AI-Driven GUI Adaptation for User Preference and Accessibility
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Solution Overview
Problem
Existing systems lack an efficient way to dynamically reconfigure user interfaces based on user input and preferences, including varying communication methods, language, and accessibility requirements.
Innovation Solution
An artificial intelligence-based system that trains an AI engine using natural language data to adapt user interfaces in real-time, transforming communications based on user settings and preferences, and incorporating feedback for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static user interfaces are used, then system simplicity is maintained, but adaptability to user preferences and accessibility requirements deteriorates
Solution Approach 1:
The patent implements dynamic user interfaces that automatically adapt to user preferences, accessibility requirements, and contextual factors. The system transitions from static to dynamic configuration, allowing interface elements to change in real-time based on user needs without requiring complex manual reconfiguration.
Solution Approach 2:
The system employs machine learning models that automatically analyze user behavior patterns and preferences to self-configure the interface. The AI engine autonomously makes decisions about interface customization without requiring explicit user instructions for each change, reducing the perceived complexity for users.
2Ease of operation
If personalized interface customization is implemented, then user experience quality improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of user preferences and behavior patterns during off-peak times or initial setup phases. Machine learning models pre-process and store user profile data, so that when interface customization is needed, the system can quickly retrieve and apply pre-analyzed preferences without significant processing delays.
Solution Approach 2:
The patent utilizes parameter changes in machine learning model inference to rapidly generate personalized interface configurations. By optimizing model parameters and using efficient inference techniques, the system can adapt interfaces in real-time with minimal processing time, balancing personalization quality with response speed.
3Measurement precision
If comprehensive user preference tracking is implemented, then interface personalization accuracy improves, but data privacy concerns increase
Solution Approach 1:
The patent implements differential privacy techniques that add controlled noise to user data at specific points in the collection and processing pipeline. This allows the system to maintain high accuracy in detecting user preferences while protecting individual user privacy by ensuring that no single user's data can be easily identified or reconstructed.
Solution Approach 2:
The system introduces privacy-preserving intermediaries such as federated learning architectures where machine learning models are trained locally on user devices and only model updates (not raw user data) are transmitted to the central server. This intermediary approach enables comprehensive preference tracking while maintaining data privacy by keeping sensitive user data localized.
Data Source
AI summary
A system is provided for artificial intelligence-based dynamic generation of graphical user interfaces. In particular, the system may train an artificial intelligence (“AI”) engine based on natural language data in written, auditory, and/or visual forms. The AI engine may serve as a translational model that may interface between users to dynamically adapt incoming and/or outgoing communications on the user devices to be customized to each user's preferences and/or inputs. The system may further use context-dependent adaptations based on whether the communications are internal or external to a particular reference entity. In this way, the system may provide an intelligence and efficient way to customize interface elements on a per-user basis.