Query Entigen Group Processing for Text Ambiguity Resolution
Find Innovative SolutionsGenerate Solutions
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
1Quantity of substance
If traditional pattern recognition and statistical reasoning techniques are used to process text, then the system can handle large volumes of data, but the accuracy of information extraction deteriorates due to ambiguities in word meanings and variations in text interpretation across languages and dialects
Solution Approach 1:
The patent introduces an intermediary layer between raw text and interpretation that identifies and resolves contradictions in word meanings. This intermediary process analyzes multiple possible interpretations of ambiguous terms and selects the most appropriate meaning based on context, thereby maintaining high accuracy while processing large volumes of diverse textual data from different languages and dialects
Solution Approach 2:
The system dynamically changes the parameter of word meaning interpretation by identifying contradictions and selecting from multiple possible meanings. Instead of using a fixed statistical approach, the system adapts the meaning parameters based on contextual analysis, allowing accurate extraction of information even when the same word has different meanings across languages and dialects
2Ease of operation
If grammar-based techniques are used to classify words into grammatical types, then the system can process text structure, but the ability to identify actual word meanings deteriorates as words are forced to support grammatical operations without identifying what they are trying to describe
Solution Approach 1:
The patent segments the text processing function into two independent parts: grammatical structure analysis and meaning identification. By separating these functions, the system can classify words into grammatical types for structural processing while simultaneously identifying the actual meanings of words through contradiction analysis, preventing loss of semantic information
Solution Approach 2:
An intermediary meaning identification process is introduced that operates alongside grammatical classification. This intermediary layer analyzes what words are actually trying to describe rather than just forcing them into grammatical categories, preserving word meaning information while maintaining text processing capability
3Adaptability or versatility
If the system attempts to interpret text with multiple possible meanings, then the system can handle linguistic variations, but the time required to process and resolve ambiguities increases
Solution Approach 1:
The system performs preliminary identification of potential contradictions and their possible meanings before final interpretation is required. By pre-analyzing ambiguous terms and preparing multiple candidate meanings, the system reduces the time needed for final resolution while maintaining the ability to handle linguistic variations across different languages and dialects
Solution Approach 2:
The contradiction identification and resolution process uses feedback mechanisms where the system continuously refines its interpretation by analyzing contextual clues and eliminating inconsistent meanings. This feedback loop enables efficient processing by quickly converging on the correct interpretation rather than exhaustively evaluating all possible meanings
Data Source
AI summary
A method includes generating a query entigen group for a query in accordance with identigen rules. The query entigen group represents a most likely interpretation of the query. The method further includes obtaining an embellished entigen group from a knowledge database based on the query entigen group. The embellished entigen group substantially includes the query entigen group and a set of embellishing entigens. The method further includes selecting a set of response entigens from the embellished entigen group in accordance with a response embellishment approach to produce a response entigen group. The set of response entigens includes at least one embellishing entigen of the set of embellishing entigens. The method further includes generating a response phrase based on the response entigen group. The response entigen group represents a most likely interpretation of the response phrase.