Patent Issued for Sensing peripheral heuristic evidence, reinforcement, and engagement system (USPTO 11869328): State Farm Mutual Automobile Insurance Company

2024 JAN 25 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States) has been issued patent number 11869328, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Brannan, Joseph Robert (Bloomington, IL, US), Hunt, Dana C. (Normal, IL, US), Kawakita, Christopher N. (Normal, IL, US), Williams, Aaron (Congerville, IL, US).

This patent was filed on July 7, 2022 and was published online on January 9, 2024.

From the background information supplied by the inventors, news correspondents obtained the following quote: “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.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “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.”

The claims supplied by the inventors are:

“1. A computer-implemented method for identifying a condition associated with an individual in a home environment, comprising: training, by one or more processors, a neural network model using a dataset associated with a home environment, wherein training the neural network model comprises adding one or more layers to the neural network model, 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; capturing data detected by a plurality of sensors associated with the home environment; analyzing, by the one or more processors, the captured data using the trained neural network model to identify one or more abnormalities or anomalies; determining, by the one or more processors, that the one or more abnormalities or anomalies indicate one or more atypical behaviors of the individual in the home environment; identifying, by the one or more processors, a condition associated with the one or more atypical behaviors of the individual; and generating, by the one or more processors, a notification indicating the condition associated with the individual, wherein the notification comprises a snapshot report generated periodically and the snapshot report includes an indication of the condition associated with the individual and a change from a prior snapshot report.

“2. The computer-implemented method of claim 1, wherein the plurality of sensors associated with the home environment include one or more sensors configured to capture data indicative of electricity use by devices associated with the home environment.

“3. The computer-implemented method of claim 2, wherein the data indicative of electricity use includes an indication of at least one selected from a group consisting of: which device is using electricity; a time at which electricity is used by a particular device; a date at which electricity is used by a particular device; a duration of electricity use by a particular device; and a power source for the electricity use.

“4. The computer-implemented method of claim 1, wherein the analyzing, by the one or more processors, the captured data to identify one or more abnormalities or anomalies comprises: analyzing, by the one or more processors, over a period of time, the data detected by the plurality of sensors to identify one or more patterns in the data; and comparing, by the one or more processors, the data detected by the plurality of sensors to the identified patterns in the data in order to identify instances in which the detected data is inconsistent with the identified patterns.

“5. The computer-implemented method of claim 1, wherein the determining, by the one or more processors, based upon the one or more identified abnormalities or anomalies, a condition associated with an individual in the home environment comprises: determining, by the one or more processors, that the one or more abnormalities or anomalies indicate one or more atypical behaviors of the individual in the home environment; and identifying, by the one or more processors, a condition associated with the one or more atypical behaviors of the individual.

“6. The computer-implemented method of claim 1, wherein the condition associated with the individual is a medical condition.

“7. The computer-implemented method of claim 1, wherein the condition associated with the individual is an emergency medical condition, the method further comprising: requesting, by the one or more processors, based upon the emergency medical condition, an emergency service to be provided to the individual.

“8. A computer system for identifying a condition associated with an individual in a home environment, comprising: one or more sensors associated with the home environment; one or more processors configured to interface with the one or more sensors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computer system to: train a neural network model using a dataset associated with a home environment and add one or more layers to the neural network model, 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; capture data detected by the one or more sensors; analyze the captured data using the trained neural network model to identify one or more abnormalities or anomalies; determine that the one or more abnormalities or anomalies indicate one or more atypical behaviors of the individual in the home environment; identify a condition associated with the one or more atypical behaviors of the individual; and generate a notification indicating the condition associated with the individual, wherein the notification comprises a snapshot report generated periodically and the snapshot report includes an indication of the condition associated with the individual and a change from a prior snapshot report.

“9. The computer system of claim 8, wherein the one or more sensors associated with the home environment include one or more sensors configured to capture data indicative of electricity use by devices associated with the home environment.

“10. The computer system of claim 9, wherein the data indicative of electricity use includes an indication of at least one selected from a group consisting of: which device is using electricity; a time at which electricity is used by a particular device; a date at which electricity is used by a particular device; a duration of electricity use by a particular device; and a power source for the electricity use.

“11. The computer system of claim 8, wherein the computer system analyzes the captured data to identify one or more abnormalities or anomalies by: analyzing, over a period of time, the data detected by the plurality of sensors to identify one or more patterns in the data; and comparing the data detected by the plurality of sensors to the identified patterns in the data in order to identify instances in which the detected data is inconsistent with the identified patterns.

“12. The computer system of claim 8, wherein the computer system determines, based upon the one or more identified abnormalities or anomalies, a condition associated with an individual in the home environment by: determining that the one or more abnormalities or anomalies indicate one or more atypical behaviors of the individual in the home environment; and identifying a condition associated with the one or more atypical behaviors of the individual.

“13. The computer system of claim 8, wherein the condition associated with the individual is a medical condition.

“14. The computer system of claim 8, wherein the condition associated with the individual is an emergency medical condition, wherein the instructions that, when executed by the one or more processors, further cause the computer system to: request, based upon the emergency medical condition, an emergency service to be provided to the individual.

“15. A non-transitory computer-readable storage medium having stored thereon a set of instructions, executable by a processor, for identifying a condition associated with an individual in a home environment, the set of instructions comprising instructions for: training a neural network model using a dataset associated with a home environment, wherein training the neural network model comprises adding 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; obtaining data captured by a plurality of sensors associated with the home environment; analyzing the captured data using the trained neural network model to identify one or more abnormalities or anomalies; determining that the one or more abnormalities or anomalies indicate one or more atypical behaviors of the individual in the home environment; identifying a condition associated with the one or more atypical behaviors of the individual; and generating, to a caregiver of the individual, a notification indicating the condition associated with the individual, wherein the notification comprises a snapshot report generated periodically and the snapshot report includes an indication of the condition associated with the individual and a change from a prior snapshot report.

“16. The non-transitory computer-readable storage medium of claim 15, wherein the plurality of sensors associated with the home environment include one or more sensors configured to capture data indicative of electricity use by devices associated with the home environment.

“17. The non-transitory computer-readable storage medium of claim 16, wherein the data indicative of electricity use includes an indication of at least one selected from a group consisting of: which device is using electricity; a time at which electricity is used by a particular device; a date at which electricity is used by a particular device; a duration of electricity use by a particular device; and a power source for the electricity use.

“18. The non-transitory computer-readable storage medium of claim 15, wherein the set of instructions further comprise instructions for: analyzing, over a period of time, the data detected by the plurality of sensors to identify one or more patterns in the data; and comparing the data detected by the plurality of sensors to the identified patterns in the data in order to identify instances in which the detected data is inconsistent with the identified patterns.”

There are additional claims. Please visit full patent to read further.

For the URL and additional information on this patent, see: Brannan, Joseph Robert. Sensing peripheral heuristic evidence, reinforcement, and engagement system. U.S. Patent Number 11869328, filed July 7, 2022, and published online on January 9, 2024. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11869328)&db=USPAT&type=ids

(Our reports deliver fact-based news of research and discoveries from around the world.)