Entigen Group Generation for Reliable Query Responses
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computing systems face challenges in extracting useful information from large volumes of data due to issues like data accuracy and variations in how text is interpreted across languages and dialects, making it difficult to generate meaningful knowledge.
Innovation Solution
A computing system that utilizes AI servers to ingest content, analyze queries, and generate knowledge by selecting appropriate content sources, processing text to identify relevant information, and providing responses through a network of user devices and servers, employing modules like collections, identigen entigen intelligence, and query modules to enhance data representation and interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern recognition techniques and statistical reasoning are used to process text, then the ability to interpret text and overcome ambiguities is improved, but the complexity of the system increases
Solution Approach 1:
The system segments text processing into distinct grammatical types (functional words, nouns, adjectives, verbs, adverbs) and processes each type through specific grammatical operations. This segmentation allows complex text interpretation to be broken down into manageable, rule-based components that reduce overall system complexity while maintaining interpretation accuracy.
Solution Approach 2:
The system changes the parameter of text representation by classifying words into grammatical types and applying grammatical operations. This transformation converts raw text into a structured format with defined grammatical properties, enabling more precise text interpretation through rule-based processing rather than relying solely on complex statistical reasoning.
2Manufacturing precision
If grammar based techniques are used to distribute words within sentences, then grammatical correctness is improved, but the ability to identify actual word meanings deteriorates
Solution Approach 1:
The system segments words into grammatical types and processes each type through specific grammatical operations while preserving the original word forms. This allows grammatical correctness to be enforced through structure without losing the semantic information contained in the actual words, as the grammatical classification is an additional layer rather than a replacement of word meanings.
Solution Approach 2:
The system introduces grammatical types as an intermediary layer between raw words and sentence construction. This intermediary classification system enables grammatical rules to be applied systematically while the original words and their meanings are preserved throughout the process, preventing information loss.
3Reliability
If multiple content sources are ingested to improve knowledge extraction, then the comprehensiveness of information is improved, but the difficulty of processing and analyzing data increases
Solution Approach 1:
The system segments data processing into distinct modules: collections module for data gathering, identigen entigen intelligence module for knowledge extraction, and query module for response generation. This segmentation allows multiple content sources to be processed systematically through specialized components, reducing the overall difficulty of handling diverse data while improving knowledge accuracy.
Solution Approach 2:
The system introduces intermediate representation layers (identigens and entigens) that mediate between raw data from multiple sources and final knowledge representations. These intermediaries standardize diverse data formats and structures, making subsequent processing and analysis more manageable while comprehensively integrating information from multiple content sources.
Data Source
AI summary
A method includes generating an initial entigen group for a query from existing entigens in a knowledge database and determining whether the initial entigen group provides a reliable response to the query. When the initial entigen group does not provide a reliable response to the query, the method further includes determining a desired entigen to add to the initial entigen group to produce an updated entigen group that leads to the reliable response to the query and creating the desired entigen based on obtained targeted data. The method further includes adding the desired entigen to the initial entigen group to produce the updated entigen group and determining whether the updated entigen group provides the reliable response to the query. When the updated entigen group provides the reliable response to the query, the method further includes outputting the reliable response.