Automated Color Matching With Graph-Based Paint Selection
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Solution Overview
Problem
Existing methods fail to accurately manage color matching for projects due to variations in material, lighting, and texture, leading to unexpected results and customer dissatisfaction.
Innovation Solution
A system and method utilizing a graph structure for organizing project elements, selecting target colors, finding matching paints, and adjusting based on comparisons, with AI-based tools for data input and paint selection, including a database of paint SKUs and customizable color gamuts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the same dye is applied to different materials and surfaces, then the coloring process is simple and efficient, but the resulting appearance varies due to material, lighting, texture, and other factors
Solution Approach 1:
The system changes the parameters of color selection by considering multiple factors including material type, lighting conditions, texture, and environmental background. Instead of using a fixed color, the system adjusts color parameters dynamically to account for these varying conditions, enabling accurate color matching across different applications while maintaining efficient processing
Solution Approach 2:
The system incorporates feedback mechanisms by comparing the applied color against the target color and making adjustments. This feedback loop allows the system to learn from previous applications and refine future color selections, improving accuracy without significantly increasing the time required for color matching
2Ease of operation
If paint is applied to previously painted surfaces, then the coloring process is straightforward, but the actual result differs from expectations due to surface absorption characteristics
Solution Approach 1:
The system performs preliminary analysis of the surface characteristics, including previous paint layers, surface texture, and absorption properties. By preparing this information in advance, the system can predict how the new paint will interact with the existing surface and adjust the color selection accordingly, improving predictability without complicating the painting process
Solution Approach 2:
The system adjusts paint parameters such as color, concentration, and application characteristics based on the identified surface properties. This dynamic parameter adjustment ensures that the paint formulation is optimized for the specific surface being painted, leading to more predictable and accurate color results
3Adaptability or versatility
If color matching is performed manually, then the process is flexible and adaptable, but it is time-consuming and prone to errors
Solution Approach 1:
The system performs self-service by automatically analyzing project requirements, selecting appropriate colors, and generating paint formulations without requiring manual intervention. The automated system maintains flexibility by adapting to different project types and conditions while significantly reducing the time investment compared to manual color matching processes
Data Source
AI summary
Disclosed herein are a system and method for managing color matching for a project of a customer. In one aspect, the method comprises, defining a coloring project using a graph structure, receiving input user data for the coloring project, organizing elements of the coloring project using the graph structure, selecting a target color for each element or group of elements, for each element or group of elements, finding a respective paint that has a color matching the selected target color of the element or group of elements, after the selected paint is applied to the element or group of elements, comparing the paint applied to the element or group of elements with the respective target color, and making an adjustment to the selection when further optimization of the selection of the paint is needed based on the comparison.