Predictive GUI Element Prioritization for Intent-Based Layout
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Solution Overview
Problem
Existing graphical user interfaces (GUIs) face challenges in displaying relevant commands to users, as they often overpopulate with frequently used commands, pushing out less relevant ones, and fail to consider the user's intent during specific usage sessions, leading to user frustration and inefficient navigation, especially on mobile devices with limited screen space.
Innovation Solution
A system that receives in-session user activity and aggregates it with historical data using predictive algorithms to predict the user's intent, post-processing the data to coordinate the display of an altered GUI that filters and prioritizes relevant GUI elements based on the predicted intent, ensuring that only necessary commands are readily accessible.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predictive algorithms display frequently used commands on the GUI, then user access efficiency is improved, but the GUI becomes overpopulated and pushes out other relevant commands
Solution Approach 1:
The GUI dynamically adjusts the display of commands based on real-time analysis of user intent during the current session. The system monitors user interactions, determines current task context, and selectively displays relevant commands while hiding unrelated ones, making the GUI composition changeable rather than static
Solution Approach 2:
The system pre-filters and prepares command recommendations based on predicted user intent before the user actually needs them. By analyzing user behavior patterns and current session context in advance, the system proactively organizes the GUI to display the most relevant commands, preventing information overload
2Ease of operation
If all commands are displayed without sub-menus, then command accessibility is improved, but device complexity and screen space requirements increase
Solution Approach 1:
The system pre-analyzes user intent and pre-organizes commands into a flattened, context-aware layout before user interaction. By predicting which commands the user will need based on current session context and historical patterns, the system prepares an optimized GUI structure that eliminates the need for nested sub-menus while maintaining logical organization
Solution Approach 2:
Different regions or sections of the GUI are assigned different levels of command visibility based on their relevance to the current user intent. Frequently needed commands in the current context are displayed prominently in the main view, while less relevant commands are either hidden or placed in secondary areas, creating a non-uniform but context-optimized layout
3Productivity
If historical usage patterns are used to customize GUI, then frequently used commands are prioritized, but user intent during specific sessions is not considered
Solution Approach 1:
The system continuously monitors user interactions during the current session and uses this real-time feedback to adjust command prioritization. By comparing actual user behavior in the current session against historical patterns, the system dynamically recalibrates which commands to display and in what order, creating a feedback loop that adapts to both long-term habits and immediate needs
Solution Approach 2:
The command prioritization mechanism transitions from a static, history-based ranking to a dynamic system that continuously adjusts based on current session context. The GUI reorganizes commands in real-time as user intent evolves during the session, making the prioritization adaptable rather than fixed
Data Source
AI summary
A system comprising one or more processors and one or more non-transitory computer-readable storage devices storing computing instructions that, when executed to run on the one or more processors, cause the one or more processors to perform: receiving in-session user activity entered into on a graphical user interface (GUI) from a user electronic device of a user; generating, using a predictive algorithm, a ranked list of one or more likely intents of the user to perform one or more actions on the GUI; processing the in-session user activity to determine one or more intents of the in-session user activity; comparing the one or more intents of the in-session user activity; and coordinating a display of a likely-to-be-used GUI element on the GUI. Other embodiments are disclosed herein.