AI Image Curation System for Vehicle Photography Compliance
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Solution Overview
Problem
Existing systems for modifying and displaying images of products, such as vehicles, are time-consuming and costly due to the need for manual manipulation using complex software and human expertise, resulting in suboptimal uniformity and quality.
Innovation Solution
A machine learning artificial intelligence system that automatically tags features in images and applies predetermined compliance rules to modify and display images uniformly, including removing unwanted elements, enhancing image quality, and ensuring consistent presentation guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual image manipulation using complex software and human expertise is used, then image quality and compliance can be controlled, but time consumption and cost increase significantly
Solution Approach 1:
The system enables self-service by allowing the machine learning model to automatically detect, tag, and modify images without requiring human expertise. The system autonomously identifies compliance issues, applies corrections, and generates final images, eliminating the need for manual intervention while maintaining quality standards.
Solution Approach 2:
The patent replaces manual mechanical image manipulation with automated machine learning systems. The ML model automatically performs tasks such as detecting unwanted elements, identifying backgrounds, and applying corrections that would otherwise require manual editing software and human expertise, thereby reducing time consumption.
2Manufacturing precision
If manual image manipulation using complex software and human expertise is used, then image quality and compliance can be controlled, but cost increases significantly
Solution Approach 1:
The system enables self-service by allowing the machine learning model to automatically detect, tag, and modify images without requiring human expertise. The system autonomously identifies compliance issues, applies corrections, and generates final images, eliminating the need for manual intervention while maintaining quality standards.
Solution Approach 2:
The patent replaces manual mechanical image manipulation with automated machine learning systems. The ML model automatically performs tasks such as detecting unwanted elements, identifying backgrounds, and applying corrections that would otherwise require manual editing software and human expertise, thereby reducing time consumption.
3Loss of time
If automated machine learning systems are used, then time and cost are reduced, but image uniformity and quality may be suboptimal
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously refines its performance based on evaluated image quality. Compliance rules are applied iteratively, and the system can adjust its processing based on feedback about image uniformity and quality metrics, ensuring automated outputs meet desired standards.
Solution Approach 2:
The patent employs parameter changes by adjusting machine learning model parameters and compliance rule thresholds to optimize image quality. The system can modify detection sensitivity, correction intensity, and uniformity thresholds to achieve desired image characteristics while maintaining automated processing efficiency.
4Manufacturing precision
If multiple compliance rules are applied to ensure consistent presentation guidelines, then image uniformity improves, but processing complexity increases
Solution Approach 1:
The system segments the compliance rule application process into distinct modular stages: detection phase (identifying unwanted elements), tagging phase (categorizing issues), correction phase (applying specific fixes), and evaluation phase (verifying compliance). This segmentation allows complex rules to be managed systematically through separate processing steps rather than monolithic complexity.
Data Source
AI summary
The present disclosure is directed to automatically curating a set of images according to a predetermined set of compliance rules and displaying those images. The machine learning and artificial intelligence technology can differentiate between images and identify quality control features within images. The features may include excessive glare in the image; poor image resolution; dirt on the vehicle surface in the image; trash in the image; flags in the image; signs in the image; people or animals in the image, paper floor mats in the vehicle, etc. The technology is trained to identify these features in the images and automatically generate a quality control score based on these features. The system tags the images with information relating to the quality control score and may use this tag to automatically modify the image to raise the quality control score to the point where the image can be published.