Adaptive Design Exploration GUI for Interactive Experiment Plan Comparison
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Solution Overview
Problem
Conventional tools for exploring statistical configurations lack flexibility for interactive exploration and adjustment, making it difficult to refine, compare, and explore different options efficiently.
Innovation Solution
A computer-program product that includes a graphical user interface for generating and managing design of experiment plans, allowing users to create and compare different plans and test plans interactively, with features like plan settings containers, test plan containers, and dynamic graphical representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional tools follow a structured step-by-step approach for exploring statistical configurations, then the process is systematic and organized, but the flexibility for interactive exploration and adjustment is limited
Solution Approach 1:
The system enables dynamic adjustment of statistical configurations through interactive graphical user interfaces where users can modify plan settings, test plans, and experimental run plans in real-time. The system transitions from a static, rigid structured approach to a dynamic environment where configurations can be changed fluidly without requiring complete redesign, directly addressing the need for flexibility while maintaining systematic organization.
Solution Approach 2:
The system divides the experimental design into separate controllable components including plan settings container objects, test plan container objects, and graphical entry sections. This segmentation allows users to adjust individual configuration elements independently while maintaining the overall structured framework, resolving the contradiction between systematic organization and flexible adjustment.
2Adaptability or versatility
If users need to evaluate alternative statistical configurations side-by-side, then comprehensive comparison is enabled, but the time required for exploration increases
Solution Approach 1:
The system pre-generates multiple statistical configurations and stores them in container objects that can be quickly accessed and displayed. Users can select from pre-computed alternatives without having to regenerate each configuration from scratch, significantly reducing the time required for side-by-side evaluation while maintaining comprehensive comparison capabilities.
Solution Approach 2:
The system creates graphical representations and visual copies of statistical configurations that can be displayed and compared simultaneously. These visual copies allow users to evaluate alternative configurations side-by-side without working with the full complexity of the underlying data structures, reducing evaluation time while maintaining adaptability.
3Manufacturing precision
If the system generates detailed plan settings and test plans, then comprehensive experimental design is achieved, but the complexity of managing multiple configurations increases
Solution Approach 1:
The system implements a nested structure where plan settings container objects contain test plan container objects, which in turn contain graphical entry sections displaying experimental run plans. This nesting organizes the detailed configuration information hierarchically, allowing users to access comprehensive experimental design details while the structured organization reduces the perceived complexity of managing multiple configurations.
Data Source
AI summary
A computer-implemented system, computer-implemented method, and computer-program product includes receiving, via a graphical user interface, a request to generate an initial design of experiment plan based on one or more experiment factors; generating a first plan settings container object that represents the initial design of experiment plan in computer memory; and instantiating, within the graphical user interface, a plan explorer section that includes a plan settings graphical entry that displays a graphical representation of a plan factors data object and a plan model data object associated with the first plan settings container object.


