Navigation Prediction System Prerendering Web Content
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Solution Overview
Problem
Current high-speed Internet services still result in significant delays when users navigate web pages, leading to lost productivity due to the time spent waiting for data to load, as existing technologies do not effectively preemptively fetch content based on user behavior.
Innovation Solution
A method and system that predicts user navigation events by analyzing navigation history, including individual and aggregate data, to prerender content before the user selects a link, using directed graphs weighted by navigation frequency and confidence values to minimize loading delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content is fetched on-demand when user selects a link, then network bandwidth is conserved, but user waits for data to load causing productivity loss
Solution Approach 1:
The system performs preliminary actions by predicting which web pages a user is likely to navigate to next and prerendering those pages in advance. This is achieved by analyzing navigation history, detecting navigation events, calculating confidence scores for predicted destinations, and preemptively fetching content before the user actually clicks any links. This resolves the contradiction by eliminating wait time through advance content preparation while conserving bandwidth by only fetching predicted content.
2Measurement precision
If navigation history data is collected and stored, then prediction accuracy improves, but user privacy concerns increase
Solution Approach 1:
The system extracts only the essential navigation event data needed for prediction (URLs visited, navigation sequences) while deliberately excluding personally identifiable information. The navigation history is processed to capture browsing patterns without storing sensitive user identifiers, thus achieving accurate predictions while protecting user privacy.
Solution Approach 2:
The system uses an intermediary approach by processing navigation data through anonymization layers. Navigation events are captured and transformed into pattern-based predictions without directly linking browsing behavior to specific user identities. This intermediary processing layer maintains prediction accuracy while shielding user privacy.
Data Source
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AI summary
A method 300 and system 200 for predicting a next navigation event are described. Aspects of the disclosure minimize the delay between a navigation event and a network response by predicting the next navigation event. The system 200 and method 300 may then prerender content associated with the next navigation event 306. For example, the method 300 and system 200 may predict a likely next uniform resource locator during web browsing to preemptively request content from the network before the user selects the corresponding link on a web page. The methods describe a variety of manners of predicting the next navigation event, including examining individual 400 and 500, and aggregate historical data 600, 700 and 800, text entry prediction 1000, and cursor input monitoring 1100.