公开(公告)号：US12014426B2

公开(公告)日：2024-06-18

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 ,  G06N20/00 ,  G06Q40/08 ,  G06V10/82 ,  G06V30/19 ,  G06V40/16 ,  H04N7/18 ,  G06Q30/0207

CPC classification number: G06Q40/08 ,  G06N3/04 ,  G06N3/08 ,  G06N20/00 ,  G06V10/82 ,  G06V30/19173 ,  G06V40/169 ,  H04N7/185 ,  G06Q30/0207

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.

公开(公告)号：US20240079117A1

公开(公告)日：2024-03-07

申请号：US18506880

申请日：2023-11-10

Applicant: State Farm Mutual Automobile Insurance Company

Inventor： Dingchao Zhang ,  Jeffrey S. Myers ,  Michael Bernico ,  Marigona Bokshi-Drotar ,  Edward W. Breitweiser ,  Peter Laube ,  Utku Pamuksuz

IPC: G16H30/40 ,  G06T7/00 ,  G06V40/16 ,  G16H15/00 ,  G16H50/50

CPC classification number: G16H30/40 ,  G06T7/0012 ,  G06V40/168 ,  G16H15/00 ,  G16H50/50 ,  A61B5/0533

Abstract: A method and system may use computer vision techniques and machine learning analysis to automatically identify a user's biometric characteristics. A user's client computing device may capture a video of the user. Feature data and movement data may be extracted from the video and applied to statistical models for determining several biometric characteristics. The determined biometric characteristic values may be used to identify individual health scores and the individual health scores may be combined to generate an overall health score and longevity metric. An indication of the user's biometric characteristics which may include the overall health score and longevity metric may be displayed on the user's client computing device.

公开(公告)号：US11640717B2

公开(公告)日：2023-05-02

申请号：US17829170

申请日：2022-05-31

Applicant: State Farm Mutual Automobile Insurance Company

Inventor： Yuntao Li ,  Dingchao Zhang ,  Jeffrey S. Myers

IPC: G06V20/59 ,  B60R25/30 ,  B60R25/25 ,  G08B21/22 ,  B60R25/102 ,  G06V40/16

Abstract: Systems and methods for using image analysis techniques to assess abnormal vehicle operating conditions are disclosed. According to aspects, a computing device may access and analyze image data depicting an individual(s) within a vehicle. Based on the depicted individuals(s) and optionally on other data, the computing device may determine that an abnormal condition exists. In response, the computing device may generate a notification and transmit the notification to an electronic device of an individual associated with the vehicle.

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.

公开(公告)号：US10275670B1

公开(公告)日：2019-04-30

申请号：US15914745

申请日：2018-03-07

Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

Inventor： Yuntao Li ,  Dingchao Zhang ,  Jeffrey S. Myers

IPC: G06K9/00 ,  B60R25/30 ,  B60R25/25 ,  G08B21/22 ,  B60R25/102

Abstract: Systems and methods for using image analysis techniques to assess abnormal vehicle operating conditions are disclosed. According to aspects, a computing device may access and analyze image data depicting an individual(s) within a vehicle. Based on the depicted individuals(s) and optionally on other data, the computing device may determine that an abnormal condition exists. In response, the computing device may generate a notification and transmit the notification to an electronic device of an individual associated with the vehicle.

公开(公告)号：US12271952B2

公开(公告)日：2025-04-08

申请号：US18237689

申请日：2023-08-24

Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

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

IPC: G06Q40/08 ,  G06N3/04 ,  G06N3/08 ,  G06N20/00 ,  G06V10/82 ,  G06V30/19 ,  G06V40/16 ,  H04N7/18 ,  G06Q30/0207

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.

公开(公告)号：US20230252578A1

公开(公告)日：2023-08-10

申请号：US18134373

申请日：2023-04-13

Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

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

IPC: G06Q40/08 ,  G06N3/08 ,  H04N7/18 ,  G06N3/04 ,  G06N20/00 ,  G06V10/82 ,  G06V30/19 ,  G06V40/16

CPC classification number: G06Q40/08 ,  G06N3/08 ,  H04N7/185 ,  G06N3/04 ,  G06N20/00 ,  G06V10/82 ,  G06V30/19173 ,  G06V40/169 ,  G06Q30/0207

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.

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.

公开(公告)号：US20220144193A1

公开(公告)日：2022-05-12

申请号：US17580396

申请日：2022-01-20

Applicant: State Farm Mutual Automobile Insurance Company

Inventor： Yuntao Li ,  Dingchao Zhang ,  Jeffrey S. Myers

IPC: B60R16/037 ,  B60R21/015 ,  G06V20/59 ,  B60K35/00 ,  B60N2/02 ,  G06V40/16

Abstract: Systems and methods for using image analysis techniques to facilitate adjustments to vehicle components are disclosed. According to aspects, a computing device may access and analyze image data depicting an individual(s) within a vehicle, and in particular determine a positioning of the individual(s) within the vehicle. Based on the positioning, the computing device may determine how to adjust a vehicle component(s) to its optimal configuration, and may facilitate adjustment of the vehicle component(s) accordingly.

公开(公告)号：US20210295441A1

公开(公告)日：2021-09-23

申请号：US15628391

申请日：2017-06-20

Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

Inventor： Christina P. Mullen ,  Jeffrey S. Myers ,  Andrew Karl Pulkstenis ,  Stephen Russell Prevatt ,  Robert T. Trefzger

IPC: G06Q40/08 ,  G05D1/02 ,  G08G1/052 ,  G08G1/056 ,  G08G1/00 ,  G01S7/41

Abstract: A computer-implemented method of determining an indication of whether a vehicle in a collision is a total loss. The method may include (1) receiving a first set of sensor data and telematics data associated with a first vehicle; (2) receiving a second set of sensor data and telematics data associated with a second vehicle; (3) determining a make, model, and age of the first vehicle; (4) determining a direction and an amount of a crash force exerted upon the first vehicle based upon the first and second sets of sensor data and telematics data; and (5) determining the indication of whether the first vehicle is a total loss based upon the make, model, and age of the first vehicle, and based upon the direction and amount of the crash force. By determining the indication of total loss based upon such data, time may be saved and resources may be conserved.