Word String Interpretation via Entigen Grouping
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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 expressing knowledge.
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 context-aware responses to queries through a network of user devices and content sources.
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 system still struggles with accurate interpretation across different languages and dialects
Solution Approach 1:
The patent introduces an intermediary representation layer between raw text and semantic meaning. Words are first mapped to identigens (identifiers of things), which are then grouped into entigens (entities with consistent meaning across languages). This intermediary layer of identigens and entigens serves as a mediator that decouples language-specific surface forms from language-independent conceptual meanings, enabling accurate cross-lingual interpretation without requiring language-specific processing for each concept.
Solution Approach 2:
The patent segments the meaning representation into hierarchical levels: words map to identigens (fine-grained identifiers), which group into entigens (coarser conceptual entities), which then form concepts. This segmentation allows the system to handle linguistic variations at the word level while maintaining stable meaning at the entigen level, resolving the contradiction between precision in individual word interpretation and adaptability across languages.
2Quantity of substance
If the system processes large volumes of data, then more information can be extracted, but the complexity of processing and interpreting the data increases
Solution Approach 1:
The system performs self-service through automated batch processing of large datasets. The background process automatically ingests content from multiple sources, extracts identigens and entigens, and builds the knowledge base without manual intervention. This automation allows the system to handle large data volumes while keeping the user interface simple, as the complex processing occurs in the background.
Solution Approach 2:
The system performs preliminary action by pre-processing and indexing large datasets in advance to build a comprehensive knowledge base of identigens and entigens. This pre-computed knowledge structure is then available for rapid querying without re-processing the entire dataset, reducing the complexity of handling large data volumes during actual operation.
3Measurement precision
If the system creates detailed representations of meaning, then interpretation accuracy improves, but the computational resources required increase
Solution Approach 1:
The patent applies local quality by creating detailed meaning representations only where needed. Instead of uniformly processing all text with high computational intensity, the system uses simpler word-to-identigen mapping for straightforward cases and reserves complex entigen grouping and concept formation for specific processing stages. This localized application of computational intensity maintains precision for critical interpretation tasks while reducing overall resource consumption.
Data Source
AI summary
A method includes obtaining a string of words and determining whether two or more words of the string of words are in a word group. When the two or more words are in the word group, the method further includes retrieving a set of word group identigens for the word group and retrieving sets of word identigens for remaining words of the string of words. The method further includes determining whether a word group identigen of the set of word group identigens and word identigens of the sets of word identigens creates an entigen group that is a valid interpretation of the string of words. When the entigen group is the valid interpretation of the string of words, the method further includes outputting the entigen group.