Allstate Insurance Company

Arity International Limited

Eric D Huls

Thomas Michael Warden

Daniel Kraft

Nathan M Bryer

Margaret A Brinkmann

Joseph Alan Kleinhenz

Mark V Slusar

Grady Irey

Jennifer A Brandmaier

William Loo

Dana Ferguson

Philip Peter Ramirez

Matthew James Manzella

Caryl M Styrsky

Christine L Bischoff

Meg G Walters

William Polisson

Ralph Adam Benjamin Tyner

David MacInnis

Jeffrey Fitzgerald

Soren K Lundsgaard

Ryan Dunn

Michael A LaVigne

Frank Kazas

Alexander Doyle

Cristina I Gheorghe

Christopher Joseph DeCleene

Carolyn Parker

Richard D Bischoff

Nicholas J Reed

Timothy S Lenahan

David Chudzicki

Pradraic Sheerin

A driving analysis server may be configured to receive vehicle location data and/or operation data from one or more vehicle systems, identify driving trips and/or driving patterns based on the vehicle data, determine risk assessment values corresponding to the driving trips and driving patterns, and calculate driver scores based on the analyzed driving trip and driving pattern data. Destination locations may be identified for a vehicle's driving trips, and information relating to the destination locations may be retrieved and analyzed to determine risk factors and risk assessment values associated with driving to and from the destination, as well as parking at the destination. Specific driving trip types or purposes may be identified, and driving scores may be calculated based on the vehicle location and time data, including the risk factors, risk assessment values, and the determined trip types or purposes.

Driving trip and pattern analysis

A system and method are disclosed to promote good driving among users. A user-customizable good driving (GD) wish list may be created, populated, modified, and/or used to promote good driving. The GD wish list may be used in conjunction with a vehicle telematics unit (VTU) to monitor, process, detect, and/or record good driving events, and communicate and/or generate positive feedback for the user. In addition to positive feedback, good driving (GD) points may be accumulated based on driving behavior. The accumulated GD points may be used in conjunction with a product and/or service offering, such as by an insurance company. Furthermore, in some examples, the system encourages users to promote good driving behavior by pledging themselves to a challenging GD wish list that will earn them more GD points and the associated benefits.

Vehicle telematics system to promote good driving behavior using positive feedback and award points

A driving analysis server may be configured to receive vehicle operation data from vehicle sensors and telematics devices of a first vehicle, and may use the data to identify a potentially high-risk or unsafe driving behavior by the first vehicle. The driving analysis server also may retrieve corresponding vehicle operation data from one or more other vehicles, and may compare the potentially high-risk or unsafe driving behavior of the first vehicle to corresponding driving behaviors in the other vehicles. A driver score for the first vehicle may be calculated or adjusted based on the comparison of the driving behavior in the first vehicle to the corresponding driving behaviors in the other vehicles.

Traffic based driving analysis

Aspects of the disclosure relate to multicomputer processing of vehicle operational data from telematics devices and other sources with centralized event control. An event control computing platform may receive vehicle operational data from a telematics device associated with a user. Subsequently, the event control computing platform may identify, based on the received data, whether at least one criterion associated with the user has been satisfied. If the received data indicates that the at least one criterion associated with the user has been satisfied, then the event control computing platform may generate a command configured to cause a change to a subunit of user data and then may transmit the generated command to a subunit provisioning server.

Multicomputer processing of user data with centralized event control

Methods, computer-readable media, systems and apparatuses for determining and implementing dynamic usage-based insurance policies are presented. A cost per day and a cost per mile associated with the dynamic usage-based insurance policy may be determined. The cost per mile may be computed independently for each of a plurality of road segments comprising a trip based on a time of day that the vehicle traveled each road segment, a road type associated with each road segment. The cost per mile of each road segment of the driving trip may further include whether or not hard braking or hard cornering events were encountered during a specified cumulative driving distance that may include, at least a portion, of a plurality of driving trips. The cost per mile for each of the plurality of driving trips may be adjusted once a threshold distance has been traveled by the vehicle based on the number of hard braking events and hard cornering events occurred during that distance.

Dynamic usage-based policies

At least a system for providing telematics data associated with a vehicle being driven by a driver is described. The vehicular telematics data may be obtained by tracking the movements of a wireless communications device of a driver of the vehicle. The telematics data may provide, among other things, speed, acceleration, deceleration, times of operation, duration of operation, mileage driven per day, and day of the week the vehicle has been used. At least a system for determining risk behavior of a driver is also described. While a vehicle is being driven, data is obtained related to the position and movement of a wireless communications device. The data may indicate the type of behavior exhibited by the driver while the vehicle is being driven.

Telematics based on handset movement within a moving vehicle

A driving analysis server may be configured to receive vehicle operation data from vehicle sensors, and may use the data to identify a potentially high-risk or unsafe driving event by the vehicle. The driving analysis server also may receive corresponding image data, video, or object proximity data from the vehicle or one or more other data sources, and may use the image, video, or proximity data to analyze the potentially high-risk or unsafe driving event. A driver score for the vehicle or driver may be calculated or adjusted based on the analysis of the data and the determination of one or more causes of the driving event.

Driving event data analysis

At least a method for determining risk behavior of a driver is described. While a vehicle is being driven, data is obtained related to the position and movement of a wireless communications device. The data may indicate the type of behavior exhibited by the driver while the vehicle is being driven.

Risk behavior detection methods based on tracking handset movement within a moving vehicle

One or more devices in a data analysis computing system may be configured to receive and analyze movement data, and determine driving trips associated driving data based on the received movement data. Movement data may be collected by one or more mobile devices, such as smartphones, tablet computers, and the like. One or more driving trips may be identified based on the movement data collected by the mobile devices, including acceleration data, speed data, and rotation rate data. After identifying a driving trip, the previously collected movement data and/or additional driving data may be analyzed to determine additional driving trip data, such as the driver, vehicle, and/or additional driving factors.

Driving analysis based on movement data

A computer-assisted method for providing re-quotations for insurance coverage may include receiving a list of insurance leads corresponding to individuals who received a previous quotation for insurance coverage but did not purchase the insurance coverage and identifying a difference between the previous quotation and a new quotation. This difference may include an increase in offered insurance coverage and/or a reduction in cost. A computing device may calculate a probability for each of the individuals on the list using a regression model based, at least in part, on the identified difference. In some cases, the regression model may be associated with individual states. In other cases, the regression model may correspond to a plurality of states. The regression model may output a probability that a resident of a particular state will purchase insurance in response to a re-quotation for insurance coverage, where individuals may then be ranked based on the probability.

Prioritization of insurance requotations

Disclosed herein is a traveling-based insurance rating system comprising a telematics device associated with a vehicle having one or more sensors arranged therein, a mobile device associated with a passenger of the vehicle, and a server computer comprising hardware, including a processor and memory. The server computer may receive driving data of a driver of the vehicle from the one or more sensors via the telematics device. The server computer may then identify one or more values of the driving data to be within one or more predetermined ranges and classify the driving data as a trip of the passenger. Based on the driving data from the trip, the server computer may determine one or more driving behaviors of the driver, calculate an insurance rating for the passenger of the vehicle based on the one or more driving behaviors of the driver, and transmit the insurance rating to the mobile device.

Traveling-based insurance ratings

Growing community of inventors

Chicago, IL, United States of America

Eric D Huls