Interactive GUI with ML Override Guidance
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Solution Overview
Problem
Graphical user interfaces (GUIs) in computing systems are limited in functionality, failing to provide adequate visual guidance for users to identify and correct errors in datasets, leading to subjective and inaccurate data point overrides.
Innovation Solution
An interactive GUI system utilizing machine-learning models to automatically identify potential errors in datasets and provide guidance on override values, using visual cues and markers to help users make accurate decisions, with the system ensuring that override values conform to recommended boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional GUIs are used for data visualization, then the system is simple and easy to operate, but the functionality is extremely limited and cannot provide adequate visual guidance for error identification
Solution Approach 1:
The patent introduces machine-learning models as intermediary components between the user and the dataset. These models automatically identify potential errors and provide guidance on override values, acting as a mediator that enhances GUI functionality without requiring the user to directly implement complex error-detection algorithms. The visual cues and markers generated by the models serve as intermediate representations that guide user interaction.
Solution Approach 2:
The system enables self-service by allowing the machine-learning models to automatically analyze the dataset, identify problematic data points, and generate visual guidance. This automatic error identification and guidance generation reduces the need for manual inspection and simplifies the user's task, while the underlying complex analysis is performed autonomously by the system.
2Measurement precision
If users manually override data points without guidance, then the process is simple and quick, but the accuracy is subjective and unreliable
Solution Approach 1:
The patent implements feedback by having the machine-learning models provide guidance on whether data points should be overridden and what override values are appropriate. The visual cues and markers serve as feedback mechanisms that inform users about the likelihood of errors and suggested corrections, enabling more accurate and objective data validation while maintaining user control.
3Measurement precision
If the GUI provides detailed visual guidance for error identification, then data accuracy improves, but the interface complexity increases
Solution Approach 1:
The patent applies local quality by providing visual cues and markers only at specific locations in the GUI where errors are likely to occur, rather than overwhelming the entire interface with complex information. The machine-learning models generate targeted guidance for individual data points or specific regions of the dataset, allowing users to focus their attention on problematic areas while maintaining overall interface simplicity.
Data Source
AI summary
One exemplary system can receive a selection of a dataset via a graphical user interface (GUI). The dataset can represent a time-series projection. The system can feed the dataset into a first machine-learning model to obtain an output indicating whether the time-series projection has a data value that should be overridden with an override value. If the first machine-learning model indicates that the time-series projection has the data value that should be overridden, the system can feed the data value as input to a second machine-learning model to obtain an output indicating whether the override value should be greater than or less than the data value. The system can then render a visual directionality cue within the GUI based on the output from the second machine-learning model. The visual directionality cue can provide guidance for overriding the data value.