Dual Authentication Text Matching Using Levenshtein and Keyboard Distance
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Solution Overview
Problem
Current methods for matching user-inputted data to stored data in databases are inadequate in distinguishing between new entries and existing entries with spelling variations, abbreviations, or misspellings, leading to potential duplicate entries and database inconsistencies.
Innovation Solution
A dual authentication method utilizing the Levenshtein distance algorithm and keyboard distance algorithm to compare user-inputted text with stored text, identifying matches by determining the number of differences and physical keyboard distances between characters, and adjusting thresholds based on the number of matching fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exact matching methods are used to compare user-inputted text to stored data, then database consistency is maintained, but the system cannot identify non-exact matches (spelling variations, abbreviations, misspellings) leading to duplicate entries
Solution Approach 1:
The patent changes the matching parameter from exact string equality to distance-based metrics (Levenshtein distance and keyboard distance). By introducing numerical thresholds for these distance metrics, the system can flexibly identify matches even when text differs slightly, thus resolving the contradiction between maintaining consistency and identifying non-exact matches
Solution Approach 2:
The patent introduces intermediate computational metrics (Levenshtein distance calculation and keyboard distance calculation) as mediators between the user-inputted text and stored data. These intermediaries transform the binary match/no-match decision into a graded assessment, allowing the system to distinguish between true duplicates and legitimate variations
2Adaptability or versatility
If the Levenshtein distance algorithm is used to determine text similarity, then spelling variations can be identified, but the system cannot distinguish between accidental typos and intentional different entries
Solution Approach 1:
The patent merges two different distance metrics (Levenshtein distance and keyboard distance) into a composite matching system. By combining these metrics with different thresholds, the system achieves more precise discrimination between accidental typos (which would have low keyboard distance) and intentional different entries (which would have high keyboard distance), resolving the precision problem
3Measurement precision
If multiple authentication methods are used to verify text matches, then match accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by first calculating the Levenshtein distance as a quick initial filter. Only when this distance falls within an acceptable range does the system proceed to the more computationally intensive keyboard distance calculation. This staged approach maintains high accuracy while minimizing unnecessary processing time for clearly non-matching entries
Data Source
AI summary
A computer-implemented method for matching user inputted text to stored text. The user inputted text is compared to each of the text strings stored in a database using a Levenshtein distance algorithm. For each comparison, the Levenshtein distance is analyzed to determine exact matches, non-matches, and probable matches. Probable matches are further analyzed using a keyboard distance algorithm to differentiate between matches and non-matches.