Automated Dictionary Hierarchy Generation for Catalog Items
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Solution Overview
Problem
Existing manual methods for generating dictionaries for online catalogs are inefficient due to the large number of product items and attributes, making it impractical to define categories and attributes manually, especially in databases with thousands of distinct models.
Innovation Solution
An automated system that uses a dictionary manager with engines for item input, brand matching, and correlated segment extraction to generate dictionaries hierarchically based on product titles, leveraging repetition and regularity in title structures to create a dictionary hierarchy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual methods are used to generate dictionaries for online catalogs, then the dictionary structure can be carefully defined, but the process becomes inefficient and impractical due to the large number of product items and attributes
Solution Approach 1:
The system enables self-service by automatically generating dictionary hierarchies using correlation analysis of product titles and attributes. The automated process extracts correlated segments and builds hierarchical structures without manual intervention, allowing the system to serve itself in creating and maintaining product catalogs.
Solution Approach 2:
The patent replaces the manual mechanical process of dictionary creation with an automated computational system. The brand matching engine, correlated segment engine, and hierarchy generator automatically process product data to create hierarchical dictionaries, substituting human manual work with algorithmic processing.
2Measurement precision
If dictionaries are generated manually for thousands of distinct product models, then accuracy can be maintained, but the time and resources required become excessive
Solution Approach 1:
The system uses feedback mechanisms where the correlation analysis results from product titles and attributes feed into the hierarchy generation process. The brand matching engine provides feedback by identifying matched substrings, which then guide the correlated segment extraction to refine the dictionary structure iteratively.
Solution Approach 2:
The automated system creates copies of hierarchical structures by extracting correlated segments from multiple product titles and synthesizing them into standardized dictionary hierarchies. This copying approach allows rapid replication of accurate structures across thousands of product models without manual recreation.
3Loss of information
If comprehensive product attributes are tracked manually, then complete information can be captured, but the complexity of management increases significantly
Solution Approach 1:
The patent segments product information into distinct hierarchical levels (brand, product line, model, attributes) through automated correlation analysis. The correlated segment engine divides product titles into meaningful segments and organizes them hierarchically, reducing management complexity by breaking down comprehensive product data into structured components.
Solution Approach 2:
The system transforms flat product attribute data into a multi-dimensional hierarchical structure. The hierarchy generator creates additional dimensional layers (brand → product line → model → attributes) from two-dimensional product title strings, enabling comprehensive information capture while simplifying management through structured organization.
Data Source
AI summary
A plurality of items included in a catalog may be obtained, each item associated with an item category. Brand indicators may be obtained, each brand indicator associated with the item category. Brand indicators associated with each of the items may be determined, and the each item may be assigned to a partition group associated with the brand indicator that is associated with the each item. Correlated string tokens that are correlated, greater than a predetermined correlation threshold value, with the brand indicator associated with the partition group that is associated with the each one of the items, the correlated string tokens associated with the each one of the plurality of items, may be determined. A dictionary hierarchy may be generated based on the one or more correlated string tokens.