Media Clearance Risk Detection From Production Planning Data
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Solution Overview
Problem
Media production risk assessment is labor-intensive due to the need to identify and clear numerous potential copyright and regulatory issues in video and audio productions, which are often inadvertently included in studio environments or background elements.
Innovation Solution
A computer-implemented method using machine learning and deep neural networks to analyze electronic datasets for media production, identifying potential risk elements by comparing them to a database of defined risk elements, and generating risk assessment measures, which can be context-sensitive and adjusted for legal and geographic considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human reviewers manually assess risk elements in media production, then accuracy and context understanding are maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent introduces an intermediary system comprising machine learning models, deep neural networks, and automated recognition algorithms that act as mediators between the media production content and human reviewers. This intermediary automatically identifies and flags potential risk elements (trademarks, copyrighted material, regulated content) from electronic datasets, reducing the manual review burden while maintaining assessment accuracy through context-sensitive analysis.
2Productivity
If automated systems are used to identify risk elements, then productivity and efficiency improve, but system complexity and development costs increase
Solution Approach 1:
The patent implements a universal automated risk assessment system that handles multiple types of risk elements (trademarks, copyrighted material, regulated content, proprietary information) through a single multi-functional platform. The system processes various media formats (video, audio, text, images) and delivers comprehensive risk assessments, reducing the need for separate specialized tools while managing complexity through integrated architecture.
Solution Approach 2:
The system incorporates feedback mechanisms where risk assessment results and reviewer corrections are fed back into the machine learning models to continuously improve accuracy. This feedback loop allows the system to learn from actual production contexts and reviewer decisions, refining its risk identification capabilities over time without requiring complete system redesign.
3Reliability
If comprehensive risk element identification is performed across all media content, then clearance completeness improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by performing risk element identification during the pre-production planning phase using electronic datasets (scripts, storyboards, shot lists) before actual production begins. This early detection allows producers to identify and address potential clearance issues in advance, ensuring comprehensive coverage while reducing time pressure during production and post-production stages.
Solution Approach 2:
The system segments the media production content into distinct analyzable units (scenes, shots, audio tracks, text elements) and processes them individually through specialized recognition algorithms. This segmentation enables parallel processing of different content types and facilitates targeted risk assessment without requiring complete re-analysis of entire productions, thereby reducing overall processing time while maintaining comprehensive coverage.
Data Source
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AI summary
Automatic assessment of clearance risks in media production includes accessing an electronic dataset for planning a media production that includes potential risk elements, and a database of electronic records each correlated prior or anticipated current risk elements. A processor identifies risk elements in the electronic dataset, at least in part by comparing the prior or anticipated risk elements to potential risk elements detected in the electronic dataset by the one or more processors and generates a set of risk assessment measures each signifying a level of risk for a corresponding one of the risk elements comprising a level of risk. The processor may save the set in a computer memory for use by a clearance team.