IEI Module Knowledge Curation via Entigen Grouping
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Solution Overview
Problem
Current technologies are inadequate in understanding and extracting meaningful information from vast amounts of data, as they rely on word-based pattern matching and statistical modeling, failing to comprehend the true meaning of text, which limits their ability to provide insightful knowledge and analyze data effectively.
Innovation Solution
A computing system that utilizes an Identigen Entigen Intelligence (IEI) module to interpret and analyze content, transforming words into groupings and identifying elements to produce a truest meaning representation, enabling the generation of knowledge and answers to queries by mapping human expressions to precise computer representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If word-based pattern matching and statistical modeling are used to process data, then data processing speed is improved, but the ability to comprehend true meaning of text deteriorates
Solution Approach 1:
The patent segments text into elements and groupings of elements, creating a hierarchical structure that allows both rapid pattern recognition and meaningful interpretation. This segmentation enables the system to process data quickly while maintaining comprehension accuracy by organizing information in structured units.
Solution Approach 2:
The patent introduces an intermediary representation layer between raw text and meaning interpretation. This intermediary structure transforms words into groupings and elements that bridge the gap between statistical processing and true comprehension, allowing the system to maintain both speed and accuracy.
2Measurement precision
If sophisticated pattern matching algorithms are applied to identify similar word patterns, then query matching capability is improved, but the ability to understand implicit meaning deteriorates
Solution Approach 1:
The patent adds a new dimension to text processing by transforming words into groupings of elements, creating an additional layer of abstraction. This dimensional transformation allows the system to capture implicit meaning that traditional pattern matching misses, while maintaining query matching precision through the structured element hierarchy.
3Quantity of substance
If massive data files are stored for analysis, then data availability is improved, but the ability to extract useful information deteriorates
Solution Approach 1:
The patent extracts meaningful elements from massive data files by transforming text into structured groupings of elements. This extraction process isolates useful information from the overwhelming volume of data, enabling effective analysis while maintaining data availability for future queries.
Data Source
AI summary
A method includes generating a plurality of entigen groups from a plurality of phrases, where the plurality of entigen groups represents a plurality of most likely meanings for the plurality of phrases. The method further includes determining an initial interpretation of the related topic based on the plurality of most likely meanings for the plurality of phrases and generating a plurality of scores for the plurality of entigen groups based on the initial interpretation and source information of the plurality of phrases. The method further includes interpreting the plurality of scores in relation to the initial interpretation to determine a confidence level of the initial interpretation and when the confidence level of the initial interpretation compares favorably to a confidence threshold, indicating that the initial interpretation is reliable.