Adaptive User Interface Based on Device and User Context
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Solution Overview
Problem
Administrators in diverse computing environments face challenges in performing tasks on various devices due to unfamiliarity with specific user interface elements and orders, which can impede successful task completion, especially when devices are repurposed or experiencing issues.
Innovation Solution
A system that learns how to adapt user interfaces by identifying device and user contexts using artificial intelligence techniques, storing knowledge in a database, and retrieving it to present optimized user interface content and routing for task performance, considering both device and user contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If administrators manually learn and adapt to each device's user interface, then task completion accuracy improves, but time consumption and training requirements increase significantly
Solution Approach 1:
The system performs self-learning by automatically observing user interactions with device user interfaces and extracting task completion patterns without requiring external training input. The AI model continuously improves its understanding of device-specific UI behaviors through passive observation, enabling it to provide accurate task guidance without consuming administrator training time
Solution Approach 2:
The system implements feedback loops where user interactions with device user interfaces are continuously monitored and fed back to the AI model. This feedback mechanism allows the system to learn from actual usage patterns and refine its task completion predictions, improving accuracy over time without requiring additional administrator intervention or training time
2Device complexity
If a standardized user interface approach is used across diverse devices, then system complexity is reduced, but adaptability to specific device contexts and user preferences deteriorates
Solution Approach 1:
The system applies local quality by tailoring user interface guidance to specific device contexts and individual user preferences. Instead of providing generic instructions, the AI model analyzes the particular device being used and the specific user's interaction patterns to deliver customized task completion guidance that adapts to local device characteristics and user behaviors
Solution Approach 2:
The system implements dynamics by making user interface guidance adaptive and changeable based on observed user behaviors and device contexts. The AI model continuously evolves its recommendations by learning from ongoing user interactions, allowing the interface guidance to dynamically adjust to changing device states, user preferences, and task requirements rather than relying on static standardized procedures
3Loss of information
If comprehensive user interface documentation is provided for all devices, then task completion information is improved, but information overload and usability decrease
Solution Approach 1:
The system extracts only the essential task completion information needed for specific user contexts from the vast amount of available device documentation. The AI model identifies and extracts relevant UI elements, navigation paths, and task steps based on the current device context and user's apparent goals, presenting only the necessary information rather than comprehensive documentation
Solution Approach 2:
The system applies partial action by providing selective, context-specific guidance rather than complete documentation for all possible tasks. The AI model determines the minimum necessary information required to complete the current task based on observed user behavior and device state, avoiding information overload by presenting only what is needed at that moment rather than exhaustive documentation
4Measurement precision
If AI learning processes continuously monitor user interactions, then user interface adaptation accuracy improves, but user privacy concerns and system resource consumption increase
Solution Approach 1:
The system extracts only the minimal necessary behavioral patterns needed for task completion analysis from user interactions. Instead of monitoring all user activities, the AI model focuses specifically on extracting task-relevant actions, UI element interactions, and navigation patterns that are essential for improving task guidance accuracy while leaving other personal information uncollected
Solution Approach 2:
The system introduces an intermediary layer of anonymization and aggregation between individual user interactions and the AI learning process. User behaviors are aggregated into pattern data that preserves task completion information while removing personally identifiable details, serving as a mediator that enables accurate behavior analysis without directly exposing or storing sensitive user privacy information
Data Source
AI summary
A device context of a device is identified based on device data including a current state of the device, and a user context of a user is identified based on user data including user interaction with a user interface for the device. A current task that the user is performing on the device in relation to the current state of the device is identified, and knowledge regarding how the user is performing the current task using the user interface is extracted, using an artificial intelligence technique with respect to the identified device context and the identified user context. The extracted knowledge for the identified current task is stored within a knowledge base, and can be used to adapt the user interface for another user performing the current task on the device in the current state.