State Farm Mutual Automobile Insurance Company

Ashish Sawhney

Scott T Christensen

Jaime Skaggs

Holly Lambert

Shane Tomlinson

Jody Ann Thoele

Daniel J Green

Jason Beckman

Amber L Wyatt

Eric Brown

Jose Gonzalez

Andrea Foster

Angela Glusick

Tracey Herring

Akilah Hugine

Neill Broadstone

Gustufus Phillip Theofanis

Chris Ray

Tim Husarik

Stephen Tinglestad

John Freymann

A system for instructing a user on repairing a vehicle configured to (i) receive, from the user, a request to repair a vehicle, including information about the vehicle; (ii) present, to the user via a user computer device, a user interface to allow the user to search for a repair facility to repair the vehicle; (iii) receive, from the user via the user interface, a selection of a repair facility; (iv) determine whether the selected repair facility is a select service location, where a select service location is a pre-authorized repair facility; and (v) if the selected repair facility is a select service location, transfer the information about the vehicle to a computer device associated with the selected repair facility.

Systems and methods for performing repairs to a vehicle

Systems and methods for building a vehicle data repository (VDR) are provided. In some embodiments, the VDR is constructed by adding standardized build sheets to it. The standardized build sheets may be constructed by selecting data from various data sources (e.g., original equipment manufacturer (OEM) databases, National Highway Traffic Safety Administration (NHTSA) databases, Highway Loss Data Institute (HLDI) databases, and/or Insurance Institute for Highway Safety (IIHS) databases). Furthermore, a common ontology may be created and applied to the various data sources to aide in the selection of data between the data sources to construct the standardized build sheets.

Systems and methods of building a consistent vehicle data repository

Systems and methods for determining a reparability of a vehicle are provided. In some embodiments, vehicle data is obtained, and a list of variables is generated from the obtained data. A machine learning algorithm may then be trained to generate a reparability metric by: (i) generating correlation metrics between the variables and costs to repair the vehicle, (ii) removing variables with correlation metrics below a threshold, and (iii) training the machine learning algorithm based upon unremoved variables.

Systems and methods of determining vehicle reparability

Growing community of inventors

Bloomington, IL, United States of America

Ashish Sawhney