Google Patentsopen access

Systems and methods of using artificial intelligence and machine learning in a … — Rom Technologies, Inc. (US11915816B2)

ABSTRACT

Abstract

Methods, systems, and computer-readable mediums for generating, by an artificial intelligence engine, treatment plans for optimizing a user outcome. The method comprises receiving attribute data associated with a user. The attribute data comprises one or more symptoms associated with the user. The method also comprises, while the user uses a treatment apparatus to perform a first treatment plan for the user, receiving measurement data associated with the user. The method further comprises generating, by the artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user. The generating is based on at least the attribute data associated with the user and the measurement data associated with the user. The second treatment plan comprises a description of one or more predicted disease states of the user. The method also comprises transmitting, to a computing device, the second treatment plan for the user.

Description

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent application Ser. No. 17/736,891, filed May 4, 2022, titled “Systems and Methods for Using Artificial Intelligence to Implement a Cardio Protocol via a Relay-Based System,” which is a continuation-in-part of U.S. patent application Ser. No. 17/379,542, filed Jul. 19, 2021, titled “System and Method for Using Artificial Intelligence in Telemedicine-Enabled Hardware to Optimize Rehabilitative Routines Capable of Enabling Remote Rehabilitative Compliance,” which is a continuation of U.S. patent application Ser. No. 17/146,705, filed Jan. 12, 2021, titled “System and Method for Using Artificial Intelligence in Telemedicine-Enabled Hardware to Optimize Rehabilitative Routines Capable of Enabling Remote Rehabilitative Compliance,” which is a continuation-in-part of U.S. patent application Ser. No. 17/021,895, filed Sep. 15, 2020, titled “Telemedicine for Orthopedic Treatment,” which claims priority to and the benefit of U.S. Provisional Patent Application Ser. No. 62/910,232, filed Oct. 3, 2019, titled “Telemedicine for Orthopedic Treatment,” the entire disclosures of which are hereby incorporated by reference for all purposes. The application U.S. patent application Ser. No. 17/146,705 also claims priority to and the benefit of U.S. Provisional Patent Application Ser. No. 63/113,484, filed Nov. 13, 2020, titled “System and Method for Use of Artificial Intelligence in Telemedicine-Enabled Hardware to Optimize Rehabilitative Routines for Enabling Remote Rehabilitative Compliance,” the entire disclosures of which are hereby incorporated by reference for all purposes.

This application also claims priority to and the benefit of U.S. Provisional Patent Application Ser. No. 63/314,646 filed Feb. 28, 2022, titled “Systems and Methods of Using Artificial Intelligence and Machine Learning in a Telemedical Environment to Predict User Disease States,” the entire disclosure of which is hereby incorporated by reference for all purposes.

BACKGROUND

Differential diagnosis is a process wherein a healthcare professional differentiates between two or more conditions (referred to herein as disease states) that could each be the cause, or a contributor to the cause, of a user's symptoms. In such a diagnostic approach, the healthcare professional attempts to eliminate as many differential diagnoses as possible, leaving one or at least a small number of such differential diagnoses as causal in some way. Thus, differential diagnosis is, at least in part, a process of elimination. Telemedicine is an option for a healthcare professional to communicate with a user and provide user care when a user does not want to or cannot easily go to the healthcare professional's office. Telemedicine, however, has substantive limitations as the healthcare professional cannot conduct physical examinations of the user. Rather, the healthcare professionals must rely on verbal communication and/or limited remote observation of the user.

SUMMARY

Artificial intelligence and machine learning can be used for predicting disease states of a user based on user performance while the user performs a treatment plan. Artificial intelligence and machine learning can also be used for generating an updated treatment plan for a user, where the updated treatment plan accounts for predicted disease states of the user. Accordingly, the present disclosure provides methods, systems, and non-transitory computer-readable media for, among other things, generating, by an artificial intelligence engine, treatment plans for one or more predicted disease states.

The present disclosure provides a method for generating, by an artificial intelligence engine, treatment plans for optimizing a user outcome. The method comprises receiving attribute data associated with a user. The attribute data comprises one or more symptoms associated with the user. The method also comprises receiving measurement data associated with the user while the user uses a treatment apparatus to perform a first treatment plan for the user. The method further comprises generating, by the artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user. The generating is based on at least the attribute data associated with the user and the measurement data associated with the user. The second treatment plan comprises a description of one or more predicted disease states of the user. The method also comprises transmitting, to a computing device, the second treatment plan for the user.

The present disclosure also provides a system for generating, by an artificial intelligence engine, treatment plans for optimizing a user outcome. The system comprises, in one implementation, a memory device and a processing device. The memory device stores instructions. The processing device is communicatively coupled to the memory device. The processing device is configured to execute the instructions to receive attribute data associated with a user. The attribute data comprises one or more symptoms associated with the user. The processing device is also configured to execute the instructions to receive measurement data associated with the user while the user uses a treatment apparatus to perform a first treatment plan for the user. The processing device is further configured to execute the instructions to generate, by the artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user. The generating is based on at least the attribute data associated with the user and the measurement data associated with the user. The second treatment plan comprises a description of one or more predicted disease states of the user. The processing device is also configured to execute the instructions to transmit, to a computing device, the second treatment plan.

The present disclosure further provides a tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to receive attribute data associated with a user. The attribute data comprises one or more symptoms associated with the user. The instructions also cause the processing device to receive measurement data associated with the user while the user uses a treatment apparatus to perform a first treatment plan for the user. The instructions further cause the processing device to generate, by an artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user. The generating is based on at least the attribute data associated with the user and the measurement data associated with the user. The second treatment plan comprises a description of one or more predicted disease states of the user. The instructions also cause the processing device to transmit, to a computing device, the second treatment plan.

Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present disclosure and its advantages, reference is now made to the following description, taken in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not necessarily to-scale. On the contrary, the dimensions of the various features may be—and typically are—arbitrarily expanded or reduced for the purpose of clarity.

FIG. 1 is a block diagram of an example of a system for generating treatment plans for optimizing user outcome, in accordance with some implementations of the present disclosure.

FIGS. 2 A and 2 B are perspective views of an example of a treatment apparatus included in the system of FIG. 1 , in accordance with some implementations of the present disclosure.

FIG. 3 is a perspective view of an example of a user using the treatment apparatus of FIGS. 2 A and 2 B , in accordance with some implementations of the present disclosure.

FIG. 4 is a block diagram of an example of a computer system, in accordance with some implementations of the present disclosure.

FIG. 5 is a diagram of an example of an overview display of a clinical portal included in the system of FIG. 1 , in accordance with some implementations of the present disclosure.

FIG. 6 is a block diagram of an example of training a machine learning model to output, based on attribute data associated with a user, a treatment plan for the user, in accordance with some implementations of the present disclosure.

FIG. 7 is a diagram of an example of an overview display of a clinical portal presenting in real-time during a telemedicine session both recommended treatment plans and excluded treatment plans, in accordance with some implementations of the present disclosure.

FIG. 8 is a diagram of an example overview display of the clinical portal presenting, in real-time during a telemedicine session, recommended treatment plans that have changed as a result of user data changing, in accordance with some implementations of the present disclosure.

FIG. 9 is a flow diagram of an example of a method for generating treatment plans for optimizing user outcome, in accordance with some implementations of the present disclosure.

FIG. 10 is a flow diagram of an example of a method for training one or more machine learning models, in accordance with some implementations of the present disclosure.

NOTATION AND NOMENCLATURE

Various terms are used to refer to particular system components. A particular component may be referred to commercially or otherwise by different names. Further, a particular component (or the same or similar component) may be referred to commercially or otherwise by different names. Consistent with this, nothing in the present disclosure shall be deemed to distinguish between components that differ only in name but not in function. In the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to . . . .” Also, the term “couple” or “couples” is intended to mean either an indirect or direct connection. Thus, if a first device couples to a second device, that connection may be through a direct connection or through an indirect connection via other devices and connections.

The terminology used herein is for the purpose of describing particular example implementations only, and is not intended to be limiting. As used herein, the singular forms “a,” “an,” and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order discussed or illustrated, unless specifically identified as an order of performance. It is also to be understood that additional or alternative steps may be employed.

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 should not be limited by these terms. These terms may be only used to distinguish one 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 a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer, or section discussed below could be termed a second element, component, region, layer, or section without departing from the teachings of the example implementations. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. 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, and A and B and C. In another example, the phrase “one or more” when used with a list of items means there may be one item or any suitable number of items exceeding one.

Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,” “lower,” “above,” “upper,” “top,” “bottom,” “inside,” “outside,” “contained within,” “superimposing upon,” and the like, may be used herein. These spatially relative terms can be used for ease of description to describe one element's or feature's relationship to another element(s) or feature(s) as illustrated in the figures. The spatially relative terms may also be intended to encompass different orientations of the device in use, or operation, in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as “below” or “beneath” other elements or features would then be oriented “above” the other elements or features. Thus, the example term “below” can encompass both an orientation of above and below. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptions used herein interpreted accordingly.

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, with each session period including a particular exercise for treating a body part of a person. For example, a treatment plan for post-operative rehabilitation after a 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 active exercise sessions performed 4 times per day starting 4 days after surgery. A treatment plan may also include information pertaining to a medical procedure to perform on a person, a treatment protocol for the person using a treatment apparatus, a diet regimen, a medication regimen, a sleep regimen, additional regimens, or some combination thereof.

“Remote medical assistance,” also referred to, inter alia, as remote medicine, telemedicine, telemed, teletherapeutic, telmed, tel-med, or telehealth, is an at least two-way communication between a healthcare professional or professionals, such as a physician or a physical therapist, and a patient (e.g., a user) using audio and/or audiovisual and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., neurostimulative)) communications (e.g., via a computer, a smartphone, or a tablet).

A “healthcare professional” may refer to a doctor, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, physical trainer, coach, personal trainer, neurologist, cardiologist, or the like. A “healthcare professional” may also refer to any person with a credential, license, degree, or the like in the field of medicine, physical therapy, rehabilitation, or the like. As used herein, and without limiting the foregoing, a “healthcare professional” may be a human being, a robot, a virtual assistant, a virtual assistant in virtual and/or augmented reality, or an artificially intelligent entity, such entity including a software program, integrated software and hardware, or hardware alone.

“Real-time” may refer to less than or equal to 2 seconds. “Near real-time” may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent application Ser. No. 17/736,891, filed May 4, 2022, titled “Systems and Methods for Using Artificial Intelligence to Implement a Cardio Protocol via a Relay-Based System,” which is a continuation-in-part of U.S. patent application Ser. No. 17/379,542, filed Jul. 19, 2021, titled “System and Method for Using Artificial Intelligence in Telemedicine-Enabled Hardware to Optimize Rehabilitative Routines Capable of Enabling Remote Rehabilitative Compliance,” which is a continuation of U.S. patent application Ser. No. 17/146,705, filed Jan. 12, 2021, titled “System and Method for Using Artificial Intelligence in Telemedicine-Enabled Hardware to Optimize Rehabilitative Routines Capable of Enabling Remote Rehabilitative Compliance,” which is a continuation-in-part of U.S. patent application Ser. No. 17/021,895, filed Sep. 15, 2020, titled “Telemedicine for Orthopedic Treatment,” which claims priority to and the benefit of U.S. Provisional Patent Application Ser. No. 62/910,232, filed Oct. 3, 2019, titled “Telemedicine for Orthopedic Treatment,” the entire disclosures of which are hereby incorporated by reference for all purposes. The application U.S. patent application Ser. No. 17/146,705 also claims priority to and the benefit of U.S. Provisional Patent Application Ser. No. 63/113,484, filed Nov. 13, 2020, titled “System and Method for Use of Artificial Intelligence in Telemedicine-Enabled Hardware to Optimize Rehabilitative Routines for Enabling Remote Rehabilitative Compliance,” the entire disclosures of which are hereby incorporated by reference for all purposes.

This application also claims priority to and the benefit of U.S. Provisional Patent Application Ser. No. 63/314,646 filed Feb. 28, 2022, titled “Systems and Methods of Using Artificial Intelligence and Machine Learning in a Telemedical Environment to Predict User Disease States,” the entire disclosure of which is hereby incorporated by reference for all purposes.

BACKGROUND

Differential diagnosis is a process wherein a healthcare professional differentiates between two or more conditions (referred to herein as disease states) that could each be the cause, or a contributor to the cause, of a user's symptoms. In such a diagnostic approach, the healthcare professional attempts to eliminate as many differential diagnoses as possible, leaving one or at least a small number of such differential diagnoses as causal in some way. Thus, differential diagnosis is, at least in part, a process of elimination. Telemedicine is an option for a healthcare professional to communicate with a user and provide user care when a user does not want to or cannot easily go to the healthcare professional's office. Telemedicine, however, has substantive limitations as the healthcare professional cannot conduct physical examinations of the user. Rather, the healthcare professionals must rely on verbal communication and/or limited remote observation of the user.

SUMMARY

Artificial intelligence and machine learning can be used for predicting disease states of a user based on user performance while the user performs a treatment plan. Artificial intelligence and machine learning can also be used for generating an updated treatment plan for a user, where the updated treatment plan accounts for predicted disease states of the user. Accordingly, the present disclosure provides methods, systems, and non-transitory computer-readable media for, among other things, generating, by an artificial intelligence engine, treatment plans for one or more predicted disease states.

The present disclosure provides a method for generating, by an artificial intelligence engine, treatment plans for optimizing a user outcome. The method comprises receiving attribute data associated with a user. The attribute data comprises one or more symptoms associated with the user. The method also comprises receiving measurement data associated with the user while the user uses a treatment apparatus to perform a first treatment plan for the user. The method further comprises generating, by the artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user. The generating is based on at least the attribute data associated with the user and the measurement data associated with the user. The second treatment plan comprises a description of one or more predicted disease states of the user. The method also comprises transmitting, to a computing device, the second treatment plan for the user.

The present disclosure also provides a system for generating, by an artificial intelligence engine, treatment plans for optimizing a user outcome. The system comprises, in one implementation, a memory device and a processing device. The memory device stores instructions. The processing device is communicatively coupled to the memory device. The processing device is configured to execute the instructions to receive attribute data associated with a user. The attribute data comprises one or more symptoms associated with the user. The processing device is also configured to execute the instructions to receive measurement data associated with the user while the user uses a treatment apparatus to perform a first treatment plan for the user. The processing device is further configured to execute the instructions to generate, by the artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user. The generating is based on at least the attribute data associated with the user and the measurement data associated with the user. The second treatment plan comprises a description of one or more predicted disease states of the user. The processing device is also configured to execute the instructions to transmit, to a computing device, the second treatment plan.

The present disclosure further provides a tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to receive attribute data associated with a user. The attribute data comprises one or more symptoms associated with the user. The instructions also cause the processing device to receive measurement data associated with the user while the user uses a treatment apparatus to perform a first treatment plan for the user. The instructions further cause the processing device to generate, by an artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user. The generating is based on at least the attribute data associated with the user and the measurement data associated with the user. The second treatment plan comprises a description of one or more predicted disease states of the user. The instructions also cause the processing device to transmit, to a computing device, the second treatment plan.

Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present disclosure and its advantages, reference is now made to the following description, taken in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not necessarily to-scale. On the contrary, the dimensions of the various features may be—and typically are—arbitrarily expanded or reduced for the purpose of clarity.

FIG. 1 is a block diagram of an example of a system for generating treatment plans for optimizing user outcome, in accordance with some implementations of the present disclosure.

FIGS. 2 A and 2 B are perspective views of an example of a treatment apparatus included in the system of FIG. 1 , in accordance with some implementations of the present disclosure.

FIG. 3 is a perspective view of an example of a user using the treatment apparatus of FIGS. 2 A and 2 B , in accordance with some implementations of the present disclosure.

FIG. 4 is a block diagram of an example of a computer system, in accordance with some implementations of the present disclosure.

FIG. 5 is a diagram of an example of an overview display of a clinical portal included in the system of FIG. 1 , in accordance with some implementations of the present disclosure.

FIG. 6 is a block diagram of an example of training a machine learning model to output, based on attribute data associated with a user, a treatment plan for the user, in accordance with some implementations of the present disclosure.

FIG. 7 is a diagram of an example of an overview display of a clinical portal presenting in real-time during a telemedicine session both recommended treatment plans and excluded treatment plans, in accordance with some implementations of the present disclosure.

FIG. 8 is a diagram of an example overview display of the clinical portal presenting, in real-time during a telemedicine session, recommended treatment plans that have changed as a result of user data changing, in accordance with some implementations of the present disclosure.

FIG. 9 is a flow diagram of an example of a method for generating treatment plans for optimizing user outcome, in accordance with some implementations of the present disclosure.

FIG. 10 is a flow diagram of an example of a method for training one or more machine learning models, in accordance with some implementations of the present disclosure.

NOTATION AND NOMENCLATURE

Various terms are used to refer to particular system components. A particular component may be referred to commercially or otherwise by different names. Further, a particular component (or the same or similar component) may be referred to commercially or otherwise by different names. Consistent with this, nothing in the present disclosure shall be deemed to distinguish between components that differ only in name but not in function. In the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to . . . .” Also, the term “couple” or “couples” is intended to mean either an indirect or direct connection. Thus, if a first device couples to a second device, that connection may be through a direct connection or through an indirect connection via other devices and connections.

The terminology used herein is for the purpose of describing particular example implementations only, and is not intended to be limiting. As used herein, the singular forms “a,” “an,” and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order discussed or illustrated, unless specifically identified as an order of performance. It is also to be understood that additional or alternative steps may be employed.

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 should not be limited by these terms. These terms may be only used to distinguish one 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 a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer, or section discussed below could be termed a second element, component, region, layer, or section without departing from the teachings of the example implementations. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. 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, and A and B and C. In another example, the phrase “one or more” when used with a list of items means there may be one item or any suitable number of items exceeding one.

Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,” “lower,” “above,” “upper,” “top,” “bottom,” “inside,” “outside,” “contained within,” “superimposing upon,” and the like, may be used herein. These spatially relative terms can be used for ease of description to describe one element's or feature's relationship to another element(s) or feature(s) as illustrated in the figures. The spatially relative terms may also be intended to encompass different orientations of the device in use, or operation, in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as “below” or “beneath” other elements or features would then be oriented “above” the other elements or features. Thus, the example term “below” can encompass both an orientation of above and below. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptions used herein interpreted accordingly.

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, with each session period including a particular exercise for treating a body part of a person. For example, a treatment plan for post-operative rehabilitation after a 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 active exercise sessions performed 4 times per day starting 4 days after surgery. A treatment plan may also include information pertaining to a medical procedure to perform on a person, a treatment protocol for the person using a treatment apparatus, a diet regimen, a medication regimen, a sleep regimen, additional regimens, or some combination thereof.

“Remote medical assistance,” also referred to, inter alia, as remote medicine, telemedicine, telemed, teletherapeutic, telmed, tel-med, or telehealth, is an at least two-way communication between a healthcare professional or professionals, such as a physician or a physical therapist, and a patient (e.g., a user) using audio and/or audiovisual and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., neurostimulative)) communications (e.g., via a computer, a smartphone, or a tablet).

A “healthcare professional” may refer to a doctor, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, physical trainer, coach, personal trainer, neurologist, cardiologist, or the like. A “healthcare professional” may also refer to any person with a credential, license, degree, or the like in the field of medicine, physical therapy, rehabilitation, or the like. As used herein, and without limiting the foregoing, a “healthcare professional” may be a human being, a robot, a virtual assistant, a virtual assistant in virtual and/or augmented reality, or an artificially intelligent entity, such entity including a software program, integrated software and hardware, or hardware alone.

“Real-time” may refer to less than or equal to 2 seconds. “Near real-time” may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will generally be less than 10 seconds (or any suitable proximate difference between two different times) but greater than 2 seconds.

“Results” may refer to medical results or medical outcomes. Results and outcomes may refer to responses to medical actions. A “medical action(s)” may refer to any suitable action(s) performed by a healthcare professional, and such action or actions may include diagnoses, prescriptions for treatment plans, prescriptions for treatment apparatuses, and the making, composing and/or executing of appointments, telemedicine sessions, prescription of medicines, telephone calls, emails, text messages, and the like.

DETAILED DESCRIPTION

The following discussion is directed to various implementations of the present disclosure. Although one or more of these implementations may be preferred, the implementations disclosed should not be interpreted, or otherwise used, as limiting the scope of the present disclosure, including the claims. In addition, one skilled in the art will understand that the following description has broad application, and the discussion of any implementation is meant only to be exemplary of that implementation, and not intended to intimate that the scope of the disclosure, including the claims, is limited to that implementation.

FIG. 1 is a block diagram of an example of a system 100 for generating treatment plans for optimizing a user outcome, in accordance with some implementations of the present disclosure. The system 100 illustrated in FIG. 1 includes a treatment apparatus 102 , a server 104 , a user computing device 106 , and a clinical computing device 108 . The system 100 illustrated in FIG. 1 is provided as one example of such a system. The methods described herein may be used with systems having fewer, additional, or different components in different configurations than the system 100 illustrated in FIG. 1 . For example, in some implementations, the system 100 may include fewer or additional computing devices, may include additional treatment apparatuses, may include additional user support apparatuses, and may include additional servers.

The various components of the system 100 may communicate with each other using suitable wireless and/or wired communication protocols over a communications network 110 . The communications network 110 may be a wired network, a wireless network, or both. All or parts of the communications network 110 may be implemented using various networks, for example and without limitation, a cellular data network, the Internet, a Bluetooth™ network, a Near-Field Communications (NFC) network, a Z-Wave network, a ZigBee network, a wireless local area network (for example, Wi-Fi), a wireless accessory Personal Area Networks (PAN), cable, an Ethernet network, satellite, a machine-to-machine (M2M) autonomous network, and a public switched telephone network. In some implementations, communications with other external components (not shown) may occur over the communications network 110 .

The treatment apparatus 102 is configured to be manipulated by the user and/or to manipulate one or more specific body parts of the user for performing activities according, for example, to a treatment plan. In some implementations, the treatment apparatus 102 may take the form of an exercise and rehabilitation apparatus configured to perform and/or to aid in the performance of a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, and the treatment includes rehabilitation of a specific body part of the user, such as a joint or a bone or a 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, a bone, or a muscle group, such as one or more vertebrae, a tendon, or a ligament. The treatment apparatus 102 may be any suitable medical, rehabilitative, therapeutic, etc. apparatus configured to be controlled distally via a computing device to treat a user and/or exercise the user. The treatment apparatus 102 may be an electromechanical machine including one or more weights, an electromechanical bicycle, an electromechanical spin-wheel, a smart-mirror, a treadmill, a balance board, or the like. The treatment apparatus 102 may include, for example, pedals 112 on opposite sides. The treatment apparatus 102 may be operated by a user engaging the pedals 112 with their feet or their hands and rotating the pedals 112 . Examples of the treatment apparatus 102 are further described below in relation to FIGS. 2 A and 2 B .

The server 104 is configured to store and to provide data related to managing treatment plans. The server 104 may include one or more computers and may take the form of a distributed and/or virtualized computer or computers. The server 104 may be configured to store data regarding the treatment plans. For example, the server 104 may be configured to hold system data, such as data pertaining to treatment plans for treating one or more users. The server 104 may also be configured to store data regarding performance by a user in following a treatment plan. For example, the server 104 may be configured to hold user data, such as data pertaining to one or more users, including data representing each user's performance within the treatment plan. In addition, the server 104 may store attributes (e.g., personal, performance, measurement, etc.) of users, the treatment plans followed by users, and the results of the treatment plans may use correlations and other statistical or probabilistic measures to enable the partitioning of or to partition the treatment plans into different user cohort-equivalent databases. For example, the data for a first cohort of first users having a first similar injury, a first similar medical condition, a first similar medical procedure performed, a first treatment plan followed by the first user, and a first result of the treatment plan may be stored in a first user database. The data for a second cohort of second users having a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment plan followed by the second user, and a second result of the treatment plan may be stored in a second user database. Any single attribute or any combination of attributes may be used to separate the cohorts of users. In some implementations, the different cohorts of users may be stored in different partitions or volumes of the same database. There is no specific limit to the number of different cohorts of users allowed, other than as limited by mathematical combinatoric and/or partition theory.

This treatment plan data and results data may be obtained from numerous treatment apparatuses and/or computing devices over time and stored, for example, in a database (not shown). The treatment plan data and results data may be correlated in user-cohort databases. The attributes of the users may include personal information, performance information, measurement information, or a combination thereof.

In addition to historical information about other users stored in the user cohort-equivalent databases, real-time or near-real-time information based on the current user's attributes about a current user being treated may be stored in an appropriate user cohort-equivalent database. The attributes of the user may be determined to match or be similar to the attributes of another user in a particular cohort (e.g., cohort A) and the user may be assigned to that cohort.

In some implementations, the server 104 executes an artificial intelligence (AI) engine 114 that uses one or more machine learning models 116 to perform at least one of the implementations disclosed herein. The server 104 may include a training engine 118 capable of generating the one or more machine learning models 116 . The training engine 118 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, an Internet of Things (IoT) device, any other desired computing device, or any combination of the above. The training engine 118 may be cloud-based, a real-time software platform, or an embedded system (e.g., microcode-based and/or implemented) and it may include privacy software or protocols, and/or security software or protocols.

The user computing device 106 may be used by a user of the treatment apparatus 102 to obtain information about treatment plans. The user computing device 106 may also be used by the user to adjust settings on the treatment apparatus 102 . The user computing device 106 may also be used by the user to provide feedback about treatment plans. The user computing device 106 may also be used by the user to communicate with a healthcare professional. The user computing device 106 illustrates in FIG. 1 includes a user portal 120 . The user portal 120 is configured to communicate information to a user and to receive feedback from the user. The user portal 120 may include one or more input devices (e.g., a keyboard, a mouse, a touch-screen input, a gesture sensor, a microphone, a processor configured for voice recognition, a telephone, a trackpad, or a combination thereof). The user portal 120 may also include one of more output devices (e.g., a computer monitor, a display screen on a tablet, smartphone, or a smart watch). The one or more output devices may include other hardware and/or software components such as a projector, virtual reality capability, augmented reality capability, etc. The one or more output devices may incorporate various different visual, audio, or other presentation technologies. For example, at least one of the output devices may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds such as tones, chimes, and/or melodies, which may signal different conditions and/or directions. At least one of the output devices may include one or more different display screens presenting various data and/or interfaces or controls for use by the user. At least one of the output devices may include graphics, which may be presented by a web-based interface and/or by a computer program or application (App.).

The clinical computing device 108 may be used by a healthcare professional to remotely communicate with and monitor a user. The clinical computing device 108 may also be used by the healthcare professional to remotely monitor and adjust settings on the treatment apparatus 102 . The clinical computing device 108 illustrates in FIG. 1 includes a clinical portal 122 . The clinical portal 122 is configured to communicate information to a healthcare professional and to receive feedback from the healthcare professional. The clinical portal 122 may include one or more input devices such as any of the ones described above in relation to the user portal 120 . The clinical portal 122 may also include one or more output devices such as any of the ones described above in relation to the user portal 120 . The clinical portal 122 may be configured for use by a person having responsibility for the treatment plan, such as an orthopedic surgeon.

The clinical portal 122 may be used by a healthcare professional, to remotely communicate with the user portal 120 and/or the treatment apparatus 102 . Such remote communications may enable the assistant to provide assistance or guidance to a user using the system 100 . More specifically, the clinical portal 122 may be configured to communicate a telemedicine signal via, for example, the communications network 110 . A telemedicine signal may comprises one of an audio signal, an audio-visual signal, an interface control signal for controlling a function of the user portal 120 , an interface monitor signal for monitoring a status of the user portal 120 , an apparatus control signal for changing an operating parameter of the treatment apparatus 102 , and/or an apparatus monitor signal for monitoring a status of the treatment apparatus 102 . In some implementations, each of the control signals may be unidirectional, conveying commands from the clinical portal 122 to the user portal 120 . In some implementations, in response to successfully receiving a control signal and/or to communicate successful and/or unsuccessful implementation of the requested control action, an acknowledgement message may be sent from the user portal 120 to the clinical portal 122 . In some implementations, each of the monitor signals may be unidirectional, status-information commands from the user portal 120 to the clinical portal 122 . In some implementations, an acknowledgement message may be sent from the clinical portal 122 to the user portal 120 in response to successfully receiving one of the monitor signals.

In some implementations, the user portal 120 may be configured as a pass-through for the apparatus control signals and the apparatus monitor signals between the treatment apparatus 102 and one or more other devices, such as the clinical portal 122 and/or the server 104 . For example, the user portal 120 may be configured to transmit an apparatus control signal in response to an apparatus control signal within the telemedicine signal from the clinical portal 122 .

In some implementations, one or more portions of the telemedicine signal may be generated from a prerecorded source (e.g., an audio recording, a video recording, or an animation) for presentation by the user portal 120 of the user computing device 106 . For example, a tutorial video may be streamed from the server 104 and presented upon the user portal 120 . Content from the prerecorded source may be requested by the user via user portal 120 . Alternatively, via a control on the clinical portal 122 , the healthcare professional may cause content from the prerecorded source to be played on the user portal 120 .

In some implementations, clinical portal 122 may be configured to provide voice-based functionalities, with hardware and/or software configured to interpret spoken instructions by the healthcare professional by using one or more microphones. The clinical portal 122 may include functionality provided by or similar to existing voice-based assistants such as Siri by Apple, Alexa by Amazon, Google Assistant, or Bixby by Samsung. The clinical portal 122 may include other hardware and/or software components. The clinical portal 122 may include one or more general purpose devices and/or special-purpose devices.

The clinical portal 122 may take one or more different forms including, for example, a computer monitor or display screen on a tablet, a smartphone, or a smart watch. The clinical portal 122 may include other hardware and/or software components such as projectors, virtual reality capabilities, or augmented reality capabilities, etc. The clinical portal 122 may incorporate various different visual, audio, or other presentation technologies. For example, the clinical portal 122 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds such as tones, chimes, melodies, and/or compositions, which may signal different conditions and/or directions. The clinical portal 122 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the assistant. The clinical portal 122 may include graphics, which may be presented by a web-based interface and/or by a computer program or application (App.).

In some implementations, the system 100 may provide computer translation of language from the clinical portal 122 to the user portal 120 and/or vice-versa. The computer translation of language may include computer translation of spoken language and/or computer translation of text, wherein the text and/or spoken language may be any language, formal or informal, current or outdated, digital, quantum or analog, invented, human or animal (e.g., dolphin) or ancient, with respect to the foregoing, e.g., Old English, Zulu, French, Japanese, Klingon, Kobaïan, Attic Greek, Modern Greek, etc., and in any form, e.g., academic, dialectical, patois, informal, e.g., “electronic texting,” etc. Additionally or alternatively, the system 100 may provide voice recognition and/or spoken pronunciation of text. For example, the system 100 may convert spoken words to printed text and/or the system 100 may audibly speak language from printed text. The system 100 may be configured to recognize spoken words by any or all of the user and the healthcare professional. In some implementations, the system 100 may be configured to recognize and react to spoken requests or commands by the user. For example, the system 100 may automatically initiate a telemedicine session in response to a verbal command by the user (which may be given in any one of several different languages).

In some implementations, the server 104 may generate aspects of the clinical portal 122 for presentation by the clinical portal 122 . For example, the server 104 may include a web server configured to generate the display screens for presentation upon the clinical portal 122 . For example, the artificial intelligence engine 114 may generate treatment plans for users and generate the display screens including those treatment plans for presentation on the clinical portal 122 . In some implementations, the clinical portal 122 may be configured to present a virtualized desktop hosted by the server 104 . In some implementations, the server 104 may be configured to communicate with the clinical portal 122 via the communications network 110 . In some implementations, the user portal 120 and the treatment apparatus 102 may each operate from a user location geographically separate from a location of the clinical portal 122 . For example, the user portal 120 and the treatment apparatus 102 may be used as part of an in-home rehabilitation system, which may be aided remotely by using the clinical portal 122 at a centralized location, such as a clinic or a call center.

In some implementations, the clinical portal 122 may be one of several different terminals (e.g., computing devices) that may be physically, virtually or electronically grouped together, for example, in one or more call centers or at one or more healthcare professionals' offices. In some implementations, multiple instance of the clinical portal 122 may be distributed geographically. In some implementations, a person may work as an assistant remotely from any conventional office infrastructure, including a home office. Such remote work may be performed, for example, where the clinical portal 122 takes the form of a computer and/or telephone. This remote work functionality may allow for work-from-home arrangements that may include full-time, part-time and/or flexible work hours for an assistant.

FIGS. 2 A and 2 B is a perspective view of an example of the treatment apparatus 102 . More specifically, FIGS. 2 A and 2 B show the <figure-callout id="102" label="treatment apparatus" filenames="US11915816-20240227-D00000.png,

CLAIMS

Claims ( 30 )

What is claimed is:

1. A method for generating, by an artificial intelligence engine, treatment plans for optimizing a user outcome, the method comprising:

receiving attribute data associated with a user, wherein the attribute data comprises one or more symptoms associated with the user;

while the user uses an electromechanical machine to perform a first treatment plan for the user, receiving measurement data associated with the user;

generating, by the artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user, wherein the generating is based on at least the attribute data associated with the user and the measurement data associated with the user, and wherein the second treatment plan comprises a description of one or more predicted disease states of the user; and

transmitting, to a computing device, the second treatment plan for the user.

2. The method of claim 1 , wherein the method further comprises:

determining, by the artificial intelligence engine, one or more associations between one or more confirmed disease states of the user and at least one selected from the group consisting of the attribute data associated with the user, the measurement data associated with the user, and the one or more predicted disease states of the user;

generating, by the artificial intelligence engine, a set of training data based on at least the one or more associations; and

generating, by training the one or more machine learning models with the set of training data, one or more updated machine learning models.

3. The method of claim 2 , wherein the user is a first user, wherein the method further comprises generating, by the artificial intelligence engine configured to use the one or more updated machine learning models, a third treatment plan for a second user, and wherein the generating is based on at least attribute data associated with the second user and measurement data associated with the second user.

4. The method of claim 1 , wherein the method further comprises:

generating, by the artificial intelligence engine configured to use the one or more machine learning models, a set of questions related to the one of more symptoms of the user; and

prompting the user to provide one or more answers to the set of questions,

wherein, based on the one or more answers provided by the user, the artificial intelligence engine is further configured to generate the second treatment plan for the user.

5. The method of claim 4 , wherein the method further comprises:

generating, by the artificial intelligence engine configured to use the one or more machine learning models, a set of additional questions concerning the one of more symptoms of the user, wherein the generating is based on at least the one or more answers provided by the user; and

prompting the user to provide one or more additional answers to the set of additional questions,

wherein, based on the one or more additional answers provided by the user, the artificial intelligence engine is further configured to generate the second treatment plan.

6. The method of claim 1 , wherein each of the one or more predicted disease states of the user has a corresponding probability score.

7. The method of claim 1 , wherein the method further comprises:

sending one or more control signals to the electromechanical machine; and

adjusting, in response to the electromechanical machine receiving the one or more control signals, one or more portions of the electromechanical machine, wherein such adjustment complies with one or more operating parameters specified in the second treatment plan.

8. The method of claim 1 , wherein the computing device comprises a clinical portal of a healthcare professional, and wherein the second treatment plan is transmitted to the clinical portal, in real-time or near real-time during a telemedicine session in which the clinical portal is engaged with a user portal of the user, of the healthcare professional.

9. The method of claim 1 , wherein the computing device comprises a user portal of the user, and wherein the second treatment plan is transmitted to the user portal, in real-time or near real-time during a telemedicine session in which the user portal is engaged with a clinical portal of a healthcare professional, of the user.

10. The method of claim 1 , wherein the second treatment plan is for at least one selected from the group consisting of habilitation, prehabilitation, rehabilitation, post-habilitation, exercise, strength training, pliability training, flexibility training, weight stability, weight gain, weight loss, cardiovascular health, endurance improvement, and pulmonary health.

11. A system for generating, by an artificial intelligence engine, treatment plans for optimizing a user outcome, the system comprising:

a memory device for storing instructions; and

a processing device communicable coupled to the memory device, the processing device configured to execute the instructions to:

receive attribute data associated with a user, wherein the attribute data comprises one or more symptoms associated with the user,

while the user uses an electromechanical machine to perform a first treatment plan for the user, receive measurement data associated with the user,

generate, by the artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user, wherein the generating is based on at least the attribute data associated with the user and the measurement data associated with the user, and wherein the second treatment plan comprises a description of one or more predicted disease states of the user, and

transmit, to a computing device, the second treatment plan.

12. The system of claim 11 , wherein the processing device is further configured to execute the instructions to:

determine, by the artificial intelligence engine, one or more associations between one or more confirmed disease states of the user and at least one selected from the group consisting of the attribute data associated with the user, the measurement data associated with the user, and the one or more predicted disease states of the user,

generate, by the artificial intelligence engine, a set of training data based on at least the one or more associations, and

generate, by training the one or more machine learning models with the set of training data, one or more updated machine learning models.

13. The system of claim 12 , wherein the user is a first user, wherein the processing device is further configured to execute the instructions to generate, by the artificial intelligence engine configured to use the one or more updated machine learning models, a third treatment plan for a second user, and wherein the generating is based on at least attribute data associated with the second user and measurement data associated with the second user.

14. The system of claim 11 , wherein the processing device is further configured to execute the instructions to:

generate, by the artificial intelligence engine configured to use the one or more machine learning models, a set of questions concerning the one of more symptoms of the user, and

prompt the user to provide one or more answers to the set of questions,

wherein, based on the one or more answers provided to the user, the artificial intelligence engine is further configured to generate the second treatment plan for the user.

15. The system of claim 14 , wherein the processing device is further configured to execute the instructions to:

determine, by the artificial intelligence engine configured to use the one or more machine learning models, a set of additional questions concerning the one of more symptoms of the user, wherein the determining is based on at least the one or more answers provided by the user, and

prompt the user to provide one or more additional answers to the set of additional questions,

wherein, based on the one or more additional answers provided by the user, the artificial intelligence engine is further configured to generate the second treatment plan.

16. The system of claim 11 , wherein each of the one or more predicted disease states of the user has a corresponding probability score.

17. The system of claim 11 , wherein the processing device is further configured to execute the instructions to send one or more control signals to the electromechanical machine, wherein the electromechanical machine is configured to adjust, in response to the electromechanical machine receiving the one or more control signals, one or more portions of the electromechanical machine, and wherein such adjustment complies with one or more operating parameters specified in the second treatment plan.

18. The system of claim 11 , wherein the computing device comprises a clinical portal of a healthcare professional, and wherein the second treatment plan is transmitted to the clinical portal, in real-time or near real-time during a telemedicine session in which the clinical portal is engaged with a user portal of the user, of the healthcare professional.

19. The system of claim 11 , wherein the computing device comprises a user portal of the user, and wherein the second treatment plan is transmitted to the user portal, in real-time or near real-time during a telemedicine session in which the user portal is engaged with a clinical portal of a healthcare professional, of the user.

20. The system of claim 11 , wherein the second treatment plan is for at least one selected from the group consisting of habilitation, prehabilitation, rehabilitation, post-habilitation, exercise, strength training, pliability training, flexibility training, weight stability, weight gain, weight loss, cardiovascular health, endurance improvement, and pulmonary health.

21. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:

receive attribute data associated with a user, wherein the attribute data comprises one or more symptoms associated with the user;

while the user uses an electromechanical machine to perform a first treatment plan for the user, receive measurement data associated with the user;

generate, by an artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user, wherein the generating is based on at least the attribute data associated with the user and the measurement data associated with the user, and wherein the second treatment plan comprises a description of one or more predicted disease states of the user; and

transmit, to a computing device, the second treatment plan.

22. The non-transitory computer-readable medium of claim 21 , wherein the instructions further cause the processing device to:

determine, by the artificial intelligence engine, one or more associations between one or more confirmed disease states of the user and at least one selected from the group consisting of the attribute data associated with the user, the measurement data associated with the user, and the one or more predicted disease states of the user,

generate, by the artificial intelligence engine, a set of training data based on at least the one or more associations, and

generate, by training the one or more machine learning models with the set of training data, one or more updated machine learning models.

23. The non-transitory computer-readable medium of claim 22 , wherein the user is a first user, wherein the instructions further cause the processing device to generate, by the artificial intelligence engine configured to use the one or more updated machine learning models, a third treatment plan for a second user, and wherein the generating is based on at least attribute data associated with the second user and measurement data associated with the second user.

24. The non-transitory computer-readable medium of claim 21 , wherein the instructions further cause the processing device to:

generate, by the artificial intelligence engine configured to use the one or more machine learning models, a set of questions concerning the one of more symptoms of the user, and

prompt the user to provide one or more answers to the set of questions,

wherein, based on the one or more answers provided by the user, the artificial intelligence engine is further configured to generate the second treatment plan for the user.

25. The non-transitory computer-readable medium of claim 24 , wherein the instructions further cause the processing device to:

determine, by the artificial intelligence engine configured to use the one or more machine learning models, a set of additional questions concerning the one of more symptoms of the user, wherein the determining is based on at least the one or more answers provided by the user, and

prompt the user to provide one or more additional answers to the set of additional questions,

wherein, based on the one or more additional answers provided by the user, the artificial intelligence engine is further configured to generate the second treatment plan.

26. The non-transitory computer-readable medium of claim 21 , wherein each of the one or more predicted disease states of the user has a corresponding probability score.

27. The non-transitory computer-readable medium of claim 21 , wherein the instructions further cause the processing device to send one or more control signals to the electromechanical machine, wherein the electromechanical machine is configured to adjust, in response to the electromechanical machine receiving the one or more control signals, one or more portions of the electromechanical machine, and wherein such adjustment complies with one or more operating parameters specified in the second treatment plan.

28. The non-transitory computer-readable medium of claim 21 , wherein the computing device comprises a clinical portal of a healthcare professional, and wherein the second treatment plan is transmitted to the clinical portal, in real-time or near real-time during a telemedicine session in which the clinical portal is engaged with a user portal of the user, of the healthcare professional.

29. The non-transitory computer-readable medium of claim 21 , wherein the computing device comprises a user portal of the user, and wherein the second treatment plan is transmitted to the user portal, in real-time or near real-time during a telemedicine session in which the user portal is engaged with a clinical portal of a healthcare professional, of the user.

30. The non-transitory computer-readable medium of claim 21 , wherein the second treatment plan is for at least one selected from the group consisting of habilitation, prehabilitation, rehabilitation, post-habilitation, exercise, strength training, pliability training, flexibility training, weight stability, weight gain, weight loss, cardiovascular health, endurance improvement, and pulmonary health.

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Systems and methods of using artificial intelligence and machine learning in a telemedical environment to predict user disease states

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