State Farm Mutual Automobile Insurance Company

Andrew Joseph Zeglin

Kristopher Keith Gaudin

Aaron C Williams

Joseph P Harr

Craig Benjamin Cope

Melinda Teresa Magerkurth

Jessica Lynn Shull

David Turrentine

Edward W Breitweiser

Daryoush Hakimi-Boushehri

Ellakate LeFebre

Wayne Walk

A system and computer-implemented method detect and act upon deactivated vehicle components. The system and method include receiving measurements data associated with driving activity. The measurements data includes an indication that at least one feature of an Advanced Driver Assistance System (ADAS) of a vehicle has been deactivated for a driving activity. The system and method may include receiving historical driving data including a history of at least one driving activity aided by activation of the alert from the ADAS feature. The system and method may compare the measurements data to the historical driving data, determine a likelihood level that the feature of the ADAS would have provided the alert had the feature been activated based upon the comparing, and set, based at least upon the determining, at least a portion of an operator profile associated with an operator of the vehicle with the likelihood level.

Automatically tracking driving activity

A system and computer-implemented method detect and act upon operator reliance to vehicle alerts. The system and method include receiving user profile data of an operator that includes a baseline of at least one driving activity aided by activation of an alert from a feature of an Advanced Driver Assistance System (ADAS). The system and method may include receiving historical ADAS alert frequency data including a history of at least one driving activity aided by activation of the alert from the ADAS feature. The system and method may compare the user profile data with the historical ADAS alert frequency data, determine a reliance level based upon the comparing, and set at least a portion of an operator profile associated with the operator with the reliance level. As a result, a risk averse driver, and/or proper responsiveness to vehicle alerts may be rewarded with insurance-cost savings, such as increased discounts.

Evaluating operator reliance on vehicle alerts

A data aggregation and processing engine optimizes underwriting decisions in the life insurance industry by using social media data to reduce the level of underwriting required. Social media data enables underwriting thresholds based on risk assessments and credibility correlations within publicly available social media data sets with options for opting into more detailed analysis of private social data. Activity monitors further refine appropriate underwriting actions together with correlations with provided biographical data.

Social media data aggregation to optimize underwriting

Systems and methods are provided for identifying vehicle care that may reduce risk in fields not directly associated with a vehicle itself. One or more electronic sources may provide an indication of care (e.g., service, maintenance, or part configuration) of a vehicle by a user, and the vehicle care may be determined to be associated with a lowered risk for an entity (e.g., a home or business) operated by the same user in another field not associated with operation of the vehicle itself. In response, a user-specific reward may be generated and transmitted to the user, to further incentivize vehicle care and other risk averse behavior in the other field.

Vehicle risk aversion and reward system

Growing community of inventors

Normal, IL, United States of America

Andrew Joseph Zeglin