Natural Language Processing Automation Workflow Generation
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Solution Overview
Problem
Existing low-code automation solutions require users to build automation flows step by step, necessitating a significant learning curve and potentially leading to errors, inefficiencies, and increased computational resources.
Innovation Solution
The implementation of natural language processing (NLP) to generate automations, where users can describe automation tasks in plain language, and the system automatically builds and deploys the corresponding workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users build automation flows step by step using existing low-code solutions, then automation functionality is achieved, but learning curve and complexity increase
Solution Approach 1:
The patent introduces natural language processing as an intermediary layer between the user and the automation building system. Users provide simple natural language descriptions of desired automations, and the NLP system translates these into structured automation workflows, eliminating the need for users to manually configure complex automation parameters while maintaining full automation functionality
Solution Approach 2:
The patent replaces the mechanical step-by-step automation building process with an automated natural language processing system. Instead of requiring users to manually assemble automation components through a complex interface, the system uses NLP to automatically generate automation workflows from simple text descriptions, substituting computational processing for manual configuration
2Productivity
If users build automation flows step by step, then automation is achieved, but time consumption increases
Solution Approach 1:
The patent performs preliminary processing of automation logic through natural language interpretation before the actual automation execution. The NLP system pre-processes user intent into structured workflows in advance, so that when automation is needed, it can be deployed immediately without requiring users to spend time on manual configuration during critical periods
Solution Approach 2:
The patent substitutes manual step-by-step automation building with automated natural language processing. The system uses computational NLP models to rapidly translate natural language descriptions into executable automation workflows, dramatically reducing the time required to create automations from minutes or hours to seconds
3Reliability
If users build automation flows manually, then customization is achieved, but error rate increases
Solution Approach 1:
The patent introduces natural language processing as an intermediary that acts as a smart translator between user intent and automation logic. This intermediary layer reduces errors by using standardized NLP interpretation rules and validation mechanisms, ensuring that user descriptions are accurately and consistently converted into correct automation workflows without manual configuration errors
Solution Approach 2:
The patent implements feedback mechanisms in the NLP system where the system validates user natural language inputs against known automation patterns and provides corrections or clarifications. This feedback loop ensures that even if users provide imperfect descriptions, the system can identify and correct potential errors before generating the final automation, improving reliability while maintaining simplicity
Data Source
AI summary
Methods for generating automations via natural language processing are performed by computing systems. Natural language input is received from a user interface, and an automation workflow is generated based on the natural language input. The automation workflow includes steps to build an automation. One or more of the steps is provided to the user interface, and a first field and a second field that each correspond to the one or more steps are populated in the user interface. The first field is populated with a parameter value based on the natural language input, and the second field is populated based on the parameter value. The automation is then enabled to be built and deployed.