Symbol-Space Pattern Compression for Balanced Distributed Recognition
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Solution Overview
Problem
Existing pattern recognition systems face scalability and accuracy issues due to uneven distribution and correlation of patterns, leading to overburdened and inactive machines, and require manual intervention for efficient symbol reduction.
Innovation Solution
The method involves symbol-space based pattern compression, where combinations of symbols above a threshold are replaced with unique symbols, creating a more balanced and de-correlated pattern-space that can be distributed across multiple machines, improving scalability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pattern handling is distributed across multiple machines, then system scalability is improved, but load imbalance occurs with some machines overburdened and others inactive
Solution Approach 1:
The patent transforms the pattern space by applying parameter changes to the symbol representations. By encoding patterns with probability information and correlation data, the system redistributes the computational load more evenly across machines, allowing each machine to handle patterns based on their statistical characteristics rather than raw frequency alone.
Solution Approach 2:
The patent adds another dimension to pattern representation by incorporating probability and correlation metadata alongside the pattern symbols themselves. This dimensional enrichment allows the distributed system to make informed routing decisions, assigning patterns to appropriate machines based on their statistical properties, thereby achieving better load balance while maintaining scalability.
2Measurement precision
If manual symbol reduction is performed, then pattern recognition accuracy is improved, but system complexity and manual intervention requirements increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform symbol reduction and pattern compression without manual intervention. The algorithm autonomously identifies redundant patterns, calculates their probabilities, and consolidates them into compressed representations, thereby maintaining high recognition accuracy while eliminating the need for manual symbol reduction processes.
Solution Approach 2:
The system automatically changes parameters by dynamically adjusting symbol representations based on observed pattern frequencies and correlations. This automated parameter optimization achieves the same accuracy benefits as manual reduction but through algorithmic processes that adapt to the data distribution, removing the need for human expertise in symbol consolidation.
3Measurement precision
If all patterns are stored in each machine, then pattern recognition accuracy is maintained, but storage requirements and system complexity increase
Solution Approach 1:
The patent applies copying by creating compressed representations of patterns that can be efficiently distributed and stored. Instead of storing all raw pattern data in every machine, the system creates condensed symbol-space representations that capture the essential pattern characteristics, probabilities, and correlations, thereby reducing storage requirements while maintaining recognition accuracy.
Solution Approach 2:
The system transforms pattern data by changing its representation parameters from detailed raw patterns to compressed symbol-space encodings. This parameter transformation reduces the quantity of data that needs to be stored and transmitted across the distributed system, while the embedded probability and correlation information ensures that recognition accuracy is preserved.
Data Source
AI summary
A method and system for symbol-space based pattern compression is provided. The method includes identifying a plurality of combinations of symbols in an input sequence, each identified combination of symbols appearing in the input sequence above a predefined threshold, the input sequence having a first length; generating an output sequence having a second length by replacing each identified combination of symbols with a unique symbol, wherein each unique symbol is not a previously used symbol, wherein the second length is shorter than the first length; and storing the output sequence as a data layer.