Kernel String Descriptors for Typo-Tolerant Text Matching
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Solution Overview
Problem
Current data analysis methods lack tools to reliably find patterns in text strings with variability and typing error tolerance, as they typically compare characters forcefully without considering proximity or similarity.
Innovation Solution
A method that generates numeric descriptors for text units by parsing text strings into words, applying mappings to characters, normalizing values, and using kernel-based transformations to create fixed-size numeric vectors for pattern identification and comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If character-by-character comparison methods are used to compare text strings, then comparison precision is improved, but the system cannot accommodate typing error tolerance and variability
Solution Approach 1:
The patent transforms text strings into numeric descriptors by mapping characters to numeric values and applying kernel transformations. This parameter change from character space to numeric space enables both precise pattern recognition and tolerance for typing errors, as the numeric descriptors capture semantic similarity rather than exact character matches.
Solution Approach 2:
The patent introduces kernel-based numeric descriptors as an intermediary between raw text strings and comparison operations. These descriptors serve as a mediator that preserves the essential pattern information while being robust to character-level variations and typing errors, enabling flexible yet accurate text analysis.
2Reliability
If forceful character matching methods are applied to ensure accurate pattern identification, then pattern recognition reliability is improved, but the method lacks flexibility for variable-length strings and typing errors
Solution Approach 1:
The patent applies kernel transformations to map text strings into a numeric descriptor space where pattern recognition becomes more robust. This transformation maintains reliability for pattern identification while naturally accommodating variable-length strings and typing errors through the mathematical properties of kernel functions.
Solution Approach 2:
The patent transitions from comparing strings in their original character space to comparing them in a transformed numeric descriptor space. This dimensionality change enables the system to capture patterns at a higher level of abstraction, making it simultaneously reliable for pattern recognition and adaptable to variations in string length and character accuracy.
3Measurement precision
If exact character matching is performed for each position in text strings, then measurement accuracy is improved, but the complexity of handling variable-length strings and typing errors increases
Solution Approach 1:
The patent simplifies the comparison process by transforming text strings into fixed-dimensional numeric descriptors through kernel transformations. This parameter change from variable-length strings to fixed-size numeric vectors maintains measurement accuracy for pattern recognition while significantly reducing the complexity of handling variable lengths and typing errors.
Data Source
AI summary
An aspect of generating a descriptor representative of a text unit includes receiving as input a text string into a memory accessible to a processor and parsing the text string into words. For each word in the text string, a word is selected to form a selected word and, for each character in the selected word, a selected mapping is applied to a character of the selected word to create a mapped value, the mapped value is normalized to create a normalized value, and a numeric descriptor is generated using the normalized value. The numeric descriptors are collected for the selected word to create a word descriptor and the collected numeric descriptors are saved in a storage device by the processor.