ML Workflow Step Prediction via Natural Language
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Solution Overview
Problem
Developers face challenges in creating computerized workflows due to the complexity of graphical user interfaces and the need to learn coding languages, which can lead to inefficient workflow generation and a lack of adherence to best practices.
Innovation Solution
A machine learning-based system that predicts and implements additional steps in a computerized workflow using natural language inputs, allowing users to generate workflows without requiring knowledge of executable coding languages, by employing a text-to-text model that converts user descriptions into API calls for automation flow builders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If developers use graphical user interfaces to create computerized workflows, then workflow generation becomes more accessible, but the complexity of learning the development tool increases
Solution Approach 1:
The patent introduces an intermediary system that translates natural language descriptions into executable workflow steps. This intermediary layer allows users to interact with complex workflow systems using simple natural language, without needing to learn the underlying graphical interface or coding language, thus resolving the contradiction between accessibility and tool complexity
Solution Approach 2:
The patent replaces the mechanical interaction with graphical user interfaces and visual drag-and-drop operations with a language-based interaction model. By substituting the visual/mechanical interface with natural language processing, users can create workflows through text descriptions rather than manipulating complex graphical elements, reducing the learning curve while maintaining accessibility
2Adaptability or versatility
If developers learn coding languages to create workflows, then workflow functionality increases, but training time and learning curve increase
Solution Approach 1:
The patent extracts the coding language requirement from the workflow creation process. By separating the natural language input from the executable workflow generation, the system eliminates the need for users to learn programming languages while still producing functional workflows, thus maintaining versatility without the time cost of training
Solution Approach 2:
The patent changes the input parameter from coded commands to natural language descriptions. This parameter change allows the system to interpret high-level intent descriptions and automatically generate the detailed workflow steps, maintaining full workflow functionality while eliminating the need for users to learn complex syntax and programming concepts
3Manufacturing precision
If developers manually create each workflow step, then precision and control increase, but productivity decreases
Solution Approach 1:
The patent performs preliminary action by automatically generating complete workflow sequences from high-level natural language descriptions. The system pre-computes the necessary workflow steps, intermediate actions, and edge case handling before execution, allowing rapid workflow creation while maintaining precision through automated validation and best practice enforcement
Solution Approach 2:
The patent implements self-service by enabling the workflow system to automatically generate, validate, and optimize workflow steps without manual intervention. The system serves itself by interpreting natural language input and autonomously creating precise workflow implementations, significantly increasing productivity while maintaining quality through built-in validation mechanisms
4Ease of operation
If inexperienced users create workflows without guidance, then ease of use increases, but workflow quality and adherence to best practices decrease
Solution Approach 1:
The patent incorporates feedback mechanisms that automatically analyze generated workflows for quality and best practice adherence. The system provides real-time feedback on workflow correctness, suggests improvements, and validates against established patterns, ensuring high-quality workflows are produced even by inexperienced users while maintaining ease of use
Solution Approach 2:
The patent applies beforehand cushioning by pre-establishing best practice templates and validation rules that automatically guide workflow generation. These pre-configured quality safeguards cushion against poor workflow design by inexperienced users, ensuring reliability without adding complexity to the user interface or reducing accessibility
Data Source
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AI summary
An indication to predict one or more additional steps to be added to a partially specified computerized workflow based at least in part on the partially specified computerized workflow is received. Text descriptive of at least a portion of the partially specified computerized workflow is generated. Machine learning inputs based at least in part on the descriptive text are provided to a machine learning model to determine an output text descriptive of the one or more additional steps to be added. One or more processors are used to automatically implement the one or more additional steps to be added to the partially specified computerized workflow.