Transliterated Data Records for Cross-Language Matching
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Solution Overview
Problem
Service providers face inefficiencies in processing data records from multiple sources, especially when these records include data in different languages, leading to inaccurate matching and search results due to the challenges of handling heterogeneous language combinations.
Innovation Solution
A records processing service generates transliterated data records in a reference phonetic language, such as katakana, to improve the accuracy of data record linkage by processing both original and transliterated data records using matching models, which helps in identifying and merging matching data records across different languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data records from multiple sources in different languages are processed directly, then data processing speed is maintained, but matching accuracy deteriorates due to heterogeneous language combinations
Solution Approach 1:
The patent introduces transliterated data records as an intermediary representation between original multi-language data records and matching models. By converting data to a reference phonetic language (katakana), it creates a common intermediary format that enables accurate matching while maintaining processing efficiency. This intermediary layer resolves the contradiction by providing a standardized representation that improves matching accuracy without requiring complex multi-language processing logic in the matching models themselves.
2Measurement precision
If transliterated data records are generated for all original data records, then matching accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by generating transliterated data records in advance before the matching process. The featurization component processes original data records and creates corresponding transliterated versions stored in data stores. This preliminary transformation ensures that when matching models execute, they work with pre-prepared phonetic representations, improving matching accuracy without adding time pressure during the actual matching operation. The transliteration work is completed beforehand, decoupling the time-consuming language conversion from the time-sensitive matching process.
Data Source
AI summary
A data records service is configured to receive original data records and, in parallel, generate a transliterated version of the original data record into a phonetic based language. Individual fields of data records can be transliterated by identifying a primary language, generating language specific tokens for individual text portions, and transliterating the token. The records processing service can then execute matching models on both original data records and transliterated data records to detect matching data records.


