Adaptive Pattern Recognition Platform for Biological Signals

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Solution Overview

Problem

Conventional pattern recognition systems require predefined parameters and are rigid in their recognition abilities, making them time-consuming and inflexible in learning and detecting patterns, especially in biological signal analysis where dynamic and adaptive approaches are needed.

Innovation Solution

The development of a flexible pattern recognition platform that allows for dynamic adjustment of pattern recognition configurations, using knowledge elements and markers to identify and learn patterns without requiring users to input specific parameters, and can handle various data types including biological signals, images, and audio.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional pattern recognition systems use predefined parameters and fixed recognition rules, then the system structure is simple and easy to implement, but the system lacks adaptability and requires extensive manual parameter tuning which is time-consuming

Engineering Contradiction:

Improvepattern recognition adaptabilityVSAvoidpattern learning time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system enables self-service through automatic parameter learning where the pattern recognition system autonomously learns optimal parameters from training data without manual intervention. The system automatically adjusts recognition thresholds, feature weights, and classification rules based on the provided training patterns, eliminating the need for time-consuming manual parameter tuning while maintaining high adaptability to different pattern types

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically changes parameters during the learning process by adjusting recognition thresholds, feature importance weights, and decision boundaries based on training data analysis. This parameter adaptation allows the system to optimize its performance for specific pattern recognition tasks automatically, resolving the contradiction between adaptability and time consumption

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If the pattern recognition system uses fixed recognition rules and predefined parameters, then the system complexity is low, but the system cannot dynamically adjust to different pattern types and data characteristics

Engineering Contradiction:

Improveconfiguration flexibilityVSAvoidsystem structure complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system transitions from static fixed rules to dynamic adaptive configurations by implementing learnable parameters that automatically adjust based on training data. The recognition system dynamically modifies its internal parameters, thresholds, and decision rules during the learning phase, enabling flexibility without requiring complex manual reconfiguration for different pattern types

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system uses training patterns as templates or copies to establish recognition rules. By copying the essential characteristics from labeled training patterns, the system automatically generates recognition parameters and rules that are tailored to specific pattern types, achieving flexibility through automated template-based learning rather than manual rule design

Inventive Principle:

Principle #26Copying

3Ease of operation

If manual parameter input is required for pattern recognition, then the system maintains precise control over recognition criteria, but the ease of operation decreases and requires specialized knowledge

Engineering Contradiction:

Improvesystem usabilityVSAvoidparameter accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system performs self-service by automatically learning optimal parameters from training data without requiring user input. This eliminates the need for operators to have specialized knowledge for parameter tuning while maintaining or improving parameter accuracy through data-driven optimization, thereby significantly improving ease of operation

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses feedback from labeled training patterns to automatically adjust and optimize recognition parameters. By incorporating feedback mechanisms that learn from correct and incorrect classifications, the system automatically refines its parameters to achieve high precision without manual intervention, making the system both easy to operate and accurate

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20230097178A1Pattern recognition system

Publication Date: 2023.03.30 DATASHAPES INC

AI summary

Methods, apparatuses and systems directed to pattern learning, recognition, and metrology. 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 certain implementations, the present invention provides for methods and systems suitable for analyzing and recognizing patterns in biological signals such as multi-electrode array waveform data. In other 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. In other implementations, the present invention provides methods and systems for dynamic learning of patterns in supervised and unsupervised manners.