AI Web Page Content Optimization via ROI Feedback
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Solution Overview
Problem
Current web page optimization methods are static, requiring significant time and resources to modify business rules and content, leading to inefficiencies in displaying relevant objects and widgets based on user interactions and marketing analysis.
Innovation Solution
A system utilizing AI algorithms and a feedback loop to optimize web page content by selecting screen objects and widgets based on Return On Investment (ROI) values, dynamically adjusting the layout and content to maximize revenue generation, and automating the process of updating business rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If web pages use static business rules to govern content display, then the logic is simple and easy to implement, but the system cannot adapt to changing user preferences and marketing insights in real-time
Solution Approach 1:
The patent transforms static business rules into dynamic optimization algorithms that automatically adapt web page content based on real-time user behavior data and marketing insights. The system continuously learns from user interactions and adjusts content selection without requiring manual rule rewrites, enabling the web page to evolve dynamically while maintaining manageable complexity through automated decision-making.
Solution Approach 2:
The patent implements feedback loops where user interaction data is collected, analyzed, and fed back into the optimization system to refine future content selections. This closed-loop approach enables the system to learn from actual user preferences and marketing performance, continuously improving adaptability while managing complexity through systematic data processing and algorithmic decision-making.
2Productivity
If web page content is manually optimized by rewriting business rules, then the optimization logic is clear and controllable, but the process requires significant time and resources for each change
Solution Approach 1:
The patent enables the optimization system to self-adjust by automatically analyzing user behavior data and selecting optimal content without requiring manual intervention. The system performs self-optimization through automated algorithms that process user interactions and update content selection in real-time, dramatically increasing productivity while eliminating the time-consuming manual rewriting process.
Solution Approach 2:
The patent pre-processes and stores user behavior data and marketing insights in advance, enabling the optimization system to make rapid decisions about content selection without requiring real-time manual analysis. This preliminary data preparation and processing allows the system to respond quickly to changing conditions, significantly reducing the time needed for optimization while maintaining clear and controllable logic through pre-established algorithms.
3Adaptability or versatility
If new objects or widgets are introduced on web pages, then the functionality and user experience are enhanced, but the business rules must be rewritten to accommodate the new elements
Solution Approach 1:
The patent segments the web page into discrete content elements and uses automated algorithms to determine their optimal selection and arrangement. This segmentation allows new objects and widgets to be added as independent units without requiring comprehensive rewrites of business rules, as the optimization system can incorporate new elements through targeted algorithmic adjustments rather than global rule changes.
Solution Approach 2:
The patent replaces the mechanical process of manually rewriting business rules with automated algorithmic systems that handle the integration of new objects and widgets. Instead of requiring developers to update rules for each new element, the system uses machine learning and optimization algorithms to automatically adapt to new content, significantly improving ease of manufacture while enhancing functionality.
4Productivity
If web pages are optimized using automated algorithms, then the optimization process is efficient and scalable, but the system requires sophisticated data processing and analysis capabilities
Solution Approach 1:
The patent designs a universal optimization framework that handles multiple types of data and content elements through a single integrated system. The algorithmic approach is designed to be multi-functional, capable of processing diverse user behavior data, marketing insights, and content types without requiring separate specialized systems for each function, thereby improving efficiency while managing overall system complexity.
Data Source
AI summary
In one embodiment, a method is illustrated as including defining a set of perspective objects capable of being placed onto a modified web page, monitoring parameters of a web page, the parameters including a number of times a current object is executed on the web page, using an Artificial Intelligence (AI) algorithm to determine a perspective object with a preferred Return On Investment (ROI), and selecting the perspective object to be placed onto the modified web page.