Smart Web Link Contextual Preview System
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Solution Overview
Problem
Users face frustration and inefficiency when trying to find relevant content in shared web pages, as they often have to manually scroll through large amounts of information or rely on browser search features, especially when the preview does not align with the context of the conversation.
Innovation Solution
A system and method for providing smart web links that use natural language processing and content analysis to identify the most relevant sections of a web page, allowing users to generate custom previews and highlight relevant content, automatically scrolling to the most relevant portion when loaded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually scroll through web pages to find relevant content, then they can access the complete webpage, but it wastes time and causes frustration
Solution Approach 1:
The system performs preliminary action by analyzing the web page content and conversation context before the user needs to access the page. The server identifies relevant sections in advance and modifies the web page to highlight them, so when the user opens the page, the relevant content is already prepared and visible, eliminating the need for manual scrolling through the entire page.
Solution Approach 2:
The system uses feedback from the conversation context to dynamically modify the web page display. By analyzing the conversation history and identifying what sections are most relevant to the current discussion, the system provides feedback-driven customization of the web page view, highlighting only the sections that matter to the user in that specific context.
2Adaptability or versatility
If the web page preview shows the primary purpose or topic, then it provides a general overview, but it may have nothing to do with the context of the conversation
Solution Approach 1:
The system makes the web page preview dynamic by adjusting it based on the conversation context. Instead of showing a static general overview, the system dynamically modifies the preview to highlight specific sections that are most relevant to the current conversation, making the preview adaptable to different contextual situations.
Solution Approach 2:
The system applies local quality by customizing different parts of the web page display based on relevance. Rather than uniformly presenting the entire page, it selectively highlights specific local sections (paragraphs, sections) that are most relevant to the conversation context, while leaving other parts in their original state.
3Measurement precision
If users use browser search features to find keywords, then they can locate specific information, but it requires reading through large amounts of content first
Solution Approach 1:
The system segments the web page content into smaller, manageable sections (paragraphs, sections) and analyzes each segment's relevance to the conversation context. By working with segmented portions rather than the entire page at once, the system can quickly identify and highlight the most relevant sections without requiring users to read through large amounts of content.
Data Source
AI summary
Systems and methods presented herein provide smart web links that display the most relevant portion of a shared web page based on the context in which the web page is shared. An agent on a user device can detect a uniform resource locator (“URL”) shared on a communication channel. The agent can send the URL and content from the communication channel to a server. The server can retrieve a web page of the URL and identify sections of it. The server can compare the sections to the communication content to determine which section is the most semantically similar. The server can modify the web page to generate a custom preview, highlight the semantically similar content, and cause the web page to automatically scroll to the highest scoring section. The agent can change the shared URL to a new URL that directs to the modified web page.