“Sensing Peripheral Heuristic Evidence, Reinforcement, And Engagement System” in Patent Application Approval Process (USPTO 20220139190): State Farm Mutual Automobile Insurance Company

2022 MAY 25 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent application by the inventors Brannan, Joseph Robert (Bloomington, IL, US); Hunt, Dana C. (Normal, IL, US); Kawakita, Christopher N. (Bloomington, IL, US); Williams, Aaron (Congerville, IL, US), filed on January 13, 2022, was made available online on May 5, 2022, according to news reporting originating from Washington, D.C., by NewsRx correspondents.

This patent application is assigned to State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “As individuals age, many develop cognitive conditions or health conditions making it difficult and/or unsafe for them to live independently in a home environment. However, because the signs of such cognitive conditions and/or health conditions may be subtle, or may develop slowly over time, it may be difficult for caregivers to determine whether an individual is capable of safely living independently.”

In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventors’ summary information for this patent application: “In one aspect, a computer-implemented method for identifying a condition associated with an individual in a home environment may be provided. The method may include, via one or more local or remote processors, servers, transceivers, and/or sensors: (1) capturing data detected by a plurality of sensors associated with a home environment; (2) analyzing, by a processor, the captured data to identify one or more abnormalities or anomalies; and/or (3) determining, by a processor, based upon the identified one or more abnormalities or anomalies, a condition associated with an individual in the home environment. The method may additionally include (4) generating, by a processor, to a caregiver of the individual, a notification indicating the condition associated with the individual. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“In another aspect, a computer system for identifying a condition associated with an individual in a home environment may be provided. The computer system may include one or more sensors associated with a home environment, one or more processors configured to interface with the one or more sensors, and/or one or more memories storing non-transitory computer executable instructions. The non-transitory computer executable instructions, when executed by the one or more processors, cause the computer system to (1) capture data detected by the one or more sensors; (2) analyze the captured data to identify one or more abnormalities or anomalies; (3) determine, based upon the identified one or more abnormalities or anomalies, a condition associated with an individual in the home environment; and/or (4) generate, to a caregiver of the individual, a notification indicating the condition associated with the individual. The system may include additional, less, or alternate functionality, including that discussed elsewhere herein.

“In still another aspect, a computer-readable storage medium having stored thereon a set of non-transitory instructions, executable by a processor, for identifying a condition associated with an individual in a home environment may be provided. The instructions include instructions for (1) obtaining data detected by a plurality of sensors associated with a home environment; (2) analyzing the captured data to identify one or more abnormalities or anomalies; (3) determining, based upon the identified one or more abnormalities or anomalies, a condition associated with an individual in the home environment; and/or (4) generating, to a caregiver of the individual, a notification indicating the condition associated with the individual. The instructions may direct additional, less, or alternate functionality, including that discussed elsewhere herein.

“In still another aspect, a computer-implemented method for training a machine learning module to identify abnormalities or anomalies in sensor data corresponding to conditions associated with individuals in home environments may be provided. The computer-implemented method may include (1) receiving, by a processor, historical data detected by a plurality of sensors associated with a plurality of home environments; (2) receiving, by a processor, historical data indicating conditions associated with individuals in each of the plurality of home environments; (3) analyzing, by a processor, using a machine learning module, the historical data detected by the plurality of sensors associated with the plurality of home environments and the historical data indicating conditions associated with individuals in each of the plurality of home environments; and/or (4) identifying, by a processor, using the machine learning module, based upon the analysis, one or more abnormalities or anomalies in the historical data detected by the plurality of sensors corresponding to conditions associated with the individuals in the home environments. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“In still another aspect, a computer system for training a machine learning module to identify abnormalities or anomalies in sensor data corresponding to conditions associated with individuals in home environments may be provided. The computer system may include one or more processors and one or more memories storing non-transitory computer executable instructions. When executed by the one or more processors, the non-transitory computer executable instructions may cause the computer system to: (1) receive historical data detected by a plurality of sensors associated with a plurality of home environments; (2) receive historical data indicating conditions associated with individuals in each of the plurality of home environments; (3) analyze, using a machine learning module, the historical data detected by the plurality of sensors associated with the plurality of home environments and the historical data indicating conditions associated with individuals in each of the plurality of home environments; and/or (4) identify, using the machine learning module, based upon the analysis, one or more abnormalities or anomalies in the historical data detected by the plurality of sensors corresponding to conditions associated with the individuals in the home environments. The system may include additional, less, or alternate functionality, including that discussed elsewhere herein.

“In still another aspect, a computer-readable storage medium having stored thereon a set of non-transitory instructions, executable by a processor, for training a machine learning module to identify abnormalities or anomalies in sensor data corresponding to conditions associated with individuals in home environments may be provided. The instructions may include instructions for: (1) receiving historical data detected by a plurality of sensors associated with a plurality of home environments: (2) receiving historical data indicating conditions associated with individuals in each of the plurality of home environments; (3) analyzing, using a machine learning module, the historical data detected by the plurality of sensors associated with the plurality of home environments and the historical data indicating conditions associated with individuals in each of the plurality of home environments; and/or (4) identifying, using the machine learning module, based upon the analysis one or more abnormalities or anomalies in the historical data detected by the plurality of sensors corresponding to conditions associated with the individuals in the home environments. The instructions may direct additional, less, or alternate functionality, including that discussed elsewhere herein.

“Advantages will become more apparent to those skilled in the art from the following description of the preferred embodiments which have been shown and described by way of illustration. As will be realized, the present embodiments may be capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.

“The Figures depict preferred embodiments for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the systems and methods illustrated herein may be employed without departing from the principles of the invention described herein.”

The claims supplied by the inventors are:

“1.-11. (canceled)

“12. A computer-implemented method for identifying abnormal conditions, the method comprising: capturing sensor data by one or more sensors associated with a home environment; analyzing the captured sensor data using a trained machine learning model, the trained machine learning model being trained using historical sensor data collected at a plurality of home environments and the historical condition data, the historical condition data indicating conditions associated with individuals in the plurality of home environments; determining an abnormal condition of an individual in the home environment based upon the analysis using the trained machine learning model, the abnormal condition of the individual being a fall; generating an electronic notification indicating the abnormal condition of the individual via one or more processors; and transmitting, via the one or more processors, the electronic notification indicating the abnormal condition of the individual to a caregiver device.

“13. The computer-implemented method of claim 12, wherein the captured sensor data comprises image data captured by one or more home-mounted cameras.

“14. The computer-implemented method of claim 13, wherein the trained machine learning model comprises a neural network.

“15. The computer-implemented method of claim 14, wherein the neural network model is trained using the historical sensor data, wherein the historical sensor data comprises historical image data.

“16. The computer-implemented method of claim 14, wherein the neural network comprises one or more layers, wherein at least one layer of the one or more layers is associated with at least one selected from a group consisting of an activation function, a loss function, and an optimization function.

“17. The computer-implemented method of claim 12, wherein the trained machine learning model comprises an image recognition model.

“18. The computer-implemented method of claim 12, wherein the determining an abnormal condition of an individual in the home environment comprises determining an impact level of the fall using the trained machine learning model based on the captured sensor data.

“19. The computer-implemented method of claim 12, wherein the transmitting the electronic notification indicating the abnormal condition of the individual comprises requesting an emergency service to be provided to the individual.

“20. A computer-implemented method for identifying abnormal conditions, comprising: receiving, via one or more processors of a caregiver device, a notification indicating an abnormal condition of an individual in a home environment from a device, wherein the device is configured to capture sensor data one or more sensors associated with a home environment and analyze the captured sensor data using a trained machine learning model to determine the abnormal condition of the individual, wherein (i) the abnormal condition of the individual is a fall, and (ii) the trained machine learning model is trained using historical sensor data collected at a plurality of home environments and the historical condition data, the historical condition data indicating conditions associated with individuals in the plurality of home environments; in response to receiving the notification indicating the abnormal condition of the individual, presenting one or more options via the one or more processors of the caregiver device; and in response to receiving a selection of the one or more options, requesting an emergency service to be provided to the individual via the one or more processors of the caregiver device.

“21. The computer-implemented method of claim 20, wherein the captured sensor data comprises image data captured by one or more home-mounted cameras.

“22. The computer-implemented method of claim 21, wherein the trained machine learning model comprises a neural network.

“23. The computer-implemented method of claim 22, wherein the neural network model is trained using the historical sensor data, wherein the historical sensor data comprises historical image data.

“24. The computer-implemented method of claim 22, wherein the neural network comprises one or more layers, wherein at least one layer of the one or more layers is associated with at least one selected from a group consisting of an activation function, a loss function, and an optimization function.

“25. The computer-implemented method of claim 20, wherein the trained machine learning model comprises an image recognition model.

“26. The computer-implemented method of claim 20, wherein the device is further configured to determine an impact level of the fall using the trained machine learning model based on the captured sensor data.

“27. A computer system comprising: a first device comprising: one or more sensors configured to capture sensor data associated with a home environment; and one or more processors coupled to the one or more sensors and configured to: analyze the captured sensor data using a trained machine learning model, the trained machine learning model being trained using historical sensor data collected at a plurality of home environments and the historical condition data, the historical condition data indicating conditions associated with individuals in the plurality of home environments; determine an abnormal condition of an individual in the home environment based upon the analysis using the trained machine learning model, the abnormal condition of the individual being a fall; generate a notification indicating the abnormal condition of the individual; and transmit the notification indicating the abnormal condition of the individual to a caregiver device; and the caregiver device comprising at least one processor and configured to: receive the notification indicating the abnormal condition of the individual; in response to receiving the notification indicating the abnormal condition of the individual, present one or more options; and in response to receiving a selection of the one or more options, request an emergency service to be provided to the individual.

“28. The computer system of claim 27, wherein the captured sensor data comprises image data captured by one or more home-mounted cameras.

“29. The computer system of claim 28, wherein the trained machine learning model comprises a neural network.

“30. The computer system of claim 29, wherein the neural network model is trained using the historical sensor data, wherein the historical sensor data comprises historical image data.

“31. The computer system of claim 29, wherein the neural network comprises one or more layers, wherein at least one layer of the one or more layers is associated with at least one selected from a group consisting of an activation function, a loss function, and an optimization function.

“32. The computer system of claim 27, wherein the first device is further configured to determine an impact level of the fall using the trained machine learning model based on the captured sensor data.”

URL and more information on this patent application, see: Brannan, Joseph Robert; Hunt, Dana C.; Kawakita, Christopher N.; Williams, Aaron. Sensing Peripheral Heuristic Evidence, Reinforcement, And Engagement System. Filed January 13, 2022 and posted May 5, 2022. Patent URL: https://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=%2220220139190%22.PGNR.&OS=DN/20220139190&RS=DN/20220139190

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