Media Asset Lifecycle Manager for Copyright and Decency Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The rapid creation of media assets due to digital technology overwhelms conventional manual processes for managing their lifecycles, particularly in identifying copyright violations and decency issues, leading to inefficiencies in large-scale media asset management systems like user-submitted content websites.
Innovation Solution
A system and method utilizing a Media Asset Lifecycle Manager (MALM) with tagging, grouping, and executive filters to efficiently manage media assets by importing, evaluating, and filtering metadata, separating assets based on criteria such as copyright and decency, and executing actions like deletion or publication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used to review media assets for copyright and decency violations, then review accuracy can be maintained, but the system cannot scale to handle large volumes of user-submitted content
Solution Approach 1:
The patent segments the media asset review process into multiple automated filtering stages (tagging filters, grouping filters, executive filters) that process different aspects of content evaluation. Each filter handles specific tasks such as identifying copyrighted material, assessing decency violations, and prioritizing reviews, thereby distributing the review burden across multiple automated systems rather than requiring single manual review of entire content libraries
Solution Approach 2:
The patent introduces automated filtering systems as intermediary components between content upload and final human review. These filters act as mediators that pre-process and triage media assets, identifying and flagging problematic content before it reaches human reviewers, thus reducing the volume of content requiring manual examination while maintaining review accuracy
2Measurement precision
If brute-force comparison with copyrighted material is used, then copyright violation detection can be thorough, but the process does not scale efficiently
Solution Approach 1:
The patent applies preliminary tagging filters that automatically analyze uploaded media assets for potential copyright indicators before comprehensive comparison with copyrighted material databases. These filters perform preliminary actions such as identifying watermarks, recognizing logos, detecting metadata patterns, and flagging suspicious content, thereby pre-screening assets and reducing the number requiring full brute-force comparison
Solution Approach 2:
The patent implements different levels of review intensity for different media assets based on filter outcomes. Assets flagged by tagging filters undergo thorough copyright comparison, while assets passing through multiple filter stages without issues receive minimal or no human review. This local quality approach applies varying degrees of detection precision to different portions of the content library based on their risk profiles
3Reliability
If extensive human review resources are allocated, then content quality can be maintained, but operational costs and complexity increase
Solution Approach 1:
The patent implements a dynamic, multi-stage filtering system where the level of human review required for each asset is determined by automated filter outcomes. The system dynamically adjusts resource allocation based on content characteristics, prioritizing human review for high-risk content while automating low-risk content processing. This dynamic approach maintains content quality assurance for problematic material while reducing overall system complexity through selective automation
Data Source
AI summary
There is provided a method for managing the lifecycles of one or more media assets. The method comprises importing the one or more media assets into a system for managing the lifecycles of the one or more media assets, determining one or more metadata tags for association with the one or more media assets by evaluating the one or more media assets with one or more tagging filters, associating the one or more metadata tags with the one or more media assets after determining one or more metadata tags for association with the one or more media assets, and grouping the one or more media assets according to the one or more metadata tags associated with the one or more media assets by evaluating the one or more metadata tags with one or more grouping filters to generate one or more media asset groups.