Image-Based Total Loss Prediction Using Viewpoint Analysis
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Solution Overview
Problem
Insurance companies rely on manual processes for property damage estimation, which are inefficient and prone to inconsistencies due to human involvement.
Innovation Solution
An intelligent prediction system utilizing neural networks for image analysis to automate total loss determination by processing uploaded images, determining point of view and angle of view, and generating an automated predicted total loss based on total loss scores and processed images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used for property damage estimation, then human specialists can review and assess damage, but the process is inefficient and prone to inconsistencies
Solution Approach 1:
The patent replaces the mechanical system of manual human review with an automated image analysis system using neural networks and computer vision algorithms. The system automatically processes property damage images, extracts features, and generates loss predictions, eliminating the need for manual inspection while maintaining or improving consistency through standardized algorithmic evaluation.
Solution Approach 2:
The system enables self-service by allowing the image analysis model to autonomously perform damage assessment without human intervention. The neural network automatically processes uploaded images, determines point of view and angle of view, and generates total loss predictions independently, freeing human specialists from routine evaluation tasks.
2Productivity
If automated image analysis is implemented, then processing efficiency is improved, but system complexity increases due to neural network models
Solution Approach 1:
The patent segments the complex image analysis task into distinct functional modules: image preprocessing, point of view determination, angle of view determination, feature extraction, and loss prediction. Each module is handled by specialized neural network components, allowing the system to manage complexity through modular architecture while maintaining high processing speed.
Solution Approach 2:
The system employs a multi-functional analysis platform that handles various types of property damage images (vehicles, buildings, personal property) using the same core neural network architecture. The universal model can adapt to different property types and damage scenarios, reducing the need for multiple specialized systems and thereby managing complexity.
3Measurement precision
If point of view and angle of view determination is added to image analysis, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The system performs point of view and angle of view determination as preliminary steps before the main damage assessment. By pre-processing the images to establish camera perspective and orientation, the neural network can more accurately interpret damage features in subsequent processing stages, improving overall prediction accuracy without significantly increasing total processing time.
Solution Approach 2:
The patent implements continuous processing where point of view and angle of view determination occurs simultaneously with feature extraction rather than as separate sequential steps. The neural network processes multiple aspects of the image in parallel, maintaining continuous useful action and minimizing additional processing time while enhancing prediction precision.
Data Source
AI summary
Intelligent prediction systems and methods of use to analyze one or more uploaded images to generate one or more processed images via a data analytics module, determine by a neural network model a point of view and angle of view determination for each processed image, retrieve a claim identifier and associated total loss score, and generate an automated predicted total loss determination based on the total loss score and the one or more processed images from the data analytics module.