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    RISK EVALUATION BASED ON VEHICLE OPERATOR BEHAVIOR
    41.
    发明申请
    RISK EVALUATION BASED ON VEHICLE OPERATOR BEHAVIOR 有权
    基于车辆操作者行为的风险评估

    公开(公告)号：US20140278569A1

    公开(公告)日：2014-09-18

    申请号：US13897650

    申请日：2013-05-20

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    Inventor： Kenneth J. Sanchez ,  Aaron S. Chan ,  Matthew R. Baker ,  Matthew Zettinger ,  John A. Nepomuceno ,  Brian Fields

    IPC: G06Q40/08

    CPC classification number: G06Q40/08 ,  G06Q40/00

    Abstract: A method for ascertaining the risk associated with the driver of a vehicle utilizes three-dimensional (3D) motion sensing data. A server gathers motion sensing data from one or more motion sensing modules and clusters the motion sensing data into movement categories. The server then assigns an indication of risk to at least some of the movement categories and combines the motion sensing data from a plurality of movement categories to generate a collective measure of risk associated with the driver of the vehicle.

    Abstract translation: 用于确定与车辆驾驶员相关联的风险的方法利用三维（3D）运动感测数据。 服务器从一个或多个运动感测模块收集运动感测数据，并将运动感测数据聚类成移动类别。 然后，服务器向至少一些运动类别分配风险指示，并组合来自多个运动类别的运动感测数据以产生与车辆驾驶员相关联的风险的集体测量。

    Method of controlling for undesired factors in machine learning models
    48.
    发明授权
    Method of controlling for undesired factors in machine learning models 有权

    公开(公告)号：US11315191B1

    公开(公告)日：2022-04-26

    申请号：US16720665

    申请日：2019-12-19

    Applicant: State Farm Mutual Automobile Insurance Company

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

    IPC: G06K9/62 ,  G06Q40/08 ,  G06N3/08 ,  H04N7/18 ,  G06V10/44 ,  G06V10/75 ,  G06V30/194

    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.

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