Aptitude Level Entigen Group Conversion for Text Interpretation
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Solution Overview
Problem
Current computing systems face challenges in extracting useful information from large datasets due to issues like data volume, accuracy, 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, analyze queries, and generate knowledge by transforming and interpreting text through modules like collections, identigen entigen intelligence, and answer resolution, which select and gather content based on quality thresholds to provide accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern recognition techniques are used to process text, then the system can attempt to overcome ambiguities in word meanings, but the accuracy of information extraction remains limited due to variations in language and dialect interpretation
Solution Approach 1:
The patent introduces an intermediary representation layer between raw text and final information extraction. Text is first converted to identigens (identity tokens) that capture linguistic meaning, then to entigens (concept tokens) that represent universal concepts. This intermediary layer of standardized representations acts as a mediator that translates diverse language variations into a unified representation space, enabling accurate information extraction across different languages and dialects.
Solution Approach 2:
The system changes the representation parameters of text from raw linguistic forms to standardized identigen-entigen pairs. By transforming text into this standardized parameter space where concepts are represented consistently regardless of linguistic variation, the system achieves both high accuracy in information extraction and adaptability to different languages and dialects.
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 patent segments the complex processing task into distinct modular components: text processing module that converts text to identigens, identigen to entigen translation module, and information extraction module. This segmentation divides the large-scale data processing into manageable stages, each handled by specialized modules, reducing the overall system complexity while enabling processing of large data volumes.
Solution Approach 2:
The system creates standardized copies of concepts through identigen-entigen pairs that represent universal meanings. Instead of processing each unique text variation individually, the system creates standardized representations that can be reused across multiple data items, reducing processing complexity while handling large data volumes efficiently.
3Measurement precision
If the system uses standardized representations to improve accuracy, then information extraction quality improves, but the difficulty of detecting and measuring language variations increases
Solution Approach 1:
The identigen-entigen representation system serves as an intermediary that makes language variations detectable and measurable. By translating diverse text into standardized representations, the system can easily measure and compare concepts across different languages and dialects, turning the previously difficult task of detecting variations into a straightforward comparison of standardized tokens.
Data Source
AI summary
A method performed by a computing device includes generating a first aptitude level entigen group for a first aptitude level phrase in accordance with identigen rules. The first aptitude level entigen group represents a most likely interpretation of the first aptitude level phrase. The method further includes obtaining a multiple aptitude level entigen group from a knowledge database based on the first aptitude level entigen group. The multiple aptitude level entigen group includes the first aptitude level entigen group. The method further includes generating a second aptitude level entigen group utilizing the multiple aptitude level entigen group. The method further includes generating a second aptitude level phrase based on the second aptitude level entigen group. The second aptitude level entigen group represents a most likely interpretation of the second aptitude level phrase.