Mobile Device Event Prediction for Background Content Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mobile computing devices face challenges in managing battery life and data usage efficiently due to constraints on battery capacity and cellular data limits, leading to delayed access to updated content for users.
Innovation Solution
A mobile device system that monitors environmental, system, and user events to predict future occurrences, allowing for proactive adjustments in resource allocation and background data processing, ensuring that frequently used applications are updated and ready for use without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the mobile device waits for user invocation to download and update application content, then battery power is conserved, but the user experiences delayed access to updated content
Solution Approach 1:
The system performs preliminary actions by predicting when users will invoke applications and proactively downloading updated content in the background before the actual invocation occurs. This eliminates waiting time for content updates while managing battery power through intelligent prediction and scheduling of background operations.
2Ease of operation
If the mobile device proactively downloads and updates application content in the background, then user experience is improved with immediate access to updated content, but battery power is consumed
Solution Approach 1:
The system uses feedback mechanisms by monitoring user invocation patterns and application usage statistics to continuously improve prediction accuracy. This feedback loop enables the system to optimize background download scheduling, providing improved user experience while minimizing unnecessary battery consumption through data-driven decision making.
3Productivity
If the mobile device monitors and predicts system events to preemptively launch applications, then productivity is improved by reducing wait time, but device complexity increases
Solution Approach 1:
The system implements self-service by automatically monitoring system events, analyzing usage patterns, and making autonomous decisions about when to launch applications or download content. This eliminates the need for manual user intervention while managing complexity through automated algorithms that learn from observed patterns rather than requiring complex configuration.
Data Source
AI summary
In some implementations, a mobile device can be configured to monitor environmental, system and user events associated with the mobile device and/or a peer device. The occurrence of one or more events can trigger adjustments to system settings. The mobile device can be configured to keep frequently invoked applications up to date based on a forecast of predicted invocations by the user. In some implementations, the mobile device can receive push notifications associated with applications that indicate that new content is available for the applications to download. The mobile device can launch the applications associated with the push notifications in the background and download the new content. In some implementations, before running an application or communicating with a peer device, the mobile device can be configured to check energy and data budgets and environmental conditions of the mobile device and/or a peer device to ensure a high quality user experience.