Dynamic Search Caption Augmentation via Social Stream Analysis
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Solution Overview
Problem
Current search engine results pages (SERPs) have static captions that do not fully utilize user-generated annotations and social media signals, failing to provide dynamic and relevant information about web content.
Innovation Solution
The architecture dynamically augments search result captions with keywords derived from social network updates, filtering out duplicate content by comparing social update text to webpage titles, and presenting popular social topics as dynamic social content fragments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If static captions are used in search results, then the search result page structure is simple and easy to maintain, but the information relevance and user engagement are insufficient
Solution Approach 1:
The patent transforms static captions into dynamic captions that automatically update based on real-time social media signals. The system monitors social networks for mentions of searched terms and dynamically generates captions reflecting current user discussions, making the search results adaptive and context-aware without requiring manual updates
Solution Approach 2:
The patent introduces social media platforms as intermediary sources of information. Instead of directly scraping or analyzing all social media data, the system uses social media APIs and aggregation services as intermediaries to fetch relevant signals, which are then processed to generate enhanced captions
2Productivity
If social media signals are integrated into search results, then user engagement and information quality improve, but the processing complexity and computational resources increase
Solution Approach 1:
The patent extracts only the most relevant features from social media signals rather than processing complete posts or comments. It identifies and extracts key elements such as sentiment indicators, topic tags, and engagement metrics, discarding redundant information to reduce processing complexity while maintaining engagement quality
Solution Approach 2:
The patent transforms unstructured social media text into structured parameters that can be efficiently processed. It converts social media posts into standardized fields such as sentiment score, topic category, and engagement level, enabling systematic analysis and integration into search results
3Loss of information
If real-time social media data is used to augment captions, then the information freshness and relevance improve, but the data processing time and system load increase
Solution Approach 1:
The patent performs preliminary processing of social media data by pre-aggregating signals, pre-computing sentiment analyses, and pre-categorizing topics before search queries are submitted. This preparation work is done in advance so that when a search is executed, the system can quickly retrieve and integrate pre-processed information rather than analyzing raw data in real-time
Data Source
AI summary
Architecture that augments a search result entry caption with keywords related to topics currently being shared and discussed in other social network information sources. This can provide a much better idea of the content of the website or webpage. The architecture obtains a link from updates of social topics of social network information sources (e.g., social networks for people places professionals, etc.), extracts title content of a document title associated with the link, compares the title content to document text for similarity to create non-duplicative content, creates keywords related to popular social topics from the non-duplicative content, and then augments a search result entry of a search result page with the keywords of the popular social topics.