Task History UI Clustering Algorithm for Mobile Multitasking
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Solution Overview
Problem
Current user multitasking systems burden users with window management in multitasking environments, especially in mobile settings, where performing multiple tasks simultaneously is cumbersome due to the need for desktop paradigms of windows and application bars.
Innovation Solution
The implementation of unsupervised machine learning methods, specifically clustering algorithms, to group discrete user interface states into meaningful tasks, allowing users to interact with these tasks without explicit window management, using a hierarchical presentation that enables easy navigation and return to previous states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional window management is used for multitasking, then users can perform multiple tasks in parallel, but the user burden of window management increases significantly
Solution Approach 1:
The system automatically manages task organization and window arrangement without requiring user intervention. The clustering algorithm autonomously groups related windows and applications into task categories, and the system self-adjusts window positions and sizes based on task relationships, eliminating the need for users to manually manage windows while maintaining parallel task execution
Solution Approach 2:
A task clustering intermediary layer is introduced between the user and the window management system. This intermediary automatically organizes discrete windows into meaningful task groups based on content analysis and user behavior patterns, mediating the complexity of window management and presenting simplified task-level interfaces to users
2Productivity
If desktop paradigms of windows and application bars are used, then multiple tasks can be performed simultaneously, but screen space optimization is reduced
Solution Approach 1:
Window arrangements are made dynamic and adaptive rather than static. The system continuously monitors task relationships and user interactions, automatically adjusting window positions, sizes, and groupings in real-time to optimize screen space utilization while maintaining accessibility to all active tasks
Solution Approach 2:
The system transitions from traditional two-dimensional window arrangement to a multi-dimensional task organization model. Tasks are organized in hierarchical clusters with multiple levels of grouping, and windows can be accessed through spatial relationships and task contexts rather than requiring dedicated screen real estate for each application
3Adaptability or versatility
If windowing systems are used for multitasking, then users can open and close windows for different tasks, but the complexity of window management increases
Solution Approach 1:
The window management system is segmented into autonomous task clusters rather than treating all windows as a single managed entity. Each cluster independently manages its member windows based on task relationships, dividing the overall management complexity into smaller, manageable units that can be handled separately
Data Source
AI summary
The aspects of the disclosed embodiments include clustering a set of discrete user interface states into groups; presenting the groups on a display of a device; and enabling selection of any state within a presented group, wherein selection of a state returns the user interface to the selected state.