Automated Similar Category Identification System
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Solution Overview
Problem
In electronic commerce systems, identifying mutually similar categories among a large number of classified products is time-consuming and labor-intensive, as the similarity between product categories is often determined manually.
Innovation Solution
An information processing system that includes comparison target deducing means to identify objects for comparison based on user operations and similar category determination means to automatically determine similar categories based on the frequency of object comparisons, reducing the need for manual categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual checking of category names and objects is performed to identify similar categories, then identification accuracy is improved, but time consumption and labor cost increase significantly
Solution Approach 1:
The patent replaces the manual mechanical checking process with an automated information processing system that uses computer algorithms to analyze category data, extract features, and determine similarity automatically, thereby eliminating the need for human manual inspection while maintaining or improving accuracy
Solution Approach 2:
The system enables categories to be automatically evaluated and compared against each other using predefined criteria and algorithms, allowing the categorization system to self-assess and identify similar categories without external human intervention, thus reducing both time and labor requirements
2Adaptability or versatility
If the number of categories is increased to accommodate more products, then product classification capability is improved, but the complexity of identifying similar categories increases
Solution Approach 1:
The patent extracts key features and attributes from category definitions and product data, separating the essential similarity-determining characteristics from the complete category information. This extraction process simplifies the comparison task by focusing only on the most relevant features, making the identification process manageable even as the number of categories grows
Solution Approach 2:
The system transforms the complex categorical data into standardized parameters and metrics that can be systematically compared. By changing the representation of category information into comparable parameters, the system can efficiently handle increased numbers of categories without proportionally increasing identification complexity
3Manufacturing precision
If manual category classification is used to ensure accurate product categorization, then categorization precision is improved, but productivity decreases
Solution Approach 1:
The patent replaces manual categorization operations with automated information processing that uses computer algorithms to perform classification tasks, thereby maintaining precision through systematic analysis while dramatically improving productivity by eliminating manual labor constraints
Solution Approach 2:
The system incorporates feedback mechanisms where categorization results are continuously evaluated and refined based on comparison data and similarity metrics. This feedback loop ensures that automated categorization maintains high precision by learning from and adjusting to the characteristics of the data being processed
Data Source
AI summary
To reduce time and labor in identifying a similar category among a plurality of categories into which objects are classified. Based on a predetermined operation performed by a user with respect to two objects among a plurality of objects classified into some of a plurality of predetermined categories, the information processing system deduces the two objects as comparison targets. Then, based on the number of times at which the two objects are deduced as comparison targets, the information processing system determines two categories into which the two objects are respectively classified as similar categories.