Cloud Buffering for Live Stream Policy Violation Detection
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Solution Overview
Problem
Current live and streaming broadcast systems face challenges in monitoring content for policy violations due to the impracticality of human moderators, high turnover rates, and inefficiencies in automated systems that generate false positives, leading to inappropriate content being broadcast and causing psychological damage to moderators.
Innovation Solution
Implementing a cloud-based system that buffers live streams and uses machine learning algorithms to detect policy violations, allowing for temporary modifications such as deprecation or replacement of inappropriate content, and flagging for human review, thereby reducing the need for continuous human monitoring and minimizing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human moderators are used to monitor live broadcasts for policy violations, then content compliance can be maintained, but the system becomes infeasible due to the overwhelming number of simultaneous broadcasts and the psychological damage to moderators
Solution Approach 1:
The patent replaces the mechanical system of human moderators with an automated content analysis system that uses technology to detect policy violations. This substitution eliminates the need for human moderators to directly view harmful content while maintaining content compliance monitoring capabilities across tens or hundreds of thousands of simultaneous broadcasts.
Solution Approach 2:
The patent introduces an intermediary automated system that sits between the broadcast content and the moderation process. This intermediary system analyzes content for policy violations using automated techniques, preventing harmful content from reaching human moderators while still enabling compliance enforcement through subsequent human review of flagged content only.
2Productivity
If automated content detection systems are used to stop broadcasts with copyrighted content, then processing speed increases, but the system generates many false positives and cannot consider contextual factors like fair use
Solution Approach 1:
The patent segments the content moderation process into multiple stages: an initial automated detection phase that quickly identifies potential policy violations, followed by a secondary review phase that applies more sophisticated analysis. This segmentation allows the system to maintain high processing speed for the majority of content while applying more precise, context-aware analysis only when needed.
Solution Approach 2:
The patent changes the detection parameters dynamically based on the type of content being analyzed and the confidence level of initial detection. Rather than using fixed, rigid detection criteria that generate false positives, the system adjusts its analysis parameters to consider contextual factors such as fair use, transforming the approach from simple pattern matching to more nuanced parameter-based evaluation.
Data Source
AI summary
Example implementations described herein are directed to systems, methods, and computer programs for publishing videos, which can involve publishing a video for public access; processing the publically accessible published video with an algorithm configured to detect a policy violation, the policy violation comprising audio keywords or visual depictions indicated as a policy violation; and for the processing indicative of the policy violation existing in a video beyond a threshold for a specified period of time, setting a status of the video as being demonetized while maintaining the published video as publically accessible.