Feature-Based Dictionary Segmentation for Mobile Spell Checking
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Solution Overview
Problem
Conventional spell checking and auto-correction systems are resource intensive, consuming large CPU processing cycles and volatile memory space, which is a challenge in mobile devices that require real-time performance and accuracy.
Innovation Solution
The system splits an original digital dictionary into sub-dictionaries based on common features such as high-frequency words or vector spaces, organizing them in a hierarchical structure to reduce memory usage and enable efficient searching, allowing for real-time linguistic error detection and correction with reduced CPU and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional spell checking and auto-correction systems are used, then high accuracy in error detection and correction is achieved, but large CPU processing cycles and volatile memory space are consumed
Solution Approach 1:
The patent divides the original digital dictionary into multiple sub-dictionaries based on common features such as high-frequency words, vector spaces, or contextual categories. This segmentation reduces the memory footprint and processing requirements while maintaining comprehensive spell checking capability, as the system only needs to load and process relevant sub-dictionaries rather than the entire dictionary.
Solution Approach 2:
The patent creates context-sensitive sub-dictionaries tailored to specific domains, languages, or usage scenarios. Each sub-dictionary is optimized for particular contexts (e.g., technical terms for programming, common words for general communication), allowing the system to use higher quality, more accurate data structures for specific cases while reducing overall resource requirements by not loading all possible data.
2Speed
If real-time performance is demanded for spell checking and auto-correction in mobile devices, then user experience is improved, but CPU capacity and memory space become limiting factors
Solution Approach 1:
By segmenting the dictionary into smaller sub-dictionaries, the system reduces memory allocation requirements and enables faster loading times. This allows mobile devices with limited resources to maintain real-time spell checking performance without requiring excessive CPU or memory capacity.
Solution Approach 2:
The patent implements dynamic sub-dictionary selection based on context analysis. The system dynamically determines which sub-dictionary to use based on the input text's characteristics, such as language type, domain, or detected error patterns. This dynamic approach optimizes performance by loading only the necessary sub-dictionary for the current context rather than maintaining all sub-dictionaries in memory simultaneously.
Data Source
AI summary
A method for organizing and processing feature based data structures that can be used in linguistic spell checking and auto-correction, comprising: splitting an original dictionary into sub-dictionaries based on different values of a common feature such as high frequency words; receiving an input text that contains errors; determining a sub-dictionary selection feature from the input human-readable text; selecting the sub-dictionary based on the determined sub-dictionary selection feature; executing a first matching in the selected sub-dictionary, wherein a match is found if a similarity between the characters, words, or phrases in proximity of the errors in the input text and a character, word, or phrase in the sub-dictionary is above a threshold; if a unique match is found, the result is returned as an output to correct the errors; otherwise, executing a second matching with a raised threshold, and repeating the second matching until a unique match is found.