Allstate Insurance Company

Jean Utke

Cory Campagna

Yusuf Mansour

Dylan Stark

Tia Miceli

Charles F Cox

Meg G Walters

Lakshmi Prabha Nattamai Sekar

Xiaolan Wu

Lizette Lemus Gonzalez

Amber Parsons

Emily Pavey

Deborah-Anna Reznek

Ryan Rosenberg

Adam Sturt

Weizhou Wang

Jeremy Werner

Stephanie Wong

Adam Austin

Stephen Cole

Chris Jonas

Michael Bradley Henry

Ann Rebecca Wei

Patrick Figliozzi

Anna Varentsova

Keith Matera

Intelligent prediction systems and methods of use to analyze one or more uploaded images to generate one or more processed images via a data analytics module, determine by a neural network model a point of view and angle of view determination for each processed image, retrieve a claim identifier and associated total loss score, and generate an automated predicted total loss determination based on the total loss score and the one or more processed images from the data analytics module.

Systems and methods for predicted total loss based on image analysis and point of view determinations

A system may receive video of a damaged vehicle, perform image analysis of the video to determine one or more frames of the video that include a damaged portion of the vehicle, further analyze the one or more frames of the video that include a damaged portion of the vehicle to determine a damaged cluster of parts of the vehicle, determine whether the damaged cluster of parts should be repaired or replaced, map the damaged cluster of parts to one or more parts in a vehicle-specific database of parts, and generate, based on the mapping, a list of parts for repair or replacement.

Analyzing images and videos of damaged vehicles to determine damaged vehicle parts and vehicle asymmetries

Aspects of the disclosure relate to using computer vision methods for asset evaluation. A computing platform may receive historical images of a plurality of properties and corresponding historical inspection results. Using the historical images and historical inspection results, the computing platform may train a roof waiver model (which may be a computer vision model) to output inspection prediction information directly from an image. The computing platform may receive a new image corresponding to a particular residential property. Using the roof waiver model, the computing platform may analyze the new image to output of a likelihood of passing inspection. The computing platform may send, to a user device and based on the likelihood of passing inspection, inspection information indicating whether or not a physical inspection should be performed and directing the user device to display the inspection information, which may cause the user device to display the inspection information.

Computer vision methods for loss prediction and asset evaluation based on aerial images

Aspects of the disclosure relate to using computer vision methods to forecast damage. A computing platform may receive historical images comprising aerial images of residential properties and historical loss data corresponding to the residential properties. Using the historical images and the historical loss data, the computing platform may train a computer vision model, which may configure the computer vision model to output loss prediction information directly from an image. The computing platform may receive a new image corresponding to a particular residential property, and may analyze the new image, using the computer vision model, which may directly result in a likelihood of damage score. Based on the likelihood of damage score, the computing platform may send likelihood of damage information and one or more commands directing a user device to display the likelihood of damage information, which may cause the user device to display the likelihood of damage information.

Intelligent prediction systems and methods of use to train a neural network model to analyze images of property damage to detect and predict property damage of a property, the neural network model during training configured to (1) switch between one or more synthetic images comprising pixel-based masked annotations of damaged property from a synthetic engine and one or more real images comprising bounding box annotations of damaged property from a real database, and (2) freeze inactive class training to prevent learning on one or more inactive classes comprising one or more pre-determined missing annotated labels in the one or more synthetic images and/or the one or more real images.

Systems and methods for automated property damage estimations and detection based on image analysis and neural network training

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Lisle, IL, United States of America

Jean Utke