Patent Application Titled “System And Method For Predictive Health Monitoring” Published Online (USPTO 20190344120)

2019 NOV 28 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventor Fung, Coleman (Spicewood, TX), filed on July 31, 2019, was made available online on November 14, 2019.

The assignee for this patent application is Blue Goji LLC (Austin, Texas, United States).

Reporters obtained the following quote from the background information supplied by the inventors: “Field of the Art

“The disclosure relates to the field of health monitoring devices, specifically the field of wearable health monitoring devices connected to cloud-based predictive networks.

“Discussion of the State of the Art

“It is currently possible for an athlete of any skill or dedication to go into a gym and find many types of exercise machinery, some of which may have computer chips and various levels of software on them, and some of which may be entirely mechanical in nature. Software-ready electronics are common in stationary bikes, elliptical machines, and treadmills, and in some cases exist for more specialized uses such as measuring the force exerted by a punch for boxing and other martial arts. These electronics and the software systems running on them can measure things such as estimated burned calories in a workout, the force and speed of punches or of running, the Revolutions Per Minute (RPM) of a bike and what this means for distance based on a user’s settings on a stationary bike, and in some cases treadmills, elliptical machines and stationary bikes may even allow music or TV to be streamed to the user to enhance the pleasure of working out.

“However, electronics with specialized software are noticeably lacking in the area of weight training, of virtually all kinds. There exists no common system which may determine the stresses an individual is undergoing while lifting in a variety of positions and warn them of, for example, poor form, uneven stresses in muscles such as if they are bench pressing, out of bounds positions such as overextending your arms during lateral pulldowns and other exercises, and more.

“As well, none of the systems even in exercise machines currently, utilize machine learning and a large number of factors and biometric data to determine and accurately predict health events relevant to a user’s exercise before they occur, nor do they often consider or warn a user of improper form during exercise, or for over-training or over-exertion, or myriad other concerns when engaging in strenuous physical activity. This results in users often achieving sub-par results from athletic activity, and being at-risk for health events from improper or overly taxing exercise, with workout equipment ill-equipped to aid or even consider these possibilities or users who have already suffered health events at all.

“What is needed is a system and methods for a predictive health monitoring system utilizing a smart exercise belt which may aid in exercises for users to monitor their health and exercise form and progression, and more, and communicate with users to warn them of any health-related or exercise-related issues, with the goal of preventing future incidents if possible, warning users of impending or possible incidents in the near future, and aiding users in exercising both effectively and safely.”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventor’s summary information for this patent application: “Accordingly, the inventor has conceived and reduced to practice, a system and method for predictive health monitoring using neural networks, comprising a wearable device with biometric sensors, a database containing data from multiple users across many categories of health-related factors, a first set of neural networks trained on the database that makes health predictions based on a single health factor, and a second neural network that makes health predictions based on a combination of the predictions made by the first set of neural networks. The following non-limiting summary of the invention is provided for clarity, and should be construed consistently with embodiments described in the detailed description below.

“The disclosed invention makes use of at least a plurality of sensors attached to a wearable device, including pressure sensors, oximeters, accelerometers, gyroscopes, EEG, EMG, and heart rate monitors, to learn patterns of user activity, predict health events, and assist with athletic training including assisting with preventing over-training and assisting with exercise form, as well as tracking performance over time, and to help with medical rehabilitation by measuring performance and biometric feedback during activities after a medical event, for example during physical therapy after a stroke.

“A system for predictive health monitoring is disclosed, comprising: a cloud-based health prediction engine comprising: a plurality of first-stage neural networks, each configured to make a first health prediction based on a health-related factor; a second-stage neural network, configured to make a second health prediction based on a combination of the first health predictions from at least two of the plurality of first-stage neural networks; a data storage device configured to store a history of biometric data and a history of health predictions for a user of a wearable biometric monitoring and feedback device; a network-connected server comprising a memory, a processor, and a plurality of programming instructions, wherein the programming instructions, when operating on the processor, cause the network-connected server to: receive biometric data from a wearable biometric monitoring and feedback device for the user; retrieve the history of biometric data and the history of health predictions for the user from the data storage device; process the biometric data, history of biometric data, and the history of health predictions through at least two of the plurality of first-stage neural networks; receive the first health prediction from each first-stage neural network through which the biometric data was processed; process the first health predictions received through a second-stage neural network; receive the second health prediction from the second-stage neural network; send the second health prediction to the wearable biometric monitoring and feedback device, and a wearable biometric monitoring and feedback device comprising: a plurality of sensors for gathering biometric data from the user of the wearable biometric monitoring and feedback device; a network device configured to connect to the cloud-based health prediction system; a screen for providing feedback to the user; and a computing device comprising a memory, a processor, and a plurality of programming instructions, wherein the programming instructions, when operating on the processor, cause the computing device to: obtain biometric data from at least two of the plurality of sensors for the user of the wearable biometric monitoring and feedback device; send the biometric data obtained to the cloud-based health prediction engine using the network device; receive a second health prediction from a cloud-based health prediction engine; and display the second health prediction to the user.

“Further, a method for predictive health monitoring is disclosed, comprising the steps of: obtaining biometric data for a user of a wearable biometric monitoring and feedback device; retrieving a history of biometric data and a history of health predictions for the user from a data storage device; processing the biometric data, history of biometric data, and the history of health predictions through at least two of a plurality of first-stage neural networks, the plurality of first-stage neural networks, each configured to make a first health prediction based on a separate health-related factor; receiving a first health prediction from each first-stage neural network through which the biometric data was processed; processing the first health predictions received through a second-stage neural network, the second-stage neural network configured to make a second health prediction based on a combination of the first health predictions from at least two of the plurality of first-stage neural networks; receiving the second health prediction from the second-stage neural network; and displaying the second health prediction to the user of the wearable biometric monitoring and feedback device.”

The claims supplied by the inventors are:

“1. A system for predictive health monitoring, comprising: a cloud-based health prediction engine comprising: a plurality of first-stage neural networks, each configured to make a first health prediction based on a health-related factor; a second-stage neural network, configured to make a second health prediction based on a combination of the first health predictions from at least two of the plurality of first-stage neural networks; a data storage device configured to store a history of biometric data and a history of health predictions for a user of a wearable biometric monitoring and feedback device; a network-connected server comprising a memory, a processor, and a plurality of programming instructions, wherein the programming instructions, when operating on the processor, cause the network-connected server to: receive biometric data from a wearable biometric monitoring and feedback device for the user; retrieve the history of biometric data and the history of health predictions for the user from the data storage device; process the biometric data, history of biometric data, and the history of health predictions through at least two of the plurality of first-stage neural networks; receive the first health prediction from each first-stage neural network through which the biometric data was processed; process the first health predictions received through a second-stage neural network; receive the second health prediction from the second-stage neural network; send the second health prediction to the wearable biometric monitoring and feedback device, and a wearable biometric monitoring and feedback device comprising: a plurality of sensors for gathering biometric data from the user of the wearable biometric monitoring and feedback device; a network device configured to connect to the cloud-based health prediction system; a screen for providing feedback to the user; and a computing device comprising a memory, a processor, and a plurality of programming instructions, wherein the programming instructions, when operating on the processor, cause the computing device to: obtain biometric data from at least two of the plurality of sensors for the user of the wearable biometric monitoring and feedback device; send the biometric data obtained to the cloud-based health prediction engine using the network device; receive a second health prediction from a cloud-based health prediction engine; and display the second health prediction to the user.

“2. The system of claim 1, wherein the user can enter an age, a health profile, a type of exercise, or a training goal, and wherein the data processed by the neural networks further comprises the user’s age, the health profile, the type of exercise, or the training goal.

“3. The system of claim 2, wherein the data processed by the neural networks further comprises statistical data for a group of users similar in at least one aspect to the user of the wearable biometric monitoring and feedback device.

“4. The system of claim 3, wherein the statistical data comprises data representative of a global or national group.

“5. The system of claim 3, wherein the statistical data comprises data representative of a local or regional group.

“6. The system of claim 1, wherein the wearable biometric monitoring and feedback device further comprises sensors that collect non-biometric data, and wherein the data processed by the neural networks further comprises the non-biometric data.

“7. The system of claim 1, wherein the wearable biometric monitoring and feedback device uses the network device to obtain weather data from the Internet, and wherein the data processed by the neural networks further comprises the weather data.

“8. A method for predictive health monitoring, comprising the steps of: obtaining biometric data for a user of a wearable biometric monitoring and feedback device; retrieving a history of biometric data and a history of health predictions for the user from a data storage device; processing the biometric data, history of biometric data, and the history of health predictions through at least two of a plurality of first-stage neural networks, the plurality of first-stage neural networks, each configured to make a first health prediction based on a separate health-related factor; receiving a first health prediction from each first-stage neural network through which the biometric data was processed; processing the first health predictions received through a second-stage neural network, the second-stage neural network configured to make a second health prediction based on a combination of the first health predictions from at least two of the plurality of first-stage neural networks; receiving the second health prediction from the second-stage neural network; and displaying the second health prediction to the user of the wearable biometric monitoring and feedback device.

“9. The method of claim 8, wherein the user can enter an age, a health profile, a type of exercise, or a training goal, and wherein the data processed by the neural networks further comprises the user’s age, the health profile, the type of exercise, or the training goal.

“10. The method of claim 9, wherein the data processed by the neural networks further comprises statistical data for a group of users similar in at least one aspect to the user of the wearable biometric monitoring and feedback device.

“11. The method of claim 10, wherein the statistical data comprises data representative of a global or national group.

“12. The method of claim 10, wherein the statistical data comprises data representative of a local or regional group.

“13. The method of claim 8, wherein the wearable biometric monitoring and feedback device further comprises sensors that collect non-biometric data, and wherein the data processed by the neural networks further comprises the non-biometric data.

“14. The method of claim 8, wherein the wearable biometric monitoring and feedback device further comprises a network device that obtains weather data from the Internet, and wherein the data processed by the neural networks further comprises the weather data.”

For more information, see this patent application: Fung, Coleman. System And Method For Predictive Health Monitoring. Filed July 31, 2019 and posted November 14, 2019. Patent URL: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220190344120%22.PGNR.&OS=DN/20190344120&RS=DN/20190344120

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