Granted Patent B2

US 12,749,384 · App. 18/736,034 · Granted Sep 29, 2026

Inventor: Jennifer Curiel (Anna, TX)

Assignee: State Farm Mutual Automobile Insurance Company

G08B13/24B60L53/60

Abstract

Claims (95)

1 . A sensor for detecting hazards, the sensor comprising:

at least one memory with instructions stored thereon; and

at least one processor in communication with the at least one memory, wherein the instructions, when executed by the at least one processor, cause the at least one processor to:

generate sensor profile data associated with a location proximate to the sensor based upon sensor data generated by the sensor and a plurality of other sensors associated with the location and in communication with the sensor, the plurality of other sensors including an electric vehicle (EV) sensor for monitoring charging of an EV;

apply the sensor profile data to a sensor model profile associated with the location and stored in the at least one memory, wherein the sensor model profile includes a plurality of parameter levels for the location proximate to the sensor generated by a machine learning model;

identify a discrepancy between the sensor profile data and the sensor model profile;

determine a potential electrical hazard associated with charging of the EV at the location based upon the discrepancy between the sensor profile data and the sensor model profile; and

generate an alert based upon the potential electrical hazard at the location.

2 . The sensor of claim 1 , wherein the plurality of other sensors are different from the sensor, and wherein the instructions further cause the at least one processor to:

receive additional sensor data from the plurality of other sensors; and

generate the sensor profile data further based upon the additional sensor data.

3 . The sensor of claim 1 , wherein the instructions further cause the at least one processor to:

receive an input indicating that normal conditions are present at the location;

generate initial sensor profile data based upon the location;

input the initial sensor profile data to the machine learning model;

receive the sensor model profile as an output from the machine learning model; and

store the sensor model profile in the at least one memory as being associated with the location.

4 . The sensor of claim 1 , wherein the instructions further cause the at least one processor to:

receive another input indicating that the sensor has been moved to a different location;

generate updated sensor profile data associated with the different location based upon updated sensor data generated by the sensor proximate to the different location;

input the updated sensor profile data to the machine learning model;

receive an updated sensor model profile as an output from the machine learning model, wherein the updated sensor model profile includes a plurality of updated parameter levels for the different location; and

store the updated sensor model profile in the at least one memory as being associated with the different location.

5 . The sensor of claim 4 , wherein the instructions further cause the at least one processor to:

generate new updated sensor profile data associated with the different location proximate to the sensor based upon new updated sensor data generated by the sensor;

apply the new updated sensor profile data to the updated sensor model profile;

identify a new discrepancy between the new updated sensor profile data and the updated sensor model profile;

determine a potential hazard at the different location based upon the new discrepancy between the new updated sensor profile data and the updated sensor model profile; and

generate a second alert, the second alert based upon the potential hazard at the different location.

6 . The sensor of claim 1 , wherein the instructions further cause the at least one processor to:

determine a severity level of the potential electrical hazard at the location; and

determine the alert from a plurality of alert options based upon the severity level, wherein the plurality of alert options include an audible alert outputted by the sensor and an alert message transmitted by the sensor to a computer device associated with the location.

7 . A sensor system for detecting hazards, the sensor system comprising:

at least one sensor;

at least one memory with instructions stored thereon; and

at least one processor in communication with the at least one memory, wherein the instructions, when executed by the at least one processor, cause the at least one processor to:

generate sensor profile data associated with a location proximate to the at least one sensor based upon sensor data generated by the at least one sensor and a plurality of other sensors associated with the location and in communication with the at least one sensor, the plurality of other sensors including an electric vehicle (EV) sensor for monitoring charging of an EV;

apply the sensor profile data to a sensor model profile associated with the location and stored in the at least one memory, wherein the sensor model profile includes a plurality of parameter levels for the location of the at least one sensor generated by a machine learning model;

identify a discrepancy between the sensor profile data and the sensor model profile;

determine a potential electrical hazard associated with charging of the EV at the location based upon the discrepancy between the sensor profile data and the sensor model profile; and

generate an alert based upon the potential electrical hazard at the location.

8 . The sensor system of claim 7 , wherein the plurality of other sensors are different from the at least one sensor, and wherein the instructions further cause the at least one processor to:

receive additional sensor data from the plurality of other sensors; and

generate the sensor profile data further based upon the additional sensor data.

9 . The sensor system of claim 7 , wherein the instructions further cause the at least one processor to:

receive an input indicating that normal conditions are present at the location;

generate initial sensor profile data based upon the location;

input the initial sensor profile data to the machine learning model;

receive the sensor model profile as an output from the machine learning model; and

store the sensor model profile in the at least one memory as being associated with the location.

10 . The sensor system of claim 7 , wherein the instructions further cause the at least one processor to:

receive another input indicating that the at least one sensor has been moved to a different location;

generate updated sensor profile data associated with the different location based upon updated sensor data generated by the at least one sensor at the different location;

input the updated sensor profile data to the machine learning model;

receive an updated sensor model profile as an output from the machine learning model, wherein the updated sensor model profile includes a plurality of updated parameter levels for the different location; and

store the updated sensor model profile in the at least one memory as being associated with the different location.

11 . The sensor system of claim 10 , wherein the instructions further cause the at least one processor to:

generate new updated sensor profile data associated with the different location of the at least one sensor based upon new updated sensor data generated by the at least one sensor;

apply the new updated sensor profile data to the updated sensor model profile;

identify a new discrepancy between the new updated sensor profile data and the updated sensor model profile;

determine a potential hazard at the different location based upon the new discrepancy between the new updated sensor profile data and the updated sensor model profile; and

generate a second alert, the second alert based upon the potential hazard at the different location.

12 . The sensor system of claim 7 , wherein the instructions further cause the at least one processor to:

determine a severity level of the potential electrical hazard at the location; and

determine the alert from a plurality of alert options based upon the severity level, wherein the plurality of alert options include an audible alert outputted by the at least one sensor and an alert message transmitted by the at least one sensor to a computer device associated with the location.

13 . A computer-implemented method for detecting hazards implemented by at least one processor in communication with at least one memory, the computer-implemented method comprising:

generating sensor profile data associated with a location proximate to a sensor based upon sensor data generated by the sensor and a plurality of other sensors associated with the location and in communication with the sensor, the plurality of other sensors including an electric vehicle (EV) sensor for monitoring charging of an EV;

applying the sensor profile data to a sensor model profile associated with the location and stored in the at least one memory, wherein the sensor model profile includes a plurality of parameter levels for the location proximate to the sensor generated by a machine learning model;

identifying a discrepancy between the sensor profile data and the sensor model profile;

determining a potential electrical hazard associated with charging of the EV at the location based upon the discrepancy between the sensor profile data and the sensor model profile; and

generating an alert based upon the potential electrical hazard at the location.

14 . The computer-implemented method of claim 13 , wherein the plurality of other sensors are different from the sensor, and the computer-implemented method further comprising comprises:

receiving additional sensor data from the plurality of other sensors; and

generating the sensor profile data further based upon the additional sensor data.

15 . The computer-implemented method of claim 13 , further comprising:

receiving an input indicating that normal conditions are present at the location;

generating initial sensor profile data based upon the location;

inputting the initial sensor profile data to the machine learning model;

receiving the sensor model profile as an output from the machine learning model; and

storing the sensor model profile in the at least one memory as being associated with the location.

16 . The computer-implemented method of claim 13 , further comprising:

receiving another input indicating that the sensor has been moved to a different location;

generating updated sensor profile data associated with the different location based upon updated sensor data generated by the sensor proximate to the different location;

inputting the updated sensor profile data to the machine learning model;

receiving an updated sensor model profile as an output from the machine learning model, wherein the updated sensor model profile includes a plurality of updated parameter levels for the different location; and

storing the updated sensor model profile in the at least one memory as being associated with the different location.

17 . The computer-implemented method of claim 16 , further comprising:

generating new updated sensor profile data associated with the different location proximate to the sensor based upon new updated sensor data generated by the sensor;

applying the new updated sensor profile data to the updated sensor model profile;

identifying a new discrepancy between the new updated sensor profile data and the updated sensor model profile;

determining a potential hazard at the different location based upon the new discrepancy between the new updated sensor profile data and the updated sensor model profile; and

generating a second alert, the second alert based upon the potential hazard at the different location.

18 . The computer-implemented method of claim 13 , further comprising:

determining a severity level of the potential hazard at the location; and

determining the alert from a plurality of alert options based upon the severity level, wherein the plurality of alert options include an audible alert outputted by the sensor and an alert message transmitted by the sensor to a computer device associated with the location.

Assignments (1)

Continuity (2)

References Cited (55)