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AI Biotech/Diagnostics: Neurology

Search All Patents in AI Biotech/Diagnostics: Neurology


Patent 10825318


Issued 2020-11-03

Sensing Peripheral Heuristic Evidence, Reinforcement, And Engagement System

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.


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Much More than Average Length Specification


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2 Independent Claims

  • Independent Claim 1. A computer-implemented method for training a machine learning module to identify abnormalities or anomalies in sensor data corresponding to conditions associated withindividuals in home environments, comprising: receiving, by a processor, historical sensor data detected by a plurality of sensors associated with a plurality of home environmentsreceiving, by a processor, historical condition data indicatingconditions associated with individuals in each of the plurality of home environmentsanalyzing, by a processor, using the machine learning module, the historical sensor data detected by the plurality of sensors associated with the plurality of homeenvironments and the historical condition data indicating conditions associated with individuals in each of the plurality of home environments, the historical sensor data comprising at least one of a body temperature, a heart rate, a breathing rate, aglucose/ketone level, medication adherence data, eye movement data, exercise data, body control data, fine motor control data, and health and/or nutrition data, and the historical condition data comprising data indicating at least one of a medicalcondition, a health condition, an urgent condition, and a cognitive conditionidentifying, by a processor, using the machine learning module, based upon the analysis, one or more abnormalities or anomalies in the historical sensor data detected by theplurality of sensors corresponding to conditions associated with the individuals in the home environmentsand modifying, by a processor, the machine learning module based upon the analysis and the identified one or more abnormalities or anomalies withcorresponding conditions.

  • Independent Claim 8. 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, comprising: one or more processorsand one or morememories storing non-transitory 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 a plurality of homeenvironmentsreceive historical condition data indicating conditions associated with individuals in each of the plurality of home environmentsanalyze, using the machine learning module, the historical sensor data detected by the plurality of sensorsassociated with the plurality of home environments and the historical condition data indicating conditions associated with individuals in each of the plurality of home environments, the historical sensor data comprising at least one of a bodytemperature, a heart rate, a breathing rate, a glucose/ketone level, medication adherence data, eye movement data, exercise data, body control data, fine motor control data, and health and/or nutrition data, and the historical condition data comprisingdata indicating at least one of a medical condition, a health condition, an urgent condition, and a cognitive conditionidentify, using the machine learning module, based upon the analysis, one or more abnormalities or anomalies in the historical datadetected by the plurality of sensors corresponding to conditions associated with the individuals in the home environmentsand modify the machine learning module based upon the analysis and the identified one or more abnormalities or anomalies withcorresponding conditions.