Mobile App Content Pre-fetching via Sensor-Based Timing Prediction
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Solution Overview
Problem
Users of mobile applications often experience delays when viewing content streams due to the need for manual content requests, which can result in the presentation of stale or irrelevant content, exacerbated by factors like network availability and user interaction timing.
Innovation Solution
A mobile application that automatically predicts when a user is likely to view their content stream by analyzing temporal, location, and activity state data, allowing for pre-fetching of relevant content items from a server before the user initiates a request, using sensors like GPS, accelerometers, and activity recognition services to establish predefined conditions for timely content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual content request is used, then user controls content loading, but content delivery delay occurs
Solution Approach 1:
The system performs preliminary actions by automatically requesting and pre-loading content items before the user actually views them. The mobile application monitors user behavior patterns and proactively fetches content in advance, so that when the user opens the application or navigates to the content stream, the content is already available and displayed immediately without waiting for manual refresh or network requests.
2Loss of time
If content is pre-fetched, then content freshness is improved, but network bandwidth is consumed
Solution Approach 1:
The system applies partial action by selectively pre-fetching only certain content items based on predicted user interest, rather than fetching all available content. The mobile application analyzes user behavior patterns and preferences to identify which content items are most likely to be viewed, then pre-loads only those specific items. This approach balances content freshness with efficient network bandwidth utilization.
3Loss of time
If automated content request is implemented, then content delivery speed is improved, but device complexity increases
Solution Approach 1:
The mobile application implements self-service by automatically monitoring its own usage patterns and behavior data, then using this information to trigger content requests without user intervention. The system analyzes temporal patterns, location data, and activity state information to autonomously determine when content should be pre-fetched, reducing the need for complex manual control logic while improving content delivery speed.
4Loss of information
If context-aware pre-fetching is used, then content relevance is improved, but data processing requirements increase
Solution Approach 1:
The system processes only the necessary portion of available data to achieve content relevance. Instead of analyzing all possible context information, the mobile application selectively processes key signals such as temporal patterns, location changes, and activity state transitions that are most predictive of user content viewing behavior. This partial processing approach maintains high content relevance while minimizing energy consumption.
Data Source
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AI summary
Techniques for populating a content stream of a mobile application are described. Consistent with some embodiments, the delays associated with waiting for a server to respond to a user's manual request for content items are eliminated by using various sensors of a mobile computing device to predict when a user is likely to view a personalized content stream. Accordingly, a background process identifies when certain conditions are satisfied, and an automatic content request is communicated to a content server, such that content items for presentation to a member of a social networking service are pre-fetched.