Automated Model Building Tools for Simulation of Target Features
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Solution Overview
Problem
Analyzing large data sets to determine complex relationships between components is time-consuming and resource-intensive, making it difficult to simulate outcomes effectively using existing tools.
Innovation Solution
The system trains automated model building tools using variational autoencoders and principal component analysis to identify relevant features and simulate output distributions when a target feature is fixed at a specific value, enabling efficient simulation and visualization of related features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analysis tools are used to analyze large data sets, then comprehensive data analysis can be performed, but processing time and computational resources increase significantly
Solution Approach 1:
The patent extracts only the necessary information by identifying and selecting features that are most relevant to the target variable. Through feature selection techniques, the system extracts key relationships from the data without processing all data points exhaustively, thereby reducing processing time while maintaining analysis comprehensiveness.
Solution Approach 2:
The patent segments the data analysis process into distinct stages: data preprocessing, feature selection, model training, and simulation. This segmentation allows each stage to be optimized independently and enables the system to handle large data sets more efficiently by processing them in manageable portions rather than as a monolithic task.
2Measurement precision
If traditional analysis tools are used to determine relationships between components, then comprehensive relationship analysis can be performed, but computational resources increase significantly
Solution Approach 1:
The system extracts and focuses computational resources only on the most relevant features and relationships. By using feature selection methods to identify which components have the strongest relationships with the target variable, the system avoids wasting computational resources analyzing irrelevant data, thereby reducing overall computational resource consumption while maintaining relationship detection accuracy.
Solution Approach 2:
The patent performs preliminary feature selection and data preprocessing before conducting relationship analysis. This preliminary action filters out unnecessary data and pre-identifies key relationships, so that subsequent computational analysis can focus only on the most promising candidates, significantly reducing the computational resources required for relationship detection.
3Reliability
If simulations are performed with large data sets, then accurate simulation results can be obtained, but the difficulty of determining relationships between components increases
Solution Approach 1:
The system extracts and isolates the most relevant features and relationships from the complex data set before performing simulations. By removing irrelevant features and focusing only on the most significant relationships, the system maintains simulation accuracy while reducing the apparent complexity of the relationships that need to be modeled.
Solution Approach 2:
The patent segments the simulation process into modular components, where each component handles a specific aspect of the relationship analysis. This segmentation makes the overall simulation more manageable and easier to interpret, as complex relationships are broken down into smaller, more manageable sub-problems that can be analyzed independently.
Data Source
AI summary
A method includes receiving an input data set, each entry including multiple features. The method includes receiving a user input identifying a target feature of the multiple features and a target value of the target feature. The method includes determining, one or more correlated features of the multiple features. The method includes providing the input data set to multiple neural networks (including multiple VAEs) to train the multiple neural networks. The method includes generating a simulated data set based on the input data set, each entry including at least the target feature and the one or more correlated features. Values of the one or more correlated features are randomized or pseudorandomized and the target feature is fixed at the target value. The method includes providing the simulated data set to the multiple neural networks to generate output data and displaying a GUI based on the output data.