Narrative Video Clip Generation Using Subtitle-Frame Alignment
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Solution Overview
Problem
Consumers of video streaming services often struggle to watch full-length content in a single session due to busy schedules, leading to lost narrative threads and a desire for condensed, coherent segments that capture key moments.
Innovation Solution
An AI-powered system analyzes video content using natural language processing and image recognition to identify narrative elements, generating condensed video clips that maintain narrative coherence by prioritizing key scenes and moments based on user preferences and viewing habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users watch full-length video content in a single session, then they experience complete narrative coherence, but it consumes excessive time and is impractical for busy schedules
Solution Approach 1:
The patent segments video content into discrete scenes based on narrative analysis using NLP and image recognition. Each scene is identified by detecting narrative elements in subtitles and visual changes in video frames, then grouping them into meaningful segments that can be independently viewed while maintaining narrative coherence.
Solution Approach 2:
The patent extracts key narrative scenes from the full video content by analyzing subtitle text for narrative elements and detecting visual changes in video frames. The system identifies and extracts only the essential scenes that advance the narrative, removing redundant or less important portions to create a condensed version.
2Adaptability or versatility
If users pause and restart video content multiple times, then they can fit viewing into busy schedules, but they lose the narrative thread and coherence
Solution Approach 1:
The patent performs preliminary narrative analysis of the entire video content before user viewing, identifying all key scenes and their narrative relationships in advance. This pre-processing creates a structured breakdown of the content that enables users to pause and resume at scene boundaries without losing narrative context, as each scene is self-contained with clear narrative significance.
3Manufacturing precision
If the system analyzes video content using NLP and image recognition to identify narrative elements, then it can generate coherent condensed clips, but the processing complexity and computational resources increase
Solution Approach 1:
The patent uses subtitles as an intermediary to bridge audio/visual content and narrative analysis. By analyzing subtitle text through NLP to identify narrative elements and cross-referencing with visual frame analysis, the system creates a more accurate and efficient narrative understanding without requiring direct analysis of all audio and visual data.
Solution Approach 2:
The patent performs image recognition analysis at periodic intervals rather than continuously analyzing every video frame. The system detects visual changes at specific time points and uses these periodic samples to identify scene boundaries and narrative elements, reducing computational complexity while maintaining analysis accuracy.
Data Source
AI summary
A system, a method and a processor are for AI-powered generation and delivery of video clips. The processor is configured to: load a first video file of a first video content item, the first video file comprising video frames associated with timestamps; load a first subtitle file of the first video content item, the first subtitle file comprising subtitle text associated with the timestamps; execute a natural language processing (NLP) model with the subtitle text as input, the NLP model including language pre-processing steps for classifying words, names or phrases in the subtitle text and associating initial classifiers with the subtitle text, the NLP model including one or more of a recurrent neural network (RNN), a Bidirectional Encoder Representations from Transformers (BERT) model, or a generative pre-trained transformer (GPT) model for a dialogue analysis comprising processing sequences of dialogue in the subtitle text in view of the initial classifiers to associate one or more portions of the dialogue with one or more first classifiers of first narrative elements; execute an image recognition model with at least some of the video frames as input, the image recognition model including a convolutional neural network (CNN) for an object detection analysis and a facial recognition analysis comprising processing video sequences to associate one or more of the video frames with one or more second classifiers of second narrative elements; generate a narrative map of the first video content item by temporally aligning the first narrative elements with the second narrative elements based on the timestamps associated with the video frames and the first subtitle file; and generate a video clip including at least one segment of the first video content item, the at least one segment including selected video frames associated with at least one of the first or second narrative elements identified from the narrative map and selected for inclusion in the video clip.