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    Sensing peripheral heuristic evidence, reinforcement, and engagement system
    111.
    发明授权
    Sensing peripheral heuristic evidence, reinforcement, and engagement system 有权

    公开(公告)号：US11887461B2

    公开(公告)日：2024-01-30

    申请号：US17368771

    申请日：2021-07-06

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    Inventor： Aaron Williams ,  Joseph Robert Brannan ,  Christopher N. Kawakita ,  Dana C. Hunt

    IPC: G08B21/04 ,  G16H80/00 ,  G06N20/00

    CPC classification number: G08B21/0484 ,  G06N20/00 ,  G08B21/0423 ,  G08B21/0469 ,  G08B21/0476 ,  G16H80/00

    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.

    TELEMATICS SERVICE DETECTION AND ACTION MESSAGING BASED ON MACHINE LEARNING FOR ASSISTING CAR SHARING PLATFORM
    118.
    发明申请
    TELEMATICS SERVICE DETECTION AND ACTION MESSAGING BASED ON MACHINE LEARNING FOR ASSISTING CAR SHARING PLATFORM 有权

    公开(公告)号：US20230120749A1

    公开(公告)日：2023-04-20

    申请号：US18080737

    申请日：2022-12-13

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    Inventor： Joseph Robert Brannan ,  Ryan Gross

    IPC: G06Q30/0208 ,  G06N20/00 ,  H04L51/046 ,  G08G1/00 ,  G06Q50/30 ,  G06Q10/20 ,  H04L51/224

    Abstract: A method includes training artificial neural network models; analyzing first data to determine vehicle imperatives; and generating a respective action value by analyzing actions using the of trained artificial neural network models. A non-transitory computer readable medium containing program instructions that when executed, cause a computer system to: train artificial neural network models; analyze first data to determine vehicle imperatives; and generate a respective action value by analyzing actions using the of trained artificial neural network models. A computing system includes: a processor; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: train artificial neural network models; analyze first data to determine vehicle imperatives; and generate a respective action value by analyzing actions using the of trained artificial neural network models.

    Systems and methods for generating mobility insurance products using ride-sharing telematics data
    119.
    发明授权
    Systems and methods for generating mobility insurance products using ride-sharing telematics data 有权

    公开(公告)号：US11599947B1

    公开(公告)日：2023-03-07

    申请号：US16780507

    申请日：2020-02-03

    Applicant: State Farm Mutual Automobile Insurance Company

    Inventor： Ryan Michael Gross ,  Joseph Robert Brannan ,  Brian N. Harvey

    IPC: G06Q40/00 ,  G06Q40/08

    Abstract: A personalized insurance (“PI”) computing device for determining an optimal insurance product for a driver operating a vehicle for a transportation network company (“TNC”) during a period of increased demand includes at least one processor in communication with at least one memory. The processor is configured to: (i) receive, from a TNC, data indicating increased demand for transportation services, (ii) retrieve driver data that includes the driver history, (iii) generate an optimal pricing model for the driver based upon the increased demand and the driver data, (iv) execute the model to determine an optimal insurance product having characteristics reflecting at least one risk factor associated with the increased demand for transportation services and a risk profile determined from analyzing the driver data, and (v) transmit an offer to the driver to provide transportation services at an increased earnings rate and with the determined optimal insurance product.

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