Multi-Layer Symbol Compression for Balanced Pattern Distribution
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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 symbol reduction.
Innovation Solution
The method involves symbol-space based compression of patterns through a Cortex function that transforms input patterns into a larger, balanced, de-correlated, and invariant pattern-space, using iteration rules based on pattern distribution to create data layers that can be spread across multiple machines, reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pattern handling is distributed across multiple machines, then system scalability is improved, but pattern distribution becomes uneven causing some machines to be overburdened while others remain inactive
Solution Approach 1:
The patent transforms the pattern distribution problem by changing the parameter space through symbolic compression. Patterns are encoded into compressed symbols that capture statistical properties (frequency, correlation) rather than distributing raw patterns uniformly. This parameter transformation allows balanced distribution of compressed representations across machines while maintaining the ability to retrieve accurate similarity measurements.
Solution Approach 2:
The patent introduces compressed symbols as intermediary representations between the original patterns and the distributed storage system. These symbols act as mediators that encapsulate pattern statistics and correlations, allowing machines to handle compact representations rather than full pattern data, thus achieving balanced load distribution while preserving pattern relationships.
2Measurement precision
If all patterns are stored in each machine for accurate similarity measurement, then measurement precision is improved, but system complexity and storage requirements increase
Solution Approach 1:
The patent extracts essential statistical properties (frequency, correlation, occurrence probability) from the full pattern data and stores only these compressed representations. This extraction allows similarity measurements to be performed on compressed symbols rather than full patterns, reducing storage complexity while maintaining measurement precision through the preserved statistical relationships.
Solution Approach 2:
The patent changes the representation parameters from full pattern data to compressed symbolic representations that encode key statistical properties. This parameter transformation reduces the dimensionality and complexity of stored data while preserving the information necessary for accurate similarity measurement through statistical comparison of compressed symbols.
3Quantity of substance
If manual symbol reduction is performed to compress patterns, then data compactness is improved, but automation level decreases requiring human intervention
Solution Approach 1:
The patent implements self-service automation where the system automatically performs symbol compression by computing statistical properties (frequency, correlation, occurrence probability) and generating compressed symbolic representations without human intervention. The automated process includes identifying common patterns, calculating their statistics, and creating compressed symbols that represent multiple patterns, thereby achieving data compactness while eliminating manual work.
Solution Approach 2:
The patent replaces manual mechanical symbol reduction with an automated computational system that uses algorithms to identify patterns, compute statistical properties, and generate compressed representations. This substitution of manual operations with automated computational processes achieves the same compression goal while eliminating the need for human intervention.
Data Source
AI summary
A method and apparatus for symbol-space based compression of patterns are provided. The method comprises receiving an input sequence, the input sequence being of a first length and comprising a plurality of symbols; extracting all common patterns within the input sequence, wherein a common pattern includes at least two symbols; generating an output sequence responsive of the extraction of all common patterns, wherein the output sequence has a second length that is shorter than the first length; and storing in a memory the output sequence as a data layer, wherein the output sequence is provided as a new input sequence for a subsequent generation of a data layer.