Semantic Entigen Mapping for Text Interpretation
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Solution Overview
Problem
Current computing systems face challenges in extracting useful information from large datasets due to data volume, accuracy issues, and variations in how text is interpreted across languages and dialects, leading to ambiguities in word meanings.
Innovation Solution
A computing system that utilizes AI servers to ingest content, identify elements, interpret queries, and generate knowledge by transforming words into groupings and entigens, enabling accurate and precise representation of human expressions, and answering questions based on a fact-based database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern recognition techniques and statistical reasoning are used to process text, then the system can attempt to overcome ambiguities in word meanings, but the system still struggles with accurate interpretation across different languages and dialects
Solution Approach 1:
The patent introduces an intermediary layer of semantic representation that mediates between raw text and computational processing. Words are transformed into semantic objects with standardized meanings, allowing the system to handle language variations by mapping different linguistic expressions to common semantic concepts rather than relying on direct pattern matching.
Solution Approach 2:
The system changes the representation parameters of text from literal word forms to semantic concepts. By transforming text into a standardized semantic representation format, the system can accurately interpret meaning across different languages and dialects while maintaining computational efficiency.
2Quantity of substance
If the system processes large volumes of data, then more information can be analyzed, but the complexity of extracting useful information increases
Solution Approach 1:
The patent extracts meaningful semantic information from raw text data by identifying and isolating key semantic objects and relationships. This extraction process transforms unstructured data into organized semantic representations that can be efficiently stored and queried, reducing the complexity of processing large datasets.
Solution Approach 2:
The system segments text processing into distinct stages: tokenization, semantic analysis, and knowledge representation. By dividing the complex task of information extraction into manageable segments, the system can efficiently process large volumes of data while maintaining manageable computational complexity.
3Ease of operation
If grammar based techniques are used to classify words into grammatical types, then the system can study word distribution to form grammatical sentences, but the system cannot accurately identify what each word is actually trying to describe
Solution Approach 1:
The patent introduces semantic objects as intermediaries between grammatical structure and meaning. Words are classified into grammatical types for sentence formation, while simultaneously being mapped to semantic concepts that capture their actual meaning. This intermediary layer allows the system to maintain both grammatical correctness and semantic accuracy.
Solution Approach 2:
The system adds a semantic dimension to the traditional grammatical representation. By mapping words to semantic concepts in addition to their grammatical categories, the system gains the ability to identify what words are actually describing while maintaining their grammatical functionality for sentence formation.
Data Source
AI summary
A method includes identifying a set of identigens for each word of a first phrase of a phrase group to produce a first plurality of sets of identigens and determining whether first and second identigen rules are applicable to the first plurality of sets of identigens. When the first and second identigen rules are applicable to the first plurality of sets of identigens, the method further includes identifying a set of identigens for each word of a second phrase of the phrase group to produce a second plurality of sets of identigens and determining that the first identigen rules are applicable to the second plurality of sets of identigens. The method further includes identifying one valid identigen of each set of identigens of the first plurality of sets of identigens by applying the first identigen rules to the first plurality of sets of identigens to produce a first entigen group.