Vehicle Damage Imaging With Orientation Models for Automated Assessment
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Solution Overview
Problem
The existing methods for assessing vehicle damage are inefficient, labor-intensive, and prone to errors, leading to inconsistent and costly repairs due to subjective appraisals by repair facilities or insurance providers.
Innovation Solution
A network-based system using a mobile device with an orientation model to capture and analyze vehicle damage images, transmitting the data to a damage estimator computing device for accurate damage assessment, including machine learning algorithms to classify and estimate repair costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual inspection methods are used, then human expertise can be applied to damage assessment, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated image processing system. Mobile devices capture images of vehicle damage, and computer vision algorithms automatically analyze these images to assess damage extent and generate repair estimates, eliminating the need for manual physical inspection while maintaining assessment accuracy
Solution Approach 2:
The system creates digital copies of the vehicle damage through photographs captured by mobile devices. These image copies are then processed by machine learning models to assess damage, replacing the need for physical manual inspection and enabling rapid, consistent evaluation without human labor
2Ease of operation
If subjective appraisal methods are used by repair facilities or insurance providers, then local expertise can be applied, but errors and biases occur in damage estimation
Solution Approach 1:
The system incorporates feedback mechanisms where the image processing system continuously refines its damage assessment based on multiple image analyses and compares results against established damage databases. This iterative feedback process reduces human bias and error while maintaining operational simplicity
Solution Approach 2:
The patent transforms the appraisal process from subjective human judgment to objective parameter-based assessment. Machine learning models analyze image parameters such as damage location, extent, and type to generate consistent, bias-free estimates that are reproducible and transparent
3Reliability
If multiple sequential steps are used in the inspection and estimation process, then thoroughness can be maintained, but the overall process time increases
Solution Approach 1:
The system performs preliminary damage assessment automatically through image processing before any human review is needed. Machine learning models pre-analyze the images to identify damage extent and generate initial estimates, which then guide any subsequent human review, significantly reducing total process time while maintaining thoroughness
Solution Approach 2:
The patent merges multiple sequential steps into a single integrated automated process. Image capture, damage analysis, estimate generation, and report creation are combined into one streamlined workflow that executes simultaneously rather than sequentially, reducing total process time while maintaining comprehensive assessment
Data Source
AI summary
A system and computer-implemented method for facilitating a user of a mobile device obtaining image data of damage to a vehicle for damage assessment includes capturing image data of a vehicle with the mobile device. The mobile device may include an orientation model for capturing the image data. The captured image data is analyzed, and a determination is made of the orientations of the images of the captured image data. In addition, a determination is made as to whether the captured image data can be used for the damage assessment. The captured image data may then be transmitted to a damage estimator computing device for estimating an amount of damage to the vehicle.