Multi-Sample 2D Data Pattern Extraction Methodology
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Solution Overview
Problem
Current methods for analyzing two-dimensional data sets, such as mass spectroscopy, face challenges in efficiently identifying patterns and mathematical relationships between multiple samples, leading to difficulties in deriving accurate conclusions from the data.
Innovation Solution
A pattern extraction methodology that analyzes multi-sample, two-dimensional data sets by normalizing and comparing loci across samples, grouping similar loci, and identifying patterns through arithmetic or geometric relationships, allowing for automated pattern recognition and alerting operators to significant data sets requiring attention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to identify patterns in multi-sample two-dimensional data sets, then analysis accuracy can be maintained through human expertise, but the time and energy required becomes cost-prohibitive
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computer-based system that performs pattern identification through electronic data processing. The system automatically identifies patterns by comparing loci across multiple samples, calculating relationships, and generating results without human intervention, thereby resolving the contradiction between maintaining accuracy and reducing time consumption.
Solution Approach 2:
The system enables self-service pattern identification where the computer automatically performs data processing, pattern recognition, and relationship calculation without requiring manual analysis. The automated system serves itself by taking raw data as input and producing analyzed results with identified patterns, eliminating the need for costly manual analysis while maintaining consistent accuracy.
2Productivity
If automated pattern recognition systems are implemented, then analysis speed and efficiency improve, but the complexity of the system increases
Solution Approach 1:
The patent segments the pattern recognition process into distinct modular components: data import, loci identification, pattern type determination, relationship calculation, and result generation. Each module performs a specific function, making the overall complex system manageable through division into smaller, independent units that can be processed sequentially.
Solution Approach 2:
The system handles complexity by operating in multiple dimensional layers: first identifying individual loci in the raw data, then determining pattern types based on loci relationships, calculating mathematical relationships between loci, and finally generating comprehensive results. This multi-dimensional approach breaks down complex analysis into manageable stages.
3Loss of information
If comprehensive pattern analysis is performed across all loci in multi-sample data sets, then complete pattern identification is achieved, but the computational energy and processing requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for pattern identification by focusing on loci relationships and pattern types rather than processing every data point in detail. The system identifies and extracts relevant loci, determines their pattern types, and calculates only the necessary mathematical relationships, thereby reducing computational energy requirements while maintaining complete pattern detection.
Solution Approach 2:
The system performs partial analysis by identifying pattern types and calculating relationships for identified patterns rather than exhaustively analyzing all possible loci combinations. This partial action approach achieves complete pattern detection by focusing computational resources on relevant patterns while avoiding unnecessary calculations on non-pattern data.
Data Source
AI summary
The present invention utilizes a pattern extraction methodology to elucidate significant patterns and mathematical relationships that exist between and among pluralities of two-dimensional sample data sets of the same data type. In one instance, the present invention analyzes multi-sample, two-dimensional mass spectroscopy data, while in an alternate instance, another user-specified, preset, or automatically determined data type, modality, submodality, etc., is analyzed.