Natural Language Workflow Implementation for Simpler Automation
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Solution Overview
Problem
Developers face challenges in creating computer instructions due to the complexity of development tools, often overwhelming them and preventing the utilization of best practices, especially when using graphical user interfaces for workflow automation.
Innovation Solution
A machine learning model processes natural language descriptions of workflows to predict and implement computerized flows, reducing the need for manual processing and feature engineering, and adapting to user input variations with large pre-trained language models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If developers use graphical user interfaces for workflow automation, then automation capability is enabled, but the complexity of the development tool overwhelms developers and prevents utilization of best practices
Solution Approach 1:
The patent introduces a natural language processing intermediary layer between the user and the complex graphical user interface. Users provide simple text descriptions of desired workflows, and the system automatically translates these into automated flow implementations, eliminating the need for users to navigate complex GUI options while still enabling workflow automation.
Solution Approach 2:
The system automatically generates workflow implementations by processing user text inputs through machine learning models. The development tool performs self-service by autonomously creating, configuring, and optimizing automated flows without requiring manual intervention through complex GUI operations, thereby reducing perceived complexity while maintaining automation capabilities.
2Manufacturing precision
If developers manually create computer instructions using development tools, then precise control over automation flows is achieved, but the learning curve and time required increase significantly
Solution Approach 1:
The system performs preliminary action by pre-processing user text inputs through context analysis and machine learning models to predict the desired workflow structure. This preliminary processing automatically generates the framework for automation flows, preserving precise control over the final implementation while dramatically reducing the time required compared to manual creation through GUIs.
3Adaptability or versatility
If developers navigate many options within the development tool, then comprehensive workflow automation is possible, but best practices are not utilized due to overwhelming complexity
Solution Approach 1:
The system incorporates feedback mechanisms where machine learning models analyze user text inputs and provide intelligent suggestions for workflow implementations. The system learns from user preferences and automatically adjusts its recommendations to align with best practices, maintaining versatility in workflow automation while improving ease of operation through adaptive, context-aware assistance.
Data Source
AI summary
A user provided text description of at least a portion of a desired workflow is received. Context information associated with the desired workflow is determined. Machine learning inputs based at least in part on the text description and the context information are provided to a machine learning model to determine an implementation prediction for the desired workflow. One or more processors are used to automatically implement the implementation prediction as a computerized workflow implementation of at least a portion of the desired workflow.