Context Engine for Automatic Mobile Interface Adaptation
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Solution Overview
Problem
Existing mobile devices require manual user intervention to switch between contexts, which are predefined and static, failing to adapt automatically to changing user environments and preferences.
Innovation Solution
A context engine that automatically changes the mobile device's user interface context based on detected device platform and application information, using rules and weights to determine the most relevant context, and learns from user feedback to optimize context selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user intervention is used to switch between contexts, then the device complexity is reduced, but the ease of operation deteriorates because users must manually switch contexts
Solution Approach 1:
The context engine automatically monitors device platform information and application information, then autonomously determines and switches between contexts without requiring user intervention. The system serves itself by learning from user feedback and automatically optimizing context selection, thereby improving ease of operation while managing complexity through automated intelligence.
Solution Approach 2:
The system incorporates user feedback mechanisms where user interactions with automatically selected contexts are monitored and fed back to the context engine. This feedback loop enables the system to learn from user behavior and continuously optimize context selection, resolving the contradiction by making the system smarter rather than simpler.
2Adaptability or versatility
If predefined static contexts are used, then the device complexity is reduced, but the adaptability deteriorates because contexts cannot automatically adapt to changing user environments
Solution Approach 1:
The patent transforms static predefined contexts into dynamic adaptive contexts. The context engine continuously monitors changes in device platform information (such as location, time, network status) and application information, then dynamically adjusts and switches contexts in real-time. This dynamic behavior enables the system to adapt to changing user environments while managing complexity through systematic monitoring and rule-based decision making.
Solution Approach 2:
The context engine autonomously performs context adaptation by monitoring device and application information, comparing current state against stored context definitions, and automatically switching contexts without user intervention. This self-service capability provides high adaptability while containing complexity within the automated context management system.
3Ease of operation
If automatic context switching is implemented, then the ease of operation is improved, but the device complexity increases due to the context engine and automated rules
Solution Approach 1:
The context engine operates autonomously to manage context switching, learning from user feedback and automatically optimizing context selection without requiring complex user configuration or intervention. This self-service approach improves ease of operation by handling complexity internally within the automated system.
Solution Approach 2:
The system uses feedback from user interactions to continuously refine context selection, reducing the need for complex predefined rules. The feedback loop enables the system to learn and adapt, managing complexity through intelligent optimization rather than exhaustive rule sets.
4Loss of time
If static contexts are used, then the loss of time is reduced in terms of system processing, but the loss of time increases for users who must manually switch contexts
Solution Approach 1:
The context engine automatically monitors device and application information and switches contexts without user intervention, eliminating the time users would spend manually switching contexts. The system serves itself by autonomously managing context transitions, thereby reducing time loss for users while maintaining efficient automated processing.
Solution Approach 2:
The system pre-defines multiple contexts with associated rules and criteria in advance. When context switching is needed, the pre-prepared contexts can be quickly activated based on current device and application information, reducing the time required for context transitions compared to creating or configuring contexts in real-time.
Data Source
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AI summary
Example methods and apparatus to display mobile device contexts are disclosed. An example method includes displaying a first context (210) in a user interface (102) of a mobile device (100) based on first device platform information, the first context (210) including a first arrangement of information, graphics, and application icons that are associated with the first device platform information, determining that second device platform information received after the first device platform information corresponds to a second context (230), the second context (230) being different from the first context (210) and including a second arrangement of information, graphics, and applications that are associated with the second device platform information, and displaying the second context (230) in the user interface (102) replacing the first context (210) without prompting a user of the mobile device (100).