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    METHOD OF CONTROLLING FOR UNDESIRED FACTORS IN MACHINE LEARNING MODELS
    2.
    发明申请
    METHOD OF CONTROLLING FOR UNDESIRED FACTORS IN MACHINE LEARNING MODELS 有权

    公开(公告)号：US20220261918A1

    公开(公告)日：2022-08-18

    申请号：US17733465

    申请日：2022-04-29

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

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

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

    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.

    Method of controlling for undesired factors in machine learning models
    4.
    发明授权
    Method of controlling for undesired factors in machine learning models 有权

    公开(公告)号：US10769729B1

    公开(公告)日：2020-09-08

    申请号：US16352038

    申请日：2019-03-13

    Applicant: State Farm Mutual Automobile Insurance Company

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

    IPC: G06K9/00 ,  G06Q40/08 ,  G06K9/62 ,  H04N7/18 ,  G06N3/08 ,  G06K9/66 ,  G06K9/46

    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.

    Using images and voice recordings to facilitate underwriting life insurance
    5.
    发明授权
    Using images and voice recordings to facilitate underwriting life insurance 有权

    公开(公告)号：US10296982B1

    公开(公告)日：2019-05-21

    申请号：US15266118

    申请日：2016-09-15

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    Inventor： Michael L. Bernico ,  Jeffrey Myers

    IPC: G06F19/00 ,  G06Q40/08

    Abstract: A system and method for evaluating an insurance applicant as part of an underwriting process to determine one or more appropriate terms of life or other insurance coverage, such as premiums. A processing element employing a neural network is trained to correlate aspects of appearance and/or voice with personal and/or health-related characteristic. A database of images and/or voice recordings of individuals with known personal and/or health-related characteristics is provided for this purpose. The processing element is then provided with an image and/or voice recording of the insurance applicant. The image may be an otherwise non-diagnostic image, such as an ordinary “selfie.” The trained processing element analyzes the image of the insurance applicant, with their permission or affirmative consent, to determine the personal and/or health-related characteristic for the insurance applicant, and then, based upon that analysis, facilitates the underwriting process and/or suggests the one or more appropriate terms of insurance coverage.

    METHOD OF CONTROLLING FOR UNDESIRED FACTORS IN MACHINE LEARNING MODELS
    9.
    发明申请
    METHOD OF CONTROLLING FOR UNDESIRED FACTORS IN MACHINE LEARNING MODELS 有权

    公开(公告)号：US20230032355A1

    公开(公告)日：2023-02-02

    申请号：US17963397

    申请日：2022-10-11

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

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

    IPC: G06N3/04 ,  G06N3/08

    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 analysis 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.

    METHOD OF CONTROLLING FOR UNDESIRED FACTORS IN MACHINE LEARNING MODELS
    10.
    发明申请
    METHOD OF CONTROLLING FOR UNDESIRED FACTORS IN MACHINE LEARNING MODELS 有权

    公开(公告)号：US20220156844A1

    公开(公告)日：2022-05-19

    申请号：US17591633

    申请日：2022-02-03

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

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

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

    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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