Adaptive GUI Layout for Small Screens Using Inferred User Intent
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Solution Overview
Problem
Existing graphical user interfaces (GUIs) often fail to balance long-term user interaction patterns with short-term user intent, leading to inefficient experiences, especially on devices with small screens where navigation is already challenging.
Innovation Solution
A system and method that gather both historical and in-session user interaction data to determine user intent using predictive algorithms, allowing for real-time adjustments to the GUI to better align with the user's current intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If the GUI is designed based on long-term user interaction patterns, then the interface maintains consistency with established user habits, but it fails to adapt to short-term user intent and becomes inefficient for current tasks
Solution Approach 1:
The GUI transitions from a static design based solely on long-term patterns to a dynamic system that adapts in real-time. The interface composition changes based on inferred user intent, allowing it to remain consistent with user habits while responding to current task requirements through continuous adjustment of displayed elements and navigation options.
Solution Approach 2:
The system implements feedback loops by continuously monitoring user interactions, inferring intent from these interactions, and adjusting the GUI accordingly. This creates a closed-loop system where the interface learns from user behavior and adapts its composition to better serve both long-term habits and short-term goals.
2Stability of the object's composition
If the system focuses strictly on long-term use patterns, then it maintains established interaction trends, but it neglects short-term user intent expression
Solution Approach 1:
The system dynamically balances long-term pattern recognition with short-term intent detection. By continuously analyzing interaction data and adjusting the GUI in real-time, it maintains stability in core interaction patterns while adapting to emerging user intentions, effectively combining both temporal dimensions of user behavior.
Solution Approach 2:
The system performs preliminary analysis of user interactions to infer intent before the user completes their task. By proactively detecting user goals from early interaction signals, it can prepare and present appropriate GUI adjustments in advance, ensuring both long-term pattern consistency and short-term intent responsiveness.
3Area of stationary object
If the GUI is optimized for small screen devices, then navigation becomes more compact, but it becomes more difficult to navigate and less efficient
Solution Approach 1:
The GUI on small screens becomes dynamic rather than static, adjusting its layout and element prioritization based on inferred user intent. This allows the interface to maintain compact screen space utilization while improving navigation ease by presenting only the most relevant elements and pathways for the current task, reducing cognitive load and interaction complexity.
Solution Approach 2:
The system applies local quality by selectively optimizing different portions of the GUI based on user intent. Rather than uniformly simplifying the entire interface, it focuses computational and design resources on enhancing the specific navigation paths and elements most relevant to the user's current goal, thereby improving ease of operation where it matters most while maintaining compact overall layout.
Data Source
AI summary
Systems and methods including one or more processors and one or more non-transitory computer readable storage devices storing computing instructions configured to run on the one or more processing modules and perform: gathering first data comprising first interactions of a user with a first graphical user interface; storing the first data comprising the first interactions of the user with the first graphical user interface as at least one first vector by adding to the at least one first vector for each level of a hierarchical categorization of the first user interface; gathering second data comprising second interactions of the user with a second graphical user interface; storing the second data comprising the second interactions of the user with the second graphical user interface as at least one second vector; determining an intent of the user using the at least one first vector, the at least one second vector, and a predictive algorithm; and transmitting instructions to display a third graphical user interface for the user based upon the intent of the user. Other embodiments are disclosed herein.