Rom Technologies, Inc.

Anonos Inc.

Rehab2fit Technologies, Inc.

Anonos IP LLC

Steven Mason

S Adam Hacking

Peter Arn

Daniel Posnack

Jonathan Greene

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

Malcolm Gary Lafever

Howard Kaushansky

Samantha L Hampton

Daniel Lipszyc

Jeff Cote

Sucheta Tamragouri

Joe Guaneri

Mikael Taveras

Colin James Smith

Eric Mundt

Micheal Muller

Mark Little

David Scheck

Colin W Wetherbee

Bill McGrail

Gordon J Hull

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.

Method and system for using artificial intelligence to present a user interface representing a user's progress in various domains

A computer-implemented system for processing medical claims is disclosed. The system includes a medical device configured to be manipulated by a user while the user performs a treatment plan; a patient interface associated with the medical device, the patient interface comprising an output configured to present telemedicine information associated with a telemedicine session; and a processor. During the telemedicine session, the processor is configured to receive information from a medical device. Using the device-generated information, the processor is further configured to determine device-based medical coding information. The processor is further configured to transmit the device-based medical coding information to a claim adjudication server.

System and method for processing medical claims

A computer-implemented system includes an electromechanical device configured to be manipulated by a patient while performing an exercise session, and a processor in communication with the electromechanical device. The processor is configured to receive data, generate a map packet, and transmit the map packet. The processor is configured to use the data to generate continuity packets, where each of the continuity packets includes a contiguous portion of the data, and transmit the continuity packets. The processor is configured to use the map packet and the continuity packets to cause an output file to be generated.

System and method for transmitting data and ordering asynchronous data

A method is disclosed for using an artificial intelligence engine to interact with a user of an exercise device during an exercise session. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive data as input, and based on the data, providing an output. While a user performs an exercise using the exercise device, the method includes receiving the data from an input peripheral of a computing device associated with the user. Based on the data being received from the input peripheral, the method includes determining, via the machine learning model, the output to control an aspect of the exercise device.

Method and system for using artificial intelligence to interact with a user of an exercise device during an exercise session

A system for processing medical claims, comprising a processor configured to receive device-generated information from a medical device. The device-generated information includes performance information. The processor generates a first biometric signature, and using the device-generated information, generates a second biometric signature, wherein the second biometric signature uses the performance information. Using the first and second biometric signatures, the processor generates a signature comparison. Using the signature comparison, the processor generates a signature indicator and transmits the signature indicator.

System and method for processing medical claims using biometric signatures

A method includes receiving data pertaining to a user that uses a treatment apparatus to perform a treatment plan. The data includes characteristics of the user, the treatment plan, and a result of the treatment plan. The method includes assigning the user to a cohort representing people having similarities to the characteristics of the user. The method includes receiving second data pertaining to a second user, the second data comprises characteristics of the second user. The method includes determining whether at least some of the characteristics of the second user match with at least some of the characteristics of the user, assigning the second user to the first cohort, and selecting, via a trained machine learning model, the treatment plan for the second user, and controlling, based on the treatment plan, the treatment apparatus while the second user uses the treatment apparatus.

Method and system for using artificial intelligence to assign patients to cohorts and dynamically controlling a treatment apparatus based on the assignment during an adaptive telemedical session

Growing community of inventors

Las Vegas, NV, United States of America

Steven Mason