GUI Personalization via Behavioral Simplification
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Solution Overview
Problem
Existing graphical user interface (GUI) design methods require manual effort to create customized versions for different user segments, making it impractical to generate multiple versions, leading to degraded user experience for many users as their individual preferences and actions are not optimized.
Innovation Solution
A computer-implemented method that tracks user interactions, computes behavioral simplifications, and modifies the GUI to consolidate frequent actions, enabling automatic personalization of the GUI for each user, using a behavior-driven GUI subsystem and machine learning techniques to optimize the interface in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If multiple customized versions of GUI are generated for different user segments, then user experience for specific segments is improved, but the process becomes primarily manual and time-consuming
Solution Approach 1:
The system automatically generates customized GUI versions by monitoring user interactions and using machine learning to create personalized interfaces without manual intervention. The GUI framework self-adapts to individual user preferences and behaviors, eliminating the need for manual customization processes.
Solution Approach 2:
The GUI is designed to be dynamic and adaptable, automatically reconfiguring itself based on real-time user interaction data. The system continuously learns from user behavior patterns and transforms the GUI layout, elements, and functionality to match individual user preferences, making the customization process ongoing and automatic rather than static and manual.
2Ease of manufacture
If only a few customized versions of GUI are generated, then the manual process remains manageable, but many users are left without a customized version
Solution Approach 1:
Each user receives a personalized GUI automatically generated by the system based on their individual interaction patterns. The machine learning framework processes user data and creates unique customized versions for every user without requiring manual intervention, enabling unlimited scalability of customization.
Solution Approach 2:
The system uses machine learning to analyze user interaction parameters and dynamically adjusts GUI parameters such as layout, element positioning, and functionality based on individual user preferences. This allows the generation of infinitely varied customized versions by changing parameters according to each user's unique behavior patterns.
3Device complexity
If a single GUI is provided for all users, then the design process is simplified, but individual user preferences and outcomes are not optimized
Solution Approach 1:
The system segments users into individual customization groups, with each user receiving a personalized GUI based on their unique interaction patterns. The machine learning framework processes each user's data separately to create tailored interfaces, effectively segmenting the customization process at the individual user level rather than using broad segments or a single universal design.
Solution Approach 2:
The GUI customization is applied locally to each individual user based on their specific preferences and behaviors. The system creates unique local variations of the GUI for each user, optimizing elements such as frequently accessed features, layout preferences, and interaction patterns specific to that user rather than applying a uniform design to all users.
Data Source
AI summary
A graphical user interface (“GUI”) personalization application automatically generates a GUI for a software application. In operation, the GUI personalization application tracks interactions between a user and a first GUI that is associated with the software application and a GUI framework. The GUI personalization application then computes a behavioral simplification for the first GUI based on the interactions and the GUI framework. Subsequently, the GUI personalization application modifies the first GUI based on the behavioral simplification to generate a second GUI for the user that is associated with the software application and the GUI framework. Advantageously, the GUI personalization application can more efficiently and reliably improve the quality of the overall user experience when interacting with the software application across a wider range of users relative to prior art approaches.