Application US20210043058

SENSING PERIPHERAL HEURISTIC EVIDENCE, REINFORCEMENT, AND ENGAGEMENT SYSTEM
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US20210043058

1.-11. (canceled) (0)

12. 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: (4) (5)

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;

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.

13. The computer-implemented method of claim 12, further comprising: (1) (2)

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.

14. The computer-implemented method of claim 13, further comprising: (3) (2)

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.

15. The computer-implemented method of claim 14, further comprising: (0) (1)

18. The computer-implemented method of claim 14, wherein the current condition associated with the individual is a medical condition. (0)

19. The computer-implemented method of claim 14, wherein the current condition associated with the individual is an emergency medical condition, the method further comprising: (0) (1)

16. The computer-implemented method of claim 12, 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. (1) (0)

17. The computer-implemented method of claim 16, wherein the data indicative of electricity use includes an indication of at least one of: (0) (5)

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.

20. The computer-implemented method 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. (0)

21. The computer-implemented method of claim 12, wherein the historical condition data comprising data indicating at least one of a medical condition, a health condition, an urgent condition, and a cognitive condition. (0)

22. 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: (4) (2)

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;

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.

23. The computer system of claim 22, wherein the computer executable instructions further cause the computer system to: (1) (2)

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.

24. The computer system of claim 23, wherein the computer executable instructions further cause the computer system to: (3) (2)

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.

25. The computer system of claim 24, wherein the computer executable instructions further cause the computer system to: (0) (1)

28. The computer system of claim 24, wherein the current condition associated with the individual is a medical condition. (0)

29. The computer system of claim 24, wherein the current condition associated with the individual is an emergency medical condition, (0) (1)

26. The computer system of claim 22, 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. (1) (0)

27. The computer system of claim 26, wherein the data indicative of electricity use includes an indication of at least one of: (0) (5)

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.

30. The computer system of claim 22, 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. (0)

31. The computer system of claim 22, wherein the historical condition data comprising data indicating at least one of a medical condition, a health condition, an urgent condition, and a cognitive condition. (0)

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Application US20210043058

SENSING PERIPHERAL HEURISTIC EVIDENCE, REINFORCEMENT, AND ENGAGEMENT SYSTEM

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