Web Experience Augmentation via Local and Global Content Preferences
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional web browsing solutions fail to provide personalized content to users without compromising their anonymity, as they often require users to create accounts and track their activities, leading to generic content presentation and user frustration.
Innovation Solution
A web experience augmentation system predicts and modifies webpages based on both local and global content preferences, using machine learning to identify augmentation data that aligns with user behavior during a browsing session, allowing for personalized content delivery without account creation or tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If websites track user activities and create accounts to customize digital content, then content personalization is improved, but user anonymity and system complexity worsen
Solution Approach 1:
The system performs self-service by automatically analyzing user navigation behavior and generating personalized content recommendations without requiring user intervention for account creation or preference specification. The web experience augmentation system autonomously tracks navigation paths, infers content preferences, and modifies webpages to include relevant augmentation data, eliminating the need for manual user setup while maintaining personalization.
Solution Approach 2:
The patent introduces a web experience augmentation system as an intermediary layer between the user and the website content. This intermediary system anonymously analyzes navigation behavior and injects personalized augmentation data into webpages without requiring direct user-account relationships or complex tracking infrastructure. The intermediary handles personalization in the background, resolving the contradiction between customization and system complexity.
2Device complexity
If websites provide generic digital content to all users, then system complexity is reduced, but user experience and content relevance worsen
Solution Approach 1:
The system applies local quality by customizing content based on individual user navigation patterns while maintaining a generic base structure. Each user receives augmentation data tailored to their specific navigation path and inferred preferences, while the core webpage structure remains unchanged. This allows personalized content delivery without requiring complete system redesign or complex content management infrastructure.
Solution Approach 2:
The web experience augmentation system performs preliminary action by pre-analyzing user navigation behavior and pre-generating personalized augmentation data before the user requests content. The system infers content preferences from navigation paths and prepares relevant augmentation data in advance, so that when users access webpages, they immediately receive personalized content without experiencing delays or complexity in the content delivery process.
3Adaptability or versatility
If users manually specify preferences or create accounts, then content customization is improved, but ease of operation and user convenience worsen
Solution Approach 1:
The system performs self-service by automatically analyzing user navigation behavior and generating personalized content recommendations without requiring user intervention for account creation or preference specification. The web experience augmentation system autonomously tracks navigation paths, infers content preferences, and modifies webpages to include relevant augmentation data, eliminating the need for manual user setup while maintaining personalization.
Solution Approach 2:
The system implements feedback by continuously monitoring user navigation behavior and using this information to dynamically adjust and personalize content delivery. The web experience augmentation system analyzes navigation paths in real-time, infers content preferences from browsing patterns, and automatically modifies webpages to include relevant augmentation data, creating a closed-loop system that improves customization without requiring manual user input.
Data Source
AI summary
A web experience augmentation system predicts, during a web browsing session of a user, augmentation data that the user is likely to want to view during the web browsing session. This prediction is based on both local content preferences for the user and global content preferences. The local content preferences for the user refer to an indication of the webpages accessed during the current web browsing session of the user. The global content preferences refer to analytics for webpages on a website obtained over an extended period of time that extends prior to the web browsing session of the user. The web experience augmentation system also modifies a webpage to which the user navigates to include the predicted augmentation data.


