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    Method of controlling for undesired factors in machine learning models
    52.
    发明授权
    Method of controlling for undesired factors in machine learning models 有权

    公开(公告)号：US10909453B1

    公开(公告)日：2021-02-02

    申请号：US15383567

    申请日：2016-12-19

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    Inventor： Jeffrey S. Myers ,  Kenneth J. Sanchez ,  Michael L. Bernico

    IPC: G06N3/08 ,  G06Q40/08 ,  G06N20/00

    Abstract: A method of training and using a machine learning model that controls for consideration of undesired factors which might otherwise be considered by the trained model during its subsequent analyses of new data. For example, the model may be a neural network trained on a set of training images to evaluate an insurance applicant based upon an image or audio data of the insurance applicant as part of an underwriting process to determine an appropriate life or health insurance premium. The model is trained to probabilistically correlate an aspect of the applicant's appearance with a personal and/or health-related characteristic. Any undesired factors, such as age, sex, ethnicity, and/or race, are identified for exclusion. The trained model receives the image (e.g., a “selfie”) of the insurance applicant, analyzes the image without considering the identified undesired factors, and suggests the appropriate insurance premium based only on the remaining desired factors.

    System and method for automatically modifying brake light intensity
    56.
    发明授权
    System and method for automatically modifying brake light intensity 有权

    公开(公告)号：US10207635B1

    公开(公告)日：2019-02-19

    申请号：US15872668

    申请日：2018-01-16

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    Inventor： Kenneth J. Sanchez ,  Aaron Scott Chan

    IPC: B60Q1/44 ,  B60Q1/46 ,  B60Q1/50 ,  B60Q9/00

    Abstract: Systems and methods for detecting distracted drivers and reducing risks posed by the distracted drivers are disclosed. According to certain aspects, an electronic device may capture and analyze image data that depicts an operator of a vehicle in various levels of distraction. The electronic device may determine, based on the analysis, whether the operator is distracted, and determine an additional vehicle that may be located in front of the vehicle. The electronic device may generate and transmit a command to an additional electronic device that, upon execution, causes brake lights of the additional vehicle to modify in intensity.

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