Dynamic GUI Adaptation via User Intent Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing graphical user interfaces (GUIs) face challenges in adapting to the specific intents and contexts of different users, leading to friction between the actual and perceived user experience, particularly in online commercial applications like ecommerce, resulting in reduced conversion rates and revenue.
Innovation Solution
A method and system that utilize machine learning models to collect and analyze usage data from users, group them based on similar intentions, and predict user intents to dynamically modify the GUI, including control and content components, to better serve individual user needs and intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single GUI serves multiple users with different intents, then the GUI can be simple and universal, but user experience friction increases and conversion rates decrease
Solution Approach 1:
The patent segments users into different groups based on their usage data and behavior patterns. By clustering users with similar intents together, the system can provide customized GUI experiences for each segment rather than a single universal interface, thereby reducing user experience friction while maintaining system manageability.
Solution Approach 2:
The patent implements dynamic GUI modification based on real-time usage data analysis. The system continuously monitors user interactions and adjusts the GUI layout, content, and functionality dynamically to match the detected user intent, making the interface adaptable rather than static.
2Ease of operation
If the GUI is dynamically updated based on usage data, then user experience improves, but system complexity increases
Solution Approach 1:
The system employs machine learning models that automatically analyze usage data and determine user intents without requiring manual intervention. The models self-adjust and improve over time by learning from accumulated data, reducing the need for complex manual configuration and management of the dynamic GUI updates.
Solution Approach 2:
The patent implements a feedback loop where usage data from user interactions is continuously collected, analyzed, and used to refine the GUI modifications. This closed-loop system allows the interface to automatically adapt based on real-world performance, reducing the need for manual system tuning and management.
3Measurement precision
If machine learning models are used to predict user intent, then GUI customization accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent pre-processes and stores usage data in structured formats suitable for machine learning analysis. By preparing the data infrastructure in advance and using pre-trained models, the system can quickly infer user intents during actual interactions without requiring extensive real-time computation, thus reducing processing delays.
Data Source
AI summary
The present method and system provides for recognizing user intent and updating a graphical user interface. In an example, the method and system includes collecting usage data from users, grouping users based on usage data, assigning a user intent to each group of users, training an intent prediction model using machine learning, providing access to the intent prediction model, assigning an intent to a new user using the intent prediction model, and, modifying the graphical user interface to facilitate the assigned intent of the user.