Image-Based Total Loss Prediction for Faster Property Claims

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Solution Overview

Problem

Insurance companies face inefficiencies in generating total loss estimates for property damage, relying on manual processes that are time-consuming and resource-intensive, particularly in handling large volumes of insurance claims.

Innovation Solution

An intelligent prediction system utilizing image analysis and machine learning algorithms to automate the total loss determination process by analyzing uploaded images, matching property types, and generating automated predictions based on total loss scores and property matches.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual processes are used for total loss estimates, then accuracy can be maintained through human review, but time consumption and resource intensity increase significantly

Engineering Contradiction:

Improveaccuracy of total loss estimateVSAvoidtime for claim processing

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces the mechanical human review process with an automated image analysis system using machine learning algorithms. The system processes property damage images through trained models that automatically generate total loss estimates, eliminating the need for manual human review while maintaining estimation accuracy through sophisticated computer vision techniques.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent creates a digital copy of the human expert's decision-making process through trained machine learning models. These models are trained on historical claim data and expert assessments to replicate human judgment patterns, enabling automated systems to produce estimates that mirror human expert accuracy without the time and resource constraints.

Inventive Principle:

Principle #26Copying

2Reliability

If manual processes are used for total loss estimates, then complex judgment can be applied, but resource consumption and costs increase

Engineering Contradiction:

Improvequality of assessment judgmentVSAvoidresource consumption

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The patent substitutes human specialists and adjusters with an automated image analysis system. The system uses machine learning models trained on extensive datasets to perform complex assessment judgments automatically, eliminating the need for human resource consumption while maintaining or improving assessment quality through consistent, data-driven decision-making.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent enables the system to perform assessments autonomously without human intervention. The machine learning models self-evaluate property damage images, automatically generate estimates, and make determination decisions based on trained patterns, making the system self-sufficient in performing complex judgment tasks that previously required human expertise.

Inventive Principle:

Principle #25Self-service

3Productivity

If automated image analysis is implemented, then processing speed and efficiency improve, but system complexity increases

Engineering Contradiction:

Improveclaim processing throughputVSAvoidcomplexity of analysis system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent divides the complex image analysis task into distinct processing stages: image preprocessing, feature extraction, damage detection, severity assessment, and estimate generation. Each stage is handled by specialized machine learning components that process specific aspects of the claim, allowing the system to manage complexity through modular architecture while maintaining high processing throughput.

Inventive Principle:

Principle #1Segmentation

4Extent of automation

If automated predictions are generated, then manual intervention is minimized, but verification accuracy must be maintained

Engineering Contradiction:

Improvelevel of automated determinationVSAvoidaccuracy of loss determination

Core Design Contradiction:

Extent of automationVSMeasurement precision

Solution Approach 1:

The patent implements feedback mechanisms where automated estimates are validated against historical data patterns, and results are continuously refined based on performance metrics. The system incorporates verification steps that cross-check automated determinations with established criteria and allows for human review of edge cases, ensuring accuracy is maintained while maximizing automation.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12469083B1Systems and methods for predicted total loss determinations based on image analysis

Publication Date: 2025.11.11 ALLSTATE INSURANCE COMPANY

AI summary

Intelligent prediction systems and methods of use to analyze one or more uploaded and labeled images to generate one or more processed images via a data analytics module, determine an identified property type from the processed images, generate a match between the identified property type and a reported property type associated with a claim identifier and a total loss score, and generate an automated predicted total loss determination based on the total loss score, the one or more processed images from the data analytics module, and the match.