State Farm Mutual Automobile Insurance Company

John Westhues

John Dillard

Leann Dionesotes

David Ruby

Douglas Albert Graff

Ronald R Duehr

Justin Devore

Shane Tomlinson

Gary Foreman

Jason Beckman

Carlee A Clymer

Stephen E Wilson

Bradley J Walder

Yohan Santos

Joshua T Monk

Aaron Ames

John G McConkey

Denson Smith

Michael Cicilio Fresquez

Himanshu Chhita

Vincent M Hummel

Michele Wittman

Alexis Danielle Cates

Stephen Young

Chad Mychal Hirst

Rajesh Panicker

Carrie A Read

David A Pluimer

Michael Brown

Pavan Kumar Bhagavatula

Tracey Leigh Knorr

Robin A Rose

Erin E Miller

Dee Engel

Dan Boser

Dustin Meyer

Techniques relating to clustering team data and providing the resulting cluster information as a service. The cluster information provided as a service can be efficiently incorporated into tools and services of utility for the teams from which the team data is gathered.

Team data clustering as a service

A method for providing smart auto rental estimatics to a user includes training a machine learning model to predict repair information, receiving a report, analyzing the report to identify a coverage, determining a vendor, receiving a chosen vendor, determining a branch, receiving a chosen branch, analyzing the report and a volume data using the trained machine learning model to determine loss information, calculating out-of-pockets, and transmitting the out-of-pockets to the user.

Smart estimatics methods and systems

A method of determining an injury segment includes receiving a loss report, analyzing the loss report using a trained model to determine a severity of an injury, determining, based on the severity of the injury, an injury segment, and storing, via a processor, an indication of the injury segment. A computer system includes a processor configured to receive a loss report, analyze the loss report using a trained model to determine the severity of an injury, determine an injury segment, and store an indication of the injury segment. A non-transitory computer readable medium containing program instructions that when executed cause a computer to receive a loss report, analyze the loss report using a trained model to determine the severity of an injury, determine an injury segment, and store an indication of the injury segment.

Methods and systems for injury segment determination

A method of identifying a vehicle total loss claim includes retrieving a plurality of historical vehicle records, labeling the records as repaired or total loss, calculating mean cost values, training a regression model, optimizing a probability threshold, analyzing a plurality of inputs to generate a prediction, and transmitting the prediction. A computing system includes a transceiver; a processor; and a memory storing instructions that, when executed by the processor, cause the computing system to receive answers, transmit the answers, receive a prediction, when the prediction is repairable, generate a repair suggestion, and when the prediction is total loss, generate a settlement offer. A non-transitory computer readable medium containing program instructions that when executed, cause a computer to receive answers, transmit the answers, receive a prediction, when the prediction is repairable, generate a repair suggestion, and when prediction is total loss, generate a settlement offer.

Method and system for early identification and settlement of total loss claims

A method of determining an injury segment includes training a machine learning model, receiving a loss report, analyzing the loss report using the trained model to determine a severity of an injury, determining, based on the severity of the injury, an injury segment, and storing, via a processor, an indication of the injury segment. A computer system includes a processor configured to train a machine learning model, receive a loss report, analyze the loss report using the trained model to determine the severity of an injury, determine an injury segment, and store an indication of the injury segment. A non-transitory computer readable medium containing program instructions that when executed cause a computer to train a machine learning model, receive a loss report, analyze the loss report using the trained model to determine the severity of an injury, determine an injury segment, and store an indication of the injury segment.

Systems and methods are disclosed with respect to providing seamless customer experience transactions, in particular, linking disparate systems to facilitate a lower friction customer experience. An exemplary embodiment includes receiving recorded data from one or more connected devices at a geographic location; analyzing the recorded data, wherein analyzing the recorded data includes determining that an collision has occurred involving one or more vehicles; generating a transaction including the data indicative of the collision based upon the analysis; and transmitting the transaction to at least one other participant in the distributed ledger network.

Methods and systems for providing seamless customer experience transactions

A method of assigning and/or routing an auto claim to an appropriate claim handling tier to mitigate delay may include training a machine learning model using historical claim data to determine a severity corresponding to an injury claim, receiving a loss report corresponding to an auto accident, analyzing the loss report using the trained machine learning model to determine the severity of at least one injury corresponding to the loss report, determining an injury segment, and storing the indication of the injury segment in association with the loss report.

Methods and systems for smart claim routing and smart claim assignment

Growing community of inventors

Normal, IL, United States of America

John Westhues