Rom Technologies, Inc.

Anonos Inc.

Rehab2fit Technologies, Inc.

Anonos IP LLC

Steven Mason

S Adam Hacking

Peter Arn

Jonathan Greene

Daniel Posnack

Wendy Para

Micheal Mueller

Joseph Guaneri

Joel Rosenberg

Malcolm Gary LaFever

Ted N Myerson

Michael Bissonnette

James D Steidl

Philip Powers

Luis Berga

Jay L Waddell

Howard Kaushansky

Samantha L Hampton

Mark Little

Malcolm Gary Lafever

Daniel Lipszyc

Jeff Cote

Sucheta Tamragouri

Mikael Taveras

Joe Guaneri

Eric Mundt

Colin James Smith

Micheal Muller

David Scheck

Colin W Wetherbee

Bill McGrail

Gordon J Hull

The present disclosure provides a method for performing a treatment plan, wherein the method comprises: receiving first patient data, wherein the first patient data includes at least a first patient identifier associated with the first patient and a first treatment plan; receiving second patient data, wherein the second patient data includes a second patient identifier associated with the second patient and a second treatment plan; receiving first measurement data associated with a first performance level of the first treatment plan by the first patient; receiving second measurement data associated with a second performance level of the second treatment plan by the second patient; determining differential data, wherein the determining is based on a contrast of the first or the second measurement data or first or second patient data; and generating, based on the differential data, an instruction to modify an operating state of the treatment apparatus.

Systems and methods for an artificial intelligence engine to optimize a peak performance

A computer-implemented system includes one or more processing devices configured to receive user information associated with a user, generate a selected set of the user information, determine, based on the selected set of the user information, at least one of a first probability of surviving one or more procedures and a second probability indicating an improvement, resulting from the one or more procedures, in quality-of-life metrics for the user, generate, based on the at least one of the first probability and the second probability and on the selected set of the user information, one or more recommendations of whether the user should undergo the one or more procedures, and generate, based on the one or more recommendations, a treatment plan that includes one or more exercises directed to modifying the at least one of the first probability and the second probability.

System and method for determining, based on advanced metrics of actual performance of an electromechanical machine, medical procedure eligibility in order to ascertain survivability rates and measures of quality-of-life criteria

A computer-implemented method is disclosed. The method includes receiving, from one or more sensors, one or more measurements associated with a user, where the one or more measurements are received while the user performs a treatment plan, and an electromechanical machine is configured to be manipulated by the user while the user is performing the treatment plan. The method also includes determining, via one or more machine learning models, one or more content items to present to the user, where the determining is based on the one or more measurements and one or more characteristics of the user. A presentation of the one or more content items is provided on an interface while the user performs the treatment plan using the electromechanical machine.

System and method for enabling residentially-based cardiac rehabilitation by using an electromechanical machine and educational content to mitigate risk factors and optimize user behavior

Systems, methods, and computer-readable mediums for generating, by an artificial intelligence engine, one or more alignment plans for aligning a user with an imaging sensor. The method comprises generating one or more machine learning models trained to identify alignment plans. The method also comprises receiving user data and determining that a targeted portion of a body of the user is outside of a field of view of the imaging sensor. The method further comprises generating the one or more alignment plans using the one or more machine learning models. Each of the one or more alignment plans comprises a target location within the field of view of the imaging sensor and one or more elements for adjusting the targeted portion of the body from a first location to the target location. The method also comprises transmitting the one or more alignment plans to a computing device.

Systems and methods of using artificial intelligence and machine learning for generating alignment plans to align a user with an imaging sensor during a treatment session

Various systems, computer-readable media, and computer-implemented methods of providing improved data privacy, anonymity and security by enabling subjects to which data pertains to remain 'dynamically anonymous,' i.e., anonymous for as long as is desired—and to the extent that is desired—are disclosed herein. Embodiments include systems that create, access, use, store and/or erase data with increased privacy, anonymity, and security—thereby facilitating the availability of more qualified and accurate information. When personal data is authorized by data subjects to be shared with third parties, embodiments described herein may facilitate the sharing of information in a dynamically-controlled manner that also enables the delivery of temporally-, geographically-, and/or purpose-limited information to the receiving party. In one example, the disclosed techniques may be used to functionally separate geospatial information, such that it remains “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent or degree that is desired.

Systems and methods for dynamic de-identification and anonymity for user input to artificial intelligence (AI)-based models

A system that comprises a memory device storing instructions, and a processing device communicatively coupled to the memory device. The processing device executes the instructions to: receive user data obtained from records associated with a user; generate a modified treatment plan based on the user data; and send, to a treatment apparatus accessible to the user, the modified treatment plan, wherein the modified treatment plan causes the treatment apparatus to update at least one operational aspect of the treatment apparatus, and update at least one operational aspect of at least one other device communicatively coupled to the treatment apparatus.

Method and system for implementing dynamic treatment environments based on patient information

In one embodiment, a computer-implemented system includes an electromechanical machine configured to be manipulated by a user while the user performs a treatment plan. The treatment plan includes a high-intensity interval training (HIIT) session. A processing device is configured to initiate, using the electromechanical machine, the HIIT session, receive, via one or more sensors, one or more measurements pertaining to the electromechanical machine, determine whether the one or more measurements exceed one or more of one or more corresponding thresholds, and in response to determining that the one or more measurements exceed one or more of the one or more corresponding thresholds, modify the treatment plan to cause operation of the electromechanical machine to be modified.

Systems and methods for using artificial intelligence and machine learning to generate treatment plans having dynamically tailored cardiac protocols for users to manage a state of an electromechanical machine

A computer-implemented system includes processing devices configured to receive attribute data associated with a user, determine, based on the attribute data, at least one of a probability of being eligible for the first bariatric procedure to be performed on the user, and generate, based on the at least one probability, a treatment plan that includes one or more exercises directed to modifying the at least one of the first probability and the second probability, and a treatment apparatus configured for implementation of the treatment plan.

System and method for using AI ML and telemedicine to perform bariatric rehabilitation via an electromechanical machine

A computer-implemented system includes a processing device configured to receive a plurality of user and blood vessel characteristics associated with a user, generate a selected set of user and blood vessel characteristics, determine, based on the selected set of the user and blood vessel characteristics, a probability that angiogenesis will occur, and generate, based on the probability and the selected set of the user and blood vessel characteristics, a treatment plan that includes one or more exercises directed to modifying the probability that angiogenesis will occur, and a treatment apparatus configured to implement the treatment plan while the treatment apparatus is being manipulated by the user.

System and method for using AI/ML to generate treatment plans to stimulate preferred angiogenesis

A system and method for installing devices on a user can include an indicator set comprising a marker to place markings on the user, and a set of adhesive pads to be placed on the user. A knee pivot anchor can be attached to the user. A template having a proximal portion can be pivotally mounted to the knee pivot anchor. A distal portion of the template can be positioned at different locations on the user. The template also can have an aperture. A set of pods can be interchangeably located in the aperture of the template. Each pod can be secured to respective locations on the user. The template and the knee pivot anchor can be removed from the user with the pods remaining in place on the user to support a goniometer.

System, method and apparatus for anchoring an electronic device and measuring a joint angle

Systems and methods for positioning one or more sensors on a user. The system has user sensors, apparatus sensors, and treatment sensors. A processing device, executing computer readable instructions stored in a memory, cause the processing device to: generate an enhanced environment representative of an environment; receive apparatus data representative of a location of the apparatus in the environment; generate an apparatus avatar in the enhanced environment; receive user data representative of a location of the user in the environment; generate a user avatar in the enhanced environment; receive treatment data representative of one or more locations of the treatment sensors in the environment; generate, treatment sensor avatars in the enhanced environment; calculate a treatment location for each treatment sensor, wherein the treatment location is associated with an anatomical structure of a user; and generate instruction data representing an instruction for positioning the treatment sensors at the treatment location.

Augmented reality placement of goniometer or other sensors

A computer-implemented system includes a treatment device configured to be manipulated by a user while the user performs a treatment plan, a patient interface, and a computing device configured to: receive treatment; write to an associated memory, configured to be accessed by an artificial intelligence engine, treatment data, the artificial intelligence engine being configured to use at least one machine learning model to, using the treatment data, generate at least one of a treatment scheduling output prediction and an appointment output; receive, from the artificial intelligence engine, the at least one of the treatment scheduling output prediction and the appointment output; and selectively modify, using the at least one of the treatment scheduling output prediction and the appointment output, the at least one aspect of the treatment plan.

Method and system for using artificial intelligence and machine learning to provide recommendations to a healthcare provider in or near real-time during a telemedicine session

A computer-implemented method including receiving, at a computing device, a first treatment plan designed to treat a cardiovascular health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the cardiovascular health issue, enable the user to perform an exercise at different exertion levels. While the user uses a treatment apparatus to perform the first treatment plan for the user, the computing device receives cardiovascular data from one or more sensors configured to measure the cardiovascular data associated with the user, and transmits the cardiovascular data. Wherein a machine learning model is used to generate a second treatment plan. The second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the cardiovascular data, and the cardiovascular health issue of the user. The method includes receiving the second treatment plan.

Systems and methods for using artificial intelligence to implement a cardio protocol via a relay-based system

A computer-implemented system includes a treatment apparatus configured to be manipulated by a patient while performing a treatment plan and a server computing device configured to execute an artificial intelligence engine to generate the treatment plan and a billing sequence associated with the treatment plan. The server computing device receives information pertaining to the patient, generates, based on the information, the treatment plan including instructions for the patient to follow, and receives a set of billing procedures associated with the instructions. The set of billing procedures includes rules pertaining to billing codes, timing, constraints, or some combination thereof. The server computing device generates, based on the set of billing procedures, the billing sequence for at least a portion of the instructions. The billing sequence is tailored according to a certain parameter. The server computing device transmits the treatment plan and the billing sequence to a computing device.

Method and system to analytically optimize telehealth practice-based billing processes and revenue while enabling regulatory compliance

A method is disclosed for using an artificial intelligence engine to present a user interface capable of presenting the progress of a user in domains. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive measurements as input, and outputting, based on the measurements, a user interface that causes icons to dynamically change position on the user interface. While a user performs an exercise using the exercise device, the method includes receiving the measurements from sensors associated with the exercise device. The method includes presenting, on a computing device associated with the exercise device, sections of the user interface, wherein the sections are each related to a separate domain comprising the domains and wherein, based on the measurements, each section includes the icons placed.

Growing community of inventors

Las Vegas, NV, United States of America

Steven Mason