Semantic Bookmark Management for Duplicate Detection
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Solution Overview
Problem
Current web browsers lack effective methods to manage bookmarks, leading to issues such as duplicate bookmarks, broken links, and outdated information, with no capability to track changes or notify users of updates.
Innovation Solution
A system and method that utilizes a server-based service to analyze bookmarks using semantic similarity analysis, including knowledge graphs and document embedding techniques, to identify duplicates, broken links, and changes in web page content, allowing users to manage their bookmarks more intelligently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users continuously create bookmarks to save web pages, then the number of saved bookmarks increases, but the bookmark library becomes congested with duplicates and broken links
Solution Approach 1:
The system performs preliminary actions by automatically analyzing bookmarks upon creation, comparing them against existing bookmarks using semantic similarity analysis, and identifying duplicates before they congest the library. This proactive approach prevents the accumulation of redundant bookmarks rather than reacting to congestion after it occurs.
Solution Approach 2:
The bookmark management system performs self-service by automatically detecting duplicates, validating links, and organizing bookmarks without requiring user intervention. The system autonomously compares new bookmarks against the existing library using semantic analysis and maintains library quality independently.
2Device complexity
If web browsers use simple URL or page title comparison to identify duplicates, then the detection process is fast and simple, but duplicate bookmarks with different URLs or titles are not detected
Solution Approach 1:
The system changes the parameters used for duplicate detection from simple URL or title strings to semantic representations. By transforming bookmark data into vector embeddings that capture the meaning and content of web pages, the system achieves more accurate duplicate identification while maintaining computational efficiency through optimized similarity calculations.
Solution Approach 2:
The patent replaces the mechanical string-matching system with a semantic analysis system. Instead of mechanically comparing URLs or titles character-by-character, the system uses machine learning models to understand the semantic meaning of content, enabling detection of duplicates based on conceptual similarity rather than surface-level text matching.
3Extent of automation
If users manually monitor bookmarked web pages for updates, then users can track changes, but users have no way of knowing when bookmarked pages are outdated
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring bookmarked web pages and automatically notifying users of changes. The service periodically retrieves and analyzes bookmarked pages, compares them against the original version using semantic similarity, and provides feedback to users when updates are detected, eliminating the need for manual checking.
Solution Approach 2:
The patent introduces an intermediary service that acts between the user and the bookmarked web pages. This service autonomously monitors page changes, performs semantic analysis to detect meaningful updates, and communicates changes to users, serving as a mediator that bridges the gap between static bookmarks and dynamic web content.
4Measurement precision
If the system performs semantic similarity analysis on all bookmarked pages, then duplicate detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system segments the bookmark analysis process into multiple stages: initial filtering using lightweight metadata comparison, intermediate analysis of potentially duplicate bookmarks, and detailed semantic similarity analysis only for candidates that pass previous filters. This segmented approach maintains high accuracy while reducing overall processing time by applying intensive analysis only where necessary.
Data Source
AI summary
Systems and methods are described for managing saved web pages on a user device. In an example, when a user bookmarks a web page at the user device, the user device can send the bookmark to a server. A service on the server can gather information about web page and associate a plurality of tags with the web page based on that information. The service can compare the web page's tags to tags of other previously bookmarked web pages to identify a possible matching web page. The service can perform a semantic similarity analysis between the two web pages to determine whether their similarity exceeds a threshold. Where the similarity does exceed the threshold, the service can notify the user device. The user device can notify the user and present options for the new bookmarked web page.