Neural Network Knowledge Extraction System
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Solution Overview
Problem
Current data mining and reasoning engines are not scalable, expensive to maintain, and have limited reuse value, making it challenging for organizations to effectively extract and apply knowledge from the increasing volume of raw data.
Innovation Solution
A computerized method and system for knowledge extraction and prediction that uses historical data and predetermined heuristics to create causal maps with hierarchically composed nodes, allowing for automatic recognition of patterns and trend prediction, optimized using evolutionary algorithms and the bureaucracy design pattern.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current data mining and reasoning engines are used, then knowledge extraction can be performed, but scalability is poor and maintenance costs are high
Solution Approach 1:
The patent replaces traditional mechanical data mining and reasoning engines with a neural network-based system. The neural network automatically learns patterns and relationships from data, eliminating the need for complex manual rule-based reasoning engines while improving scalability and reducing maintenance costs.
Solution Approach 2:
The neural network system performs self-learning and self-optimization through automated training on historical data. The system automatically adjusts its internal parameters and structures without requiring manual intervention for maintenance, thereby improving scalability while reducing operational complexity.
2Productivity
If current data mining and reasoning engines are used, then knowledge extraction can be performed, but maintenance costs are expensive
Solution Approach 1:
The neural network system performs self-learning and self-optimization through automated training on historical data. The system automatically adjusts its internal parameters and structures without requiring manual intervention for maintenance, thereby improving scalability while reducing operational complexity.
Solution Approach 2:
The neural network architecture provides a universal platform that can handle multiple types of data mining tasks and reasoning functions through a single system. This multi-functionality eliminates the need for separate specialized engines for different tasks, reducing overall maintenance costs while maintaining high productivity.
3Productivity
If current data mining and reasoning engines are used, then some knowledge extraction is possible, but reuse value is limited
Solution Approach 1:
The neural network architecture provides a universal platform that can handle multiple types of data mining tasks and reasoning functions through a single system. This multi-functionality eliminates the need for separate specialized engines for different tasks, reducing overall maintenance costs while maintaining high productivity.
Solution Approach 2:
The neural network system is dynamic and adaptable, automatically adjusting its structure and parameters based on the specific task requirements. This dynamic nature allows the extracted knowledge to be easily reused across different domains and applications, significantly improving versatility and reuse value compared to static rule-based systems.
Data Source
AI summary
Methods and systems for knowledge extraction and prediction are described. In an example, a computerized method, and system for performing the method, can include receiving historical data pertaining to a domain of interest, receiving predetermined heuristics design data associated with the domain of interest, and using the predetermined heuristics design and historical data, automatically creating causal maps including a hierarchy of nodes, each node of the hierarchy of nodes being associated with a plurality of quantization points and reference temporal patterns, the plurality quantization points being known reference spatial patterns. In an example the computerized method, and system for performing the method, can further include receiving, at each node, a plurality of unknown patterns pertaining to a cause associated with the domain of interest, automatically mapping the plurality of unknown patterns to the quantization points using spatial similarities of the unknown patterns and the quantization points, automatically pooling the quantization points into a temporal pattern, the temporal pattern being a sequence of spatial patterns that represent the cause, automatically mapping the temporal pattern to a reference temporal pattern, automatically creating a sequence of the temporal patterns, and automatically recognizing the cause using the sequence of temporal patterns.