Name Comparison System Tokenization Duplicate Resolution
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Solution Overview
Problem
Contact databases often face issues with duplicate records due to inconsistent name entries, variations in name sequences, nickname usage, and typographical errors, making it difficult to identify and merge duplicate records accurately.
Innovation Solution
A name comparison system comprising modules such as comparison control, name tokenizer, token deduplication, nickname identifier, token cross-multiplier, and string comparison modules, which tokenize names, identify distinct tokens, account for nicknames, generate permutations, and calculate a name comparison score to determine if records should be merged.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple name comparison is used to identify duplicate records, then the process is fast and simple, but accuracy is poor due to inconsistent name entries, variations in sequences, nicknames, and typographical errors
Solution Approach 1:
The name comparison system segments a name into multiple tokens (e.g., first name, middle initial, last name) and compares corresponding tokens between two names. This segmentation allows the system to handle inconsistencies in name formatting by comparing individual components rather than treating the entire name as a single string, thereby improving duplicate identification accuracy while managing complexity through structured tokenization.
2Measurement precision
If detailed name analysis is performed to account for nicknames and spelling variations, then duplicate identification accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary tokenization of names into standardized components before comparison. By pre-processing names into tokens and establishing a token comparison framework in advance, the system prepares data for accurate matching while reducing the computational burden during actual comparison operations, thus improving accuracy without proportionally increasing processing time.
3Reliability
If multiple name variations and permutations are considered, then comprehensive duplicate detection is achieved, but computational complexity increases
Solution Approach 1:
The system segments the name comparison task into discrete token comparisons rather than evaluating all possible permutations of entire names. By dividing names into tokens and comparing corresponding tokens positionally, the system achieves comprehensive duplicate detection through structured segment comparison, reducing algorithmic complexity while maintaining reliability.
Data Source
AI summary
The present disclosure extends to comparing two or more names in a database of contact records. In embodiments, systems of the present disclosure execute a comparison of submitted names to determine if the records should be linked and/or merged. Embodiments of the present disclosure can compensate for irregularities in the database of contact records, including inconsistent name prefixes and suffixes, misspellings, typographical errors, misordered first/last names, and nicknames.