Gradient Feature Vector Analysis for Numeric Feature Extraction
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Solution Overview
Problem
Manual extraction of numeric feature combinations from large datasets is challenging, as existing methods fail to automatically identify combinations that can effectively improve specific indicators like quality or yield in manufacturing processes.
Innovation Solution
An analysis system that automatically extracts numeric feature combinations by calculating a gradient feature vector from parent population data, determining the significance of these combinations based on their impact on key performance indicators, and visualizing the relationships between parameter combinations and indicators to guide user decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction of numeric feature combinations is performed, then extraction accuracy can be maintained through human judgment, but productivity decreases due to the difficulty of processing large datasets
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computational system that calculates gradient feature vectors to identify important numeric feature combinations. The system substitutes human judgment with mathematical optimization algorithms that automatically process large datasets and extract meaningful feature combinations based on gradient magnitude calculations.
2Productivity
If automated extraction methods are implemented, then productivity increases through automatic processing, but measurement precision decreases as existing methods cannot effectively identify combinations that improve specific indicators
Solution Approach 1:
The patent changes the approach from traditional correlation-based feature selection to gradient-based optimization. By calculating gradient feature vectors that measure the rate of change of target indicators with respect to numeric features, the system identifies combinations that directly improve specific indicators like yield or quality, rather than merely finding correlated features.
3Measurement precision
If all numeric feature combinations are analyzed, then extraction accuracy improves through comprehensive evaluation, but device complexity increases due to the large number of combinations to process
Solution Approach 1:
The patent extracts only the most important numeric feature combinations by filtering based on gradient feature vector thresholds. Instead of analyzing all possible combinations, the system calculates gradients for candidate combinations and selectively extracts those exceeding predetermined thresholds, significantly reducing the number of combinations that require detailed evaluation while maintaining extraction accuracy.
Data Source
AI summary
An analysis system is configured to acquire data of a first numeric feature combination by referring to the parent population data. The data of the first numeric feature combination includes a score of a target indicator for each of category combinations. Each category combination of the category combinations is composed of categories of the numeric features of the first numeric feature combination. The analysis system is configured to determine a score at coordinates corresponding to each of the category combinations in a space having each of the numeric features of the first numeric feature combination as an axis, calculate a gradient feature vector representing a gradient for the score in the space, and determine, based on the gradient feature vector, whether to include the first numeric feature combination in numeric feature combinations to be extracted from the parent population data.