Personalized Video Summary Generation via Semantic Clip Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face difficulty in selecting digital content, such as movies and TV shows, due to generic video previews that may not include relevant or interesting portions, failing to account for individual user preferences and interests.
Innovation Solution
The system generates customized video content summaries by analyzing user preferences and separating content into semantic clips, scoring their relevance, and selecting segments for inclusion based on user interests, creating tailored summaries that highlight preferred themes and scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic video previews are used, then content selection process is simple, but user interest and relevance are low
Solution Approach 1:
The video content is divided into multiple semantic clips based on scene detection and semantic analysis. Each clip represents a distinct segment with specific themes or actions, allowing the system to selectively assemble relevant portions into personalized summaries rather than treating the entire video as a single unit.
Solution Approach 2:
Different portions of the video content are treated differently based on their relevance to user preferences. The system assigns varying weights and selection probabilities to different semantic clips according to their alignment with user interests, ensuring that high-relevance segments are prominently featured while low-relevance segments are excluded or minimized.
2Measurement precision
If user preference analysis is performed, then content relevance improves, but processing time increases
Solution Approach 1:
User preferences are analyzed and stored in advance before video processing begins. The system maintains pre-computed user profiles containing preference vectors that can be quickly compared against video content during summary generation, avoiding the need for real-time preference analysis when creating summaries.
Solution Approach 2:
The system uses automated machine learning models and algorithms to analyze user preferences and match them with video content, replacing manual or heuristic-based selection methods. This automated approach efficiently processes large amounts of data and generates accurate matches without requiring significant user intervention or time-consuming manual curation.
3Manufacturing precision
If semantic clip separation is implemented, then content accuracy improves, but computational complexity increases
Solution Approach 1:
The system introduces semantic analysis as an intermediary layer between raw video frames and final summary generation. Scene detection algorithms first identify potential segments, then semantic analysis refines these segments by analyzing visual content, audio, and metadata to determine thematic coherence. This intermediary processing step ensures accurate segmentation while maintaining manageable system complexity through modular architecture.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for systems and methods for customized video content summary generation. Example methods may include determining a first segment of digital content including a first set of frames, first textual content, and first audio content. Example methods may include determining a first event that occurs in the first set of frames, determining a first theme of the first event, generating first metadata indicative of the first theme, and determining a meaning of a first sentence that occurs in the first textual content. Some methods may include determining a second theme of the first sentence, generating second metadata indicative of the second theme, determining that user preference data associated with an active user profile includes the first theme and the second theme, generating a video summary that includes a portion of the first segment of digital content, and presenting the video summary.