Contextual Video Segment Selection for Text Overlay
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Solution Overview
Problem
Contemporary video editing tools are difficult for amateur users to navigate, requiring extensive manual adjustments and lacking context-sensitive video selection capabilities, making it challenging to create video-filled text effectively.
Innovation Solution
A video editing application with a context determination module and video analysis module that automatically selects video segments matching the context of the text selection, generating a composite video by superimposing text characters onto relevant video segments, thereby simplifying the process and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If contemporary video editing tools are used to create video-filled text, then the precision and control over video segments can be improved, but the device complexity and ease of operation deteriorate
Solution Approach 1:
The system performs automatic video segment selection and text synchronization without requiring manual user intervention. The video editing application autonomously analyzes video content, identifies relevant segments, and matches them with text elements, eliminating the need for users to manually adjust multiple video layers and synchronize timing.
Solution Approach 2:
The patent replaces manual mechanical editing operations with automated computational processes. Instead of requiring users to manually manipulate video layers, adjust timing, and synchronize content, the system uses automatic analysis and matching algorithms to perform these tasks, significantly reducing operational complexity.
2Manufacturing precision
If manual adjustments are made in contemporary video editing tools, then the manufacturing precision of video-filled text can be improved, but the loss of time and productivity deteriorate
Solution Approach 1:
The system performs preliminary analysis of video content to pre-identify suitable video segments before the actual video-filled text creation process. The video analysis module examines video content in advance, tags relevant segments with contextual information, and prepares them for automatic matching, eliminating the need for time-consuming manual adjustments during the creation process.
Solution Approach 2:
The automatic video segment selection and synchronization process eliminates manual adjustment operations entirely. The system autonomously performs all necessary tasks including video analysis, segment identification, text matching, and temporal synchronization, dramatically reducing the time required to create video-filled text while maintaining high precision.
3Ease of operation
If contemporary video editing tools are used, then the ease of operation can be improved for experienced users, but the adaptability to different user skill levels deteriorates
Solution Approach 1:
The video editing application provides a universal solution that serves both amateur and experienced users through automatic video segment selection. For amateur users, the system performs all complex operations automatically, making the tool accessible without extensive knowledge. For experienced users, the same system maintains precision and control while reducing manual effort, thus adapting to different skill levels with a single interface.
4Adaptability or versatility
If context-sensitive video selection is implemented, then the adaptability to text message context can be improved, but the device complexity and loss of time deteriorate
Solution Approach 1:
The system performs preliminary tagging of video segments with contextual information during the video analysis phase. By pre-analyzing video content and assigning relevant tags before the actual matching process, the system reduces the complexity of real-time context analysis while maintaining high adaptability to text message context.
Solution Approach 2:
The patent replaces complex manual context analysis with automated computational processes. The system uses algorithms to automatically analyze video content, identify contextual elements, and match them with text messages, eliminating the need for manual context evaluation while achieving high adaptability through intelligent automated analysis.
Data Source
AI summary
A video editing application generates video-filled text based on context-sensitive video segments. For example, the video editing application receives a text selection including multiple characters. A text selection context that identifies a characteristic of the text selection is determined, the context including a category of the text selection and a tag identifying an entity associated with the text selection. Scores are computed for multiple video segments, each score indicating a match between an attribute of the respective video segment and the text selection context. Video segments with attributes that match the context are selected, based on a comparison of each score to a threshold. The video editing application generates a composite video that includes a combination of a selected video segment and a character from the text selection, the combination including an outline of the character and the selected video segment.