Patents by Inventor Jeffrey S. Myers
Jeffrey S. Myers has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20220292852Abstract: 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.Type: ApplicationFiled: May 31, 2022Publication date: September 15, 2022Inventors: Yuntao Li, Dingchao Zhang, Jeffrey S. Myers
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Patent number: 11436846Abstract: 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.Type: GrantFiled: September 3, 2020Date of Patent: September 6, 2022Assignee: State Farm Mutual Automobile Insurance CompanyInventors: Yuntao Li, Dingchao Zhang, Jeffrey S. Myers
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Publication number: 20220261918Abstract: 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.Type: ApplicationFiled: April 29, 2022Publication date: August 18, 2022Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Jeffrey S. Myers, Kenneth J. Sanchez, Michael L. Bernico
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Patent number: 11348183Abstract: 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.Type: GrantFiled: June 5, 2020Date of Patent: May 31, 2022Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Jeffrey S. Myers, Kenneth J. Sanchez, Michael L. Bernico
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Publication number: 20220156844Abstract: 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.Type: ApplicationFiled: February 3, 2022Publication date: May 19, 2022Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Jeffrey S. Myers, Kenneth J. Sanchez, Michael L. Bernico
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Publication number: 20220144193Abstract: 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.Type: ApplicationFiled: January 20, 2022Publication date: May 12, 2022Inventors: Yuntao Li, Dingchao Zhang, Jeffrey S. Myers
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Patent number: 11315191Abstract: 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.Type: GrantFiled: December 19, 2019Date of Patent: April 26, 2022Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Jeffrey S. Myers, Kenneth J. Sanchez, Michael L. Bernico
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Patent number: 11254270Abstract: 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.Type: GrantFiled: May 2, 2018Date of Patent: February 22, 2022Assignee: State Farm Mutual Automobile Insurance CompanyInventors: Yuntao Li, Dingchao Zhang, Jeffrey S. Myers
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Publication number: 20210295441Abstract: 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.Type: ApplicationFiled: June 20, 2017Publication date: September 23, 2021Inventors: Christina P. Mullen, Jeffrey S. Myers, Andrew Karl Pulkstenis, Stephen Russell Prevatt, Robert T. Trefzger
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Patent number: 10909453Abstract: 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.Type: GrantFiled: December 19, 2016Date of Patent: February 2, 2021Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Jeffrey S. Myers, Kenneth J. Sanchez, Michael L. Bernico
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Patent number: 10825564Abstract: 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.Type: GrantFiled: December 11, 2017Date of Patent: November 3, 2020Assignee: State Farm Mutual Automobile Insurance CompanyInventors: Dingchao Zhang, Michael Bernico, Peter Laube, Utku Pamuksuz, Jeffrey S. Myers, Marigona Bokshi-Drotar, Edward W. Breitweiser
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Patent number: 10824852Abstract: 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.Type: GrantFiled: October 29, 2019Date of Patent: November 3, 2020Assignee: State Farm Mutual Automobile Insurance CompanyInventors: Dingchao Zhang, Michael Bernico, Peter Laube, Utku Pamuksuz, Jeffrey S. Myers, Marigona Bokshi-Drotar, Edward W. Breitweiser
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Patent number: 10783386Abstract: 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.Type: GrantFiled: February 12, 2019Date of Patent: September 22, 2020Assignee: State Farm Mutual Automobile Insurance CompanyInventors: Yuntao Li, Dingchao Zhang, Jeffrey S. Myers
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Patent number: 10776644Abstract: 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.Type: GrantFiled: March 7, 2018Date of Patent: September 15, 2020Assignee: State Farm Mutual Automobile Insurance CompanyInventors: Dingchao Zhang, Yuntao Li, Jeffrey S. Myers
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Patent number: 10769729Abstract: 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.Type: GrantFiled: March 13, 2019Date of Patent: September 8, 2020Assignee: State Farm Mutual Automobile Insurance CompanyInventors: Jeffrey S. Myers, Kenneth J. Sanchez, Michael L. Bernico
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Patent number: 10769518Abstract: 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.Type: GrantFiled: December 19, 2016Date of Patent: September 8, 2020Assignee: State Farm Mutual Automobile Insurance CompanyInventors: Jeffrey S. Myers, Kenneth J. Sanchez, Michael L. Bernico
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Patent number: 10503970Abstract: 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.Type: GrantFiled: December 11, 2017Date of Patent: December 10, 2019Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Dingchao Zhang, Michael Bernico, Peter Laube, Utku Pamuksuz, Jeffrey S. Myers, Marigona Bokshi-Drotar, Edward W. Breitweiser
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Patent number: 10282789Abstract: 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.Type: GrantFiled: December 19, 2016Date of Patent: May 7, 2019Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Jeffrey S. Myers, Kenneth J. Sanchez, Michael L. Bernico
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Patent number: 10275670Abstract: 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.Type: GrantFiled: March 7, 2018Date of Patent: April 30, 2019Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Yuntao Li, Dingchao Zhang, Jeffrey S. Myers
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Patent number: 9934021Abstract: An information handling system, includes a processor to execute an application and an update system to determine to provide an update to the application. The update system includes an identity context engine to determine an identity context for the information handling system and a system context engine to determine a system context for the information handling system. The update system determines whether to retrieve an update manifest for the update based upon the identity context, the system context, and a manifest policy, determines whether to download the update based upon the identity context, the system context, and a download policy, and determines whether to install update based upon the identity context, the system context, and an installation policy.Type: GrantFiled: March 11, 2015Date of Patent: April 3, 2018Assignee: DELL PRODUCTS, LPInventors: Alberto Nieves, Daniel L. Hamlin, Jeffrey S. Myers