Patents by Inventor Michael L. Bernico
Michael L. Bernico 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: 20240027780Abstract: 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.Type: ApplicationFiled: September 29, 2023Publication date: January 25, 2024Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Michael L. Bernico, Jeffrey S. Myers
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Publication number: 20230401647Abstract: 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: August 24, 2023Publication date: December 14, 2023Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Jeffrey S. Myers, Kenneth J. Sanchez, Michael L. Bernico
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Patent number: 11828949Abstract: 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.Type: GrantFiled: March 25, 2019Date of Patent: November 28, 2023Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Michael L. Bernico, Jeffrey S. Myers
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Publication number: 20230360143Abstract: 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.Type: ApplicationFiled: July 21, 2023Publication date: November 9, 2023Applicant: State Farm Mutual Automobile Insurance CompanyInventors: Michael L. Bernico, Jeffrey S. Myers
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Patent number: 11769213Abstract: 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: April 29, 2022Date of Patent: September 26, 2023Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Jeffrey S. Myers, Kenneth J. Sanchez, Michael L. Bernico
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Publication number: 20230252578Abstract: 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 13, 2023Publication date: August 10, 2023Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Jeffrey S. Myers, Kenneth J. Sanchez, Michael L. Bernico
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Patent number: 11676217Abstract: 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: February 3, 2022Date of Patent: June 13, 2023Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Jeffrey S. Myers, Kenneth J. Sanchez, Michael L. Bernico
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Publication number: 20230032355Abstract: 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.Type: ApplicationFiled: October 11, 2022Publication date: February 2, 2023Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Jeffrey S. Myers, Kenneth J. Sanchez, Michael L. Bernico
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Patent number: 11501133Abstract: 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.Type: GrantFiled: June 4, 2020Date of Patent: November 15, 2022Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANYInventors: Jeffrey S. Myers, Kenneth J. Sanchez, Michael L. Bernico
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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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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: 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: 10825095Abstract: 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.Type: GrantFiled: September 15, 2016Date of Patent: November 3, 2020Assignee: State Farm Mutual Automobile Insurance CompanyInventors: Michael L. Bernico, Jeffrey Myers
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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: 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: 10296982Abstract: 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.Type: GrantFiled: September 15, 2016Date of Patent: May 21, 2019Assignee: State Farm Mutual Automobile Insurance CompanyInventors: Michael L. Bernico, Jeffrey Myers
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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