Ontological Subject Composition Analysis for Automated Knowledge Discovery

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Solution Overview

Problem

Current methods for analyzing large bodies of data are inefficient, requiring extensive expertise and time to extract valuable information, and are prone to biases due to human investigators' knowledge and experiences, limiting the speed and accuracy of knowledge discovery.

Innovation Solution

A systematic, computer-implementable method for investigating compositions of ontological subjects using value significance measures, association strength measures, and relational measures to identify significant constituents and relationships within data compositions, enabling automated knowledge extraction and processing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If human investigators analyze large bodies of data manually, then expertise and experience can be applied to interpret complex information, but the process requires extensive time and is prone to biases

Engineering Contradiction:

Improveaccuracy of knowledge discoveryVSAvoidtime required for data analysis

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces the mechanical system of human manual analysis with an automated computer-implementable method. The system uses algorithms to perform value significance measures, association strength measures, and relational measures on compositions of ontological subjects, eliminating the time-consuming manual process while maintaining analytical capability through systematic computational approaches.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by allowing the computer system to autonomously analyze compositions of ontological subjects without requiring continuous human intervention. The automated method processes data independently, performing extraction, analysis, and interpretation of significant constituents and relationships within data compositions.

Inventive Principle:

Principle #25Self-service

2Productivity

If automated systems process data, then speed and throughput are improved, but the system lacks the expertise and experience of human investigators

Engineering Contradiction:

Improvespeed of knowledge discoveryVSAvoidaccuracy of knowledge discovery

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent introduces an intermediary layer of structured ontological subjects and systematic measures that bridge the gap between automated processing and expert-level analysis. The value significance measures, association strength measures, and relational measures act as intermediaries that translate raw data into meaningful insights, enabling automated systems to achieve accuracy comparable to human expertise.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If manual analysis methods are used, then expertise can be applied, but the throughput and scalability are limited

Engineering Contradiction:

Improvequality of analysisVSAvoidthroughput of knowledge discovery

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent creates a universal system that can handle multiple types of data compositions and ontological subjects through a single automated framework. The method is designed to process diverse compositions systematically, enabling the same analytical approach to be applied across different domains and data types, thereby achieving both high quality and high throughput.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Loss of information

If human investigators conduct thorough analysis, then comprehensive knowledge can be extracted, but the process is costly in terms of time and resources

Engineering Contradiction:

Improvecompleteness of information extractionVSAvoidtime and resource cost

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The patent focuses on extracting only the most significant constituents and relationships from compositions of ontological subjects using value significance measures. Instead of analyzing every detail manually, the system identifies and extracts key information elements that contribute most to knowledge discovery, achieving comprehensive information extraction with reduced time and resource costs.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS10795949B2Methods and systems for investigation of compositions of ontological subjects and intelligent systems therefrom

Publication Date: 2020.10.06 HATAMI HANZA HAMID

AI summary

Methods and systems are given for investigation of compositions of ontological subjects in accordance with various aspects of significance. Accordingly, the present invention provide a unified method and process of investigating the compositions of ontological subjects, modeling an unknown system, and obtaining as much worthwhile information and knowledge as possible about the system or the composition or the body of knowledge along with exemplary services utilizing such investigations. The data structures built and the knowledge acquired by a machine through executing the investigation methods of the present disclosure enables artificial intelligent systems, machines, and agents to perform intelligent tasks and jobs.