Thought Representation System for NLP Time Sense and Possibility
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Solution Overview
Problem
Current Natural Language Processing (NLP) and Natural Language Understanding (NLU) systems are limited in processing sequences of phonetics-based words, missing aspects like time sense and possibility, and fail to accurately represent human thoughts, especially in informal languages and spoken language, which are not well-formed grammatical sentences.
Innovation Solution
A system comprising an entity look-up subsystem, a controller for thought representation formation and reasoning, a multi-word entities buffer, an entity knowledge base, and a predictive word meaning memory, which converts sequences of words and concurrent non-verbal data into thought representations, using a language-independent phonetic representation inspired by IPA standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current NLP/NLU systems process sentences one-by-one using rule-based or neural network algorithms, then processing speed and simplicity are maintained, but the ability to capture time sense, possibility, and represent human thoughts accurately is lost
Solution Approach 1:
The system segments the input speech stream into discrete phonetic units and processes them sequentially through multiple reasoning layers. Each layer captures different aspects of human thought (time sense, possibility, certainty), breaking down the complex task of thought representation into manageable segments that can be processed independently and then integrated.
Solution Approach 2:
The patent introduces a temporal dimension by processing phonetic sequences in order rather than treating sentences as isolated units. This sequential processing through multiple reasoning layers adds dimensions of time sense, possibility, and certainty to the representation, transforming flat sentence processing into multi-dimensional thought representation.
2Adaptability or versatility
If NLP systems are designed to handle only well-formed grammatical sentences, then processing accuracy for formal language is improved, but the ability to process informal spoken language and sequences of phonetics is deteriorated
Solution Approach 1:
The system changes the fundamental parameter of input representation from grammatical sentences to sequences of phonetic units. This parameter change allows the system to accept any spoken language input regardless of grammatical correctness, while the multi-layer reasoning process maintains processing accuracy by capturing the semantic meaning through phonetic sequence analysis rather than grammatical structure analysis.
3Loss of information
If systems process sentences in isolation without contextual information, then processing simplicity is maintained, but the ability to capture time sense and contextual meaning is lost
Solution Approach 1:
The system performs preliminary processing of each phonetic unit as it arrives in the sequence, maintaining a running context representation that is continuously updated. This preliminary action allows the system to capture time sense and contextual meaning incrementally without waiting for the complete sentence or requiring reprocessing, thus retaining information while maintaining processing efficiency.
Data Source
AI summary
A system to convert sequences of words, along with concurrent non-verbal data, into thought representations, said system being used in association with a language understanding system, where words to thought transformation is needed, is disclosed. Said system comprises: an entity look-up subsystem that comprises: a pre-processing unit, a word database, and a cache; a controller or thought representation formation and reasoning unit; a multi-word entities buffer; an entity knowledge base; a predictive word meaning memory; and an output thought representation unit.