Adaptive UI Element Layout Using Interaction Weight Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing user interface configurations lack dynamic and efficient methods for updating element layouts based on user interaction data, leading to suboptimal user experience.
Innovation Solution
A method that initializes element layouts randomly and updates them based on derived weight metrics and time series models, using machine learning algorithms to predict future user interactions, applying layout modes such as focus, sort, and disable modes to optimize element placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If user interface element layouts are updated dynamically based on user interaction data, then user experience and usability are improved, but system complexity and computational resources increase
Solution Approach 1:
The patent implements dynamic layout adaptation by continuously monitoring user interaction data and automatically repositioning UI elements based on derived weight metrics. The system transitions from static to dynamic configuration, where element positions are adjusted in real-time according to user behavior patterns, thereby improving usability without requiring manual reconfiguration.
Solution Approach 2:
The system performs self-configuration by automatically analyzing user interaction data, deriving element weight metrics, and adjusting layouts without external intervention. The machine learning model autonomously learns from user behavior and applies layout modes (focus, sort, disable) to optimize the interface, eliminating the need for manual UI design adjustments.
2Measurement precision
If machine learning algorithms are used to predict future user interactions, then layout optimization accuracy is improved, but processing time and computational power increase
Solution Approach 1:
The system pre-processes user interaction data to derive weight metrics for UI elements before layout updates are needed. By continuously analyzing and storing element interaction weights in advance, the system prepares prediction models that can quickly generate layout recommendations when triggered, reducing real-time processing delays.
Solution Approach 2:
The patent applies layout modes selectively based on predicted user needs rather than continuously optimizing all elements. The system uses prediction thresholds to determine when layout updates are necessary, applying focus mode to high-priority elements and disable mode to low-priority ones, thereby reducing overall processing requirements while maintaining prediction accuracy for critical elements.
3Adaptability or versatility
If element layouts are randomly initialized based on user clustering, then adaptability to different users is improved, but initial configuration time increases
Solution Approach 1:
The system pre-clusters users based on identification information and randomly initializes element layouts for each cluster before actual user interaction begins. This preliminary configuration allows the system to present users with pre-optimized layouts based on their cluster characteristics, reducing the time needed for initial setup while maintaining high adaptability to different user types.
Data Source
AI summary
Techniques are described with regard to user interface configuration in a computing environment. An associated computer-implemented method includes initializing an element layout within a set of user interface layers for a certain user based upon random determination, wherein the element layout includes a plurality of elements on which the certain user operates. Responsive to determining that a predefined user history data threshold is exceeded, the method further includes deriving weight metrics for the plurality of elements in association with each of the set of user interface layers based upon user history data, applying at least one layout mode to respective elements among the plurality of elements associated with each of the set of user interface layers based upon the derived weight metrics, and updating the element layout within each of the set of user interface layers for the certain user consequent to applying the at least one layout mode.