Dynamic GUI Adaptation via Machine Learning Models
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Solution Overview
Problem
Graphical user interfaces (GUIs) do not automatically adapt to user behavior, new data, or contextual changes, requiring manual modifications by users to maintain relevance and usability.
Innovation Solution
Implementing a system that uses machine learning models to dynamically determine and generate GUI components based on user attributes, resource attributes, and contextual factors, allowing for automatic adaptation of the GUI over time and across users, while maintaining a consistent user experience through a combination of predetermined and dynamically-determined components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a GUI uses a same or similar set of components for different users, then the device complexity is reduced and ease of manufacture is improved, but the adaptability to user behavior and contextual changes deteriorates
Solution Approach 1:
The patent implements dynamic GUI components that automatically adapt to user behavior and contextual changes. The system monitors user interactions, availability of new data, and contextual factors to dynamically modify GUI components without requiring manual user intervention. This resolves the contradiction by making the GUI adaptable while managing complexity through automated adaptation mechanisms.
Solution Approach 2:
The GUI system performs self-service by automatically detecting user behavior patterns and contextual changes, then autonomously modifying its own components. The system uses machine learning models to analyze user interactions and automatically adjust the GUI configuration, eliminating the need for manual modifications while maintaining adaptability.
2Adaptability or versatility
If a GUI allows each user to make manual modifications to components, then the adaptability to user preferences is improved, but the loss of time for manual modifications increases
Solution Approach 1:
The system enables self-service customization by automatically observing user behavior and autonomously modifying GUI components to match user preferences. The machine learning model continuously learns from user interactions and automatically adjusts the GUI configuration, providing personalized customization without requiring users to spend time on manual modifications.
Solution Approach 2:
The system implements feedback loops where user interactions with the GUI are continuously monitored and fed back to the machine learning model. The model processes this feedback and automatically adjusts GUI components to better match user preferences over time, enabling adaptive customization without manual intervention.
3Adaptability or versatility
If the GUI components remain constant until manual modifications are performed, then the stability of the user experience is maintained, but the adaptability to new data and contextual changes deteriorates
Solution Approach 1:
The patent implements dynamic adaptation mechanisms that allow GUI components to automatically respond to contextual changes and new data while maintaining overall structural stability. The system uses machine learning models to detect meaningful changes in user behavior and context, then selectively modifies only the necessary components rather than the entire GUI structure, balancing adaptability with stability.
Solution Approach 2:
The system applies local quality changes by modifying only specific GUI components that are relevant to detected contextual changes or user behavior patterns, rather than changing the entire GUI structure. This approach maintains the stability of the overall GUI framework while enabling targeted adaptations to specific areas that need to respond to contextual changes.
4Productivity
If the GUI is automatically modified to adapt to user attributes and context, then the productivity and user experience are improved, but the device complexity increases
Solution Approach 1:
The system achieves automatic GUI adaptation through self-service mechanisms where machine learning models autonomously analyze user attributes and contextual factors, then automatically modify the GUI configuration. This eliminates the need for manual user intervention and improves productivity by providing a dynamically optimized interface, while the automated nature of the system manages the complexity internally.
Solution Approach 2:
The patent replaces manual mechanical interactions (users manually modifying GUI components) with automated intelligent systems (machine learning models that automatically adapt the GUI). This substitution improves productivity by eliminating manual modification steps while managing system complexity through automated algorithms that can process and respond to multiple user attributes and contextual factors simultaneously.
Data Source
AI summary
A system includes a processor configured to perform operations, including receiving, from a client device, a request, associated with a user identifier, for rendering of a graphical user interface (GUI), and obtaining, based on the request and from persistent storage, a framework definition that specifies a first plurality of components and a layout thereof within the GUI. The operations also include determining a second plurality of components by way of a machine learning model and based on attributes associated with the user identifier, and determining, for each respective component of the second plurality of components, a corresponding visual format and a corresponding position within the GUI. The operations further include updating the framework definition by combining the first plurality of components and the second plurality of components based on the corresponding visual format and the corresponding position, and transmitting the framework definition as updated to the client device.