Multi-Channel Content Remix Using Graph-Based AI Workflows
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Solution Overview
Problem
Creating content for multiple content provider channels requires significant computing resources, time, and manual effort due to the need for separate content generation for each type of channel.
Innovation Solution
A system utilizing machine learning and artificial intelligence to transform source content into new content tailored for various channels, reducing resource and time consumption by leveraging existing content through a multi-channel content remix process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate content generation is performed for each content provider channel, then content can be optimized for each channel, but computing resources, time, and manual effort are significantly consumed
Solution Approach 1:
The content creation process is segmented into source content generation and channel-specific adaptation. A single source content item is created once, then segmented and adapted into multiple channel-specific versions through automated remixing, reducing redundant creation effort while maintaining channel optimization
Solution Approach 2:
Instead of creating entirely new content for each channel, the system creates a master source content item and generates copies/adaptations for different channels by remixing and transforming the source content, significantly reducing computing resources and manual effort
2Adaptability or versatility
If separate content generation is performed for each content provider channel, then content can be tailored to channel requirements, but time consumption increases
Solution Approach 1:
The system performs preliminary action by creating a single source content item that contains the core content, then uses automated remixing processes to generate channel-specific versions. This preliminary creation avoids repeated time-consuming content generation for each channel while maintaining tailored content requirements
Solution Approach 2:
The system creates channel-specific content copies by remixing and transforming the source content rather than creating entirely new content from scratch for each channel, significantly reducing the time required for content creation while maintaining channel-specific tailoring
3Adaptability or versatility
If separate content generation is performed for each content provider channel, then content can be optimized for each channel, but manual effort is significantly consumed
Solution Approach 1:
The system performs self-service by automatically remixing and transforming source content into channel-specific versions without requiring manual intervention for each channel adaptation. The automated process handles the transformation and optimization for different channels, significantly reducing manual effort while maintaining channel-specific optimization
Solution Approach 2:
Instead of manually creating content for each channel, the system generates channel-specific content copies through automated remixing processes, reducing manual creation effort while maintaining channel-specific optimization through intelligent content transformation
Data Source
AI summary
Systems and methods are provided for multichannel content remix. A flow is defined for generating new content based upon source content. The flow is defined with nodes arranged according to a graph structure such that downstream nodes utilize output from upstream nodes as input. An execution engine processes each node of the flow by identifying an executor associated with a node type of a node. If the node is a downstream node, then an output of an upstream node is used to as input for the downstream node.The node is executed to generate a prompt that is input into a machine learning model to create a node result for the node. Once the execution engine has processed the nodes of the graph structure, the node results are used to generate the new content that is provided through a content provider channel.