Entigen Group Correction for Knowledge Database Accuracy
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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, allowing for accurate and context-specific responses to user queries through a network of user devices, content sources, and transactional servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pattern recognition techniques and statistical reasoning are used to process text, then the system can attempt to overcome word ambiguities, but the volume of available data and interpretation variances make accurate knowledge extraction difficult
Solution Approach 1:
The system segments the knowledge extraction process into distinct modules: an interpretation module that breaks down text into conceptual units, a knowledge base that stores structured knowledge, and a query processing module that matches queries against the knowledge base. This segmentation allows the system to handle large volumes of data by processing it in manageable chunks through structured transformation steps.
Solution Approach 2:
The patent introduces an intermediary representation layer between raw text and final knowledge representations. The interpretation module acts as a mediator that transforms ambiguous text into standardized conceptual structures, which then interact with the knowledge base through well-defined interfaces, enabling reliable knowledge extraction despite data volume and interpretation variances.
2Adaptability or versatility
If the same or similar words are used to represent different concepts across languages and dialects, then communication can be simplified, but ambiguities in word meanings increase
Solution Approach 1:
The system applies local quality by maintaining language-specific interpretation modules that preserve the nuances of different languages and dialects while transforming them into a unified conceptual representation. Each language variant is handled with its specific interpretive rules, allowing the system to adapt to cross-language queries while preventing information loss through localized interpretation followed by global standardization.
Solution Approach 2:
The patent resolves word ambiguity by adding a new dimension of representation - transforming words from their surface form (which can be ambiguous across languages) into a deeper conceptual representation layer. This dimensional transformation maps multiple language variants and their ambiguous meanings into a unified conceptual space where the true meaning is preserved despite surface-level variations.
3Ease of manufacture
If grammar based techniques are used to classify words into grammatical types, then the system can force words to support grammatical operations, but the words do not necessarily identify what they are actually trying to describe
Solution Approach 1:
The system segments the analysis into two independent stages: first, grammatical analysis that ensures structural integrity by classifying words into grammatical types and verifying sentence structure; second, semantic analysis that identifies the actual meaning of words and their relationships. This segmentation allows the system to maintain both grammatical correctness and semantic accuracy without forcing meaning onto structure.
Solution Approach 2:
The patent introduces an intermediary semantic representation layer between the grammatical structure and the final knowledge extraction. The interpretation module serves as a mediator that receives grammatically valid structures and transforms them into meaningful conceptual representations, ensuring that grammatical operations do not override actual semantic meaning while maintaining structural integrity.
Data Source
AI summary
A method includes detecting a defective entigen group within a knowledge database. The defective entigen group includes entigens and one or more entigen relationships between at least some of the entigens. The defective entigen group represents knowledge of a topic. The method further includes obtaining corrective content for the topic based on the defective entigen group and generating a corrective entigen group based on the corrective content. The method further includes updating the defective entigen group utilizing the corrective entigen group to produce a curated entigen group.