Intelligent Vehicle Repair Estimation Using Image Processing
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Solution Overview
Problem
Current computer-assisted vehicle repair estimation systems rely heavily on human judgment, leading to inaccuracies, omissions, and inconsistencies, and are limited by the speed of human input, resulting in suboptimal estimates.
Innovation Solution
An intelligent vehicle repair estimation system that uses a combination of front-end image processing and back-end hybrid estimate completion, leveraging machine learning and statistical analysis to automatically determine necessary parts and labor operations based on image attributes, jurisdictional requirements, and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of parts and labor operations by insurance adjuster is used, then contextual accuracy and compliance with jurisdictional regulations can be achieved through human judgment, but processing speed is limited by human ability and human error leads to omissions and inconsistencies
Solution Approach 1:
The patent replaces the mechanical system of manual human judgment with an automated image processing system that uses computer vision and machine learning algorithms to analyze vehicle damage images, identify damaged parts, and generate repair estimates automatically, thereby eliminating human speed limitations while maintaining or improving accuracy through consistent application of trained models
Solution Approach 2:
The system enables self-service estimation by automatically processing damage images and generating complete repair estimates without requiring human adjuster intervention for basic estimation tasks, allowing the system to serve itself in identifying damage, selecting parts, calculating labor operations, and producing final estimates
2Reliability
If comprehensive menu of parts and labor operations is provided for manual selection, then completeness of estimate can be achieved through thorough human review, but complexity of the estimation process increases and time consumption increases
Solution Approach 1:
The system performs preliminary actions by automatically pre-selecting relevant parts and labor operations based on image analysis before human review, pre-populating estimate templates with confidence-scored recommendations that guide adjusters through the estimation process rather than presenting blank comprehensive menus
Solution Approach 2:
The patent introduces an intermediary intelligent system that acts as a mediator between the damage images and the final estimate, translating visual damage information into structured estimation data through automated part identification and labor operation selection, thereby simplifying the interface between image data and estimate completion
3Measurement precision
If human adjuster knowledge and personal judgment are relied upon, then contextual accuracy and jurisdictional compliance can be achieved, but consistency across different adjusters and estimates decreases
Solution Approach 1:
The system transforms the variable human judgment parameter into a stable algorithmic parameter by encoding jurisdictional regulations and repair standards into fixed rules and constraints within the estimation system, ensuring consistent application across all estimates while maintaining contextual accuracy through programmed compliance requirements
Data Source
AI summary
Intelligent vehicle repair estimating techniques include an image processing component that extracts image attributes from one or more images of a damaged vehicle, and utilizes the attributes to predict an initial set of parts that are globally-identified. Based on a jurisdiction associated with the damaged vehicle, the initial set of parts is transformed into a set of jurisdictionally-based repairs (e.g., parts, labor operations, time intervals, costs, etc.), which may be included in a draft vehicle repair estimate. An estimate refinement component iteratively modifies/refines the draft estimate using a machine-only loop nested within a larger human-machine loop, where system-generated modifications are incrementally incorporated into the draft within the smaller loop, and user-generated modifications are incrementally incorporated into the draft within the larger loop. User-facing draft estimates may be of a WYSIWYG format, and draft estimate contents and/or guidance annotations are updated, via the nested loops, in-line upon each individual/unitary user modification.