Web Content Prerendering via Navigation Prediction
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Solution Overview
Problem
Current high-speed Internet services still result in significant delays when users navigate to web pages, leading to lost productivity due to the time spent waiting for content to load.
Innovation Solution
A method and system that predicts likely user navigation events by analyzing metadata and navigation history, allowing for prerendering of web content before the user selects a link, thereby minimizing wait times by preemptively requesting and loading necessary resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If web content is loaded on-demand when user selects a link, then network bandwidth is conserved and unnecessary content is not transmitted, but user wait time increases and productivity is reduced
Solution Approach 1:
The system performs preliminary actions by predicting which web content the user is likely to navigate to next based on analysis of navigation history, metadata correlations, and browsing patterns. The predicted content is prerendered and staged in advance, so when the user actually selects the link, the content is already prepared and can be displayed immediately without waiting for network transmission, thus resolving the contradiction between reducing wait time and conserving bandwidth.
Solution Approach 2:
The system applies preliminary anti-action by preemptively counteracting the potential delay before it occurs. By analyzing patterns in navigation history and metadata, the system identifies likely navigation targets and pre-loads them, thereby neutralizing the future wait time issue before the user even clicks the link, while still maintaining bandwidth efficiency by only pre-loading predicted content.
2Speed
If all web content is prerendered in advance, then user access speed is maximized and no wait time is experienced, but network bandwidth is wasted and unnecessary content is transmitted
Solution Approach 1:
The system performs preliminary actions selectively rather than universally. It analyzes navigation history and metadata to predict which specific pieces of content are most likely to be accessed next, then prerenders only those predicted items. This approach maintains high access speed for relevant content while avoiding the bandwidth waste of prerendering all possible content, thus resolving the contradiction between speed and energy consumption.
Solution Approach 2:
The system applies local quality by making different parts of the content delivery system have different properties based on predicted user needs. Instead of uniformly prerendering all content or none, it selectively prerenders only the specific content segments that are predicted to be accessed, creating a localized optimization that balances speed improvement with bandwidth conservation.
3Measurement precision
If the system analyzes detailed metadata and navigation history to improve prediction accuracy, then prerendering precision is improved and bandwidth is saved, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring actual user navigation behavior and comparing it with predicted navigation targets. This feedback loop allows the system to refine its prediction algorithms and metadata analysis over time, improving prediction accuracy without requiring a proportional increase in system complexity, as the learning process automates the optimization.
Solution Approach 2:
The system applies self-service by automatically analyzing navigation history and metadata patterns to improve its own prediction capabilities without requiring manual configuration or complex external control systems. The system serves itself by learning from user behavior and automatically adjusting its prediction model, thereby achieving high precision while keeping operational complexity manageable.
Data Source
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AI summary
A method (1100) and system (100) for improving access to network content are described. Aspects of the disclosure minimize the delay between a navigation event and a network response by prerendering the next navigation event. For example, the method (1100) and system (100) may predict a likely next navigation event during web browsing (1102) to preemptively request content from the network before the user selects the corresponding link on a web page. The prediction operation may identify correlations between metadata associated with a navigation history (1104). The metadata may include the relationship of the selected navigation events to a web page (1106). By making predictions using link page relationships links may be accurately predicted (1108) in circumstances where two links share common relationships to their source page but different destination addresses.