Configurable Data Shapes for Hierarchical Pattern Recognition
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Solution Overview
Problem
Existing pattern recognition systems are inefficient and resource-wasteful due to fixed vector widths, lack of data type flexibility, and inability to handle complex recognition tasks effectively, limiting their practical applications.
Innovation Solution
A flexible pattern recognition system with configurable vector widths, data types, and hierarchical recognition operations, allowing for dynamic partitioning and pluggable comparison techniques, enabling real-time, data-agnostic pattern identification and recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed vector widths are used in pattern recognition systems, then system simplicity is maintained, but resource efficiency deteriorates and adaptability to different data types is limited
Solution Approach 1:
The patent implements dynamic vector width configuration where the system can adaptively adjust the width of vectors based on the specific pattern recognition task requirements. This allows the system to optimize resource usage for different data types and complexity levels while maintaining a unified underlying architecture, resolving the contradiction between adaptability and system simplicity.
Solution Approach 2:
The system enables parameter changes in vector dimensions and data type representations without fundamental architectural changes. By allowing flexible configuration of vector widths and data type parameters, the system achieves adaptability to diverse pattern recognition applications while maintaining structural consistency.
2Measurement precision
If traditional pattern recognition algorithms are used, then implementation simplicity is maintained, but recognition accuracy for complex tasks deteriorates
Solution Approach 1:
The patent segments the pattern recognition process into distinct operational phases including data preprocessing, feature extraction, pattern matching, and result validation. This segmentation allows complex recognition algorithms to be organized into manageable modules, improving accuracy while maintaining implementation clarity through structured processing stages.
Solution Approach 2:
The system performs preliminary actions through comprehensive data preprocessing and feature extraction before the actual pattern matching occurs. By preparing and optimizing the input data structure in advance, the system enables simpler and more accurate pattern recognition operations, improving overall accuracy without proportionally increasing algorithm complexity.
3Productivity
If resource allocation is optimized for specific tasks, then processing efficiency improves, but system versatility deteriorates
Solution Approach 1:
The patent implements a universal pattern recognition platform that can handle multiple data types and recognition tasks through a unified architecture. The system allocates computational resources dynamically based on task requirements while maintaining the same underlying infrastructure, achieving both processing efficiency for specific tasks and versatility across different applications.
Solution Approach 2:
The system employs dynamic resource allocation mechanisms that adjust computational resource distribution based on the specific pattern recognition task being performed. This dynamic adaptation allows the system to optimize processing efficiency for each task while maintaining the capability to handle diverse recognition problems, balancing productivity and versatility.
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.