Image Interpretation System for Automated Damage Assessment
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Solution Overview
Problem
The insurance claim settlement process faces challenges in automating the analysis of images for damage assessment due to the complexity of mapping images to desired answers, requiring extensive and expensive training data, and lacking reasoning components in AI systems, which hinders accurate negotiation and interpretation.
Innovation Solution
A modular image interpretation system using separate training of image segmentation and visual inference models, decoupling image perception from repair calculation, and employing a repair-relevant property vector to generate structured repair reports, allowing for efficient data usage and interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive training data is used to train AI systems for damage assessment, then measurement precision is improved, but loss of substance increases due to expensive training data requirements
Solution Approach 1:
The patent segments the image analysis task into multiple specialized models: image segmentation models that identify damaged regions, visual inference models that determine repair relevance, and property vector generation models that extract specific damage characteristics. Each model is trained on targeted data subsets rather than requiring comprehensive training data for all functions, reducing overall data requirements while maintaining precision.
Solution Approach 2:
The patent introduces intermediate representations (property vectors, segmentation masks, inferred attributes) that bridge raw images and final repair estimates. These intermediaries structure the information flow, allowing each model component to operate with smaller, more focused datasets rather than requiring all models to process complete training sets from scratch.
2Productivity
If AI systems are designed to perform comprehensive damage analysis, then productivity is improved, but device complexity increases due to lack of reasoning components
Solution Approach 1:
The system divides the complex damage analysis task into sequential processing stages: image segmentation to identify damaged regions, visual inference to determine repair relevance, property vector generation to extract characteristics, and repair estimation to calculate costs. This segmentation allows each component to be simpler and more specialized, reducing overall system complexity while maintaining high productivity through automated processing.
Solution Approach 2:
The patent introduces intermediate structures (property vectors, segmentation masks, inferred attributes) that serve as reasoning intermediaries between image input and repair estimation output. These intermediaries provide structured representations that facilitate logical processing and interpretation, enabling the system to perform comprehensive analysis without requiring overly complex monolithic architecture.
3Reliability
If image analysis is performed manually to ensure interpretability, then reliability is improved, but loss of time increases due to extensive data exchange and processing
Solution Approach 1:
The system enables automated self-service processing where the image analysis models independently perform damage assessment, property extraction, and repair estimation without requiring manual intervention at each step. The models generate structured outputs (property vectors, segmentation masks) that are automatically processed through the reasoning pipeline, maintaining interpretability while eliminating time-consuming manual data exchange and processing steps.
Solution Approach 2:
The structured intermediate representations (property vectors with damaged object properties, segmentation masks with spatial information) serve as interpretable intermediaries that preserve reasoning transparency. These intermediaries allow the automated system to maintain the interpretability traditionally associated with manual analysis by providing clear, structured evidence chains that can be reviewed and understood, while achieving faster processing through automation.
Data Source
AI summary
In one embodiment, a method includes accessing an image of a damaged object. The method further includes determining, using a plurality of image segmentation models, a plurality of objects in the image. The method further includes determining, using a plurality of visual inference models and the determined plurality of objects from the image segmentation models, a repair-relevant property vector for the damaged object in the image. The repair-relevant property vector includes a plurality of damaged object properties. The method further includes generating a repair report using the repair-relevant property vector and a price catalogue. The repair report includes an indication of the damaged object and a price associated with the repair or replacement of the damaged object. The method further includes providing the generated report for display on an electronic display device.