State Farm Mutual Automobile Insurance Company

Ronald R Duehr

Jeffrey David Hevrin

Shawn M Call

Eric Bellas

Gregory Dwayne Carter

Sara Elizabeth Collins

Rajiv C Shah

Lucas Allen

Taylor Griffin Smith

Sripriya Sundararaman

Jing Wan

Jeremy Cunningham

Brad Sliz

Shannon Morrison

Abigail A Scott

Douglas Albert Graff

Justin Devore

Shane Tomlinson

Gary Foreman

Jason Beckman

John Westhues

Carlee A Clymer

Stephen E Wilson

Yohan Santos

Bradley J Walder

Rajesh Panicker

Carrie A Read

Stephen Young

Denson Smith

Robin A Rose

Vincent M Hummel

Michael Brown

Chad Mychal Hirst

Pavan Kumar Bhagavatula

David A Pluimer

Tracey Leigh Knorr

Erin E Miller

Joshua T Monk

Aaron Ames

John G McConkey

Michael Cicilio Fresquez

Himanshu Chhita

Michele Wittman

Alexis Danielle Cates

A method includes receiving initial sensor data and additional sensor data; and generating combined sensor data. A computing device for includes one or more processors; and one or more memories coupled to the one or more processors, the one or more memories including non-transitory computer executable instructions stored therein that, when executed by the one or more processors, cause the one or more processors to: receive initial sensor data and additional sensor data; and generate combined sensor data. A computer readable storage medium includes non-transitory computer readable instructions stored thereon for generating data regarding a vehicle crash, wherein the instructions when executed on one or more processors cause the one or more processors to: receive initial sensor data and additional sensor data; and generate combined sensor data.

System and method for generating data regarding a vehicle crash

A computer-implemented method for detecting vehicle crashes is presented. The method may include detecting, at a first mobile computing device a condition corresponding to a potential crash of a vehicle. The method may also include operating a beacon receive mode in response to detecting the potential crash condition, and receiving data from a second mobile computing device operating in a beacon transmit mode, the data indicating that the second mobile computing device detected an actual crash condition of the vehicle. The method may further include confirming, based on the potential crash condition and the data received from the second mobile computing device, that the potential crash is an actual crash.

System and method for indicating whether a vehicle crash has occurred

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 computer-implemented method for generating an indication of whether a vehicle crash has occurred is presented. It may be determined whether data output by a base sensor(s) of a mobile computing device indicates that a vehicle crash condition has occurred. An additional sensor(s) of the mobile computing device may begin outputting additional sensor data when the data output by the base sensor(s) indicates that the vehicle crash condition has occurred. The additional sensor(s) may be caused to output the additional sensor data for an amount of time after the additional sensor(s) begins outputting the additional sensor data. An indication of whether a crash of a vehicle has occurred may be generated based on the additional sensor data output by the additional sensor(s) for the amount of time after the additional sensor(s) begins outputting the additional sensor data.

A computer-implemented method for determining features of a dataset that are indicative of anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) executing a time series decomposition algorithm on a portion of the features of the data to generate a first list of features, (3) implementing a plurality of traffic dispersion graphs to generate a second list of features, and (4) implementing an autoencoder and a random forest regressor to generate a third list of features.

Detection of anomalous computer behavior

A computer-implemented method for detecting anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) applying a first plurality of algorithms to determine features of the data which may contribute to anomalous behavior of the computers, and (3) applying a second plurality of algorithms to determine which computers are behaving anomalously based upon the features.

A method for generating data regarding a vehicle crash includes receiving initial sensor data, determining that the initial sensor data includes sensor data indicating a crash has occurred, receiving additional sensor data; and generating combined sensor data. A computing device for generating data regarding a vehicle crash includes a processor and a memory including instructions that when executed cause the processor to receive initial sensor data, determine that the initial sensor data includes sensor data indicating that a crash has occurred, receive additional sensor data; and generate combined sensor data. A computer readable storage medium includes non-transitory computer readable instructions stored thereon for generating data regarding a crash, wherein the instructions when executed cause a processor to receive initial sensor data; receive additional sensor data, determine that the initial sensor data includes sensor data indicating that a crash has occurred; and generate combined sensor data.

A computer-implemented method for generating vehicle crash data is presented. The method may include determining whether sensor data output of a sensor of a mobile computing device within a vehicle indicates that a crash of a vehicle has occurred. The method may further include displaying a notification prompting a user of the mobile computing device to confirm that the crash of the vehicle has occurred and receiving an indication from the user that the crash of the vehicle has occurred. Still further, the method may include transmitting the sensor data output to a remote computing device and receiving a communication from the remote computing device, wherein the communication prompts the user to take a post-crash action.

System and method for generating vehicle crash data

Methods, systems, apparatus, and non-transitory computer readable media are described for identifying users who are likely to have unauthorized access to secure data files in an organizational network. Various aspects may include presenting the identified users on a display for a system administrator and/or security analyst to resolve. For example, the display may include a graph data structure with users represented as nodes and connections between users represented as edges. Each connection may be a pair of users belonging to the same security group. Nodes of the graph data structure may be clustered to indicate that each of the users in the cluster belong to the same security group. Moreover, the users who are connected to multiple clusters may be identified as a potential risk of having unauthorized access to secure data files. The authorized access may then be remedied or taken away.

Method and system for identifying security risks using graph analysis

A computer-implemented method for generating vehicle crash data is presented. It may be determined whether a first mobile computing device has detected that a crash of a vehicle has occurred. It may also be determined whether a second mobile computing device(s) has detected that the crash has occurred. Sensor data output by at least one sensor of the first mobile computing device and/or the second mobile computing device(s) may be received when it is determined that the first mobile computing device and/or the second mobile computing device(s) has detected that the crash has occurred. Vehicle crash data may be generated based on such sensor data, and may include an initial confirmation that the crash has occurred, an indication of crash severity, an indication of a number of vehicle occupants, and/or an indication that the crash has not occurred.

A computer-implemented method for generating data regarding a vehicle crash is presented. Initial sensor data output by a sensor(s) of a mobile computing device may be received. It may be determined whether the initial sensor data includes sensor data indicating that a crash of a vehicle has occurred. Additional sensor data output by the sensor(s) of the mobile computing device may be received when it is determined that the initial sensor data includes sensor data indicating the crash has occurred. The additional sensor data may be received for a first amount of time after it is determined that the initial sensor data includes sensor data indicating the crash has occurred. Combined sensor data may be generated based on the initial sensor data and the additional sensor data, where the combined sensor data may include sensor data corresponding to time before the crash and corresponding to time after the crash.

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Normal, IL, United States of America

Ronald R Duehr