Antique Search Knowledge Base Using Image and Text Extraction
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Solution Overview
Problem
The antiques and collectables industry faces challenges with limited and fragmented information, outdated information collection methods, reliance on text search which requires specialized terminology, and inadequate image search capabilities that fail to consider the context and consistency of item outlines, as well as copyright restrictions and the scarcity of experts to verify information.
Innovation Solution
A method utilizing computer vision and natural language processing to create a comprehensive knowledge base that supports both text and image search, extracting and combining searchable features to improve relevancy, and automating the copyright granting process to include a wider range of items, while leveraging expert inputs for validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If text search is used for finding antiques and collectables, then search results can be obtained, but the search requires specialized terminology and years of formal training to use correctly
Solution Approach 1:
The patent introduces an image search intermediary that bridges the gap between users and antique identification. Instead of requiring users to directly use complex text search with specialized terminology, the system provides image-based search as an intermediary interface. Users can upload photos of items they wish to identify, and the system handles the complex text matching and knowledge base queries automatically, returning relevant results without requiring user expertise in antique terminology.
2Adaptability or versatility
If generic image search is used, then images can be found, but the search does not consider the context of antiques/collectables and relies on color and page ranking
Solution Approach 1:
The patent fundamentally changes the search parameters from generic image search parameters (color, page ranking) to antique-specific parameters. The system extracts and compares multiple parameters including shape, outline, texture, historical context, and categorical information. By transforming the search into a multi-parameter comparison that considers the specific context of antiques and collectables, the system achieves both adaptability to various item types and precision in identification.
3Reliability
If multiple experts review and verify information, then the quality and authority of data can be ensured, but the number of experts required increases significantly
Solution Approach 1:
The patent segments the expert verification process into specialized categories and subcategories. Instead of requiring all experts to review all items, the system divides the antique domain into distinct categories (ceramics, metalwork, textiles, etc.) and assigns experts to specific categories. This segmentation allows the system to maintain high reliability through expert verification while reducing the overall number of experts needed by matching items to relevant expert domains only.
4Object-affected harmful factors
If copyright permission is negotiated with each item owner, then copyright restrictions can be respected, but the process is time-consuming and creates uncertainty
Solution Approach 1:
The patent implements preliminary copyright clearance actions during the data collection and indexing phase. Before items are added to the knowledge base, the system performs preliminary checks and obtains necessary permissions in advance. This preliminary action eliminates the need for time-consuming copyright negotiations during subsequent search operations, allowing the system to respect copyright protection while minimizing time loss through proactive copyright management.
Data Source
AI summary
Generating a knowledge base in a database, the knowledge base including a first field which specifies a plurality of known brands of a plurality of known objects, a second field which specifies a plurality of known categories corresponding to the plurality of known objects, and a third field which specifies a plurality of sets of known image-based parameters of the plurality of known objects; receiving in one or more computer memories an indication of a brand, an indication of a category, and an image-based description parameter for a particular object; comparing, the indications of the brand, the category, and the image-based description parameter for the particular object with one or more of the plurality of known brands, known categories, and sets of known image based parameters, respectively, and providing an indication of whether the particular object is one or more of the plurality of known objects, based, on the comparisons.