Personalized Recap Video Annotation via Relevancy Analysis
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Solution Overview
Problem
Viewers face difficulties in keeping track of episodic content, such as television series, due to irregular consumption patterns and the spread of topics and entities across episodes, leading to confusion in storyline follow-up.
Innovation Solution
A method and system that automatically generates a personalized recap video by analyzing user viewership data and identifying relevant content from previously viewed episodes to annotate unwatched episodes, using speech-to-text, image recognition, topic modeling, and change point algorithms to create a compilation of video footage that is relevant to the intended episode.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users watch episodes irregularly over time, then they can consume content at their own pace, but they lose track of storyline and character developments
Solution Approach 1:
The system performs preliminary analysis of episode content to identify key plot points, character developments, and important events before the user watches. When generating a recap, it pre-assembles relevant footage and information based on what the user has previously watched, so the contextual information is ready and waiting to be presented when the user returns to watch a new episode.
Solution Approach 2:
The recap video acts as an intermediary between previously watched episodes and the current episode the user is about to watch. It bridges the temporal gap and information gap by presenting a synthesized summary of relevant prior content, allowing the user to maintain storyline awareness without having to continuously rewatch previous episodes.
2Loss of information
If users rewatch previous episodes to follow storyline, then they maintain understanding, but they waste time watching content they have already seen
Solution Approach 1:
The system extracts only the essential and relevant portions from previously watched episodes - key plot points, character introductions, and important events - and compiles them into a condensed recap video. This selective extraction allows users to review only the necessary information without rewatching entire episodes they have already seen.
Solution Approach 2:
Instead of providing a complete review of all previous episodes (excessive action), the system provides a partial summary containing only the most relevant content needed for understanding the current episode. This partial action approach saves user time while still achieving the goal of maintaining storyline comprehension.
3Loss of information
If the system generates detailed recaps of all previous episodes, then users get complete context, but the recap video becomes too long and loses user attention
Solution Approach 1:
The system applies local quality by making different parts of the recap video have different levels of detail and importance. Key plot points and character developments receive more screen time and emphasis, while less critical information is condensed or omitted. The recap structure varies in detail based on the specific episode context and what the user has already watched.
Solution Approach 2:
The system deliberately provides a partial summary rather than a complete review of all previous content. It identifies and includes only the essential elements needed for understanding the current episode, accepting that some less critical information will be omitted to keep the recap concise and engaging.
4Measurement precision
If the system analyzes all previously viewed videos to create personalized recap, then it provides accurate relevant content, but the processing complexity and time increase
Solution Approach 1:
The system segments the analysis process into distinct components: identifying key plot points, detecting character developments, extracting important events, and determining relevancy to the current episode. Each segment handles a specific aspect of content analysis, making the overall complex task more manageable and efficient through modular processing.
Solution Approach 2:
The system performs preliminary analysis and tagging of video content as it is watched, pre-identifying key elements and their relevancy. This preliminary action creates an organized database of content features that can be quickly queried and assembled into recaps without requiring complex real-time analysis when the user wants to watch a new episode.
Data Source
AI summary
A method for automatically annotating an intended video with at least one personalized recap video based on previously viewed videos is provided. The method may include automatically tracking user viewership of the previously viewed videos, and in response to detecting an intention to view the intended video: automatically identifying and extracting a subset of video footage from one or more of the previously viewed videos based on the tracked user viewership and based on a determined relevancy of the subset of video footage to content in the intended video; generating the at least one personalized recap video by compiling and sorting the extracted subset of video footage from the one or more previously viewed videos into a compilation video; and annotating the intended video with the at least one personalized video by presenting the at least one personalized recap video on the intended video.