Sensing peripheral heuristic evidence, reinforcement, and engagement system

Patent metadata

Field	Value
Publication number	US-12573283-B2
Application number	US-202318138558-A
Country	US
Kind code	B2
Filing date	Apr 24, 2023
Priority date	Apr 9, 2018
Publication date	Mar 10, 2026
Grant date	Mar 10, 2026

Abstract

Systems and methods for identifying a condition associated with an individual in a home environment are provided. Sensors associated with the home environment detect data, which is captured and analyzed by a local or remote processor to identify the condition. In some instances, the sensors are configured to capture data indicative of electricity use by devices associated with the home environment, including, e.g., which devices are using electricity, what date/time electricity is used by each device, how long each device uses electricity, and/or the power source for the electricity used by each device. The processor analyzes the captured data to identify any abnormalities or anomalies, and, based upon any identified abnormalities or anomalies, the processor determines a condition (e.g., a medical condition) associated with an individual in the home environment. The processor generates and transmits a notification indicating the condition associated with the individual to a caregiver of the individual.

First claim

What is claimed is:

1 . A computer-implemented method for training a machine learning module to identify abnormalities or anomalies corresponding to historically identified conditions associated with one or more individuals in a home environment, comprising:
identifying, by a processor, one or more abnormalities or anomalies in historical sensor data detected by one or more sensors associated with the home environment; analyzing, by the processor, using the machine learning module, the one or more abnormalities or anomalies in the historical sensor data and historical condition data indicating historically identified conditions associated with one or more individuals in the home environment; identifying, by the processor, using the machine learning module, based upon the analyzing, one or more abnormalities or anomalies in the historical sensor data corresponding to one or more of the historically identified conditions associated with the one or more individuals in the home environment; capturing current data detected by the one or more 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 machine learning module.

2 . The computer-implemented method of claim 1 , 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 historically identified conditions associated with the one or more individuals in the home environment; and determining, by the processor, based upon the comparing, a current condition associated with an individual in the home environment using the machine learning module.

3 . The computer-implemented method of claim 2 , further comprising:
generating, by the processor, a notification indicating the current condition associated with the individual in the home environment.

4 . The computer-implemented method of claim 2 , wherein the current condition associated with the individual is a medical condition.

5 . The computer-implemented method of claim 2 , wherein the current condition associated with the individual is an emergency medical condition, the computer-implemented method further comprising:
requesting, by the processor, based upon the emergency medical condition, an emergency service to be provided to the individual.

6 . The computer-implemented method of claim 2 , wherein the current condition associated with the individual is a gait of the individual.

7 . The computer-implemented method of claim 2 , wherein the current condition associated with the individual is whether or not the individual is sleeping.

8 . 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.

9 . The computer-implemented method of claim 1 , further comprising:
modifying, by the processor, the machine learning module based upon the analyzing and the identified one or more abnormalities or anomalies with corresponding historically identified conditions.

10 . The computer-implemented method of claim 1 , 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.

11 . The computer-implemented method of claim 10 , 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.

12 . A computer system for training a machine learning module to identify abnormalities or anomalies corresponding to historically identified conditions associated with one or more individuals in a home environment, 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: identify one or more abnormalities or anomalies in historical sensor data detected by one or more sensors associated with the home environment; analyze, using the machine learning module, the one or more abnormalities or anomalies in the historical sensor data and historical condition data indicating historically identified conditions associated with one or more individuals in the home environment; identify, using the machine learning module, based upon the analyzing, one or more abnormalities or anomalies in the historical sensor data corresponding to one or more of the historically identified conditions associated with the one or more individuals in the home environment; capture current data detected by the one or more 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 machine learning module.

13 . The computer system of claim 12 , 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 historically identified conditions associated with the one or more individuals in the home environment; and determine, based upon the comparing, a current condition associated with an individual in the home environment using the machine learning module.

14 . The computer system of claim 13 , 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.

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

16 . The computer system of claim 13 , 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.

17 . The computer system of claim 12 , 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.

18 . The computer system of claim 17 , 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.

19 . The computer system of claim 12 , 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.

2

Assignees

• State Farm Mutual Automobile Insurance Co

Inventors

• Hunt Dana C
• Brannan Joseph Robert
• Kawakita Christopher N
• Williams Aaron

Classifications

• G06N20/00

  Machine learning · CPC title

• G16H80/00

  ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring · CPC title

• G08B21/0469

  Presence detectors to detect unsafe condition, e.g. infrared sensor, microphone (G08B21/0476 takes precedence) · CPC title

• G08B21/0476

  Cameras to detect unsafe condition, e.g. video cameras · CPC title

• G08B21/0423 Primary

  detecting deviation from an expected pattern of behaviour or schedule · CPC title