Transliteration Device Using Sub-Phonetic Units to Reduce Memory
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Solution Overview
Problem
Existing electronic devices face inefficiencies in transliteration due to memory-intensive methods and dependency on specific language configurations, making it difficult for users to input text in their native language using devices designed for another language.
Innovation Solution
A method and device that convert input text into target language sub-phonetic units, using a database to rank and identify candidate words based on predefined criteria, eliminating the need for complex rules and reducing memory usage by learning on sub-phonetic units rather than characters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static mapping of source language character with target language character is stored in the electronic device, then transliteration can be performed, but excessive memory use occurs
Solution Approach 1:
The patent segments the transliteration problem from character-level mapping to sub-phonetic unit level. Instead of storing complete character mappings for all possible source-target language combinations, the system breaks down words into sub-phonetic units and stores only the essential mapping relationships at this granular level, significantly reducing memory requirements while maintaining transliteration capability
Solution Approach 2:
The patent introduces sub-phonetic units as an intermediary layer between source language characters and target language characters. This intermediary representation allows the system to perform transliteration without storing exhaustive character mapping tables, as the sub-phonetic units serve as a compact intermediate form that captures the essential phonetic information needed for translation
2Measurement precision
If decision tree with context-based rules is created for each source language character, then transliteration accuracy improves, but large amount of memory is required
Solution Approach 1:
The patent applies segmentation by breaking down the complex context-dependent translation problem into smaller sub-phonetic unit translations. Instead of creating decision trees for entire characters with all their contextual variations, the system segments words into sub-phonetic units and applies simpler mapping rules at this granular level, reducing memory requirements while maintaining accuracy
Solution Approach 2:
The patent changes the parameter of analysis from character-level to sub-phonetic unit level. This parameter change allows the system to use simpler mapping relationships at the sub-phonetic level rather than complex character-level decision trees, achieving comparable or better accuracy with significantly reduced memory usage
3Measurement precision
If bilingual dictionary with word-pairs is used for transliteration, then transliteration quality enhances, but large amount of memory is required to store segments and words
Solution Approach 1:
The patent segments complete words into sub-phonetic units and stores mapping information at this smaller scale. Instead of requiring a bilingual dictionary to store every possible word-pair combination, the system stores sub-phonetic unit mappings that can be recombined to form complete translations, dramatically reducing the memory footprint while maintaining translation quality
Solution Approach 2:
The patent transitions from storing transliteration data in a two-dimensional word-pair format to a more granular sub-phonetic unit level. This dimensional change allows the system to build complete word translations from smaller building blocks, reducing the overall data storage requirements while preserving translation accuracy
4Adaptability or versatility
If training model is executed for each instance of source language, then transliteration adapts to specific language pairs, but processing time increases
Solution Approach 1:
The patent creates a universal sub-phonetic unit mapping system that can handle multiple source-language-to-target-language combinations with a single training model. The sub-phonetic units serve as a universal intermediate representation that works across different language pairs, eliminating the need to execute separate training models for each language instance and significantly reducing processing time
Data Source
AI summary
A method for transliteration includes receiving input such as a word, a sentence, a phrase, and a paragraph, in a source language, creating source language sub-phonetic units for the word and converting the source language sub-phonetic units for the word to target language sub-phonetic units, retrieving ranking for each of the target language sub-phonetic units from a database and creating target language words for the word in the source language based on the target language sub-phonetic units and ranking of the each of the target language sub-phonetic units. The method further includes identifying candidate target language words based predefined criteria, and displaying candidate target language words.