United Services Automobile Association (Usaa)

Daniel Diaz

Breanna Nicole Allerkamp

Matthew C Dahlman

Andrew Luke Enochs

Shawn D Low

Manfred Amann

Eric David Schroeder

Robert Wiseman Simpson

Donnette Moncrief Brown

Jess Witman Gingrich

Karen Marie Shackelford-George

Abbey Erin May

Jay Wesley Mullen

David Edward Garrison

Michael James Leach

Bradly Jay Billman

Reynaldo Medina, Iii

Arthur Quentin Smith

Oscar Guerra

Qunying Kou

Justin R Nash

A supply delivery system includes one or more processors and one or more memory devices storing instructions executable by the one or more processors to cause the one or more processors to instruct an aerial vehicle to travel within a geographical area, instruct an imaging device carried by the aerial vehicle to capture image data as the aerial vehicle travels through the geographical area, analyze the image data to identify an identification code on a physical target, instruct the aerial vehicle to dispense one or more items from the aerial vehicle at or near the physical target.

Systems and methods for targeted delivery of supplies during emergency events

To enable efficient processing of an insurance claim that is submitted by a client, the presently disclosed embodiments include systems and methods that enable an insurance computing system to autonomously assess the risk of fraud associated with a claim by generating a risk score. The risk score may provide an indication of a likelihood of fraud associated with the claim based on similar insurance claims in the same geographical area, client information indicative of a likelihood to commit fraud, and/or a type of the claim. The insurance computing system may compare the risk score with a threshold number, and if the threshold number is exceeded, the insurance computing system may prompt the client to contact a service representative to further process or validate the claim. Otherwise, if the threshold number is not exceeded, the insurance computing system may continue processing the claim autonomously.

Fraud identification system

A technique for assessing a vehicle condition is provided that determines whether damage has occurred to the vehicle using electrical property data acquired by one or more electrical property detection systems disposed on one or more surfaces of the vehicles. The electrical property detection system include a conductive polymer material that changes in response to chemical deformations and/or mechanical deformations in the conductive polymer material, which may result from objects and/or other vehicles damaging the conductive polymer material. Based on the electrical property data, a control system may generate a damage assessment output, such as an alert for a vehicle owner.

Systems and methods for assessing vehicle damage

An advanced lens system includes an advanced lens configured to be worn by an individual. The advanced lens system includes a camera configured to obtain one or more images of a property. The advanced lens system includes one or more processors communicatively coupled to the advanced lens and the camera, the one or more processors being configured to receive the one or more images of the property and process the one or more images to identify primary damage to the property. The one or more processors are also configured to predict, based on the primary damage and previous damage data stored in a database, secondary damage that is likely to be present with the primary damage. The one or more processors are further configured to provide an output indicative of the secondary damage that is likely to be present with the primary damage via a display of the advanced lens.

Advanced lens system for assessing property damage

A system and method for capturing and processing aerial images of structures to assess structural damage is disclosed. The system comprises an aerial system associated with an aerial vehicle, which is in contact with a ground system. The aerial system is used to capture images of selected locations associated with known structures and crop those images around the selected locations. The cropped images are then fed into a damage classifier that automatically classifies the degree of damage done to structures at the selected locations. The system and method may be deployed to quickly assess damage of structures in a disaster area.

System and method for assessing damage from aerial imagery

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San Antonio, TX, United States of America

Daniel Diaz