Mobile Web Content Pre-Caching via User Behavior Feedback
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Solution Overview
Problem
Current mobile web browsing technologies lack the ability to intelligently pre-emptively cache content on devices without user request, leading to inefficient use of limited device memory and increased bandwidth usage during peak hours, as they do not provide feedback on user viewing habits to optimize caching decisions.
Innovation Solution
A method involving a remote computer that logs and replicates user viewing data to a server, which uses this information to determine when to automatically send and cache updated content based on factors like content change frequency, user access patterns, time, and operator promotions, allowing for optimized caching strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-defined content is sent and cached pre-emptively on devices without user request, then content availability and user experience are improved, but device memory usage increases inefficiently
Solution Approach 1:
The system performs preliminary actions by sending and caching content pre-emptively on user devices before explicit requests are made. This allows content to be instantly available when users access it, improving reliability and user experience while the intelligent selection mechanisms ensure memory is used efficiently
Solution Approach 2:
The system implements feedback loops by monitoring user viewing habits, content change frequency, and access patterns. This feedback enables the server to intelligently decide what content to cache pre-emptively on which devices, optimizing the balance between content availability and memory usage by only caching relevant content
2Reliability
If standard cache management is used to store recently viewed pages, then content can be viewed offline, but only limited memory capacity allows storing perhaps only the last 10-20 WAP pages
Solution Approach 1:
Instead of waiting for users to view content and then caching it, the system performs preliminary actions by pre-caching content based on predicted user interest, access patterns, and content importance. This allows a much larger variety of content to be available offline beyond just the last 10-20 pages
Solution Approach 2:
The system uses feedback from user viewing habits and content change frequency to dynamically adjust what content is cached offline. This intelligent selection based on actual user behavior allows the limited memory to store the most relevant and frequently accessed content, maximizing both offline accessibility and content variety
3Ease of manufacture
If content is cached for a given period of time with expiration, then memory management is simplified, but the browser is forced to reload pages during peak hours increasing bandwidth usage
Solution Approach 1:
The system performs preliminary actions by pre-caching content during off-peak hours when bandwidth is more available and costs are lower. This shifts the content transfer load from peak hours to off-peak hours, reducing bandwidth usage during high-demand periods while maintaining simple expiration-based cache management
Solution Approach 2:
The system implements periodic action by scheduling content updates and cache refreshes during off-peak hours rather than continuously or on-demand. This periodic batching of content transfers during low-usage periods reduces peak bandwidth consumption while maintaining cache management simplicity through time-based expiration policies
4Extent of automation
If Dynamic Service Delivery technology is used to send content pre-emptively, then content can be cached locally without user requests, but there is no feedback about what content users are viewing making intelligent caching decisions impossible
Solution Approach 1:
The system implements comprehensive feedback mechanisms by monitoring user viewing habits, content change frequency, and access patterns. This feedback enables intelligent caching decisions to be made automatically, combining the automation of pre-emptive caching with the adaptability of user-behavior-based content selection
Solution Approach 2:
The system enables self-service by allowing the caching mechanism to automatically make intelligent decisions about what content to cache based on monitored user behavior and content characteristics. The system serves itself by using its own collected data to optimize caching decisions without requiring manual intervention or external control
Data Source
AI summary
Content is automatically provided to a mobile web browsing device from a web server, by the following process: (a) receiving at a computer, remotely connected to the device, a log of data identifying content that has been viewed by the device; (b) automatically sending updated content stored on the web server to the device; (c) causing that updated content to be automatically stored in device memory. Because user activity is replicated back from the device to the remote computer, the content cached on a given device can be completely optimised for the user of that device and no-one else.