Ideographic String Matching via Phonetic Shape Feature Extraction
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Solution Overview
Problem
Conventional fuzzy string matching algorithms are not well-suited for ideographic or non-English characters, such as Chinese or Japanese characters, as they are designed for English pronunciations and do not account for different dialects or the 'sound-like' and 'look-like' errors common in human input, making them inefficient for large datasets.
Innovation Solution
A computer-implemented technology that extracts phonetic and shape features from input and candidate strings, using n-grams and conversion to Latin-based representations to determine a match score, enabling efficient and accurate matching of ideographic characters across different dialects and languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional phonetic algorithms (Soundex, Levenshtein distance) are used for fuzzy matching, then English string matching accuracy is improved, but suitability for ideographic and non-English characters deteriorates
Solution Approach 1:
The patent transforms ideographic characters into phonetic representations (e.g., Pinyin for Chinese characters) and extracts shape features, changing the parameter space from original ideographic form to a hybrid phonetic-visual representation that can be processed by fuzzy matching algorithms while preserving language-specific characteristics
Solution Approach 2:
The patent introduces phonetic transcription and shape feature extraction as intermediary steps between ideographic characters and the matching algorithm, allowing English-based phonetic algorithms to work with non-English characters through a mediating representation layer
2Measurement precision
If conventional phonetic algorithms with Levenshtein distance are used, then phonetic matching capability is improved, but computational cost for large datasets deteriorates
Solution Approach 1:
The patent segments the matching process into independent feature extraction (phonetic and shape) and scoring components, allowing parallel processing of multiple features and enabling optimization of each segment separately to improve overall processing efficiency
Solution Approach 2:
The patent extracts and compares only the most discriminative phonetic and shape features rather than processing complete character strings, reducing the computational burden while maintaining matching accuracy through selective feature comparison
3Measurement precision
If conventional phonetic algorithms are used, then English pronunciation matching is improved, but handling of different dialects and pronunciations deteriorates
Solution Approach 1:
The patent creates a universal matching framework that handles multiple dialects and pronunciations by extracting both phonetic and shape features, making the system multi-functional to accommodate various linguistic variations without requiring dialect-specific algorithms
4Measurement precision
If Levenshtein distance is used for string comparison, then edit distance calculation is improved, but scalability to longer strings and large datasets deteriorates
Solution Approach 1:
The patent extracts key phonetic and shape features from complete character strings before comparison, removing unnecessary computational overhead from processing entire long strings while retaining the essential information needed for accurate matching
Data Source
AI summary
Described herein is a technology for facilitating searching and matching of data. In accordance with one implementation, first and second feature sets are extracted. The first feature set is associated with an input data string including one or more first ideographic elements, while the second feature set is associated with a candidate string including one or more second ideographic elements. A match score of the candidate string is determined based on the first and second feature sets.