Adaptive GUI Components Using Emotional AI Preference Modeling
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Solution Overview
Problem
Existing graphical user interfaces (GUIs) are not dynamically configured based on user preferences or abilities, especially for users with disabilities, and managing multiple user preferences in a network is inefficient and error-prone.
Innovation Solution
A system utilizing an emotional AI engine trained on historical user interaction data to automatically generate and configure GUI components in real-time, considering user preferences and abilities, reducing manual intervention and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual configuration of GUI components is performed for each user, then user-specific preferences can be accommodated, but the complexity and time required for configuration increases significantly
Solution Approach 1:
The system performs preliminary action by collecting user interaction data and training the machine learning model in advance. The model is pre-trained on historical interaction data so that when a user accesses the platform, the GUI can be automatically configured based on predictions from the pre-trained model, eliminating the need for manual configuration at the time of user access.
Solution Approach 2:
The system implements self-service by using the machine learning model to automatically generate and configure GUI components based on user interaction data. The platform serves itself by autonomously analyzing user behavior patterns and dynamically adjusting the interface without requiring manual intervention from administrators or developers.
2Ease of operation
If GUI components are dynamically configured for each user, then user experience is improved, but computing resources and network traffic increase
Solution Approach 1:
The system applies local quality by configuring only the specific GUI components that are most relevant to each user based on their interaction patterns, rather than redesigning the entire interface. The machine learning model identifies and adjusts individual components (such as navigation elements, display preferences, or feature visibility) that have the greatest impact on user experience while minimizing overall system resource consumption.
3Measurement precision
If extensive user data is collected to determine preferences, then accurate GUI configuration is achieved, but security risks and data management complexity increase
Solution Approach 1:
The system extracts only the essential features and patterns from user interaction data that are necessary for GUI configuration, rather than storing or processing all raw user data. The machine learning model processes interaction data to extract meaningful preferences while the system architecture ensures that sensitive information is not retained, thereby reducing security risks while maintaining configuration accuracy.
Data Source
AI summary
Systems, computer program products, and methods are described herein for dynamically configuring graphical user interface components based on interface interaction data. The present invention is configured to identify a user device associated with a user account; identify at least one user access to a platform from the user device; determine, by an emotional artificial intelligence (AI) engine, at least one user platform preference for the user account, wherein the emotional AI engine is pre-trained on historical user platform preference data for the user account; generate, by the emotional AI engine, a user platform interface component based on the at least one user platform preference; and transmit the user platform interface component to the user device, wherein the transmission of the user platform interface component triggers a configuration of the GUI of the user device.