Visual Display Personalization Using User Reaction Analysis
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Solution Overview
Problem
Current techniques in dynamic content and responsive web design focus on a single point of interest determined by designers, failing to personalize visual experiences for individual users, and lack effective methods to dynamically adjust content based on user preferences and reactions.
Innovation Solution
An AI-driven method that creates user profiles based on online activity, analyzes user reactions using convolutional and recurrent neural networks, and applies personalized visual treatments such as image cropping, zooming, rotating, and text addition/removal to enhance user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single point of interest is determined by designers for all users, then design consistency is maintained, but user personalization is lost
Solution Approach 1:
The system performs preliminary actions by creating user profiles in advance based on monitored online activity, storing this data for future personalization. This allows the system to quickly personalize content without complex real-time processing during user interaction
Solution Approach 2:
The patent introduces an intermediary layer (the personalization system with neural networks) that sits between the fixed design templates and the user, translating user profile data into personalized content arrangements. This intermediary handles the complexity, keeping the base design system simple while enabling personalization
2Productivity
If dynamic content changes based on user behavior, then user engagement is improved, but processing time and computational resources increase
Solution Approach 1:
User profiles and behavior patterns are built in advance through continuous monitoring and analysis of online activity. This preliminary data collection and processing enables rapid content personalization without requiring extensive real-time computation when users interact with the system
Solution Approach 2:
The system uses neural networks to identify patterns and create simplified representations of user preferences. Once patterns are learned from comprehensive data analysis, the system can quickly apply these learned patterns to personalize content without re-processing the entire dataset each time
3Loss of information
If visual elements are dynamically rearranged based on user preferences, then content relevance is increased, but layout consistency deteriorates
Solution Approach 1:
The system applies local quality by making specific visual elements (such as images, text, or layout components) personally relevant to each user based on their profile, while maintaining the overall page structure and design framework consistent. Not all elements are changed - only those locally relevant to user preferences are dynamically adjusted
Data Source
AI summary
In an approach to improve adaptive content and responsive web content, embodiments of create a profile associated with a user based on monitoring online activity of the user and analyze and classify content displayed to the user based on collected data associated with a reaction from the user. Further, embodiments derive a preliminary set of topics of interests based on the reaction of the user and the analyzed and classified content and associate the preliminary set of topics of interest with the reaction of the user and the analyzed and classified content. Additionally, embodiments, analyze a location of interest to the user on associated with the content based on the preliminary set of topics of interests to personalize a display of the location for interaction by the user, and present, by a user interface, a personalized display of the location to the user.