Dynamic GUI Navigation Tool Allocation via Machine Learning
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Solution Overview
Problem
Existing graphical user interface (GUI) designs lack dynamic allocation of navigation tools based on learned user interaction, leading to subjective effectiveness and inefficient navigation experiences.
Innovation Solution
A system that uses machine learning algorithms to classify and dynamically allocate navigation tools on a GUI by generating a training dataset from user and peer interactions, predicting optimal placement of unseen navigation tools based on historical data and gesture analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional static GUI design is used, then the interface structure is simple and easy to implement, but the navigation effectiveness is subjective and not optimized for individual users
Solution Approach 1:
The patent implements dynamic allocation of navigation tools by transitioning from static GUI layouts to adaptive layouts that change based on real-time user interaction data. The system continuously monitors user behavior and repositions navigation tools dynamically, making the interface adaptable rather than fixed, thereby resolving the contradiction between operational effectiveness and system complexity.
Solution Approach 2:
The system employs machine learning algorithms that enable the GUI to automatically optimize its own layout based on user interaction patterns. The navigation interface serves itself by learning from user behavior and autonomously adjusting tool placements without requiring manual redesign, thus improving navigation effectiveness while managing complexity through automation.
2Productivity
If dynamic allocation of navigation tools is implemented, then navigation efficiency is improved, but the system complexity increases due to machine learning algorithms and data processing
Solution Approach 1:
The system performs preliminary data collection and analysis by monitoring user interactions with navigation tools before making optimization decisions. By gathering interaction data in advance and processing it through machine learning models, the system prepares optimization recommendations proactively, improving navigation efficiency while managing complexity through structured advance processing rather than reactive complex computations.
Solution Approach 2:
The patent implements a feedback loop where user interaction data is continuously collected, analyzed by machine learning algorithms, and used to adjust navigation tool placements. This closed-loop feedback mechanism enables the system to automatically improve navigation efficiency based on actual user behavior, resolving the contradiction by making the system adaptive rather than statically complex.
3Adaptability or versatility
If machine learning algorithms are used to predict navigation tool placement, then user experience is personalized, but the computational resources and processing time increase
Solution Approach 1:
The system applies machine learning algorithms selectively to predict only the optimal placement positions for navigation tools rather than analyzing or redrawing the entire GUI. By focusing computational resources on the specific task of predicting navigation tool positions based on user interaction data, the system achieves personalization capability while minimizing unnecessary computational resource consumption.
4Ease of operation
If navigation tools are repositioned based on user behavior, then accessibility is improved, but the interface stability and consistency deteriorate
Solution Approach 1:
The patent changes the positional parameters of navigation tools based on user interaction patterns while maintaining their functional identities. The machine learning model predicts optimal positions and adjusts placement parameters dynamically, allowing the interface to adapt to user preferences and improve accessibility while preserving the recognizable nature of navigation tools through controlled parameter adjustments.
Data Source
AI summary
Systems, computer program products, and methods are described herein for dynamic allocation of navigation tools based on learned user interaction. The present invention is configured to generate a training dataset based on at least the information associated with the interaction of the user with the one or more GUI grids, information associated with the one or more interactions of the one or more peers with the one or more GUI grids, information associated with the user, and information associated with the one or more peers; initiate one or more machine learning algorithms on the training dataset; receive, via the user computing device, a user selection of an unseen navigation tool for placement on the GUI; and classify the unseen navigation tool using the first set of parameters to predict a placement of the unseen navigation tool in at least one of one or more GUI grids associated with the GUI.