USPTO Patent Grant for Structural Damage Assessment in Aerial Images

ChangeBridge: Patent Grants - Business Methods (G06Q)

Published March 24th, 2026

Detected March 25th, 2026

Summary

The USPTO has granted a patent to United Services Automobile Association (USAA) for a system and method to assess structural damage in occluded aerial images. The technology uses a generative model trained on simulated occluded images to clarify real-world occluded images for damage classification.

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What changed

The United States Patent and Trademark Office (USPTO) has issued patent US12586372B1 to United Services Automobile Association (USAA) for a novel system and method designed to assess structural damage in aerial images that are occluded. The patented technology utilizes a generative model trained on pairs of clear and simulated occluded aerial images. This automated training process allows the model to generate a clarified image from real occluded images, thereby revealing previously hidden structural details and enabling automated damage classification for properties.

This patent grant is primarily relevant for entities involved in property assessment, insurance claims processing, and disaster response. While not a regulatory mandate, the technology could influence industry standards and practices in how structural damage is evaluated, particularly in scenarios where visual obstruction is a factor. Compliance officers in the insurance sector should be aware of potential technological advancements that may impact claims handling and risk assessment methodologies.

Source document (simplified)

← USPTO Patent Grants

System and method for assessing structural damage in occluded aerial images

Grant US12586372B1 Kind: B1 Mar 24, 2026

Assignee

United Services Automobile Association (USAA)

Inventors

Yangqiu Hu, David Lamar Rogers, Ting Lu, Jess W. Gingrich

Abstract

A system and method for processing occluded aerial images of structures to assess structural damage is disclosed. The system employs a generative model that is trained on data created by adding occlusive features to clear aerial images. Both the clear images and their simulated occluded counterparts (image pairs) are inputted into the generative model as training data, and the model learns to recognize and discriminate patterns between the pairs. This process is fully automated and does not require manual labeling. Following training, the model is capable of receiving real (non-simulated) occluded images and generating a clarified image that can reveal structural details previously not apparent to automatically determine a damage classification for the property.