Content Meaning Interpretation Through Identigen Pairing
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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 such as data accuracy and variance in language interpretation, leading to difficulties in producing useful information.
Innovation Solution
A computing system that utilizes AI servers to ingest content, extract knowledge, and interact with user devices to facilitate the generation and utilization of knowledge, including pattern recognition and grammatical analysis to improve data interpretation.
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 word ambiguities, but the accuracy of data interpretation remains insufficient due to language variance and regional dialects
Solution Approach 1:
The patent introduces an intermediary layer between raw text data and computer processing. This intermediary involves translating text into a standardized internal representation format that mediates between the variability of human language and the need for consistent computational processing, thereby improving interpretation accuracy while maintaining language adaptability
Solution Approach 2:
The system changes the parameters of text representation by transforming variable linguistic forms into standardized semantic representations. This parameter transformation allows the system to handle language variance and regional dialects while maintaining consistent interpretation accuracy across different text formats
2Adaptability or versatility
If the same or similar words are used to represent different concepts in many languages and regional dialects, then language versatility is maintained, but data accuracy and interpretation consistency deteriorate
Solution Approach 1:
The patent segments the text processing into distinct functional layers: linguistic analysis, semantic interpretation, and knowledge representation. This segmentation allows the system to handle multiple languages and dialects at the linguistic level while maintaining consistent knowledge representation at the semantic level, resolving the contradiction between language versatility and interpretation reliability
Solution Approach 2:
The system creates standardized copies of semantic meanings that are independent of the specific words or languages used. By copying the essential meaning into a standardized representation format, the system maintains consistency across different linguistic expressions while preserving language versatility
3Quantity of substance
If large volumes of data are stored in information systems, then data availability increases, but the difficulty of producing useful information increases due to data volume and variance
Solution Approach 1:
The patent extracts useful information from large volumes of data by separating the signal from the noise. The system extracts meaningful patterns and knowledge representations from the data sea, removing irrelevant information and reducing processing complexity while maintaining the ability to handle large data volumes
Solution Approach 2:
The system transforms data from a two-dimensional storage format into a three-dimensional knowledge structure by adding semantic and contextual dimensions. This dimensional transformation allows the system to manage large data volumes more effectively by organizing information in multiple layers of abstraction, reducing processing complexity
Data Source
AI summary
A method for execution by a computing device includes identifying sets of identigens for words of a phrase. The method further includes identifying levels of pairing of sequentially adjacent identigens between each adjacent set of identigens and interpreting every permutation of sequentially adjacent identigens to produce permutations of entigen groups. The method further includes determining permutation scores for the permutations of entigen groups and selecting an entigen group based on the permutation scores to produce a selected entigen group that represents a most likely meaning interpretation of the phrase.