Adaptive Page Layout Selection for Client Latency Constraints
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Solution Overview
Problem
Contemporary websites face challenges in optimizing content page layout to minimize latency across various client devices and network connections, which can lead to user abandonment and negatively impact user experience and conversion rates.
Innovation Solution
A system that generates a score for predicted latency based on client device and network connection properties, using a machine learning engine to select an optimized page layout that balances richness and rendering efficiency, serving a less taxing layout for high latency and a richer layout for low latency scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a rich page layout with numerous features and widgets is served to all clients, then the information completeness and functionality are improved, but the latency increases and user experience deteriorates for clients with limited resources
Solution Approach 1:
The patent applies local quality by serving different page layouts tailored to specific client device characteristics. Instead of a uniform layout, the system analyzes client properties (processor speed, memory, network bandwidth) and delivers optimized layouts where each client receives content appropriate to their specific capabilities, ensuring information completeness for capable devices while reducing latency for constrained devices.
Solution Approach 2:
The system changes parameters of the page layout based on client device parameters. By monitoring client device characteristics and network conditions, the system dynamically adjusts layout parameters (number of widgets, content density, feature inclusion) to optimize the balance between information completeness and loading latency for each specific client scenario.
2Loss of time
If a simple page layout is served to reduce latency, then the loading speed is improved, but the information completeness and user experience are degraded
Solution Approach 1:
The patent implements dynamics by making the page layout adaptive rather than static. The system continuously monitors client device properties and network conditions, then dynamically selects or generates appropriate layout configurations. This dynamic approach allows the layout to be simple when latency is critical but rich when the client can handle it, optimizing the balance in real-time.
Solution Approach 2:
The system changes layout parameters based on client capabilities and network conditions. By adjusting parameters such as widget count, content density, and feature inclusion according to measured client properties (processor speed, memory, bandwidth), the system ensures that simple layouts are served when needed for low latency while maintaining information completeness when client capabilities permit.
3Ease of operation
If customized page layouts are generated for each client device, then the user experience is improved, but the system complexity and computational resources required increase
Solution Approach 1:
The patent applies segmentation by dividing clients into segments based on their device characteristics and network conditions. Instead of handling each client completely individually (which would be overly complex), the system segments clients into groups with similar properties and applies appropriate layout strategies to each segment, reducing system complexity while still providing customized experiences.
Solution Approach 2:
The system implements universality by creating a flexible page layout generation framework that can serve multiple client types with a single system. The layout engine is designed to handle various client device configurations and network conditions through a unified approach, reducing system complexity while maintaining the ability to customize layouts for different user experiences.
Data Source
AI summary
Disclosed are various embodiments for selecting page layouts based upon an outcome prediction associated with a request for a content page. Session variables associated with a request can be extracted. A score can be calculated based upon the session variables. The score can be generated by a machine learning engine that is trained using archived session data. A page layout can be selected based upon the generated score and a respective content page generated.