Real-Time Video Editing via Machine Learning Personalization
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Solution Overview
Problem
Existing video editing and playback systems do not allow for real-time personalization based on user preferences, requiring users to manually search through content to find desired segments.
Innovation Solution
A machine-learning assisted system that uses AI to analyze user preferences and edit video content in real-time, allowing users to input commands via text or voice to customize their playback experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually search through video content to find desired segments, then they can access specific content, but the process is time-consuming and inefficient
Solution Approach 1:
The system automatically analyzes user preferences and edits video content without requiring manual user intervention. The machine learning model processes user profiles, viewing history, and real-time commands to autonomously select and edit relevant content segments, eliminating the need for users to manually search through entire videos.
Solution Approach 2:
The system performs preliminary analysis of user preferences and content metadata before playback begins. By pre-processing user profiles, viewing patterns, and content characteristics, the system prepares customized content selections in advance, enabling fast and efficient content delivery without manual searching.
2Adaptability or versatility
If existing systems automatically process content based on pre-configured settings, then content personalization is achieved, but users cannot make real-time dynamic requests to alter content during playback
Solution Approach 1:
The system transitions from static pre-configured personalization to dynamic real-time customization. Users can issue commands during playback to immediately alter content selection, and the system responds by re-editing the video stream in real-time, making the personalization process adaptive and flexible rather than fixed.
Solution Approach 2:
The system incorporates real-time user feedback through commands during playback and uses this feedback to dynamically adjust content selection. The machine learning model continuously processes user inputs and modifies content delivery accordingly, creating a closed-loop system that adapts to user preferences in real-time.
3Productivity
If the system analyzes user preferences and edits video content in real-time, then personalized playback is achieved, but processing time and computational resources increase
Solution Approach 1:
The system divides video content into discrete segments or clips that can be independently processed and edited. By working with segmented content rather than entire videos, the system reduces computational complexity and enables faster real-time editing while maintaining personalization accuracy.
Solution Approach 2:
The system applies machine learning analysis and content editing only to the necessary portions of the video based on user preferences and real-time commands, rather than processing the entire content stream. This selective processing approach reduces computational resource consumption while achieving the required personalization level.
Data Source
AI summary
The invention provides a system for machine-learning assisted real-time video editing and playback. Users select content, and the system uses machine learning to analyze preferences and edit the content in real time. The system is distinguished by its ability to process real-time user commands (via text or voice) during playback, adjusting the video dynamically. Personalized versions of movies, shows, or other video content are then generated and played back to the user, based on preferences provided or inferred through the system.