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    AUTOMATIC BUILDING ASSESSMENT
    29.
    发明申请
    AUTOMATIC BUILDING ASSESSMENT 有权
    自动建筑评估

    公开(公告)号：US20140270492A1

    公开(公告)日：2014-09-18

    申请号：US13839634

    申请日：2013-03-15

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    Inventor： Nicholas U. Christopulos ,  Jonathan D. Maurer ,  Nathan L. Tofte ,  Roger D. Schmidgall ,  James M. Freeman

    IPC: G06K9/00

    CPC classification number: G06K9/00637 ,  G06K9/00536 ,  G06K9/00704 ,  G06K9/6267 ,  G06K9/6268 ,  G06K9/6279 ,  G06K9/628 ,  G06Q40/08 ,  G06Q50/16

    Abstract: Disclosed systems and methods automatically assess buildings and structures. A device may receive one or more images of a structure, such as a building or portion of the building, and then label and extract relevant data. The device may then train a system to automatically assess other data describing similar buildings or structures based on the labeled and extracted data. After training, the device may then automatically assess new data, and the assessment results may be sent directly to a client or to an agent for review and/or processing.

    Abstract translation: 公开的系统和方法自动评估建筑物和结构。 设备可以接收结构的一个或多个图像，例如建筑物或建筑物的一部分，然后标记和提取相关数据。 该装置然后可以训练系统以基于标记和提取的数据来自动评估描述类似建筑物或结构的其他数据。 培训后，设备可以自动评估新数据，并将评估结果直接发送给客户或代理进行审查和/或处理。

    Automobile Monitoring Systems and Methods for Detecting Damage and Other Conditions
    30.
    发明申请
    Automobile Monitoring Systems and Methods for Detecting Damage and Other Conditions 有权

    公开(公告)号：US20220284517A1

    公开(公告)日：2022-09-08

    申请号：US17752702

    申请日：2022-05-24

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    Inventor： Gregory L. Hayward ,  Meghan Sims Goldfarb ,  Nicholas U. Christopulos ,  Erik Donahue

    IPC: G06Q40/08 ,  G06N3/08 ,  G06V30/194 ,  G06V20/00

    Abstract: A method of determining damage to property includes inputting historical data into a machine learning model to identify an insured type, features, and/or characteristics. The method may include identifying a peril, repair and/or replacement cost of the vehicle by analyzing a digital image from a device of an insured, the digital image depicting damage to the vehicle, The method may include inputting the digital image into the trained machine learning model to identify a type, feature, and/or characteristic of the vehicle, and may include identifying a peril, repair, and/or replacement cost associated with the vehicle. A method may include receiving and/or retrieving free-form text associated with an insurance claim and/or a vehicle, identifying at least one key word composing the free-form text, and determining based on the at least one key word a cause of loss and/or peril that caused damage to the vehicle.

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