Patent Issued for Sensing peripheral heuristic evidence, reinforcement, and engagement system (USPTO 11423758): State Farm Mutual Automobile Insurance Company

2022 SEP 14 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent 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 October 22, 2020, was published online on August 23, 2022, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11423758 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, NewsRx journalists 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 training a machine learning module to identify abnormalities or anomalies corresponding to conditions associated with individuals in a plurality of home environments, comprising: receiving, by a processor, historical sensor data detected by a plurality of sensors associated with the plurality of home environments; receiving, by the processor, historical condition data indicating conditions associated with individuals in each of the plurality of home environments, wherein the conditions include one or more of: a medical condition, a health condition, a cognitive condition, a forgetfulness condition, an insomnia condition, a fatigue condition, a hygiene condition, an emergency condition, or an urgent condition; analyzing, by the processor, using the machine learning module, the historical sensor data and the historical condition data; identifying, by the processor, using the machine learning module, based upon the analysis, one or more abnormalities or anomalies in the historical sensor data corresponding to conditions associated with the individuals in the plurality of home environments; and modifying, by the processor, the machine learning module based upon the analysis and the identified one or more abnormalities or anomalies with corresponding conditions.

“2. The computer-implemented method of claim 1, further comprising: capturing current data detected by a plurality of sensors associated with a home environment; and analyzing, by the processor, the captured current data to identify one or more abnormalities or anomalies in the current data using the modified machine learning module.

“3. The computer-implemented method of claim 2, further comprising: comparing, by the processor, the one or more abnormalities or anomalies in the current data to the abnormalities or anomalies in the historical sensor data corresponding to conditions associated with the individuals in the plurality of home environments; and determining, by the processor, based upon the comparison, a current condition associated with an individual in the home environment using the modified machine learning module.

“4. The computer-implemented method of claim 3, further comprising: generating, by the processor, a notification indicating the current condition associated with the individual in the home environment.

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

“6. The computer-implemented method of claim 5, wherein the data indicative of electricity use includes an indication of at least one 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.

“7. The computer-implemented method of claim 3, wherein the current condition associated with the individual is a medical condition.

“8. The computer-implemented method of claim 3, wherein the current condition associated with the individual is an emergency medical condition, the method further comprising: requesting, by the processor, based upon the emergency medical condition, an emergency service to be provided to the individual.

“9. The computer-implemented method of claim 1, wherein the historical sensor data comprising at least one of a body temperature, a heart rate, a breathing rate, a glucose level, a ketone level, medication adherence data, eye movement data, exercise data, body control data, fine motor control data, health data, and nutrition data.

“10. A computer system for training a machine learning module to identify abnormalities or anomalies corresponding to conditions associated with individuals in a plurality of home environments, comprising: one or more processors; and one or more non-transitory memories storing computer executable instructions that, when executed by the one or more processors, cause the computer system to: receive historical sensor data detected by a plurality of sensors associated with the plurality of home environments; receive historical condition data indicating conditions associated with the individuals in the plurality of home environments, wherein the conditions include one or more of: a medical condition, a health condition, a cognitive condition, a forgetfulness condition, an insomnia condition, a fatigue condition, a hygiene condition, an emergency condition, or an urgent condition; analyze, using the machine learning module, the historical sensor data and the historical condition data; identify, using the machine learning module, based upon the analysis, one or more abnormalities or anomalies in the historical sensor data corresponding to conditions associated with the individuals in the plurality of home environments; and modify the machine learning module based upon the analysis and the identified one or more abnormalities or anomalies with the corresponding conditions.

“11. The computer system of claim 10, wherein the computer executable instructions further cause the computer system to: capture current data detected by a plurality of sensors associated with a home environment; and analyze the captured current data to identify one or more abnormalities or anomalies in the current data using the modified machine learning module.

“12. The computer system of claim 11, wherein the computer executable instructions further cause the computer system to: compare the one or more abnormalities or anomalies in the current data to the abnormalities or anomalies in the historical sensor data corresponding to conditions associated with the individuals in the home environments; and determine, based upon the comparison, a current condition associated with an individual in the home environment using the modified machine learning module.

“13. The computer system of claim 12, wherein the computer executable instructions further cause the computer system to: generate a notification indicating the current condition associated with the individual in the home environment.

“14. The computer system of claim 10, wherein the plurality of sensors associated with the plurality of home environments include one or more sensors configured to capture data indicative of electricity use by devices associated with the plurality of home environments.

“15. The computer system of claim 14, wherein the data indicative of electricity use includes an indication of at least one 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.

“16. The computer system of claim 12, wherein the current condition associated with the individual is a medical condition.

“17. The computer system of claim 12, wherein the current condition associated with the individual is an emergency medical condition, wherein the computer executable instructions further cause the computer system to: request, based upon the emergency medical condition, an emergency service to be provided to the individual.

“18. The computer system of claim 10, wherein the historical sensor data comprising at least one of a body temperature, a heart rate, a breathing rate, a glucose level, a ketone level, medication adherence data, eye movement data, exercise data, body control data, fine motor control data, health data, and nutrition data.”

URL and more information on this patent, see: Brannan, Joseph Robert. Sensing peripheral heuristic evidence, reinforcement, and engagement system. U.S. Patent Number 11423758, filed October 22, 2020, and published online on August 23, 2022. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11423758.PN.&OS=PN/11423758RS=PN/11423758

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