Fingerprint-Based Intelligent Content Pre-Fetching for Mobile Devices
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Solution Overview
Problem
Conventional pre-fetching methods for mobile devices are inefficient due to their user-centric approach, which leads to excessive memory usage, battery drain, and unnecessary data consumption, as they push all content to the device without considering the user's actual needs, often providing unviewed content and missing viewed content.
Innovation Solution
Adaptive asynchronous refresh using fingerprint-based, intelligent content pre-fetching, which selects content based on predictive approaches, including user history, patterns, and location, and shares intelligence through a proxy service, allowing proactive, crowd-sourced, and predictive content pre-fetching, reducing the burden on mobile devices by using fingerprints instead of actual content and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional pre-fetching pushes all content to the mobile device, then content availability is improved, but memory usage and battery drain increase
Solution Approach 1:
The patent extracts only the essential identifying information (fingerprints) from content and pushes it to the mobile device, rather than pushing the entire content. This allows the device to identify and request only the specific content items it needs, significantly reducing memory usage and energy consumption while maintaining content availability.
Solution Approach 2:
The system performs partial pre-fetching by pushing only fingerprints (partial information) rather than complete content. This partial action reduces the burden on mobile device resources while still enabling intelligent content retrieval when needed.
2Reliability
If conventional pre-fetching pushes all content to the mobile device, then content availability is improved, but data consumption increases
Solution Approach 1:
The system extracts and transmits only fingerprints (minimal data) rather than complete content items. This dramatically reduces data consumption while maintaining the ability to provide content availability when users request it.
3Quantity of substance
If conventional pre-fetching provides all content from visited websites, then content completeness is improved, but relevance to user needs deteriorates
Solution Approach 1:
The system uses user interaction feedback (clicks, views, time spent) to learn which content items are actually relevant to the user. This feedback mechanism allows the system to filter and prioritize content pre-fetching based on demonstrated user interest, improving relevance while maintaining reasonable content completeness.
Solution Approach 2:
The system performs preliminary analysis of user behavior patterns to predict which content items will be of interest before actually pre-fetching them. This preliminary action ensures that pre-fetched content is more likely to be relevant to user needs.
Data Source
AI summary
Example apparatus and methods concern fingerprint-based, intelligent, content pre-fetching. An example apparatus may have a memory that is configured to store content items or fingerprints derived from content items. The apparatus may include a set of logics that are configured to selectively asynchronously provide a content item or a fingerprint derived from the content item to a data store on a mobile computing device. The items are provided in response to an event other than a request for content from the mobile computing device. The apparatus may be configured to select the content item based on a proactive, crowd-sourced, predictive and adaptive method. The apparatus may provide the content item or the fingerprint to the mobile device and to other related mobile devices or users. The apparatus may consider the state (e.g., available memory, available battery, available communication channels) of the mobile device before providing content.