Automated Property Damage Restoration Estimation via Image Analysis

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Solution Overview

Problem

Current property damage restoration and repair processes are inefficient, with lengthy turn-around times and resource exhaustion due to manual claims processing and human bias, as well as vulnerabilities to fraudulent claims.

Innovation Solution

A system that uses digital image processing and machine learning to programmatically generate property damage restoration estimates by analyzing digital images and associated metadata, including fraud detection models to verify image authenticity and predict repair costs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual claims processing is used, then human judgment and flexibility are maintained, but turn-around time increases and resource exhaustion occurs

Engineering Contradiction:

Improveturn-around timeVSAvoidprocessing complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical processing with an automated computer vision system that uses machine learning models to analyze damage images, extract features, and generate repair estimates automatically, eliminating human labor bottlenecks while maintaining processing quality

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by allowing customers to upload damage photos through a mobile interface and receive automated repair estimates without human intervention, with the AI model independently completing fraud detection, damage assessment, and cost estimation

Inventive Principle:

Principle #25Self-service

2Reliability

If manual processing is used, then flexibility in judgment is maintained, but human bias and fraud vulnerabilities increase

Engineering Contradiction:

Improvefraud detection accuracyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary fraud detection by analyzing image metadata, EXIF data, and digital fingerprints before damage assessment begins, identifying potentially fraudulent images early in the workflow to prevent resource waste on invalid claims

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system incorporates feedback loops where AI model predictions are continuously refined based on historical claim data and verification outcomes, with fraud detection results feeding back into the overall assessment process to improve accuracy over time

Inventive Principle:

Principle #23Feedback

3Measurement precision

If detailed manual assessment is performed, then estimation accuracy is improved, but resource usage and time consumption increase

Engineering Contradiction:

Improvedamage estimation accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments the damage assessment process into distinct modular stages: image quality validation, fraud detection, damage feature extraction, part identification, and cost calculation, allowing parallel processing and optimized resource allocation at each stage

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system applies partial action by focusing computational resources only on relevant damage areas identified through initial image analysis, using selective attention mechanisms to assess only the damaged portions rather than processing entire images uniformly

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11514526B1Systems and methods for property damage restoration predictions based upon processed digital images

Publication Date: 2022.11.29 LIBERTY MUTUAL INSURANCE CO

AI summary

Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting property damage restoration estimates. In one embodiment, a computing entity or apparatus is configured to receive, from a client device, a property damage restoration estimate request comprising one or more digital image files; retrieve policy data associated with a user of the client device, the policy data comprising user identification properties and policy properties; programmatically generate, by fraud detection/prediction circuitry and based on the one or more digital image files, a first predictive value, wherein the first predictive value represents a likelihood that at least one of the digital image files was fraudulently altered; upon identifying that the first predictive value does not exceed a fraud threshold, programmatically generate, by property restoration estimate prediction circuitry and based on the one or more digital image files, a second predictive value, wherein the second predictive value represents a property damage restoration estimate, wherein the second predictive value is based at least on the property properties contained in the policy data and the one or more digital image files; and substantially instantaneously transmit a property damage restoration estimate response comprising the property damage restoration estimate to the client device.