Dynamic Tag Matching for Unique Product Search
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Solution Overview
Problem
Existing electronic transaction systems struggle to effectively identify unique products with low supply in response to electronic queries, particularly in markets like art and collectibles, which are antiquated, fragmented, and filled with friction.
Innovation Solution
A method for executing search requests in data structures based on electronic tag matching, where products are associated with tags generated from input information like images or text, and these tags are matched to identify candidate products and entities in a hierarchical relationship.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search systems are used in fragmented markets, then system simplicity is maintained, but search accuracy and product identification capability deteriorate
Solution Approach 1:
The system segments product identification into multiple independent tagging dimensions (visual tags from images, attribute tags from text, category tags from hierarchical classification). Each dimension operates independently and contributes to the overall search accuracy, allowing the system to handle fragmented markets with diverse product characteristics without requiring a monolithic complex system.
Solution Approach 2:
The patent introduces an intermediary tagging layer between the raw product data and the search query processing. This tagging system acts as a mediator that translates diverse product information (images, text descriptions) into standardized tags that can be efficiently matched against search queries, improving accuracy without directly increasing system complexity.
2Manufacturing precision
If comprehensive product tagging is implemented, then search precision improves, but computational resources increase
Solution Approach 1:
The system applies local quality by assigning different levels of tagging depth to different product attributes based on their importance and data availability. Critical attributes like product category and key visual characteristics receive detailed tagging, while less important attributes receive simpler tagging. This selective approach maintains high matching precision for essential product identification while reducing overall computational resource consumption.
Solution Approach 2:
The patent implements preliminary action by pre-computing and storing product tags during the data ingestion phase, before search queries are received. This allows the system to build a richly tagged product database in advance, so that during actual search operations, the system only needs to match pre-computed tags against queries rather than performing complex real-time analysis, significantly reducing operational computational resources.
3Productivity
If automated tag assignment is used, then processing speed increases, but tag accuracy may deteriorate
Solution Approach 1:
The system merges multiple automated tagging approaches together: visual recognition algorithms process product images to generate visual tags, while text processing algorithms analyze product descriptions to generate attribute tags. By combining these parallel automated processes, the system achieves both high processing speed and high tag accuracy, as each automated component contributes specialized tags that complement each other.
Solution Approach 2:
The patent implements a universal tagging framework that can process multiple types of input data (images, text, structured attributes) through a single integrated system. This multi-functional approach allows the same automated tagging infrastructure to handle diverse product information formats, maintaining both speed and accuracy across different product types and data sources without requiring separate specialized systems for each data type.
Data Source
AI summary
The present solution relates to an improved system and method for executing search requests in data structures based on electronic tag matching. The method can include maintaining a product data structure including a plurality of products and receiving, from the client device, a query comprising at least one of an image or a text string. The method can also include generating a plurality of first tags according to a tag policy from the query and identifying one or more candidate products of the plurality of products. The method can also include identifying one or more candidate first entities associated with the one or more candidate products and providing for presentation at the client device an identification of the one or more candidate products and the corresponding candidate first entities.