# US 2023/0410976 A1 · Systems and methods for using artificial intelligence and machine learning to improve cardiovascular health such that the need for a cardiac intervention is mitigated > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0410976 A1 - Document ID: US20230410976A1 - Status: Published application - Kind: A1 - Application number: 18/459,646 - Filed: 2023-09-01 - Published: 2023-12-21 - Earliest claimed: 2019-10-03 - Assignees: ROM Technologies, Inc. - Applicants: ROM Technologies, Inc. - Inventors: Joel Rosenberg; Jay L. Waddell; Steven Mason - Web page: https://patentgrep.com/patent/US20230410976A1 ## CPC - A61H 1/0214: {by rotating cycling movement} - G16H 20/30: relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising - A61H 1/024: {Knee (A61H1/0255 takes precedence)} - A63B 21/00178: {for active exercising, the apparatus being also usable for passive exercising} - A63B 21/00181: {comprising additional means assisting the user to overcome part of the resisting force, i.e. assisted-active exercising} - A63B 21/0058: {using motors} - A63B 22/0605: {performing a circular movement, e.g. ergometers} - A63B 24/0062: {Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance} - A63B 24/0075: {Means for generating exercise programmes or schemes, e.g. computerized virtual trainer, e.g. using expert databases} - A63B 24/0087: {Electric or electronic controls for exercising apparatus of groups A63B21/00 - A63B23/00, e.g. controlling load} - G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof - G16H 40/63: for local operation - G16H 40/67: for remote operation - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/30: for calculating health indices; for individual health risk assessment - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G16H 80/00: ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring - A61H 2201/1215: Rotary drive - A61H 2201/1261: combined with active exercising of the patient - A61H 2201/1633: Seat (A61H2201/0149 takes precedence) - A61H 2201/164: Feet or leg, e.g. pedal - A61H 2201/1642: Holding means therefor - A61H 2201/1671: rotational - A61H 2201/501: connected to external computer devices or networks - A61H 2201/5012: using the internet - A61H 2201/5043: Displays - A61H 2201/5046: Touch screens - A61H 2201/5048: Audio interfaces, e.g. voice or music controlled - A61H 2201/5061: Force sensors - A61H 2201/5064: Position sensors - A61H 2201/5069: Angle sensors - A61H 2201/5071: Pressure sensors - A61H 2201/5092: Optical sensor - A61H 2201/5097: wireless - A61H 2203/0431: in 90°/90°-position, like on a chair - A61H 2205/10: Leg - A61H 2230/06: Heartbeat rate - A61H 2230/202: Glucose - A61H 2230/207: partial O2-value - A61H 2230/30: Blood pressure (A61H2230/04 takes precedence) - A61H 2230/42: Rate - A63B 2022/0623: {Cranks of adjustable length} - A63B 2022/0652: {for cycling in a recumbent position} - A63B 2024/0065: {Evaluating the fitness, e.g. fitness level or fitness index} - A63B 2024/0093: {the load of the exercise apparatus being controlled by performance parameters, e.g. distance or speed} - A63B 2071/063: {Spoken or verbal instructions} - A63B 2071/0652: {Visualisation or indication relating to symmetrical exercise, e.g. right-left performance related to spinal column} - A63B 2071/0655: {Tactile feedback} - A63B 2071/0663: {worn on the wrist, e.g. wrist bands} - A63B 2071/068: {Input by voice recognition} - A63B 2071/0683: {Input by handheld remote control} - A63B 2220/13: Relative positions - A63B 2220/16: Angular positions - A63B 2220/30: Speed - A63B 2220/51: Force - A63B 2220/52: Weight, e.g. weight distribution - A63B 2225/20: with means for remote communication, e.g. internet or the like - A63B 2225/50: Wireless data transmission, e.g. by radio transmitters or telemetry - A63B 2230/06: heartbeat rate only - A63B 2230/202: glucose - A63B 2230/207: P-O2, i.e. partial O2 value - A63B 2230/30: blood pressure (A63B2230/04 takes precedence) - A63B 2230/42: rate - A63B 2230/50: temperature - A61H: PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY (electrotherapy, magnetotherapy, radiation therapy or ultrasound therapy A61N) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - A63B: APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT (apparatus for passive exercising, massage A61H) ## Abstract Systems, methods, and computer-readable media for improving cardiovascular health such that the need for a cardiac intervention is mitigated. The system includes one or more processing devices and an electromechanical machine. The one or more processing device are configured to receive a plurality of risk factors associated with a cardiac-related event for a user. The one or more processing device are also configured to generate a selected set of the risk factors. The one or more processing device are further configured to determine, based on the selected set of the risk factors, a probability that a cardiac intervention will occur. The one or more processing device are also configured to generate, based on the probability and the selected set of the risk factors, a treatment plan including exercises directed to reducing the probability that the cardiac intervention will occur. The electromechanical machine is configured to implement the treatment plan. ## Claims ### Claim 1 A computer-implemented system, comprising: one or more processing devices configured to: receive a plurality of risk factors associated with a cardiac-related event for a user, generate a selected set of the risk factors, determine, based on the selected set of the risk factors, a probability that a cardiac intervention will occur, and generate, based on the probability and the selected set of the risk factors, a treatment plan including one or more exercises directed to reducing the probability that the cardiac intervention will occur; and an electromechanical machine configured to implement the treatment plan while the electromechanical machine is being manipulated by the user. ### Claim 2 (depends on 1) The computer-implemented system of claim 1, wherein the one or more processing devices are configured to execute a risk factor model, and wherein, to generate the selected set of the risk factors, the risk factor model is configured to at least one of assign weights to the risk factors, rank the risk factors, and filter the risk factors. ### Claim 3 (depends on 2) The computer-implemented system of claim 2, wherein the one or more processing devices are configured to execute a risk factor model, and wherein, to generate the selected set of the risk factors, the risk factor model is configured to at least one of assign weights to the risk factors, rank the risk factors, and filter the risk factors. ### Claim 4 (depends on 3) The computer-implemented system of claim 3, wherein the probability model is configured to determine the probability based on respective probabilities associated with individual ones of the selected set of the risk factors. ### Claim 5 (depends on 3) The computer-implemented system of claim 3, wherein the one or more processing devices are configured to execute a treatment plan model, wherein the treatment plan model is configured to generate the treatment plan based on individual probabilities of the cardiac intervention of respective ones of the selected set of the risk factors. ### Claim 6 (depends on 5) The computer-implemented system of claim 5, wherein the treatment plan model is configured to generate the treatment plan based on an identified one of the selected set of the risk factors having a largest contribution to the probability that the cardiac intervention will occur. ### Claim 7 (depends on 6) The computer-implemented system of claim 6, wherein, subsequent to implementing the treatment plan using the electromechanical machine, the one or more processing devices are configured to modify the treatment plan based on a determination of whether the treatment plan reduced either one of (i) the probability that the cardiac intervention will occur and (ii) the identified one of the selected set of the risk factors. ### Claim 8 (depends on 7) The computer-implemented system of claim 7, wherein the one or more processing devices are configured to transmit the modified treatment plan to cause the electromechanical machine to implement at least one modified exercise of the modified treatment plan. ### Claim 9 (depends on 1) The computer-implemented system of claim 1, wherein the cardiac intervention is for minimizing one or more negative effects of the cardiac-related event. ### Claim 10 (depends on 1) The computer-implemented system of claim 1, wherein the one or more processing devices are configured to initiate, while the user performs the treatment plan, a telemedicine session between a computing device of the user and a computing device of a healthcare professional. ### Claim 11 (depends on 1) The computer-implemented system of claim 1, wherein the one or more risk factors comprise modifiable risk factors and non-modifiable risk factors. ### Claim 12 A computer-implemented method, comprising: receiving a plurality of risk factors associated with a cardiac-related event for a user; generating a selected set of the risk factors; determining, based on the selected set of the risk factors, a probability that a cardiac intervention will occur; generating, based on the probability and the selected set of the risk factors, a treatment plan including one or more exercises directed to reducing the probability that the cardiac intervention will occur; and using an electromechanical machine to implement the treatment plan while the electromechanical machine is being manipulated by the user. ### Claim 13 (depends on 12) The computer-implemented method of claim 12, further comprising: using a risk factor machine learning model to generate the selected set of the risk factors, wherein the risk factor machine learning model is configured to at least one of assign weights to the risk factors, rank the risk factors, and filter the risk factors; and using a probability machine learning model to determine the probability that the cardiac intervention will occur. ### Claim 14 (depends on 13) The computer-implemented method of claim 13, further comprising using the probability machine learning model to determine the probability based on respective probabilities associated with individual ones of the selected set of the risk factors. ### Claim 15 (depends on 14) The computer-implemented method of claim 14, further comprising using a treatment plan machine learning model to generate the treatment plan based on individual probabilities of the cardiac intervention of respective ones of the selected set of the risk factors. ### Claim 16 (depends on 14) The computer-implemented method of claim 14, further comprising generating the treatment plan based on an identified one of the selected set of the risk factors having a largest contribution to the probability that the cardiac intervention will occur. ### Claim 17 (depends on 16) The computer-implemented method of claim 16, further comprising, subsequent to implementing the treatment plan using the treatment apparatus, modifying the treatment plan based on a determination of whether the treatment plan reduced either one of (i) the probability that the cardiac intervention will occur and (ii) the identified one of the selected set of the risk factors. ### Claim 18 (depends on 12) The computer-implemented method of claim 12, wherein the cardiac intervention is for minimizing one or more negative effects of the cardiac-related event. ### Claim 19 (depends on 12) The computer-implemented method of claim 12, wherein the one or more risk factors comprise modifiable risk factors and non-modifiable risk factors. ### Claim 20 One or more tangible, non-transitory computer-readable media storing instructions that, when executed, cause one or more processing devices to: receive a plurality of risk factors associated with a cardiac-related event for a user; generate a selected set of the risk factors; determine, based on the selected set of the risk factors, a probability that a cardiac intervention will occur; generate, based on the probability and the selected set of the risk factors, a treatment plan including one or more exercises directed to reducing the probability that the cardiac intervention will occur; and use an electromechanical machine to implement the treatment plan while the electromechanical machine is being manipulated by the user.