State Farm Mutual Automobile Insurance Company

Matthew Eric Riley, Sr

J Lynn Wilson

Brian N Harvey

Joseph Robert Brannan

Ryan Michael Gross

David W Thurber

Nicholas R Baker

Brian Mark Fields

Benjamin F Bowne

Bryan T Wilkerson

Duane Lee Marzinzik

James M Freeman

Gregory Dwayne Carter

Vaidya Balasubramanian Pillai

Nick U Christopulos

Jufeng “James” Peng

Jason Chang

Benjamin Joel Tucker

Michael Aaron Myers

Darwin Gene Beachy

Scott T Christensen

Nicholas U Christopulos

Mark E Clauss

Richard J Lovings

Steven Joseph Balbach

Jufeng James Peng

Duane Lee Marzinik

James Peng

A computing device for generating on-demand products based upon real-time geolocation data is configured to receive and store at least one product definition associated with a respective product. Each product definition defines an availability of the product and includes a product availability location. The computing device is also configured to receive real-time location data from a user computing device, and periodically query a memory device with the real-time location data. The computing device detects that the user computing device has entered a first location by retrieving, in response to a successful query of the memory device, a first product definition associated with a first product having a product availability location including the first location. The computing device is also configured to generate a product identification message including at least a portion of the first product definition, and transmit the product identification message to the user computing device.

Systems and methods for generating on-demand products responsive to real-time geolocation and telematics

Described herein are methods and systems for generating and applying vehicle maintenance models to vehicle telematics data. A vehicle telematics analytics (VTA) computing device is configured to receive vehicle telematics data associated with operation of a subject vehicle, and apply the received vehicle telematics data to a vehicle maintenance model to determine whether the operation of the subject vehicle is better or worse than standard operation of like vehicles. The VTA computing device may adjust a maintenance cost and/or determine if a user of the vehicle is eligible for a reward based upon this determination. The VTA computing device is configured to transmit an itemized report to the user including the adjusted maintenance cost and/or the reward as well as one or more other vehicle costs.

Vehicle telematics systems and methods

Embodiments described herein, inter alia, receive telematics data collected over a period of time, wherein the telematics data is indicative of operation of a vehicle by a potential renter during the period of time; identify, upon analyzing the telematics data, driving behavior(s) of the renter during the period of time; determine, for each driving behavior, a corresponding state of an environment of the vehicle when the driving behavior occurred; determine renter eligibility value(s) for the renter based on the driving behavior(s) and the corresponding state of an environment of the vehicle; compare the renter eligibility value(s) to user preference value(s) of a profile of a vehicle owner, wherein the user preference value(s) define one or more criteria for vehicle renters with whom the vehicle can be shared; and cause an indication of the vehicle associated with the profile to be displayed only if the renter satisfies the criteria.

System and methods for determining rental eligibility based on contextual telematics data

A matching computer system for electronically generating, matching, and providing online user profiles, and determining a trust score for a user based upon at least social media data and insurance data is provided. The matching computer system may be configured to register users within the matching computer system, receive consent from the users to capture the social media data, and collect the social media data and the insurance data from each registered user. The matching computer system may also be configured to retrieve the social media data and the insurance data associated with each registered user. The matching computer system may be further configured to determine a trust score for each registered user based upon each respective social media data and each respective insurance data. Each trust score represents a level of trustworthiness of the user.

Systems and methods for electronically matching online user profiles

A data analytics computing device for generating refined risk models responsive to vehicle-based telematics data is configured to receive geolocation data and telematics data associated with a plurality of vehicles. The geolocation data is captured at a location sensor of a vehicle computing device and representative of a location of the vehicle, and the telematics data is captured at a plurality of sensors of at least one of the vehicle or the vehicle computing device and representative of movement of the vehicle. The plurality of vehicles is associated with a geographic area. The data analytics computing device is also configured to apply machine-learning techniques to the geolocation data and the telematics data to define standard driving behavior within the geographic area, and generate driving behavior risk models for the geographic area based upon the standard driving behavior.

Systems and methods for generating refined risk models responsive to vehicle-based telematics data

A matching computer system for electronically generating, matching, and providing online user profiles, and determining a sharing score between the online user profiles is provided. The matching computer system may be configured to generate online user profiles associated with users of the systems. The matching computer system may be also configured to calculate a base score based upon the generated online user profile. Each base score represents a level of trustworthiness of each respective user. The matching computer system may be further configured to determine a sharing score between users based upon the base scores. Each sharing score represents a level of matching between the online user profiles.

Embodiments described herein receive telematics data collected over a period of time, wherein the telematics data is indicative of operation of a vehicle by an owner of the vehicle during the period of time; analyze the telematics data to identify driving behavior(s) of the owner during the period of time; predict one or more user preference values of a vehicle-sharing platform profile of the owner based on the identified one or more driving behaviors, wherein the one or more user preference values define one or more criteria for vehicle renters with whom the first vehicle can be shared; apply the one or more criteria to a potential vehicle renter; and cause an indication of the first vehicle of the owner to be displayed via a mobile device of the potential renter only if the potential vehicle renter satisfies the one or more criteria.

System and methods for determining owner's preferences based on vehicle owners telematics data

In a method for applying penalties or incentives to a driver of a rented vehicle, an indication that the driver has agreed to terms for renting the vehicle from the vehicle owner is received, with the terms including the potential application of penalties or incentives to the driver based on driving behavior. Telematics data, indicative of operation of the rented vehicle by the driver during a period of time, is also received. By analyzing the telematics data, one or more driving behaviors of the driver during the time period is/are identified. One or more characteristics of the rented vehicle are also determined. One or more penalties or incentives are caused to be applied to the driver, based on the driving behavior(s) and the one or more characteristics of the rented vehicle.

Determining acceptable driving behavior based on vehicle specific characteristics

Embodiments described herein generate a first set of prompts, the first set of prompts configured to prompt a vehicle owner for a first set of answers used to learn preferred vehicle renter characteristics; receive the first set of answers; generate, based upon the first set of answers, a second set of prompts, the second set of prompts configured to prompt the owner for a second set of answers used to learn additional preferred vehicle renter characteristics; receive the second set of answers; predict user preference value(s) of a profile of the owner based upon the second set of answers, wherein the user preference value(s) define criteria for sharing a vehicle associated with the profile with vehicle renters who satisfy the criteria; apply the criteria to potential vehicle renters; and cause an indication of the vehicle to be displayed only to the potential vehicle renters who satisfy the criteria.

System and methods for predicting rental vehicle use preferences

In a method for applying penalties or incentives to a driver of a rented vehicle, an indication that the driver has agreed to terms for renting the vehicle from the vehicle owner is received, with the terms including the potential application of penalties or incentives to the driver based on driving behavior. Telematics data, indicative of operation of the rented vehicle by the driver during a period of time, is received. By analyzing the telematics data, one or more driving behaviors of the driver during the time period is/are identified. For each driving behavior, a corresponding state of an environment of the rented vehicle when the driving behavior occurred is determined. One or more penalties or incentives are caused to be applied to the driver, based on the driving behavior(s) and the corresponding state(s) of the environment of the rented vehicle.

Growing community of inventors

Heyworth, IL, United States of America

Matthew Eric Riley, Sr