Phonetic Search Engine Using String Segmentation for Fast Retrieval
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Solution Overview
Problem
Conventional phonetic search engine systems face challenges in achieving both accuracy and speed in retrieving phonetically similar words, with existing algorithms being resource-intensive and slow due to the need for multiple searches across large datasets.
Innovation Solution
A phonetic search engine system that generates primary and secondary strings by omitting specific letters from the search argument, and tertiary strings by transposing letters, allowing for efficient marking and retrieval of phonetically similar words through equivalence searches in an index file, significantly reducing the number of searches required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional phonetic algorithms (Soundex, metaphone) are used to retrieve phonetically similar words, then retrieval accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the search process into multiple stages: first generating and searching primary strings (exact matches), then secondary strings (omitting one letter), and finally tertiary strings (transposing adjacent letters). This segmentation allows the system to quickly eliminate exact matches before proceeding to more complex phonetic variations, significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing primary, secondary, and tertiary string variations of search terms in an index structure before actual search queries are processed. This preliminary preparation allows rapid retrieval during user queries without needing to compute all variations in real-time, thus reducing processing time while maintaining comprehensive search coverage.
2Measurement precision
If multiple search queries are performed to handle different types of spelling errors, then retrieval accuracy is improved, but the number of searches and computational complexity increase
Solution Approach 1:
The patent merges multiple search strategies into a unified algorithm that handles exact matches, one-letter omissions, and adjacent transpositions within a single cohesive search process. By combining these approaches and using a unified index structure that stores all string variations, the system reduces overall complexity compared to executing separate search queries for each error type.
Solution Approach 2:
The patent creates a universal search algorithm that can handle multiple types of spelling errors (exact matches, one-letter omissions, adjacent transpositions) through a single multi-functional search process. The index structure is designed to store and query all string variations simultaneously, making the system universally applicable to different error types without requiring separate specialized searches for each case.
3Measurement precision
If the search system indexes all possible string variations of words, then retrieval accuracy for misspelled words is improved, but the size of the index and memory requirements increase
Solution Approach 1:
The patent applies local quality by selectively creating and storing only the necessary string variations (primary, secondary, and tertiary) for each search term based on its specific characteristics and the types of errors most likely to occur. This selective indexing approach ensures that the index contains sufficient variations to handle common spelling errors while avoiding the need to store all possible permutations, thus controlling index size.
Solution Approach 2:
The patent uses partial action by generating and indexing only a limited set of string variations (omitting one letter, transposing adjacent letters) rather than all possible variations. This partial approach provides sufficient coverage for the most common spelling errors while keeping the index size manageable, avoiding the excessive action of indexing every possible permutation.
Data Source
AI summary
Useful search results are obtained when the search logic retrieves, not only text strings, or “words,” that match exactly the search arguments but also when the search logic retrieves phonetically similar words, which are misspelled. The method and system disclosed herein provide both results faster than known conventional methods and systems, and in some embodiments such speed may be achieved using conventional resources as described.