US 12,573,283 B2 · Sensing peripheral heuristic evidence, reinforcement, and engagement system

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.

Claims21

Dependent claims

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.

3 more elements

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.

6 more elements

Dependencies are inferred from the claim text.

Sensing peripheral heuristic evidence, reinforcement, and engagement system

Life of this patent

Sensing peripheral heuristic evidence, reinforcement, and engagement system

Life of this patent

Abstract

Claims21