AI-Guided Vehicle Repair Estimation Using ML Damage Analysis
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Solution Overview
Problem
Current vehicle repair estimation methods are inefficient and labor-intensive, often resulting in inaccurate and time-consuming repair estimates.
Innovation Solution
A system utilizing machine learning models trained on historical images of damaged vehicles to automatically generate and refine vehicle repair estimates, integrating with a user interface that allows users to interactively add or edit estimates, providing relevance and severity values to prioritize repair lines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual vehicle repair estimation methods are used, then estimators can exercise judgment and adapt to complex cases, but the process is labor-intensive and time-consuming
Solution Approach 1:
The system enables self-service estimation by allowing estimators to capture images of damage and receive automated ML-generated repair line recommendations, reducing manual effort while maintaining accuracy through human review and adjustment of the AI-generated estimates
Solution Approach 2:
The patent replaces manual mechanical estimation processes with an automated ML-based system that analyzes images and generates repair line recommendations, significantly reducing the time required while maintaining reliability through human-in-the-loop validation
2Productivity
If automated ML-based estimation is used, then processing speed increases and labor is reduced, but the system requires training data and model development time
Solution Approach 1:
The system performs preliminary actions by pre-training ML models on historical estimation data before deployment, and by generating initial repair line recommendations automatically from images before human estimators review and adjust them, thus improving efficiency while managing complexity through advance preparation
3Measurement precision
If more detailed repair estimates are generated, then accuracy improves, but the time and labor required increase
Solution Approach 1:
The system performs self-service by automatically generating detailed repair line recommendations with associated costs from images, providing precise estimates without requiring manual line-by-line analysis, thus improving measurement precision while reducing time loss through automated processing
Data Source
AI summary
A computer-implemented method comprises: generating a user interface operable by a user to generate one or more vehicle repair estimate lines for repairing a damaged vehicle and/or request an automated review of the line(s); generating one or more first vehicle repair estimate lines, adding the one or more first vehicle repair estimate lines to a vehicle repair estimate data structure, and presenting a first view of the data structure in the user interface; obtaining images of the damaged vehicle, providing the images to one or more trained machine learning (ML) models, which provide first output comprising second vehicle repair estimate lines for the vehicle repair estimate, adding second vehicle repair estimate lines to the data structure, and presenting a second view of the data structure in the user interface; and generating a vehicle repair estimation document based on the vehicle repair estimate data structure.