Natural Language Orchestration Workflow Generation
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Solution Overview
Problem
Developing cloud orchestration workflows from templates is resource-intensive and requires significant administrative effort due to the need for customization, troubleshooting, and debugging, even when using existing workflow templates.
Innovation Solution
Employing machine learning-based natural language processing to automate the creation of cloud orchestration workflows from input data, using a natural language processing engine that trains on labeled data to generate optimized workflows in specific cloud orchestration languages, and allowing for user feedback to refine the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud orchestration workflows are developed from templates with customization, then the workflows can be tailored to specific use cases, but the administrative effort and resource consumption increase significantly
Solution Approach 1:
The system enables self-service workflow generation by allowing users to input natural language descriptions of desired workflows, which are then automatically translated into executable orchestration workflows. This eliminates the need for manual template customization and reduces administrative effort while maintaining adaptability to specific use cases.
Solution Approach 2:
The patent replaces the mechanical process of manual workflow development and template customization with an automated natural language processing system. The NLP engine translates user intentions directly into workflows, substituting the manual mechanical process of template editing and customization with an intelligent automated system.
2Adaptability or versatility
If manual customization and debugging of workflow templates is performed, then specific use cases can be addressed, but the time and resources required increase
Solution Approach 1:
The system performs self-service by automatically generating customized workflows from natural language inputs without requiring manual intervention for customization or debugging. The NLP engine handles the entire workflow creation process autonomously, maintaining use case specificity while dramatically improving creation speed.
Solution Approach 2:
The system performs preliminary action by pre-processing natural language inputs and automatically generating complete, ready-to-execute workflows in advance. This eliminates the need for subsequent manual debugging and customization steps, thereby improving productivity while maintaining adaptability.
3Extent of automation
If automated natural language processing is used to generate workflows, then administrative burden is reduced, but the complexity of the system increases
Solution Approach 1:
The patent introduces an intermediary natural language processing engine that mediates between user intentions and workflow execution. This intermediary layer handles the complexity of automation internally while presenting a simple natural language interface to users, thereby achieving high automation without increasing perceived system complexity.
Solution Approach 2:
The system uses copying by training the NLP engine on existing workflow templates and patterns. The engine learns from copied examples and generates new workflows based on these patterns, which simplifies the automation process while maintaining system manageability through pattern-based generation.
Data Source
AI summary
A technique includes receiving, by a computer, data representing a task to be automated in association with a computing environment. The technique includes applying, by the computer, natural language processing to the data to generate a sequence of statements describing operations to be executed to perform the task. The sequence of statements is associated with a predetermined orchestration workflow language.