Web Browser Navigation Prediction Prerendering
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Solution Overview
Problem
Current web browsing experiences are hindered by delays when users select links or enter URLs, as there is a wait time for data to be requested, sent, and rendered, leading to lost productivity despite high-speed Internet access.
Innovation Solution
A method and system that predicts user navigation events by tracking text inputs, consulting probability data to determine likely network navigation destinations, and prerendering these destinations using a web browser, reducing wait times by preemptively requesting content based on historical and aggregate navigation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If web content is rendered only after user request, then network bandwidth is conserved and unnecessary data transmission is avoided, but user wait time increases and productivity decreases
Solution Approach 1:
The system performs preliminary actions by predicting user navigation events before they occur and prerendering the corresponding web content in the background. This allows the content to be ready immediately when the user navigates to it, eliminating wait time while only consuming network bandwidth for predicted (and thus likely useful) content
Solution Approach 2:
The system dynamically adjusts its behavior based on real-time user interactions and historical data. By continuously learning from user navigation patterns, the system optimizes which content to prerender, adapting to changing user preferences and behaviors to balance bandwidth consumption with wait time reduction
2Productivity
If navigation prediction functionality is added to the web browser, then user productivity is improved by reducing wait times, but device complexity increases due to additional tracking and processing components
Solution Approach 1:
The navigation prediction system serves itself by automatically tracking user behavior, learning patterns, and making predictions without requiring manual configuration or intervention. The system self-optimizes by continuously processing user interactions and adjusting its prediction model, reducing the need for complex external management while improving productivity
3Measurement precision
If user navigation data is collected and stored for prediction purposes, then prediction accuracy is improved, but user privacy is compromised through collection of personally identifiable information
Solution Approach 1:
The system extracts and removes personally identifiable information from the collected navigation data, retaining only the behavioral patterns and navigation sequences needed for accurate prediction. This separation allows the system to maintain high prediction accuracy while eliminating privacy risks associated with storing identifiable user information
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 304 during web browsing 302 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, 500 and aggregate historical data 600, 700, 800 text entry prediction 900, and cursor input monitoring 1100, 1200.