Natural-Language Integration Process Construction With Generative AI
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Solution Overview
Problem
Existing integration platforms require manual construction of processes element by element, lacking the ability to build processes using natural language.
Innovation Solution
A method utilizing a generative AI model to construct integration processes through natural language input, generating prompts and definitions to automatically create integration processes from user requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual construction of integration processes is used, then precision and control over each element is improved, but productivity and ease of operation deteriorate
Solution Approach 1:
The system enables self-service by allowing the integration process to construct itself automatically from natural language descriptions. The generative AI model autonomously translates user intent into process elements, connections, and configurations without requiring manual assembly, thereby maintaining precision while dramatically improving productivity.
Solution Approach 2:
The patent replaces the mechanical drag-and-drop construction system with an automated linguistic system. Instead of manually manipulating visual elements on a canvas, users simply describe the desired integration process in natural language, and the system automatically generates the corresponding process definition, substituting manual mechanical operations with AI-driven automated generation.
2Manufacturing precision
If manual construction of integration processes is used, then control and precision over each element is improved, but ease of operation deteriorates
Solution Approach 1:
The patent replaces the mechanical drag-and-drop construction system with an automated linguistic system. Instead of manually manipulating visual elements on a canvas, users simply describe the desired integration process in natural language, and the system automatically generates the corresponding process definition, substituting manual mechanical operations with AI-driven automated generation.
Solution Approach 2:
The system enables self-service by allowing the integration process to construct itself automatically from natural language descriptions. The generative AI model autonomously translates user intent into process elements, connections, and configurations without requiring manual assembly, thereby maintaining precision while dramatically improving productivity.
3Productivity
If automated generation using generative AI is used, then productivity and ease of operation are improved, but device complexity increases
Solution Approach 1:
The patent introduces a generative AI model as an intermediary between the user's natural language description and the integration process construction. This intermediary automatically translates linguistic input into structured process definitions, handling the complexity of process generation while presenting a simple interface to users, thereby improving productivity without requiring users to manage the underlying complexity.
4Ease of operation
If automated generation using generative AI is used, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent introduces a generative AI model as an intermediary between the user's natural language description and the integration process construction. This intermediary automatically translates linguistic input into structured process definitions, handling the complexity of process generation while presenting a simple interface to users, thereby improving productivity without requiring users to manage the underlying complexity.
Data Source
AI summary
Currently, there is no means for a user to build an integration process using natural language. Accordingly, in an embodiment, a user submits a user request via natural language (e.g., via interaction with a screen-based or voice-based chatbot). Embodiments wrap the user request with a contextual wrapper to generate a prompt, submit the prompt to a generative AI model (e.g., large language model) to produce an actionable set of objectives, and then programmatically and recursively generate software instances of each necessary component, including the final integration process, based on the set of objectives and using the generative AI model.