Flexible Pattern Recognition Engines for Diverse Data Matching
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Solution Overview
Problem
Existing pattern recognition technologies, particularly those based on artificial neural networks, have limited practical real-world applications due to inefficiencies in adapting to specific recognition configurations and handling diverse data types.
Innovation Solution
The development of a flexible pattern recognition platform that includes dynamically adjustable engines for specific configurations, supports multi-level recognition schemes, and operates in a parallel architecture to enable real-time pattern identification and recognition across various data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If artificial neural networks are used for pattern recognition, then the system can model complex relationships between inputs and outputs, but the system has limited adaptability to specific recognition configurations and diverse data types
Solution Approach 1:
The pattern recognition system is divided into multiple independent decision elements, each capable of handling specific recognition tasks. These decision elements can be independently configured and combined to form recognition engines that adapt to specific applications without requiring complete system redesign.
Solution Approach 2:
The system employs a universal decision element design that can be configured to handle diverse data types and recognition configurations. By making the basic building blocks universal and configurable, the system achieves adaptability across different applications while maintaining a consistent underlying architecture.
2Adaptability or versatility
If traditional pattern recognition systems are used, then the system structure is fixed, but the system cannot be dynamically adjusted for different applications
Solution Approach 1:
The system implements dynamic configuration capabilities where decision elements and recognition engines can be adjusted at runtime to match specific application requirements. This dynamic nature allows the system to adapt to different data types and recognition tasks without physical reconfiguration.
Solution Approach 2:
The system uses virtual decision elements that can be instantiated and configured as needed, allowing multiple copies of recognition logic to be created and tailored for different applications. This virtualization approach enables flexible copying and reuse of recognition patterns across diverse contexts.
3Productivity
If parallel architecture is implemented for real-time recognition, then recognition speed increases, but system complexity and resource requirements increase
Solution Approach 1:
The parallel architecture is segmented into independent decision elements that can operate simultaneously. Each element processes a portion of the recognition task, and their results are combined to achieve real-time recognition. This segmentation enables parallel processing without requiring a completely complex monolithic architecture.
Solution Approach 2:
The system employs a nested structure where decision elements are contained within recognition engines, which are in turn contained within the overall pattern recognition system. This nested organization allows parallel operation at multiple levels while maintaining manageable complexity through hierarchical structuring.
Data Source
AI summary
Methods, apparatuses and systems directed to pattern identification and pattern recognition. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In some implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes.