Real-Time Query Handling Using Identifier Embeddings
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Solution Overview
Problem
Data retrieval systems face challenges when dealing with inconsistent identifiers across different data sources, leading to slow or inaccurate data retrieval and issues with generating complete and fast-loading user interfaces.
Innovation Solution
A computer system that performs word tokenization on identifiers, obtains weighting values based on frequency of occurrence, generates embeddings for identifiers, and calculates similarity metrics to create a similarity profile that maps identifiers from one data source to another, enabling rapid and accurate data retrieval and user interface generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional identifier matching methods are used across different data sources, then data retrieval can be performed based on identifiers, but inconsistent identifiers lead to inaccurate or slow data retrieval
Solution Approach 1:
The system performs preliminary processing by tokenizing identifiers and generating embeddings before actual data retrieval operations. Similarity profiles are pre-computed and stored, allowing the system to quickly match inconsistent identifiers during query execution without performing complex calculations in real-time, thus resolving the contradiction between accuracy and speed
Solution Approach 2:
The patent introduces embeddings as an intermediary representation between raw identifiers and data retrieval operations. These embeddings capture semantic meaning and enable similarity-based matching, serving as a mediator that translates inconsistent identifier formats into a unified representation space for accurate and efficient retrieval
2Adaptability or versatility
If data sources maintain different identifier formats, then each data source can preserve its own formatting preferences, but this causes inconsistencies that result in incomplete data retrieval
Solution Approach 1:
The system transforms identifiers from their original format parameters into embedding space parameters. By changing the representation parameters from exact string matches to continuous vector representations, the system maintains adaptability to different formats while preventing information loss through semantic similarity matching
3Speed
If exact identifier matching is used, then data retrieval is fast when identifiers match perfectly, but any inconsistency causes retrieval failures or omissions
Solution Approach 1:
The patent replaces the mechanical exact-matching system with a similarity-based matching system using embeddings. This substitution maintains speed by using efficient vector operations while dramatically improving reliability by tolerating inconsistencies through similarity thresholds and ranking mechanisms
Data Source
AI summary
In an aspect, a computer-implemented method may include: performing word tokenization on identifiers in a first identifier set to obtain a first set of tokens; performing word tokenization on identifiers in a second identifier set to obtain a second set of tokens; obtaining a weighting value for each of a plurality of tokens in the first set of tokens and the second set of tokens based on a frequency of occurrence; for each identifier of a plurality of identifiers in the first identifier set and of a plurality of identifiers in the second identifier set, obtaining embeddings for the identifier based on the embeddings of tokens obtained by performing word tokenization on that identifier and based on the weighting value for such tokens; obtaining similarity metrics. The similarity metrics may be used for rapid query handling to generate user interface screens in real time.