Language-Agnostic Embeddings Resolve Catalog Identifier Inconsistencies
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Solution Overview
Problem
Existing electronic commerce systems face inefficiencies in identifying and managing items with the same item identifier across multiple geographies and languages, leading to inconsistent item information and increased computational resources required for pairwise comparisons.
Innovation Solution
The system employs a natural language processing approach using language-agnostic embeddings to model item information as distributions, enabling unsupervised learning to detect inconsistencies and scale across languages and geographies, reducing computational load by mapping items into a shared embedding space without translation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pairwise comparison methods are used to identify items with the same identifier across multiple geographies and languages, then identification accuracy is improved, but computational resources and processing time increase significantly
Solution Approach 1:
The patent introduces language-agnostic embeddings as an intermediary representation that translates item information from multiple languages into a unified vector space. This mediator enables direct comparison of items across languages without requiring pairwise translation or comparison, thereby maintaining identification accuracy while dramatically reducing computational resources and processing time
Solution Approach 2:
The patent transforms item information from textual representations in multiple languages into numerical embedding vectors in a shared vector space. This parameter change from text to vectors enables efficient mathematical operations and comparisons, allowing the system to identify items with the same identifier across geographies and languages with both high accuracy and computational efficiency
2Adaptability or versatility
If translation is used to compare item information across languages, then language compatibility is improved, but processing time and computational load increase
Solution Approach 1:
Instead of translating item information into a common language, the patent uses language-agnostic embeddings as an intermediary that directly maps text from any language into a shared vector space. This approach achieves language compatibility without the time-consuming translation process, as the embeddings inherently capture semantic meaning across languages in a unified representation
Solution Approach 2:
The patent replaces the mechanical translation process with a neural network-based embedding system. Rather than linguistically translating text from one language to another, the system uses trained embedding models to directly map text from any language into vector representations that preserve semantic relationships, eliminating the need for translation while maintaining language compatibility
3Adaptability or versatility
If multiple item identifiers are allowed for different geographies and languages, then catalog coverage is improved, but data consistency and reliability deteriorate
Solution Approach 1:
The patent merges item information from multiple geographies and languages into a unified representation in a shared vector space. By combining item data and applying clustering algorithms, the system identifies items with the same identifier across different regions and languages, ensuring that catalog coverage is maintained while data consistency is enforced through the unified embedding representation
Solution Approach 2:
The patent implements a feedback mechanism where item embeddings are continuously refined based on their relationships with other items in the catalog. The system uses clustering results and similarity measurements to provide feedback that helps maintain consistent item identifiers across geographies and languages, ensuring that items with the same identifier are correctly identified and grouped together
Data Source
AI summary
Systems and methods are provided for using language-agnostic embedding data to analyze a plurality of items associated with an item identifier in a catalog and determine a characteristic associated with the plurality of items or the item identifier. A distribution may be generated based on the language-agnostic embedding data and groups of items may be identified based on the distribution. Based on the groups of items, the item identifier may be classified as a consistent item identifier or an inconsistent item identifier across the catalog. A primary group of items and secondary groups of items can be identified for the plurality of items. The primary group of items may include items with a verified association with the item identifier and the secondary groups of items may include items with an unverified association with the item identifier.