Generative AI Video Summarization Using Text-Clip Timestamp Matching
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Solution Overview
Problem
Existing video summarization methods are time-intensive and lack the ability to autonomously produce high-quality, user-defined summaries from long videos, consuming significant computing resources and storage.
Innovation Solution
A generative AI-powered technique for extractive video summarization that extracts and stitches discrete video clips based on user objectives, using large language models and embedding models to create concise summaries without adding new content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional video summarization is outsourced to agencies with huge computing resources, then summary quality can be improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical video analysis by agencies with an automated AI system. The AI model autonomously processes videos, extracts key frames, and generates summaries without human intervention, eliminating the need for time-consuming manual review while maintaining high summary quality through intelligent algorithms.
Solution Approach 2:
The system enables self-service video summarization where the AI model independently performs the entire summarization workflow. The model automatically identifies important content, selects representative frames, and assembles summaries without requiring external agency involvement, making the process both faster and more autonomous.
2Productivity
If huge computing resources are allocated to video summarization, then processing capability is improved, but resource consumption and storage requirements increase
Solution Approach 1:
The patent extracts only the essential and most representative video frames rather than processing or storing the entire video content. The AI model identifies and selects key frames that capture the core information, significantly reducing computational requirements and storage needs while preserving the essential content for summarization.
Solution Approach 2:
The video is segmented into discrete frames, and the AI model processes only the most relevant segments for summarization. This segmentation approach allows the system to focus computational resources on critical portions of the video rather than uniformly processing all content, improving efficiency and reducing overall resource consumption.
3Manufacturing precision
If manual review processes are used for video summarization, then quality control is improved, but automation level decreases
Solution Approach 1:
The patent replaces manual quality control review with automated AI-based quality assessment. The AI model inherently maintains quality standards through its trained capabilities in identifying important content and assembling coherent summaries, eliminating the need for separate manual quality control steps while achieving full automation.
Data Source
AI summary
The present disclosure relates to a technique for summarizing long videos to short videos using generative artificial intelligence (AI). A method for converting an original video into a merged video by first receiving the original video, text based on the original video, and a list of summarization instructions. Thereafter, to first generate timestamps for first discrete chunks of the text, each timestamp defining a start time of the corresponding first discrete chunk within the original video. Further, receiving a summary of the text based on the summarization instructions, that includes second discrete chunks from the text. The method then discloses first and second vectorizing the first and second discrete chunks of the text, respectively, to define first and second vectors, respectively. Thereafter, matching each of the second vectors to its corresponding one of the first vectors and identify, for the matched second vectors, the timestamp for the corresponding first vector. Accordingly, extract video clips from the original video and stitch the extracted video clips together to create the merged video.