Dynamic Thumbnail Selection for Social Media Engagement
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Solution Overview
Problem
Static video thumbnails fail to maximize engagement across different social media webpages, as they do not account for varying user preferences and emotional connections with influencers, leading to suboptimal engagement on webpages with different user demographics.
Innovation Solution
A system that analyzes content on social media webpages and videos to identify prominent influencers and select dynamic thumbnails based on characteristics that resonate with the target audience, using a framework that includes influencer identification, ranking, and thumbnail selection algorithms to create personalized thumbnails for each webpage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static video thumbnail is used for all social media webpages, then the thumbnail presentation is simple and consistent, but user engagement is suboptimal because it does not account for varying user preferences and emotional connections with influencers
Solution Approach 1:
The patent applies dynamics by transitioning from a static thumbnail to a dynamic thumbnail selection system that adapts based on webpage content and user preferences. The system analyzes webpage content to identify prominent influencers and selects thumbnails accordingly, making the thumbnail presentation dynamic and context-aware rather than fixed and universal.
Solution Approach 2:
The patent applies local quality by customizing thumbnail selection for different local contexts (specific webpages) based on their content and audience characteristics. Instead of using a single thumbnail for all webpages, the system selects different thumbnails for different webpages based on the prominent influencers identified in each webpage's content, thereby optimizing engagement for each local context.
2Productivity
If the same video thumbnail is presented on all social media webpages, then the implementation is straightforward, but engagement fails to maximize across different user demographics and influencer preferences
Solution Approach 1:
The patent applies preliminary action by analyzing webpage content and identifying prominent influencers before the video is viewed. The system performs this analysis in advance to determine which thumbnail to present, ensuring that the most engaging thumbnail is selected based on pre-identified user preferences and influencer relevance, thereby maximizing video views before the user even interacts with the content.
Solution Approach 2:
The patent applies feedback by using engagement metrics and user interaction data to refine future thumbnail selections. The system learns from user behavior patterns and adjusts its thumbnail selection algorithm to better predict which thumbnails will generate the highest engagement, creating a feedback loop that continuously improves video view productivity.
3Adaptability or versatility
If influencer identification and ranking algorithms are implemented to select dynamic thumbnails, then user engagement is maximized, but the system complexity and computational resources required increase
Solution Approach 1:
The patent applies copying by creating a simplified representation or model of the complex task. Instead of analyzing every possible aspect of webpage content and user behavior in full detail, the system uses simplified algorithms to identify prominent influencers and generate thumbnail selections that capture the essential characteristics needed for engagement optimization.
Solution Approach 2:
The patent applies parameter changes by adjusting the complexity and depth of analysis based on available data and computational resources. The system can modify its influencer identification and ranking algorithms to operate at different levels of detail, changing parameters such as the number of influencers analyzed, the depth of content analysis, and the complexity of ranking algorithms to balance customization benefits with system complexity constraints.
Data Source
AI summary
A framework generates a thumbnail to represent a video on a webpage based on a prominent individual appearing in both the video and content of the webpage. Content of a webpage on which a video is to be posted is analyzed to identify individuals represented in the webpage content. Frames of the video are also analyzed to identify individuals in the video. A first individual that appears in both the webpage content and the video is selected based on a score for the first individual determined based on the webpage content. Subsequent to selecting the first individual, frames of the video that include the first individual are analyzed to select a first frame of the video that includes the first individual. A thumbnail to represent the video on the webpage is generated from the first frame, and the thumbnail is provided for presentation on the webpage to represent the video.