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    Manual control re-engagement in an autonomous vehicle
    1.
    发明授权
    Manual control re-engagement in an autonomous vehicle 有权

    公开(公告)号：US12130622B2

    公开(公告)日：2024-10-29

    申请号：US18206414

    申请日：2023-06-06

    申请人： STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    发明人： Brian Mark Fields ,  Leo Nelson Chan ,  Kristopher Keith Gaudin ,  Aaron Williams

    IPC分类号： G05D1/00 ,  B60W50/00

    CPC分类号： G05D1/0061 ,  G05D1/0088 ,  B60W2050/007 ,  B60W2050/0072

    摘要： Vehicles may have the capability to navigate according to various levels of autonomous capabilities, the vehicle having a different set of autonomous competencies at each level. In certain situations, the vehicle may shift from one level of autonomous capability to another. The shift may require more or less driving responsibility from a human operator. Sensors inside the vehicle collect human operator parameters to determine an alertness level of the human operator. An alertness level is determined based on the human operator parameters and other data including historical data or human operator-specific data. Notifications are presented to the user based on the determined alertness level that are more or less intrusive based on the alertness level of the human operator and on the urgency of an impending change to autonomous capabilities. Notifications may be tailored to specific human operators based on human operator preference and historical performance.

    ERROR CHECKING FOR CODE
    2.
    发明公开
    ERROR CHECKING FOR CODE 审中-公开

    公开(公告)号：US20240311271A1

    公开(公告)日：2024-09-19

    申请号：US18462071

    申请日：2023-09-06

    申请人： STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    发明人： Brian Mark Fields ,  Nathan L. Tofte ,  Joseph Robert Brannan ,  Vicki King ,  Justin Davis

    IPC分类号： G06F11/36 ,  G06F8/65 ,  G09B5/06 ,  H04L51/02

    CPC分类号： G06F11/3604 ,  G06F8/65 ,  G06F11/3688 ,  G09B5/06 ,  H04L51/02

    摘要： Apparatuses, systems and methods are provided for checking code for errors. The apparatuses, systems and methods may send a target code and a prompt for code checking to a machine learning (ML) chatbot to cause the ML chatbot to check the target code for errors. The apparatuses, systems and methods may determine whether there is an error in the target code based at least partially on a response from the ML chatbot. The apparatuses, systems and methods may, responsive to determining that there is an error in the target code, determine, via an interaction with the ML chatbot, a solution to fix the error. The apparatuses, systems and methods may analyze the solution to determine a number of at least one of (i) a set of steps or (ii) a set of interactions required by the solution. The apparatuses, systems and methods may, responsive to determining that the number exceeds a predetermined threshold, fix the error by implementing the solution with respect to the target code.

    Using machine learning techniques to calculate damage of vehicles involved in an accident
    7.
    发明授权
    Using machine learning techniques to calculate damage of vehicles involved in an accident 有权

    公开(公告)号：US11989759B2

    公开(公告)日：2024-05-21

    申请号：US17826250

    申请日：2022-05-27

    申请人： STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    发明人： Brian Mark Fields ,  Lee Marvin John Assam

    IPC分类号： G06Q30/02 ,  G06Q40/08

    CPC分类号： G06Q30/0278 ,  G06Q40/08

    摘要： Disclosed systems and methods incorporate machine learning to assess damage to vehicles. An example method includes: accessing image data representing one or more digital images of damage to a vehicle; selecting, using one or more processors, an applicable machine learning algorithm from a plurality of different trained machine learning algorithms, wherein the plurality of different machine learning algorithms are trained for respective different combinations of one or more of vehicle make, vehicle model, vehicle year, or area of damage; processing the image data, with the applicable machine learning algorithm using one or more processors, to determine assessed damage for the vehicle; accessing actual damage information for the vehicle; determining, using one or more processors, differences between the actual damage and the assessed damage; and iterating, using one or more processors, the applicable machine learning algorithm based on the differences to improve its damage assessment accuracy.