Automated Product Attribute Generation via Image Matching
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Solution Overview
Problem
The manual process of entering and organizing product attributes into data pools within the Global Data Synchronization Network (GDSN) is labor-intensive and time-consuming, requiring significant human effort to accurately categorize and describe products, especially as products evolve and new ones are introduced.
Innovation Solution
A method that compares digitized images of products to pre-existing images in a database, using taxonomy and ontology to automatically retrieve and associate relevant attribute data, thereby generating product data and placing products within the correct taxonomy nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data entry is used to populate product attributes in data pools, then data accuracy can be maintained through human review, but labor costs and time consumption increase significantly
Solution Approach 1:
The system enables automatic self-service data population by comparing product images against the taxonomy ontology to generate attribute data without manual intervention. The computer automatically retrieves and populates product attributes by matching image features with taxonomy nodes, eliminating the need for manual data entry while maintaining data accuracy through automated verification processes.
Solution Approach 2:
The patent replaces the mechanical manual data entry process with an automated computer-based image analysis system. The system uses image comparison algorithms and ontology-based reasoning to automatically extract and populate product attributes, substituting human labor with computational processes that are both faster and equally accurate.
2Measurement precision
If comprehensive product attributes are collected for complex products, then product identification accuracy improves, but the time and effort required to enter and organize data increases
Solution Approach 1:
The taxonomy ontology is pre-structured with comprehensive product attributes, categories, and relationships before actual product data entry. This preliminary organization of the data framework allows the system to automatically map product images to appropriate attributes and categories, eliminating the need for time-consuming manual data organization while ensuring complete and accurate product identification.
Solution Approach 2:
The system uses pre-existing product images from the database as templates to copy and transfer attribute data to new product entries. By comparing the query image against stored images and their associated attributes, the system automatically replicates relevant product information, significantly reducing the time required to collect and organize comprehensive product attributes.
3Adaptability or versatility
If the taxonomy structure is updated to include new product types, then the system remains adaptable to market changes, but the effort to maintain and update the taxonomy increases
Solution Approach 1:
The system incorporates feedback mechanisms where image comparison results and matching patterns provide information about emerging product types and attributes. This feedback loop enables automatic identification of gaps in the existing taxonomy, allowing for streamlined updates that maintain adaptability to new products while reducing the complexity of taxonomy maintenance through data-driven insights.
4Productivity
If automated image comparison is implemented, then manual labor is reduced, but the complexity of the image processing system increases
Solution Approach 1:
The patent introduces an intermediary ontology layer that bridges image processing and product data management. The ontology serves as a standardized intermediate representation that simplifies image comparison by providing a common framework for attribute extraction and matching, reducing the overall system complexity while maintaining high automation capabilities.
Data Source
AI summary
Disclosed is a method to generate data describing a product. The method includes the steps of comparing a digitized query image of the product to digitized pre-existing product images in a pre-existing product database. The pre-existing product database is organized using a taxonomy and an ontology. The pre-existing product images are linked to a corresponding node in the taxonomy and are also linked to attribute data and attribute value data in the ontology. At least one pre-existing product image is then retrieved that most closely matches the query image based on at least one matching criterion selected in whole or in part by a user. From the pre-existing product database is extracted the node in the taxonomy, the attribute data, or the attribute value data linked to the pre-existing product image retrieved earlier. In this fashion, product data relevant to the item depicted in the query image can be generated automatically from existing product data.