Name Phrase Scoring Without Parsing
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Solution Overview
Problem
Comparing names without parsing them first is challenging due to the need for pre-processing, which adds time or requires additional memory/storage, and existing methods fail to accurately distinguish given names from surnames in names like 'Juan Beltran' and 'Juan Cabrera'.
Innovation Solution
A method scores each name in a name phrase based on field frequency in a database to determine a primary given name and surname, calculating a transition score to assess similarity without parsing, using country-specific data to adjust scores for accurate comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-processing is performed to parse names before comparison, then accuracy in distinguishing given names from surnames is improved, but runtime overhead and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing field frequency data for names in a database during an offline phase. This allows the system to quickly retrieve and use pre-computed frequency information during runtime comparisons without performing complex parsing operations, thus maintaining accuracy while reducing processing time.
Solution Approach 2:
The patent replaces the mechanical parsing process with a statistical approach using field frequency data. Instead of using complex rule-based or algorithmic parsing mechanisms during runtime, the system substitutes this with simple frequency-based scoring that leverages pre-computed statistics to determine whether names are given names or surnames.
2Measurement precision
If pre-processing is performed to parse names before comparison, then accuracy in distinguishing given names from surnames is improved, but additional memory or storage space is required
Solution Approach 1:
The patent extracts only the essential information needed for name comparison - the field frequency data indicating whether names appear in given_name or surname fields - and stores this compressed statistical information. This extraction approach stores only necessary data without retaining full parsed name structures, reducing memory requirements while maintaining comparison accuracy.
3Productivity
If field frequency scoring is used to determine name similarity without parsing, then runtime overhead is reduced, but ability to handle complex name structures may be compromised
Solution Approach 1:
The patent changes the parameter used for name comparison from structural analysis to statistical frequency scoring. By using field frequency data that reflects how names actually appear in given_name versus surname fields, the system adapts to complex name structures across different cultures and languages without requiring explicit parsing rules, maintaining both efficiency and reliability.
Data Source
AI summary
A system for determining a similarity between a name phrase and a comparison name phrase, for each name in the name phrase, scores the name. The scoring is based on the field frequency of the name in a name database, where the field frequency indicates a given name frequency and/or a surname frequency in the database. The system uses the scoring to determine a transition from a given name to a surname in the name phrase. The system determines a primary given name and a primary surname in the name phrase based on the scoring and the transition. The system uses the primary given name and primary surname to determine a similarity between the name phrase and a comparison name phrase, where the comparison name phrase comprises a comparison given name and a comparison surname.