State Farm Mutual Automobile Insurance Company

Jeffrey S Myers

Michael L Bernico

Dingchao Zhang

Kenneth Jason Sanchez

Yuntao Li

Edward W Breitweiser

Marigona Bokshi-Drotar

Utku Pamuksuz

Peter Laube

Christina P Mullen

Stephen R Prevatt

Robert T Trefzger

Andrew Karl Pulkstenis

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

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.

Biometric characteristic application using audio/video analysis

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.

Technologies for using image analysis to facilitate adjustments of vehicle components

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.

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.

Using vehicle data, geographic area type data, and vehicle collision data in determining an indication of whether a vehicle in a vehicle collision is a total loss

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.

Image analysis technologies for assessing safety of vehicle operation

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.

Using images and voice recordings to facilitate underwriting life insurance

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.

Method and system for identifying biometric characteristics using machine learning techniques

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.

Growing community of inventors

Normal, IL, United States of America

Jeffrey S Myers