Media Licensing Platform Using Recognition Models for Content Compliance
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Solution Overview
Problem
Conventional rights clearance and license management processes are overwhelmed by the increasing use of modern media generation methods and distribution, particularly in social media, leading to a pronounced increase in the need for license obtainment and validation, especially with the use of generative artificial intelligence.
Innovation Solution
Employing recognition models to determine media content with protected content and compliance models to evaluate licensing rules, allowing for retraining based on interactions to ensure compliance and granting media licenses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional rights clearance and license management processes are used, then license validation can be performed, but the processes become overwhelmed by the increasing volume of media content generated through modern methods and social media distribution
Solution Approach 1:
The patent replaces manual rights clearance processes with automated machine learning models including recognition models that identify protected content in media and compliance models that evaluate licensing rules. This substitution of mechanical human review with automated AI systems enables the platform to handle the increasing volume of social media content without proportional increases in system complexity or manual intervention.
2Productivity
If generative artificial intelligence is used to generate media, then media creation efficiency increases, but the number of opportunities to generate media with protected content increases pronouncedly
Solution Approach 1:
The patent implements preliminary action by having the compliance model evaluate licensing rules and the licensing engine obtain necessary licenses before media content is generated or distributed. The system proactively identifies protected content through recognition models and secures proper authorization in advance, preventing copyright infringement rather than reacting to it after occurrence. This preliminary licensing approach allows generative AI to maintain high productivity while operating within legal boundaries.
3Reliability
If manual license review and monitoring processes are used, then license compliance can be validated, but significant effort is expended by rights holders in reviewing proposed uses and monitoring media
Solution Approach 1:
The patent implements self-service by enabling the system to automatically monitor media distribution and validate license compliance without requiring continuous manual intervention from rights holders. The compliance models and licensing engine autonomously track protected content through distribution networks, verify licensing conditions, and generate reports. This automated self-monitoring maintains high reliability in compliance validation while dramatically reducing the time and effort rights holders must invest in manual review and monitoring activities.
Data Source
AI summary
Embodiments are directed to managing media licenses. Recognition models may be employed to determine media content that includes protected content. Compliance models may be employed to evaluate the media content based on licensing rules for the protected content. Results of the evaluation may be employed to perform further actions, including: identifying portions of the protected content for the media content that may be non-compliant with the licensing rules such that information associated with the non-compliance of the media content maybe provided to a creator or a rights holder of the media content; retraining the compliance models based on metrics associated with interactions by the creator or the rights holder with the media content or the results of the evaluation; employing the retrained compliance models to reevaluate the media content for compliance such that the reevaluated media content that is compliant is granted a media license.