Semantic RPA Builder for Natural-Language Workflow Configuration
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Solution Overview
Problem
Novice developers face time-consuming processes when manually configuring activities in RPA workflows, as they require coding and logical thinking skills, which are not familiar to most novice developers.
Innovation Solution
A semantic automation builder that uses machine learning to generate workflows from natural language descriptions, providing a zero-code intuitive user interface for automatic activity configuration and enhanced discoverability of activities, including speech-to-text features and trained models to identify actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration of activities is used in RPA workflow designer applications, then developers can configure activities with full control, but the process becomes time-consuming and requires coding and logical thinking skills that novice developers lack
Solution Approach 1:
The patent introduces an intermediary component (the semantic automation builder system including NLP processor, activity mapper, and workflow generator) that mediates between the user's natural language input and the RPA workflow configuration. This intermediary automatically translates spoken or written instructions into structured automation activities, eliminating the need for users to manually configure complex workflow parameters while maintaining full control over the automation process.
Solution Approach 2:
The patent replaces the mechanical system of manual drag-and-drop activity configuration with an automated linguistic processing system. Instead of requiring users to physically manipulate workflow elements and understand coding concepts, the system uses natural language processing to automatically generate and configure activities based on verbal or text instructions, substituting manual mechanical operations with intelligent automated processing.
2Manufacturing precision
If manual configuration of activities is used in RPA workflow designer applications, then developers can ensure precision in activity setup, but the complexity of the process increases and becomes difficult for novice developers
Solution Approach 1:
The patent enables the system to serve itself by automatically generating precise activity configurations from natural language inputs. The semantic automation builder autonomously performs tasks such as activity selection, parameter extraction, and workflow structuring without requiring user expertise in RPA concepts. The system self-corrects and self-validates the generated workflows, maintaining precision while eliminating complexity for the end user.
Solution Approach 2:
The patent fundamentally changes the input parameter from complex technical specifications requiring coding knowledge to simple natural language expressions. By accepting high-level descriptive inputs and automatically translating them into detailed technical configurations, the system maintains manufacturing precision in activity setup while reducing the apparent complexity to a level accessible to novice developers.
3Reliability
If coding and logical thinking skills are required for RPA development, then automation workflows can be precisely controlled, but the accessibility of the technology decreases for novice developers
Solution Approach 1:
The patent segments the complex RPA development process into distinct automated components: natural language input processing, semantic interpretation, activity mapping, and workflow generation. Each segment handles a specific aspect of the translation from human language to machine-executable automation, allowing novice developers to interact with the system through simple language while the backend segments ensure reliable and precise automation control through structured processing of each component.
Data Source
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AI summary
A method is provided. The method is implemented by a semantic automation builder executed on a processor. The method generating semantic automations of a robotic process automation. The method includes receiving user inputs corresponding to a target application, a written automation task, or steps to identify actions of the robotic process automation. The method also includes mapping each action to an activity to generate mapped activities, transforming the actions into the semantic automations based on the mapped activities, and providing the semantic automations in a user interface of the semantic automation builder to enable editing of the actions.