Entigen Query Interpretation for Multilingual Knowledge Extraction
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Solution Overview
Problem
Existing data processing systems struggle to effectively generate and utilize knowledge from large volumes of data due to issues with data accuracy and linguistic ambiguities, particularly in handling multiple languages and regional dialects.
Innovation Solution
The system employs an entigen construct, including AI servers that ingest content, extract knowledge, and interact with user devices to facilitate the generation and utilization of knowledge through pattern recognition and statistical reasoning, utilizing modules like the collections module, IEI module, and query module to interpret and respond to queries.
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 linguistic ambiguities, but the accuracy of data interpretation remains insufficient for handling multiple languages and regional dialects
Solution Approach 1:
The patent introduces an entigen as an intermediary construct between human language and computer processing. The entigen serves as a mediator that captures the essential meaning of text while being independent of specific languages or dialects, allowing accurate interpretation across multiple languages without requiring language-specific processing for each dialect variant
Solution Approach 2:
The system changes the fundamental parameter of data representation from raw text to entigen constructs. By transforming text into entigens that represent core concepts rather than surface-level words, the system achieves language-independent accuracy while maintaining the ability to handle diverse linguistic expressions
2Adaptability or versatility
If the same or similar words are used to represent different concepts in different languages and regional dialects, then communication can be maintained, but the system struggles to produce useful information due to linguistic ambiguities
Solution Approach 1:
The patent segments the meaning representation into distinct entigen components that separate conceptual meaning from linguistic form. By dividing the interpretation task into identifying concepts (entigens) versus identifying specific words, the system can handle linguistic variations without losing knowledge accuracy
Solution Approach 2:
The system creates a copy of the essential meaning (entigen) that is independent of the original linguistic expression. This entigen copy preserves the core concept while being free from the ambiguities of specific word choices across different languages and dialects
3Ease of operation
If grammar based techniques are used to classify words into major grammatical types, then words can be forced to support grammatical operations, but the system cannot identify what the word is actually trying to describe
Solution Approach 1:
The entigen acts as an intermediary that bridges grammatical structure and semantic meaning. Rather than forcing words into grammatical categories without understanding their meaning, the entigen captures the actual concept being described, allowing both grammatical processing and accurate meaning identification to work together
Data Source
AI summary
A method performed by a computing device includes identifying a symbolic representation of a query of a topic to produce a plurality of tokens. The method further includes generating a first equation package for the plurality of tokens that corresponds to a first permutation of a plurality of permutations of interpretation of the plurality of tokens based on one or more different meanings of the symbolic representations of the query. The method further includes updating, utilizing a knowledge database, the first equation package for the plurality of tokens that optimizes an interpretation confidence level for the plurality of tokens to produce a second equation package that includes a sequence of second selected equation elements that corresponds to a second permutation of the plurality of permutations of interpretation of the plurality of tokens representing a most likely interpretation of the query.