Automated Video Editor Biasing via User Frame Ratings
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Solution Overview
Problem
Current automated video editing solutions lack understanding of the meaning of video and audio content, leading to errors such as removing important scenes or inserting cuts inappropriately, and require manual post-editing that loses automated editing benefits and reintroduces complexity.
Innovation Solution
A system and method that allows users to rate specific frames of digital content, which are then used to bias an automated editor to make editing decisions, ensuring important content is preserved and edited accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated editing rules based on color and motion analysis are used, then editing automation is improved, but understanding of video meaning deteriorates
Solution Approach 1:
The patent introduces an intermediary component (content understanding module) that bridges the gap between automated editing rules and video meaning. This module analyzes video content to extract semantic information such as scene transitions, important moments, and contextual relationships, which then guide the automated editing process to make decisions that preserve video meaning while maintaining automation.
Solution Approach 2:
The system implements feedback mechanisms where the automated editing process continuously evaluates its own output against the extracted video meaning. If editing decisions conflict with the understood video content (such as cutting important scenes), the system adjusts its decisions accordingly, creating a closed-loop control that maintains both automation and reliability.
2Reliability
If manual post-editing is performed to correct automated editing errors, then understanding of video meaning is improved, but editing complexity deteriorates
Solution Approach 1:
The patent applies preliminary action by performing content understanding and semantic analysis before the editing process begins. The system pre-identifies important scenes, transitions, and meaningful segments, storing this information for use during automated editing. This preliminary preparation enables the automated editor to make informed decisions without requiring subsequent manual intervention, thus maintaining reliability while avoiding the complexity of manual post-editing.
3Reliability
If conventional video editing interface is used for post-editing, then understanding of video meaning is improved, but ease of operation deteriorates
Solution Approach 1:
The system implements self-service by enabling the automated editing engine to independently understand video content and make editing decisions without requiring manual intervention. The content understanding module automatically extracts meaning from video, and the editing engine uses this understanding to autonomously generate edited output, eliminating the need for users to interact with complex conventional editing interfaces while maintaining high-quality results that reflect video meaning.
Data Source
AI summary
A system and method for automated editing of content is disclosed. The system includes a biasable editing engine. A user is provided with content. The user rates the content according to user preferences. The system receives the ratings implements the editor engine, which edits the content and which is biased in its decision making by user ratings.