Web Page Server Optimization System for Visitor Context Adaptation
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Solution Overview
Problem
Current web page testing methods are inflexible and time-consuming, assuming a single optimal page and failing to account for varying visitor contexts, leading to inefficient ad campaign deployment and suboptimal conversion rates.
Innovation Solution
A web page server optimization system that includes a visitor context analysis component and a real-time optimizer, continuously analyzing visitor interactions to dynamically create and test variations of web pages, selecting the most effective page based on accumulated context data for real-time decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional A/B testing or multivariate testing is used to evaluate web pages, then statistical validity can be achieved, but the testing process becomes time-consuming and inflexible
Solution Approach 1:
The patent implements dynamic web page optimization by continuously analyzing visitor context and automatically selecting or generating optimized pages in real-time, replacing static traditional testing approaches. The system dynamically adjusts page content based on visitor characteristics, behavior patterns, and contextual signals without requiring lengthy predetermined testing cycles.
Solution Approach 2:
The system performs self-optimization by automatically analyzing visitor context, evaluating multiple web page variations, and selecting the most effective page without human intervention. The automated optimization engine continuously learns from visitor interactions and independently makes decisions about page selection, eliminating the need for manual A/B testing setup and analysis.
2Productivity
If multiple web pages are created and rotated to optimize for different audiences, then conversion efficiency can be improved, but the complexity of managing and testing these pages increases
Solution Approach 1:
The patent creates a universal optimization system that handles multiple web page variations and visitor contexts through a single automated platform. The system serves multiple functions: analyzing visitor context, evaluating page performance, selecting optimal pages, and continuously learning from interactions, replacing multiple separate testing and management processes.
Solution Approach 2:
The automated optimization engine acts as an intermediary between the visitor context data and the web page selection process. It mediates by analyzing contextual signals, evaluating multiple page options, and selecting the most appropriate page based on learned patterns, simplifying the complex relationship between diverse visitor segments and numerous page variations.
3Productivity
If web pages are optimized for specific visitor contexts, then conversion efficiency improves, but the system requires continuous analysis and real-time decision-making capability
Solution Approach 1:
The system implements continuous feedback loops by monitoring visitor interactions with web pages, analyzing the effectiveness of different page variations across various visitor contexts, and using this feedback to continuously refine its optimization decisions. The automated engine learns from actual conversion data and contextual signals to improve its page selection accuracy over time.
Solution Approach 2:
The system performs preliminary analysis of visitor context and pre-evaluates multiple web page options before the actual visitor interaction occurs. By pre-processing contextual signals and having optimized pages ready for immediate deployment, the system enables real-time decision-making without requiring complex computations during the actual page delivery moment.
Data Source
AI summary
In one embodiment, a method includes accessing a current-visitor context of a current visitor to a web page in a current web-browsing session. The current-visitor context includes one or more data associated with or concerning the current visitor. The method includes selecting based on the current-visitor context a particular one of multiple possible instances of the web page for presentation to the current visitor. The particular one of the multiple possible instances of the web page is substantially most likely to generate a highest expected outcome from interaction with the web page by the current visitor as indicated by the current-visitor context.