Model-Driven Media Annotation Workflow Management
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Solution Overview
Problem
Conventional media content annotation tools are typically specific to certain types of content and lack flexibility, requiring human collaboration with specialized knowledge, which limits their ability to efficiently and accurately annotate the growing variety of media content.
Innovation Solution
A media content annotation system that determines an annotation workflow based on a data model corresponding to the content type, actively manages workflow processing by identifying and distributing tasks among human and machine contributors, and stores annotations in a non-relational database for rich connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional authoring tools are designed to be specific to a certain type of media content, then they can provide specialized workflows for that content type, but they cannot be flexibly applied to multiple types of media content
Solution Approach 1:
The patent implements a universal authoring tool platform that can handle multiple types of media content (movies, television programming, etc.) through a common infrastructure. The system uses configurable workflows and metadata schemas that can be adapted to different content types without requiring separate specialized tools for each media type, thus achieving multi-functionality while maintaining manageable complexity through standardization.
2Productivity
If conventional authoring tools passively process inputs from human contributors, then they are simple to operate, but they cannot actively manage workflow processing and distribution among contributors
Solution Approach 1:
The system enables self-service workflow management where the authoring tool automatically distributes tasks among human contributors based on their expertise and availability, tracks progress, and manages annotations without requiring manual intervention. This automated self-management increases productivity while keeping the interface simple for users, effectively resolving the contradiction between active workflow management and ease of operation.
3Measurement precision
If specialized knowledge is required for media content annotation, then annotation accuracy can be maintained, but the process becomes slower and less efficient
Solution Approach 1:
The patent introduces an intermediary system that acts as a bridge between specialized knowledge and annotation tasks. The system automatically matches annotation tasks with human contributors who possess the required specialized knowledge, and provides them with contextual information and guidelines through the interface. This intermediary matching and information provision mechanism ensures high annotation accuracy while streamlining the process to improve overall productivity.
Data Source
AI summary
According to one implementation, a media content annotation system includes a computing platform including a hardware processor and a system memory storing a model-driven annotation software code. The hardware processor executes the model-driven annotation software code to receive media content for annotation, identify a data model corresponding to the media content, and determine a workflow for annotating the media content based on the data model, the workflow including multiple tasks. The hardware processor further executes the model-driven annotation software code to identify one or more annotation contributors for performing the tasks included in the workflow, distribute the tasks to the one or more annotation contributors, receive inputs from the one or more contributors responsive to at least some of the tasks, and generate an annotation for the media content based on the inputs.