AI Narrative Mapping for Coherent Video Clip Segmentation
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Solution Overview
Problem
Users often struggle to watch full-length video content due to busy schedules, leading to lost narrative threads and a desire for condensed, coherent video 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, segments the content into clips based on prioritization rules, and delivers personalized clips based on user preferences and viewing habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users watch full-length video content, then they experience complete narrative, but it consumes excessive time and disrupts their busy schedules
Solution Approach 1:
The patent segments video content into discrete clips based on narrative elements identified through NLP and image recognition. Each clip represents a coherent narrative unit that can be consumed independently, allowing users to watch only selected portions rather than entire videos, thus reducing time consumption while preserving narrative integrity.
Solution Approach 2:
The system extracts key narrative moments and scenes from full-length video content using AI analysis of subtitles and visual elements. These extracted clips capture essential story beats, character interactions, and plot developments, enabling users to experience the core narrative without watching the complete original video.
2Ease of operation
If users pause and restart video content multiple times, then they can manage their viewing around busy schedules, but they lose the narrative thread and coherence
Solution Approach 1:
The system performs preliminary analysis of video content using NLP on subtitles and image recognition on visual elements to identify and segment narrative moments before user viewing. This pre-processing creates self-contained clips with inherent narrative context, so users can start any clip without prior viewing and maintain coherence without needing to watch content sequentially from the beginning.
3Loss of time
If the system segments video content into multiple clips, then users can watch shorter segments, but the system complexity increases due to AI analysis requirements
Solution Approach 1:
The patent employs multi-functional AI models that perform multiple tasks: NLP models analyze subtitles for narrative elements, character interactions, and plot points, while image recognition models simultaneously identify visual elements, scenes, and actions. These models serve dual purposes of both content analysis and clip segmentation, reducing overall system complexity despite the sophisticated processing required.
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.