 97, an interface control signal 98a for controlling a function of the patient interface 50, a interface monitoring 98b to monitor a status of the patient interface 50, an apparatus control signal 99a for changing an operating parameter of the treatment device 70 and/or an apparatus monitoring signal signal 99b for monitoring a status of the treatment device 70. In some embodiments, each of the Control 98a, 99a may consist of unidirectional transmission commands from the assistant interface 94 to the patient interface 50. In some embodiments, in response to successful receipt of a control signal 98a, 99a and/or to communicate successful and/or failed implementation of the requested control action, a confirmation message may be sent from the patient interface 50. to the wizard interface 94. In some embodiments, each of the monitoring signal signals 98b, 99b may consist of unidirectional status information commands from the patient interface 50 to the assistant interface 94. In some embodiments, a confirmation message may be sent from the assistant interface 94 to the patient interface 50 in response to successful reception of one of the monitoring signals 98b, 99b.

[0136] In some embodiments, the patient interface 50 may be configured as a direct passage for the device control signals 99a and the device monitoring signals 99b between the treatment device 70 and one or more devices, such as the interface assistant 94 and/or server 30. For example, patient interface 50 can be configured to transmit a

QCCP Ln/Zznz/E/YIAI device control 99a in response to a device control signal 99a in the telemedicine signal 96, 97, 98a, 98b, 99a, 99b of the assistant interface 94.

[0137] In some embodiments, the assistant interface 94 may be presented on a shared physical device, such as the clinician interface 20. For example, the clinician interface 20 may include one or more displays that implement the assistant interface 94. Alternatively or additionally, the clinician interface 20 may include additional hardware components, such as a video camera, a speaker, and/or a microphone, to implement aspects of the assistant interface 94.

[0138] In some embodiments, one or more portions of the telemedicine signal 96, 97, 98a, 98b, 99a, 99b may be generated from a prerecorded source (e.g., an audio recording, a video recording, or an animation ) for presentation through the output device 54 of the patient interface 50. For example, a video tutorial can be transmitted from the server 30 and presented on the patient interface 50. The patient can request the content of the prerecorded source through the patient interface 50. Alternatively, through a control on the assistant interface 94, the healthcare provider can cause the content of the prerecorded source to be requested. play on patient interface 50.

[0139] The assistant interface 94 includes an assistant input device 22 and a visual presentation of the assistant 24, which may be referred to collectively as an assistant and user interface 22, 24. The assistant input device 22 may include one or more of a telephone, a keyboard, a mouse, a touch-sensitive area or a touch screen, for example. Alternatively or additionally, the assistant input device 22 may include one or more microphones. In some embodiments, the microphone(s) may take the form of a handset, headset, wide area microphone, or microphones configured for the healthcare provider to speak with a patient through the patient interface 50. In some embodiments, the assistant input device 22 may be configured to provide voice-based functions, with hardware and/or software configured to interpret spoken instructions from the healthcare provider with the use of the microphone(s). The assistant input device 22 may include functions provided by or similar to those of existing voice-based assistants, such as Apple's Siri,

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ

Amazon's Alexa, Google Assistant or Samsung's Bixby. The wizard input device 22 may include other hardware and/or software components. The assistant input device 22 may include one or more general purpose devices and/or specific use devices.

[0140] The visual presentation of the assistant 24 may take one or more different forms, such as a computer monitor or a display screen on a tablet, smartphone, or smart watch. The visual presentation of the assistant 24 may include other hardware and/or software components, such as projectors, virtual reality capabilities, or augmented reality capabilities, among others. The visual presentation of the assistant 24 may incorporate various visual, audio or other types of presentation technologies. For example, the visual presentation of the assistant 24 may include a non-visual representation, such as an audio signal, which may include spoken language and/or other sounds, such as tones, timbres, melodies and/or compositions that may indicate different conditions and/or instructions. The visual presentation of the assistant 24 may comprise one or more different display screens that present various data and/or interfaces or controls for use by the healthcare provider. The visual presentation of the assistant 24 may include graphics, which may be presented through a web-based interface and/or through a computer program or application (App.).

[0141] In some embodiments, system 10 may allow computer language translation, from assistant interface 94 to patient interface 50 and/or vice versa. Computer translation of language may include computer translation of spoken language and/or computer translation of text. In addition or alternatively, system 10 may allow recognition of speech and/or spoken pronunciation of the text. For example, system 10 may convert spoken words into printed text and/or system 10 may audibly speak a language from printed text. The system 10 may be configured to recognize words spoken by any or all of: the patient, the clinician, or the healthcare provider. In some embodiments, system 10 may be configured to recognize and react to verbal requests or commands from the patient. For example, system 10 may automatically initiate a telemedicine session in response to a verbal command from the patient (which may be given in any of several languages).

QCCP ίη/77Π7/Ε/ΥΙΛΙ

[0142] In some embodiments, the server 30 may generate aspects of the visual presentation of the wizard 24 for presentation via the wizard interface 94. For example, the server 30 may include a web server configured to generate the display screens for display. presentation in the assistant visual presentation 24. For example, the artificial intelligence engine 11 may generate recommended treatment plans and/or excluded treatment plans for patients and generate visual display screens that include the recommended treatment plans and/or external treatment plans for display on the screen. visual presentation of wizard 24 of wizard interface 94. In some embodiments, the display of assistant 24 may be configured to present a virtualized desktop hosted by server 30. In some embodiments, server 30 may be configured to communicate with assistant interface 94 over first network 34. In some embodiments, server 30 may be configured to communicate with assistant interface 94 over first network 34. In some embodiments, the first network 34 may include a local area network (LAN), such as an Ethernet network.

[0143] In some embodiments, the first network 34 may include the Internet, and the security of communications between the server 30 and the assistant interface 94 may be established through privacy-enhancing technologies, such as by using encryption in a virtual private network (VPN). Alternatively or additionally, the server 30 may be configured to communicate with the assistant interface 94 through one or more networks independent of the first network 34 and/or other means of communication, such as a direct wired communication channel or wireless. In some embodiments, each of the patient interface 50 and the treatment device 70 may operate from a patient location geographically separate from a location of the assistant interface 94. For example, the patient interface 50 and the treatment device 70 can be used as part of a home rehabilitation system, which can receive remote assistance through the use of the assistant interface 94 in a centralized location, such as a clinic or a call center.

[0144] In some embodiments, the assistant interface 94 may be one of several different terminals (e.g., computing devices) that may be grouped together, for example, in one or more call centers or in one or more doctor's offices. In some embodiments, a plurality of assistant interfaces 94 may be geographically distributed. In some embodiments, a person

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ can work as a healthcare provider remotely from any conventional office infrastructure. Such remote work may be performed, for example, when the assistant interface 94 takes the form of a computer and/or a telephone. This remote work feature may allow for work-from-home arrangements that may include part-time and/or flexible work schedules for a healthcare provider.

[0145] Figures 2-3 show one embodiment of a treatment device 70. More specifically, Figure 2 generally illustrates a treatment device 70 in the form of a stationary cycling machine 100, which may be referred to as a bicycle static for short. The stationary cycling machine 100 includes a set of pedals 102, each attached to a pedal arm 104 for rotation about an axis 106. In some embodiments, and as generally illustrated in Figure 2, the pedals 102 can be moved on the pedal arms 104 to adjust a range of motion used by the patient when pedaling. For example, pedals that are located on the inside, towards the axis 106, correspond to a smaller range of motion than when the pedals are located on the outside, away from the axis 106. A pressure sensor 86 is attached to or integrated into one of the pedals 102 to measure the degree of force applied by the patient on the pedal 102. The pressure sensor 86 may communicate wirelessly with the treatment device 70 and/or with the patient interface 50.

[0146] Figure 4 generally illustrates a person (a patient) using the treatment device of Figure 2, and showing the sensors and various data parameters connected to a patient interface 50. The example of a patient interface patient 50 is a tablet, computer or smartphone, or a phablet, such as a ¡Pad, an ¡Phone, an Android device or a Surface tablet, that is manually held by the patient. In other embodiments, the patient interface 50 may be integrated into or attached to the treatment device 70.

[0147] Figure 4 generally illustrates the patient wearing the ambulation sensor 82 on the wrist, with a note showing “TODAY'S STEPS 1355”, which indicates that the ambulation sensor 82 has recorded and transmitted that step count to the patient interface 50. Figure 4 generally also illustrates the patient wearing the goniometer 84 on the right knee, with a note showing the “KNEE ANGLE 72either", which indicates that the goniometer 84 is measuring and ocher Ln/zznz/E/YiAi transmitting that knee angle to the patient interface 50. Figure 4 also generally illustrates a right side of one of the pedals 102 with a pressure sensor 86 that displays a “FORCE of 12.5 pounds,” which indicates that the right pedal pressure sensor 86 is measuring and transmitting that force measurement to the patient interface 50.

[0148] Figure 4 generally also illustrates a left side of one of the pedals 102 with a pressure sensor 86 showing a "FORCE of 27 pounds", which indicates that the left pedal pressure sensor 86 is measuring and transmitting that force measurement to the patient interface 50. Figure 4 generally also illustrates other patient data, such as a “SESSION TIME 0:04:13” indicator, which indicates that the patient has been using the treatment device 70 for 4 minutes and 13 seconds. This session time can be determined by the patient interface 50 based on the information received from the treatment device 70. Figure 4 generally also illustrates an indicator showing “PAIN LEVEL 3”. Such a pain level may be obtained from the patient in response to a request, such as a question, presented at the patient interface 50.

[0149] Figure 5 is an exemplary embodiment of a visual presentation of the general information 120 of the assistant interface 94. Specifically, the visual presentation of the general information 120 presents several different controls and interfaces for the care provider. The physician remotely assists a patient in using the patient interface 50 and/or the treatment device 70. This remote assistance feature can also be referred to as telemedicine or telehealth.

[0150] Specifically, the general information display 120 includes a patient profile display 130 that presents biographical information related to a patient using the treatment device 70. The patient profile display 130 may take the form of a portion or region of the general information display 120, as generally illustrated in Figure 5, although the patient profile display 130 may take the form of a portion or region of the general information display 120, as generally illustrated in Figure 5, although the patient profile display 130 may take other forms, such as a standalone screen or a pop-up window.

[0151] In some embodiments, the visual presentation of the patient profile 130 may include a limited subset of patient biographical information. More specifically, the data presented in the patient profile display 130 may depend on the need that

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ health care provider needs to look up that information. For example, a healthcare provider who is helping the patient with a medical problem may be provided with information from the patient's medical history, while a technician troubleshooting a problem with the treatment device 70 may be provided with a series much more limited information related to the patient. The technician, for example, could only be provided with the patient's name.

[0152] The visual presentation of the patient profile 130 may include pseudonymous patient data and/or anonymized patient data, or use any privacy-enhancing technology to prevent confidential patient data from being communicated in a manner that could violate patient confidentiality requirements. These privacy-enhancing technologies may enable compliance with laws, regulations, or other government rules, such as, but not limited to, the Health Insurance Portability and Accountability Act (HIPAA) or the General Rules. of Data Protection (General Data Protection Regulation, GDPR), where the patient can be considered a “registered person”.

[0153] In some embodiments, the visual display of the patient profile 130 may present information related to the treatment plan for the patient to follow with the use of the treatment device 70. Such treatment plan information may be limited to a provider. of medical care. For example, a healthcare provider assisting the patient with a problem related to the treatment regimen may be provided information about the treatment plan, while a technician troubleshooting a problem with the treatment device 70 may not be provided with information about the treatment plan. cannot provide you with any information related to the patient's treatment plan.

[0154] In some embodiments, one or more recommended treatment plans and/or excluded treatment plans may be presented to the healthcare provider through the visual display of the patient profile 130. The artificial intelligence engine 11 of the server 30 may generate the recommended treatment plan(s) and/or excluded treatment plans and receive them from the server 30 in real time, among others, during a telemedicine or telehealth session. Below is an example of the presentation of the recommended treatment plan(s) and/or discarded treatment plans.

QCCP Ln/Zznz/E/YIAI is described with reference to Figure 7.

[0155] The example general information display 120 generally illustrated in Figure 5 also includes a patient status display 134 that presents status information related to a patient using the treatment device. The patient status display 134 may take the form of a portion or region of the general information display 120, as generally illustrated in Figure 5, although the patient status display 134 may take other forms, such as a standalone screen or a pop-up window.

[0156] The visual display of patient status 134 includes sensor data 136 from one or more of the external sensors 82, 84, 86 and/or from one or more internal sensors 76 of the treatment device 70. In some embodiments, the Visual presentation of patient status 134 may include sensor data from one or more sensors of one or more wearable devices worn by the patient while using the treatment device 70. The wearable device(s) may include a watch, bracelet, necklace, chest strap, and the like. The wearable device(s) may be configured to monitor a patient's heart rate, temperature, blood pressure, one or more vital signs, and the like while the patient uses the treatment device 70. In some embodiments, the visual presentation of the patient's status 134 may present other data 138 related to the patient, such as the last reported level of pain or progress on a treatment plan.

[0157] User access controls may be used to limit access, including what data is available for viewing and/or modification, on any or all of the user interfaces 20, 50, 90, 92, 94 of system 10. In some embodiments, user access controls may be employed to control what information is available to any given person using the system 10. For example, data presented in the assistant interface 94 may be controlled by user access controls, with permissions set based on the healthcare provider/user's need and/or restrictions on viewing that information.

[0158] The example of general information display 120 generally illustrated in Figure 5 also includes a help data display 140 that presents information

QCCP Ln/Zznz/E/YIAI for the healthcare provider to use when providing assistance to the patient. The help data display 140 may take the form of a portion or region of the general information display 120, as generally illustrated in Figure 5. The help data display 140 may take other forms. , such as a stand-alone screen or a pop-up window. The visual presentation of assistive data 140 may include, for example, the presentation of answers to frequently asked questions related to the use of the patient interface 50 and/or the treatment device 70.

[0159] The visual presentation of assistive data 140 may also include research data or best practices. In some embodiments, the visual display of assistive data 140 may present scripts for answers or explanations in response to questions posed by the patient. In some embodiments, the visual presentation of assistive data 140 may present flowcharts or guides for the healthcare provider to use to determine a root cause and/or solution to a patient's problem.

[0160] In some embodiments, the assistant interface 94 may present two or more visual displays of help data 140, which may be the same or different, for simultaneous presentation of the help data for use by the software provider. medical attention. For example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient's problem, and a second help data display may present script information. for the healthcare provider to read to the patient; Such information should preferably include instructions for the patient to take some action, which may help reduce or solve the problem. In some embodiments, based on data entries in the troubleshooting flowchart in the first help data display, the second help data display may be automatically populated with script information.

[0161] The example of general information display 120 generally illustrated in Figure 5 also includes a patient interface control 150 that displays information related to the patient interface 50 and/or for modifying one or more settings of the interface of patient 50. The patient interface control 150 may take the form of a part or region of the presentation

Visual QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ of the general information 120, as generally illustrated in Figure 5. The patient interface control 150 may take other forms, such as a stand-alone screen or a pop-up window. The patient interface control 150 may present information communicated to the assistant interface 94 through one or more of the interface monitoring signals 98b.

[0162] As generally illustrated in Figure 5, the patient interface control 150 includes a transmission of the visual presentation 152 of the visual presentation presented by the patient interface 50. In some embodiments, the transmission of the Visual display 152 may include an active copy of the display screen currently being presented to the patient via patient interface 50. In other words, the visual display transmission 152 may present an image of what is presented on a display screen of the patient interface 50.

[0163] In some embodiments, the transmission of the visual presentation 152 may include abbreviated information related to the display screen that is currently being presented by the patient interface 50, such as a screen name or a screen number. The patient interface control 150 may include a patient interface settings control 154 so that the healthcare provider can adjust or control one or more settings or aspects of the patient interface 50. In some embodiments, the settings control Patient interface 154 may cause assistant interface 94 to generate and/or transmit an interface control signal 98 to control a function or setting of patient interface 50.

[0164] In some embodiments, control of patient interface settings 154 may include collaborative navigation or co-browsing capability so that the healthcare provider can remotely view and/or control patient interface 50. For example, patient interface settings control 154 may allow the healthcare provider to remotely enter text into one or more text entry fields on patient interface 50 and/or remotely control a cursor on the patient interface 50 with the use of a mouse or touch screen of the assistant interface 94.

[0165] In some embodiments, with the use of the patient interface 50, the patient interface settings control 154 may allow the healthcare provider to change a setting that the patient cannot. change. For example, the patient interface 50 may not be able to access a language setting to prevent a patient from inadvertently changing, in the patient interface 50, the language used for visual presentations, while controlling interface settings. Patient interface 154 may allow the healthcare provider to change the language settings of the patient interface 50. In another example, the patient interface 50 may not be able to change a font size setting to a smaller size to prevent a patient from inadvertently changing the font size used for visual displays on the patient interface 50, so such that the visual presentation is legible to the patient, while the patient interface settings control 154 may allow the healthcare provider to change the font size setting of the patient interface 50.

[0166] The example general information display 120 generally illustrated in Figure 5 also includes an interface communications display 156 showing the status of communications between the patient interface 50 and one or more devices 70, 82, 84, such as the treatment device 70, the ambulation sensor 82 and/or the goniometer 84. The interface communications display 156 may take the form of a portion or region of the general information display 120, as generally illustrated in Figure 5.

[0167] The interface communications display 156 may take other forms, such as a stand-alone screen or a pop-up window. The interface communications display 156 may include controls for the healthcare provider to remotely modify communications with one or more of the other devices 70, 82, 84. For example, the healthcare provider may remotely command the patient interface 50 to restart communication with one of the other devices 70, 82, 84, or to establish communications with a new one of the other devices 70,82, 84. This functionality can be used, for example, when the patient has a problem with one of the other devices 70, 82, 84, or when the patient receives a new or replacement device for one of the other devices 70, 82, 84.

[0168] The example of general information display 120 generally illustrated in Figure 5 also includes an apparatus control 160 for the healthcare provider to display and/or control information related to the treatment device 70. The control of device

QCCP Ln/Zznz/E/YIAI

160 It may take the form of a portion or region of the general information display 120, as generally illustrated in Figure 5. The appliance control 160 may take other forms, such as a stand-alone display or a pop-up window. The device control 160 may include a visual display of the status of the device 162 with information about the current state of the device. The display of the status of the device 162 may present information communicated to the assistant interface 94 through one or more of the device monitoring signals 99b. Visual display of the status of the device 162 may indicate whether the treatment device 70 is currently communicating with the patient interface 50. The visual display of the status of the device 162 may present other current or historical information related to the status of the treatment device 70. [0169] The device control 160 may include an device settings control 164 for the healthcare provider to adjust or control one or more aspects of the treatment device 70. The apparatus setting control 164 may cause the assistant interface 94 to generate and/or transmit an apparatus control signal 99 (e.g., which may be referred to as treatment plan data input, as described) to change an operating parameter and/or one or more characteristics of the treatment device 70 (e.g., a pedal radius setting, a resistance setting, a target RPM value, other suitable features of the treatment device 70 or a combination thereof).

[0170] The device settings control 164 may include a mode button 166 and a position control 168, which may be used together for the healthcare provider to place an activator 78 of the treatment device 70 in a mode. manual, after which a setting, such as a position or speed of the actuator 78, can be changed with the use of the position control 168. Mode button 166 may allow a setting, such as a position, to toggle between automatic and manual modes.

[0171] In some embodiments, one or more adjustments can be made at any time and without having an associated automatic/manual mode. In some embodiments, the healthcare provider may change an operating parameter of the treatment device 70, such as a pedal radius setting, while the patient is actively using the treatment device 70. Such “on the fly” adjustment may or not be available to the patient using patient interface 50.

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ

[0172] In some embodiments, device setting control 164 may allow the healthcare provider to change a setting that the patient cannot change, using the patient interface 50. For example, the patient interface 50 may not be able to change a preconfigured setting, such as a height or tilt setting of the treatment device 70, while the device settings control 164 may allow the healthcare provider to change adjusting the height or inclination of the treatment device 70.

[0173] The example of visual presentation of general information 120 generally illustrated in Figure 5 also includes a patient communications control 170 for controlling an audio or audiovisual communications session with the patient interface 50. The communication session with The patient interface 50 may comprise an active transmission of the assistant interface 94 for presentation by the output device of the patient interface 50. The active stream may take the form of an audio stream and/or a video stream. In some embodiments, the patient interface 50 may be configured to provide two-way audio or audiovisual communications with a person with the use of the assistant interface 94. Specifically, the communications session with the patient interface 50 may include bidirectional (two-way) video or audiovisual transmissions, where each of the patient interface 50 and the assistant interface 94 presents the other's video. .

[0174] In some embodiments, the patient interface 50 may present video from the assistant interface 94, while the assistant interface 94 only presents audio or the assistant interface 94 does not present any active audio or visual signal from the interface. of patient 50. In some embodiments, the assistant interface 94 may present video from the patient interface 50, while the patient interface 50 only presents audio or the patient interface 50 does not present any active audio or visual signal from the assistant interface 94. .

[0175] In some embodiments, the audio or audiovisual communications session with the patient interface 50 may take place, at least in part, while the patient is performing the rehabilitation regimen for the respective body part. The patient communications control 170 may take the form of a portion or region of the general information display 120, as generally illustrated in Figure 5. The patient communications control 170 can

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ take other forms, such as a stand-alone screen or a pop-up window.

[0176] Audio and/or audiovisual communications may be processed or directed by the assistant interface 94 and/or other device or devices, such as a telephone system or a video conferencing system used by the healthcare provider while the healthcare provider healthcare uses assistant interface 94. Alternatively or additionally, audio and/or audiovisual communications may include communications with third parties. For example, system 10 may allow the healthcare provider to initiate a three-way conversation regarding the use of specific hardware or software, both with the patient and with a subject matter expert, such as a healthcare provider or specialist. . The example of patient communications control 170 generally illustrated in Figure 5 includes call controls 172 for use by the healthcare provider in managing various aspects of audio or audiovisual communications with the patient. Call controls 172 include a disconnect button 174 for the healthcare provider to end the audio or audiovisual communications session. The call controls 172 also include a mute button 176 to temporarily mute an audio or audiovisual signal from the assistant interface 94. In some embodiments, the call controls 172 may include other attributes, such as a hold button (not included). sample).

[0177] The call controls 172 also include one or more record/playback controls 178, such as record, play and pause buttons to control, with the patient interface 50, the recording and/or playback of audio and/or video of the teleconference session. The call controls 172 also include a video feed display 180 for presenting still and/or video images from the patient interface 50, as well as a self-video display 182 showing the current image of the call provider. healthcare using the 94 assistant interface. The self-video display 182 may be presented in a picture-in-picture format, in a section of the video stream display 180, as generally illustrated in Figure 5. Alternatively or additionally , the self-video display 182 may be presented separately and/or independently of the video stream display 180.

[0178] The example of visual presentation of general information 120 generally illustrated

QCCP Ln/Zznz/E/YIAI in Figure 5 also includes a third party communications control 190 for use when conducting audio and/or audiovisual communications with third parties. The third-party communications control 190 may take the form of a portion or region of the general information display 120, as generally illustrated in Figure 5. Third-party communications monitoring 190 may take other forms, such as a visual presentation on a separate screen or a pop-up window.

[0179] Third party communications control 190 may include one or more controls, such as a contact list and/or buttons or controls for contacting a third party in connection with the use of specific hardware or software, e.g. , a subject matter expert, such as a healthcare provider or specialist. Third party communications control 190 may include conference call capability for the third party to simultaneously communicate with the healthcare provider via assistant interface 94 and with the patient via patient interface 50. For example, system 10 may allow the healthcare provider to initiate a three-way conversation with the patient and the third party.

[0180] Figure 6 generally illustrates an example block diagram of training a machine learning model 13 to generate, based on patient-related data 600, a treatment plan 602 for the patient in accordance with the present description. Data related to other patients may be received by server 30. The other patients may have used various treatment devices to carry out treatment plans.

[0181] The data may include characteristics of the other patients, details of the treatment plans made by the other patients, and/or the results of having made the treatment plans (for example, a percentage of recovery of a body part of patients, a level of recovery of a part of the patients' body, a level of increase or decrease in muscle strength of the part of the patients' body, a level of increase or decrease in the range of motion of the patients body part, etc.).

[0182] As depicted, the data has been assigned to different cohorts. Cohort A includes data from patients who have similar first characteristics, first treatment plans, and first outcomes. Cohort B includes data from patients who have second characteristics,

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ second treatment plans and similar second results. For example, Cohort A may include early characteristics of patients between the ages of twenty and twenty-nine who have no medical condition and who have undergone surgery for a broken limb; Your treatment plans may include a certain treatment protocol (for example, using the treatment device 70 for 30 minutes, 5 times a week, for 3 weeks, where the values of the device properties, configurations and/or adjustments Treatment rates 70 are set to X (where X is a numerical value) for the first two weeks, and to Y (where Y is a numerical value) for the last week.

[0183] Cohort A and Cohort B may be included in a training data set used to train the machine learning model 13. The machine learning model 13 may be trained to look for pattern matches between the characteristics of each cohort and generate the treatment plan that provides the result. Accordingly, when data 600 of a new patient is entered into the trained machine learning model 13, the trained machine learning model 13 can match the characteristics included in the data 600 with the characteristics of cohort A or cohort B and generate the appropriate 602 treatment plan. In some embodiments, the machine learning model 13 may be trained to generate one or more excluded treatment plans that the new patient should not perform.

[0184] Figure 7 generally illustrates one embodiment of a visual presentation of the general information 120 of the assistant interface 94 that presents in real time the recommended treatment plans and the excluded treatment plans during a telemedicine session according to the present description. As depicted, the general information display 120 only includes sections for the patient profile 130 and the video stream display 180, including the self-video display 182. Any suitable configuration of controls and interfaces of the general information display 120 described with reference to Figure 5 may be presented in addition to or instead of the patient profile 130, the video stream display 180, and the self-report display. -video 182.

[0185] The healthcare provider using the assistant interface 94 (e.g., a computing device) during the telemedicine session may be presented in the self-video 182 in

QCCP Ln/Zznz/E/YIAI a portion of the visual presentation of general information 120 (e.g., in the user interface presented on a display screen 24 of the assistant interface 94) that also presents a video of the patient in the visual presentation of the 180 video stream. Additionally, the visual presentation of the video stream 180 may also include a graphical user interface (GUI) object 700 (e.g., a button) that allows the healthcare provider to share in real time or near real time, during the telemedicine session, the recommended treatment plans and/or the excluded treatment plans with the patient at the patient interface 50. The healthcare provider may select GUI object 700 to share recommended treatment plans and/or excluded treatment plans. As shown, another part of the display of general information 120 includes the display of the patient profile 130.

[0186] The visual presentation of the patient profile 130 presents two examples of recommended treatment plans 600 and one example of an excluded treatment plan 602. As described herein, treatment plans may be recommended based on the characteristics of the patient. patient being treated. To generate recommended treatment plans 600 that the patient should follow to obtain the desired result, a pattern matches between the characteristics of the patient being treated and a cohort of other people who have used the treatment device 70 to perform a treatment plan can be searched using one or more machine learning models 13 of the artificial intelligence engine 11. Each of the recommended treatment plans can be generated based on the different desired results.

[0187] For example, as depicted, the visual presentation of patient profile 130 presents “The characteristics of the patient match the characteristics of the uses in cohort A. The following treatment plans are recommended for the patient based on its characteristics and desired results. Next, the visual presentation of patient profile 130 presents the recommended treatment plans of Cohort A with each treatment plan providing different results.

[0188] As depicted, treatment plan “A” indicates that “Patient Patient X has type 2 diabetes; and Patient

QCCP ίη/77Π7/Ε/ΥΙΛΙ who have type 2 diabetes). Consequently, the generated treatment plan increases the range of motion by Y%. As can be seen, the treatment plan also includes a recommended medication (e.g., Drug Z) that will be prescribed to the patient to manage pain in light of a known medical condition (e.g., type 2 diabetes) of the patient. That is, the medication recommended to the patient not only does not conflict with the patient's medical condition, but improves the likelihood of a superior clinical outcome for the patient. This specific example and all examples described elsewhere in this document are not intended to limit in any way the generated treatment plan from recommending multiple medications or addressing the recognition, opinion, diagnosis and/or treatment of diseases or conditions. comorbid.

[0189] The recommended treatment plan “B” may specify, based on a different desired outcome of the treatment plan, a different treatment plan that includes a different treatment protocol for a treatment device, a different medication regimen, etc

[0190] As depicted, the visual presentation of the patient profile 130 may also present the excluded treatment plans 602. These types of treatment plans are displayed to the healthcare provider who uses the assistant interface 94 to notify the provider. health care provider should not recommend certain parts of a treatment plan to the patient. For example, the excluded treatment plan could specify the following: “Patient X should not use the treatment device for more than 30 minutes per day due to a heart condition; Patient X has type 2 diabetes; and Patient Specifically, the excluded treatment plan notes a limitation of a treatment protocol in which, due to a heart condition, Patient X should not exercise for more than 30 minutes per day. The discarded treatment plan also states that Patient X should not be prescribed medication M because it conflicts with the medical condition of type 2 diabetes.

[0191] The healthcare provider may select the treatment plan for the patient in the visual display of general information 120. For example, the healthcare provider may use an input peripheral (e.g., a mouse, a screen touch, a microphone,

QCCP Ln/Zznz/E/YIAI a keyboard, etc.) to choose from 600 treatment plans for the patient. In some embodiments, during the telemedicine session, the healthcare provider may discuss with the patient the pros and cons of the recommended treatment plans 600.

[0192] In any case, the healthcare provider can choose the treatment plan that the patient should follow to obtain the desired result. The selected treatment plan may be transmitted to the patient interface 50 for display. The patient can view the selected treatment plan on the patient interface 50. In some embodiments, the healthcare provider and the patient may discuss details (e.g., treatment protocol with use of treatment device 70, dietary regimen, medication regimen, etc.) during the telemedicine session. ) in real time or near real time. In some embodiments, the server 30 may control, based on the selected treatment plan and during the telemedicine session, the treatment device 70 as the user uses the treatment device 70.

[0193] Figure 8 generally illustrates an embodiment of the visual presentation of the general information 120 of the assistant interface 94 that presents in real time, during a telemedicine session, the recommended treatment plans that have been modified as result of the modification made to the patient's data in accordance with this description. As can be seen, the treatment device 70 and/or any computing device (e.g., patient interface 50) may transmit data while the patient uses the treatment device 70 to perform a treatment plan. The data may include updated patient characteristics and/or other treatment data. For example, updated features may include new performance information and/or measurement information. The performance information may include a speed of a portion of the treatment device 70, a range of motion achieved by the patient, a force exerted on a portion of the treatment device 70, a heart rate of the patient, a blood pressure of the patient, patient's respiratory rate, among others.

[0194] In some embodiments, data received at server 30 may be input into trained machine learning model 13, which may determine that characteristics indicate that the patient is ready for the current treatment plan. Determining that the patient is ready for the current treatment plan can make the 13 trained machine learning model can

QCCP Ln/Zznz/E/YIAI adjust a parameter of the treatment device 70. The adjustment may be based on a next stage of the treatment plan to further improve patient performance.

[0195] In some embodiments, data received at server 30 may be input into trained machine learning model 13, which may determine that characteristics indicate that the patient is not ready (e.g., running late, not able to maintain a speed, are unable to reach a certain range of motion, are in too much pain, etc.) for the current treatment plan or are ahead of schedule (for example, if they exceed a certain speed, if you exercise more than specified without pain, if you exert greater force than specified, etc.) for the current treatment plan.

[0196] The trained machine learning model 13 may determine that the characteristics of the patient no longer match the characteristics of the patients in the cohort to which the patient is assigned. Accordingly, the trained machine learning model 13 can reassign the patient to another cohort that includes characteristics that meet the patient's characteristics. Thus, the trained machine learning model 13 can select a new treatment plan from the new cohort and control the treatment device 70 based on the new treatment plan.

[0197] In embodiments, prior to controlling the treatment device 70, the server 30 may provide the new treatment plan 800 to the assistant interface 94 for display in the patient profile 130. As depicted, the patient profile patient 130 indicates “The patient characteristics have changed and now match the characteristics of the uses of cohort B. The following treatment plan is recommended for the patient based on their characteristics and desired results. Patient profile 130 then presents new treatment plan 800 (“Patient can select the new treatment plan 800 and the server 30 can receive the selection. The server 30 may control the treatment device 70 based on the new treatment plan 800. In some embodiments, the new treatment plan 800 may be transmitted to the patient interface 50, such that the patient can view the details of the treatment. new 800 treatment plan.

QCCP Ln/Zznz/E/YIAI

[0198] In some embodiments, while the patient uses the treatment device 70 to perform the treatment plan, the server 30 may receive treatment data related to the patient. As described, the treatment plan may correspond to a rehabilitation treatment plan, a prehabilitation treatment plan, an exercise-based treatment plan, or other appropriate treatment plan. The treatment data may include various characteristics of the patient, (e.g., such as those described herein), various measurement information related to the user while the patient uses the treatment device 70 (e.g., such as the described in this document), various characteristics of the treatment device (for example, such as those described in this document), the treatment plan, other suitable data or a combination thereof.

[0199] In some embodiments, at least part of the treatment data may include sensor data 136 from one or more of the external sensors 82, 84, 86 and/or from one or more internal sensors 76 of the treatment device 70 In some embodiments, at least part of the treatment data may include sensor data from one or more sensors of one or more wearable devices worn by the patient while using the treatment device 70. The wearable device(s) may include a watch, bracelet, necklace, chest strap, elastic headband, elastic wrist band, any other suitable elastic band, any other suitable wearable item, or a combination thereof while the patient uses the treatment device 70, the wearable device(s) may be configured to monitor a heart rate, a temperature, a blood pressure, one or more vital and similar signs of the patient.

[0200] In some embodiments, the server 30 may generate treatment information with the use of treatment data. The treatment information may include a formatted summary about the user's performance of the treatment plan while using the treatment device, such that the treatment data can be presented on a computing device of a responsible healthcare provider. of the user's performance of the treatment plan. In some embodiments, the visual presentation of the patient profile 120 may include and/or display treatment information.

[0201] The server 30 can be configured to provide the treatment information in the

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ display of the general information 120. For example, the server 30 may store the treatment information to access the treatment information by displaying the general information 120 and/or communicate the information treatment to the visual presentation of general information 120. In some embodiments, the server 30 may provide the treatment information to the patient profile display 130 or other suitable section, part or component of the general information display 120 or to any other suitable display or interface.

[0202] In some embodiments, the healthcare provider assisting the patient while using the treatment device 70 may review the treatment information and determine whether the treatment plan and/or one or more features of the treatment device should be modified. 70. For example, the healthcare provider may review treatment information and compare the treatment information to the treatment plan the patient is undergoing.

[0203] While the patient uses the treatment device 70, the healthcare provider may compare one or more parts or portions of the expected information related to the patient's ability to perform the treatment plan with one or more corresponding parts or portions. of measurement information (e.g. those indicated by the treatment information) related to the patient while the patient uses the treatment device 70 to carry out the treatment plan. The expected information may include one or more vital signs of the user, a respiratory rate of the user, a heart rate of the user, a temperature of the user, a blood pressure of the user, other suitable information of the user, or a combination thereof. The health care provider may determine that the treatment plan is having the desired effect if one or more parts or portions of the measurement information are within an acceptable range of one or more corresponding parts or portions of the expected information. Conversely, the health care provider may determine that the treatment plan is not having the desired effect if one or more parts or portions of the measurement information fall outside the acceptable range of one or more corresponding parts or portions of the measurement information. expected information.

[0204] In some embodiments, while the patient uses the treatment device 70 to

QCCP ίη/77Π7/Ε/ΥΙΛΙ perform the treatment plan, the healthcare provider may compare the respective expected characteristics of the treatment device 70 with the corresponding characteristics of the treatment device 70 indicated by the treatment information. For example, the healthcare provider may compare an expected resistance setting of the treatment device 70 with an actual resistance setting of the treatment device 70 indicated by the treatment information.

[0205] The healthcare provider may determine that the patient is performing the treatment plan correctly if the actual characteristics of the treatment device 70 indicated by the treatment information are within a range of the expected characteristics of the treatment device 70 . Conversely, the healthcare provider may determine that the patient is not performing the treatment plan correctly if the actual characteristics of the treatment device 70 indicated by the treatment information are outside the range of the expected characteristics of the treatment device. 70.

[0206] If the healthcare provider determines that the treatment information indicates that the patient is performing the treatment plan correctly and/or that the treatment plan is having the desired effect, the healthcare provider may determine not to modify the treatment plan. treatment plan or the characteristics of the treatment device 70. Conversely, if the health care provider determines that the treatment information indicates that the patient is not performing the treatment plan correctly and/or that the treatment plan is not having the desired effect, the health care provider may determine modifying the treatment plan and/or the characteristic(s) of the treatment device 70 while the user uses the treatment device 70 to perform the treatment plan.

[0207] In some embodiments, while the patient uses the treatment device 70 to perform the modified treatment plan, the server 30 may receive subsequent treatment data related to the patient. For example, after the healthcare provider provides data input that modifies the treatment plan and/or controls the characteristic(s) of the treatment device 70, the patient may continue to perform the modified treatment plan with the use of the treatment device 70. Subsequent treatment data may correspond to treatment data generated while the patient uses the treatment device 70 to

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ perform the modified treatment plan. In some embodiments, the subsequent treatment data may correspond to the treatment data generated while the patient continues to perform the treatment plan with the use of the treatment device 70, after the healthcare provider has received the treatment information and has determined not to modify the treatment plan and/or control one or more characteristics of the treatment device 70.

[0208] The server 30 may also modify the treatment plan and/or control the characteristic(s) of the treatment device 70 based on the subsequent treatment plan data input received from the general information display 120. The subsequent data entry about the treatment plan may correspond to a data entry provided by the healthcare provider, in the visual presentation of the general information 120, in response to the receipt and/or review of the subsequent treatment information. corresponding to subsequent treatment data. It should be understood that the server 30 may continuously and/or periodically provide treatment information to the patient profile display 130 and/or other sections, parts or components of the general information display 120 based on the data. of treatment received continuously and/or periodically.

[0209] The healthcare provider may receive and/or review treatment information continuously or periodically while the user uses the treatment device to carry out the treatment plan. The healthcare provider may determine whether to modify the treatment plan and/or monitor the characteristic(s) of the treatment device based on one or more trends indicated by treatment information received on a continuous and/or periodic basis. For example, the trend(s) may indicate an increase in heart rate or changes in the other applicable trends that indicate that the user is not performing the treatment plan correctly and/or that the user's performance of the treatment plan is not correct. is having the desired effect.

[0210] Figure 9 is a flow chart generally illustrating a method 900 for monitoring the performance of a treatment plan by a user using a treatment device and for selectively modifying the treatment plan and a or more characteristics of the treatment device according to the present description. Method 900 is performed using logic

QCCP Ln/Zznz/E/YIAI processing that may include hardware (circuits, dedicated logic, etc.), software (such as running on a general-purpose computing system or a dedicated machine), or a combination of both . Method 900 and/or each of its individual functions, routines, subroutines or operations may be performed by one or more processors of a computing device (e.g., any component of Figure 1, such as server 30 running the engine of artificial intelligence 11). In some embodiments, method 900 may be performed through a single processing cycle. Alternatively, method 900 may be performed by two or more processing cycles, where each cycle implements one or more individual functions, routines, subroutines, or operations of the methods.

[0211] For purposes of simplicity of explanation, method 900 is represented and described as a series of operations. However, operations according to this description may take place in a different order and/or simultaneously and/or with other operations that are not presented or described in this document. For example, the operations represented in method 900 may occur in combination with any other operation of any other method described herein. Furthermore, not all of the operations illustrated may be required to implement method 900 in accordance with the disclosed subject matter. Furthermore, those skilled in the art will understand and note that method 900 could alternatively be represented as a series of interrelated states via a state or event diagram.

[0212] At 902, the processing device may receive treatment data related to a user using a treatment device, such as treatment device 70, to perform a treatment plan. The treatment data may include characteristics of the user, measurement information related to the user while the user uses the treatment device 70, characteristics of the treatment device 70, the treatment plan, other suitable data, or a combination thereof.

[0213] At 904, the processing device may generate treatment information with the use of the treatment data. The treatment information may include a summary of the user's performance of the treatment plan while using the treatment device 70. The treatment information may be in a format such that the treatment data may be presented on a computing device of a healthcare provider responsible for the user's performance of the treatment plan.

[0214] At 906, the processing device may be configured to provide (e.g., store for access, make available, transmit, and the like), on the healthcare provider's computing device, treatment information. At 908, the processing device may be configured to provide the treatment information on an interface of the healthcare provider's computing device. For example, the processing device may store the treatment information to access the treatment information through the healthcare provider's computing device and/or communicate (e.g., or transmit) the treatment information to the healthcare provider's computing device. healthcare provider for display in the patient profile display 130 of the general information display 120. As described, the general information display 120 may be configured to receive data input, such as data input about the treatment plan, indicating one or more modifications made to the treatment plan and/or to one or more characteristics. of the treatment device 70. The healthcare provider may interact with the various controls, data entry fields, and other aspects of the visual presentation of the general information 120 to provide data entry about the treatment plan.

[0215] At 910, the processing device may modify the treatment plan in response to receiving input data about the treatment plan that includes at least one modification of the treatment plan. For example, the processing device may modify various attributes and characteristics of the treatment plan based on at least one modification indicated by the input of data about the treatment plan.

[0216] At 912, the processing device may selectively control the treatment device 70 with the use of the modified treatment plan. For example, the processing device may modify one or more characteristics of the treatment device 70 based on modifications made to the treatment plan. Additionally or alternatively, the processing device may adapt, modify, adjust or otherwise control one or more features based on input data about the treatment plan. For example, entering data about the

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ treatment plan may indicate at least one modification made to one or more features of the treatment device 70. The processing device may modify the feature(s) of the treatment device 70 based on the or the modifications indicated by the data entry on the treatment plan.

[0217] Figure 10 is a flow chart generally illustrating an alternative method 1000 for monitoring the performance of a treatment plan by a user using a treatment device and for selectively modifying the treatment plan and one or more characteristics of the treatment device according to the present description. Method 1000 includes operations performed by the processors of a computing device (e.g., any component of Figure 1, such as the server 30 running the artificial intelligence engine 11). In some embodiments, one or more operations of method 1000 are implemented in computer instructions stored in a memory device and executed by a processing device. Method 1000 may be performed in the same or similar manner as described above in connection with Method 900. The operations of Method 1000 may be performed in combination with any of the operations of any of the methods described herein.

[0218] At 1002, during a telemedicine session, the processing device may receive first treatment data related to a user using a treatment device, such as treatment device 70, to perform a treatment plan. The first treatment data includes at least measurement information related to the user while the user uses the treatment device 70 to perform the treatment plan. The first treatment data may correspond to sensor data, such as data from sensor 136, from one or more of the external sensors, such as external sensors 82, 84, 86, and/or from one or more internal sensors, such as such as the internal sensors 76, of the treatment device 70.

[0219] In some embodiments, at least part of the first treatment data may include sensor data from one or more sensors associated with one or more corresponding wearable devices worn by the user while using the treatment device 70. The wearable device(s) may include a watch, bracelet, necklace, chest strap, elastic headband, wrist band, any other elastic band, and any other device

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ suitable portable, or a combination thereof. The wearable device(s) may be configured to monitor a user's heart rate, temperature, blood pressure, one or more vital signs, and the like while the user uses the treatment device 70.

[0220] At 1004, the processing device may generate first treatment information with the use of the first treatment data. The first treatment information may include a summary of the user's performance of the treatment plan while using the treatment device 70. The first treatment information may be in a format such that the first treatment data may be presented on a computing device of a healthcare provider responsible for the user's performance of the treatment plan.

[0221] At 1006, the processing device may be configured to write to an associated memory, to access the first treatment information at the healthcare provider's computing device, and/or to provide the first treatment information at the healthcare provider computing device. At 1008, the processing device may be configured to provide the first treatment information at an interface of the healthcare provider's computing device. For example, the processing device may be configured to provide the first treatment information in the patient profile display 130 of the general information display 120. As described, the general information display 120 may be configured to receive data input, such as data input about the treatment plan, indicating one or more modifications made to the treatment plan and/or to one or more characteristics. of the treatment device 70. The healthcare provider may interact with the various controls, data entry fields, and other aspects of the visual presentation of the general information 120 to provide data entry about the treatment plan.

[0222] At 1010, the processing device may receive a first input of data about the treatment plan in response to the first treatment information. The first treatment plan data entry may indicate at least one modification made to the treatment plan. In some embodiments, the first input of data about the treatment plan may be provided by the healthcare provider in the manner described above. In some modalities, with

QCCP Ln/Zznz/E/YIAI Based on the first treatment information, the artificial intelligence engine 11 can generate the first data entry about the treatment plan.

[0223] At 1012, the processing device may modify the treatment plan in response to receiving the first data entry about the treatment plan that includes at least one modification made to the treatment plan. For example, the processing device may modify various attributes and characteristics of the treatment plan based on at least one modification indicated by the first data input about the treatment plan.

[0224] At 1014, the processing device may selectively control the treatment device 70 with the use of the modified treatment plan. For example, the processing device may modify one or more characteristics of the treatment device 70 based on modifications made to the treatment plan. Additionally or alternatively, the processing device may adapt, modify, adjust or otherwise control one or more features based on the first input of data about the treatment plan. For example, the first data entry about the treatment plan may indicate at least one modification made to one or more features of the treatment device 70. The processing device may modify the characteristic(s) of the treatment device 70 based on the modification(s) indicated by the first data entry about the treatment plan.

[0225] At 1016, the processing device may receive a second input of data about the treatment plan in response to a second treatment information generated with the use of second treatment data. For example, the processing device may receive the second treatment data related to the user while the user uses the treatment device 70. The second treatment data may include treatment data received by the processing device after the first treatment data. In some embodiments, the second treatment data may be related to the user while the user uses the treatment device 70 to perform the modified treatment plan.

[0226] In some embodiments, the second treatment data may be related to the user while the user uses the treatment device 70 to perform the treatment plan (for example, in cases where the health care provider does not modify the ocher Ln/zznz/E/YiAi treatment plan, as described). The processing device may generate the second treatment information based on the second treatment data. The processing device may receive the second input of data about the treatment plan that indicates at least one modification made to the treatment plan.

[0227] As described, the processing device may be configured to provide the second treatment information to the patient profile display 130 and/or any other suitable section, part or component of the general information display 120. or to any other suitable visual presentation or interface. The healthcare provider (e.g., and/or the artificial intelligence engine 11) may review the second treatment information and determine whether to modify and/or further modify the treatment plan based on the second treatment information.

[0228] At 1018, with the use of the second data entry about the treatment plan, the processing device can modify the treatment plan. For example, the processing device may further modify (e.g., in cases where the processing device has already modified the treatment plan) and/or modify (e.g., in cases where the processing device has not has previously modified the treatment plan) various attributes and characteristics of the treatment plan based on the modification(s) indicated by the second data entry about the treatment plan.

[0229] At 1020, with the use of the modified treatment plan, the processing device can selectively control the treatment device 70. For example, based on the modifications made to the treatment plan, the processing device can modify one or more features of the treatment device 70. Additionally or alternatively, the processing device may adapt, modify, adjust or otherwise control one or more features based on the second data input about the treatment plan. For example, the second treatment plan data entry may indicate at least one modification made to one or more features of the treatment device 70. The processing device may modify the characteristic(s) of the treatment device 70 based on the modification(s) indicated by the second data input about the treatment plan.

QCCP Ln/Zznz/E/YIAI

[0230] Figure 11 is a flow chart generally illustrating an alternative method 1100 for monitoring the performance of a treatment plan by a user using a treatment device and for selectively modifying the treatment plan and one or more characteristics of the treatment device according to the present description. Method 1100 includes operations performed by the processors of a computing device (e.g., any component of Figure 1, such as the server 30 running the artificial intelligence engine 11). In some embodiments, one or more operations of method 1100 are implemented in computer instructions stored in a memory device and executed by a processing device. Method 1100 may be performed in the same or similar manner as described above in connection with method 900 and/or method 1000. The operations of method 1100 may be performed in combination with any of the operations of any of the methods described. in this document.

[0231] At 1102, the processing device may receive treatment data related to a user using a treatment device, such as treatment device 70, to carry out the treatment plan. Treatment data may include any of the data described in this document. The treatment data may correspond to sensor data, such as data from sensor 136, from one or more of the external sensors, such as external sensors 82, 84, 86, and/or from one or more internal sensors, such as the internal sensors 76, of the treatment device 70. In some embodiments, at least part of the treatment data may include sensor data from one or more sensors associated with one or more corresponding wearable devices worn by the user while using the treatment device 70. The wearable device(s) may include a watch, bracelet, necklace, chest strap, elastic headband, wrist band, any other elastic band, any other suitable wearable device, or a combination of the same. The wearable device(s) may be configured to monitor a user's heart rate, temperature, blood pressure, one or more vital signs, and the like while the user uses the treatment device 70.

[0232] At 1104, the processing device may generate treatment information with the use of the treatment data. Treatment information may include a summary of the

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ performance of the treatment plan by the user while using the treatment device 70. The treatment information may be in a format, such that the treatment data can be presented on a computing device a healthcare provider responsible for the user's performance of the treatment plan.

[0233] At 1106, the processing device may be configured to provide the treatment information to at least one of the healthcare provider's computing devices and to a machine learning model executed by the artificial intelligence engine 11.

[0234] At 1108, the processing device may receive a data input about the treatment plan in response to the treatment information. The treatment plan data entry may indicate at least one modification made to the treatment plan. In some embodiments, data entry about the treatment plan may be provided by the healthcare provider in the manner described above. In some embodiments, based on the treatment information, the artificial intelligence engine 11 running the machine learning model may generate data input about the treatment plan.

[0235] At 1110, the processing device determines whether the data entry about the treatment plan indicates at least one modification made to the treatment plan. If the processing device determines that the input of data about the treatment plan does not indicate at least one modification made to the treatment plan, the processing device returns to step 1102 and continues to receive treatment data related to the user while the The user uses the treatment device 70 to carry out the treatment plan. If the processing device determines that the data entry about the treatment plan indicates at least one modification made to the treatment plan, the processing device proceeds to step 1112.

[0236] At 1112, with the use of data input on the treatment plan, the processing device may modify the treatment plan. For example, with the use of the modification(s) made to the treatment plan indicated by the input of data about the treatment plan, the processing device may modify the treatment plan. Based on the modification(s) indicated by the treatment plan data entry, the treatment device

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ processing may modify various attributes and characteristics of the treatment plan.

[0237] At 1114, with the use of the modified treatment plan, the processing device can selectively control the treatment device 70. For example, based on the modification(s) made to the treatment plan, the processing device may modify one or more characteristics of the treatment device 70. Additionally or alternatively, the processing device may adapt, modify, adjust or otherwise control one or more features based on input data about the treatment plan. For example, the treatment plan data entry may indicate at least one modification made to one or more features of the treatment device 70. Based on the modification(s) indicated by the input of data about the treatment plan, the processing device may modify the characteristic(s) of the treatment device 70. The processing device may return to step 1102 and continue receiving related data. with the user while the user uses the treatment device 70 to carry out the treatment plan.

[0238] Figure 12 generally illustrates an example of a computer system 1200 that can perform any or more of the methods described herein, in accordance with one or more aspects of the present description. In an example, the computing system 1200 may include a computing device and correspond to the assistant interface 94, the reporting interface 92, the monitoring interface 90, the clinician interface 20, the server 30 (including the AI engine 11), the patient interface 50, the ambulatory sensor 82, the goniometer 84, the treatment device 70, the pressure sensor 86 or any suitable component of Figure 1. The computer system 1200 may execute instructions that implement one or more machine learning models 13 of the artificial intelligence engine 11 of Figure 1. The computer system may connect (e.g., network) to other computer systems on a LAN. , an intranet, an extranet or the Internet, including through the cloud or a network between users.

[0239] The computing system may operate with the capacity of a server in a client-server network environment. The computing system may be a personal computer (PC), a tablet, a wearable device (e.g. a bracelet), a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a camera, a camera video, an Internet of Things (IoT) device or

QCCP Ln/Zznz/E/YIAI any device capable of executing a set of instructions (sequential or otherwise) that specify the actions that the device must perform. Furthermore, although only one computer system is illustrated, it should be understood that the term "computer" also includes any group of computers that individually or jointly execute a set (or multiple sets) of instructions to perform one or more of the methods that are described in this document.

[0240] The computing system 1200 includes a processing device 1202, a main memory 1204 (e.g., a read-only memory (ROM), a flash memory, solid state drives (SSD), a dynamic random access memory (DRAM), such as a synchronous DRAM (SDRAM)), a static memory 1206 (for example, a flash memory, solid state drives (SSD), a static random access memory (SRAM)), as well as a data storage device 1208 that communicates with other devices through a bus 1110.

[0241] The processing device 1202 represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, or the like. More particularly, the processing device 1202 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor that triples other instruction sets or processors that implement a combination of instruction sets. The processing device 1402 may also be one or more purpose-specific processing devices, such as an application-specific integrated circuit (ASIC), a system on a chip, a field programmable gate array (FPGA), a data processor. digital signals (DSP), a network processor or the like. The processing device 1402 is configured to execute instructions to perform any of the operations and steps described herein.

[0242] The computer system 1200 may also include a network interface device 1212. The computing system 1200 may also include a video display 1214 (e.g., a liquid crystal display (LCD), a light-emitting diode (LED), an organic light-emitting diode (OLED), a quantum LED, a cathode ray tube (CRT), a CRT shadow mask, a CRT aperture grating, a monochrome CRT), one or more input devices 1216 (for example, a keyboard and/or a mouse or a

QCCP Ln/Zznz/E/YIAI game-like control), and one or more speakers 1218 (for example, a horn). In an illustrative example, the video display 1214 and input devices 1216 may be combined into a single component or device (e.g., an LCD touch screen).

[0243] Data storage device 1216 may include a computer-readable medium 1220 on which instructions 1222 incorporating one or more of the methods, operations, or functions described herein are stored. The instructions 1222 may also reside, in whole or at least partially, in the main memory 1204 and/or in the processing device 1202 during their execution by the computing system 1200. Thus, the main memory 1204 and the processing device 1202 also constitute computer-readable media. Instructions 1222 may also be transmitted or received over a network using network interface device 1212.

[0244] Although the computer-readable storage medium 1220 is generally illustrated in the illustrative examples as a single medium, the term “computer-readable storage medium” should be understood to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the instruction set(s). It should also be understood that the term “computer-readable storage medium” includes any medium capable of storing, encoding or carrying a set of instructions for execution by the machine and causing the machine to perform one or more of the methodologies herein description. Accordingly, the term “computer readable storage medium” will be understood to include, but is not limited to, solid state memories, optical media, and magnetic media.

[0245] Determine an optimal treatment plan for a patient who presents certain characteristics (e.g., demographic, geographic, diagnostic, measurement or test-based, medically historical, etiological, cohort-associated, differentially diagnostic, surgical, therapeutic from a physical, pharmacological and other recommended treatments, etc.) may constitute a technically difficult problem. For example, establishing a treatment plan may involve a large amount of information, which can lead to inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitation setting, part

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ of that large amount of information considered may include a type of patient injury, types of medical procedures available to perform, treatment regimens, medication regimens, as well as patient characteristics. Patient characteristics can be very broad and can include medications of the patient, previous injuries of the patient, previous medical procedures performed on the patient, measurements (e.g., body fat, weight, etc.) of the patient, allergies of the patient, medical conditions of the patient. patient, historical patient information, vital signs (e.g., temperature, blood pressure, heart rate) of the patient, symptoms of the patient, family medical information of the patient, among others.

[0246] Likewise, in addition to the information described above, it may be appropriate to consider additional historical information, such as clinical information related to the results of treatment plans made with the use of a treatment device on other people. Clinical information may include clinical studies, clinical trials, evidence-based guidelines, journal articles, meta-analyses, and related information. Clinical information may be written by people with certain academic titles (for example, medical doctor, osteopathic doctor, physical therapist, etc.), certifications, etc. Clinical information can be retrieved from any suitable data source.

[0247] In some embodiments, clinical information may describe individuals seeking to receive treatment for a specific ailment (e.g., injury, illness, any applicable medical condition, among others). Clinical information may describe that certain outcomes are obtained when individuals perform or have performed specific treatment plans (e.g., medical procedures, treatment protocols with the use of treatment devices, medication regimens, dietary regimens, among others). ). Clinical information may also include specific characteristics of the individuals described. Direct or indirect reference can be made to the values of the characteristics contained therein. It may be appropriate to compare the characteristics of the patient with the characteristics of the persons referred to in the clinical information to determine what the optimal treatment plan would be for the patient, so that the patient can obtain the desired result. The processing of this historical information can be overwhelming, ineffective and/or unfeasible, from a computational point of view, through the use of techniques

QCCP ίη/77Π7/Ε/ΥΙΛΙ conventional.

[0248] Accordingly, the embodiments of the present disclosure relate to recommending optimal treatment plans using real-time and historical data correlations including databases equivalent to patient cohorts. In some embodiments, an artificial intelligence engine can be trained to recommend the optimal treatment plan based on patient characteristics and clinical information. For example, the artificial intelligence engine can be trained to look for pattern matches between the characteristics of the patient and the people referred to in different clinical information. Based on the pattern, the AI engine can generate a treatment plan for the patient, where the treatment plan has generated a desired outcome in clinical information for one or more similarly matched individuals. In that sense, the generated treatment plan may be “optimal” based on the desired outcome (e.g., speed, effectiveness, both speed and effectiveness, life expectancy, etc.). In other words, based on the characteristics of the patient, to obtain the desired result there may be certain medical procedures, certain medications, certain rehabilitation exercises, among others, that must be included in an optimal treatment plan in order to obtain the result. wanted.

[0249] Depending on what the desired outcome is, the artificial intelligence engine can be trained to generate various optimal or optimized treatment plans. For example, one outcome may include recovering to a threshold level (e.g., 75% range of motion) in a faster amount of time, while another outcome may include full recovery (e.g., 75% range of motion). 100%) regardless of the amount of time. The clinical information may indicate that a first treatment plan provides the first outcome for people with similar characteristics to the patient, and that a second treatment plan provides the second outcome for people with similar characteristics to the patient.

[0250] Additionally, the artificial intelligence engine can also be trained to generate treatment plans that are not optimal (referred to as “discarded treatment plans”) for the patient. For example, if a patient has diabetes, a specific medication may not be approved or appropriate for the patient and that medication may be marked off the patient's treatment plan.

QCCP Ln/Zznz/E/YIAI

[0251] As described above, processing clinical and patient information in real time may not be feasible using conventional techniques due to the large amount of data that must be processed. Accordingly, in some embodiments, the received clinical information and/or patient information may be translated into descriptive medical language. Descriptive medical language can refer to coding configured to be processed efficiently by the artificial intelligence engine. For example, a clinical trial can be received and analyzed, optionally with the addition of an attribute grammar; and then you can search for keywords related to the target information. Target information values can be identified. A canonical format defined by the descriptive medical language can be defined and/or generated, where the canonical format includes tags that identify the values of the information the target information and, optionally, tags that implement an attribute grammar for the language. descriptive medical.

[0252] Descriptive medical language can be extended to include any property of an object-oriented programming language or artificial intelligence. Descriptive medical language may define other methods or procedures. Descriptive medical language can implement the concept of “objects,” which can contain data, in the form of fields (often known as attributes or properties), and code, in the form of procedures (often known as methods). Descriptive medical language can encapsulate data and functions that manipulate the data to protect it from interference and misuse. Descriptive medical language can also implement data hiding or masking, which prevents certain aspects of the data or functions from being accessible to another component. Descriptive medical language can implement inheritance, which arranges components as “is a type of” relationships, where a first component can be a type of a second component and the first component inherits the functions and data of the second component. . Descriptive medical language can also implement polymorphism, which is the provision of a single interface for components of different types.

[0253] Clinical information may be translated into descriptive medical language before the artificial intelligence engine determines optimal treatment plans and/or discarded treatment plans. The artificial intelligence engine can be trained with the use of medical language

Descriptive QCCP Ln/Zznz/E/YIAI that represents clinical information, so that the artificial intelligence engine can more effectively determine optimal treatment plans instead of using the initial data formats in which the information is received. clinical information. Furthermore, the artificial intelligence engine may continuously or constantly receive the clinical information and include the clinical information in the training data to update the artificial intelligence engine.

[0254] In some embodiments, optimal treatment plans and/or discarded treatment plans may be presented to a medical professional. The medical professional can select a specific optimal treatment plan for the patient to have that treatment plan transmitted to the patient. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, establishment of treatment plans, and pharmacological and/or rehabilitation prescriptions, the artificial intelligence engine may receive and/or operate remotely from the source. or the patient's clinical and/or remote information. In such cases, recommended treatment plans and/or discarded treatment plans may be presented during a telemedicine or telehealth session on a user interface of a medical professional's computing device simultaneously with a real-time video of the patient. real. Video may also include audio, text, and other multimedia information. Real time can refer to less than 2 seconds.

[0255] The presentation of treatment plans generated by the artificial intelligence engine together with a presentation of the patient's video can provide an improved user interface, as the medical professional can continue to communicate visually or otherwise with the patient while also reviewing treatment plans in the same user interface. The improved user interface may improve the experience of the medical professional using the computing device and may encourage the medical professional to return to using the user interface. This technique can also reduce computing resources (e.g., processing, memory, network) because the clinician does not have to switch to another user interface screen to enter a query about a treatment plan to recommend based on the patient's characteristics. The artificial intelligence engine dynamically provides optimal treatment plans and discarded treatment plans on the fly.

[0256] In some embodiments, the treatment apparatus may be adaptive and/or

Customized QCCP Ln/Zznz/E/YIAI because its properties, configurations and positions can be tailored to the needs of a specific patient. For example, the pedals can be adjusted dynamically and on the fly (e.g., through a telemedicine session or based on programmed settings in response to certain detected measurements) in order to increase or decrease a range of motion. to comply with a treatment plan designed for the user. Such adaptive nature can improve a patient's recovery outcomes.

[0257] Clause 1. A method comprising: receiving treatment data related to a user using a treatment device to carry out a treatment plan, wherein the treatment data comprises at least one of characteristics of the user, measurement information related to the user while the user uses the treatment devices, features of the treatment device, and at least one aspect of the treatment plan; generate treatment information with the use of the treatment data; writing it to an associated memory, to access treatment information through a computing device of a healthcare provider; communicating with an interface, on the healthcare provider's computing device, wherein the interface is configured to receive a data input regarding the treatment plan; and modify the aspect(s) of the treatment plan in response to receipt of data input regarding the treatment plan that includes at least one modification made to the aspect(s) of the treatment plan.

[0258] Clause 2. The method in accordance with any clause herein also includes monitoring, based on the modified aspect(s) of the treatment plan, the treatment device while the user uses the treatment device.

[0259] Clause 3. The method in accordance with any clause herein also includes controlling, based on the modified aspect(s) of the treatment plan, the treatment device while the user uses the treatment device during a treatment session. telemedicine.

[0260] Clause 4. The method in accordance with any clause of this document, wherein the measurement information includes at least one of a vital sign of the user, a respiratory rate of the user, a heart rate of the user, a temperature of the user and a

QCCP Ln/Zznz/E/YIAI user's blood pressure.

[0261] Clause 5. The method in accordance with any clause herein, wherein at least part of the treatment data corresponds to sensor data from a sensor associated with the treatment device.

[0262] Clause 6. The method in accordance with any clause of this document, wherein at least part of the treatment data corresponds to sensor data from a sensor associated with a portable device used by the user while using the device treatment.

[0263] Clause 7. The method in accordance with any clause herein also comprises receiving subsequent treatment data related to the user while the user uses the treatment device to carry out the treatment plan.

[0264] Clause 8. The method in accordance with any clause of this document, also includes modifying the modified treatment plan in response to the receipt of a subsequent data entry about the treatment plan that includes at least one additional modification made to the modified aspect(s). of the treatment plan, where the subsequent data entry about the treatment plan is based on at least one of the treatment data and the subsequent treatment data.

[0265] Clause 9. A tangible, non-transitory computer-readable medium that stores instructions that, when executed, cause a processing device: receive treatment data related to a user using a treatment device to carry out a treatment plan, wherein the treatment data comprises at least one of characteristics of the user, measurement information related to the user while the user uses the device of treatment, characteristics of the treatment device and at least one aspect of the treatment plan; generate treatment information using the treatment data; write to an associated memory, to access treatment information on a computing device of a healthcare provider; communicates with an interface, on the healthcare provider's computing device, wherein the interface is configured to receive a data input regarding the treatment plan; and modify the aspect(s) of the treatment plan in response to receipt of data entry about the treatment plan that includes at least one modification made to the

QCCP ίη/77Π7/Ε/ΥΙΛΙ treatment plan.

[0266] Clause 10. The computer-readable medium in accordance with any clause hereof, wherein the processing device is also configured to control, based on the modified aspect(s) of the treatment plan, the treatment device while the user uses the treatment device.

[0267] Clause 11. The computer-readable medium in accordance with any clause hereof, wherein the processing device is also configured to control, based on the modified aspect(s) of the treatment plan, the treatment device while the user uses the treatment device during a telemedicine session.

[0268] Clause 12. The computer-readable medium in accordance with any clause of this document, wherein the measurement information includes at least one of a vital sign of the user, a respiratory rate of the user, a heart rate of the user, a temperature of the user and a blood pressure of the user.

[0269] Clause 13. The computer-readable medium in accordance with any clause hereof, wherein at least part of the treatment data corresponds to sensor data from a sensor associated with the treatment device.

[0270] Clause 14. The computer-readable medium in accordance with any clause of this document, where at least part of the treatment data corresponds to sensor data from a sensor associated with a portable device used by the user while using the treatment device.

[0271] Clause 15. The computer-readable medium in accordance with any clause hereof, wherein the processing device is also configured to receive subsequent treatment data related to the user while the user uses the treatment device to perform the treatment plan.

[0272] Clause 16. The computer-readable medium in accordance with any clause of this document, wherein the processing device is also configured to modify the modified aspect(s) of the treatment plan in response to receipt of subsequent data entry about the treatment plan which includes at least one additional modification made to the ocher Ln/zznz/E/YiAi treatment plan, wherein the subsequent data entry about the treatment plan is based on at least one of the treatment data and the subsequent treatment data.

[0273] Clause 17. A system comprising: a memory device that stores instructions; a processing device communicatively coupled to the memory device, the processing device executes instructions to: receiving treatment data related to a user using a treatment device to carry out a treatment plan, wherein the treatment data comprises at least one of characteristics of the user, measurement information related to the user while the user uses the device of treatment, characteristics of the treatment device and at least one aspect of the treatment plan; generate treatment information with the use of the treatment data; writing to an associated memory, to access treatment information on a computing device of a healthcare provider; communicating with an interface, on the healthcare provider's computing device, wherein the interface is configured to receive a data input regarding the treatment plan; and modify the aspect(s) of the treatment plan in response to receipt of data input about the treatment plan that includes at least one modification made to the treatment plan.

[0274] Clause 18. The system in accordance with any clause hereof, wherein the processing device is also configured to control, based on the modified aspect(s) of the treatment plan, the treatment device while the user uses the treatment device.

[0275] Clause 19. The system in accordance with any clause hereof, wherein the processing device is also configured to control, based on the modified aspect(s) of the treatment plan, the treatment device while the user uses the treatment device during a telemedicine session.

[0276] Clause 20. The system in accordance with any clause of this document, wherein the measurement information includes at least one of a vital sign of the user, a respiratory rate of the user, a heart rate of the user, a temperature of the user and a blood pressure of the user.

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ

[0277] Clause 21. The system in accordance with any clause herein, wherein at least part of the treatment data corresponds to sensor data from a sensor associated with the treatment device.

[0278] Clause 22. The system in accordance with any clause herein, wherein at least part of the treatment data corresponds to sensor data from a sensor associated with a portable device used by the user while using the device treatment.

[0279] Clause 23. The system in accordance with any clause herein, wherein the processing device is also configured to receive subsequent treatment data related to the user while the user uses the treatment device to carry out the treatment plan .

[0280] Clause 24. The system in accordance with any clause of this document, wherein the processing device is also configured to modify the modified aspect(s) of the aspect(s) and any other aspect of the treatment plan in response to receipt of a data entry subsequent review of the treatment plan that includes at least one additional modification made to the treatment plan, wherein the subsequent data entry about the treatment plan is based on at least one of the treatment data and the subsequent treatment data.

METHOD AND SYSTEM FOR DESCRIBING AND RECOMMENDING OPTIMAL TREATMENT PLANS IN ADAPTIVE TELEMEDICINE OR OTHER TYPE SETTINGS

[0281] Figure 13 shows a block diagram of a 2010 computer-implemented system, hereinafter referred to as “the system” for managing a treatment plan. Treatment plan management may include using an artificial intelligence engine to recommend optimal treatment plans and/or provide discarded treatment plans that should not be recommended to a patient. A treatment plan may include one or more treatment protocols, and each treatment protocol includes one or more treatment sessions. Each treatment session comprises several session periods, where each session period includes a specific activity to treat the patient's body part. For example, a treatment plan for postoperative rehabilitation after knee surgery may include a treatment protocol

Initial QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ with stretching sessions twice a day for the first 3 days after surgery and a more intensive treatment protocol with active exercise sessions performed 4 times a day starting on day 4 after surgery. Surgery. A treatment plan may also include information related to a medical procedure to be performed on the patient, a treatment protocol for the patient using a treatment device, a dietary regimen for the patient, a medication regimen for the patient, a sleep regimen for the patient, additional regimens, or some combination thereof.

[0282] System 2010 also includes a server 2030 configured to store and provide data related to treatment plan management. Server 2030 may include one or more computers and may take the form of one or more distributed and/or virtualized computers. The server 2030 also includes a first communication interface 2032 configured to communicate with the clinician interface 2020 over a first network 2034. In some embodiments, the first network 2034 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communications (NFC), cellular data network, among others. The server 2030 includes a first processor 2036 and a first machine-readable storage memory 2038, which may be referred to as "memory" for short, which contains first instructions 2040 for performing the various actions of the server 2030 for execution by the first processor 2036. Server 2030 is configured to store data related to the treatment plan. For example, memory 2038 includes system data storage 2042 configured to contain system data, such as data related to treatment plans for treating one or more patients. Server 2030 is also configured to store data related to the performance of a patient following a treatment plan. For example, memory 2038 includes patient data storage 2044 configured to retain patient data, such as data related to the patient(s), including data representing the performance of each patient within the treatment plan.

[0283] Additionally, the characteristics of the individuals, the treatment plans followed by the individuals, the level of compliance with the treatment plans, as well as the outcomes of the treatment plans, may use correlations and other statistical or probabilistic measures to split

QCCP Ln/Zznz/E/YIAI the treatment plans in different databases equivalent to patient cohorts in the 2044 patient data storage. For example, data from a first cohort of first patients who have a similar first injury, a similar first medical condition, a similar first medical procedure performed, a first treatment plan followed by the first patient, as well as a first outcome of the plan of treatment, can be stored in a database of first patients. Data from a second cohort of second patients who have a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment plan followed by the second patient, as well as a second outcome of the treatment plan, can be stored in a second patient database. Any combination of characteristics can be used to separate patient cohorts. In some embodiments, different patient cohorts may be stored in different partitions or volumes of the same database.

[0284] These characteristic data, treatment plan data, and outcome data may be obtained from clinical information that describes the characteristics of individuals who undertook certain treatment plans and the results of those treatment plans. Feature data, treatment plan data, and outcomes data can be correlated across patient cohort databases in the 2044 patient data warehouse. Characteristics of people may include medications prescribed to people, injuries of people, medical procedures performed on people, measurements of people, allergies of people, medical conditions of people, historical information of people, vital signs of people. persons, symptoms of persons, family medical information of persons, other information of persons, or some combination thereof.

[0285] In addition to historical information about other individuals stored in databases equivalent to patient cohorts, real-time information based on current patient characteristics about a current patient being treated may be stored in a database. equivalent to adequate patient cohorts. Patient characteristics may include patient medications, patient injuries, medical procedures performed on the patient, patient measurements, patient allergies, patient medical conditions, historical information

Patient's QCCP Ln/Zznz/E/YIAI, patient's vital signs, patient's symptoms, patient's family health information, other patient information, or combinations thereof.

[0286] In some embodiments, the server 2030 may run an artificial intelligence (AI) engine 2011 that uses one or more machine learning models 2013 to perform at least one of the embodiments described herein. The server 2030 may include a training engine 9 capable of generating one or more machine learning models 2013. 2013 machine learning models can be trained to generate and recommend optimal treatment plans using real-time and historical data correlations that include patient cohort equivalents, among other things. The training engine 209 may generate the machine learning model(s) 2013 and may be implemented into executable computer instructions by one or more processing devices of the training engine 209 and/or the servers 2030. To generate one or more learning models automatic 2013, the training engine 209 may train one or more machine learning models 2013. The 2011 AI Engine may use one or more 2013 Machine Learning models.

[0287] The training engine 209 may be a rack-mount server, a router computer, a personal computer, a personal digital assistant, a smartphone, a laptop, a tablet, an ultraportable (netbook), a computer desktop, an Internet of Things (loT) device, any other desired computing device, or any combination thereof. The training engine 9 may be cloud-based or a real-time software platform, and may include privacy software or protocols, and/or security software or protocols.

[0288] To train one or more machine learning models 2013, the training engine 209 may use a training data set of a corpus of keywords representing target information to identify clinical information. The training data set may also include a corpus of clinical information (e.g., clinical trials, meta-analyses, evidence-based guidelines, journal articles, etc.) having a first data format. Clinical information may include characteristics of individuals, treatment plans followed by individuals, and outcomes of treatment plans, among other things. The set of

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ training data may also include examples of descriptive medical language including labels for target information, telemedical information, and values embedded in the labels. The machine learning model(s) can be trained to translate clinical information from the first data format into automatic description language having a canonical format (e.g., tag-value pair and/or attribute grammar). Training can be performed by identifying keywords from the target information, identifying values of the keywords, and generating the canonical value that includes labels for the target information and values for the target information.

[0289] The machine learning model(s) 2013 may also be trained to translate patient characteristics received in real time (e.g., from an electronic medical record (EMR) system) into descriptive medical language for storage in databases. equivalent data to appropriate patient cohorts. The 2013 machine learning model(s) may be trained to match patterns of characteristics of a patient described by descriptive medical language to the characteristics of other people described by descriptive medical language that represent clinical information. In some embodiments, descriptive medical language representing clinical information may be stored in the various patient cohort equivalent databases of patient data storage 2044. Accordingly, in some embodiments, the machine learning model(s) 2013 may access databases equivalent to cohorts of patients during their training or when recommending optimal treatment plans for a patient. Computing resources, processing efficiency, accuracy, and error minimization can be improved by using descriptive medical language in a canonical format, rather than using the full bodies of text and/or EMR records. In particular, accuracy can be improved and errors minimized by the use of formal descriptive medical language that can be parsed for meaning, while informal descriptions can give rise to more than one potentially semantically overloaded and unresolvable meaning. .

[0290] Different machine learning models 2013 can be trained to recommend different optimal treatment plans for different desired outcomes. For example, one machine learning model can be trained to recommend optimal treatment plans for more effective recovery, while another machine learning model can be trained to recommend optimal treatment plans based on speed. recovery.

[0291] Using training data that includes training data inputs and corresponding target outputs, one or more machine learning models 2013 may refer to model artifacts created by the training engine 209. The training engine 209 may find patterns in the training data where such patterns map the training data input to the target output, and generate machine learning models 2013 that capture these patterns. In some embodiments, the artificial intelligence engine 2011, database 2033, and/or training engine 209 may reside in another component (e.g., assistant interface 2094, clinician interface 2020, etc.) depicted. in Figure 13.

[0292] As described above in more detail, the machine learning model(s) 2013 may comprise, for example, a single level of linear or nonlinear operations (e.g., a support vector machine [SVM]) or machine learning models 2013 can be a deep learning network, that is, a machine learning model that comprises multiple levels of non-linear operations. Examples of deep learning networks are neural networks which include generative adversarial networks, convolutional neural networks, recurrent neural networks with one or more hidden layers, and fully connected neural networks (for example, each neuron can transmit its output signal to the input of data from the remaining neurons, as well as itself). For example, the machine learning model may include a large number of layers and/or hidden layers that perform calculations (e.g., dot products) using multiple neurons.

[0293] System 2010 also includes a patient interface 2050 configured to communicate information to a patient and to receive feedback from the patient. In particular, the patient interface includes an input device 2052 and an output device 2054, which may be referred to collectively as a patient and user interface 2052,2054. Input device 2052 may include one or more devices, such as a keyboard, a mouse, a touch screen data input, a gesture sensor, and/or a microphone and processor configured for speech recognition. The output device 2054 may take one or more different forms, such as a display monitor.

QCCP Ln/Zznz/E/YIAI computer or a display screen on a tablet, smartphone, or smart watch. The output device 2054 may include other hardware and/or software components, such as a projector, virtual reality capability, augmented reality capability, among others. The output device 2054 may incorporate various visual, audio, or other presentation technologies. For example, output device 2054 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds, such as tones, ringers and/or melodies, which may indicate different conditions. and/or instructions. The output device 2054 may comprise one or more different displays that present various data and/or interfaces or controls for use by the patient. The output device 2054 may include graphics, which may be presented through a web-based interface and/or through a computer program or application (App.).

[0294] As shown in Figure 13, the patient interface 2050 includes a second communication interface 2056, which may also be referred to as a remote communication interface configured to communicate with the server 2030 and/or the clinician interface 20 through a second network 2058. In some embodiments, the second network 2058 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the second network 2058 may include the Internet, and security of communications between the patient interface 2050 and the server 2030 and/or the clinician interface 2020 may be established through encryption, such as, for example, by the use of a virtual private network (VPN). In some embodiments, the second network 2058 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communications (NFC), cellular data network, among others. In some embodiments, the second network 2058 may be the same as the first network 2034 and/or be operatively coupled to it.

[0295] The patient interface 2050 includes a second processor 2060 and a second machine-readable storage memory 2062 that contains second instructions 2064 for execution by the second processor 2060 to perform various actions of the patient interface 2050. The second machine-readable storage memory 2062 also includes a local data store 2066 configured to contain data, such as data related to a treatment plan and/or patient data, such as data representing the performance of

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ a patient within a treatment plan. The patient interface 2050 also includes a local communication interface 2068 configured to communicate with various devices for use by the patient near the patient interface 2050. The local communication interface 2068 may include wired and/or wireless communications. In some embodiments, the local communication interface 2068 may include a local wireless network, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communications (NFC), cellular data network, among others.

[0296] System 2010 also includes a treatment apparatus 2070 configured to be manipulated by the patient and/or to manipulate a part of the patient's body to perform activities in accordance with the treatment plan. In some embodiments, the treatment apparatus 2070 may take the form of an exercise and rehabilitation apparatus configured to perform and/or assist in performing a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, and the treatment includes rehabilitation of a part of the patient's body, such as a joint, bone, or muscle group. The body part may include, for example, a spine, a hand, a foot, a knee, or a shoulder. The body part may include a part of a joint, bone, or muscle group, such as one or more vertebrae, a tendon, or a ligament. As shown in Figure 13, the treatment device 2070 includes a controller 2072, which may include one or more processors, computer memory and/or other components. The treatment apparatus 2070 also includes a fourth communication interface 2074 configured to communicate with the patient interface 2050 through the local communication interface 2068. The treatment apparatus 2070 also includes one or more internal sensors 2076 and an activator 2078, just like an engine. The activator 2078 may be used, for example, to move the patient's body part or to resist forces by the patient.

[0297] The internal sensors 2076 may measure one or more operating characteristics of the treatment apparatus 2070, such as a force, a position, a speed, and/or a velocity. In some embodiments, the internal sensors 2076 may include a position sensor configured to measure at least one of a linear motion or an angular motion of a part of the patient's body. For example, an internal sensor 2076 in the form of a position sensor can measure a distance up to which the patient is able to move a part of the treatment apparatus 2070, in

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ where the distance can correspond to a range of motion that the patient's body part can achieve. In some embodiments, the internal sensors 2076 may include a force sensor configured to measure a force applied by the patient. For example, an internal sensor 2076 in the form of a force sensor may measure a force or weight that the patient is able to apply, with a specific body part, to the treatment apparatus 2070.

[0298] The system 10 shown in Figure 13 also includes an ambulation sensor 2082, which communicates with the server 2030 through the local communication interface 2068 of the patient interface 2050. The ambulation sensor 2082 can perform Track and store a series of steps taken by the patient. In some embodiments, the ambulation sensor 2082 may take the form of a bracelet, wristwatch, or smartwatch. In some embodiments, the wander sensor 2082 may be integrated into a telephone, such as a smartphone.

[0299] The system 2010 shown in Figure 13 also includes a goniometer 2084, which communicates with the server 2030 through the local communication interface 2068 of the patient interface 2050. The goniometer 2084 measures an angle of the part of the patient's body. For example, the 2084 goniometer can measure the flexion angle of the patient's knee, elbow, or shoulder.

[0300] The system 2010 shown in Figure 13 also includes a pressure sensor 2086, which communicates with the server 2030 through the local communication interface 68 of the patient interface 2050. The pressure sensor 2086 measures a degree of pressure or weight applied by a part of the patient's body. For example, pressure sensor 2086 may measure a degree of force applied by a patient's foot when pedaling a stationary bicycle.

[0301] The system 2010 shown in Figure 13 also includes a monitoring interface 2090 that may be similar or identical to the clinician interface 2020. In some embodiments, the monitoring interface 2090 may have enhanced functionality beyond what which is provided in the clinician interface 2020. The monitoring interface 2090 can be configured for use by a person responsible for the treatment plan, such as an orthopedic surgeon.

[0302] The system 2010 shown in Figure 13 also includes a reporting interface 2092 that may be similar or identical to the clinician interface 2020. In some embodiments, the reporting interface 2092 may have less functionality. that

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ provided in the 2020 Clinician Interface. For example, the 2092 Reporting Interface may not have the ability to modify a treatment plan. Such reporting interface 2092 may be used, for example, by a biller to determine the use of system 2010 for billing purposes. In another example, the reporting interface 2092 may not have the ability to display patient identifying information and may only display pseudonymous patient data and/or anonymous patient data for certain data fields related to a registered person and/or for certain data fields related to a quasi-identifier of the registered person. For example, a researcher can use the reporting interface 2092 to determine the various effects of a treatment plan on different patients.

[0303] System 2010 includes an assistant interface 2094 for an assistant, such as a doctor, nurse, physical therapist, or technician to communicate remotely with the patient interface 2050 and/or the treatment apparatus 2070. These remote communications may allow the assistant to provide assistance or advice to a patient using the 2010 system. More specifically, the assistant interface 2094 is configured to communicate a telemedicine signal 2096, 2097, 2098a, 2098b, 2099a, 2099b to the patient interface 2050 through a network connection, such as through the first network 2034 and/or the second network 2058. The telemedicine signal 2096, 2097, 2098a, 2098b, 2099a, 2099b comprises one of an audio signal 2096, an audiovisual signal 2097, an interface control signal 2098a to control a function of the patient interface 2050, a interface monitoring 2098b to monitor a status of the patient interface 2050, an apparatus control signal 2099a to change an operating parameter of the treatment apparatus 2070 and/or an apparatus monitoring signal signal 2099b to monitor a status of the treatment apparatus 2070. In some embodiments, each of the Control 2098a, 2099a may consist of unidirectional transmission commands from the assistant interface 2094 to the patient interface 2050. In some embodiments, in response to successful receipt of a control signal 2098a, 2099a and/or to communicate successful and/or failed implementation of the requested control action, a confirmation message may be sent from the patient interface 2050. towards the wizard interface 2094. In some embodiments, each of the signal signals

QCCP Ln/Zznz/E/YIAI surveillance 2098b, 2099b may consist of unidirectional status information commands from the patient interface 2050 to the assistant interface 2094. In some embodiments, a confirmation message may be sent from the assistant interface 2094 to the patient interface 2050 in response to successful receipt of one of the monitoring signals 2098b, 2099b.

[0304] In some embodiments, the patient interface 2050 may be configured as a direct passage for the device control signals 2099a and the device monitoring signals 2099b between the treatment device 2070 and one or more devices, such as the interface of assistant 2094 and/or server 2030. For example, the patient interface 2050 may be configured to transmit a device control signal 2099a in response to a device control signal 2099a in the telemedicine signal 2096, 2097, 2098a, 2098b, 2099a, 2099b of the assistant interface. 2094.

[0305] In some embodiments, the assistant interface 2094 may be presented on a shared physical device, such as the clinician interface 2020. For example, the clinician interface 2020 may include one or more displays that implement the assistant interface 2094. Alternatively or additionally, the clinician interface 2020 may include additional hardware components, such as a video camera, speaker, and/or microphone, to implement aspects of the assistant interface 2094.

[0306] In some embodiments, one or more portions of the telemedicine signal 2096, 2097, 2098a, 2098b, 2099a, 2099b may be generated from a prerecorded source (e.g., an audio recording, a video recording, or an animation ) for presentation through the output device 2054 of the patient interface 2050. For example, a tutorial video can be transmitted from the server 2030 and presented on the patient interface 2050. The patient can request the content of the prerecorded source through the patient interface 2050. Alternatively, through a control on the assistant interface 2094, the assistant can cause the content of the prerecorded source to be played on the 2050 patient interface.

[0307] The assistant interface 2094 includes an assistant input device 2022 and an auxiliary display 2024, which may be referred to collectively as an assistant and user interface 2022, 2024. The assistant input device 2022 may include one or more of a telephone, keyboard, mouse, touch-sensitive area or touch screen, for example. Alternatively or

Additional QCCP ίη/77Π7/Ε/ΥΙΛΙ, the assistant input device 2022 may include one or more microphones. In some embodiments, the microphone(s) may take the form of a handset, headset, wide area microphone, or microphones configured for the assistant to speak with a patient through the patient interface 2050. In some embodiments, the assistant input device 2022 may be configured to provide voice-based functions, with hardware and/or software configured to interpret instructions spoken by the assistant with the use of the microphone(s). The 2022 assistant input device may include functions provided by or similar to those of existing voice-based assistants, such as Apple's Siri, Amazon's Alexa, Google Assistant, or Samsung's Bixby. The assistant input device 2022 may include other hardware and/or software components. The assistant input device 2022 may include one or more general purpose devices and/or specific use devices.

[0308] The visual presentation of the assistant 2024 may take one or more different forms, such as a computer monitor or a display screen on a tablet, smartphone, or smart watch. The visual presentation of the assistant 2024 may include other hardware and/or software components, such as projectors, virtual reality capabilities or augmented reality capabilities, among others. The visual presentation of Assistant 2024 may incorporate different visual, audio or other types of presentation technologies. For example, the visual presentation of the assistant 2024 may include a non-visual representation, such as an audio signal, which may include spoken language and/or other sounds, such as tones, timbres, melodies and/or compositions that may indicate different conditions and/or instructions. The visual presentation of the assistant 2024 may comprise one or more different display screens that present various data and/or interfaces or controls for use by the assistant. The visual presentation of the 2024 assistant may include graphics, which may be presented through a web-based interface and/or through a computer program or application (App.).

[0309] In some embodiments, the system 2010 may allow computer language translation, from the assistant interface 2094 to the patient interface 2050 and/or vice versa. Computer translation of language may include computer translation of spoken language and/or computer translation of text. In addition or alternatively, the 2010 system can

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ allow voice recognition and/or spoken pronunciation of the text. For example, system 2010 may convert spoken words into printed text and/or system 2010 may audibly speak a language from printed text. The 2010 system can be configured to recognize words spoken by any or all of the patient, clinician, or assistant. In some embodiments, system 2010 may be configured to recognize and react to verbal requests or commands from the patient. For example, the 2010 system can automatically start a telemedicine session in response to a verbal command from the patient (which can be given in any of several languages).

[0310] In some embodiments, server 2030 may generate aspects of the visual presentation of wizard 2024 for display via wizard interface 2094. For example, server 2030 may include a web server configured to generate display screens for display. presentation in the assistant 2024 visual presentation. For example, the 2011 artificial intelligence engine can generate recommended optimal treatment plans and/or excluded treatment plans for patients and generate display screens that include the recommended optimal treatment plans and/or excluded treatment plans for presentation in the visual presentation of the assistant 2024 of the assistant interface 2094. In some embodiments, the display of the assistant 2024 may be configured to present a desktop virtualized and hosted by the server 2030. In some embodiments, the server 2030 may be configured to communicate with the assistant interface 2094 over the first network 2034. In In some embodiments, the first network 2034 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the first network 2034 may include the Internet, and the security of communications between the server 2030 and the assistant interface 2094 may be established through privacy-enhancing technologies, such as through the use of encryption. a virtual private network (VPN). Alternatively or additionally, the server 2030 may be configured to communicate with the assistant interface 2094 through one or more networks independent of the first network 2034 and/or other means of communication, such as a direct wired communication channel or wireless. In some embodiments, each of the patient interface 2050 and the treatment apparatus 2070 may operate from a patient location geographically separate from a location of the assistant interface 2094. For example, the

QCCP Ln/Zznz/E/YIAI patient interface 2050 and treatment apparatus 2070 can be used as part of a home rehabilitation system, which can receive remote assistance by using the assistant interface 2094 in a centralized location , such as a clinic or call center.

[0311] In some embodiments, the assistant interface 2094 may be one of several different terminals (e.g., computing devices) that may be grouped, for example, into one or more call centers or one or more medical offices. In some embodiments, a plurality of assistant interfaces 2094 may be geographically distributed. In some embodiments, a person can work as an assistant remotely from any conventional office infrastructure. Such remote work may be performed, for example, when the assistant interface 94 takes the form of a computer and/or a telephone. This remote work feature may allow for work-from-home arrangements that may include part-time and/or flexible work schedules for an assistant.

[0312] Figures 14-15 show an embodiment of a treatment apparatus 2070. More specifically, Figure 14 shows a treatment apparatus 2070 in the form of a stationary cycling machine 2100, which may be referred to as a stationary bicycle for short. . The stationary cycling machine 2100 includes a set of pedals 2102, each attached to a pedal arm 2104 for rotation about an axis 2106. In some embodiments, and as shown in Figure 14, the pedals 2102 can be moved on the pedal arms 2104 to adjust a range of motion used by the patient when pedaling. For example, pedals that are located on the inside, towards the axis 2106, correspond to a smaller range of motion than when the pedals are located on the outside, away from the axis 2106. A pressure sensor 2086 is attached to or integrated into one of the pedals 2102 to measure the degree of force applied by the patient on the pedal 2102. The pressure sensor 2086 may communicate wirelessly with the treatment apparatus 2070 and/or with the 2050 patient interface.

[0313] Figure 16 shows a person (a patient) using the treatment apparatus of Figure 14, and showing the sensors and various data parameters connected to a patient interface 2050. The example of patient interface 2050 is a tablet, computer or smartphone, or a phablet, such as a iPad, iPhone, Android device, or Surface tablet,

QCCP ίη/77Π7/Ε/ΥΙΛΙ that is held manually by the patient. In other embodiments, the patient interface 2050 may be integrated into or attached to the treatment apparatus 2070. Figure 16 shows the patient wearing the ambulation sensor 2082 on the wrist, with a note showing “TODAY'S STEPS 21355,” indicating that the ambulation sensor 2082 has recorded and transmitted that step count to the interface. of patient 2050. Figure 16 also shows the patient wearing the 2084 goniometer on the right knee, with a note showing “KNEE ANGLE 72either”, which indicates that the goniometer 2084 is measuring and transmitting that knee angle to the patient interface 2050. Figure 16 also shows a right side of one of the pedals 2102 with a pressure sensor 2086 showing a “FORCE of 12.5 pounds”, which indicates that the right pedal pressure sensor 2086 is measuring and transmitting that force measurement to the patient interface 2050. Figure 16 also shows a left side of one of the pedals 2102 with a pressure sensor 2086 displaying a “FORCE of 27 pounds,” which indicates that the left pedal pressure sensor 2086 is measuring and transmitting that force measurement. to the 2050 patient interface. Figure 16 also shows other patient data, such as a “SESSION TIME 0:04:13” indicator, which indicates that the patient has been using the treatment device 2070 for 4 minutes and 13 seconds. This session time can be determined by the patient interface 2050 based on the information received from the treatment apparatus 2070. Figure 16 also shows an indicator showing “PAIN LEVEL 3”. Such a pain level may be obtained from the patient in response to a request, such as a question, presented to the patient interface 2050.

[0314] Figure 17 is an exemplary embodiment of a visual presentation of the general information 2120 of the assistant interface 2094. Specifically, the visual presentation of the general information 2120 presents several different controls and interfaces for the assistant to assist in remotely direct a patient to use the patient interface 2050 and/or the treatment apparatus 2070. This remote assistance function may also be referred to as telemedicine or telehealth.

[0315] Specifically, the general information display 2120 includes a patient profile display 2130 that presents biographical information related to a patient using the treatment apparatus 2070. The patient profile display 2130 may take the form of a part or region of the visual presentation of general information 2120,

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ as shown in Figure 17, although the visual presentation of the patient profile 2130 may take other forms, such as a stand-alone screen or a pop-up window. In some embodiments, the visual presentation of the patient profile 2130 may include a limited subset of the patient's biographical information. More specifically, the data presented in the patient profile display 2130 may depend on the assistant's need to view that information. For example, a medical professional who is assisting the patient with a medical problem may be provided with information from the patient's medical history, while a technician troubleshooting a problem with the treatment apparatus 2070 may be provided with a variety of information. much more limited patient-related. The technician, for example, could only be provided with the patient's name. The visual presentation of the patient profile 2130 may include pseudonymous patient data and/or anonymized patient data, or use any privacy-enhancing technology to prevent confidential patient data from being communicated in a manner that could violate the requirements. of patient confidentiality. These privacy-enhancing technologies may enable compliance with laws, regulations, or other government rules, such as, but not limited to, the Health Insurance Portability and Accountability Act (HIPAA) or the General Rules. of Data Protection Regulation (GDPR), where the patient can be considered a “registered person”.

[0316] In some embodiments, the visual presentation of the patient profile 2130 may present information related to the treatment plan for the patient to follow with the use of the treatment apparatus 2070. The treatment plan information may be limited to an assistant who is a medical professional, such as a doctor or physical therapist. For example, a medical professional assisting the patient with a problem related to the treatment regimen may be provided information about the treatment plan, while a technician troubleshooting a problem with the treatment apparatus 2070 cannot be provided with information about the treatment plan. provide any information related to the patient's treatment plan.

[0317] In some embodiments, one or more recommended optimal treatment plans and/or discarded treatment plans may be presented to the assistant through the visual presentation of the

QCCP Ln/Z7nz/E/YIAI patient profile 2130. The artificial intelligence engine 2011 of server 2030 can generate the recommended optimal treatment plan(s) and/or discarded treatment plans and receive them from server 2030 in real time, among others. , during a telemedicine or telehealth session. Below, an example presentation of the recommended optimal treatment plan(s) and/or discarded treatment plans is described with reference to Figure 18.

[0318] The example general information display 2120 shown in Figure 17 also includes a patient status display 2134 that presents status information related to a patient using the treatment apparatus. The display of the patient statue 2134 may take the form of a portion or region of the display of general information 2120, as shown in Figure 17, although the display of the status of the patient 2134 may take other forms, such as a stand-alone screen or pop-up window. The visual presentation of the status of the patient 2134 includes sensor data 2136 from one or more of the external sensors 2082, 2084, 2086 and/or from one or more internal sensors 2076 of the treatment apparatus 2070. In some embodiments, the visual presentation of the Patient status 2134 may present other data 2138 related to the patient, such as the last reported level of pain or progress on a treatment plan.

[0319] User access controls may be used to limit access, including what data is available for viewing and/or modification, on any or all of the user interfaces 2020, 2050, 2090, 2092, 2094 of system 2010. In some embodiments, user access controls may be used to control what information is available to any given person using System 2010. For example, data presented in the assistant interface 2094 may be controlled by user access controls, with permissions set based on the assistant/user's need and/or restrictions on viewing that information.

[0320] The example general information display 2120 shown in Figure 17 also includes a help data display 2140 that presents information for the assistant to use when providing assistance to the patient. The help data display 2140 may take the form of a portion or region of the general information display 2120, as shown in Figure 17. The visual presentation of help data 2140 may take other forms, such as a stand-alone display or a pop-up window. The visual presentation of help data 2140 may include, for example, the presentation of answers to frequently asked questions related to the use of the patient interface 2050 and/or the treatment apparatus 2070. The visual presentation of help data 2140 may also include research data or best practices. In some embodiments, the visual display of assistive data 2140 may present scripts for answers or explanations in response to questions posed by the patient. In some embodiments, the visual presentation of assistive data 2140 may present flowcharts or guides for the assistant to use to determine a root cause and/or solution to a patient's problem. In some embodiments, the assistant interface 2094 may present two or more displays of help data 2140, which may be the same or different, for simultaneous presentation of the help data for use by the assistant. For example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient's problem, and a second help data display may present script information. for the assistant to read to the patient; Such information should preferably include instructions for the patient to take some action, which may help reduce or solve the problem. In some embodiments, based on data entries in the troubleshooting flowchart in the first help data display, the second help data display may be automatically populated with script information.

[0321] The example display of general information 2120 shown in Figure 17 also includes a patient interface control 2150 that displays information related to the patient interface 2050 and/or to modify one or more settings of the patient interface 2050. The patient interface control 2150 may take the form of a portion or region of the general information display 2120, as shown in Figure 17. The patient interface control 2150 may take other forms, such as a stand-alone display or a pop-up window. The patient interface control 2150 may present information communicated to the assistant interface 2094 through one or more of the interface monitoring signals 2098b. As shown in Figure 17, the patient interface control 2150 includes a transmission of the presentation

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ visual 2152 of the visual presentation presented by the patient interface 2050. In some embodiments, the transmission of the visual presentation 2152 may include an active copy of the display screen that is currently being presented to the patient through the 2050 patient interface. In other words, the visual presentation transmission 2152 may present an image of what is presented on a display screen of the patient interface 2050. In some embodiments, the visual presentation transmission 2152 may include abbreviated information related to the display screen that is currently being presented by the patient interface 2050, such as a screen name or a screen number. The patient interface control 2150 may include a patient interface settings control 2154 so that the assistant can adjust or control one or more settings or aspects of the patient interface 2050. In some embodiments, the patient interface settings control Patient 2154 may cause assistant interface 2094 to generate and/or transmit an interface control signal 2098 to control a function or setting of patient interface 2050.

[0322] In some embodiments, control of patient interface settings 2154 may include collaborative navigation or co-browsing capability so that the assistant can remotely view and/or control patient interface 2050. For example, patient interface settings control 2154 may allow the assistant to remotely enter text into one or more text entry fields on patient interface 2050 and/or remotely control a cursor on the patient interface 2050. patient 2050 with the use of a mouse or touch screen of the assistant interface 2094.

[0323] In some embodiments, with the use of patient interface 2050, patient interface setting control 2154 may allow the assistant to change a setting that the patient cannot change. For example, the patient interface 2050 may not be able to access a language setting to prevent a patient from inadvertently changing, in the patient interface 2050, the language used for visual presentations, while controlling interface settings. Patient interface 2154 may allow the assistant to change the language settings of the patient interface 2050. In another example, the patient interface 2050 may not be able to change a font size setting to a smaller size to prevent a patient from inadvertently changing the font size used for visual displays in the patient interface 2050, so such that the

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ visual presentation is legible to the patient, while the patient interface settings control 154 may allow the assistant to change the font size setting of the patient interface 50.

[0324] The example general information display 2120 shown in Figure 17 also includes an interface communications display 2156 showing the status of communications between the patient interface 2050 and one or more devices 2070, 2082, 2084, such as the treatment apparatus 2070, the ambulation sensor 2082 and/or the goniometer 2084. The interface communications display 2156 may take the form of a portion or region of the general information display 2120, as shown in Figure 17. The interface communications display 2156 may take other forms, such as as a standalone screen or popup window. The interface communications display 2156 may include controls for the assistant to remotely modify communications with one or more of the other devices 2070, 2082, 2084. For example, the assistant may remotely command the patient interface 2050 to restart communication with one of the other devices 2070, 2082, 2084, or to establish communications with a new one of the other devices 2070, 2082, 2084. This functionality may be used, for example, when the patient has a problem with one of the other devices 2070, 2082, 2084, or when the patient receives a new or replacement one of the other devices 2070, 2082, 2084.

[0325] The example display of general information 2120 shown in Figure 17 also includes an apparatus control 2160 for the assistant to display and/or control information related to the treatment apparatus 2070. The apparatus control 2160 may take the form of a portion or region of the general information display 2120, as shown in Figure 17. The device control 2160 may take other forms, such as a stand-alone display or a pop-up window. The device control 2160 may include a visual display of the status of the device 2162 with information about the current state of the device. The visual display of the status of the device 2162 may present information communicated to the assistant interface 2094 through one or more of the device monitoring signals 2099b. The visual display of the status of the device 2162 may indicate whether the treatment device 2070 is currently communicating with

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ the patient interface 2050. The visual display of the status of the device 2162 may present other current or historical information related to the status of the treatment device 2070.

[0326] The appliance control 2160 may include an appliance settings control 2164 for the assistant to adjust or control one or more aspects of the treatment apparatus 2070. The appliance settings control 2164 may cause the assistant interface 2094 to generate and/or transmit an apparatus control signal 2099 to change an operating parameter of the treatment apparatus 2070 (e.g., a pedal radius setting, a resistance setting, a target RPM value, etc.). The apparatus settings control 2164 may include a mode button 2166 and a position control 2168, which may be used together for the assistant to place an activator 2078 of the treatment apparatus 2070 in a manual mode, after which A setting, such as a position or speed of the actuator 2078, can be changed with the use of position control 2168. Mode button 2166 may allow a setting, such as a position, to toggle between automatic and manual modes. In some embodiments, one or more adjustments can be made at any time and without having an associated automatic/manual mode. In some embodiments, the assistant may change an operating parameter of the treatment apparatus 2070, such as a pedal radius setting, while the patient is actively using the treatment apparatus 2070. Such “on the fly” adjustment may or may not be available to the patient using the patient interface 2050. In some embodiments, the device settings control 2164 may allow the assistant to change a setting that the patient cannot change, using the 2050 patient interface. For example, the patient interface 2050 may not be able to change a preconfigured setting, such as a height or tilt setting of the treatment apparatus 2070, while the apparatus settings control 2164 may allow the assistant to change the setting of height or inclination of the treatment apparatus 2070.

[0327] The example display of general information 2120 shown in Figure 17 also includes a patient communications control 2170 for controlling an audio or audiovisual communications session with the patient interface 2050. The communication session with the patient interface 2050 may comprise an active transmission of the assistant interface 94 for presentation by the output device of the patient interface 2050. The active stream may take the form of an audio stream and/or a video stream. In some

QCCP Ln/Zznz/E/YIAI modalities, the patient interface 2050 can be configured to provide two-way audio or audiovisual communications with a person with the use of the assistant interface 2094. Specifically, the communications session with the patient interface patient 2050 may include bidirectional (two-way) video or audiovisual transmissions, where each of the patient interface 2050 and the assistant interface 2094 presents the video of the other. In some embodiments, the patient interface 2050 may present video from the assistant interface 2094, while the assistant interface 2094 only presents audio or the assistant interface 2094 does not present any active audio or visual signal from the patient interface 2050. . In some embodiments, the assistant interface 2094 may present video from the patient interface 2050, while the patient interface 2050 only presents audio or the patient interface 2050 does not present any active audio or visual signal from the assistant interface 2094. .

[0328] In some embodiments, the audio or audiovisual communications session with the patient interface 2050 may occur, at least in part, while the patient is performing the rehabilitation regimen for the respective body part. The patient communications control 2170 may take the form of a portion or region of the general information display 2120, as shown in Figure 17. The patient communications control 2170 may take other forms, such as a stand-alone display or a pop-up window. Audio and/or audiovisual communications may be processed or directed through the assistant interface 2094 and/or other device or devices, such as a telephone system or a video conferencing system used by the assistant while the assistant uses the assistant interface 2094. Alternatively or additionally, audio and/or audiovisual communications may include communications with third parties. For example, system 2010 may allow the assistant to initiate a three-way conversation regarding the use of specific hardware or software, both with the patient and with a subject matter expert, such as an assistant or specialist. The example patient communications control 2170 shown in Figure 17 includes call controls 2172 for the assistant to use in managing various aspects of audio or audiovisual communications with the patient. Call controls 2172 include a disconnect button 2174 for the attendant to end the audio or audiovisual communications session. The 2172 call controls also include a button

QCCP Ln/Zznz/E/YIAI squelch 2176 to temporarily mute an audio or audiovisual signal from the assistant interface 2094. In some embodiments, the call controls 2172 may include other attributes, such as a hold button (not shown ). The call controls 2172 also include one or more record/playback controls 2178, such as record, play and pause buttons to control, with the patient interface 2050, the recording and/or playback of audio and/or video of the teleconference session. The call controls 2172 also include a display of the video stream 2180 to present still and/or video images from the patient interface 2050, as well as a self-video display 2182 that displays the current image of the assistant who use the wizard interface. The self-video display 2182 may be presented in a picture-in-picture format, in a section of the video stream display 2180, as shown in Figure 17. Alternatively or additionally , the self-video display 2182 may be presented separately and/or independently of the video stream display 2180.

[0329] The example visual presentation of general information 2120 shown in Figure 17 also includes a third-party communications control 2190 for use when conducting audio and/or audiovisual communications with third parties. The third-party communications control 2190 may take the form of a portion or region of the general information display 2120, as shown in Figure 17. Third-party communications monitoring 2190 may take other forms, such as a visual presentation on a separate screen or a pop-up window. Third-party communications control 2190 may include one or more controls, such as a contact list and/or buttons or controls for contacting a third party regarding the use of specific hardware or software, e.g., an expert in the matter, such as a medical professional or specialist. Third party communications control 2190 may include conference call capability for the third party to simultaneously communicate with the assistant via the assistant interface 2094 and with the patient via the patient interface 2050. For example , System 2010 may allow the assistant to initiate a three-way conversation with the patient and the third party.

[0330] Figure 18 shows an exemplary embodiment of the visual presentation of the general information 2120 of the assistant interface 2094 that presents in real time the treatment plans

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100 recommended optimal treatment plans and discarded treatment plans during a telemedicine session in accordance with this description. As depicted, the general information display 2120 only includes sections for the patient profile 2130 and the video stream display 2180, including the self-video display 2182. Any suitable configuration of controls and interfaces of the general information display 2120 described with reference to Figure 17 may be presented in addition to or instead of the patient profile 2130, the video stream display 2180, and the self-report display. -video 2182.

[0331] The assistant (e.g., a medical professional) using the assistant interface 2094 (e.g., a computing device) during the telemedicine session may be presented in the self-video 2182 in a portion of the visual presentation. of general information 2120 (for example, in the user interface is presented on a display screen 2024 of the assistant interface 2094) that also presents a video of the patient in the visual presentation of the video stream 2180. As shown, another part of the visual presentation of General information 2120 includes the visual presentation of the patient profile 2130.

[0332] The visual presentation of the patient profile 2130 presents two examples of optimal treatment plans 2600 and one example of an excluded treatment plan 2602. As described herein, optimal treatment plans may be recommended based on the different clinical information and the characteristics of the patient being treated. Clinical information may include information related to the characteristics of other people, treatment plans followed by other people, and results of treatment plans. To generate the recommended optimal treatment plans 2600 that the patient should follow to obtain a desired result, matches of a pattern between the characteristics of the patient being treated and the other people can be searched using one or more machine learning models 2013 of the artificial intelligence engine 2011. Each of the recommended optimal treatment plans can be generated based on the different desired results.

[0333] For example, in the following assumption: Treatment plan “A” states that “Patient Patient X has type 2 diabetes; and Patient

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101 “Drug Z for pain management during the treatment plan (Drug Z is approved for people with type 2 diabetes).” Consequently, the optimal treatment plan generated increases the range of motion by Y%. As can be seen, the optimal treatment plan also includes a recommended medication (e.g., drug Z) that will be prescribed to the patient to manage pain in light of a known medical condition (e.g., type 2 diabetes) of the patient. That is, the medication recommended to the patient not only does not conflict with the patient's medical condition, but improves the likelihood of a superior clinical outcome for the patient.

[0334] The recommended optimal treatment plan “B” may specify, based on a different desired outcome of the treatment plan, a different treatment plan that includes a different treatment protocol for a treatment device, a different medication regimen , etc.

[0335] As depicted, the visual presentation of the patient profile 2130 may also present discarded treatment plans 2602. These types of treatment plans are displayed to the assistant who uses the assistant interface 2094 to advise the assistant not to recommend certain parts of a treatment plan to the patient. For example, the discarded treatment plan might specify: “Patient X should not use the treatment device for more than 30 minutes per day due to a heart condition; Patient X has type 2 diabetes; and Patient Specifically, the discarded treatment plan points out a limitation of a treatment protocol in which, due to a heart condition, Patient X should not exercise for more than 30 minutes a day. The discarded treatment plan also states that Patient X should not be prescribed medication M because it conflicts with the medical condition of type 2 diabetes.

[0336] The assistant may select the optimal treatment plan for the patient in the display of general information 2120. For example, the assistant may use an input peripheral (e.g., a mouse, a touch screen, a microphone, a keyboard, etc.) to choose from ocher Ln/zznz/E/YiAi

102 2600 optimal treatment plans for the patient. In some embodiments, during the telemedicine session, the assistant may discuss with the patient the pros and cons of the recommended optimal treatment plans 2600.

[0337] In any case, the assistant can select the optimal treatment plan that the patient should follow to obtain the desired result. The selected optimal treatment plan may be transmitted to the patient interface 2050 for display. The patient can view the selected optimal treatment plan on the patient interface 2050. In some embodiments, the assistant and the patient can talk during the telemedicine session about the details (for example, the treatment protocol with the use of the treatment apparatus 2070, the diet, the medication regimen, etc.) in time. real.

[0338] Figure 19 shows an exemplary embodiment of a server 2030 that translates clinical information 2700 into a descriptive medical language 2702 for processing by an artificial intelligence engine 2011 in accordance with the present description. The 2700 clinical information may be written by a person who holds a certain professional credential, license, or degree. In the example depicted, clinical information 2700 includes a portion of the meta-analysis for a clinical trial titled “EFFECT OF USING A TREATMENT PLAN FOR HIP OSTEOARTHRITIS PAIN.” That part includes a “Results” section and a “Conclusions” section. There may be many other parts (e.g., trial procedure details, subject biographies, etc.) of the clinical information 2700 that, for purposes of clarity of explanation, are not represented.

[0339] One or more machine learning models 2013 may be trained to analyze a body of structured or unstructured text (e.g., clinical information 700) for a corpus of keywords representing the target information. The target information may be included in one or more parts of the clinical information 2700. Target information may refer to any appropriate information of interest, such as characteristics of individuals (e.g., vital signs, medical conditions, medical procedures, allergies, family medical information, measurements, etc.), treatment plans followed by individuals , results of treatment plans, clinical trial information, treatment devices used for the treatment plan, and the like.

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103

[0340] Using tags that represent the target information and values associated with the tags, the machine learning model(s) 2013 can generate a canonical format defined by descriptive medical language. The values may be numbers, characters, alphanumeric characters, strings, arrays, and the like, which are obtained from the parts of the clinical information 2700 (including the target information). Target information can be organized into parent-child relationships based on the structure, organization, and/or relationships of the information. For example, the keyword “Results” can be identified and determined to be a parent-level label due to its objective information covering the offspring, such as trials, subjects, treatment plan, treatment devices, subject characteristics, and conclusions. . Thus, a parent-level tag for “<results>” can include child-level tags for “<trials>”, “<subjects>”, “<treatment plan>”, “<treatment apparatus>”, “< subject characteristics>” and “<conclusions>”. Each tag can have a corresponding end tag (for example, “<results> ... </results>”).

[0341] Described below is an embodiment of the operations that a trained machine learning model 2013 performs to encode the portion of the clinical information 2700 into the descriptive medical language 2702. The trained machine learning model 2013 identified the keywords “ treatment plan” and “treatment device” in clinical information part 2700. Once identified, the trained 2013 machine learning model can analyze words nearby (e.g., to the left and right) of the keywords to determine, based on training data, whether the words match a recognized context. The trained 2013 machine learning model can also determine, based on training data and based on attributes of the data, whether words are recognized as being associated with keywords. In Figure 19, the trained machine learning model can determine that the words “range of motion (ROM)” fit the context of the keyword “treatment device” and also that they are likely to be recognized as being associated with the keyword “treatment device”. Therefore, the “ROM” value is placed between the “<treatment apparatus>” and “</treatment apparatus>” tags that represent the target information. The other tags that represent the target information in the canonical format of the descriptive medical language 2702 can be populated in a similar way. The descriptive medical language 2702 that represents the part of the clinical information 2700

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104 may be stored in patient data storage 2044 in a suitable patient cohort equivalent database.

[0342] Figure 20 shows an exemplary embodiment of a method 2800 for recommending an optimal treatment plan in accordance with the present disclosure. Method 2800 is performed using processing logic that may include hardware (circuits, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), or a combination of both. . Method 2800 and/or each of its individual functions, routines, subroutines or operations may be performed by one or more processors of a computing device (e.g., any component of Figure 13, such as server 2030 running the engine of artificial intelligence 2011 13 In certain embodiments, method 2800 can be performed through a single processing cycle. Alternatively, method 2800 may be performed using two or more processing cycles, where each cycle implements one or more individual functions, routines, subroutines, or operations of the methods.

[0343] For purposes of simplicity of explanation, method 2800 is represented and described as a series of operations. However, operations according to this description may take place in a different order and/or simultaneously and/or with other operations that are not presented or described in this document. For example, the operations represented in method 2800 may occur in combination with any other operation of any other method described herein. Additionally, not all of the operations illustrated may be required to implement Method 2800 in accordance with the disclosed subject matter. Furthermore, those skilled in the art will understand and note that method 2800 could alternatively be represented as a series of interrelated states via a state or event diagram.

[0344] At 2802, the processing device may receive, from a data source 2015, clinical information 2700 related to the results of carrying out specific treatment plans with the use of the treatment apparatus 2070 for individuals exhibiting certain characteristics. Clinical information has a first data format, which may include natural language text in the form of words arranged in sentences that are arranged in paragraphs. The first data format may be a report or description, where the report or description may

QCCP ίη/ΖΖΠΖ/Ε/ΥΙΛΙ

105 include information related to clinical trials, medical research, meta-analyses, evidence-based guidelines, journals and the like. The first data format may include information arranged in an unstructured manner and may have a first data size (e.g., bytes, kilobytes, etc.).

[0345] Certain characteristics of people may include medications prescribed to people, injuries of people, medical procedures performed on people, measurements of people, allergies of people, medical conditions of people, first historical information of people. persons, vital signs of persons, symptoms of persons, family medical information of persons, or combinations thereof. Characteristics may also include the following information related to individuals: demographic, geographic, diagnostic, measurement or test-based, medically historical, etiological, cohort-associated, differentially diagnostic, surgical, physically therapeutic, pharmacological, and other recommended treatments.

[0346] At 2804, the processing device may translate a portion of the clinical information from the first data format into a descriptive medical language 2702 used by the artificial intelligence engine 2011. The descriptive medical language 2702 may include a second data format that structures the unstructured data of clinical information 2700. For example, descriptive medical language 2702 may include the use of tag-value pairs, where the tags identify the type of value stored between the tags. The descriptive medical language 2702 may have a second data size (e.g., bits) that is less than the first data size of the clinical information 2700. The descriptive medical language may include telemedicine data.

[0347] At 2806, the processing device may determine, based on the portion of clinical information 2700 described by descriptive medical language 2702 and a set of characteristics related to a patient, the optimal treatment plan 2600 that the patient should follow. follow when using the 2070 treatment device to achieve the desired result. One or more machine learning models 2013 of the artificial intelligence engine 2011 may be trained to generate the optimal treatment plan 2600. For example, a machine learning model 2013 may be trained to look for pattern matches among the portion of clinical information described using descriptive medical language 2702 with the set of patient characteristics. In some

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106 modalities, the set of patient characteristics is also represented in descriptive medical language. The pattern is associated with the optimal treatment plan that can produce the desired result.

[0348] In some embodiments, the optimal treatment plan may also include information related to a medical procedure to be performed on the patient, a treatment protocol for the patient using a treatment apparatus 2070, a dietary regimen for the patient, a medication regimen for the patient, a sleep regimen for the patient, or combination thereof.

[0349] The desired result may include obtaining a certain result within a certain period of time. The certain result may include a range of motion that the patient achieves with the use of the treatment apparatus 2070, a degree of force exerted by the patient on a part of the treatment apparatus 2070, an amount of time that the patient exercises with the use of the treatment device 2070, a distance that the patient travels with the use of the treatment device 2070, a level of pain experienced by the patient when using the treatment device 2070 or some combination thereof.

[0350] In some embodiments, the processing device may determine, based on the portion of clinical information described by the descriptive medical language and the set of patient-related characteristics, a second optimal treatment plan for the patient to follow. with the use of the 2070 treatment apparatus to achieve a desired result. The desired outcome may consist of a clinical consequence of recovery and the second desired outcome may consist of recovery time. The clinical consequence of recovery may include achieving a certain threshold of functionality, mobility, movement, range of motion, etc., of a specific body part. Recovery time may include achieving a certain threshold of functionality, mobility, movement, range of motion, etc., of a specific body part at a certain time threshold. For example, some people may prefer to recover to a certain degree of mobility as quickly as possible without full recovery. As described above, different 2013 machine learning models can be trained, using different clinical information, to provide different recommended treatment plans that

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107 can produce different desired results.

[0351] In some embodiments, the processing device may determine, based on the portion of clinical information described by descriptive medical language and the set of patient-related characteristics, an excluded treatment plan 2602 that should not be recommended for for the patient to follow when using the 2070 treatment apparatus to achieve the desired result. In some embodiments, as depicted in Figure 18, the optimal treatment plans 2600 and the excluded treatment plans 2602 may be presented simultaneously in a first portion (e.g., in the display of the patient profile 2130) of the user interface while at least the video data or other multimedia data of the patient participating in the telemedicine session may be presented elsewhere (e.g. in the visual presentation of the video transmission 2180).

[0352] In some embodiments, optimal treatment plans 2600 and excluded treatment plans 2602 may be presented simultaneously while the medical professional is not participating in a telemedicine session. For example, optimal treatment plans 2600 and excluded treatment plans 2602 may be presented in the user interface before a telemedicine session begins or after a telemedicine session ends.

[0353] At 2808, the processing device may provide the optimal treatment plan for presentation in a user interface (e.g., in the visual presentation of general information 2120) on a computing device (e.g., the interface assistant 2094) of a medical professional. Additionally, any other optimal treatment plans 2600 generated may be provided to the healthcare professional's computing device. For example, different optimal treatment plans that produce different clinical consequences may be presented to the medical professional. The processing device may receive a treatment plan selected from any of the presented treatment plans. In some embodiments, the medical professional may select the optimal treatment plan based on a patient's clinical consequence preference. For example, an athlete might want to optimize their performance, while a retiree might want to optimize their quality of life without pain. The selected treatment plan can be transmitted to the patient's computing device for presentation in a user interface. In

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108 In some embodiments, optimal treatment plans may be provided to the medical professional's computing device during a telemedicine session to cause the optimal treatment plan to be presented in real time in a first portion of the user interface while a video and, of Optionally, other patient multimedia elements are presented simultaneously in a second part of the user interface. The selected treatment plan can be presented on the patient's computing device during the telemedicine session, so that the medical professional can explain the selected treatment plan to the patient.

[0354] Figure 21 shows an exemplary embodiment of a method 2900 for translating clinical information into descriptive medical language in accordance with the present disclosure. Method 2900 includes operations performed by the processors of a computing device (e.g., any component of Figure 13, such as server 2030 running artificial intelligence engine 2011). In some embodiments, one or more operations of method 2900 are implemented in computer instructions stored in a memory device and executed by a processing device. Method 2900 may be performed in the same or similar manner as described above in connection with Method 2800. The operations of Method 2900 may be performed in combination with any of the operations of any of the methods described herein.

[0355] Method 2900 may include operation 2804 of method 2800 described above and depicted in Figure 20. For example, at 2804, in method 2600, the processing device may translate a portion of the clinical information of the first format. data into a descriptive medical language used by the artificial intelligence engine.

[0356] Method 2900 of Figure 21 includes operations 2902, 2904, and 2906. Operations 2902, 2904, and 2906 may be performed by one or more machine learning models 2013 trained from the artificial intelligence engine 2011.

[0357] At 2902, the processing device may analyze clinical information. At 2904, the processing device may identify, based on keywords representing the target information in the clinical information, the part of the clinical information that has values related to the target information. At 2906, the processing device may generate a

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109 canonical format defined using descriptive medical language. The canonical format may include tags that identify the target information values. Tags can be attributes that describe specific characteristics of the target information. Specific characteristics may include the cohort class to which a person is assigned, the person's age, semantic information related to a given cohort, family history, and the like. In some embodiments, specific characteristics may include any information or indication that a person is at risk.

[0358] The canonical format may allow for more efficient processing of the portion of clinical information represented by descriptive medical language by training a machine learning model to generate optimal treatment plans for patients using the trained machine learning model. . Additionally, the canonical format may allow for more efficient processing by the trained machine learning model by looking for pattern matches between patient characteristics and the portion of clinical information represented by descriptive medical language.

[0359] Figure 22 shows an example of a computer system 21000 that can perform any or more of the methods described herein, in accordance with one or more aspects of the present description. In an example, the computing system 21000 may include a computing device and correspond to the assistant interface 2094, the reporting interface 2092, the monitoring interface 2090, the clinician interface 2020, the server 2030 (including the AI engine 2011), the patient interface 2050, the ambulatory sensor 2082, the goniometer 2084, the treatment apparatus 2070, the pressure sensor 2086 or any suitable component of Figure 13. The computer system 21000 may execute instructions that implement one or more machine learning models 2013 of the artificial intelligence engine 2011 of Figure 13. The computer system may connect (e.g., network) to other computer systems on a LAN. , an intranet, an extranet or the Internet, including through the cloud or a network between users. The computer system can operate with the capacity of a server in a client-server network environment. The computing system may be a personal computer (PC), a tablet, a wearable device (e.g. a bracelet), a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a

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110 camera, a video camera, an Internet of Things (IoT) device, or any device capable of executing a set of instructions (sequential or otherwise) that specify the actions that the device must perform. Furthermore, although only one computer system is illustrated, it should be understood that the term “computer” also includes any group of computers that individually or jointly execute a set (or multiple sets) of instructions to perform one or more of the methods are described in this document.

[0360] The computing system 21000 includes a processing device 21002, a main memory 21004 (e.g., a read-only memory (ROM), a flash memory, solid state drives (SSD), a dynamic random access memory (DRAM), such as a synchronous DRAM (SDRAM)), a static memory 21006 (for example, a flash memory, solid state drives (SSD), a static random access memory (SRAM)), as well as a data storage device 21008 that communicates with other devices through a bus 1010.

[0361] Processing device 21002 represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, or the like. More particularly, the processing device 21002 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor that implements other instruction sets or processors that implement a combination of instruction sets. The processing device 21002 may also be one or more purpose-specific processing devices, such as an application-specific integrated circuit (ASIC), a system on a chip, a field programmable gate array (FPGA), a digital signals (DSP), a network processor or the like. The processing device 21002 is configured to execute instructions to perform any of the operations and steps described herein.

[0362] The computer system 21000 may also include a network interface device 21012. The computer system 21000 may also include a video display 21014 (e.g., a liquid crystal display (LCD), a light-emitting diode (LED), an organic light-emitting diode (OLED), a quantum LED, a cathode ray tube (CRT), a CRT shadow mask, a grating

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111 aperture CRT, a monochrome CRT), one or more input devices 21016 (e.g., a keyboard and/or a mouse or a game-like controller), and one or more speakers 21018 (e.g., a speaker). In an illustrative example, the video display 21014 and input devices 21016 may be combined into a single component or device (e.g., an LCD touch screen).

[0363] The data storage device 21016 may include a computer-readable medium 21020 on which instructions 21022 that incorporate one or more of the methods, operations, or functions described herein are stored. The instructions 21022 may also reside, in whole or at least partially, in the main memory 21004 and/or in the processing device 21002 during their execution by the computing system 21000. Thus, the main memory 21004 and the processing device 21002 also constitute computer-readable media. Instructions 21022 may also be transmitted or received over a network using network interface device 21012.

[0364] Although the computer-readable storage medium 21020 is shown in the illustrative examples as a single medium, the term “computer-readable storage medium” should be understood to include a single medium or multiple media (e.g., a database). centralized or distributed data, and/or associated caches and servers) that store the instruction set(s). It should also be understood that the term “computer-readable storage medium” includes any medium capable of storing, encoding or carrying a set of instructions for execution by the machine and causing the machine to perform one or more of the methodologies herein description. Accordingly, the term “computer readable storage medium” will be understood to include, but is not limited to, solid state memories, optical media, and magnetic media.

[0365] Clause 25. A method for providing, using an artificial intelligence engine, an optimal treatment plan for use with a treatment apparatus, the method comprising:

[0366] receiving, from a data source, clinical information related to the results of having used the treatment device to make specific treatment plans for people who have certain characteristics, wherein the clinical information has a first data format;

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112

[0367] translate a portion of the clinical information from the first data format into a descriptive medical language used by the artificial intelligence engine;

[0368] determine, based on the portion of clinical information described using descriptive medical language and a plurality of characteristics related to a patient, the optimal treatment plan that the patient should follow with the use of the treatment apparatus to achieve a desired result; and

[0369] provide the optimal treatment plan for presentation on a computing device of a medical professional.

[0370] Clause 26. The method in accordance with any clause of this document, wherein translating the clinical information part of the first data format into a descriptive medical language used by the artificial intelligence engine also comprises:

[0371] analyze clinical information;

[0372] identify, based on keywords representing the target information in the clinical information, the part of the clinical information that has values related to the target information;

[0373] generate a canonical format defined using descriptive medical language, where the canonical format comprises tags that identify the values of the target information.

[0374] Clause 27. The method in accordance with any clause of this document, where labels are attributes that describe specific characteristics of the target information;

[0375] Clause 28. The method in accordance with any clause of this document, wherein providing the optimal treatment plan for presentation on the computing device of the medical professional also includes:

[0376] causing, during a telemedicine session, the optimal treatment plan to be presented on a user interface of the medical professional's computing device, wherein the optimal treatment plan is not presented on a display screen of a device computing, the display screen is configured for use by the patient during the telemedicine session.

[0377] Clause 29. The method in accordance with any clause of this document,

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113 also includes:

[0378] determine, based on the portion of clinical information described using descriptive medical language and the plurality of characteristics related to the patient, a ruled out treatment plan that should not be recommended for the patient to follow when using the medical device. treatment to achieve the desired result; and

[0379] provide the excluded treatment plan for presentation on the healthcare professional's computing device.

[0380] Clause 30. The method of compliance with any clause of this document also includes:

[0381] determine, based on the portion of clinical information described by descriptive medical language and the plurality of characteristics related to a patient, a second optimal treatment plan for the patient to follow when using the treatment apparatus to achieve a second desired outcome, wherein the desired outcome consists of a clinical consequence of recovery and the second desired outcome consists of a recovery time; and

[0382] providing the second optimal treatment plan for presentation on the medical professional's computing device;

[0383] receive a treatment plan selected from either the optimal treatment plan or the second optimal treatment plan; and

[0384] transmit the selected treatment plan to a patient computing device for display on a user interface of the patient computing device.

[0385] Clause 31. The method in accordance with any clause of this document, wherein the desired result comprises obtaining a certain result in a certain period of time, and the certain result comprises:

[0386] a range of motion that the patient achieves with the use of the treatment apparatus,

[0387] a degree of force exerted by the patient on a part of the treatment apparatus,

[0388] an amount of time that the patient exercises with the use of the treatment device, [0389] a distance that the patient travels with the use of the treatment device, or

[0390] some combination thereof.

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114

[0391] Clause 32. The method in accordance with any clause of this document, where:

[0392] Certain characteristics of persons include first medications prescribed to persons, first injuries of persons, first medical procedures performed on persons, first measurements of persons, first allergies of persons, first medical conditions of persons, first historical information of people, first vital signs of people, first symptoms of people, first family medical information of people, first demographic information of people, first geographical information of people, first information based on measurements or tests of people, first medically historical information of people, first etiological information of people, first information associative to cohorts of people, first differentially diagnostic information of people, first surgical information of people, first therapeutic information from a physical aspect of the people, first pharmacological information about the people, other first treatments recommended to the people or some combination thereof, and

[0393] The plurality of patient characteristics comprises second medications of the patient, second injuries of the patient, second medical procedures performed on the patient, second measurements of the patient, second allergies of the patient, second medical conditions of the patient, second historical information of the patient, seconds patient's vital signs, patient's second symptoms, patient's second family medical information, second demographic information of the patient, second geographic information of the patient, second information based on measurements or tests of the patient, second medically historical information of the patient, second etiological information of the patient, second associative information to cohorts of the patient, second differentially diagnostic information of the patient, second surgical information of the patient, second therapeutic information from a physical aspect of the patient, second pharmacological information of the patient, other second treatments recommended to the patient or some combination thereof.

[0394] Clause 33. The method of compliance with any clause of this document, wherein the clinical information is that written by a person who has a certain credential

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115 professional and comprises a journal article, a clinical trial, evidence-based guidelines, meta-analysis or some combination thereof.

[0395] Clause 34. The method in accordance with any clause of this document, wherein determining, based on the part of the clinical information described by descriptive medical language and the plurality of characteristics related to the patient, the treatment plan optimal that the patient should continue with the use of the treatment device to achieve a desired result, also includes:

[0396] search for matches of a pattern between the part of the clinical information described using descriptive medical language in the plurality of patient characteristics, where the pattern is associated with the optimal treatment plan that produces the desired result.

[0397] Clause 35. The method in accordance with any clause of this document, wherein the optimal treatment plan comprises:

[0398] a medical procedure that must be performed on the patient,

[0399] a treatment protocol for the patient using the treatment apparatus,

[0400] a dietary regimen for the patient,

[0401] a medication regimen for the patient,

[0402] a sleep regimen for the patient, or

[0403] some combination thereof.

[0404] Clause 36. A tangible, non-transitory, computer-readable medium that stores instructions that, when executed, cause a processing device:

[0405] receive, from a data source, clinical information related to the results of having used the treatment device to make specific treatment plans for people who have certain characteristics, wherein the clinical information has a first data format;

[0406] translate a portion of the clinical information from the first data format into a descriptive medical language used by the artificial intelligence engine;

[0407] determine, based on the portion of clinical information described using descriptive medical language and a plurality of characteristics related to a patient, the optimal treatment plan that the patient should follow with the use of the treatment apparatus to achieve a

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116 desired result; and

[0408] provide the optimal treatment plan for presentation on a computing device of a medical professional.

[0409] Clause 37. The computer-readable medium in accordance with any clause of this document, wherein translating the clinical information part of the first data format into the descriptive medical language used by the artificial intelligence engine also comprises:

[0410] analyze clinical information;

[0411] identify, based on keywords representing the target information in the clinical information, the part of the clinical information that has values of the target information;

[0412] generate a canonical format defined using descriptive medical language, where the canonical format comprises tags that identify the values of the target information.

[0413] Clause 38. The computer-readable medium in accordance with any clause of this document, wherein providing the optimal treatment plan for presentation on the computing device of the medical professional also includes:

[0414] causing, during a telemedicine session, the optimal treatment plan to be presented in a user interface of the computing device of the medical professional, wherein, during the telemedicine session, the optimal treatment plan is not presented in a user interface of a patient computing device.

[0415] Clause 39. The computer-readable medium in accordance with any clause of this document, wherein the processing device also:

[0416] determines, based on the portion of clinical information described by descriptive medical language and the plurality of characteristics related to a patient, a second optimal treatment plan for the patient to follow when using the treatment apparatus to achieve a second desired outcome, wherein the desired outcome consists of a clinical consequence of recovery and the second desired outcome consists of a recovery time; and

[0417] provides the second optimal treatment plan for presentation on the medical professional's computing device;

[0418] receives a treatment plan selected from either the optimal treatment plan or the

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117 second optimal treatment plan; and

[0419] transmits the selected treatment plan to a patient computing device.

[0420] Clause 40. The computer-readable medium in accordance with any clause of this document, wherein the desired result comprises obtaining a certain result in a certain period of time, and the certain result comprises:

[0421] a range of motion that the patient achieves with the use of the treatment apparatus,

[0422] a degree of force exerted by the patient on a part of the treatment apparatus,

[0423] an amount of time that the patient exercises with the use of the treatment device, [0424] a distance that the patient travels with the use of the treatment device, or

[0425] some combination thereof.

[0426] Clause 41. The computer-readable medium in accordance with any clause of this document, where:

[0427] Certain characteristics of persons include first medications prescribed to persons, first injuries of persons, first medical procedures performed on persons, first measurements of persons, first allergies of persons, first medical conditions of persons, first historical information of people, first vital signs of people, first symptoms of people, first family medical information of people, first demographic information of people, first geographical information of people, first information based on measurements or tests of people, first medically historical information of people, first etiological information of people, first information associative to cohorts of people, first differentially diagnostic information of people, first surgical information of people, first therapeutic information from a physical aspect of the people, first pharmacological information about the people, other first treatments recommended to the people or some combination thereof, and

[0428] The plurality of patient characteristics comprises second medications of the patient, second injuries of the patient, second medical procedures performed on the patient, second measurements of the patient, second allergies of the patient, second medical conditions of the patient, second historical information of the patient, seconds patient's vital signs, seconds

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118 symptoms of the patient, second family medical information of the patient, second demographic information of the patient, second geographic information of the patient, second information based on measurements or tests of the patient, second medically historical information of the patient, second etiological information of the patient, second information associative to patient cohorts, second differentially diagnostic information of the patient, second surgical information of the patient, second therapeutic information from a physical aspect of the patient, second pharmacological information about the patient, other second treatments recommended to the patient or some combination thereof.

[0429] Clause 42. The computer-readable medium in accordance with any clause of this document, wherein the clinical information is that written by a person who has a certain professional credential and comprises a journal article, a clinical trial, guidelines based in evidence or some combination thereof.

[0430] Clause 43. A system that comprises:

[0431] a memory device that stores instructions; and

[0432] a processing device communicatively coupled to the memory device, wherein the processing device executes instructions to:

[0433] receiving, from a data source, clinical information related to the results of having used the treatment device to make specific treatment plans for people who have certain characteristics, wherein the clinical information has a first data format;

[0434] translate a portion of the clinical information from the first data format into a descriptive medical language used by the artificial intelligence engine;

[0435] determine, based on the portion of clinical information described by descriptive medical language and a plurality of characteristics related to a patient, the optimal treatment plan that the patient should follow when using the treatment apparatus to achieve a result wanted; and

[0436] provide the optimal treatment plan for presentation on a computing device of a medical professional.

[0437] Clause 44. The system in accordance with any clause of this document, ocher Ln/zznz/E/YiAi

119 where translating the clinical information part of the first data format into a descriptive medical language used by the artificial intelligence engine also includes:

[0438] analyze clinical information;

[0439] identify, based on keywords representing the target information described by the clinical information, the part of the clinical information that has values of the target information;

[0440] generate a canonical format defined using descriptive medical language, wherein the canonical format comprises tags that identify the values of the target information.

METHOD AND SYSTEM THAT USES ARTIFICIAL INTELLIGENCE TO MONITOR THE USER'S CHARACTERISTICS DURING A TELEMEDICINE SESSION

[0441] Determine a treatment plan for a patient who presents certain characteristics (for example, vital signs or other types of measurements; performance; demographic, geographic, diagnostic information, based on measurements or tests, medically historical, etiological, associative to cohorts, differentially diagnostic, surgical, therapeutic from a physical aspect, behavioral, pharmacological and other recommended treatments, etc.) may constitute a technically difficult problem. For example, establishing a treatment plan may involve a large amount of information, which can lead to inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitation setting, some of that wealth of information considered may include patient characteristics, such as personal information, performance information, and measurement information. Personal information may include, for example, demographic, psychographic or other information, such as age, weight, gender, height, body mass index, medical condition, family medication history, injury, a medical procedure, a prescribed medication, behavioral or psychological states, or some combination thereof. Performance information may include, for example, an elapsed time of use of a treatment device, a degree of force exerted on a portion of the treatment device, a range of motion achieved on the treatment device, a speed of movement of a part of the treatment device, an indication of

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120 a plurality of pain levels with the use of the treatment device or some combination thereof. The measurement information may include, for example, a vital sign, respiratory rate, heart rate, temperature, blood pressure, glucose level or other biomarker, or some combination thereof. It may be desirable to process the characteristics of a large number of patients, the treatment plans made for those patients, and the results of the treatment plans for those patients.

[0442] Furthermore, another technical problem may involve the remote treatment, via a computing device during a telemedicine or telehealth session, of a patient from a location other than the location in which the patient is located. Another technical problem is controlling or enabling, from a different location, the control of a treatment device used by the patient at the location where the patient is located. Often, when a patient undergoes rehabilitation surgery (for example, knee surgery), a healthcare provider may prescribe a treatment device for the patient to use to perform a treatment protocol at home or at any mobile location or temporary home. A healthcare provider may refer a patient to a physician, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, fitness trainer, instructor, personal trainer, or the like. A healthcare provider may refer a patient to anyone who has a credential, license, degree, or similar in the field of medicine, physical therapy, rehabilitation, or the like.

[0443] When the healthcare provider is in a location other than the patient and the treatment device, it may be technically difficult for the healthcare provider to monitor the patient's actual progress (rather than relying on the patient's word). patient about his or her progress) using the treatment device, modify the treatment plan based on the patient's progress, adapt the treatment device to the personal characteristics of the patient as the patient carries out the treatment plan, and the like.

[0444] Accordingly, the use of systems and methods, such as those described herein, configured to monitor the actual progress of the patient, while the patient carries out the treatment plan with the use of the treatment device, may be convenient. In some embodiments, the systems and methods described herein may be configured to

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121 receive treatment data related to a user using a treatment device to carry out a treatment plan. The user may include a patient, user, or a person who uses the treatment device to perform various exercises.

[0445] The treatment data may include various characteristics of the user, various basal measurement information related to the user, various measurement information related to the user while the user uses the treatment device, various characteristics of the treatment device, the plan of treatment, other appropriate data or a combination thereof. In some embodiments, the systems and methods described herein may be configured to receive treatment data during a telemedicine session.

[0446] In some embodiments, while the user uses the treatment device to carry out the treatment plan, at least part of the treatment data may correspond to sensor data from a sensor configured to detect various characteristics of the treatment device and /or the user's measurement information. Additionally, or alternatively, while the user uses the treatment device to carry out the treatment plan, at least some of the treatment data may correspond to sensor data from a sensor associated with a wearable device configured to detect the information. user measurement.

[0447] Various features of the treatment device may include one or more settings of the treatment device, current revolutions per time period (e.g., one minute) of a rotating member (e.g., a wheel) of the treatment device, a resistance setting of the treatment device, other suitable characteristics of the treatment device or a combination thereof. The basal measurement information may include, while the user is resting, one or more vital signs of the user, a respiratory rate of the user, a heart rate of the user, a temperature of the user, a blood pressure of the user, a glucose level or other biomarker, other appropriate user measurement information, or a combination thereof. The measurement information may include, while the user uses the treatment device to perform the treatment plan, one or more vital signs of the user, a respiratory rate of the user, a heart rate of the user, a temperature of the user, a blood pressure of the user, a glucose level of the user or other appropriate measurement information of the user or a

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122 combination of them.

[0448] In some embodiments, the systems and methods described herein may be configured to write to an associated memory to access treatment data using an artificial intelligence engine. The artificial intelligence engine may be configured to use one or more machine learning models configured to use at least part of the treatment data to generate one or more predictions. For example, the artificial intelligence engine may use a machine learning model trained using various treatment data from multiple users. The machine learning model can be configured to receive treatment data corresponding to the user. The machine learning model may analyze the aspect(s) of the treatment data and may generate at least one prediction corresponding to the aspect(s) of the treatment data. The prediction(s) may indicate one or more expected characteristics of the user. The predicted characteristic(s) of the user may include a predicted vital sign of the user, a predicted respiratory rate of the user, a predicted heart rate of the user, a predicted temperature of the user, a predicted blood pressure of the user, a predicted performance parameter of the user. carrying out the treatment plan, an intended clinical consequence of the treatment plan that the user is carrying out, an anticipated injury to the user as a result of the user carrying out the treatment plan or other appropriate anticipated characteristics of the user.

[0449] In some embodiments, the systems and methods described herein may be configured to receive, from the artificial intelligence engine, one or more predictions. The systems and methods described herein may be configured to identify a threshold corresponding to the respective predictions received from the artificial intelligence engine. For example, the systems and methods described herein may identify one or more user characteristics indicated by a respective prediction.

[0450] The systems and methods described herein may be configured to access a database configured to associate thresholds with user characteristics and/or combinations of user characteristics. For example, the database may include information that associates a first threshold with a blood pressure of the user. In addition, or alternatively, the basis

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123 The data may include information that associates a threshold with a blood pressure of the user and a heart rate of the user. It should be understood that the database may include any number of thresholds associated with any of various user characteristics and/or any combination of user characteristics. In some embodiments, a threshold corresponding to a respective prediction may include a value or a range of values, including an upper limit and a lower limit.

[0451] In some embodiments, the systems and methods described herein may be configured to determine whether a prediction received from the artificial intelligence engine is within a range of a corresponding threshold. In some embodiments, the systems and methods described herein may be configured to compare the prediction to the corresponding threshold. The systems and methods described in this document can be configured to determine whether the prediction is within a predefined threshold range. For example, if the threshold includes a value, the predefined range may include an upper limit (for example, 0.5% or 1% on a percentage basis or, for example, 250 or 750 (a unit of measurement or other suitable numerical value), or other suitable upper limit) above the value, as well as a lower limit (for example, 0.5% or 1% on a percentage basis or, for example, 250 or 750 (a unit of measurement or other suitable numerical value), or other appropriate lower limit) below the value. Similarly, if the threshold includes a range that includes a first upper limit and a first lower limit (for example, defining an acceptable range of the user characteristic(s) corresponding to the prediction), the predefined range may include a second upper limit (e.g. 0.5% or 1% on percentage basis or e.g. 250 or 750 (a unit of measurement or other suitable numerical value) or other suitable upper limit) above the first upper limit and a second lower limit (for example, 0.5% or 1% on a percentage basis or, for example, 250 or 750 (a unit of measurement or other suitable numerical value) or other suitable lower limit) below the first lower limit. It should be understood that the threshold may include any suitable predefined interval and may include any suitable format in addition to those described in this document.

[0452] If the systems and methods described herein determine that the prediction is within the threshold range, the systems and methods described herein may be configured to communicate with (e.g., or through) a interface, in a

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124 computing device of a healthcare provider, to provide prediction and treatment data. In some embodiments, the systems and methods described herein may be configured to generate treatment information using treatment data. Treatment information may include a summary of the user's performance of the treatment plan while using the treatment device. The summary may be formatted such that the treatment data may be presented on a computing device of the healthcare provider. The systems and methods described herein may be configured to communicate treatment information with prediction and/or treatment data to the healthcare provider's computing device. Alternatively, if the systems and methods described herein determine that the prediction is outside the threshold range, the systems and methods described herein may be configured to update treatment data related to the user to indicate the prediction.

[0453] In some embodiments, the systems and methods described herein may, in response to determining that the prediction is within the threshold range, modify at least one aspect of the treatment plan and/or a or more characteristics of the treatment device based on the prediction.

[0454] In some embodiments, the systems and methods described herein may be configured to control, while the user uses the treatment device during a telemedicine session and based on a generated prediction, the treatment device. For example, the systems and methods described herein may control one or more characteristics of the treatment device based on the prediction and/or treatment plan.

[0455] The healthcare provider may include a medical professional (e.g., doctor, nurse, therapist, or the like), an exercise professional (e.g., instructor, trainer, nutritionist, and the like), or other professional who possesses at least one of the medical and exercise attributes (for example, an exercise physiologist, a physical therapist, an occupational therapist, and the like). As used herein, and without limiting the foregoing, a “health care provider” may be a human, a robot, a virtual assistant, a virtual assistant in virtual and/or augmented reality, or an entity artificially intelligent, including a software program ,

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125 integrated software and hardware, or hardware alone.

[0456] In some embodiments, the interface may include a graphical user interface configured to provide treatment information and receive data input from the healthcare provider. The interface may include one or more input fields, such as text input fields, drop-down selection input fields, radio button input fields, virtual switch input fields, virtual toggle input fields, input powered by audio, haptics, touch, biometrics and/or otherwise, other suitable input fields or a combination thereof.

[0457] In some embodiments, the healthcare provider may review treatment and/or prediction information. The healthcare provider may determine, based on review of treatment information and/or prediction, whether to modify at least one aspect of the treatment plan and/or one or more features of the treatment device. For example, the health care provider may review treatment information. Based on the review of the treatment information, the healthcare provider can compare the treatment information with the treatment plan that the user is undertaking.

[0458] The healthcare provider may compare the following (i) the expected information, which is related to the user while the user uses the treatment device to perform the treatment plan, with i) the prediction, which is related to the user while the user uses the treatment device to carry out the treatment plan.

[0459] The expected information may include one or more vital signs of the user, a respiratory rate of the user, a heart rate of the user, a temperature of the user, a blood pressure of the user, other suitable information of the user, or a combination thereof. . The health care provider can determine that the treatment plan is having the desired effect if the prediction is within an acceptable range associated with one or more corresponding parts or portions of the expected information. Alternatively, the healthcare provider may determine that the treatment plan is not having the desired effect if the prediction falls outside the range associated with one or more corresponding parts or portions of the expected information.

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126

[0460] For example, the healthcare provider may determine whether a blood pressure value indicated by the prediction (e.g., systolic pressure, diastolic pressure, and/or differential blood pressure) is within an acceptable range (e.g., plus or minus 1%, plus or minus 5%, on a percentage basis, plus or minus 1 unit of measurement (or other suitable numerical value) or any suitable numerical or percentage-based interval) of an expected blood pressure value indicated by the expected information. The healthcare provider can determine that the treatment plan is having the desired effect if the blood pressure value is within the range of the expected blood pressure value. Alternatively, the healthcare provider may determine that the treatment plan is not having the desired effect if the blood pressure value is outside the range of the expected blood pressure value.

[0461] In some embodiments, while the user uses the treatment device to carry out the treatment plan, the healthcare provider may compare the expected characteristics of the treatment device with the characteristics of the treatment device indicated by the treatment information. For example, the healthcare provider may compare an expected resistance setting of the treatment device to an actual resistance setting of the treatment device indicated by the treatment information. The healthcare provider may determine that the user is performing the treatment plan correctly if the actual characteristics of the treatment device indicated by the treatment information are within a range of characteristics corresponding to the expected characteristics of the treatment device. Alternatively, the healthcare provider may determine that the user is not performing the treatment plan correctly if the actual characteristics of the treatment device indicated by the treatment information are outside the corresponding characteristic range of the expected characteristics of the device. of treatment.

[0462] If the healthcare provider determines that the prediction and/or treatment information indicates that the user is performing the treatment plan correctly and/or that the treatment plan is having the desired effect, the healthcare provider may determine not to modify the aspect(s) of the treatment plan and/or the characteristic(s) of the treatment device. Alternatively, while the user uses the treatment device to perform

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127 the treatment plan, if the health care provider determines that the prediction and/or treatment information indicates that the user is not performing or has not performed the treatment plan correctly and/or that the treatment plan is not having or has not had the desired effect, the healthcare provider may determine to modify the aspect(s) of the treatment plan and/or the characteristic(s) of the treatment device.

[0463] In some embodiments, the healthcare provider may interact with the interface to provide input of data about the treatment plan indicating one or more modifications made to the treatment plan and/or modify one or more characteristics of the treatment device. treatment if the health care provider determines to modify the aspect(s) of the treatment plan and/or modify one or more features of the treatment device. For example, the healthcare provider may use the interface to provide data input indicating an increase or decrease in the resistance setting of the treatment device or other appropriate modification made to the characteristic(s) of the treatment device. Additionally, or alternatively, the healthcare provider may use the interface to provide a data entry indicating a modification made to the treatment plan. For example, the healthcare provider may use the interface to provide a data entry indicating an increase or decrease in an amount of time that the user should use the treatment device in accordance with the treatment plan or other appropriate modifications made. to the treatment plan.

[0464] In some embodiments, based on one or more modifications indicated by input of data about the treatment plan, the systems and methods described herein may be configured to modify at least one aspect of the treatment plan and /or one or more characteristics of the treatment device.

[0465] In some embodiments, the systems and methods described herein may be configured to receive subsequent treatment data related to the user while the user uses the treatment device to perform the modified treatment plan. For example, after the healthcare provider provides input that modifies the treatment plan and/or the treatment device feature(s), and/or after

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128 If the artificial intelligence engine modifies the treatment plan and/or one or more characteristics of the treatment device, the user may continue to use the treatment device to perform the modified treatment plan. Subsequent treatment data may correspond to treatment data generated while the user uses the treatment device to perform the modified treatment plan. In some embodiments, subsequent treatment data may correspond to treatment data generated while the user continues to use the treatment device to carry out the treatment plan, after the healthcare provider has received the treatment information and determined not to. modify the treatment plan and/or the characteristics of the treatment device, and/or the artificial intelligence engine has determined not to modify the treatment plan and/or the characteristics of the treatment device.

[0466] In some embodiments, the artificial intelligence engine may use the machine learning model(s) to generate one or more subsequent predictions based on the subsequent processing data. The systems and methods described herein can determine whether a respective posterior prediction is within a range of a corresponding threshold. The systems and methods described herein may, in response to a determination that the respective subsequent prediction is within the threshold range, communicate the subsequent treatment data, the subsequent treatment information and/or the prediction to the device. healthcare provider's computer system. In some embodiments, based on subsequent prediction, the systems and methods described herein may modify at least one aspect of the treatment plan and/or one or more characteristics of the treatment device.

[0467] In some embodiments, the systems and methods described herein may be configured to receive subsequent data input regarding the treatment plan from the healthcare provider's computing device. Based on subsequent data input about the treatment plan received from the healthcare provider's computing device, the systems and methods described herein may be configured to further modify the treatment plan and/or monitor the o the characteristics of the treatment device. Subsequent data entry about the treatment plan may correspond to data entry provided by the healthcare provider, at the interface, in response to receipt and/or review of the information.

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129 post-treatment information and/or the post-prediction corresponding to the post-treatment data. It should be understood that the systems and methods described herein may be configured to generate predictions continuously and/or periodically based on treatment data. The systems and methods described herein may be configured to provide treatment information to the healthcare provider's computing device based on treatment data received continuously and/or periodically from sensors or other suitable sources that are provided. described in this document. Additionally, or alternatively, the systems and methods described herein may be configured to continuously and/or periodically monitor the user's characteristics while the user uses the treatment device to carry out the treatment plan.

[0468] In some embodiments, the health care provider and/or the systems and methods described herein may receive and/or review, continuously or periodically, while the user uses the treatment device to carry out the plan of treatment, treatment information, treatment data and/or predictions. Based on one or more trends indicated by the treatment information, treatment data and/or predictions, the health care provider and/or the systems and methods described herein may determine to modify the treatment plan and /or modify and/or control the characteristics of the treatment device. For example, the trend(s) may indicate an increase in heart rate or other appropriate trends that indicate that the user is not performing the treatment plan correctly and/or that the user's performance of the treatment plan is not being met. desired effect.

[0469] In some embodiments, the systems and methods described herein may be configured to use artificial intelligence and/or machine learning to assign patients to cohorts and to dynamically control a treatment device based on the assignment during a adaptive telemedicine session. In some embodiments, one or more treatment devices may be provided to patients. Patients can use the treatment device(s) to carry out treatment plans at home, at a gym, at a rehabilitation center, at a hospital, at their workplace, at a hotel, at a convention center or at any suitable location, including permanent or temporary addresses.

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130

[0470] In some embodiments, the processing devices may be communicatively coupled to a server. Patient characteristics, including treatment data, may be collected before, during, or after patients make treatment plans. For example, personal information, performance information, and measurement information may be collected before, during, and/or after the individual makes treatment plans. The results (e.g., improved performance or reduced performance) of performing each exercise may be collected from the treatment device throughout the treatment plan and after the treatment plan is performed. The parameters, settings, configurations, etc. (e.g., pedal position, degree of resistance, etc.) of the treatment device may be collected before, during, or after performing the treatment plan.

[0471] Each patient characteristic, each result, and each parameter, setting, configuration, etc., may be time stamped and may be correlated to a specific stage of the treatment plan. This technique can allow you to determine which stages of the treatment plan are most likely to produce the desired results (e.g., improvement in muscle strength, range of motion, etc.) and which stages are most likely to produce diminishing returns (e.g., continuing to exercise after 3 minutes actually delays or harms recovery).

[0472] Over time, data may be collected from processing devices and/or any suitable computing devices (e.g., computing devices where personal information is entered, such as the computing device interface described herein). document, a clinician interface, a patient interface and the like) as patients use the treatment devices to carry out the various treatment plans. Data that may be collected may include patient characteristics, treatment plans made by patients, results of treatment plans, any of the data described herein, any other suitable data, or a combination thereof.

[0473] In some embodiments, data may be processed to group certain individuals into cohorts. People can be grouped by people who have certain similar or selected characteristics, treatment plans, and results of having made the treatment plans. For example, athletic people who have no medical conditions and who follow an ocher treatment plan Ln/zznz/E/YiAi

131 (for example, they use the treatment device for 30 minutes a day, 5 times a week, for 3 weeks) and who fully recover can be grouped into a first cohort. Seniors who are classified as obese and who complete a treatment plan (for example, use the treatment plan for 10 minutes a day, 3 times a week, for 4 weeks) and who improve their range of motion by 75 % can be grouped into a second cohort.

[0474] In some embodiments, an artificial intelligence engine may include one or more machine learning models that are trained using the cohorts. For example, the machine learning model(s) can be trained to receive an input of data about the characteristics of a new patient and to generate a treatment plan for the patient that produces the desired outcome. Machine learning models can match a pattern between the characteristics of the new patient and at least one patient of the patients included in a specific cohort. When searching for pattern matches, machine learning models can assign the new patient to the specific cohort and select the treatment plan associated with at least one patient. The artificial intelligence engine can be configured to remotely control the treatment device based on the treatment plan while the new patient uses the treatment device to perform the treatment plan.

[0475] As can be appreciated, the characteristics of the new patient (e.g., a new user) may change as the new patient uses the treatment device to carry out the treatment plan. For example, patient performance may improve faster than expected for people in the cohort to which the new patient is currently assigned. Accordingly, machine learning models can be trained to dynamically reassign the new patient, based on the modified characteristics, to a different cohort that includes people with characteristics similar to the currently modified characteristics, as the new patient. For example, a clinically obese patient may lose weight and no longer meet the weight criterion for the initial cohort, which may result in the patient being reassigned to a different cohort with a different weight criterion.

[0476] A different treatment plan may be selected for the new patient, and the treatment device may be controlled remotely (for example, which may be referred to as

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132 remotely) and based on the different treatment plan, while the new patient uses the treatment device to perform the treatment plan. Such techniques may provide the technical solution to remotely control a treatment device.

[0477] Additionally, the systems and methods described herein may result in faster recovery times and/or better outcomes for patients because the treatment plan that most closely matches their characteristics is selected and implemented. , in real time, at any given moment. “Real time” can also refer to near real time, which can be less than 10 seconds. As described herein, the term “outcomes” may refer to medical outcomes or medical consequences. Clinical outcomes and consequences may refer to responses to medical actions.

[0478] Depending on the desired outcome, the artificial intelligence engine can be trained to generate various treatment plans. For example, one outcome may include recovering to a threshold level (e.g., 75% range of motion) in a faster amount of time, while another outcome may include full recovery (e.g., 75% range of motion). 100%) regardless of the amount of time. Data collected from patients and classified into cohorts may indicate that a first treatment plan provides the first outcome for people with similar characteristics to the patient, and that a second treatment plan provides the second outcome for people with similar characteristics to the patient. of the patient.

[0479] Additionally, the artificial intelligence engine may be trained to generate treatment plans that are suboptimal, that is, suboptimal, non-standard, or otherwise excluded (all of which are referred to, but not limited to, as “treatment plans.” “excluded treatment options”) for the patient. For example, if a patient has high blood pressure, a specific exercise may not be approved or may not be suitable for the patient, as it may pose an unnecessary risk to the patient or even induce a hypertensive crisis and, therefore, that Exercise may be marked on the excluded treatment plan for the patient. In some embodiments, the artificial intelligence engine may monitor received treatment data while the patient (e.g., user) with, e.g., high blood pressure, uses the treatment device to make a plan.

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133 of appropriate treatment plan and may modify the appropriate treatment plan to include features of an excluded treatment plan that may provide favorable outcomes for the patient if the treatment data indicate that the patient is adhering to the appropriate treatment plan without aggravating, e.g. , the patient's high blood pressure condition.

[0480] In some embodiments, treatment plans and/or excluded treatment plans may be presented, during a telemedicine or telehealth session, to a healthcare provider. The healthcare provider may select a specific treatment plan for the patient to have that treatment plan transmitted to the patient and/or to control the treatment device based on the treatment plan. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, establishment of treatment plans and pharmacological and/or rehabilitation prescriptions, the artificial intelligence engine may receive and/or operate remotely from the patient and of the treatment device.

[0481] In such cases, the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in real time or near real time during a telemedicine or telehealth session in a user interface of a computing device from a healthcare provider. Video may also include audio, text, and other multimedia information. Real time may refer to less than or equal to 2 seconds. Near real-time can refer to any interaction of a time short enough to allow two people to engage in a conversation through that user interface, and will typically be less than 10 seconds, but greater than 2 seconds.

[0482] Presenting the treatment plans generated by the artificial intelligence engine at the same time as a presentation of the patient's video can provide an improved user interface, as the healthcare provider can continue to communicate visually or otherwise. mode with the patient while reviewing treatment plans in the same user interface. The improved user interface may improve the experience of the healthcare provider using the computing device and may encourage the healthcare provider to use the user interface again. This technique can also reduce computing resources (e.g., processing, memory, network), since the healthcare provider does not have to switch to another

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134 user interface screen to enter a query about a treatment plan to recommend based on patient characteristics. The AI engine can be configured to dynamically provide treatment plans and excluded treatment plans on the fly.

[0483] In some embodiments, the treatment device may be adaptive and/or personalized because its properties, configurations, and positions may be tailored to the needs of a specific patient. For example, the pedals can be adjusted dynamically and on the fly (e.g., through a telemedicine session or based on programmed settings in response to certain detected measurements) in order to increase or decrease a range of motion. to comply with a treatment plan designed for the user. In some embodiments, during a telemedicine session, a healthcare provider can remotely adapt the treatment device to the patient's needs by causing a control instruction to be transmitted from a server to the treatment device. Such adaptive nature can improve a patient's recovery outcomes, promoting the goals of personalized medicine, and allowing separate treatment plan personalization.

[0484] A technical issue may occur related to information regarding the patient's medical condition being received in disparate formats. For example, a server may receive information related to a patient's medical condition from one or more sources (for example, from an electronic medical record (EMR) system, application programming interface (API), or any suitable system that has information related to the patient's medical condition). That is, some fonts used by various healthcare providers may be installed on their local computing devices and may use proprietary formats. Accordingly, some embodiments of the present disclosure may use an API to obtain, through interfaces exposed by the APIs and used by the sources, the formats used by the sources. In some embodiments, when information is received from sources, the API may map, translate and/or convert the format used by the sources to a standardized format used by the artificial intelligence engine. In addition, the information mapped, translated and/or converted to the standardized format used by the artificial intelligence engine can be stored in a database that is accessed

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135 the artificial intelligence engine when performing any of the techniques described in this document. The use of information mapped, translated and/or converted to a standardized format can allow a more precise determination of the procedures to be performed by the patient and/or a billing sequence.

[0485] To this end, standardized information may allow the generation of treatment plans and/or billing sequences that have a specific format that can be processed by various applications (e.g., telehealth). For example, applications such as telehealth applications can run on various computing devices of medical professionals and/or patients. Applications (e.g., stand-alone or web-based) may be provided through a server and may be configured to process data according to a format in which treatment plans and billing sequences are implemented. Accordingly, the described modalities may provide a technical solution by (i) receiving, from various sources (e.g., EMR systems), information in non-standardized and/or different formats; (ii) the standardization of information; and (iii) generating, based on standardized information, treatment plans and billing sequences that have standardized formats capable of being processed by applications (e.g., telehealth application) running on computing devices. medical professionals and/or patients.

[0486] Figure 23 generally illustrates a block diagram of a computer-implemented system 3010, hereinafter referred to as “the system” for managing a treatment plan. Treatment plan management may include using an artificial intelligence engine to recommend treatment plans and/or providing excluded treatment plans that should not be recommended to a patient.

[0487] System 3010 also includes a server 3030 configured to store (e.g., write to an associated memory) and provide data related to treatment plan management. Server 3030 may include one or more computers and may take the form of one or more distributed and/or virtualized computers. The server 3030 also includes a first communication interface 3032 configured to communicate with (e.g., through) the clinician interface 3020 over a first network 3034. In some embodiments, the first network 3034

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136 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communications (NFC), cellular data network, among others. The server 3030 includes a first processor 3036 and a first machine-readable storage memory 3038, which may be referred to as "memory" for short, which contains first instructions 3040 for performing the various actions of the server 3030 for execution by the first 3036 processor.

[0488] Server 3030 is configured to store data related to the treatment plan. For example, memory 3038 includes system data storage 3042 configured to contain system data, such as data related to treatment plans for treating one or more patients. Server 3030 is also configured to store data related to the performance of a patient following a treatment plan. For example, memory 3038 includes patient data storage 3044 configured to retain patient data, such as data related to the patient(s), including data representing the performance of each patient within the treatment plan.

[0489] In addition, or alternatively, the characteristics (e.g., personal, performance, measurement, etc.) of the individuals, the treatment plans followed by the individuals, the level of compliance with the treatment plans, as well as the results of treatment plans, may use correlations and other statistical or probabilistic measures to allow splitting or dividing treatment plans into different databases equivalent to patient cohorts in the patient data store 3044. For example, data from a first cohort of first patients who have a similar first injury, a similar first medical condition, a similar first medical procedure performed, a first treatment plan followed by the first patient, as well as a first outcome of the plan of treatment, can be stored in a database of first patients. Data from a second cohort of second patients who have a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment plan followed by the second patient, as well as a second outcome of the treatment plan, can be stored in a second patient database. Any individual characteristic or combination of characteristics can be used to separate patient cohorts. In some embodiments, different cohorts of patients can be stored in

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137 different partitions or volumes of the same database. There is no specific limit for the number of different patient cohorts allowed, other than the limitation by mathematical combinatorial and/or partition theory.

[0490] These characteristic data, treatment plan data, and outcome data may be obtained from a large number of treatment devices and/or computing devices over time and may be stored in the database 3044. The characteristics, treatment plan data, and outcomes data may be correlated across patient cohort databases in the patient data warehouse 3044. Characteristics of individuals may include personal information, performance information, and/or measurement information.

[0491] In addition to historical information about other individuals stored in patient cohort-equivalent databases, real-time or near-real-time information based on current patient characteristics about a current patient being treated may be stored. in an appropriate patient cohort equivalent database. It may be determined that the patient's characteristics match or are similar to those of another person in a specific cohort (e.g., Cohort A) and the patient may be assigned to that cohort.

[0492] In some embodiments, the server 3030 may run an artificial intelligence (AI) engine 3011 that uses one or more machine learning models 3013 to perform at least one of the embodiments described herein. The server 3030 may include a training engine 9 capable of generating one or more machine learning models 3013. Machine learning models 3013 can be trained to assign people to certain cohorts based on their characteristics, select treatment plans using real-time and historical data correlations that include patient cohort equivalents, and control a treatment device 3070 , among other things.

[0493] The training engine 309 may generate the machine learning model(s) 3013 and may be implemented into executable computer instructions by one or more processing devices of the training engine 309 and/or the servers 3030. To generate one or more 3013 machine learning models, 309 training engine can train ocher Ln/zznz/E/YiAi

138 one or more machine learning models 3013. The artificial intelligence engine 3011 may use one or more machine learning models 3013.

[0494] The training engine 309 may be a rack-mount server, a router computer, a personal computer, a personal digital assistant, a smartphone, a laptop, a tablet, an ultraportable (netbook), a computer desktop, an Internet of Things (loT) device, any other suitable computing device or a combination thereof. The training engine 9 may be cloud-based or a real-time software platform, and may include privacy software or protocols, and/or security software or protocols.

[0495] To train the machine learning model(s) 3013, the training engine 309 may use a training data set of a corpus of the characteristics of individuals who used the treatment device 3070 to make treatment plans, the details (for example, the treatment protocol that includes the exercises, the amount of time to perform the exercises, how often the exercises are performed, an exercise schedule, the parameters/settings/adjustments of the treatment device 3070 throughout each stage of the treatment plan, etc.) of the treatment plans made by the persons using the treatment device 3070, as well as the results of the treatment plans made by people. The machine learning model(s) 3013 may be trained to match patterns of characteristics of a patient to the characteristics of other individuals assigned to a specific cohort. The term “match” may refer to an exact match, a correlative match, a substantial match, etc. The machine learning model(s) 3013 may be trained to receive the characteristics of a patient as a data input, map the characteristics to the characteristics of individuals assigned to a cohort, and select a treatment plan from that cohort. The machine learning model(s) 3013 may also be trained to control, based on the treatment plan, the machine learning apparatus 3070.

[0496] Different machine learning models 3013 can be trained to recommend different treatment plans for different desired outcomes. For example, a machine learning model can be trained to recommend treatment plans for recovery.

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139 more effective, while another machine learning model can be trained to recommend treatment plans based on recovery speed.

[0497] Using training data that includes training data inputs and corresponding target outputs, one or more machine learning models 3013 may refer to model artifacts created by the training engine 309. The training engine 309 may find patterns in the training data where such patterns map the training data input to the target output and generate machine learning models 3013 that capture these patterns. In some embodiments, the artificial intelligence engine 3011 and/or the training engine 309 may reside in another component (e.g., assistant interface 3094, clinician interface 3020, etc.) depicted in Figure 23.

[0498] The machine learning model(s) 3013 may comprise, for example, a single level of linear or nonlinear operations (e.g., a support vector machine [SVM]) or the machine learning model(s) 3013 may be a deep learning network, that is, a machine learning model that comprises more than one level (e.g., multiple levels) of nonlinear operations. Examples of deep learning networks are neural networks which include generative adversarial networks, convolutional neural networks, recurrent neural networks with one or more hidden layers, and fully connected neural networks (for example, each neuron can transmit its output signal to the input of data from the remaining neurons, as well as itself). For example, the machine learning model may include a large number of layers and/or hidden layers that perform calculations (e.g., dot products) using multiple neurons.

[0499] System 3010 also includes a patient interface 3050 configured to communicate information to a patient and to receive feedback from the patient. In particular, the patient interface includes an input device 3052 and an output device 3054, which may be referred to collectively as a patient and user interface 3052,3054. Input device 3052 may include one or more devices, such as a keyboard, a mouse, a touch screen data input, a gesture sensor, and/or a microphone and processor configured for speech recognition. The output device 3054 may take one or more different forms, such as a computer monitor or a display screen on a tablet, smartphone, or smart watch.

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140

The output device 3054 may include other hardware and/or software components, such as a projector, virtual reality capability, augmented reality capability, among others. The output device 3054 may incorporate various visual, audio, or other presentation technologies. For example, output device 3054 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds, such as tones, ringers and/or melodies, which may indicate different conditions. and/or instructions. The output device 3054 may comprise one or more different displays that present various data and/or interfaces or controls for use by the patient. The output device 3054 may include graphics, which may be presented through a web-based interface and/or through a computer program or application (App.).

[0500] As generally illustrated in Figure 23, the patient interface 3050 includes a second communication interface 3056, which may also be referred to as a remote communication interface configured to communicate with the server 3030 and/or the clinical professional 3020 over a second network 3058. In some embodiments, the second network 3058 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the second network 3058 may include the Internet, and security of communications between the patient interface 3050 and the server 3030 and/or the clinician interface 3020 may be established through encryption, such as, for example, by the use of a virtual private network (VPN). In some embodiments, the second network 3058 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communications (NFC), cellular data network, among others. In some embodiments, the second network 3058 may be the same as and/or operatively coupled to the first network 3034.

[0501] The patient interface 3050 includes a second processor 3060 and a second machine-readable storage memory 3062 that contains second instructions 3064 for execution by the second processor 3060 to perform various actions of the patient interface 3050. The second machine-readable storage memory 3062 also includes a local data store 3066 configured to contain data, such as data related to a treatment plan and/or patient data, such as data representing the performance of a patient within a treatment plan. The 3050 patient interface also includes an interface

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141 local communication interface 3068 configured to communicate with various devices for use by the patient near the patient interface 3050. The local communication interface 3068 may include wired and/or wireless communications. In some embodiments, the local communication interface 3068 may include a local wireless network, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communications (NFC), cellular data network, among others.

[0502] System 3010 also includes a treatment device 3070 configured to be manipulated by the patient and/or to manipulate a part of the patient's body to perform activities in accordance with the treatment plan. In some embodiments, the treatment device 3070 may take the form of an exercise and rehabilitation apparatus configured to perform and/or assist in performing a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, and the treatment includes rehabilitation of a part of the patient's body, such as a joint, bone, or muscle group. The treatment device 3070 may be any suitable medical, rehabilitation, therapeutic, etc. device configured to be controlled remotely via another computing device to treat a patient and/or exercise the patient. The treatment device 3070 may be an electromechanical machine that includes one or more weights, an electromechanical bicycle, an electromechanical spinning wheel, a smart mirror, a treadmill, or the like. The body part may include, for example, a spine, a hand, a foot, a knee, or a shoulder. The body part may include a part of a joint, bone, or muscle group, such as one or more vertebrae, a tendon, or a ligament. As generally illustrated in Figure 23, the treatment device 3070 includes a controller 3072, which may include one or more processors, computer memory and/or other components. The treatment device 3070 also includes a fourth communication interface 3074 configured to communicate with (e.g., through) the patient interface 3050 via the local communication interface 3068. The treatment device 3070 also includes one or more internal sensors 3076 and an activator 3078, such as a motor. The activator 3078 may be used, for example, to move the patient's body part or to resist forces by the patient.

[0503] The internal sensors 3076 may measure one or more operating characteristics of the treatment device 3070, such as a force, a position, a speed, a speed

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142 and/or an acceleration. In some embodiments, the internal sensors 3076 may include a position sensor configured to measure at least one of a linear motion or an angular motion of a part of the patient's body. For example, an internal sensor 3076 in the form of a position sensor may measure a distance up to which the patient is able to move a portion of the treatment device 3070, where the distance may correspond to a range of motion that the body part of the patient can achieve. In some embodiments, the internal sensors 3076 may include a force sensor configured to measure a force applied by the patient. For example, an internal sensor 3076 in the form of a force sensor may measure a force or weight that the patient is able to apply, with a specific part of the body, to the treatment device 3070.

[0504] The system 3010 generally illustrated in Figure 23 also includes a ambulation sensor 3082, which communicates with the server 3030 through the local communication interface 3068 of the patient interface 3050. The ambulation sensor Ambulation 3082 can track and store a series of steps taken by the patient. In some embodiments, the ambulation sensor 3082 may take the form of a bracelet, wristwatch, or smartwatch. In some embodiments, the wander sensor 3082 may be integrated into a telephone, such as a smartphone.

[0505] The system 3010 generally illustrated in Figure 23 also includes a goniometer 3084, which communicates with the server 3030 through the local communication interface 3068 of the patient interface 3050. The goniometer 3084 measures an angle of the patient's body part. For example, the 3084 goniometer can measure the flexion angle of the patient's knee, elbow, or shoulder.

[0506] The system 3010 may also include one or more additional sensors (not shown) that communicate with the server 3030 through the local communication interface 3068 of the patient interface 3050. The additional sensor(s) may measure other patient parameters, such as a heart rate, a temperature, a blood pressure, a glucose level, a level of another biomarker, one or more vital signs, among others. For example, the additional sensor(s) may be optical sensors that detect the reflection of near-infrared light from circulating blood below skin level. Optical sensors can take the form of a bracelet, a wristwatch, or

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143 a smart watch and measure the glucose level, a heart rate, a blood oxygen saturation level, one or more vital signs and the like.

[0507] In some embodiments, the additional sensor(s) may be located in a room or physical space in which the treatment device 3070 is being used, within the patient's body, arranged on the person's body (e.g., a skin patch) or included in the treatment device 3070, and the additional sensor(s) may measure various vital signs or other diagnostically relevant attributes (e.g., heart rate, sweat rate, temperature, blood pressure, oxygen levels, any appropriate vital signs, glucose level, a level of another biomarker, among others). The additional sensor(s) may transmit the patient's measurements to server 3030 for analysis and processing (e.g., for use to modify, based on the measurements, at least the patient's treatment plan).

[0508] The system 3010 generally illustrated in Figure 23 also includes a pressure sensor 3086, which communicates with the server 3030 through the local communication interface 68 of the patient interface 3050. The pressure sensor 3086 measures a degree of pressure or weight applied by a part of the patient's body. For example, pressure sensor 86 may measure a degree of force applied by a patient's foot when pedaling a stationary bicycle.

[0509] The system 3010 generally illustrated in Figure 23 also includes a monitoring interface 3090 that may be similar or identical to the clinician interface 3020. In some embodiments, the monitoring interface 3090 may have further enhanced functionality. beyond what is provided in the 3020 Clinician Interface. The monitoring interface 90 may be configured for use by a person responsible for the treatment plan, such as an orthopedic surgeon.

[0510] The system 3010 generally illustrated in Figure 23 also includes a reporting interface 3092 that may be similar or identical to the clinician interface 3020. In some embodiments, the reporting interface 3092 may have less functionality than that provided in the clinician interface 3020. For example, the reporting interface 3092 may not have the ability to modify a treatment plan. Such reporting interface 3092 may be used, for example, by a biller to

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144 determine the use of the 3010 system for billing purposes. In another example, the reporting interface 3092 may not have the ability to display patient identifying information and may only display pseudonymous patient data and/or anonymous patient data for certain data fields related to a registered person and/or for certain data fields related to a quasi-identifier of the registered person. For example, a researcher can use the reporting interface 3092 to determine the various effects of a treatment plan on different patients.

[0511] System 3010 includes an assistant interface 3094 for a healthcare provider, such as those described herein, to remotely communicate with (e.g., through) patient interface 3050 and /or the treatment device 3070. These remote communications may allow the healthcare provider to provide assistance or advice to a patient using the system 3010. More specifically, the assistant interface 3094 is configured to communicate a telemedicine signal 3096, 3097, 3098a, 3098b, 3099a, 3099b to the patient interface 3050 through a network connection, such as through the first network 3034 and/or the second network 3058. The telemedicine signal 3096, 3097, 3098a, 3098b, 3099a, 3099b comprises one of an audio signal 3096, an audiovisual signal 3097, an interface control signal 3098a to control a function of the patient interface 3050, a interface monitoring 3098b to monitor a status of the patient interface 3050, an apparatus control signal 99a for changing an operating parameter of the treatment device 3070 and/or an apparatus monitoring signal signal 3099b for monitoring a status of the treatment device 3070. In some embodiments, each of the Control 3098a, 3099a may consist of unidirectional transmission commands from the assistant interface 3094 to the patient interface 3050. In some embodiments, in response to successful receipt of a control signal 3098a, 3099a and/or to communicate successful and/or failed implementation of the requested control action, a confirmation message may be sent from patient interface 3050. to the wizard interface 3094. In some embodiments, each of the monitoring signal signals 3098b, 3099b may consist of unidirectional status information commands from the patient interface 3050 to the assistant interface 3094. In some embodiments, a confirmation message may be sent from

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145 the assistant interface 3094 to the patient interface 3050 in response to successful receipt of one of the monitoring signals 3098b, 3099b.

[0512] In some embodiments, the patient interface 3050 may be configured as a direct passage for the device control signals 3099a and the device monitoring signals 3099b between the treatment device 3070 and one or more devices, such as the interface of assistant 3094 and/or server 3030. For example, the patient interface 3050 may be configured to transmit a device control signal 3099a in response to a device control signal 3099a in the telemedicine signal 3096, 3097, 3098a, 3098b, 3099a, 3099b of the assistant interface. 3094.

[0513] In some embodiments, the assistant interface 3094 may be presented on a shared physical device, such as the clinician interface 3020. For example, the clinician interface 3020 may include one or more displays that implement the assistant interface 3094. Alternatively or additionally, the clinician interface 3020 may include additional hardware components, such as a video camera, speaker, and/or microphone, to implement aspects of the assistant interface 3094.

[0514] In some embodiments, one or more portions of the telemedicine signal 3096, 3097, 3098a, 3098b, 3099a, 3099b may be generated from a prerecorded source (e.g., an audio recording, a video recording, or an animation ) for presentation through the output device 3054 of the patient interface 3050. For example, a tutorial video can be transmitted from the server 3030 and presented on the patient interface 3050. The patient can request the content of the prerecorded source through the patient interface 3050. Alternatively, through a control on the assistant interface 3094, the healthcare provider can cause the content of the prerecorded source to be requested. play on the 3050 patient interface.

[0515] The assistant interface 3094 includes an assistant input device 3022 and an assistant display 3024, which may be referred to collectively as an assistant and user interface 3022, 3024. The assistant input device 3022 may include one or more of a telephone, a keyboard, a mouse, a touch-sensitive area or a touch screen, for example. Alternatively or additionally, the assistant input device 3022 may include one or more microphones. In some embodiments, the microphone(s) may take the form of a handset,

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146 headset, wide area microphone, or microphones configured for the healthcare provider to speak with a patient through patient interface 3050. In some embodiments, assistant input device 3022 may be configured to provide voice-based functions. , with hardware and/or software configured to interpret spoken instructions from the healthcare provider with the use of the microphone(s). The assistant input device 3022 may include functions provided by or similar to those of existing voice-based assistants, such as Apple's Siri, Amazon's Alexa, Google Assistant, or Samsung's Bixby. The wizard input device 3022 may include other hardware and/or software components. The assistant input device 3022 may include one or more general purpose devices and/or specific use devices.

[0516] The visual presentation of the assistant 3024 may take one or more different forms, such as a computer monitor or a display screen on a tablet, smartphone, or smart watch. The visual presentation of the assistant 3024 may include other hardware and/or software components, such as projectors, virtual reality capabilities, or augmented reality capabilities, among others. The visual presentation of wizard 3024 may incorporate various visual, audio, or other presentation technologies. For example, the visual presentation of assistant 3024 may include a non-visual representation, such as an audio signal, which may include spoken language and/or other sounds, such as tones, timbres, melodies and/or compositions that may indicate different conditions and/or instructions. The visual presentation of the assistant 3024 may comprise one or more different display screens that present various data and/or interfaces or controls for use by the healthcare provider. The visual presentation of the wizard 3024 may include graphics, which may be presented through a web-based interface and/or through a computer program or application (App.).

[0517] In some embodiments, the system 3010 may allow computer language translation, from the assistant interface 3094 to the patient interface 3050 and/or vice versa. Computer translation of language may include computer translation of spoken language and/or computer translation of text. Additionally or alternatively, system 3010 may allow recognition of speech and/or spoken pronunciation of text. For example, system 10 may convert spoken words into printed text and/or system 3010 may speak audibly.

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147 a language from printed text. The 3010 system can be configured to recognize words spoken by any or all of: the patient, the clinician, or the healthcare provider. In some embodiments, system 3010 may be configured to recognize and react to verbal requests or commands from the patient. For example, system 3010 may automatically initiate a telemedicine session in response to a verbal command from the patient (which may be given in any of several languages).

[0518] In some embodiments, the server 3030 may generate aspects of the visual presentation of the wizard 3024 for display via the wizard interface 3094. For example, the server 3030 may include a web server configured to generate the display screens for display. presentation in the visual presentation of the assistant 3024. For example, the artificial intelligence engine 3011 may generate recommended treatment plans and/or excluded treatment plans for patients and generate visual display screens that include the recommended treatment plans and/or external treatment plans for display on the screen. visual presentation of wizard 3024 of wizard interface 3094. In some embodiments, the display of assistant 3024 may be configured to present a virtualized desktop hosted by server 3030. In some embodiments, server 3030 may be configured to communicate with (e.g., through) assistant interface 2094 through through the first network 3034. In some embodiments, the first network 3034 may include a local area network (LAN), such as an Ethernet network.

[0519] In some embodiments, the first network 3034 may include the Internet, and the security of communications between the server 3030 and the assistant interface 3094 may be established through privacy-enhancing technologies, such as by using encryption in a virtual private network (VPN). Alternatively or additionally, the server 3030 may be configured to communicate with (e.g., through) the assistant interface 3094 through one or more networks independent of the first network 3034 and/or other means of communication, such as as a direct wired or wireless communication channel. In some embodiments, each of the patient interface 3050 and the treatment device 3070 may operate from a patient location geographically separate from a location of the assistant interface 3094. For example, the patient interface 3050 and the treatment device 3070 can be used as part of a

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148 rehabilitation at home, which can receive remote assistance by using the 3094 assistant interface at a centralized location, such as a clinic or call center.

[0520] In some embodiments, the assistant interface 3094 may be one of several different terminals (e.g., computing devices) that may be grouped together, for example, in one or more call centers or in one or more medical offices. In some embodiments, a plurality of assistant interfaces 3094 may be geographically distributed. In some embodiments, a person can work as a healthcare provider remotely from any conventional office infrastructure. Such remote work may be performed, for example, when the assistant interface 3094 takes the form of a computer and/or a telephone. This remote work feature may allow for work-from-home arrangements that may include part-time and/or flexible work schedules for a healthcare provider.

[0521] Figures 24-25 show one embodiment of a treatment device 3070. More specifically, Figure 24 generally illustrates a treatment device 3070 in the form of a stationary cycling machine 3100, which may be referred to as a stationary bicycle. To abreviate. The stationary cycling machine 3100 includes a set of pedals 3102, each attached to a pedal arm 3104 for rotation about an axis 3106. In some embodiments, and as generally illustrated in Figure 24, the pedals 3102 can be moved on the pedal arms 3104 to adjust a range of motion used by the patient when pedaling. For example, the pedals that are located on the inside, towards the axis 3106, correspond to a smaller range of movement than when the pedals are located on the outside, away from the axis 3106. In some embodiments, the pedals may be adjustable in and out of the plane of rotation. These techniques can allow the width of the patient's legs to increase and decrease as he pedals. A pressure sensor 3086 is attached to or integrated into one of the pedals 3102 to measure the degree of force applied by the patient on the pedal 3102. The pressure sensor 3086 may communicate wirelessly with the treatment device 3070 and/or with the patient interface 3050.

[0522] Figure 26 generally illustrates a person (a patient) using the treatment device of Figure 24, and showing sensors and various data parameters connected to a patient interface 3050. The example patient interface patient 3050 is a tablet, computer or ocher Ln/zznz/E/YiAi

149 smartphone, or a phablet, such as an ¡Pad, an ¡Phone, an Android device, or a Surface tablet, that is manually held by the patient. In other embodiments, the patient interface 3050 may be integrated into or attached to the treatment device 3070.

[0523] Figure 26 generally illustrates the patient wearing the ambulation sensor 3082 on the wrist, with a note showing “TODAY'S STEPS 31355”, which indicates that the ambulation sensor 3082 has recorded and transmitted that step counting to the 3050 patient interface. Figure 26 also generally illustrates the patient wearing the 3084 goniometer on the right knee, with a note showing “KNEE ANGLE 72°”, which indicates that the 3084 goniometer is measuring and transmitting that knee angle to the 3050 patient interface. Figure 36 generally also illustrates a right side of one of the pedals 3102 with a pressure sensor 3086 showing a “FORCE of 12.5 pounds,” which indicates that the right pedal pressure sensor 3086 is measuring and transmitting that force. force measurement to patient interface 3050.

[0524] Figure 26 generally also illustrates a left side of one of the pedals 4102 with a pressure sensor 3086 showing a "FORCE of 27 pounds", which indicates that the left pedal pressure sensor 3086 is measuring and transmitting that force measurement to the patient interface 3050. Figure 26 generally also illustrates other patient data, such as a “SESSION TIME 0:04:13” indicator, which indicates that the patient has been using the treatment device 3070 for 4 minutes and 13 seconds. This session time may be determined by patient interface 3050 based on information received from treatment device 3070. Figure 26 generally also illustrates an indicator showing “PAIN LEVEL 3.” Such a pain level may be obtained from the patient in response to a request, such as a question, presented to the patient interface 3050.

[0525] Figure 27 is an exemplary embodiment of a visual presentation of the general information 3120 of the assistant interface 3094. Specifically, the visual presentation of the general information 3120 presents several different controls and interfaces for the care provider. physician remotely assists a patient in using the patient interface 3050 and/or the treatment device 3070. This remote assistance feature can also be referred to as telemedicine or telehealth.

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150

[0526] Specifically, the general information display 3120 includes a patient profile display 3130 that presents biographical information related to a patient using the treatment device 3070. The patient profile display 3130 may take the form of a portion or region of the general information display 3120, as generally illustrated in Figure 27, although the patient profile display 3130 may take other forms. forms, such as a stand-alone screen or a pop-up window.

[0527] In some embodiments, the visual presentation of the patient profile 3130 may include a limited subset of patient biographical information. More specifically, the data presented in the patient profile display 3130 may depend on the healthcare provider's need to view that information. For example, a healthcare provider who is assisting the patient with a medical problem may be provided with information from the patient's medical history, while a technician troubleshooting a problem with the treatment device 3070 may be provided with a series much more limited information related to the patient. The technician, for example, could only be provided with the patient's name.

[0528] The visual presentation of the patient profile 3130 may include pseudonymous patient data and/or anonymized patient data, or use any privacy-enhancing technology to prevent sensitive patient data from being communicated in a manner that could violate patient confidentiality requirements. These privacy-enhancing technologies may enable compliance with laws, regulations, or other government rules, such as, but not limited to, the Health Insurance Portability and Accountability Act (HIPAA) or the General Rules. of Data Protection Regulation (GDPR), where the patient can be considered a “registered person”.

[0529] In some embodiments, the visual presentation of the patient profile 3130 may present information related to the treatment plan for the patient to follow with the use of the treatment device 3070. Such treatment plan information may be limited to a provider. of medical care. For example, a healthcare provider helping a patient with a

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151 A problem related to the treatment regimen can be provided with information about the treatment plan, while a technician troubleshooting a problem with the 3070 treatment device cannot be provided with any information related to the patient's treatment plan.

[0530] In some embodiments, one or more recommended treatment plans and/or excluded treatment plans may be presented to the healthcare provider through the visual display of the patient profile 3130. The artificial intelligence engine 3011 of the server 3030 may generate the recommended treatment plan(s) and/or excluded treatment plans and receive them from server 3030 in real time, among others, during a telemedicine or telehealth session. Below, an example of presentation of the recommended treatment plan(s) and/or discarded treatment plan(s) is described with reference to Figure 29.

[0531] The example general information display 3120 generally illustrated in Figure 27 also includes a patient status display 3134 that presents status information related to a patient using the treatment device. The patient status display 3134 may take the form of a portion or region of the general information display 3120, as generally illustrated in Figure 27, although the patient status display 3134 may take the form of a portion or region of the general information display 3120, as generally illustrated in Figure 27, although the patient status display 3134 may take other forms, such as a standalone screen or a pop-up window.

[0532] The visual presentation of the patient status 3134 includes sensor data 3136 from one or more of the external sensors 3082, 3084, 3086 and/or from one or more internal sensors 3076 of the treatment device 3070 and/or one or more treatment sensors (not shown), as described earlier in this document. In some embodiments, the visual display of patient status 3134 may include sensor data from one or more sensors of one or more wearable devices worn by the patient while using the treatment device 3070. The wearable device(s) may include a watch, a bracelet, a necklace, a chest strap and the like. The wearable device(s) may be configured to monitor a patient's heart rate, temperature, blood pressure, glucose level, blood oxygen saturation level, one or more vital signs, and the like while the patient uses the device. treatment 3070. In some

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152 modalities, the visual presentation of the patient's status 3134 may present other data 3138 related to the patient, such as the last reported level of pain or progress on a treatment plan.

[0533] User access controls may be used to limit access, including what data is available for viewing and/or modification, on any or all of the user interfaces 3020, 3050, 3090, 3092, 3094 of system 3010. In some embodiments, user access controls may be employed to control what information is available to any given person using the system 3010. For example, data presented in the assistant interface 3094 may be controlled by user access controls, with permissions set based on the healthcare provider/user's need and/or restrictions on viewing that information.

[0534] The general information display example 3120 generally illustrated in Figure 27 also includes a help data display 3140 that presents information for the healthcare provider to use when providing assistance to the patient. The help data display 3140 may take the form of a portion or region of the general information display 3120, as generally illustrated in Figure 27. The visual presentation of help data 3140 may take other forms, such as a stand-alone screen or a pop-up window. The visual presentation of assistive data 3140 may include, for example, the presentation of answers to frequently asked questions related to the use of the patient interface 3050 and/or the treatment device 3070.

[0535] The visual presentation of assistive data 3140 may also include research data or best practices. In some embodiments, the visual display of assistive data 3140 may present scripts for answers or explanations in response to questions posed by the patient. In some embodiments, the visual presentation of assistive data 3140 may present flowcharts or guides for the healthcare provider to use to determine a root cause and/or solution to a patient's problem.

[0536] In some embodiments, the assistant interface 3094 may present two or more visual presentations of help data 3140, which may be the same or different, for the

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153 Simultaneous presentation of aid data for use by the healthcare provider. For example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient's problem, and a second help data display may present script information. for the healthcare provider to read to the patient; Such information should preferably include instructions for the patient to take some action, which may help reduce or solve the problem. In some embodiments, based on data entries in the troubleshooting flowchart in the first help data display, the second help data display may be automatically populated with script information.

[0537] The example display of general information 3120 generally illustrated in Figure 27 also includes a patient interface control 3150 that displays information related to the patient interface 3050 and/or for modifying one or more settings of the interface The patient interface control 3150 may take the form of a portion or region of the general information display 3120, as generally illustrated in Figure 27. The patient interface control 3150 may take other forms, such as a stand-alone display or a pop-up window. The patient interface control 3150 may present information communicated to the assistant interface 3094 through one or more of the interface monitoring signals 3098b.

[0538] As generally illustrated in Figure 27, the patient interface control 3150 includes a transmission of the visual presentation 3152 of the visual presentation presented by the patient interface 3050. In some embodiments, the transmission of the presentation visual 3152 may include an active copy of the display screen that is currently being presented to the patient via the patient interface 3050. In other words, the visual display transmission 3152 may present an image of what is presented on a display screen of the patient interface 3050.

[0539] In some embodiments, the transmission of the visual presentation 3152 may include abbreviated information related to the display screen that is currently being presented by the patient interface 3050, such as a screen name or a screen number.

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154

The patient interface control 3150 may include a patient interface settings control 3154 so that the healthcare provider can adjust or control one or more settings or aspects of the patient interface 3050. In some embodiments, the settings control Patient interface 3154 may cause assistant interface 3094 to generate and/or transmit an interface control signal 3098 to control a function or setting of patient interface 3050.

[0540] In some embodiments, control of patient interface settings 3154 may include collaborative navigation or co-browsing capability so that the healthcare provider can remotely view and/or control patient interface 3050. For example, patient interface settings control 3154 may allow the healthcare provider to remotely enter text into one or more text entry fields on patient interface 3050 and/or remotely control a cursor on the patient interface 3050 with the use of a mouse or touch screen of the assistant interface 3094.

[0541] In some embodiments, with the use of patient interface 3050, patient interface setting control 3154 may allow the healthcare provider to change a setting that the patient cannot change. For example, the patient interface 3050 may not be able to access a language setting to prevent a patient from inadvertently changing, in the patient interface 3050, the language used for visual presentations, while controlling interface settings. Patient interface 3154 may allow the healthcare provider to change the language settings of the patient interface 3050. In another example, the patient interface 3050 may not be able to change a font size setting to a smaller size to prevent a patient from inadvertently changing the font size used for visual displays on the patient interface 3050, so such that the visual presentation is legible to the patient, while the patient interface settings control 3154 may allow the healthcare provider to change the font size setting of the patient interface 3050.

[0542] The example general information display 3120 generally illustrated in Figure 27 also includes an interface communications display 3156 showing the status of communications between the patient interface 3050 and one or more devices 3070, 3082 , 3084, such as the treatment device 3070, the ambulation sensor 3082 and/or the goniometer

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155

3084. The interface communications display 3156 may take the form of a portion or region of the general information display 3120, as generally illustrated in Figure 27.

[0543] The interface communications display 3156 may take other forms, such as a stand-alone display or a pop-up window. The interface communications display 3156 may include controls for the healthcare provider to remotely modify communications with one or more of the other devices 3070, 3082, 3084. For example, the healthcare provider may remotely instruct the patient interface 50 to restart communication with one of the other devices 3070, 3082, 3084, or to establish communications with a new one of the other devices 3070, 3082, 3084. . This functionality may be used, for example, when the patient has a problem with one of the other devices 3070, 3082, 3084, or when the patient receives a new or replacement one of the other devices 3070, 3082, 3084.

[0544] The example of general information display 3120 generally illustrated in Figure 27 also includes an apparatus control 3160 for the healthcare provider to display and/or control information related to the treatment device 3070. The control Apparatus 3160 may take the form of a portion or region of the general information display 3120, as generally illustrated in Figure 27. The device control 3160 may take other forms, such as a stand-alone display or a pop-up window. The device control 3160 may include a visual display of the status of the device 3162 with information about the current state of the device. The visual display of the status of the device 3162 may present information communicated to the assistant interface 3094 through one or more of the device monitoring signals 3099b. The device status display 3162 may indicate whether the treatment device 3070 is currently communicating with the patient interface 3050. The device status display 3162 may present other current or historical information related to the status of the treatment device. 3070.

[0545] Apparatus control 3160 may include apparatus settings control 3164 for the healthcare provider to adjust or control one or more aspects of the treatment device.

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156

3070. The apparatus setting control 3164 may cause the assistant interface 3094 to generate and/or transmit an apparatus control signal 3099 (e.g., which may be referred to as treatment plan data input, as described) to change an operating parameter and/or one or more characteristics of the treatment device 3070 (e.g., a pedal radius setting, a resistance setting, a target RPM value, other suitable features of the treatment device 3070 or a combination thereof).

[0546] The device settings control 3164 may include a mode button 3166 and a position control 3168, which may be used together for the healthcare provider to place an activator 3078 of the treatment device 3070 in a mode. manual, after which a setting, such as a position or speed of the actuator 3078, can be changed with the use of the position control 3168. Mode button 3166 may allow a setting, such as a position, to toggle between automatic and manual modes.

[0547] In some embodiments, one or more adjustments can be made at any time and without having an associated automatic/manual mode. In some embodiments, the healthcare provider may change an operating parameter of the treatment device 3070, such as a pedal radius setting, while the patient is actively using the treatment device 3070. Such “on the fly” adjustment may or may not be available to the patient using the patient interface 3050.

[0548] In some embodiments, device setting control 3164 may allow the healthcare provider to change a setting that the patient cannot change, using patient interface 3050. For example, the patient interface 3050 may not be able to change a preconfigured setting, such as a height or tilt setting of the treatment device 3070, while the device settings control 3164 may allow the healthcare provider to change the height or tilt adjustment of the 3070 treatment device.

[0549] The example of visual presentation of general information 3120 generally illustrated in Figure 27 also includes a patient communications control 3170 for controlling an audio or audiovisual communications session with the patient interface 3050. The communication session with The patient interface 3050 may comprise an active transmission of the assistant interface 3094 for presentation by the output device of the patient interface 3050. The ocher Ln/zznz/E/YiAi

157 Active streaming may take the form of an audio stream and/or a video stream. In some embodiments, the patient interface 3050 may be configured to provide two-way audio or audiovisual communications with a person with the use of the assistant interface 3094. Specifically, the communications session with the patient interface 3050 may include bidirectional (two-way) video or audiovisual transmissions, where each of the patient interface 3050 and the assistant interface 3094 presents the other's video. .

[0550] In some embodiments, the patient interface 3050 may present video from the assistant interface 3094, while the assistant interface 3094 only presents audio or the assistant interface 3094 does not present any active audio or visual signal from the interface of patient 3050. In some embodiments, the assistant interface 3094 may present video from the patient interface 3050, while the patient interface 3050 only presents audio or the patient interface 3050 does not present any active audio or visual signal from the assistant interface 3094. .

[0551] In some embodiments, the audio or audiovisual communications session with the patient interface 3050 may occur, at least in part, while the patient is performing the rehabilitation regimen for the respective body part. The patient communications control 3170 may take the form of a portion or region of the general information display 3120, as generally illustrated in Figure 27. The patient communications control 3170 may take other forms, such as a stand-alone display or a pop-up window.

[0552] Audio and/or audiovisual communications may be processed or directed by the assistant interface 3094 and/or other device or devices, such as a telephone system or a video conferencing system used by the healthcare provider while the healthcare provider healthcare uses assistant interface 3094. Alternatively or additionally, audio and/or audiovisual communications may include communications with third parties. For example, system 3010 may allow the healthcare provider to initiate a three-way conversation regarding the use of specific hardware or software, both with the patient and with a subject matter expert, such as a healthcare provider or specialist. . The patient communications control example 3170 generally illustrated in Figure 27 includes call controls 3172 for use by the healthcare provider in managing various aspects of audio or communications communications.

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158 audiovisual with the patient. Call controls 3172 include a disconnect button 3174 for the healthcare provider to end the audio or audiovisual communications session. The call controls 3172 also include a mute button 3176 to temporarily mute an audio or audiovisual signal from the assistant interface 3094. In some embodiments, call controls 3172 may include other attributes, such as a hold button (not shown).

[0553] The call controls 3172 also include one or more record/playback controls 3178, such as record, play and pause buttons to control, with the patient interface 3050, the recording and/or playback of audio and/or video of the teleconference session. Call controls 3172 also include a video feed display 3180 to present still and/or video images from the patient interface 3050, as well as a self-video display 3182 that displays the current image from the provider. healthcare using the 3094 assistant interface. The self-video display 3182 may be presented in a picture-in-picture format, in a section of the video stream display 3180, as generally illustrated in Figure 27. Alternatively, or Additionally, the self-video display 3182 may be presented separately and/or independently of the video stream display 3180.

[0554] The example visual presentation of general information 3120 generally illustrated in Figure 27 also includes a third-party communications control 3190 for use when conducting audio and/or audiovisual communications with third parties. The third-party communications control 3190 may take the form of a portion or region of the general information display 3120, as generally illustrated in Figure 27. Third-party communications monitoring 3190 may take other forms, such as a visual presentation on a separate screen or a pop-up window.

[0555] Third party communications control 3190 may include one or more controls, such as a contact list and/or buttons or controls for contacting a third party regarding the use of specific hardware or software, e.g. , a subject matter expert, such as a healthcare provider or specialist. Third party communications control 190 may include conference call capability for the third party to communicate simultaneously.

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159 with the healthcare provider through assistant interface 3094 and with the patient through patient interface 3050. For example, system 3010 may allow the healthcare provider to initiate a three-way conversation with the patient and with the third.

[0556] Figure 28 generally illustrates an example block diagram of training a machine learning model 3013 to generate, based on data 3600 related to the patient, a treatment plan 3602 for the patient according to the present description. Data related to other patients may be received by server 3030. The other patients may have used various treatment devices to carry out treatment plans.

[0557] The data may include characteristics of the other patients, details of the treatment plans made by the other patients, and/or the results of having made the treatment plans (for example, a percentage of recovery of a body part of patients, a level of recovery of a part of the patients' body, a level of increase or decrease in muscle strength of the part of the patients' body, a level of increase or decrease in the range of motion of the patients body part, etc.).

[0558] As represented, the data has been assigned to different cohorts. Cohort A includes data from patients who have similar first characteristics, first treatment plans, and first outcomes. Cohort B includes data from patients who have similar second characteristics, second treatment plans, and second outcomes. For example, Cohort A may include early characteristics of patients between the ages of twenty and twenty-nine who have no medical condition and who have undergone surgery for a broken limb; Your treatment plans may include a certain treatment protocol (for example, using the treatment device 70 for 30 minutes, 5 times a week, for 3 weeks, where the values of the device properties, configurations and/or adjustments Treatment rates 70 are set to X (where X is a numerical value) for the first two weeks, and to Y (where Y is a numerical value) for the last week.

[0559] Cohort A and Cohort B may be included in a training data set used to train the machine learning model 3013. The machine learning model

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3013 can be trained to look for pattern matches between the characteristics of each cohort and generate the treatment plan that provides the result. Accordingly, when data 3600 of a new patient is entered into the trained machine learning model 3013, the trained machine learning model 3013 can match the characteristics included in the data 3600 with the characteristics of cohort A or cohort B and generate the appropriate 3602 treatment plan. In some embodiments, the machine learning model 3013 may be trained to generate one or more excluded treatment plans that the new patient should not perform.

[0560] Figure 29 generally illustrates one embodiment of a visual presentation of the general information 3120 of the assistant interface 3094 that presents in real time the recommended treatment plans and the excluded treatment plans during a telemedicine session according to with this description. As depicted, the general information display 3120 only includes sections for the patient profile 3130 and the video stream display 3180, including the self-video display 3182. Any suitable configuration of controls and interfaces of the general information display 3120 described with reference to Figure 27 may be presented in addition to or instead of the patient profile 3130, the video stream display 3180, and the self-report display. -video 3182.

[0561] The healthcare provider using the assistant interface 3094 (e.g., a computing device) during the telemedicine session may be presented in the self-video 3182 in a portion of the visual presentation of the general information 3120 (e.g., in the user interface presented on a display screen 3024 of the assistant interface 3094) that also presents a video of the patient in the visual presentation of the video stream 3180. Additionally, the visual presentation of the video stream 3180 may also include a graphical user interface (GUI) object 3700 (e.g., a button) that allows the healthcare provider to share in real time or near real time, during the telemedicine session, the recommended treatment plans and/or the excluded treatment plans with the patient in the patient interface 3050. The healthcare provider may select GUI object 3700 to share recommended treatment plans and/or excluded treatment plans. As depicted, another part of the general information display 3120 includes the profile display

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161 of patient 3130.

[0562] The visual presentation of the patient profile 3130 presents two examples of recommended treatment plans 3600 and one example of an excluded treatment plan 3602. As described herein, treatment plans may be recommended based on the characteristics of the patient. patient being treated. To generate recommended treatment plans 3600 that the patient should follow to obtain the desired result, a pattern matches between the characteristics of the patient being treated and a cohort of other people who have used the treatment device 3070 to perform a treatment plan may be searched using one or more machine learning models 3013 of the artificial intelligence engine 3011. Each of the recommended treatment plans can be generated based on the different desired results.

[0563] For example, as depicted, the visual presentation of patient profile 3130 presents “Patient characteristics match the characteristics of the uses in cohort A. The following treatment plans are recommended for the patient based on its characteristics and desired results. Next, the visual presentation of patient profile 3130 presents the recommended treatment plans of Cohort A with each treatment plan providing different results.

[0564] As depicted, treatment plan “A” indicates that “Patient Patient X has type 2 diabetes; and Patient Consequently, the generated treatment plan increases the range of motion by Y%. As can be seen, the treatment plan also includes a recommended medication (e.g., Drug Z) that will be prescribed to the patient to manage pain in light of a known medical condition (e.g., type 2 diabetes) of the patient. That is, the medication recommended to the patient not only does not conflict with the patient's medical condition, but improves the likelihood of a superior clinical outcome for the patient. This specific example and all examples described elsewhere in this document are not intended to limit in any way the generated treatment plan from recommending multiple

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162 medications or addresses the recognition, opinion, diagnosis and/or treatment of comorbid diseases or conditions.

[0565] The recommended treatment plan “B” may specify, based on a different desired outcome of the treatment plan, a different treatment plan that includes a different treatment protocol for a treatment device, a different medication regimen, etc

[0566] As depicted, the visual presentation of the patient profile 3130 may also present the excluded treatment plans 3602. These types of treatment plans are displayed to the healthcare provider who uses the assistant interface 3094 to notify the provider health care provider should not recommend certain parts of a treatment plan to the patient. For example, the excluded treatment plan could specify the following: “Patient X should not use the treatment device for more than 30 minutes per day due to a heart condition; Patient X has type 2 diabetes; and Patient Specifically, the excluded treatment plan notes a limitation of a treatment protocol in which, due to a heart condition, Patient X should not exercise for more than 30 minutes per day. The discarded treatment plan also states that Patient X should not be prescribed medication M because it conflicts with the medical condition of type 2 diabetes.

[0567] The healthcare provider may select the treatment plan for the patient in the display of general information 3120. For example, the healthcare provider may use an input peripheral (e.g., a mouse, a screen touch, a microphone, a keyboard, etc.) to choose from among 3600 treatment plans for the patient. In some embodiments, during the telemedicine session, the healthcare provider may discuss with the patient the pros and cons of the recommended treatment plans 3600.

[0568] In any case, the healthcare provider can choose the treatment plan that the patient must follow to obtain the desired result. The selected treatment plan may be transmitted to the patient interface 3050 for display. The patient can view the selected treatment plan on the patient interface 3050. In some embodiments, the provider

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163 The healthcare provider and the patient can discuss details (e.g., treatment protocol using the 3070 treatment device, diet, medication regimen, etc.) in or near real-time during the telemedicine session. in real time. In some embodiments, the server 3030 may control, based on the selected treatment plan and during the telemedicine session, the treatment device 3070 as the user uses the treatment device 3070.

[0569] Figure 30 generally illustrates an embodiment of the visual presentation of the general information 3120 of the assistant interface 3094 that presents in real time, during a telemedicine session, the recommended treatment plans that have been modified as a result of the modification made to the patient's data in accordance with this description. As can be seen, the treatment device 3070 and/or any computing device (e.g., patient interface 3050) may transmit data while the patient uses the treatment device 3070 to perform a treatment plan. The data may include updated patient characteristics and/or other treatment data. For example, updated features may include new performance information and/or measurement information. The performance information may include a speed of a portion of the treatment device 3070, a range of motion achieved by the patient, a force exerted on a portion of the treatment device 3070, a heart rate of the patient, a blood pressure of the patient, patient's respiratory rate, among others.

[0570] In some embodiments, data received at server 3030 may be input into trained machine learning model 3013, which may determine that characteristics indicate that the patient is ready for the current treatment plan. Determining that the patient is ready for the current treatment plan may cause the trained machine learning model 3013 to adjust a parameter of the treatment device 3070. The adjustment can be based on a next stage of the treatment plan to further improve the patient's performance.

[0571] In some embodiments, data received at server 3030 may be input to trained machine learning model 3013, which may determine that characteristics indicate that the patient is not ready (e.g., running late, not able to maintain a speed, is not able to reach a certain range of motion, is in too much pain, etc.) to

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164 the current treatment plan or is ahead of time (for example, if you exceed a certain speed, if you exercise more than specified without pain, if you exert more force than specified, etc.) for the current treatment plan.

[0572] The trained machine learning model 3013 may determine that the characteristics of the patient no longer match the characteristics of the patients in the cohort to which the patient is assigned. Accordingly, the trained machine learning model 3013 may reassign the patient to another cohort that includes characteristics that meet the patient's characteristics. Thus, the trained machine learning model 3013 can select a new treatment plan from the new cohort and control the treatment device 3070 based on the new treatment plan.

[0573] In embodiments, prior to controlling the treatment device 3070, the server 3030 may provide the new treatment plan 3800 to the assistant interface 3094 for display in the patient profile 3130. As depicted, the patient profile patient 3130 indicates “The patient characteristics have changed and now match the characteristics of the uses of cohort B. The following treatment plan is recommended for the patient based on their characteristics and desired results. Patient profile 3130 then presents the new treatment plan 3800 (“Patient can select the new treatment plan 3800 and the server 3030 can receive the selection. The server 3030 may control the treatment device 3070 based on the new treatment plan 3800. In some embodiments, the new treatment plan 3800 may be transmitted to the patient interface 3050, such that the patient can view the details of the treatment. new 3800 treatment plan.

[0574] In some embodiments, server 3030 may be configured to receive treatment data related to a user using a treatment device 3070 to perform a treatment plan. The user may include a patient, user, or person who uses the treatment device 3070 to perform various exercises.

[0575] The processing data may include various characteristics of the user, other than

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165 basal measurement information related to the user, various measurement information related to the user while the user uses the treatment device 3070, various characteristics of the treatment device 3070, the treatment plan, other suitable data or a combination thereof. In some embodiments, server 3030 may receive treatment data during a telemedicine session.

[0576] In some embodiments, while the user uses the treatment device 3070 to carry out the treatment plan, at least part of the treatment data may include sensor data 3136 from one or more of the external sensors 3082, 3084 , 3086 and/or one or more internal sensors 3076 of the treatment device 3070. In some embodiments, at least part of the treatment data may include sensor data from one or more sensors of one or more wearable devices worn by the patient while using the treatment device 3070. One or more wearable devices may include a watch. , a bracelet, a necklace, a chest strap and the like. The wearable device(s) may be configured to monitor a patient's heart rate, temperature, blood pressure, one or more vital signs, and the like while the patient uses the treatment device 3070.

[0577] Various features of the treatment device 3070 may include one or more settings of the treatment device 3070, current revolutions per time period (e.g., such as one minute) of a rotating member (e.g., such as a wheel ) of the treatment device 3070, a resistance setting of the treatment device 3070, other suitable characteristics of the treatment device 3070 or a combination thereof. The basal measurement information may include, while the user is resting, one or more vital signs of the user, a respiratory rate of the user, a heart rate of the user, a temperature of the user, a blood pressure of the user, other appropriate measurement information of the user. user or a combination thereof. The measurement information may include, while the user uses the treatment device 3070 to perform the treatment plan, one or more vital signs of the user, a respiratory rate of the user, a heart rate of the user, a temperature of the user, a pressure user's arterial blood pressure, other suitable measurement information of the user or an ocher Ln/zznz/E/YiAi combination thereof.

166

[0578] In some embodiments, the server 3030 may write to an associated memory, to access the treatment data using the artificial intelligence engine 3011. The artificial intelligence engine 3011 may use the machine learning model(s) 3013, the which can be configured to use at least part of the treatment data to generate one or more predictions. For example, the artificial intelligence engine 3011 may use a machine learning model 3013 configured to receive treatment data corresponding to the user. The machine learning model 3013 may analyze the aspect(s) of the treatment data and may generate at least one prediction corresponding to the aspect(s) of the treatment data.

[0579] The prediction(s) may indicate one or more expected characteristics of the user. The expected characteristic(s) of the user may include a predicted vital sign of the user, a predicted respiratory rate of the user, a predicted heart rate of the user, a predicted temperature of the user, a blood pressure of the user, an expected clinical consequence of the treatment plan. that the user is performing, an anticipated injury to the user as a result of the user performing the treatment plan, or other appropriate intended characteristic of the user.

[0580] In some embodiments, the server 3030 may receive, from the artificial intelligence engine 3011, the prediction(s). The server 3030 may identify a threshold corresponding to the respective predictions received from the artificial intelligence engine 3011. For example, the server 3030 may identify one or more user characteristics indicated by a respective prediction.

[0581] The server 3030 may access a database, such as a database 3044 or other suitable database, configured to associate thresholds with user characteristics and/or combinations of user characteristics. For example, database 3044 may include information that associates a first threshold with a blood pressure of the user. Additionally, or alternatively, database 3044 may include information that associates a threshold with a blood pressure of the user and a heart rate of the user. It should be understood that database 3044 may include any number of thresholds associated with any of various user characteristics and/or any combination of user characteristics. In some embodiments, a threshold corresponding to a respective prediction may include a value or a range of values, including an upper limit and a lower limit.

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[0582] In some embodiments, the server 3030 may determine whether a prediction received from the artificial intelligence engine 3011 is within a range of a corresponding threshold. For example, server 3030 may compare the prediction with the corresponding threshold. The server 3030 may determine whether the precision is within a predefined threshold range. For example, if the threshold includes a value, the predefined range may include an upper limit (for example, 0.5% or 1% on a percentage basis or, for example, 250 or 750 (a unit of measurement or other suitable numerical value), or other suitable upper limit) above the value, as well as a lower limit (for example, 0.5% or 1% on a percentage basis or, for example, 250 or 750 (a unit of measurement or other suitable numerical value), or other appropriate lower limit) below the value. Similarly, if the threshold includes a range that includes a first upper limit and a first lower limit (for example, defining an acceptable range of the user characteristic(s) corresponding to the prediction), the predefined range may include a second upper limit (e.g. 0.5% or 1% on percentage basis or e.g. 250 or 750 (a unit of measurement or other suitable numerical value) or other suitable upper limit) above the first upper limit and a second lower limit (for example, 0.5% or 1% on a percentage basis or, for example, 250 or 750 (a unit of measurement or other suitable numerical value) or other suitable lower limit) below the first lower limit. It should be understood that the threshold may include any suitable predefined interval and may include any suitable format in addition to those described in this document.

[0583] If the server 3030 determines that the prediction is within the threshold range, the server 3030 may communicate with (e.g., through) an interface, such as displaying the general information 3120 on the display device. healthcare provider's computation that helps the user, to provide prediction and treatment data. In some embodiments, server 3030 may generate treatment information using treatment data and/or prediction. The treatment information may include a formatted summary about the user's performance of the treatment plan while using the treatment device 3070, such that the treatment data and/or prediction can be presented on the treatment computing device. a healthcare provider responsible for the user's performance of the treatment plan. In some embodiments, the visual presentation of the patient profile 3120 may

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168 include and/or display treatment information.

[0584] The server 3030 may be configured to provide the treatment information in the display of the general information 3120. For example, the server 3030 may store the treatment information to access the treatment information by displaying the information general 3120 and/or communicate the treatment information to the visual presentation of the general information 3120. In some embodiments, the server 3030 may provide the treatment information to the patient profile display 3130 or other suitable section, part or component of the general information display 3120 or to any other suitable display or interface.

[0585] In some embodiments, the server 3030 may, in response to determining that the prediction is within the threshold range, modify at least one aspect of the treatment plan and/or, based on the prediction, a or more features of the 3070 treatment device. In some embodiments, the server 3030 may control, while the user uses the treatment device 3070 during a telemedicine session and based on a generated prediction, the treatment device 3070. For example, the server 3030 may, based on the prediction and/or treatment plan, control one or more characteristics of the treatment device 3070.

[0586] The healthcare provider may include a medical professional (e.g., doctor, nurse, therapist, or the like), an exercise professional (e.g., instructor, trainer, nutritionist, and the like), or other professional who possesses at least one of the medical and exercise attributes (for example, an exercise physiologist, a physical therapist, an occupational therapist, and the like). As used herein, and without limiting the foregoing, a “health care provider” may be a human, a robot, a virtual assistant, a virtual assistant in virtual and/or augmented reality, or an entity artificially intelligent, including a software program, integrated software and hardware, or hardware alone.

[0587] In some embodiments, the interface may include a graphical user interface configured to provide treatment information and receive data input from the healthcare provider. The interface may include one or more input fields, such as

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169 text input, drop-down selection input fields, radio button input fields, virtual switch input fields, virtual toggle input fields, audio, haptic, haptic, touch, biometric and/or some other input fields otherwise, other suitable input fields or a combination thereof.

[0588] In some embodiments, the healthcare provider may review treatment and/or prediction information. The healthcare provider may determine, based on review of treatment information and/or prediction, whether to modify at least one aspect of the treatment plan and/or one or more features of the treatment device 3070. For example, the health care provider may review treatment information. Based on the review of the treatment information, the healthcare provider can compare the treatment information with the treatment plan that the user is undertaking.

[0589] The healthcare provider may compare the following: (i) the expected information, which is related to the user while the user uses the treatment device to carry out the treatment plan, with i) the prediction, the which is related to the user while the user uses the treatment device to carry out the treatment plan.

[0590] The expected information may include one or more vital signs of the user, a respiratory rate of the user, a heart rate of the user, a temperature of the user, a blood pressure of the user, other suitable information of the user, or a combination thereof. . The health care provider can determine that the treatment plan is having the desired effect if the prediction is within an acceptable range associated with one or more corresponding parts or portions of the expected information. Alternatively, the healthcare provider may determine that the treatment plan is not having the desired effect if the prediction falls outside the range associated with one or more corresponding parts or portions of the expected information.

[0591] For example, the healthcare provider may determine whether a blood pressure value indicated by the prediction (e.g., systolic pressure, diastolic pressure, and/or differential blood pressure) is within an acceptable range (e.g., plus or minus 1%, plus or minus 5%, plus or minus 1 unit of measurement (or other suitable numerical value) or any suitable range)

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170 of an expected blood pressure value indicated by the expected information. The healthcare provider can determine that the treatment plan is having the desired effect if the blood pressure value is within the range of the expected blood pressure value. Alternatively, the healthcare provider may determine that the treatment plan is not having the desired effect if the blood pressure value is outside the range of the expected blood pressure value.

[0592] In some embodiments, while the user uses the treatment device 3070 to carry out the treatment plan, the healthcare provider may compare the expected characteristics of the treatment device 3070 with the characteristics of the treatment device 3070 indicated by the information treatment and/or prediction. For example, the healthcare provider may compare an expected resistance setting of the treatment device 3070 with an actual resistance setting of the treatment device 3070 indicated by the treatment information and/or prediction. The healthcare provider may determine that the user is performing the treatment plan correctly if the actual characteristics of the treatment device 3070 indicated by the treatment information and/or prediction are within a range of characteristics corresponding to the expected characteristics. of the treatment device 3070. Alternatively, the healthcare provider may determine that the user is not performing the treatment plan correctly if the actual characteristics of the treatment device 3070 indicated by the treatment information and/or prediction are outside the corresponding characteristic range. of the expected characteristics of the 3070 treatment device.

[0593] If the healthcare provider determines that the prediction and/or treatment information indicates that the user is performing the treatment plan correctly and/or that the treatment plan is having the desired effect, the healthcare provider may determine not to modify the aspect(s) of the treatment plan and/or the characteristic(s) of the 3070 treatment device. Alternatively, while the user uses the treatment device 3070 to carry out the treatment plan, if the healthcare provider determines that the prediction and/or treatment information indicates that the user is not or has not carried out the plan of treatment correctly and/or that the treatment plan is not having or has not had the desired effect, the

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171 health care provider may determine to modify the aspect(s) of the treatment plan and/or the characteristic(s) of the treatment device 3070.

[0594] In some embodiments, if the healthcare provider determines to modify the aspect(s) of the treatment plan and/or modify one or more features of the treatment device, the healthcare provider may interact with the interface to provide input. of data about the treatment plan that indicates one or more modifications made to the treatment plan and/or modify one or more characteristics of the treatment device 3070. For example, the healthcare provider may use the interface to provide data input indicating an increase or decrease in the resistance setting of the treatment device 3070 or other appropriate modification made to the feature(s) of the treatment device 3070. Additionally, or alternatively, the healthcare provider may use the interface to provide a data entry indicating a modification made to the treatment plan. For example, the healthcare provider may use the interface to provide a data entry indicating an increase or decrease in an amount of time that the user should use the treatment device 3070 in accordance with the treatment plan or other appropriate modifications. made to the treatment plan.

[0595] In some embodiments, based on one or more modifications indicated by input of data about the treatment plan, the server 3030 may modify at least one aspect of the treatment plan and/or one or more characteristics of the treatment device. treatment 3070.

[0596] In some embodiments, while the user uses the treatment device 3070 to perform the modified treatment plan, the server 3030 may receive subsequent treatment data related to the user. For example, after the healthcare provider provides data input that modifies the treatment plan and/or the characteristic(s) of the treatment device 3070, and/or after the server 3030 using the intelligence engine artificial 3011 modifies the treatment plan and/or one or more characteristics of the treatment device 3070, the user may continue to use the treatment device 3070 to perform the modified treatment plan. Subsequent treatment data may correspond to treatment data generated while the user uses the treatment device 3070 to carry out the plan.

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172 of modified treatment. In some embodiments, after the healthcare provider has received the treatment information and determined not to modify the treatment plan and/or the characteristic(s) of the treatment device 3070 and/or the server 3030 that uses the intelligence engine artificial 3011 has determined not to modify the treatment plan and/or the characteristics of the treatment device 3070, Subsequent treatment data may correspond to treatment data generated while the user continues to use the treatment device 3070 to perform the treatment plan. In some embodiments, the subsequent treatment data may include the updated treatment data (e.g., the treatment data updated to include at least one prediction),

[0597] In some embodiments, the server 3030 may use the artificial intelligence engine 3011 that uses the machine learning model 3013 to generate one or more subsequent predictions based on the subsequent processing data. The server 3030 may determine whether a respective subsequent prediction is within a range of a corresponding threshold. The server 3030 may, in response to a determination that the respective subsequent prediction is within the threshold range, communicate the subsequent treatment data, the subsequent treatment information and/or the prediction to the healthcare provider's computing device. . In some embodiments, the server 3030 may modify at least one aspect of the treatment plan and/or one or more characteristics of the treatment device 3070 based on the subsequent prediction.

[0598] In some embodiments, server 3030 may receive subsequent data input regarding the treatment plan from the healthcare provider's computing device. Based on subsequent data input about the treatment plan received from the healthcare provider's computing device, the server 3030 may further modify the treatment plan and/or control the characteristic(s) of the treatment device 3070. Subsequent data entry about the treatment plan may correspond to data entry provided by the healthcare provider, at the interface, in response to receipt and/or review of the subsequent treatment information and/or subsequent prediction. corresponding to subsequent treatment data. It should be understood that the server 3030 using the artificial intelligence engine 3011 may generate

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173 predictions continuously and/or periodically based on treatment data. Based on treatment data received continuously and/or periodically from sensors or other suitable sources described herein, server 3030 may provide treatment information and/or predictions to the healthcare provider's computing device. Additionally, or alternatively, the server 3030 may be configured to continuously and/or periodically monitor the user's characteristics while the user uses the treatment device 3070 to carry out the treatment plan.

[0599] In some embodiments, the healthcare provider and/or the server may receive and/or review, continuously or periodically, while the user uses the treatment device 3070 to carry out the treatment plan, the treatment information , treatment data and/or predictions. Based on one or more trends indicated by the treatment information, treatment data and/or predictions, the healthcare provider and/or server 3030 may determine whether to modify the treatment plan and/or modify and /or control the characteristic(s) of the treatment device 3070. For example, the trend(s) may indicate an increase in heart rate or other appropriate trends that indicate that the user is not performing the treatment plan correctly and/or that the user's performance of the treatment plan is not being met. desired effect.

[0600] In some embodiments, the server 3030 may monitor, while the user uses the treatment device 3070 to perform the treatment plan, one or more characteristics of the treatment device 3070 based on the prediction. For example, the server 3030 may determine that the prediction is outside the corresponding threshold range. Based on the prediction, the server 3030 may identify one or more features of the treatment device 3070. The server 3030 may communicate a signal to the controller 3072 of the treatment device 3070 indicating modifications made to the feature(s) of the treatment device 3070. Based on the signal, the controller 3072 may modify the characteristic(s) of the treatment device 3070.

[0601] In some embodiments, the treatment plan including the settings, adjustments, range of motion adjustments, pain level, strength adjustments, speed adjustments, etc., of the

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174 treatment device 3070 for various exercises, can be transmitted to the controller of the treatment device 3070. In an example, if the user provides an indication, through the patient interface 3050, that he is feeling a high level of pain in a range determined movement, the controller can receive the indication. Based on the indication, the controller may electronically adjust the range of motion of the pedal 3102 by adjusting the pedal in, out, or along or about any suitable axis, through one or more actuators, hydraulics, springs, electric motors or similar. When the user indicates certain levels of pain during an exercise, the treatment plan may define alternative range of motion settings for the pedal 3102. Accordingly, once the treatment plan is uploaded to the treatment device controller 3070, the treatment device 3070 can continue to operate without additional instructions, without additional external data inputs, and the like. It should be noted that the patient (via patient interface 3050) and/or the assistant (via assistant interface 3094) may override any of the configurations or settings of the treatment device 3070 at any time. For example, The patient can use the patient interface 3050 to cause the treatment device 3070 to stop immediately if desired.

[0602] Figure 31 is a flow chart generally illustrating a method 3900 for monitoring, based on treatment data received while a user uses the treatment device 3070, the characteristics of the user while the user uses the device of treatment 3070 in accordance with the principles of the present description. Method 3900 is performed using processing logic that may include hardware (circuits, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), or a combination of both. . Method 3900 and/or each of its individual functions, routines, subroutines or operations may be performed by one or more processors of a computing device (e.g., any component of Figure 23, such as server 3030 running the engine of artificial intelligence 3011). In some embodiments, method 3900 may be performed through a single processing cycle. Alternatively, method 3900 may be performed using two or more processing cycles, where each cycle implements one or more individual functions, routines, subroutines, or operations of the methods.

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[0603] For purposes of simplicity of explanation, method 3900 is represented and described as a series of operations. However, operations according to this description may take place in a different order and/or simultaneously and/or with other operations that are not presented or described in this document. For example, the operations represented in method 3900 may occur in combination with any other operation of any other method described herein. Additionally, not all of the operations illustrated may be required to implement Method 3900 in accordance with the disclosed subject matter. Furthermore, those skilled in the art will understand and note that method 3900 could alternatively be represented as a series of interrelated states via a state or event diagram.

[0604] At 3902, the processing device may receive treatment data related to a user using a treatment device, such as treatment device 3070, to perform a treatment plan. Treatment data may include characteristics of the user, baseline measurement information related to the user, measurement information related to the user while the user uses the treatment device 3070, characteristics of the treatment device 3070, the aspect(s) of the treatment plan. treatment, other appropriate data or a combination thereof.

[0605] At 3904, the processing device may write to an associated memory, to access the treatment data by an artificial intelligence engine 3011, such as the artificial intelligence engine 3011. The artificial intelligence engine 3011 may be configured to use at least one machine learning model, such as machine learning model 3013. The machine learning model 3013 may be configured to use at least one aspect of the treatment data to generate at least one prediction.

[0606] The prediction(s) may indicate one or more expected characteristics of the user. The expected characteristic(s) of the user may include a predicted vital sign of the user, a predicted respiratory rate of the user, a predicted heart rate of the user, a predicted temperature of the user, a blood pressure of the user, an expected clinical consequence of the treatment plan. that the user is performing, an anticipated injury to the user as a result of the user performing the treatment plan, or other appropriate intended characteristic of the user.

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[0607] At 3906, the processing device may receive, from the artificial intelligence engine 3011, the prediction(s).

[0608] At 3908, the processing device may identify a threshold that corresponds to the prediction(s). For example, the processing device may identify one or more characteristics of the user indicated by a respective prediction. The processing device may access a database, such as a database 3044 or other suitable database, configured to associate thresholds with user characteristics and/or combinations of user characteristics. For example, database 3044 may include information that associates a first threshold with a blood pressure of the user. Additionally, or alternatively, database 3044 may include information that associates a threshold with a blood pressure of the user and a heart rate of the user. A threshold corresponding to a respective prediction may include a value or a range of values including an upper limit and a lower limit.

[0609] At 3910, the processing device may, in response to a determination that the prediction(s) fall within a range of a corresponding threshold, communicate with an interface on a computing device of a healthcare provider, to provide prediction(s) and treatment data. For example, the processing device may compare the prediction(s) and/or one or more user characteristics indicated by the prediction with the corresponding threshold identified by the processing device. If the processing device determines that the prediction is within the threshold range, the processing device may communicate the prediction(s) and/or treatment data to the healthcare provider's computing device.

[0610] At 3912, the processing device may, in response to determining that the prediction(s) is outside the applicable threshold range, update user-related processing data to indicate the prediction(s). The processing device may store the updated treatment data in an associated memory.

[0611] Figure 32 is a flow chart generally illustrating an alternative method 31000 for monitoring, based on treatment data received while a user uses the treatment device 3070, the characteristics of the user while the user uses the treatment device 3070. treatment device

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3070 in accordance with the principles of the present description. Method 31000 includes operations performed by the processors of a computing device (e.g., any component of Figure 23, such as the server 3030 running the artificial intelligence engine 3011). In some embodiments, one or more operations of method 31000 are implemented in computer instructions stored in a memory device and executed by a processing device. Method 31000 may be performed in the same or similar manner as described above in connection with Method 3900. The operations of Method 31000 may be performed in combination with any of the operations of any of the methods described herein.

[0612] At 31002, during a telemedicine session, the processing device may receive treatment data related to a user using a treatment device or treatment apparatus, such as treatment device 3070, to perform a treatment plan. . Treatment data may include characteristics of the user, baseline measurement information related to the user, measurement information related to the user while the user uses the treatment device 3070, characteristics of the treatment device 3070, the aspect(s) of the treatment plan. treatment, other appropriate data or a combination thereof.

[0613] At 31004, the processing device may write to an associated memory, to access the treatment data by an artificial intelligence engine 3011, such as the artificial intelligence engine 3011. The artificial intelligence engine 3011 may be configured to use at least one machine learning model, such as machine learning model 3013. The machine learning model 3013 may be configured to use at least one aspect of the treatment data to generate at least one prediction.

[0614] The prediction(s) may indicate one or more expected characteristics of the user. The expected characteristic(s) of the user may include a predicted vital sign of the user, a predicted respiratory rate of the user, a predicted heart rate of the user, a predicted temperature of the user, a blood pressure of the user, an expected clinical consequence of the treatment plan. that the user is performing, an anticipated injury to the user as a result of the user performing the treatment plan, or other appropriate intended characteristic of the user.

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[0615] At 31006, the processing device may receive, from the artificial intelligence engine 11, the prediction(s).

[0616] At 31008, the processing device may identify a threshold that corresponds to the prediction(s). For example, the processing device may identify one or more characteristics of the user indicated by a respective prediction. The processing device may access a database, such as a database 3044 or other suitable database, configured to associate thresholds with user characteristics and/or combinations of user characteristics. For example, database 3044 may include information that associates a first threshold with a blood pressure of the user. Additionally, or alternatively, database 3044 may include information that associates a threshold with a blood pressure of the user and a heart rate of the user. A threshold corresponding to a respective prediction may include a value or a range of values including an upper limit and a lower limit.

[0617] At 31010, the processing device may, in response to a determination that the prediction(s) fall within a range of a corresponding threshold, communicate with an interface on a computing device of a healthcare provider, to provide prediction(s) and treatment data. For example, the processing device may compare the prediction(s) and/or one or more user characteristics indicated by the prediction with the corresponding threshold identified by the processing device. If the processing device determines that the prediction is within the threshold range, the processing device may communicate the prediction(s) and/or treatment data to the healthcare provider's computing device.

[0618] At 31012, the processing device may receive, from the healthcare provider's computing device, a data entry about the treatment plan indicating at least one modification made to the aspect(s) of the treatment plan and any another aspect of the treatment plan.

[0619] At 31014, the processing device may modify, with the use of data input about the treatment plan, the aspect(s) of the treatment plan and any other aspect of the treatment plan.

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[0620] At 31016, the processing device may monitor, during a telemedicine session, while the user uses the treatment device 3070 and based on the modified aspect(s) of the treatment plan or any other aspect of the treatment plan, the 3070 treatment device.

[0621] Figure 33 is a flow chart generally illustrating an alternative method 31100 for monitoring, based on treatment data received while a user uses the treatment device 3070, the characteristics of the user while the user uses the device of treatment 3070 in accordance with the principles of the present description. Method 31100 includes operations performed by the processors of a computing device (e.g., any component of Figure 23, such as the server 3030 running the artificial intelligence engine 3011). In some embodiments, one or more operations of method 31100 are implemented in computer instructions stored in a memory device and executed by a processing device. Method 31100 may be performed in the same or similar manner as described above in connection with Method 3900 and/or Method 31000. The operations of Method 31100 may be performed in combination with any of the operations of any of the methods described. in this document.

[0622] At 31102, the processing device may receive treatment data related to a user using a treatment device, such as treatment device 3070, to perform a treatment plan. Treatment data may include characteristics of the user, baseline measurement information related to the user, measurement information related to the user while the user uses the treatment device 3070, characteristics of the treatment device 3070, the aspect(s) of the treatment plan. treatment, other appropriate data or a combination thereof.

[0623] At 31104, the processing device may write to an associated memory, to access the treatment data by an artificial intelligence engine 3011, such as the artificial intelligence engine 3011. The artificial intelligence engine 3011 may be configured to use at least one machine learning model, such as machine learning model 3013. The machine learning model 3013 can be configured to use at least one aspect of

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180 treatment data to generate at least one prediction.

[0624] The prediction(s) may indicate one or more expected characteristics of the user. The expected characteristic(s) of the user may include a predicted vital sign of the user, a predicted respiratory rate of the user, a predicted heart rate of the user, a predicted temperature of the user, a blood pressure of the user, an expected clinical consequence of the treatment plan. that the user is performing, an anticipated injury to the user as a result of the user performing the treatment plan, or other appropriate intended characteristic of the user.

[0625] At 31106, the processing device may receive, from the artificial intelligence engine 3011, the prediction(s).

[0626] At 31108, the processing device may generate treatment information with the use of the prediction(s). The treatment information may include a summary of the performance, while the user uses the treatment device 3070 to perform the treatment plan, of the treatment plan made by the user and the prediction(s). The treatment information may be in a format such that the treatment data and/or prediction(s) may be presented on a computing device of a healthcare provider responsible for the user's performance of the treatment plan.

[0627] At 31110, the processing device may write to an associated memory, to access treatment information and/or prediction(s) using at least one of the healthcare provider's computing device and a learning model. automatic executed using the 3011 artificial intelligence engine.

[0628] At 31112, the processing device may receive a data input about the treatment plan in response to the treatment information. The data entry about the treatment plan may indicate at least one modification made to the aspect(s) of the treatment plan and/or any other aspect of the treatment plan. In some embodiments, data entry about the treatment plan may be provided by the healthcare provider in the manner described above. In some embodiments, based on the treatment information, the artificial intelligence engine 3011 running the machine learning model 3013 may generate data input about the treatment plan.

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181

[0629] At 31114, the processing device may determine whether the input of data about the treatment plan indicates at least one modification made to the aspect(s) of the treatment plan and/or any other aspect of the treatment plan.

[0630] If the processing device determines that the data entry about the treatment plan does not indicate at least one modification made to the aspect(s) of the treatment plan and/or any other aspect of the treatment plan, the processing device Processing returns to step 31102 and continues to receive treatment data related to the user while the user uses the treatment device 3070 to perform the treatment plan. If the processing device determines that the data entry about the treatment plan indicates at least one modification made to the aspect(s) of the treatment plan and/or any other aspect of the treatment plan, the processing device proceeds to the stage 31116.

[0631] At 31116, the processing device may selectively modify the aspect(s) of the treatment plan and/or any other aspect of the treatment plan. For example, the processing device may determine whether the treatment data indicates that the treatment plan is having a desired effect. The processing device may modify, in response to the determination that the treatment plan is not having the desired effect, at least one aspect of the treatment plan to attempt to achieve the desired effect and, if not possible, at least a part of the desired effect.

[0632] At 31118, the processing device may control, while the user uses the treatment device 3070, based on the treatment plan and/or the modified treatment plan, the treatment device 3070.

[0633] Figure 34 generally illustrates an exemplary embodiment of a method 31200 for receiving a selection of an optimal treatment plan and controlling a treatment device while the patient uses the treatment device in accordance with the present description, based on in the optimal treatment plan. Method 31200 includes operations performed by the processors of a computing device (e.g., any component of Figure 23, such as the server 3030 running the artificial intelligence engine 3011). In some embodiments, one or more operations of method 31200 are implemented in computer instructions stored on a storage device.

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182 memory and executed by a processing device. Method 31200 may be performed in the same or similar manner as described above in connection with Method 3900. The operations of Method 31200 may be performed in combination with any of the operations of any of the methods described herein.

[0634] Before executing method 31200, various optimal treatment plans may be generated by one or more trained machine learning models 3013 of artificial intelligence engine 3011. For example, based on a set of treatment plans related to a condition of a patient, the trained machine learning model(s) 3013 can generate optimal treatment plans. The various treatment plans can be transmitted to the computing device(s) of a patient and/or medical professional.

[0635] In 31202 of method 31200, the processing device may receive an optimal treatment plan selected from some or all of the optimal treatment plans. The selection may have been entered into a user interface that presents the optimal treatment plans to the patient interface 3050 and/or the assistant interface 3094.

[0636] At 31204, the processing device may control, while the patient uses the treatment device 3070, based on the selected optimal treatment plan, the treatment device 3070. In some embodiments, the control may be performed remotely by the server 3030. For example, if the selection is made with the use of the patient interface 3050, one or more control signals may be transmitted from the patient interface 3050 to the treatment device 3070 to configure, according to the selected treatment plan , an adjustment of the treatment device 3070 to control the operation of the treatment device 3070. Additionally, if the selection is made with the use of the wizard interface 3094, one or more control signals may be transmitted from the wizard interface 3094 to the treatment device 3070 to configure, according to the selected treatment plan, an adjustment of the treatment device 3070 to control the operation of the treatment device 3070.

[0637] It should be noted that, when the patient uses the treatment device 3070, the sensors 3076 may transmit measurement data to a processing device. The processing device can dynamically control, according to the treatment plan, the device

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183 treatment device 3070 by modifying, based on sensor measurements, a setting of the treatment device 3070. For example, if the force measured by sensor 3076 indicates that the user is not applying enough force to a pedal 3102, the treatment plan Treatment may indicate reducing the degree of force needed for an exercise.

[0638] It should be noted that, when the patient uses the treatment device 3070, the user can use the patient interface 3050 to enter data entries regarding a level of pain experienced by the patient while the patient performs the treatment plan. For example, the user may input a high level of pain when pedaling with the pedals 3102 set at a certain range of motion on the treatment device 3070. The pain level entered by the user may be within a range or at a level that may cause the range of motion to be dynamically adjusted based on the treatment plan. For example, the treatment plan may specify alternative range of motion settings if a certain level of pain is indicated when the user is performing an exercise subject to a certain range of motion.

[0639] Figure 35 generally illustrates an example of a computer system 31300 that can perform any or more of the methods described herein, in accordance with one or more aspects of the present description. In an example, the computing system 31300 may include a computing device and correspond to the assistant interface 3094, the reporting interface 3092, the monitoring interface 3090, the clinician interface 3020, the server 3030 (including the AI engine 3011), the patient interface 3050, the ambulatory sensor 3082, the goniometer 3084, the treatment device 3070, the pressure sensor 3086 or any suitable component of Figure 23. The computer system 31300 may execute instructions that implement one or more machine learning models 3013 of the artificial intelligence engine 3011 of Figure 23. The computer system may connect (e.g., network) to other computer systems on a LAN. , an intranet, an extranet or the Internet, including through the cloud or a network between users.

[0640] The computing system may operate with the capacity of a server in a client-server network environment. The computing system may be a personal computer (PC), a tablet, a wearable device (e.g. a bracelet), a set-top box (STB), a personal digital assistant (PDA),

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184 a mobile phone, a camera, a video camera, an Internet of Things (IoT) device, or any device capable of executing a set of instructions (sequential or otherwise) that specify the actions the device must perform. Furthermore, although only one computer system is illustrated, it should be understood that the term “computer” also includes any group of computers that individually or jointly execute a set (or multiple sets) of instructions to perform one or more of the methods are described in this document.

[0641] The computing system 31300 includes a processing device 31302, a main memory 31304 (e.g., a read-only memory (ROM), a flash memory, solid state drives (SSD), a dynamic random access memory (DRAM), such as a synchronous DRAM (SDRAM)), a static memory 31306 (for example, a flash memory, solid state drives (SSD), a static random access memory (SRAM)), as well as a data storage device 31308 that communicates with other devices through a bus 31310.

[0642] Processing device 31302 represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, or the like. More particularly, the processing device 31302 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor that implements other instruction sets or processors that implement a combination of instruction sets. The processing device 31302 may also be one or more purpose-specific processing devices, such as an application-specific integrated circuit (ASIC), a system on a chip, a field programmable gate array (FPGA), a data processor. digital signals (DSP), a network processor or the like. The processing device 31302 is configured to execute instructions to perform any of the operations and steps described herein.

[0643] The computer system 31300 may also include a network interface device 31312. The computer system 31300 may also include a video display 31314 (e.g., a liquid crystal display (LCD), a light emitting diode (LED), an organic light-emitting diode (OLED), a quantum LED, a cathode ray tube (CRT), a CRT shadow mask, an ocher grating Ln/zznz/E/YiAi

185 aperture CRT, a monochrome CRT), one or more input devices 31316 (e.g., a keyboard and/or a mouse or a game-like controller), and one or more speakers 31318 (e.g., a speaker). In an illustrative example, the video display 31314 and input devices 31316 may be combined into a single component or device (e.g., an LCD touch screen).

[0644] The data storage device 31316 may include a computer-readable medium 31320 on which instructions 31322 that incorporate one or more of the methods, operations, or functions described herein are stored. The instructions 31322 may also reside, in whole or at least partially, in the main memory 31304 and/or in the processing device 31302 during their execution by the computing system 31300. Thus, the main memory 31304 and the processing device 31302 also constitute computer-readable media. Instructions 31322 may also be transmitted or received over a network using network interface device 31312.

[0645] Although computer-readable storage medium 31320 is generally illustrated in the illustrative examples as a single medium, the term “computer-readable storage medium” should be understood to include a single medium or multiple media (e.g. , a centralized or distributed database, and/or associated caches and servers) that store the instruction set(s). It should also be understood that the term “computer-readable storage medium” includes any medium capable of storing, encoding or carrying a set of instructions for execution by the machine and causing the machine to perform one or more of the methodologies herein description. Accordingly, the term “computer readable storage medium” will be understood to include, but is not limited to, solid state memories, optical media, and magnetic media.

[0646] Clause 45. A method for providing, using an artificial intelligence engine, an optimal treatment plan for use with a treatment apparatus, the method comprising:

[0647] receiving, from a data source, clinical information related to the results of having used the treatment device to make specific treatment plans for people who have certain characteristics, wherein the clinical information has a first data format;

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186

[0648] translate a portion of the clinical information from the first data format into a descriptive medical language used by the artificial intelligence engine;

[0649] determine, based on the portion of clinical information described using descriptive medical language and a plurality of characteristics related to a patient, the optimal treatment plan that the patient should follow with the use of the treatment apparatus to achieve a desired result; and

[0650] provide the optimal treatment plan for presentation on a computing device of a medical professional.

[0651] Clause 46. The method in accordance with any clause of this document, wherein translating the clinical information part of the first data format into a descriptive medical language used by the artificial intelligence engine also comprises:

[0652] analyze clinical information;

[0653] identify, based on keywords representing the target information in the clinical information, the part of the clinical information that has values related to the target information;

[0654] generate a canonical format defined using descriptive medical language, where the canonical format comprises tags that identify the values of the target information.

[0655] Clause 47. The method in accordance with any clause of this document, where labels are attributes that describe specific characteristics of the target information;

[0656] Clause 48. The method in accordance with any clause of this document, wherein providing the optimal treatment plan for presentation on the computing device of the medical professional also includes:

[0657] causing, during a telemedicine session, the optimal treatment plan to be presented on a user interface of the medical professional's computing device, wherein the optimal treatment plan is not presented on a display screen of a device computing, the display screen is configured for use by the patient during the telemedicine session.

[0658] Clause 49. The method in accordance with any clause of this document,

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187 also includes:

[0659] determine, based on the portion of clinical information described using descriptive medical language and the plurality of characteristics related to the patient, a ruled out treatment plan that should not be recommended for the patient to follow when using the medical device. treatment to achieve the desired result; and

[0660] provide the excluded treatment plan for presentation on the healthcare professional's computing device.

[0661] Clause 50. The method of compliance with any clause of this document also includes:

[0662] determine, based on the portion of clinical information described by descriptive medical language and the plurality of characteristics related to a patient, a second optimal treatment plan for the patient to follow when using the treatment apparatus to achieve a second desired outcome, wherein the desired outcome consists of a clinical consequence of recovery and the second desired outcome consists of a recovery time; and

[0663] providing the second optimal treatment plan for presentation on the medical professional's computing device;

[0664] receive a treatment plan selected from either the optimal treatment plan or the second optimal treatment plan; and

[0665] transmit the selected treatment plan to a patient computing device for display on a user interface of the patient computing device.

[0666] Clause 51. The method in accordance with any clause of this document, wherein the desired result comprises obtaining a certain result in a certain period of time, and the certain result comprises:

[0667] a range of motion that the patient achieves with the use of the treatment apparatus,

[0668] a degree of force exerted by the patient on a part of the treatment apparatus,

[0669] an amount of time that the patient exercises with the use of the treatment device, [0670] a distance that the patient travels with the use of the treatment device, or

[0671] some combination thereof.

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188

[0672] Clause 52. The method in accordance with any clause of this document, where:

[0673] Certain characteristics of persons include first medications prescribed to persons, first injuries of persons, first medical procedures performed on persons, first measurements of persons, first allergies of persons, first medical conditions of persons, first historical information of people, first vital signs of people, first symptoms of people, first family medical information of people, first demographic information of people, first geographical information of people, first information based on measurements or tests of people, first medically historical information of people, first etiological information of people, first information associative to cohorts of people, first differentially diagnostic information of people, first surgical information of people, first therapeutic information from a physical aspect of the people, first pharmacological information about the people, other first treatments recommended to the people or some combination thereof, and

[0674] The plurality of patient characteristics comprises second medications of the patient, second injuries of the patient, second medical procedures performed on the patient, second measurements of the patient, second allergies of the patient, second medical conditions of the patient, second historical information of the patient, seconds patient's vital signs, patient's second symptoms, patient's second family medical information, second demographic information of the patient, second geographic information of the patient, second information based on measurements or tests of the patient, second medically historical information of the patient, second etiological information of the patient, second associative information to cohorts of the patient, second differentially diagnostic information of the patient, second surgical information of the patient, second therapeutic information from a physical aspect of the patient, second pharmacological information of the patient, other second treatments recommended to the patient or some combination thereof.

[0675] Clause 53. The method of compliance with any clause of this document, wherein the clinical information is that written by a person who has a certain credential

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189 professional and comprises a journal article, a clinical trial, evidence-based guidelines, meta-analysis or some combination thereof.

[0676] Clause 54. The method in accordance with any clause of this document, wherein determining, based on the part of the clinical information described by descriptive medical language and the plurality of characteristics related to a patient, the treatment plan optimal that the patient should continue with the use of the treatment device to achieve a desired result, also includes:

[0677] search for matches of a pattern between the part of the clinical information described using descriptive medical language in the plurality of patient characteristics, where the pattern is associated with the optimal treatment plan that produces the desired result.

[0678] Clause 55. The method in accordance with any clause of this document, wherein the optimal treatment plan comprises:

[0679] a medical procedure that must be performed on the patient,

[0680] a treatment protocol for the patient using the treatment apparatus,

[0681] a dietary regimen for the patient,

[0682] a medication regimen for the patient,

[0683] a sleep regimen for the patient, or

[0684] some combination thereof.

[0685] Clause 56. A tangible, non-transitory, computer-readable medium that stores instructions that, when executed, cause a processing device:

[0686] receive, from a data source, clinical information related to the results of having used the treatment device to make specific treatment plans for people who have certain characteristics, wherein the clinical information has a first data format;

[0687] translate a portion of the clinical information from the first data format into a descriptive medical language used by the artificial intelligence engine;

[0688] determine, based on the portion of clinical information described using descriptive medical language and a plurality of characteristics related to a patient, the optimal treatment plan that the patient should follow with the use of the treatment apparatus to achieve a

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190 desired result; and

[0689] provide the optimal treatment plan for presentation on a computing device of a medical professional.

[0690] Clause 57. The computer-readable medium in accordance with any clause of this document, wherein translating the clinical information part of the first data format into the descriptive medical language used by the artificial intelligence engine also comprises:

[0691] analyze clinical information;

[0692] identify, based on keywords representing the target information in the clinical information, the part of the clinical information that has values of the target information;

[0693] generate a canonical format defined using descriptive medical language, where the canonical format comprises tags that identify the values of the target information.

[0694] Clause 58. The computer-readable medium in accordance with any clause of this document, wherein providing the optimal treatment plan for presentation on the computing device of the medical professional also includes:

[0695] causing, during a telemedicine session, the optimal treatment plan to be presented in a user interface of the computing device of the medical professional, wherein, during the telemedicine session, the optimal treatment plan is not presented in a user interface of a patient computing device.

[0696] Clause 59. The computer-readable medium in accordance with any clause of this document, wherein the processing device also:

[0697] determines, based on the portion of clinical information described by descriptive medical language and the plurality of characteristics related to a patient, a second optimal treatment plan for the patient to follow when using the treatment apparatus to achieve a second desired outcome, wherein the desired outcome consists of a clinical consequence of recovery and the second desired outcome consists of a recovery time; and

[0698] provides the second optimal treatment plan for presentation on the healthcare professional's computing device;

[0699] receives a treatment plan selected from either the optimal treatment plan or the

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191 second optimal treatment plan; and

[0700] transmits the selected treatment plan to a patient computing device.

[0701] Clause 60. The computer-readable medium in accordance with any clause of this document, wherein the desired result comprises obtaining a certain result in a certain period of time, and the certain result comprises:

[0702] a range of motion that the patient achieves with the use of the treatment apparatus,

[0703] a degree of force exerted by the patient on a part of the treatment apparatus,

[0704] an amount of time that the patient exercises with the use of the treatment device, [0705] a distance that the patient travels with the use of the treatment device, or

[0706] some combination thereof.

[0707] Clause 61. The computer-readable medium in accordance with any clause of this document, where:

[0708] Certain characteristics of persons include first medications prescribed to persons, first injuries of persons, first medical procedures performed on persons, first measurements of persons, first allergies of persons, first medical conditions of persons, first historical information of people, first vital signs of people, first symptoms of people, first family medical information of people, first demographic information of people, first geographical information of people, first information based on measurements or tests of people, first medically historical information of people, first etiological information of people, first information associative to cohorts of people, first differentially diagnostic information of people, first surgical information of people, first therapeutic information from a physical aspect of the people, first pharmacological information about the people, other first treatments recommended to the people or some combination thereof, and

[0709] The plurality of patient characteristics comprises second medications of the patient, second injuries of the patient, second medical procedures performed on the patient, second measurements of the patient, second allergies of the patient, second medical conditions of the patient, second historical information of the patient, seconds patient's vital signs, seconds

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192 symptoms of the patient, second family medical information of the patient, second demographic information of the patient, second geographic information of the patient, second information based on measurements or tests of the patient, second medically historical information of the patient, second etiological information of the patient, second information associative to patient cohorts, second differentially diagnostic information of the patient, second surgical information of the patient, second therapeutic information from a physical aspect of the patient, second pharmacological information about the patient, other second treatments recommended to the patient or some combination thereof.

[0710] Clause 62. The computer-readable medium in accordance with any clause of this document, wherein the clinical information is that written by a person who has a certain professional credential and comprises a journal article, a clinical trial, guidelines based in evidence or some combination thereof.

[0711] Clause 63. A system that includes:

[0712] a memory device that stores instructions; and

[0713] a processing device communicatively coupled to the memory device, wherein the processing device executes instructions to:

[0714] receiving, from a data source, clinical information related to the results of having used the treatment device to make specific treatment plans for people who have certain characteristics, wherein the clinical information has a first data format;

[0715] translate a portion of the clinical information from the first data format into a descriptive medical language used by the artificial intelligence engine;

[0716] determine, based on the portion of clinical information described by descriptive medical language and a plurality of characteristics related to a patient, the optimal treatment plan that the patient should follow when using the treatment apparatus to achieve a result wanted; and

[0717] provide the optimal treatment plan for presentation on a computing device of a medical professional.

[0718] Clause 64. The system in accordance with any clause of this document,

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193 where translating the clinical information part of the first data format into a descriptive medical language used by the artificial intelligence engine also includes:

[0719] analyze clinical information;

[0720] identify, based on keywords representing the target information described by the clinical information, the part of the clinical information that has values of the target information;

[0721] generate a canonical format defined using descriptive medical language, where the canonical format comprises tags that identify the values of the target information.

METHOD AND SYSTEM FOR USING VIRTUAL AVATARS ASSOCIATED WITH MEDICAL PROFESSIONALS DURING EXERCISE SESSIONS

[0722] Determine a treatment plan for a patient who presents certain characteristics (for example, vital signs or other types of measurements; performance; demographic, geographic, diagnostic, measurement or test-based, medically historical, etiological, associative to cohorts, differentially diagnostic, surgical, therapeutic from a physical aspect, pharmacological and other recommended treatments; levels or percentages of blood gases and/or arterial oxygenation; psychographic, etc.) can be a technically difficult problem. For example, establishing a treatment plan may involve a large amount of information, which can lead to inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitation setting, some of that wealth of information considered may include patient characteristics, such as personal information, performance information, and measurement information. Personal information may include, for example, demographic, psychographic or other information, such as age, weight, gender, height, body mass index, medical condition, family medication history, injury, a medical procedure, a prescription medication, or some combination thereof. Performance information may include, for example, an elapsed time of use of a treatment apparatus, a degree of force exerted on a portion of the treatment apparatus, a range of motion achieved in the treatment apparatus, a speed of movement of a part of the treatment device, an indication of a plurality of pain levels with the use of the treatment device or some combination of the

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194 themselves. The measurement information may include, for example, a vital sign, a respiratory rate, a heart rate, a temperature, a blood pressure, blood gas and/or arterial oxygenation levels or percentages, glucose levels or other levels of other biomarkers, or some combination thereof. It may be desirable to process the characteristics of a large number of patients, the treatment plans made for those patients, and the results of the treatment plans for those patients.

[0723] Additionally, another technical issue may involve the remote treatment, via a computing device during a telemedicine or telehealth session, of a patient from a location other than the location where the patient is located. Another technical problem is to control or enable, from a different location, the control of a treatment device used by the patient at the location where the patient is located. Often, when a patient undergoes rehabilitation surgery (for example, knee surgery), a physical therapist or other medical professional may prescribe a treatment device for the patient to use to perform a treatment protocol at home. or at any mobile location or temporary address. A medical professional may refer a patient to a physician, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, fitness trainer, or the like. A medical professional may refer a patient to anyone who has a credential, license, degree, or similar in the field of medicine, physical therapy, rehabilitation, or the like.

[0724] Because the physical therapist or other medical professional is in a different location than the patient and the treatment device, it may be technically difficult for the physical therapist or other medical professional to monitor the patient's actual progress (rather than relying on the patient's word about his or her progress) using the treatment device, modify the treatment plan based on the patient's progress, adapt the treatment apparatus to the personal characteristics of the patient as the patient carries out the treatment plan, and the like.

[0725] Accordingly, some embodiments of the present disclosure relate to the use of artificial intelligence and/or machine learning to dynamically control a treatment apparatus based on assignment during an adaptive telemedicine session. In some embodiments, a large number of treatment devices may be provided to patients. The ocher Ln/zznz/E/YiAi

195 Patients can use the treatment devices to carry out treatment plans in their homes, in a gym, in a rehabilitation center, in a hospital, in the workplace or in any suitable location, including permanent or temporary homes. In some embodiments, the processing apparatus may be communicatively coupled to a server. Patient characteristics can be collected before, during, and/or after patients make treatment plans. For example, personal information, performance information, and measurement information may be collected before, during, and/or after the individual makes treatment plans. The results (e.g., improved performance or reduced performance) of performing each exercise may be collected from the treatment device throughout the treatment plan and after the treatment plan is performed. The parameters, settings, configurations, etc. (e.g. pedal position, degree of resistance, etc.) of the treatment device may be collected before, during or after carrying out the treatment plan.

[0726] Each patient characteristic, each result, and each parameter, setting, configuration, etc., may be time stamped and may be correlated to a specific stage of the treatment plan. This technique can allow you to determine which stages of the treatment plan produce the desired results (e.g., improvement in muscle strength, range of motion, etc.) and which stages produce diminishing returns (e.g., continuing to exercise after 3 minutes in actually delays or damages recovery).

[0727] Over time, data may be collected from treatment devices and/or any suitable computing devices (e.g., computing devices where personal information is entered, such as a clinician interface or a user interface). patient) as patients use the treatment devices to carry out the various treatment plans. Data that may be collected may include patient characteristics, treatment plans made by patients, and outcomes of treatment plans.

[0728] In some embodiments, data may be processed to group certain individuals into cohorts. People can be grouped by people who have certain similar or selected characteristics, treatment plans, and results of having made the treatment plans. For example, athletic people who have no medical conditions and who are making a treatment plan

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196 (for example, they use the treatment device for 30 minutes a day, 5 times a week, for 3 weeks) and who fully recover can be grouped into a first cohort. Seniors who are classified as obese and who complete a treatment plan (for example, use the treatment plan for 10 minutes a day, 3 times a week, for 4 weeks) and who improve their range of motion by 75 % can be grouped into a second cohort.

[0729] In some embodiments, an artificial intelligence engine may include one or more machine learning models that are trained using the cohorts. For example, the machine learning model(s) can be trained to receive an input of data about the characteristics of a new patient and to generate a treatment plan for the patient that produces the desired outcome. Machine learning models can match a pattern between the characteristics of the new patient and at least one patient of the patients included in a specific cohort. When searching for pattern matches, machine learning models can assign the new patient to the specific cohort and select the treatment plan associated with at least one patient. The artificial intelligence engine can be configured to remotely control the treatment device based on the treatment plan while the new patient uses the treatment device to perform the treatment plan.

[0730] As can be seen, the characteristics of the new patient may change as the new patient uses the treatment apparatus to carry out the treatment plan. For example, patient performance may improve faster than expected for people in the cohort to which the new patient is currently assigned. Accordingly, machine learning models can be trained to dynamically reassign the new patient, based on the modified characteristics, to a different cohort that includes people with characteristics similar to the currently modified characteristics, as the new patient. For example, a clinically obese patient may lose weight and no longer meet the weight criterion for the initial cohort, which may result in the patient being reassigned to a different cohort with a different weight criterion. A different treatment plan can be selected for the new patient, and the treatment apparatus can be controlled remotely and based on the different treatment plan, the treatment apparatus while the new patient uses the treatment apparatus to carry out the plan.

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197 of treatment. Such techniques may provide the technical solution to remotely control a treatment apparatus. Additionally, the techniques can lead to faster recovery times and/or better outcomes for patients because the treatment plan that most closely matches their characteristics is selected and implemented, in real time, at any given time. Real time may refer to less than or equal to 2 seconds. Near real-time can refer to any interaction of a time short enough to allow two people to engage in a conversation through that user interface, and will typically be less than 10 seconds, but greater than 2 seconds. As described herein, the term “outcomes” may refer to medical outcomes or medical consequences. Clinical outcomes and consequences may refer to responses to medical actions.

[0731] In some embodiments, treatment plans may be presented, during a telemedicine or telehealth session, to a medical professional. The medical professional may select a specific treatment plan for the patient to have that treatment plan transmitted to the patient and/or to control the treatment device based on the treatment plan. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, establishment of treatment plans and pharmacological and/or rehabilitation prescriptions, the artificial intelligence engine may receive and/or operate remotely from the patient and of the treatment device. In such cases, the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in real time or near real time during a telemedicine or telehealth session in a user interface of a device. computation of a medical professional. Video may also include audio, text, and other multimedia information.

[0732] The presentation of treatment plans generated by the artificial intelligence engine together with a presentation of the patient's video can provide an improved user interface, as the medical professional can continue to communicate visually or otherwise with the patient while also reviewing treatment plans in the same user interface. The improved user interface may improve the experience of the medical professional using the computing device and may encourage the medical professional to return to using the user interface. This technique can also reduce computing resources (e.g.

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198 processing, memory, network), since the medical professional does not have to switch to another user interface screen to enter a query about a treatment plan to recommend based on the patient's characteristics. The artificial intelligence engine dynamically provides treatment plans and excluded treatment plans on the fly.

[0733] In some embodiments, the treatment plan may be modified by a medical professional. For example, certain procedures can be added, modified, or deleted. In the case of telehealth, there are certain procedures that may not be performed due to the distant nature of a medical professional using a computing device in a physical location other than a patient.

[0734] A potential technical problem may be information regarding the patient's medical condition that is received in disparate formats. For example, a server may receive information related to a patient's medical condition from one or more sources (for example, from an electronic medical record (EMR) system, application programming interface (API), or any suitable system that has information related to the patient's medical condition). That is, some fonts used by various medical professional entities may be installed on their local computing devices and may additionally or alternatively use proprietary formats. Accordingly, some embodiments of the present disclosure may use an API to obtain, through interfaces exposed by the APIs and used by the sources, the formats used by the sources. In some embodiments, when information is received from sources, the API may map and convert the format used by the sources to a format ("format", as used herein, shall be inclusive of all such terms), language and/or standardized (i.e. canonical) encoding used by the AI engine. Additionally, information converted to the standardized format used by the AI Engine may be stored in a database that is accessed by the AI Engine when the AI Engine performs any of the techniques described herein. The use of information converted to a standardized format may allow for a more precise determination of the procedures to be performed by the patient and/or a billing sequence for use by the patient.

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200 such as a user who indicates that they are in significant pain, a telemedicine session is initiated, either by screening or electronically. The telemedicine session sees the virtual avatar replaced on the patient's computing device by a multimedia stream from the medical professional's computing device. In some embodiments, the medical professional may choose to intervene and/or interrupt any patient's treatment plan (including, for example, but not limited to, an exercise, rehab, prehabilitation, or other session) as desired (e.g., when the medical professional determines that a sensor measurement is of an undesired type, that the patient is not responding as desired, etc.), while the other patients continue following the virtual avatar to carry out the exercise session.

[0737] In some embodiments, the treatment apparatus may be adaptive and/or personalized because its properties, configurations, and positions may be tailored to the needs of a specific patient. For example, the pedals can be adjusted dynamically and on the fly (e.g., through a telemedicine session or based on programmed settings in response to certain detected measurements) in order to increase or decrease a range of motion. to comply with a treatment plan designed for the user. In some embodiments, during a telemedicine session, a medical professional can remotely adapt the treatment apparatus to the patient's needs by causing a control instruction to be transmitted from a server to the treatment apparatus. Such adaptive nature can improve a patient's recovery outcomes, promoting the goals of personalized medicine, and allowing separate treatment plan personalization.

[0738] Figure 36 shows a block diagram of a computer-implemented system 10, hereinafter referred to as “the system” for managing a treatment plan. Treatment plan management may include using an artificial intelligence engine to recommend treatment plans and/or providing excluded treatment plans that should not be recommended to a patient.

[0739] System 4010 also includes a server 4030 configured to store and provide data related to treatment plan management. Server 4030 may include one or more computers and may take the form of one or more distributed computers and/or

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201 virtualized. The server 4030 also includes a first communication interface 4032 configured to communicate with the clinician interface 4020 over a first network 4034. In some embodiments, the first network 4034 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communications (NFC), cellular data network, among others. The server 4030 includes a first processor 4036 and a first machine-readable storage memory 4038, which may be referred to as "memory" for short, which contains first instructions 4040 for performing the various actions of the server 30 for execution by the first processor 4036. Server 4030 is configured to store data related to the treatment plan. For example, memory 4038 includes system data storage 4042 configured to contain system data, such as data related to treatment plans for treating one or more patients.

[0740] System data storage 4042 may be configured to contain data related to billing procedures, including rules and restrictions regarding billing codes, orders, deadlines, insurance regimes, laws, regulations or some combination thereof. The system data storage 4042 may be configured to store various billing sequences generated based on the billing procedures and various parameters (e.g., monetary value amount generated, patient clinical consequence, reimbursement plan, rates, a payment plan). payments for patients to pay a sum of money owed, an amount of proceeds to be paid to an insurance provider, etc.). System data storage 4042 may be configured to store optimal treatment plans generated based on various treatment plans for users with similar medical conditions, amounts of monetary value generated by the treatment plans, as well as restrictions. Any of the data stored in the system data store 4042 can be accessed by an artificial intelligence engine 4011 by performing any of the techniques described herein.

[0741] Server 4030 is also configured to store data related to the performance of a patient following a treatment plan. For example, memory 4038 includes patient data storage 4044 configured to retain patient data, such as

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202 data related to the patient(s), including data that represents the performance of each patient within the treatment plan.

[0742] Additionally, the characteristics (e.g., personal, performance, measurement, etc.) of the individuals, the treatment plans followed by the individuals, the level of compliance with the treatment plans, as well as the results of treatment plans, may use correlations and other statistical or probabilistic measures to allow splitting or dividing treatment plans into different databases equivalent to patient cohorts in the patient data warehouse 4044. For example, data from a first cohort of first patients who have a similar first injury, a similar first medical condition, a similar first medical procedure performed, a first treatment plan followed by the first patient, as well as a first outcome of the plan of treatment, can be stored in a database of first patients. Data from a second cohort of second patients who have a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment plan followed by the second patient, as well as a second outcome of the treatment plan, can be stored in a second patient database. Any individual characteristic or combination of characteristics can be used to separate patient cohorts. In some embodiments, different patient cohorts may be stored in different partitions or volumes of the same database. There is no specific limit for the number of different patient cohorts allowed, other than the limitation by mathematical combinatorial and/or partition theory.

[0743] These characteristic data, treatment plan data, and outcome data may be obtained from a large number of treatment apparatuses and/or computing devices over time and may be stored in the patient data storage 3044. characteristic data, treatment plan data, and outcomes data may be correlated across patient cohort databases in patient data storage 4044. Characteristics of individuals may include personal information, performance information, and/or measurement information.

[0744] In addition to historical information about other people stored in databases

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203 patient cohort equivalent data, real-time or near real-time information based on current patient characteristics about a current patient being treated may be stored in an appropriate patient cohort equivalent database. It may be determined that the patient's characteristics match or are similar to those of another person in a specific cohort (e.g., Cohort A) and the patient may be assigned to that cohort.

[0745] In some embodiments, the server 4030 may execute the artificial intelligence (AI) engine 4011 that uses one or more machine learning models 4013 to perform at least one of the embodiments described herein. The server 4030 may include a training engine 409 capable of generating one or more machine learning models 4013. Machine learning models 4013 can be trained to assign individuals to certain cohorts based on their characteristics, select treatment plans using real-time and historical data correlations that include patient cohort equivalents, and control a treatment apparatus 4070 , among other things. The machine learning models 4013 can be trained to generate, based on billing procedures, billing sequences and/or treatment plans tailored to various parameters (for example, the fees to be paid to a medical professional, a payment plan for the patient to pay a sum of money owed, a repayment plan, an amount of income that must be paid to an insurance provider, or some combination thereof). Machine learning models 4013 can be trained to generate, based on constraints, optimal treatment plans tailored to various parameters (e.g., amount of monetary value generated, patient clinical consequence, risk level, etc.). The training engine 9 may generate the machine learning model(s) 4013 and may be implemented into executable computer instructions by one or more processing devices of the training engine 409 and/or the servers 4030. To generate one or more learning models automatic 4013, the training engine 409 may train one or more machine learning models 4013. The artificial intelligence engine 4011 may use one or more machine learning models 4013.

[0746] The training engine 409 may be a rack-mount server, a router computer, a personal computer, a personal digital assistant, a telephone

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204 smart phone, a laptop, a tablet, a netbook, a desktop computer, an Internet of Things (IoT) device, any other desired computing device or any combination thereof. The training engine 409 may be cloud-based or a real-time software platform, and may include privacy software or protocols, and/or security software or protocols.

[0747] To train the machine learning model(s) 4013, the training engine 409 may use a training data set of a corpus of information (e.g., characteristics, medical diagnosis codes, etc.) relating to the medical conditions of people who used the 4070 treatment device to make treatment plans, the details (for example, the treatment protocol, including exercises, the amount of time the exercises should be performed, the instructions the patient should follow, the frequency with which the exercises should be performed, an exercise program, the parameters/settings/adjustments of the treatment apparatus 4070 throughout each stage of the treatment plan, etc.) of the treatment plans made by the persons using the treatment apparatus 4070, the results of the treatment plans made by the persons, a set of monetary value amounts associated with the treatment plans, a set of restrictions (for example, rules regarding billing codes associated with the set of treatment plans, laws, regulations, etc.), a set of procedures billing (e.g., rules regarding billing codes, orders, times, and restrictions) associated with treatment plan instructions, a set of parameters (e.g., a fee that must be paid to a medical professional, a payment plan for the patient to pay a sum of money owed, a reimbursement plan, an amount of income that must be paid to an insurance provider, or some combination thereof , a treatment plan, an amount of monetary value generated, a risk level, etc.), insurance schemes, etc.

[0748] The machine learning model(s) 4013 may be trained to match patterns of characteristics of a patient to the characteristics of other individuals assigned to a specific cohort. The term “match” may refer to an exact match, a correlative match, a substantial match, etc. The machine learning model(s) 4013 may be trained to receive characteristics of a patient as a data input,

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205 map the characteristics to the characteristics of people assigned to a cohort, and select a treatment plan from that cohort. One or more machine learning models 4013 may also be trained to control, based on the treatment plan, the machine learning apparatus 4070.

[0749] The machine learning model(s) 4013 may be trained to search for pattern matches of a first set of parameters (e.g., treatment plans for patients presenting with a medical condition, a set of monetary value amounts associated with the plans of treatment, clinical consequence of the patient and/or a set of restrictions) into a second set of parameters associated with an optimal treatment plan. The machine learning model(s) 4013 may be trained to receive the first set of parameters as a data input, map the features to the second set of parameters associated with the optimal treatment plan, and select the optimal treatment plan as a treatment plan. treatment. One or more machine learning models 4013 may also be trained to control, based on the treatment plan, the machine learning apparatus 4070.

[0750] The machine learning model(s) 4013 may be trained to search for pattern matches of a first set of parameters (e.g., information relating to a medical condition, treatment plans for patients presenting with a medical condition, a set of amounts of monetary value associated with treatment plans, clinical consequences of the patient, instructions that the patient must follow in a treatment plan, a set of billing procedures associated with instructions and/or a set of restrictions) into a second set of parameters associated with a billing sequence and/or optimal treatment plan. The machine learning model(s) 4013 may be trained to receive the first set of parameters as a data input, map or otherwise algorithmically associate the first set of parameters with the second set of parameters associated with the billing sequence, and /or the optimal treatment plan, and select the billing sequence and/or the optimal treatment plan for the patient. In some embodiments, one or more optimal treatment plans may be selected to be provided to a computing device of the medical professional and/or the patient. The machine learning model(s) 4013 may also be trained to

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206 control, based on the treatment plan, the machine learning device 4070.

[0751] Different machine learning models 4013 can be trained to recommend different treatment plans tailored for different parameters. For example, one machine learning model can be trained to recommend treatment plans for a maximum amount of monetary value generated, while another machine learning model can be trained to recommend treatment plans based on the patient's clinical consequence or based on any combination of monetary value amount and patient clinical consequence, or based on those and/or additional objectives. Additionally, different machine learning models 4013 can be trained to recommend different billing sequences tailored for different parameters. For example, one machine learning model can be trained to recommend billing sequences for a maximum fee to be paid to a medical professional, while another machine learning model can be trained to recommend billing sequences based on a reimbursement plan.

[0752] Using training data that includes training data inputs and corresponding target outputs, one or more machine learning models 4013 may refer to model artifacts created by the training engine 409. The training engine 409 may find patterns in the training data where such patterns map the training data input to the target output and generate machine learning models 4013 that capture these patterns. In some embodiments, the artificial intelligence engine 4011, database, and/or training engine 409 may reside in another component (e.g., assistant interface 4094, clinician interface 4020, etc.) represented in Figure 36.

[0753] One or more machine learning models 4013 may comprise, for example, a single level of linear or nonlinear operations (e.g., a support vector machine [SVM]) or the machine learning models 4013 may be a deep learning network, that is, a machine learning model that comprises multiple levels of nonlinear operations. Examples of deep learning networks are neural networks that include generative adversarial networks, convolutional neural networks, recurrent neural networks with one or more hidden layers, and fully connected neural networks (for example, each neuron can transmit its

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207 output signal to the data input of the remaining neurons, as well as to itself). For example, the machine learning model may include a large number of layers and/or hidden layers that perform calculations (e.g., dot products) using multiple neurons.

[0754] System 4010 also includes a patient interface 4050 configured to communicate information to a patient and to receive feedback from the patient. In particular, the patient interface includes an input device 4052 and an output device 4054, which may be referred to collectively as a patient and user interface 4052,4054. Input device 4052 may include one or more devices, such as a keyboard, a mouse, a touch screen data input, a gesture sensor, and/or a microphone and processor configured for speech recognition. The output device 4054 may take one or more different forms, such as a computer monitor or a display screen on a tablet, smartphone, or smart watch. The output device 4054 may include other hardware and/or software components, such as a projector, virtual reality capability, augmented reality capability, among others. The output device 4054 may incorporate various visual, audio, or other presentation technologies. For example, the output device 4054 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds, such as tones, ringers and/or melodies, which may indicate different conditions. and/or instructions. The output device 4054 may comprise one or more different displays that present various data and/or interfaces or controls for use by the patient. The output device 4054 may include graphics, which may be presented through a web-based interface and/or through a computer program or application (App.).

[0755] In some embodiments, the output device 4054 may present a user interface that may present a recommended treatment plan, billing sequence, or the like to the patient. The user interface may include one or more graphical elements that allow the user to select the treatment plan they wish to perform. In response to receiving a selection of a graphical element (e.g., the “Start” button) associated with a treatment plan through the input device 4054, the patient interface 4050 may communicate a control signal to the controller 4072. of the treatment apparatus 4070, wherein the control signal causes the treatment apparatus 4070 to begin execution of the selected treatment plan. As described ocher Ln/zznz/E/YiAi

208 The control signal may then control, based on the selected treatment plan, the treatment apparatus 4070 by operating the actuator 4078 (for example, causing a motor to drive the rotation of the pedals of the treatment apparatus to a certain speed), causing measurements to be obtained through sensor 4076 or similar. The patient interface 4050 may communicate, through a local communication interface 4068, the control signal to the treatment apparatus 4070.

[0756] As shown in Figure 36, the patient interface 4050 includes a second communication interface 4056, which may also be referred to as a remote communication interface configured to communicate with the server 4030 and/or the clinician interface 4020 through a second network 4058. In some embodiments, the second network 58 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the second network 4058 may include the Internet, and security of communications between the patient interface 4050 and the server 4030 and/or the clinician interface 4020 may be established through encryption, such as, for example, by the use of a virtual private network (VPN). In some embodiments, the second network 4058 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communications (NFC), cellular data network, among others. In some embodiments, the second network 4058 may be the same as and/or operatively coupled to the first network 4034.

[0757] The patient interface 4050 includes a second processor 4060 and a second machine-readable storage memory 4062 that contains second instructions 64 for execution by the second processor 4060 to perform various actions of the patient interface 4050. The second machine-readable storage memory 4062 also includes a local data store 66 configured to contain data, such as data related to a treatment plan and/or patient data, such as data representing the performance of a patient within a treatment plan. The patient interface 4050 also includes a local communication interface 4068 configured to communicate with various devices for use by the patient near the patient interface 4050. The local communication interface 4068 may include wired and/or wireless communications. In some embodiments, the communication interface

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209 local 4068 may include a local wireless network, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communications (NFC), cellular data network, among others.

[0758] System 4010 also includes a treatment apparatus 4070 configured to be manipulated by the patient and/or to manipulate a part of the patient's body to perform activities in accordance with the treatment plan. In some embodiments, the treatment apparatus 4070 may take the form of an exercise and rehabilitation apparatus configured to perform and/or assist in performing a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, and the treatment includes rehabilitation of a part of the patient's body, such as a joint, bone, or muscle group. Treatment apparatus 4070 may be any suitable medical, rehabilitation, therapeutic, etc. apparatus configured to be controlled remotely via another computing device to treat a patient and/or exercise the patient. The treatment apparatus 4070 may be an electromechanical machine that includes one or more weights, an electromechanical bicycle, an electromechanical spinning wheel, a smart mirror, a treadmill, a vibration device, or the like. The body part may include, for example, a spine, a hand, a foot, a knee, or a shoulder. The body part may include a part of a joint, bone, or muscle group, such as one or more vertebrae, a tendon, or a ligament. As shown in Figure 36, the treatment device 4070 includes a controller 4072, which may include one or more processors, computer memory and/or other components. The treatment apparatus 4070 also includes a fourth communication interface 4074 configured to communicate with the patient interface 4050 through the local communication interface 4068. The treatment apparatus 4070 also includes one or more internal sensors 4076 and an activator 4078, just like an engine. The activator 4078 may be used, for example, to move the patient's body part or to resist forces by the patient.

[0759] The internal sensors 4076 may measure one or more operating characteristics of the treatment apparatus 4070, such as a force, a position, a speed, a velocity, and/or an acceleration. In some embodiments, the internal sensors 4076 may include a position sensor configured to measure at least one of a linear motion or an angular motion of a part of the patient's body. For example, an internal sensor 4076 in the form of a position sensor

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210 may measure a distance up to which the patient is able to move a part of the treatment apparatus 4070, wherein the distance may correspond to a range of motion that the patient's body part can achieve. In some embodiments, the internal sensors 4076 may include a force sensor configured to measure a force applied by the patient. For example, an internal sensor 4076 in the form of a force sensor may measure a force or weight that the patient is able to apply, with a specific body part, to the treatment apparatus 4070.

[0760] The system 4010 shown in Figure 36 also includes an ambulation sensor 4082, which communicates with the server 4030 through the local communication interface 4068 of the patient interface 4050. The ambulation sensor 4082 can perform Track and store a series of steps taken by the patient. In some embodiments, the ambulation sensor 4082 may take the form of a bracelet, wristwatch, or smartwatch. In some embodiments, the ambulation sensor 4082 may be integrated into a telephone, such as a smartphone. In some embodiments, the ambulation sensor 4082 may be integrated into an article of clothing, such as a shoe, a belt, and/or a pair of pants.

[0761] The system 4010 shown in Figure 36 also includes a goniometer 4084, which communicates with the server 4030 through the local communication interface 4068 of the patient interface 4050. The goniometer 4084 measures an angle of the part of the patient's body. For example, the 4084 goniometer can measure the flexion angle of the patient's knee, elbow, or shoulder.

[0762] The system 4010 shown in Figure 36 also includes a pressure sensor 4086, which communicates with the server 4030 through the local communication interface 4068 of the patient interface 4050. The pressure sensor 4086 measures a degree of pressure or weight applied by a part of the patient's body. For example, pressure sensor 4086 may measure a degree of force applied by a patient's foot when pedaling a stationary bicycle.

[0763] The system 4010 shown in Figure 36 also includes a monitoring interface 4090 that may be similar or identical to the clinician interface 4020. In some embodiments, the monitoring interface 4090 may have enhanced functionality beyond what which is provided in the clinician interface 4020. The monitoring interface 4090 can be configured for use by a person responsible for the treatment plan, such as an orthopedic surgeon.

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211

[0764] The system 4010 shown in Figure 36 also includes a reporting interface 4092 that may be similar or identical to the clinician interface 4020. In some embodiments, the reporting interface 4092 may have less functionality. than that provided in the clinician interface 4020. For example, the reporting interface 4092 may not have the ability to modify a treatment plan. Such reporting interface 4092 may be used, for example, by a biller to determine the use of system 4010 for billing purposes. In another example, the reporting interface 4092 may not have the ability to display patient identifying information and may only display pseudonymous patient data and/or anonymous patient data for certain data fields related to a registered person and/or for certain data fields related to a quasi-identifier of the registered person. For example, a researcher can use the 4092 reporting interface to determine the various effects of a treatment plan on different patients.

[0765] The system 4010 includes an assistant interface 4094 for an assistant, such as a doctor, nurse, physical therapist, or technician to communicate remotely with the patient interface 4050 and/or the treatment apparatus 4070. These remote communications may allow the assistant to provide assistance or advice to a patient using the 4010 system. More specifically, the assistant interface 4094 is configured to communicate a telemedicine signal 4096, 4097, 4098a, 4098b, 4099a, 4099b to the patient interface 4050 through a network connection, such as through the first network 4034 and/or the second network 4058. The telemedicine signal 4096, 4097, 4098a, 4098b, 4099a, 4099b comprises one of an audio signal 4096, an audiovisual signal 4097, an interface control signal 4098a to control a function of the patient interface 4050, a interface monitoring 4098b to monitor a status of the patient interface 4050, an apparatus control signal 4099a to change an operating parameter of the treatment apparatus 4070 and/or an apparatus monitoring signal signal 4099b to monitor a status of the treatment apparatus 4070. In some embodiments, each of the Control 4098a, 4099a may consist of unidirectional transmission commands from the assistant interface 4094 to the patient interface 4050. In some embodiments, in response to successful receipt of a

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212 control signal 4098a, 4099a and/or to communicate successful and/or failed implementation of the requested control action, a confirmation message may be sent from the patient interface 4050 to the assistant interface 4094. In some embodiments, each one of the monitoring signal signals 4098b, 4099b may consist of unidirectional status information commands from the patient interface 4050 to the assistant interface 4094. In some embodiments, a confirmation message may be sent from the assistant interface 4094 to the patient interface 4050 in response to successful receipt of one of the monitoring signals 4098b, 4099b.

[0766] In some embodiments, the patient interface 4050 may be configured as a direct passage for the device control signals 4099a and the device monitoring signals 4099b between the treatment device 4070 and one or more devices, such as the interface of assistant 4094 and/or server 4030. For example, the patient interface 4050 may be configured to transmit a device control signal 4099a in response to a device control signal 4099a in the telemedicine signal 4096, 4097, 4098a, 4098b, 4099a, 4099b of the assistant interface. 4094.

[0767] In some embodiments, the assistant interface 4094 may be presented on a shared physical device, such as the clinician interface 4020. For example, the clinician interface 4020 may include one or more displays that implement the assistant interface 4094. Alternatively or additionally, the clinician interface 4020 may include additional hardware components, such as a video camera, speaker, and/or microphone, to implement aspects of the assistant interface 4094.

[0768] In some embodiments, one or more portions of the telemedicine signal 4096, 4097, 4098a, 4098b, 4099a, 4099b may be generated from a prerecorded source (e.g., an audio recording, a video recording, or an animation ) for presentation through the output device 4054 of the patient interface 4050. For example, a tutorial video can be transmitted from the server 4030 and presented on the patient interface 4050. The patient can request the content of the prerecorded source through the patient interface 4050. Alternatively, through a control on the assistant interface 4094, the assistant can cause the content of the prerecorded source to be played on the 4050 patient interface.

[0769] The wizard interface 4094 includes a wizard input device 4022 and a

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213 assistant visual presentation 4024, which may be referred to collectively as an assistant and user interface 4022, 4024. The assistant input device 4022 may include one or more of a telephone, a keyboard, a mouse, a sensitive area touch or a touch screen, for example. Alternatively or additionally, the assistant input device 4022 may include one or more microphones. In some embodiments, the microphone(s) may take the form of a handset, headset, wide area microphone, or microphones configured for the assistant to speak with a patient through the patient interface 4050. In some embodiments, the assistant input device 4022 may be configured to provide voice-based functions, with hardware and/or software configured to interpret instructions spoken by the assistant with the use of the microphone(s). Assistant input device 4022 may include functions provided by or similar to existing voice-based assistants, such as Apple's Siri, Amazon's Alexa, Google Assistant, or Samsung's Bixby. The wizard input device 4022 may include other hardware and/or software components. The assistant input device 4022 may include one or more general purpose devices and/or specific use devices.

[0770] The visual presentation of the assistant 4024 may take one or more different forms, such as a computer monitor or a display screen on a tablet, smartphone, or smart watch. The visual presentation of the assistant 4024 may include other hardware and/or software components, such as projectors, virtual reality capabilities, or augmented reality capabilities, among others. The visual presentation of the wizard 4024 may incorporate various visual, audio, or other presentation technologies. For example, the visual presentation of the assistant 4024 may include a non-visual representation, such as an audio signal, which may include spoken language and/or other sounds, such as tones, timbres, melodies and/or compositions that may indicate different conditions and/or instructions. The visual presentation of the assistant 4024 may comprise one or more different display screens that present various data and/or interfaces or controls for use by the assistant. The visual presentation of the wizard 4024 may include graphics, which may be presented through a web-based interface and/or through a computer program or application (App.).

[0771] In some embodiments, system 4010 may allow computer translation

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214 of the language, from the assistant interface 4094 to the patient interface 4050 and/or vice versa. Computer translation of language may include computer translation of spoken language and/or computer translation of text. Additionally or alternatively, system 4010 may allow recognition of speech and/or spoken pronunciation of text. For example, system 4010 may convert spoken words into printed text and/or system 4010 may audibly speak a language from printed text. The 4010 system can be configured to recognize words spoken by any or all of the patient, clinician, or assistant. In some embodiments, system 4010 may be configured to recognize and react to verbal requests or commands from the patient. For example, system 4010 may automatically initiate a telemedicine session in response to a verbal command from the patient (which may be given in any of several languages).

[0772] In some embodiments, the server 4030 may generate aspects of the visual presentation of the wizard 4024 for display via the wizard interface 4094. For example, the server 4030 may include a web server configured to generate the display screens for display. presentation in the visual presentation of the assistant 4024. For example, the artificial intelligence engine 4011 may generate treatment plans, billing sequences, and/or excluded treatment plans for patients and generate display screens that include those treatment plans, billing sequences, and/or excluded treatment plans. for presentation in the wizard display 4024 of the wizard interface 4094. In some embodiments, the display of the assistant 4024 may be configured to present a desktop virtualized and hosted by the server 4030. In some embodiments, the server 4030 may be configured to communicate with the assistant interface 4094 over the first network 4034. In In some embodiments, the first network 4034 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the first network 4034 may include the Internet, and the security of communications between the server 4030 and the assistant interface 4094 may be established through privacy-enhancing technologies, such as through the use of encryption. a virtual private network (VPN). Alternatively or additionally, the server 4030 may be configured to communicate with the assistant interface 4094 through one or more networks independent of the first network 4034 and/or other means ocher Ln/zznz/E/YiAi

215 communication, such as a direct wired or wireless communication channel. In some embodiments, each of the patient interface 4050 and the treatment apparatus 4070 may operate from a patient location geographically separate from a location of the assistant interface 4094. For example, patient interface 4050 and treatment apparatus 4070 may be used as part of a home rehabilitation system, which may receive remote assistance using assistant interface 4094 at a centralized location, such as a clinic or a call center.

[0773] In some embodiments, the assistant interface 4094 may be one of several different terminals (e.g., computing devices) that may be grouped together, for example, in one or more call centers or in one or more medical offices. In some embodiments, a plurality of assistant interfaces 4094 may be geographically distributed. In some embodiments, a person can work as an assistant remotely from any conventional office infrastructure. Such remote work may be performed, for example, when the assistant interface 94 takes the form of a computer and/or a telephone. This remote work feature may allow for work-from-home arrangements that may include part-time and/or flexible work schedules for an assistant.

[0774] Figures 37-38 show one embodiment of a treatment apparatus 4070. More specifically, Figure 37 shows a treatment apparatus 4070 in the form of a stationary cycling machine 4100, which may be referred to as a stationary bicycle for short. . The stationary cycling machine 4100 includes a set of pedals 4102, each attached to a pedal arm 4104 for rotation about an axis 4106. In some embodiments, and as shown in Figure 37, the pedals 4102 can be moved on the pedal arms 4104 to adjust a range of motion used by the patient when pedaling. For example, pedals that are located on the inside, towards the axis 4106, correspond to a smaller range of motion than when the pedals are located on the outside, away from the axis 4106. A pressure sensor 4086 is attached to or integrated into one or more of the pedals 4102 to measure the degree of force applied by the patient on the pedal 4102. The pressure sensor 4086 may communicate wirelessly with the treatment apparatus 4070 and/or or with the 4050 patient interface.

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216

[0775] Figure 39 shows a person (a patient) using the treatment apparatus of Figure 37, and showing the sensors and various data parameters connected to a patient interface 4050. The example of patient interface 4050 is a tablet, computer, or smartphone, or a phablet, such as an ¡Pad, an ¡Phone, an Android device, or a Surface tablet, that is manually held by the patient. In other embodiments, the patient interface 4050 may be integrated into or attached to the treatment apparatus 4070. Figure 39 shows the patient wearing the ambulation sensor 4082 on the wrist, with a note showing “TODAY'S STEPS 41355,” indicating that the ambulation sensor 4082 has recorded and transmitted that step count to the interface. of patient 4050. Figure 39 also shows the patient wearing the 4084 goniometer on the right knee, with a note showing “KNEE ANGLE 72either”, which indicates that the goniometer 4084 is measuring and transmitting that knee angle to the patient interface 4050. Figure 39 also shows a right side of one of the pedals 4102 with a pressure sensor 4086 showing a “FORCE of 12.5 pounds”, which indicates that the right pedal pressure sensor 4086 is measuring and transmitting that force measurement to the patient interface 4050. Figure 39 also shows a left side of one of the pedals 4102 with a pressure sensor 4086 displaying a “FORCE of 27 pounds,” which indicates that the left pedal pressure sensor 4086 is measuring and transmitting that force measurement. to the 4050 patient interface. Figure 36 also shows other patient data, such as a “SESSION TIME 0:04:13” indicator, which indicates that the patient has been using the treatment device 4070 for 4 minutes and 13 seconds. This session time can be determined by the patient interface 4050 based on the information received from the treatment apparatus 4070. Figure 36 also shows an indicator showing “PAIN LEVEL 3”. Such a pain level may be obtained from the patient in response to a request, such as a question, presented to the patient interface 4050.

[0776] Figure 40 is an exemplary embodiment of a visual presentation of the general information 4120 of the assistant interface 4094. Specifically, the visual presentation of the general information 4120 presents several different controls and interfaces for the assistant to assist in remotely direct a patient to use the patient interface 4050 and/or the treatment apparatus 4070. This remote assistance function may also be referred to as telemedicine or telehealth.

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217

[0777] Specifically, the general information display 4120 includes a patient profile display 4130 that presents biographical information related to a patient using the treatment apparatus 4070. The patient profile display 4130 may take the form of a portion or region of the general information display 4120, as shown in Figure 40, although the patient profile display 4130 may take other forms, such as a stand-alone screen or pop-up window. In some embodiments, the visual presentation of the patient profile 4130 may include a limited subset of the patient's biographical information. More specifically, the data presented in the patient profile display 4130 may depend on the assistant's need to view that information. For example, a medical professional who is assisting the patient with a medical problem may be provided with information from the patient's medical history, while a technician troubleshooting a problem with the treatment apparatus 4070 may be provided with a variety of information. much more limited patient-related. The technician, for example, could only be provided with the patient's name. The visual presentation of the patient profile 4130 may include pseudonymous patient data and/or anonymized patient data, or use any privacy-enhancing technology to prevent confidential patient data from being communicated in a manner that could violate the requirements. of patient confidentiality. These privacy-enhancing technologies may enable compliance with laws, regulations, or other government rules, such as, but not limited to, the Health Insurance Portability and Accountability Act (HIPAA) or the General Rules. of Data Protection Regulation (GDPR), where the patient can be considered a “registered person”.

[0778] In some embodiments, the visual presentation of the patient profile 4130 may present information related to the treatment plan for the patient to follow with the use of the treatment apparatus 4070. The treatment plan information may be limited to an assistant who is a medical professional, such as a doctor or physical therapist. For example, a medical professional helping a patient with a problem related to the treatment regimen may be provided with information about the treatment plan, while a technician troubleshooting a

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218 problem with the 4070 treatment device you cannot be provided with any information related to the patient's treatment plan.

[0779] In some embodiments, one or more recommended treatment plans and/or excluded treatment plans may be presented in the visual presentation of the patient profile 4130 to the assistant. The artificial intelligence engine 4011 of the server 4030 may generate one or more recommended treatment plans and/or excluded treatment plans and receive them from the server 4030 in real time, among others, during a telemedicine or telehealth session. Below, an example of presentation of the recommended treatment plan(s) and/or discarded treatment plan(s) is described with reference to Figure 42.

[0780] In some embodiments, one or more treatment plans and/or billing sequences associated with the treatment plans may be presented in the patient's visual presentation 4130 to the assistant. The artificial intelligence engine 4011 of the server 4030 may generate the treatment plan(s) and/or billing sequences associated with the treatment plans and receive them from the server 4030 in real time, among others, during a telemedicine or telehealth session. Below, an example presentation of the treatment plan(s) and/or billing sequences associated with the treatment plans is described with reference to Figure 44.

[0781] In some embodiments, one or more treatment plans and associated generated monetary value amounts, patient clinical consequences, and risks associated with the treatment plans may be presented in the visual presentation of the patient profile 4130 to the assistant. The artificial intelligence engine 4011 of the server 4030 may generate the treatment plan(s) and associated generated monetary value amounts, patient clinical consequences, and risks associated with the treatment plans and receive them from the server 4030 in real time, among others, during a telemedicine or telehealth session. Below, an example presentation of the treatment plan(s) and associated generated monetary value amounts, patient clinical consequences, and risks associated with the treatment plans is described with reference to Figure 47.

[0782] The example general information display 4120 shown in Figure 40 also includes a patient status display 4134 that presents status information related to a patient using the treatment apparatus. The presentation

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219 The patient statue display 4134 may take the form of a portion or region of the general information display 4120, as shown in Figure 40, although the patient status display 4134 may take other forms, such as a stand-alone screen or pop-up window. The visual presentation of the patient status 4134 includes sensor data 4136 from one or more of the external sensors 4082, 4084, 4086 and/or from one or more internal sensors 4076 of the treatment apparatus 4070. In some embodiments, the visual presentation of the Patient status 4134 may present other data 4138 related to the patient, such as the last reported level of pain or progress on a treatment plan.

[0783] User access controls may be used to limit access, including what data is available for viewing and/or modification, on any or all of the user interfaces 4020, 4050, 4090, 4092, 4094 of the system 4010. In some embodiments, user access controls may be employed to control what information is available to any given person using the system 4010. For example, data presented in the assistant interface 4094 may be controlled by user access controls, with permissions set based on the assistant/user's need and/or restrictions on viewing that information.

[0784] The example general information display 4120 shown in Figure 40 also includes a help data display 4140 that presents information for the assistant to use when providing assistance to the patient. The help data display 4140 may take the form of a portion or region of the general information display 4120, as shown in Figure 40. The visual presentation of help data 4140 may take other forms, such as a stand-alone screen or a pop-up window. The visual presentation of help data 4140 may include, for example, the presentation of answers to frequently asked questions related to the use of the patient interface 4050 and/or the treatment apparatus 4070. The visual presentation of help data 4140 may also include research data or best practices. In some embodiments, the visual display of assistive data 4140 may present scripts for answers or explanations in response to questions posed by the patient. In some embodiments, the visual presentation of help data 4140 may present flowcharts or guides for the assistant to use to determine a root cause and/or solution to the problem.

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220 of a patient. In some embodiments, the assistant interface 4094 may present two or more displays of help data 4140, which may be the same or different, for simultaneous presentation of the help data for use by the assistant. For example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient's problem, and a second help data display may present script information. for the assistant to read to the patient; Such information should preferably include instructions for the patient to take some action, which may help reduce or solve the problem. In some embodiments, based on data entries in the troubleshooting flowchart in the first help data display, the second help data display may be automatically populated with script information.

[0785] The example display of general information 4120 shown in Figure 40 also includes a patient interface control 4150 that displays information related to the patient interface 4050 and/or to modify one or more settings of the patient interface 4050. The patient interface control 4150 may take the form of a portion or region of the general information display 4120, as shown in Figure 40. The patient interface control 4150 may take other forms, such as a stand-alone display or a pop-up window. The patient interface control 4150 may present information communicated to the assistant interface 4094 through one or more of the interface monitoring signals 4098b. As shown in Figure 40, the patient interface control 4150 includes a transmission of the display 4152 of the display presented by the patient interface 4050. In some embodiments, the transmission of the display 4152 may include a active copy of the display screen currently being presented to the patient via patient interface 4050. In other words, the visual presentation transmission 4152 may present an image of what is presented on a display screen of the patient interface 4050. In some embodiments, the visual presentation transmission 4152 may include abbreviated information related to the display screen that is currently being presented by the patient interface 4050, such as a screen name or a screen number. interface control

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221 The patient interface 4150 may include a patient interface settings control 4154 so that the assistant can adjust or control one or more settings or aspects of the patient interface 4050. In some embodiments, the patient interface settings control 4154 can do The assistant interface 4094 generates and/or transmits an interface control signal 4098 to control a function or setting of the patient interface 4050.

[0786] In some embodiments, patient interface settings control 4154 may include collaborative navigation or co-browsing capability so that the assistant can remotely view and/or control patient interface 4050. For example, patient interface settings control 4154 may allow the assistant to remotely enter text into one or more text entry fields on patient interface 4050 and/or remotely control a cursor on the patient interface. patient 4050 with the use of a mouse or touch screen assistant interface 4094.

[0787] In some embodiments, with the use of patient interface 4050, patient interface setting control 4154 may allow the assistant to change a setting that the patient cannot change. For example, the patient interface 4050 may not be able to access a language setting to prevent a patient from inadvertently changing, on the patient interface 4050, the language used for visual presentations, while controlling interface settings. Patient interface 4154 may allow the assistant to change the language settings of the patient interface 4050. In another example, the patient interface 4050 may not be able to change a font size setting to a smaller size to prevent a patient from inadvertently changing the font size used for visual displays on the patient interface 4050, so such that the visual presentation is legible to the patient, while the patient interface settings control 4154 may allow the assistant to change the font size setting of the patient interface 4050.

[0788] The example general information display 4120 shown in Figure 40 also includes an interface communications display 4156 that shows the status of communications between the patient interface 4050 and one or more devices 4070, 4082, 4084, such as the treatment apparatus 4070, the ambulation sensor 4082 and/or the goniometer 4084. The interface communications display 4156 may take the form of a portion

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222 or region of the general information display 4120, as shown in Figure 40. The interface communications display 4156 may take other forms, such as a stand-alone display or a pop-up window. The interface communications display 4156 may include controls for the assistant to remotely modify communications with one or more of the other devices 4070, 4082, 4084. For example, the assistant can remotely instruct the patient interface 4050 to restart communication with one of the other devices 4070, 4082, 4084, or to establish communications with a new one of the other devices 4070, 4082, 4084. This functionality may be used, for example, when the patient has a problem with one of the other devices 4070, 4082, 4084, or when the patient receives a new or replacement one of the other devices 4070, 4082, 4084.

[0789] The example display of general information 4120 shown in Figure 40 also includes an apparatus control 4160 for the assistant to display and/or control information related to the treatment apparatus 4070. The apparatus control 4160 may take the form of a portion or region of the general information display 4120, as shown in Figure 40. The device control 4160 may take other forms, such as a stand-alone display or a pop-up window. The device control 4160 may include a visual display of the status of the device 4162 with information about the current state of the device. The display of the status of the device 4162 may present information communicated to the assistant interface 4094 through one or more of the device monitoring signals 4099b. The device status display 4162 may indicate whether the treatment device 4070 is currently communicating with the patient interface 4050. The device status display 4162 may present other current or historical information related to the status of the treatment device. 4070.

[0790] The appliance control 4160 may include an appliance settings control 4164 for the assistant to adjust or control one or more aspects of the treatment apparatus 4070. The appliance settings control 4164 may cause the assistant interface 4094 to generate and/or transmit an apparatus control signal 4099 to change an operating parameter of the treatment apparatus 4070 (e.g., a pedal radius setting, a resistance setting, a target RPM value, etc.). The device settings control 4164 may include a mode button 4166 and a position control 4168, both of which

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223 They can be used together for the assistant to put an activator 4078 of the treatment apparatus 4070 in a manual mode, after which a setting, such as a position or speed of the activator 4078, can be changed with the use of position control 4168. The mode button 4166 may allow a setting, such as a position, to toggle between automatic and manual modes. In some embodiments, one or more adjustments can be made at any time and without having an associated automatic/manual mode. In some embodiments, the assistant may change an operating parameter of the treatment apparatus 4070, such as a pedal radius setting, while the patient is actively using the treatment apparatus 4070. Such "on-the-fly" adjustment may or may not be available. for the patient using the 4050 patient interface. In some embodiments, device setting control 4164 may allow the assistant to change a setting that the patient cannot change, using patient interface 4050. For example, the patient interface 4050 may not be able to change a preconfigured setting, such as a height or tilt setting of the treatment apparatus 4070, while the apparatus settings control 4164 may allow the assistant to change the setting of height or inclination of the treatment apparatus 4070.

[0791] The example display of General Information 4120 shown in Figure 40 also includes a patient communications control 4170 for controlling an audio or audiovisual communications session with the patient interface 4050. The communication session with the patient interface 4050 may comprise an active transmission of the assistant interface 4094 for presentation by the output device of the patient interface 4050. The active stream may take the form of an audio stream and/or a video stream. In some embodiments, the patient interface 4050 may be configured to provide two-way audio or audiovisual communications with a person with the use of the assistant interface 4094. Specifically, the communications session with the patient interface 4050 may include bidirectional (two-way) video or audiovisual transmissions, where each of the patient interface 4050 and the assistant interface 4094 presents the other's video. . In some embodiments, the patient interface 4050 may present video from the assistant interface 4094, while the assistant interface 4094 only presents audio or the assistant interface 4094 does not present any active audio or visual signal from the patient interface 4050. In some embodiments, the interface

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224 assistant 4094 may present video from patient interface 4050, while patient interface 4050 only presents audio or patient interface 4050 does not present any active audio or visual signal from assistant interface 4094.

[0792] In some embodiments, the audio or audiovisual communications session with the patient interface 4050 may occur, at least in part, while the patient is performing the rehabilitation regimen for the respective body part. The patient communications control 4170 may take the form of a portion or region of the general information display 4120, as shown in Figure 40. The patient communications control 4170 may take other forms, such as a stand-alone display or a pop-up window. Audio and/or audiovisual communications may be processed or directed through the assistant interface 4094 and/or other device or devices, such as a telephone system or a video conferencing system used by the assistant while the assistant uses the assistant interface 4094. Alternatively or additionally, audio and/or audiovisual communications may include communications with third parties. For example, system 4010 may allow the assistant to initiate a three-way conversation regarding the use of specific hardware or software, both with the patient and with a subject matter expert, such as an assistant or specialist. The example patient communications control 4170 shown in Figure 40 includes call controls 4172 for the assistant to use in managing various aspects of audio or audiovisual communications with the patient. The call controls 4172 include a disconnect button 4174 for the attendant to end the audio or audiovisual communications session. The call controls 4172 also include a mute button 4176 to temporarily mute an audio or audiovisual signal from the assistant interface 4094. In some embodiments, the call controls 4172 may include other attributes, such as a hold button (not included). sample). The call controls 4172 also include one or more record/playback controls 4178, such as record, play and pause buttons to control, with the patient interface 4050, the recording and/or playback of audio and/or video of the teleconference session. The call controls 4172 also include a video feed display 4180 to present still and/or video images from the patient interface 4050, as well as a self-video display 4182 that displays the current image of the attending assistant.

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225 use the wizard interface. The self-video display 4182 may be presented in a picture-in-picture format, in a section of the video stream display 4180, as illustrated in Figure 40. Alternatively or additionally, The self-video display 4182 may be presented separately and/or independently of the video stream display 4180.

[0793] The example visual presentation of general information 4120 shown in Figure 40 also includes a third-party communications control 4190 for use when conducting audio and/or audiovisual communications with third parties. The third-party communications control 4190 may take the form of a portion or region of the general information display 4120, as shown in Figure 40. Third-party communications monitoring 4190 may take other forms, such as a visual presentation on a separate screen or a pop-up window. Third-party communications control 4190 may include one or more controls, such as a contact list and/or buttons or controls for contacting a third party regarding the use of specific hardware or software, e.g., an expert in the matter, such as a medical professional or specialist. Third party communications control 4190 may include conference call capability for the third party to simultaneously communicate with the assistant through the assistant interface 4094 and with the patient through the patient interface 4050. For example , system 4010 may allow the assistant to initiate a three-way conversation with the patient and the third party.

[0794] Figure 41 generally illustrates an example block diagram of training a machine learning model 4013 to generate, based on data 4600 related to the patient, a treatment plan 4602 for the patient according to the present description. Data related to other patients may be received by server 4030. The other patients may have used various treatment devices to carry out treatment plans. The data may include characteristics of the other patients, details of the treatment plans made by the other patients, and/or the results of having made the treatment plans (for example, a percentage of recovery of a part of the patients' body , a level of recovery of a part of the patients' body, a level of increase or decrease in muscle strength of the part of the patients' body, a level of increase or decrease of the ocher amplitude Ln/zznz/E/YiAi

226 of movement of the patients' body part, etc.).

[0795] As represented, the data has been assigned to different cohorts. Cohort A includes data from patients who have similar first characteristics, first treatment plans, and first outcomes. Cohort B includes data from patients who have similar second characteristics, second treatment plans, and second outcomes. For example, Cohort A may include early characteristics of patients between the ages of twenty and twenty-nine who have no medical condition and who have undergone surgery for a broken limb; Your treatment plans may include a certain treatment protocol (for example, using the 4070 treatment device for 30 minutes, 5 times a week, for 3 weeks, where the values of the properties, settings and/or adjustments of the device Treatment codes 4070 are set to X (where X is a numeric value) for the first two weeks, and to Y (where Y is a numeric value) for the last week.

[0796] Cohort A and Cohort B may be included in a training data set used to train the machine learning model 4013. The machine learning model 4013 may be trained to look for pattern matches between the characteristics of each cohort and generate the treatment plan that provides the result. Accordingly, when data 4600 of a new patient is entered into the trained machine learning model 4013, the trained machine learning model 4013 can match the characteristics included in the data 4600 with the characteristics of cohort A or cohort B and generate the appropriate 4602 treatment plan. In some embodiments, the machine learning model 4013 may be trained to generate one or more excluded treatment plans that the new patient should not perform.

[0797] Figure 42 shows one embodiment of a visual presentation of the general information of the patient interface 4050 that presents a virtual avatar 4700 that guides the patient through an exercise session in accordance with the present description. The virtual avatar 4700 may be displayed on the output device 4054 (e.g., display screen) of the patient interface 4050. As depicted, the virtual avatar 4700 may represent a person. In some embodiments, the person may be a real person, for example, the medical professional, a professional athlete, the patient, a family member, a friend, a sibling, a celebrity, etc.; in other modalities,

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227 The person may be fictional or constructed, for example, a superhero or similar. As described below, the virtual avatar may be any person, object, building, animal, being, alien, robot, or the like. For example, children may relate more to animal animations that guide them through their exercise sessions. The virtual avatar 4700 may be selected by the patient and/or the medical professional from a library of virtual avatars stored in a database on the server 4030. In some embodiments, the virtual avatar 4700 may be uploaded to the database or to private use by the patient and/or medical professional. For example, the virtual avatar library may be stored in system data storage 4042 and/or patient data storage 4044. Once the virtual avatar 4700 is selected for the patient, an identifier of the virtual avatar 4700 may be associated with an identifier of the patient in the system data store 4042 and/or the patient data store 4044.

[0798] The virtual avatar 4700 may perform one or more exercises specified in an exercise session of a treatment plan for the patient. As used throughout this description, and for the avoidance of doubt, the term “exercises” may include, for example, rehabilitative movements, high-intensity interval training, strength training, range training. movement or any physical or bodily movement capable of being performed on a treatment device specified in the treatment plan (including its modifications, changes or amendments) or reasonably substituted by a different treatment device. For example, an exercise may consist of pedaling a stationary bicycle, and the virtual avatar 4700 may be animatedly represented pedaling the bicycle in a manner desired by the patient. In some embodiments, the virtual avatar 4700 may include an actual video of a person performing the exercise session. Thus, virtual avatar 4700 may be generated by server 4030 and/or include audio, video, audiovisual, and/or multimedia data of a real person performing the exercise session. In some embodiments, the virtual avatar 4700 may represent a medical professional, such as a physical therapist, an instructor, a trainer, among others.

[0799] In some embodiments, the virtual avatar 4700 may be controlled by one or more machine learning models 4013 of the artificial intelligence engine 4011. For example, the machine learning model(s) 4013 may be trained based on historical data and data in

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228 real time or near real time. Data used to train machine learning models 4013 may include prior feedback received from users (e.g., pain levels), characteristics of patients at various stages of their treatment plans (e.g., heart rate, blood pressure, temperature, sweat rate, etc.), sensor measurements (e.g., pedal pressure, range of motion, motor speed of the treatment device 4070, etc.) received while patients were carrying out their treatment plans and/or the results obtained by patients after performing certain operations (for example, starting a telemedicine session with a multimedia transmission of the medical professional, replacing the virtual avatar 4700 with the multimedia transmission of the medical professional, emotionally representing certain auditory statements, presenting certain visual elements on the output device 4054, changing a parameter of the exercise session (e.g., reducing a degree of resistance provided by the treatment apparatus 4070), among others.

[0800] The output device 54 also presents a self-video section 4702 that displays video of the patient obtained with a camera of the patient interface 4050. The patient can use the self-video to verify whether he is correctly using the form , cadence, consistency or any other quality or quantity that can be observed and measured while performing an exercise session. While the patient performs the exercise session, the video obtained with the patient interface camera 4050 may be transmitted to the assistant interface 4094 for display. A medical professional can view the assistant interface 4094 presenting the patient video and determine whether to intervene by speaking with the patient through his patient interface 4050 and/or replace the virtual avatar 4700 with a multimedia stream from the assistant interface 4050. assistant 4094.

[0801] The output device 4054 also presents a graphical user interface (GUI) object 4704. The GUI object 4704 may be an element that allows the user to provide feedback to the server 4030. For example, the GUI object 4704 may present a scale of values that represent a level of pain that the patient is currently feeling, and GUI object 4704 may allow a patient to select a value that represents their level of pain. The selection may cause a message to be transmitted to server 4030. In some embodiments, as described below, the message including the pain level may relate to an event.

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229 trigger. The server 4030 may determine whether the level of pain the patient is experiencing exceeds a certain threshold intensity level. If the pain level exceeds that certain threshold intensity level, the server 4030 may pause the virtual avatar 4700 and/or replace the virtual avatar 4700 with an audio, visual, audiovisual, or multimedia stream from a professional's computing device. doctor.

[0802] Figure 43 shows an embodiment of the general information display 4120 of the assistant interface 4094 that receives a patient-related notification and allows the assistant (e.g., a medical professional) to initiate a telemedicine session. in real time in accordance with this description. As shown, the general information display 4120 includes a section for the patient profile 4130. Patient profile 4130 presents information related to the treatment plan that patient “John Doe” is undergoing. Treatment plan 4800 indicates that “John Doe is pedaling the treatment device for 5 miles. The pedals of the treatment device are configured to provide a 45-degree range of motion.” The visual presentation of the general information 4120 also includes a notification 4802 that is received due to a trigger event. The notification shows that “John Doe indicated that he is experiencing a high level of pain during exercise.” The visual presentation of the general information 4120 also includes a prompt 4804 for a medical professional using the assistant interface 4094. The prompt asks: “Start telemedicine session?” The visual presentation of the general information 4120 includes a graphic element (e.g., a button) 4806 that is configured to allow the medical professional to use a wired or wireless input peripheral (e.g., a touch screen, a mouse, a keyboard , a microphone, etc.) to select the start of the telemedicine session. Although the above example details that the visual presentation of general information 4120 of the wizard interface 94 presents information in the form of text, an alternative or additional way of presenting that information may be in the form of graphs, charts, or the like.

[0803] The assistant (e.g., a medical professional) using the assistant interface 94 (e.g., a computing device) during the telemedicine session may appear on the self-video 4182 in a portion of the visual presentation of the information. general 4120 (e.g., in the user interface presented on a display screen 4024 of the wizard interface 4094).

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230

The assistant interface 4094 may also present, in the same part of the general information display 4120 as that of the self-video, a video (e.g., a self-video 4182) of the patient in the transmission display. video 4180. Additionally, the visual presentation of the video stream 4180 may also include a graphical user interface (GUI) object 4808 (e.g., a button) that allows the medical professional to share in real time or near real time, during the telemedicine session, a treatment plan with the patient, at the patient interface 4050, to control an operating parameter of the treatment apparatus 4070 or the like.

[0804] Figure 44 shows one embodiment of a visual presentation of general information that is presented by the output device 4054 of the patient interface 4050. The output device 4054 presents, in real time during a telemedicine session, a transmission 4900 (e.g., multimedia preferably including audio, video, or both) of the medical professional who replaced the virtual avatar in accordance with the present description. In some embodiments, the virtual avatar may continue to be presented on the patient interface 4050, but in a paused state, and the transmission may preferably be limited to audio only when the medical professional speaks with the patient. In some embodiments, as depicted, the transmission 4900 may replace the virtual avatar. The broadcast may allow the medical professional and the patient to participate in a telemedicine session where the medical professional speaks with the patient and inquires about his or her pain level, pain characteristics (e.g., heart rate, sweating rate, etc.), , and/or one or more sensor measurements (e.g., pedal pressure, range of motion, etc.).

[0805] It should be understood that the medical professional may be viewing, monitoring, treating, diagnosing, etc., a large number of patients at the same time on the assistant interface 94. For example, as described below, each patient may be presented in a respective portion of the user interface of the assistant interface 4094. Each respective portion may present a variety of information related to the respective patient. For example, each party may present a transmission of the patient performing the exercise session using the treatment device, the patient's characteristics, the patient's treatment plan, the sensor measurements, among others. The user interface of the assistant interface 4094 may be configured to allow the medical professional to select one or more patients to make the virtual avatar that

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231 guides one or more patients through an exercise is stopped and/or replaced in real time or near real time.

[0806] Upon completion of the telemedicine session between the patient interface 4050 and the assistant interface 4094, the transmission of the medical professional's computing device may be replaced by the virtual avatar on the patient interface 4050. The virtual avatar may continue to guide the patient through the exercise session at any location and/or at any time the exercise session has been paused due to the onset of the triggering event. The assistant interface 4094 may resume viewing the transmission of the patient performing the exercise session and/or patient-related information.

[0807] Figure 45 shows an exemplary embodiment of a method 41000 for replacing a virtual avatar, based on a triggering event that occurs, by transmission from a medical professional in accordance with the present description. Method 41000 is performed using processing logic that may include hardware (circuits, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), or a combination of both. . Method 41000 and/or each of its individual functions, routines, other methods, scripts, subroutines or operations may be performed by one or more processors of a computing device (e.g., any component of Figure 36, such as the server 4030 which runs the artificial intelligence engine 4011). In certain embodiments, method 41000 may be performed through a single processing cycle. Alternatively, method 41000 may be performed using two or more processing cycles, where each cycle implements one or more individual functions or routines, or other methods, scripts, subroutines, or operations of the methods.

[0808] For purposes of simplicity of explanation, method 41000 is represented and described as a series of operations. However, operations according to this description may take place in a different order and/or simultaneously and/or with other operations that are not presented or described in this document. For example, the operations represented in method 41000 may occur in combination with any other operation of any other method described herein. Additionally, not all of the operations illustrated may be required to implement Method 41000 in accordance with the disclosed subject matter. Furthermore, experts in the technique

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232 understand and note that method 41000 could alternatively be represented as a series of interrelated states via a state diagram, a directed graph, a deterministic finite state automaton, a nondeterministic finite state automaton, a Markov diagram, or other event diagrams.

[0809] At 41002, the processing device may provide, to a computing device (e.g., patient interface 4050) of the patient, a virtual avatar for display on the patient's computing device. The virtual avatar may be configured to use a virtual representation of the treatment apparatus 4070 to guide the patient through an exercise session. The virtual avatar can be configured to use audio, video, haptic feedback, or some combination thereof, to guide the patient through the exercise session. Audio, video, haptic feedback, or some combination thereof may be provided by the patient's computing device. In some embodiments, the processing device may determine, based on a treatment plan for a patient, the exercise session to be performed. The treatment apparatus 4070 may be configured for use by the patient performing the exercise session.

[0810] In some embodiments, prior to providing the virtual avatar, the processing device may transmit, to the patient's computing device, a notification to start the exercise session. The notification may include a short pop-up message notification, a text message, a phone call, an email, or a combination thereof. Notification may be transmitted based on a schedule specified in the treatment plan. The calendar may include dates and times to perform exercise sessions, durations of exercise sessions, exercises to be performed during exercise sessions, configurations of parts (e.g. pedals, seat, etc.) of the treatment apparatus 4070 and the like. The processing device may receive, from the patient's computing device, a selection to start the exercise session to use the treatment apparatus 4070. The treatment device may transmit to the treatment device 4070 a control signal to cause the treatment device 70 to initiate the exercise session. In response to the transmission of the control signal, the processing device may provide the virtual avatar to the patient's computing device.

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233

[0811] The virtual avatar may be associated with a medical professional, such as the medical professional who prescribed or generated the treatment plan for the patient to carry out. In some modalities, the medical professional may design and generate all or part of the treatment plan. In some embodiments, the treatment plan may be generated in whole or in part by the artificial intelligence engine 4011, and the medical professional may review the treatment plan and/or modify the treatment plan before transmitting it to the patient for review. perform.

[0812] The virtual avatar may represent a proxy medical professional and may be a person, being, thing, electronic software or robot, object, etc., that guides one or more patients through a treatment plan. The virtual avatar can guide a large number of patients through treatment plans at various stages of their rehabilitation, prehabilitation, recovery, etc. It is important to note that at any time the virtual avatar may be replaced by a transmission (e.g., live audio, audiovisual, etc.) of the medical professional, where the transmission is transmitted, either directly or indirectly through the server 4030 , from the assistant interface 4094 to the patient interface 4050. The transmission may be a stream of data packets (e.g., audio, video, or both) obtained through a camera and/or microphone associated with the assistant interface 4094 in real time or near real time. The transmission may be presented on the user interface 4054 of the patient interface 4050.

[0813] In some embodiments, the virtual avatar may be initially selected by the medical professional. The virtual avatar may be a file stored in a virtual avatar library, and the medical professional may select the virtual avatar from the virtual avatar library. For example, the virtual avatar may be a real representation of a person (e.g., male, female, non-binary, or any other gender with which the person identifies). In some embodiments, the virtual avatar may be a real or virtual representation of an animal (e.g., tiger, lion, unicorn, rabbit, etc.), which may be more enjoyable and motivating for younger people (e.g., children). ). In some embodiments, the virtual avatar may be a real or virtual representation of a robot, an alien, etc.

[0814] In some embodiments, the medical professional may design his or her own virtual avatar. For example, the medical professional may be provided with a user interface in his or her interface.

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2. 3. 4 wizard 4094, and the user interface may provide user interface elements that allow configuration of a virtual avatar. The medical professional can use the user interface to generate a virtual avatar that resembles himself or any suitable person.

[0815] In some embodiments, the virtual avatar may be selected by the patient. In some embodiments, the selected virtual avatar may be associated with the patient (e.g., through a patient identifier and a virtual avatar identifier) and stored in a database. For example, some patients may prefer certain virtual avatars over other virtual avatars. In some embodiments, different virtual avatars can guide patients through the same treatment plan or through a different treatment plan. With respect to any treatment plan referred to herein, the various aspects, parts or configurations of the treatment plan may also be guided by more than one virtual or physical avatar, where each avatar is associated with an aspect, specific part or configuration of the treatment plan, and with any other avatar, to the extent that the application is associated with an unrelated aspect, part or configuration of the treatment plan. In other modalities, more than one avatar, whether physical and/or virtual, may be present at the same time, but may be performing different functions in the specific aspect, portion or configuration of the treatment plan. In group therapy sessions, a large number of patients may be undergoing the same treatment plan and each patient may have their own patient interface 4050 that simultaneously presents the same virtual avatar or a different virtual avatar or avatars that guide the patient through the treatment plan.

[0816] As patients complete the treatment plan, the medical professional can view the large number of patients in different tiles in the user interface of the assistant interface 4094. The term "mosaics" may refer to frames that include each of the transmissions of the patient's respective patient interfaces 4050 as the patient performs the treatment plan, a transmission of the patient's characteristics (e.g., heart rate, blood pressure, temperature, etc.), a transmission of measurements (for example, pressure exerted on the pedals, range of motion determined by the goniometer, number of steps, speed of the motor of the treatment apparatus 4070, etc.) or some combination thereof. In this way, with the

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235 Using the modalities described, the medical professional can be enabled to manage, monitor and/or treat a large number of patients at the same time. Computer resource usage can be reduced by having a large number of patients being treated at the same time by a medical professional, since only the assistant interface 4094 is used to view, treat, manage, monitor, etc., to the large number of patients as they carry out the treatment plan.

[0817] At 41004, the processing device may receive, from the patient computing device, a message related to a trigger event. In some embodiments, the message may include data related to a patient's pain level, a patient characteristic, a sensor measurement, or some combination thereof. The trigger event may refer to any event associated with data related to the patient's pain level, patient characteristic (e.g., heart rate, blood pressure, temperature, sweat rate, etc.), sensor measurement ( for example, pressure, range of motion, speed, etc.) or some combination thereof.

[0818] In some cases, the virtual avatar may guide the patient through the treatment plan as a pre-recorded animation, and the medical professional may not actively participate in a telemedicine session with the patient while performing the treatment plan. . In some embodiments, when the triggering event occurs, a notification may be transmitted to the healthcare professional's computing device, where the device alerts the healthcare professional about the notification. The clinician may use a wired or wireless input peripheral (e.g., touch screen, mouse, keyboard, microphone) to select the notification and initiate a telemedicine session with the patient's computing device. . This technique may minimize or otherwise optimize the use and/or cost and/or risk profile of one or more computing resources, such as network resources (e.g., bandwidth), when starting the telemedicine session only when the notification is selected and not throughout the entire treatment plan. In other embodiments, the healthcare professional's computing device, as well as the patient's computing device and the healthcare professional's computing device, may continuously or constantly participate in a telemedicine session when one or more patients perform the treatment plan. treatment. The triggering event may allow the

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236 medical professional intervenes and/or replaces the virtual avatar, pauses the virtual avatar, or both. In some embodiments, while the patients carry out the treatment plan, the medical professional may selectively choose one or more of the patients to have the virtual avatar on the patient's or patients' computing devices replaced, paused, etc. . This technique may allow the medical professional to intervene for some, but not all, patients as they carry out the treatment plan. For example, the clinician may select patients who do not meet target thresholds (e.g., pressure, range of motion, speed, etc.) in the treatment plan, select patients who indicate that they are feeling a level of threshold pain, or something similar.

[0819] In some embodiments, the virtual avatar may be controlled, in real time or near real time, by one or more machine learning models trained to receive data inputs, including sensor data (e.g., ear pressure measurements). pedals, range of motion measurements from a goniometer, velocity data, etc.), patient characteristics (e.g. e.g., sweat rate, heart rate, blood pressure, temperature, blood gas and/or arterial oxygenation levels and/or percentages, etc.), real-time feedback from the patient or other patients (e.g. , indication of pain level) or some combination thereof. The machine learning model(s) can generate an output that controls the virtual avatar. For example, the output may control the virtual avatar in such a way as to modify the way the virtual avatar performs a specific exercise (e.g., faster or slower pedals on a treatment apparatus 70), or to say phrases. encouraging messages (e.g., “You've done it,” “Keep up,” etc.), among other things. The modifications may be based on training data from other patients, where that data indicates that the modifications generate a desired performance in the patient and/or an outcome in the other patients, or an increase in the probability of achieving the performance or outcome. desired of the patient. For example, training data may indicate that providing certain audio or video when certain sensor data is detected may cause the patient to exert more force on the pedal, thereby strengthening the leg muscles in accordance with the treatment plan.

[0820] At 41006, the processing device may determine whether an intensity level

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237 of the triggering event exceeds a threshold intensity level. The threshold intensity level may be any suitable grade, value, indicator, etc. For example, in one embodiment, the threshold intensity level may be a certain level of pain that the patient is feeling. At any time during an exercise session, the patient can use any input peripheral of the patient interface 4050 to express his or her pain level. For example, the patient may touch a button on the touch screen of the patient interface 4050 and the button may indicate that the patient is experiencing a pain level of 8 on a scale of 1 to 10, with 1 being the lowest. pain and 10 is the highest pain level. In this example, the threshold intensity level may be a pain level of 5. Consequently, the pain level (8) that the patient is feeling exceeds the threshold intensity level (5). In some embodiments, the threshold intensity level may be related to the degree of force that the patient exerts on the pedals, a range of motion that the patient can achieve during pedaling, a speed that the patient can achieve, a duration of a range of motion and/or a speed that the patient can achieve, or the like. For example, the threshold intensity level can be set based on the patient pedaling at a certain range of motion for a certain period of time and, if the patient fails to achieve that range of motion in a certain period of time, or variations thereof, during an exercise session, then it is possible that the threshold intensity level will be exceeded.

[0821] At 41008, in response to the determination that the intensity level of the triggering event exceeds the threshold intensity level, the processing device may replace, on the patient computing device, the presentation of the virtual avatar with a presentation of a multimedia stream from a computing device (e.g., assistant interface 4094) of the medical professional. In some embodiments, replacing the virtual avatar with streaming media can initiate a telemedicine session between the patient and the medical professional. The processing device may receive, from the patient's or medical professional's computing device, a second message indicating that the telemedicine session has ended, and the processing device may substitute, on the patient's computing device, the presentation of multimedia transmission by the presentation of the virtual avatar. The virtual avatar can be configured to

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238 continue to guide the patient through the exercise session until its completion. That is, the exercise session and/or virtual avatar can be paused at a certain date and time when the medical professional's media stream replaces the virtual avatar, and the exercise session and/or virtual avatar can start replaying at a specific date and time when the telemedicine session has ended.

[0822] In some embodiments, at 41010, in response to determining that the intensity level of the triggering event does not exceed the threshold intensity level, the processing device may allow control of the virtual avatar to the professional's computing device. doctor, so that the medical professional remotely controls the virtual avatar to interact with the patient. In some embodiments, in response to determining that the intensity level of the triggering event does not exceed the threshold intensity level, the processing device may continue to allow control of the virtual avatar to the medical professional's computing device, thereby for the medical professional to remotely control the virtual avatar to interact with the patient.

[0823] In some embodiments, the processing device may determine, based on a second treatment plan for a second patient, the exercise session to be performed, wherein the performance of the second patient utilizes a second treatment apparatus. The processing device may present, on a second computing device (e.g., patient interface 50) of the second patient, the virtual avatar configured to guide the patient in using the treatment apparatus during the exercise session. In some embodiments, while the presentation of the virtual avatar is replaced on the patient's computing device by the presentation of the multimedia stream from the healthcare professional's computing device, the virtual avatar may continue to be presented on the second computing device of the second patient. . In some embodiments, while the presentation of the virtual avatar is replaced on the patient's computing device by the presentation of the multimedia stream from the healthcare professional's computing device, the virtual avatar may be replaced on the second patient's second computing device by multimedia streaming from the medical professional's computing device.

[0824] Figure 46 shows an exemplary embodiment of a method for providing an avatar

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239 virtual according to this description. Method 41100 includes operations performed by the processors of a computing device (e.g., any component of Figure 36, such as the server 4030 running the artificial intelligence engine 4011). In some embodiments, one or more operations of method 41100 are implemented in computer instructions stored in a memory device and executed by a processing device. Method 41100 may be performed in the same or similar manner as described above in connection with Method 41000. The operations of Method 41100 may be performed in combination with any of the operations of any of the methods described herein. Method 41100 may include other operations associated with step 41002 in method 41000 related to displaying the virtual avatar on the patient's computing device.

[0825] At 41102, the processing device may retrieve data associated with the exercise session. The data may include instructions that implement a virtual model that animates one or more movements associated with the exercise session. The virtual model can be two-dimensional, three-dimensional or n-dimensional (in terms of animations or projections in a three-dimensional virtual environment or a two-dimensional design). In some embodiments, the virtual model may be a mesh model animation, sketch animation, virtual human animation, skeleton animation, among others. For example, the virtual model can be a surface representation (which is referred to as a mesh) used to draw a character (for example, a medical professional) and a hierarchical set of interconnected parts. The virtual model can use a virtual armature to animate the mesh (for example, pose and keyframe). As used herein, an armature can refer to a kinematic chain used in computer animation to simulate the movements of virtual human or animal characters (e.g., virtual avatars). Various types of virtual trusses can be used, such as keyframe trusses (volume animation) and real-time trusses (puppet animation).

[0826] At 41104, the processing device may retrieve data associated with the virtual avatar. Data associated with the virtual avatar may include which virtual avatar is selected by the patient and/or medical professional, where such selection is made to guide the patient through the exercise session. In some embodiments, the data associated with the virtual avatar may include a

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240 identifier associated with the virtual avatar. The identifier can be used to retrieve data associated with the virtual avatar from a database. For example, the patient may have selected a superhero to be her virtual avatar. Consequently, data related to the specific superhero (e.g., gender, costume, appearance, etc.) can be retrieved from the database.

[0827] At 41106, the processing device may map the data associated with the virtual avatar to the virtual model that animates one or more movements associated with the exercise session. For example, the appearance and shape of the virtual avatar can be assigned to the mesh of the virtual model (for example, the face to a part of the head of the mesh) and manipulated and/or animated according to instructions related to the session. exercise and/or the virtual avatar. In some embodiments, the virtual avatar may perform one or more exercises with the use of the treatment apparatus 4070. The virtual avatar's performance of the exercises may be animated and/or presented on a display screen of the patient's computing device to guide the patient through the exercise session. As described herein, at any time, the virtual avatar can be replaced and/or paused to allow the presentation of a multimedia broadcast from a medical professional.

[0828] Figure 47 shows an example of a computer system 41200 that can perform any or more of the methods described herein, in accordance with one or more aspects of the present description. In an example, the computing system 41200 may include a computing device and correspond to the assistant interface 4094, the reporting interface 4092, the monitoring interface 4090, the clinician interface 4020, the server 4030 (including the AI engine 4011), the patient interface 4050, the ambulatory sensor 4082, the goniometer 4084, the treatment apparatus 4070, the pressure sensor 4086 or any suitable component of Figure 36. The computer system 41200 may execute instructions that implement one or more machine learning models 4013 of the artificial intelligence engine 4011 of Figure 36. The computer system may connect (e.g., network) to other computer systems on a LAN. , an intranet, an extranet or the Internet, including through the cloud or a network between users. The computer system can operate with the capacity of a server in a client-server network environment. The computing system may be a personal computer (PC), a tablet, a wearable device (e.g. a bracelet), a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a

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241 camera, a video camera, an Internet of Things (IoT) device, or any device capable of executing a set of instructions (sequential or otherwise) that specify the actions that the device must perform. Furthermore, although only one computer system is illustrated, it should be understood that the term "computer" also includes any group of computers that individually or jointly execute a set (or multiple sets) of instructions to perform one or more of the methods that are described in this document.

[0829] The computing system 41200 includes a processing device 41202, a main memory 41204 (e.g., a read-only memory (ROM), a flash memory, solid state drives (SSD), a dynamic random access memory (DRAM), such as a synchronous DRAM (SDRAM)), a static memory 41206 (for example, a flash memory, solid state drives (SSD), a static random access memory (SRAM)), as well as a data storage device 41208 that communicates with other devices through a bus 41210.

[0830] Processing device 41202 represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, or the like. More particularly, the processing device 41202 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor that implements other instruction sets or processors that implement a combination of instruction sets. The processing device 41202 may also be one or more purpose-specific processing devices, such as an application-specific integrated circuit (ASIC), a system on a chip, a field programmable gate array (FPGA), a data processor. digital signals (DSP), a network processor or the like. The processing device 41202 is configured to execute instructions to perform any of the operations and steps described herein.

[0831] The computer system 41200 may also include a network interface device 41212. The computer system 41200 may also include a video display 41214 (e.g., a liquid crystal display (LCD), a light-emitting diode (LED), an organic light-emitting diode (OLED), a quantum LED, a cathode ray tube (CRT), a CRT shadow mask, a grating

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242 aperture CRT, a monochrome CRT), one or more input devices 41216 (for example, a keyboard and/or a mouse or a game-like controller), and one or more speakers 41218 (for example, a speaker). In an illustrative example, the video display 41214 and input devices 41216 may be combined into a single component or device (e.g., an LCD touch screen).

[0832] The data storage device 41216 may include a computer-readable medium 41220 on which instructions 41222 that incorporate one or more of the methods, operations, or functions described herein are stored. The instructions 41222 may also reside, in whole or at least partially, in the main memory 41204 and/or in the processing device 41202 during their execution by the computing system 41200. Thus, the main memory 41204 and the processing device 41202 also constitute computer readable media. Instructions 41222 may also be transmitted or received over a network using network interface device 41212.

[0833] Although computer-readable storage medium 41220 is shown in the illustrative examples as a single medium, the term “computer-readable storage medium” should be understood to include a single medium or multiple media (e.g., a database). centralized or distributed data, and/or associated caches and servers) that store the instruction set(s). It should also be understood that the term “computer-readable storage medium” includes any medium capable of storing, encoding or carrying a set of instructions for execution by the machine and causing the machine to perform one or more of the methodologies herein description. Accordingly, the term “computer readable storage medium” will be understood to include, but is not limited to, solid state memories, optical media, and magnetic media.

[0834] Clause 65. A computer-implemented system, which comprises:

[0835] a treatment apparatus configured to be manipulated by a patient while performing an exercise session;

[0836] a patient interface configured to receive a virtual avatar, wherein the patient interface comprises an output device configured to present the virtual avatar, wherein the virtual avatar uses a virtual representation of the treatment apparatus to guide the patient to through

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243 of an exercise session, and where the virtual avatar is associated with a medical professional; and

[0837] a server computing device configured to:

[0838] providing the patient's virtual avatar to the patient interface,

[0839] receiving, from the patient interface, a message related to a trigger event, and wherein the message comprises an intensity level of the trigger event,

[0840] determining whether an intensity level of the triggering event exceeds a threshold intensity level, and

[0841] in response to the determination that the intensity level of the triggering event exceeds the threshold intensity level, replacing in the patient interface the presentation of the virtual avatar with a presentation of a multimedia stream from a computing device of the professional doctor.

[0842] Clause 66. The computer-implemented system in accordance with any clause of this document, wherein the server computing device also:

[0843] in response to determining that the intensity level of the triggering event does not exceed the threshold intensity level:

[0844] allows control of the virtual avatar to the medical professional's computing device, such that the medical professional remotely controls the virtual avatar to interact with the patient, or

[0845] continues to allow control of the virtual avatar to the medical professional's computing device, such that the medical professional remotely controls the virtual avatar to interact with the patient.

[0846] Clause 67. The computer-implemented system in accordance with any clause of this document, wherein the virtual avatar is controlled, in real time or near real time, by one or more machine learning models trained to:

[0847] receive data inputs comprising sensor data, patient characteristics, real-time feedback from the patient or other patients, or some combination thereof, and

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[0848] generate an output that controls the virtual avatar.

244

[0849] Clause 68. The computer-implemented system in accordance with any clause of this document, wherein providing the virtual avatar also includes:

[0850] retrieve data associated with the exercise session, wherein the data comprises instructions that implement a virtual model that animates one or more movements associated with the exercise session;

[0851] retrieve data associated with the virtual avatar; and

[0852] mapping the data associated with the virtual avatar to the virtual model that animates the movement(s) associated with the exercise session.

[0853] Clause 69. The computer implemented system in accordance with any clause of this document, wherein before providing the virtual avatar, the server computing device also

[0854] transmits, to the patient interface, a notification to start the exercise session, wherein the notification is transmitted based on a schedule specified in the treatment plan;

[0855] receives, from the patient interface, a selection to start the exercise session to use the treatment equipment;

[0856] transmits to the treatment apparatus a control signal to cause the treatment apparatus to initiate the exercise session; and

[0857] in response to the transmission of the control signal, provides the virtual avatar to the patient interface.

[0858] Clause 70. The computer-implemented system in accordance with any clause hereof, wherein the notification comprises a short pop-up message notification, a text message, a phone call, an email, or some combination thereof themselves.

[0859] Clause 71. The computer-implemented system in accordance with any clause of this document, wherein the server computing device also

[0860] determines, based on a second treatment plan for a second patient, the exercise session to be performed, wherein the performance of the second patient uses a second treatment apparatus;

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245

[0861] presents, in a second patient interface of the second patient, the virtual avatar configured to guide the patient on the use of the treatment device during the exercise session, where:

[0862] while the presentation of the virtual avatar is replaced on the patient interface by the presentation of the multimedia stream from the healthcare professional's computing device, the virtual avatar continues to be presented on the second patient interface, or

[0863] while the presentation of the virtual avatar is replaced at the patient interface by the presentation of the multimedia stream from the medical professional's computing device, the virtual avatar is replaced at the second patient interface by the multimedia stream from the device computing power of the medical professional.

[0864] Clause 72. The method in accordance with any clause of this document, wherein the server computing device also

[0865] receives, from the patient interface, a selection of the virtual avatar from a library of virtual avatars; and

[0866] stores the virtual avatar associated with the patient in a database.

[0867] Clause 73. The method in accordance with any clause hereof, wherein the virtual avatar is configured to use audio, video, haptic feedback, or some combination thereof to guide the patient through the exercise session.

[0868] Clause 74. A method comprising:

[0869] providing, to a patient computing device, a virtual avatar for display on the patient computing device, wherein the virtual avatar is configured to use a virtual representation of the treatment apparatus to guide the patient through an exercise session, and the virtual avatar is associated with a medical professional;

[0870] receiving, from the patient's computing device, a message related to a triggering event;

[0871] determining whether an intensity level of the triggering event exceeds a threshold intensity level; and

[0872] in response to determining that the intensity level of the ocher triggering event Ln/zznz/E/YiAi

246 exceeds the threshold intensity level, replacing, on the patient's computing device, the presentation of the virtual avatar with a presentation of a multimedia stream from a computing device of the medical professional.

[0873] Clause 75. The method in accordance with any clause of this document also includes:

[0874] in response to determining that the intensity level of the triggering event does not exceed the threshold intensity level:

[0875] allow control of the virtual avatar to the medical professional's computing device , such that the medical professional remotely controls the virtual avatar to interact with the patient, or

[0876] continue to allow control of the virtual avatar to the medical professional's computing device, such that the medical professional remotely controls the virtual avatar to interact with the patient.

[0877] Clause 76. The method in accordance with any clause of this document, wherein the virtual avatar is controlled, in real time or near real time, by one or more machine learning models trained to:

[0878] receive data inputs comprising sensor data, patient characteristics, real-time feedback from the patient or other patients, or some combination thereof, and

[0879] generate an output that controls the virtual avatar.

[0880] Clause 77. The method in accordance with any clause of this document, wherein providing the virtual avatar also includes:

[0881] retrieve data associated with the exercise session, wherein the data comprises instructions that implement a virtual model that animates one or more movements associated with the exercise session;

[0882] retrieve data associated with the virtual avatar; and

[0883] mapping the data associated with the virtual avatar to the virtual model that animates the movement(s) associated with the exercise session.

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247

[0884] Clause 78. The method in accordance with any clause of this document, wherein before providing the virtual avatar, the method also comprises:

[0885] transmitting, to the patient's computing device, a notification to start the exercise session, wherein the notification is transmitted based on a schedule specified in the treatment plan;

[0886] receiving, from the patient's computing device, a selection to start the exercise session to use the treatment apparatus;

[0887] transmitting to the treatment apparatus a control signal to cause the treatment apparatus to initiate the exercise session; and

[0888] In response to the transmission of the control signal, providing the virtual avatar to the patient's computing device.

[0889] Clause 79. The method in accordance with any clause hereof, wherein the notification comprises a short pop-up message notification, a text message, a phone call, an email, or some combination thereof.

[0890] Clause 80. The method of compliance with any clause of this document also includes:

[0891] determining, based on a second treatment plan for a second patient, the exercise session to be performed, wherein the performance of the second patient uses a second treatment apparatus;

[0892] present, on a second computing device of the second patient, the virtual avatar configured to guide the patient on the use of the treatment device during the exercise session, where:

[0893] while the presentation of the virtual avatar is replaced on the patient's computing device by the presentation of the multimedia stream from the healthcare professional's computing device, the virtual avatar continues to be presented on the second computing device, or

[0894] while the presentation of the virtual avatar is replaced on the patient's computing device by the presentation of the multimedia stream from the medical professional's computing device, the virtual avatar is replaced on the second computing device by the multimedia stream

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248 from the medical professional's computing device.

[0895] Clause 81. The method of compliance with any clause of this document also includes:

[0896] receiving, from the patient's computing device, a selection of the virtual avatar from a library of virtual avatars; and

[0897] store the virtual avatar associated with the patient in a database.

[0898] Clause 82. The method in accordance with any clause hereof, wherein the virtual avatar is configured to use audio, video, haptic feedback, or some combination thereof to guide the patient through the exercise session.

[0899] Clause 83. The method in accordance with any clause hereof, wherein the message comprises data related to a patient pain level, a patient characteristic, a sensor measurement, or some combination thereof.

[0900] Clause 84. The method in accordance with any clause herein, wherein replacing the virtual avatar with the multimedia transmission initiates a telemedicine session between the patient and the medical professional, and the method also comprises:

[0901] receiving, from the patient's or medical professional's computing device, a second message indicating that the telemedicine session has ended; and

[0902] replace, on the patient's computing device, the presentation of the multimedia stream with the presentation of the virtual avatar, where the virtual avatar is configured to continue guiding the patient through the exercise session until its completion.

[0903] Clause 85. The method in accordance with any clause of this document, also includes determining, based on a treatment plan for a patient, the exercise session to be performed, wherein the treatment apparatus is configured for its use by the patient performing the exercise session.

[0904] Clause 86. A tangible, non-transitory, computer-readable medium that stores instructions that, when executed, cause a processing device:

[0905] provide, to a computing device of a patient, a virtual avatar for display on the patient computing device, wherein the virtual avatar is configured to

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249 using a virtual representation of a treatment device to guide the patient through an exercise session, and the virtual avatar is associated with a medical professional;

[0906] receive, from the patient's computing device, a message related to a trigger event;

[0907] determine whether an intensity level of the triggering event exceeds a threshold intensity level; and

[0908] in response to the determination that the intensity level of the triggering event exceeds the threshold intensity level, replaces, on the patient's computing device, the presentation of the virtual avatar with a presentation of a multimedia stream from a device computing power of the medical professional.

[0909] Clause 87. The computer-readable medium in accordance with any clause of this document, wherein, in response to the determination that the intensity level of the triggering event does not exceed the threshold intensity level, the processing device also:

[0910] allows control of the virtual avatar to the medical professional's computing device, such that the medical professional remotely controls the virtual avatar to interact with the patient, or

[0911] continues to allow control of the virtual avatar to the medical professional's computing device, such that the medical professional remotely controls the virtual avatar to interact with the patient.

[0912] Clause 88. The computer-readable medium in accordance with any clause of this document, wherein the virtual avatar is controlled, in real time or near real time, by one or more machine learning models trained to:

[0913] receive data inputs comprising sensor data, patient characteristics, real-time feedback from the patient or other patients, or some combination thereof, and

[0914] generate an output that controls the virtual avatar.

[0915] Clause 89. The computer-readable medium in accordance with any clause of this document, wherein providing the virtual avatar also includes:

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250

[0916] retrieve data associated with the exercise session, wherein the data comprises instructions that implement a virtual model that animates one or more movements associated with the exercise session;

[0917] retrieve data associated with the virtual avatar; and

[0918] mapping the data associated with the virtual avatar to the virtual model that animates the movement(s) associated with the exercise session.

[0919] Clause 90. The computer-readable medium in accordance with any clause hereof, wherein before providing the virtual avatar, the processing device also:

[0920] transmits, to the patient's computing device, a notification to start the exercise session, wherein the notification is transmitted based on a schedule specified in the treatment plan;

[0921] receives, from the patient's computing device, a selection to start the exercise session to use the treatment apparatus;

[0922] transmits to the treatment apparatus a control signal to cause the treatment apparatus to initiate the exercise session; and

[0923] in response to the transmission of the control signal, provides the virtual avatar to the patient's computing device.

[0924] Clause 91. A system that includes:

[0925] a memory device that stores instructions;

[0926] a processing device communicatively coupled to the memory device, the processing device executes instructions to:

[0927] providing, to a computing device of a patient, a virtual avatar for display on the patient's computing device, wherein the virtual avatar is configured to use a virtual representation of a treatment apparatus to guide the patient to through an exercise session, and the virtual avatar is associated with a medical professional;

[0928] receiving, from the patient's computing device, a message related to a triggering event;

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251

[0929] determining whether an intensity level of the triggering event exceeds a threshold intensity level; and

[0930] in response to the determination that the intensity level of the triggering event exceeds the threshold intensity level, replacing, on the patient's computing device, the presentation of the virtual avatar with a presentation of a multimedia stream from a device computing power of the medical professional.

[0931] Clause 92. The system in accordance with any clause of this document, wherein, in response to the determination that the intensity level of the triggering event does not exceed the threshold intensity level, the processing device also:

[0932] allows control of the virtual avatar to the medical professional's computing device, such that the medical professional remotely controls the virtual avatar to interact with the patient, or

[0933] continues to allow control of the virtual avatar to the medical professional's computing device, such that the medical professional remotely controls the virtual avatar to interact with the patient.

[0934] Clause 93. The system in accordance with any clause of this document, wherein the computing device also:

[0935] retrieves data associated with the exercise session, wherein the data comprises instructions that implement a virtual model that animates one or more movements associated with the exercise session;

[0936] retrieves data associated with the virtual avatar; and

[0937] maps the data associated with the virtual avatar to the virtual model that animates the movement(s) associated with the exercise session.

[0938] Clause 94. The system in accordance with any clause of this document, wherein before providing the virtual avatar, the processing device also:

[0939] transmits, to the patient's computing device, a notification to start the exercise session, wherein the notification is transmitted based on a schedule specified in the treatment plan;

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252

[0940] receives, from the patient's computing device, a selection to start the exercise session to use the treatment apparatus;

[0941] transmits to the treatment apparatus a control signal to cause the treatment apparatus to initiate the exercise session; and

[0942] in response to the transmission of the control signal, provides the virtual avatar to the patient's computing device.

[0943] The various aspects, modalities, implementations or attributes of the described modalities may be used separately or in any combination. The modalities described in this document are modular in nature and can be used together or in association with 10 other modalities.

[0944] In accordance with the previous description, the examples of assemblies listed in the following clauses are specifically contemplated and are intended to constitute a set of non-limiting examples.

Contents5

219 members in 11 offices

This recordMember, by publication date1 more office is in the list below

Members219

Family members, oldest first
Document	Office	Kind
US2020275886A1	United States of America	A1	2020-09-03
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EP4021592A1	European Patent Office (EPO)	A1	2022-07-06
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Method and system for using sensor data from rehabilitation or exercise equipment to treat patients via telemedicine.

Abstract

Term

BACKGROUND OF THE INVENTION

SUMMARY OF THE INVENTION

BRIEF DESCRIPTION OF THE DRAWINGS

NOTATION AND NOMENCLATURE

DETAILED DESCRIPTION OF THE INVENTION

Members219

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Titles2

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