Automated Video Compilation System for Social Media
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Solution Overview
Problem
Users face challenges in sharing interesting and high-quality content on social networking systems due to the ease of content capture, which can lead to hesitation in posting, as they fear inundating others with uninteresting content or the burden of selecting the best content from large libraries.
Innovation Solution
A system that generates compiled videos by selecting and combining relevant video segments from source clips based on themes, using machine learning techniques for object and facial recognition, and user-specified criteria, to create condensed and engaging content for sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually select and curate video content from their libraries, then content quality can be maintained, but user effort and time consumption increase significantly
Solution Approach 1:
The system enables automated video compilation where the system itself performs the curation and selection tasks that would otherwise require manual user intervention. The automated compilation system analyzes user video libraries, identifies relevant segments, and assembles them into cohesive compilations without requiring users to manually select each clip, thereby maintaining content quality while eliminating the time and effort burden.
Solution Approach 2:
The patent replaces the mechanical process of manual video selection and curation with an automated computational system. Machine learning algorithms and automated analysis tools substitute for human manual work in identifying, selecting, and assembling video segments, transforming a labor-intensive manual process into an automated digital process that maintains or improves content quality while reducing user effort.
2Quantity of substance
If users share all captured video content, then content volume increases, but the quality and interest level of shared content decreases
Solution Approach 1:
The system extracts only the most relevant and interesting video segments from users' complete video libraries, rather than sharing all captured content. The automated compilation process identifies and extracts high-quality segments based on various criteria such as engagement potential, thematic coherence, and visual interest, thereby maintaining content quality while managing volume appropriately.
Solution Approach 2:
The patent divides complete video content into discrete, meaningful segments and selects only those that meet quality and relevance criteria for inclusion in compilations. This segmentation approach allows the system to process large volumes of raw video material while ensuring that only the most interesting and high-quality segments are shared, thus maintaining content quality despite the large underlying content volume.
3Ease of operation
If automated systems generate video compilations, then user effort is reduced, but the ability to maintain thematic coherence and quality may worsen
Solution Approach 1:
The patent replaces manual thematic analysis and curation with automated machine learning systems that can identify themes, patterns, and coherence in video content. These automated systems use computer vision, natural language processing, and other AI techniques to understand video content semantically and assemble compilations that maintain thematic coherence without requiring manual user intervention, thereby preserving quality while reducing effort.
Solution Approach 2:
The system incorporates feedback mechanisms where user preferences, engagement metrics, and compilation performance data are used to continuously improve the automated selection and assembly process. This feedback loop enables the automated system to learn from user responses and refine its thematic analysis and segment selection algorithms, ensuring that generated compilations maintain high thematic coherence and quality over time.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can determine one or more source video clips. A plurality of video segments are selected from the one or more source video clips based on video segment selection criteria. A compiled video is generated comprising the plurality of video segments.