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公开(公告)号：US10282789B1
公开(公告)日：2019-05-07
申请号：US15383659
申请日：2016-12-19
Inventor： Jeffrey S. Myers , Kenneth J. Sanchez , Michael L. Bernico
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 analyzes 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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公开(公告)号：US12014423B1
公开(公告)日：2024-06-18
申请号：US15628367
申请日：2017-06-20
Inventor： Christina P. Mullen , Jeffrey S. Myers , Andrew Karl Pulkstenis , Stephen Russell Prevatt , Robert T. Trefzger
CPC classification number: G06Q40/08
Abstract: A computer-implemented method of determining an indication of whether a vehicle in a crash is a total loss. The method may include (1) receiving (i) image data, (ii) sensor data, and/or (iii) telematics or other data indicative of a direction of a crash force; (2) determining a type of geographic area in which the crash occurred; (3) determining a make, a model, and/or a year of the vehicle; and (4) determining the indication of whether the vehicle is a total loss based upon (i) (a) the image data, (b) the sensor data, and/or (c) the data indicative of the direction of the crash force, (ii) the type of geographic area, and (iii) the make, the model, and/or the year of the vehicle. By determining the indication of whether the vehicle is a total loss based upon such data and/or factors, time may be saved and resources may be conserved.
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33.发明公开公开(公告)号：US20240010146A1
公开(公告)日：2024-01-11
申请号：US18471416
申请日：2023-09-21
Inventor： Yuntao Li , Dingchao Zhang , Jeffrey S. Myers
IPC: B60R16/037 , B60R21/015 , B60N2/02 , B60N2/56 , G06T7/70
CPC classification number: B60R16/037 , B60R21/01538 , B60N2/0244 , B60N2/56 , G06T7/70 , B60R2021/01218
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.
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公开(公告)号：US11862326B1
公开(公告)日：2024-01-02
申请号：US17081812
申请日：2020-10-27
Inventor： Dingchao Zhang , Jeffrey S. Myers , Michael Bernico , Marigona Bokshi-Drotar , Edward W. Breitweiser , Peter Laube , Utku Pamuksuz
IPC: G06K9/00 , G16H30/40 , G06T7/00 , G16H50/50 , G16H15/00 , G06V40/16 , A61B5/16 , A61B5/021 , G16H50/30 , A61B5/01 , A61B5/024 , A61B5/0533
CPC classification number: G16H30/40 , G06T7/0012 , G06V40/168 , G16H15/00 , G16H50/50 , A61B5/01 , A61B5/021 , A61B5/024 , A61B5/0533 , A61B5/165 , G06T2200/24 , G06T2207/10016 , G06T2207/20081 , G06T2207/30004 , G06T2207/30201 , G16H50/30
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.
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公开(公告)号：US20230401647A1
公开(公告)日：2023-12-14
申请号：US18237689
申请日：2023-08-24
Inventor： Jeffrey S. Myers , Kenneth J. Sanchez , Michael L. Bernico
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.
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Patent Agency Ranking
公开(公告)号：US11828949B1
公开(公告)日：2023-11-28
申请号：US16363554
申请日：2019-03-25
Inventor： Michael L. Bernico , Jeffrey S. Myers
IPC: G06Q40/08 , G02B27/09 , F21V9/30 , F21K9/61 , F21K9/64 , F21S41/16 , F21S41/14 , F21S41/39 , F21S41/24 , F21S41/20 , F21S45/47 , H01S5/00 , F21Y115/10 , F21Y115/30 , F21Y101/00
CPC classification number: G02B27/0927 , F21K9/61 , F21K9/64 , F21S41/14 , F21S41/16 , F21S41/24 , F21S41/285 , F21S41/39 , F21V9/30 , G02B27/0994 , G06Q40/08 , F21S45/47 , F21Y2101/00 , F21Y2115/10 , F21Y2115/30 , H01S5/0064
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.
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公开(公告)号：US11676217B2
公开(公告)日：2023-06-13
申请号：US17591633
申请日：2022-02-03
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 , 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 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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公开(公告)号：US20230177848A1
公开(公告)日：2023-06-08
申请号：US18162223
申请日：2023-01-31
Inventor： Dingchao Zhang , Yuntao Li , Jeffrey S. Myers
CPC classification number: G06V20/597 , B60Q9/00 , G06V40/20 , G06V40/165 , G08B3/10 , G08B5/36
Abstract: Systems and methods for using image analysis techniques to assess unsafe driving conditions by a vehicle operator are discloses. According to aspects, a computing device may access and analyze image data depicting the vehicle operator. In analyzing the image, the computing device may measure certain visible metrics as depicted in the image data and compare the metrics to corresponding threshold values, and may accordingly determine whether the vehicle operator is exhibiting an unsafe driving condition. The computing device may generate and present alerts that indicate any determined unsafe driving condition.
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39.发明申请公开(公告)号：US20230060732A1
公开(公告)日：2023-03-02
申请号：US17983273
申请日：2022-11-08
Inventor： Dingchao Zhang , Michael Bernico , Peter Laube , Utku Pamuksuz , Jeffrey S. Myers , Marigona Bokshi-Drotar , Edward W. Breitweiser
Abstract: A method and system may use machine learning analysis of audio data to automatically identify a user's biometric characteristics. A user's client computing device may capture audio of the user. Feature data may be extracted from the audio 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.
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公开(公告)号：US20230032355A1
公开(公告)日：2023-02-02
申请号：US17963397
申请日：2022-10-11
Inventor： Jeffrey S. Myers , Kenneth J. Sanchez , Michael L. Bernico
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.
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