Automated Content Annotation Using Predictive Graph Models
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Solution Overview
Problem
The vast and complex archive of content assets in movie production, comprising diverse media types and formats, poses a significant challenge for effective reuse due to heterogeneity and sparse metadata, leading to costly and inefficient manual searches, resulting in unnecessary recreation of assets.
Innovation Solution
An automated system and method for annotating heterogeneous content using a predictive model, such as a deep neural network, that scans, parses metadata, generates training data, and identifies links between content assets and labels, enabling automated annotation and search within the content database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual search by human archivist is used, then content assets can be identified, but the search process becomes costly and inefficient
Solution Approach 1:
The patent replaces the mechanical manual search process with an automated computer-based system that uses machine learning models and algorithms to identify and retrieve content assets, eliminating the need for human archivists to manually inspect each asset
Solution Approach 2:
The system enables self-service automated annotation where the content management system automatically generates annotations and metadata for content assets without requiring manual human intervention, allowing the system to serve itself in the annotation process
2Loss of information
If manual inspection of each content asset is performed, then sparse metadata can be overcome, but the process becomes completely impracticable
Solution Approach 1:
The system performs preliminary automated annotation of content assets with rich metadata and descriptive information before search operations, so that when assets are searched, comprehensive information is already available without requiring manual inspection
Solution Approach 2:
The patent replaces manual metadata creation and asset inspection with automated machine learning-based annotation systems that generate comprehensive metadata at scale, making the process practicable for large archives
3Device complexity
If content assets are sparsely labeled by metadata, then storage complexity is reduced, but identification and reuse become substantially challenging
Solution Approach 1:
The system performs preliminary automated annotation to enrich sparse metadata with comprehensive descriptive information, making content assets easily identifiable and reusable while maintaining simple archive management structures
Solution Approach 2:
The patent introduces an intermediary automated annotation layer that sits between the simple storage structure and the complex search requirements, translating sparse metadata into rich descriptive information without complicating the underlying archive management
4Quantity of substance
If existing content assets are not identified, then storage requirements are minimized, but recreation costs and delays increase
Solution Approach 1:
The system uses feedback from automated annotation and analysis to identify existing content assets that match production needs, providing feedback loops that continuously improve asset discovery and prevent unnecessary recreation
Solution Approach 2:
The content management system performs self-service automated identification and retrieval of existing assets, automatically determining when existing assets can be reused without requiring manual assessment or storage of redundant copies
Data Source
AI summary
A system includes a computing platform having a hardware processor, and a system memory storing a software code and a content labeling predictive model. The hardware processor is configured to execute the software code to scan a database to identify content assets stored in the database, parse metadata stored in the database to identify labels associated with the content assets, and generate a graph by creating multiple first links linking each of the content assets to its corresponding label or labels. The hardware processor is configured to further execute the software code to train, using the graph, the content labeling predictive model, to identify, using the trained content labeling predictive model, multiple second links among the content assets and the labels, and to annotate the content assets based on the second links.