AI Video Clip Segmentation for Narrative Coherence Under Time Limits
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Solution Overview
Problem
Users often struggle to watch full-length videos due to busy schedules, leading to lost narrative threads and a desire for condensed video content that maintains narrative coherence.
Innovation Solution
An AI-powered system analyzes video content using NLP and image recognition to identify key narrative elements, segments the video 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 information
If users watch full-length video content, then they experience complete narrative, but they consume excessive time and may lose narrative threads due to interruptions
Solution Approach 1:
The patent segments video content into multiple short clips, each capturing a specific narrative moment or scene. This segmentation allows users to watch only selected portions of video content rather than full-length content, reducing time consumption while maintaining narrative coherence through carefully selected segments that preserve story flow
Solution Approach 2:
The system extracts key narrative elements, pivotal moments, and essential scenes from full video content using AI analysis of subtitles and video frames. By taking out only the most important narrative components and presenting them as condensed clips, the system reduces viewing time while preserving narrative coherence
2Loss of time
If video content is condensed into shorter clips, then viewing time is reduced, but narrative coherence may be lost
Solution Approach 1:
The system uses feedback from AI analysis of video content, including NLP analysis of subtitles and image recognition of video frames, to identify and select clips that maintain narrative coherence. The feedback mechanism ensures that selected clips preserve story flow and contextual relationships, preventing narrative fragmentation despite condensation
Solution Approach 2:
The patent replaces manual video editing and selection with AI-powered automated analysis systems that use NLP and image recognition to identify narrative elements. This substitution enables efficient condensation of video content while maintaining narrative coherence through intelligent, data-driven selection of essential scenes
3Measurement precision
If AI analysis is used to identify narrative elements, then clip selection accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces intermediary components including NLP models for subtitle analysis and image recognition models for video frame analysis. These intermediaries bridge the gap between raw video content and meaningful narrative element identification, improving accuracy while managing complexity through modular, specialized processing stages
4Adaptability or versatility
If personalized clips are generated based on user preferences, then user satisfaction is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of video content into narrative elements and segments before user requests. This advance preparation creates a structured foundation that enables rapid generation of personalized clips when users provide preferences, improving both personalization capability and generation speed by avoiding redundant processing
Data Source
AI summary
The processor is configured to: classify a first video content item including a first video file comprising video frames associated with timestamps and a first subtitle file comprising subtitle text associated with the timestamps, wherein the first video content item is classified with narrative classifiers by: executing a natural language processing (NLP) model with the first subtitle file as input, the NLP model including a dialogue analysis for identifying first narrative elements from dialogue included in the first subtitle file and associating the first narrative elements with first timestamps, executing an image recognition model with the first video file as input, the image recognition model including an object identification analysis for identifying second narrative elements from objects or persons portrayed in the video frames of the first video file and associating the second narrative elements with second timestamps, combining a first output of the NLP model with a second output of the image recognition model, and generating a first set of timestamps associated with the narrative classifiers; define one or more segments within the first video content item, each segment comprising a starting timestamp and an ending timestamp defining a duration and having one or more of the narrative classifiers associated therewith; and generate a video clip including one or more of the segments based on prioritization rules in which some narrative classifiers are associated with a priority for inclusion in the video clip, the one or more segments selected for inclusion in the video clip so that a combined duration of the one or more segments is less than a set time value, the set time value being less than a full duration of the first video content item.