Automated Image Damage Assessment via Machine Learning

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Solution Overview

Problem

Conventional insurance claim processing for damaged property relies heavily on manual inspections by experts, which is costly and time-consuming, and existing image-based assessment systems still require technical expertise and manual intervention.

Innovation Solution

A computer-based method using machine learning algorithms for automated damage evaluation from image data, allowing non-technical users to capture images and perform assessments, including image alteration detection, damage classification, and predictive analysis for repair or replacement costs, significantly reducing human input and processing time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual inspection by experts is used to assess damage, then assessment accuracy and reliability are improved, but processing time and cost increase significantly

Engineering Contradiction:

Improvedamage assessment accuracyVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces the mechanical system of manual expert inspection with an automated image processing system using machine learning algorithms. The system captures images of damaged objects, processes them through trained algorithms to detect and classify damage, and generates assessments without human intervention, thereby eliminating the time loss associated with manual inspection while maintaining assessment reliability through algorithmic consistency

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service damage assessment where the object itself (through its images) provides the information needed for assessment. The machine learning model automatically extracts damage features from images without requiring expert interpretation, allowing the system to assess damage independently and eliminating the need for time-consuming manual evaluation by specialists

Inventive Principle:

Principle #25Self-service

2Measurement precision

If strict image capture protocols and detailed damage description protocols are required, then assessment quality is improved, but user time and complexity increase

Engineering Contradiction:

Improvedamage detection precisionVSAvoiduser operation simplicity

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system performs self-service by automatically capturing and processing images without requiring user adherence to strict protocols. The machine learning model is trained to extract relevant damage information directly from naturally captured images, eliminating the need for users to follow complex capture instructions or provide detailed descriptions, thereby maintaining precision while dramatically improving ease of operation

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary action by pre-training machine learning models on extensive damage data before deployment. This preliminary training enables the system to automatically recognize and extract damage features from diverse image conditions without requiring users to prepare images according to strict protocols, thus achieving high precision measurement while keeping the user interface simple

Inventive Principle:

Principle #10Preliminary action

3Reliability

If technical expertise is required to capture and process images, then assessment reliability is improved, but device complexity and training requirements increase

Engineering Contradiction:

Improveassessment reliabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces the need for technical expertise with an automated system that embeds the expertise within machine learning models. The complex image processing and damage assessment tasks are handled by pre-trained algorithms, eliminating the need for users to possess technical knowledge while maintaining assessment reliability through the consistency and accuracy of the automated processing pipeline

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces an intermediary layer of machine learning algorithms that mediate between the simple image capture interface and the complex damage assessment task. This intermediary automatically performs the technical processing required for reliable assessment, shielding users from complexity while ensuring consistent and accurate results through algorithmic processing

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12260536B2Automatic image based object damage assessment

Publication Date: 2025.03.25 EMERGENT NETWORK INTELLIGENCE LTD

AI summary

A computer-based method for automatically evaluating validity and extent of at least one damaged object from image data, comprising: (a) receiving image data comprising one or more images of at least one damaged object; (b) processing said one or more images using an image alteration detection algorithm to detect fraudulent manipulation of said one or more images by classifying at least one attribute of said one or more images; (c) removing any image comprising fraudulent manipulation from said one or more images; (d) processing said one or more images using at least one machine learning algorithm to identify at least one of: a damaged object in any one of said one or more images a damaged area of said damaged object, and an extent of damage of said at least one damaged area; and (e) generating a predictive analysis to repair and/or replace said at least one damaged object.