Universal Pattern Recognition System for Multi-Modality Data Analysis
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Solution Overview
Problem
Current automated pattern recognition systems are limited by specific modalities and require extensive human analysis, leading to inefficiencies and errors due to the need for manual review and processing of vast digital data sets, which often results in information loss and increased costs.
Innovation Solution
A data analysis system that uses a minimal number of algorithms to recognize patterns and detect objects across various data modalities, including imagery, acoustic, and tactile data, without requiring adaptation to specific applications or environments, allowing for rapid development and improvement while operating on native data resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated pattern recognition systems are designed for specific modalities, then measurement precision is improved, but adaptability deteriorates
Solution Approach 1:
The patent creates a universal pattern recognition system that processes multiple data modalities (seismic, medical imaging, sonar, ultrasound) through a single integrated architecture. The system uses modality-agnostic feature extraction and pattern matching algorithms that can handle diverse data types without requiring separate specialized systems, thereby achieving both adaptability across modalities and maintained measurement precision through consistent processing methods.
Solution Approach 2:
The system transforms different data modalities into a unified parameter space by converting diverse inputs (seismic waves, medical images, sonar signals) into standardized feature representations. This parameter transformation allows the same pattern recognition algorithms to operate effectively across modalities while preserving the distinctive characteristics needed for accurate detection in each domain.
2Measurement precision
If manual human analysis is used, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent replaces manual human analysis with automated computational systems that perform pattern recognition and object detection. The system uses algorithmic processing to analyze data patterns, extract features, and identify objects of interest, thereby eliminating the need for human reviewers while maintaining detection accuracy through sophisticated machine learning and pattern matching techniques.
Solution Approach 2:
The system enables self-service analysis by automatically processing data sets without human intervention. The automated pattern recognition system performs feature extraction, pattern matching, and object detection independently, generating results that would traditionally require manual analysis, thus achieving both high productivity and maintained measurement precision.
3Ease of operation
If data is processed and filtered for presentation, then ease of operation is improved, but loss of information increases
Solution Approach 1:
The system performs preliminary pattern recognition and feature extraction on the complete native data set before any presentation or filtering occurs. By identifying patterns and objects of interest in the full-resolution data first, the system can then selectively present only the relevant information to users, maintaining both ease of operation through focused presentation and minimal information loss by preserving access to the complete original data set.
Data Source
AI summary
Systems and methods for creating data samples for data analysis. The method includes imaging at least a portion of a first object having a target portion to generate a first image, analyzing the first image by running a first series of algorithms using the first image to generate a first algorithm value cache, removing the target portion from the first object to form a modified first object, imaging at least a portion of the modified first object to generate a second image, analyzing the second image by running the first series of algorithms using the second image to generate a second algorithm value cache, generating a transformed result by running a subtraction process using the first algorithm value cache and the second algorithm value cache, and storing the transformed result in a knowledge base.