Design Artifact Matching System Using Computer Vision
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Solution Overview
Problem
Current methods for creating or modifying design concepts, such as fashion or interior designs, often rely on traditional sources like magazines, which can feature products no longer available or unaffordable, and newer digital platforms may include elements that do not fit the user's style or are not professionally created.
Innovation Solution
A system that receives a model design, either as an image or description, detects objects, determines their characteristics, and correlates them with an ontology of objects to identify matching available artifacts, allowing users to find and rank suitable products for purchase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sources like magazines are used for design inspiration, then design aesthetics can be achieved, but product availability and affordability cannot be guaranteed
Solution Approach 1:
The system creates a digital copy of the design vision by capturing images or descriptions from traditional sources, then uses computer vision and NLP to extract product characteristics. This copying approach allows the design aesthetic to be preserved and translated into searchable parameters, enabling users to find available products that match the original design intent without relying on the original physical products being available.
Solution Approach 2:
The patent introduces an intermediary system that bridges traditional design sources and modern e-commerce platforms. The system acts as a mediator by processing design inputs through multiple analysis layers (visual, textual, contextual) and translating them into product search queries, thereby connecting the gap between design inspiration and product availability.
2Reliability
If digital platforms with user-generated content are used, then product availability improves, but design quality and style consistency deteriorate
Solution Approach 1:
The system implements feedback mechanisms where user preferences, style preferences, and design characteristics are continuously learned and refined. The machine learning models analyze user interactions and feedback to improve design matching accuracy, ensuring that user-generated content results are filtered and ranked according to professional design standards while maintaining availability of modern products.
Solution Approach 2:
The patent transforms design quality assessment into quantifiable parameters through computer vision and NLP analysis. By converting subjective design qualities into objective measurable parameters (colors, patterns, textures, style attributes), the system can systematically evaluate and rank products from user-generated content, maintaining design quality consistency across diverse sources.
3Measurement precision
If manual product searching is performed, then design characteristics can be carefully evaluated, but time efficiency decreases
Solution Approach 1:
The system replaces manual mechanical product searching with automated computer vision and natural language processing systems. These AI-powered tools automatically analyze design images and descriptions, extract product characteristics, and search databases for matching items, thereby maintaining high measurement precision in characteristic matching while dramatically improving search efficiency and productivity.
Solution Approach 2:
The system performs preliminary analysis of design characteristics before the actual product search begins. By pre-processing design inputs to extract and structure key features (colors, materials, styles, patterns), the system prepares search queries in advance, enabling faster and more accurate product matching without compromising evaluation precision during the search phase.
Data Source
AI summary
Analysis of descriptions accompanying designs to determine matching available artifacts. A description of a design (e.g., interior design, fashion) is analyzed to determine characteristics of the design (e.g., colors, patterns), and artifacts matching those characteristics (e.g., fabrics, furniture) are identified.