Automated Adjacent Panel Damage Appraisal
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Solution Overview
Problem
Existing automated property damage appraisal technologies are inconsistent and costly due to their reliance on user input, predictive analytics with historical bias, and the need for specialized instrumentation, failing to accurately account for adjacent panel repairs during damage assessment.
Innovation Solution
The system utilizes a deep neural network to analyze images and determine damaged vehicle parts, identifying adjacent panels and their required repair operations based on vehicle type and repair methods, incorporating an adjacency database to generate comprehensive and accurate repair estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If prior software appraisal technologies rely on user input and predictive analytics, then the system can operate without specialized instrumentation, but the damage assessments become inconsistent and inaccurate due to subjective estimating and historical bias
Solution Approach 1:
The patent replaces manual user input and traditional predictive analytics with an automated deep learning image analysis system. The deep neural network automatically processes damage images to identify damaged parts and determine repair operations, eliminating the need for user input while maintaining consistency and accuracy through automated, objective analysis rather than subjective human estimation.
2Measurement precision
If prior technologies use deformation measurement methods with specialized instrumentation, then measurement precision may be improved, but the device complexity and cost increase substantially
Solution Approach 1:
The patent uses standard imaging devices to capture copies (images) of damaged property, which are then processed by deep learning algorithms. This approach replaces the need for specialized deformation measurement instrumentation with ordinary cameras or image capture devices, significantly reducing device complexity and cost while maintaining measurement precision through advanced image analysis techniques.
3Measurement precision
If comprehensive catalog of benchmark examples is created and maintained, then measurement precision can be improved through comparison, but the loss of time and resources for creating and maintaining the catalog increases substantially
Solution Approach 1:
The deep learning system is trained on existing datasets and automatically learns to identify damaged parts and determine repair operations without requiring manual creation and maintenance of benchmark catalogs. The system serves itself by continuously learning from training data and applying learned knowledge to new damage assessments, eliminating the time-consuming process of manually curating comparison benchmarks while maintaining high accuracy.
Data Source
AI summary
A method, non-transitory computer readable medium, and apparatus that improves automated damage appraisal includes analyzing one or more obtained images of property using a deep neural network with multiple hidden layers of units between an input and output and which has stored knowledge data encoded from one or more stored property damage images to identify which area of the property has damage. Damage data on an extent of the damage in the identified area of the property is determined using the deep neural network which has stored knowledge data encoded from one or more stored property damage images. The identified damaged part and may be used to determine one or more adjacent parts based on the vehicle information and the repair operation type.