Dimensional Translator for Automated Data Structure Mapping
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Solution Overview
Problem
The challenge lies in automatically translating dimensions between different data structures used by various entities, such as retailers, which is currently a manual and time-consuming process due to proprietary and varying data structures, making it difficult for manufacturers to determine how retailers categorize new items across different channels.
Innovation Solution
A system and method utilizing a dimensional translator that compares attributes between data structures to automatically translate dimensions, including the use of keywords, meta-data, and understanding hierarchies, to determine how a provider entity's item would be categorized by a target entity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual categorization by experts is used, then accuracy is improved, but time consumption increases
Solution Approach 1:
The patent replaces manual expert categorization with an automated computer system that uses attribute comparison algorithms. The system automatically compares attributes of items from provider entities with target entity data structures to determine categories, eliminating the need for manual expert intervention while maintaining accuracy through systematic attribute matching.
Solution Approach 2:
The system enables self-service categorization by allowing provider entities to input item information and automatically receiving categorization results from the target entity's data structure. The automated translator performs the categorization task independently without requiring manual processing, making the system self-sufficient and efficient.
2Loss of time
If automated translation is implemented, then time consumption is reduced, but system complexity increases
Solution Approach 1:
The patent introduces a dimensional translator as an intermediary component that mediates between different data structures of provider entities and target entities. This translator layer handles the complexity of attribute comparison and mapping, allowing automated translation while managing system complexity through a dedicated intermediate system rather than direct complex integration.
Solution Approach 2:
The system creates a universal translator that can handle multiple provider entities with different data structures and map them to a target entity's data structure. This multi-functional approach consolidates complexity into a single reusable system that handles various categorization scenarios, reducing overall system complexity compared to separate specialized systems.
3Adaptability or versatility
If proprietary data structures are used by each entity, then data structure flexibility is improved, but interoperability deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming attributes from different data structure formats into a standardized set of comparable parameters. The system normalizes various proprietary data structure representations into common attribute categories (such as product type, category, subcategory) that can be universally compared and mapped, enabling interoperability while preserving the flexibility of original proprietary structures.
Solution Approach 2:
The dimensional translator serves as an intermediary that accepts proprietary data structures from multiple provider entities and translates them into the target entity's data structure format. This mediator layer preserves the flexibility and adaptability of different proprietary structures while providing standardized interoperability through systematic attribute mapping and comparison.
Data Source
AI summary
A dimensional translator may automatically translate a dimension from an entity to a different dimension of another entity. The dimensional translator may do so by comparing attributes of the input dimension (the dimension to be translated) to attributes of a target data structure. An attribute may include, for example, hierarchy of a data structure, relationships of a data structure, a keyword associated with a data structure, and a data value associated with the data structure. The dimensional translator may automatically determine how a target entity would categorize the item. In particular, a Universal Product Code dimension of an item provided by an entity may be translated into a data structure of a target entity such as a retailer in order to determine how an item identified by the UPC will be categorized by the retailer.