Intermediary Knowledge Layer for Reliable AI Meaning Extraction
Find Innovative SolutionsGenerate Solutions
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 word interpretation across languages and dialects, leading to inefficiencies 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 through pattern recognition and grammatical analysis, including the use of identigen entigen intelligence (IEI) modules to interpret and generate responses to queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pattern recognition techniques and statistical reasoning are used to process text, then the system can attempt to overcome word ambiguities, but the accuracy and reliability of knowledge extraction remain insufficient due to data volume and interpretation variance
Solution Approach 1:
The patent introduces an intermediary knowledge representation layer between raw text data and final knowledge extraction. This intermediary layer standardizes diverse text interpretations into unified knowledge entities, enabling reliable extraction from large volumes of data with varying language usage and dialects.
2Ease of operation
If grammatical analysis techniques are used to classify words into major grammatical types, then the system can study word distribution to form properly constructed sentences, but the system cannot accurately identify what each word is actually trying to describe
Solution Approach 1:
The patent segments the word analysis process into multiple independent components: grammatical type classification, semantic role identification, and contextual meaning determination. This segmentation allows the system to maintain grammatical correctness while precisely identifying word meanings through specialized analysis of each component.
Solution Approach 2:
The patent applies different analysis qualities to different parts of the text processing pipeline. Grammatical analysis provides structural correctness, while semantic and contextual analyses provide precise meaning identification. Each component operates with appropriate local quality rather than uniform processing.
Data Source
AI summary
A method includes determining a set of identigens for words of content to produce a sets of identigens and interpreting the sets of identigens to determine a most likely meaning interpretation of the content and produce a baseline entigen group. The method further includes recovering an incomplete entigen group for the topic from a first knowledge database based on a knowledge defect of the incomplete entigen group with regards to the topic. The method further includes obtaining an additive entigen group from a second knowledge database based on the knowledge defect and modifying the incomplete entigen group utilizing the additive entigen group to produce an updated entigen group to provide a beneficial cure for the knowledge defect of the incomplete entigen group.