Automated RPA Bot Generation via Natural Language Task Classification
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Solution Overview
Problem
Conventional methods for creating robotic process automation (RPA) bots are difficult, time-intensive, and expensive, requiring a deep understanding of business processes, data extraction from unstructured sources, and manual coding. Additionally, RPA bots are challenging to maintain due to their dependency on application interfaces and the need for complete rewriting with small changes.
Innovation Solution
A method and system for automatically generating RPA bots by receiving an automation request, optionally determining an application representation, generating a set of tasks, classifying tasks, generating instructions for each task, validating the instructions, and executing them. This approach creates human-readable task lists and RPA bots with minimal human intervention, making them more accurate and easier to maintain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual coding methods are used to create RPA bots, then the bots can perform specific tasks, but the creation process becomes extremely difficult, time-intensive, and expensive
Solution Approach 1:
The system enables self-service automation by allowing users to create RPA bots through natural language descriptions without requiring manual coding expertise. The system automatically translates user intent into executable automation workflows, eliminating the need for users to manually program each bot from scratch.
Solution Approach 2:
The patent introduces an intermediary natural language processing layer between the user and the RPA bot creation process. This intermediary automatically interprets user descriptions, extracts relevant task parameters, and generates the corresponding automation code, serving as a mediator that bridges the gap between simple user input and complex bot functionality.
2Manufacturing precision
If RPA bots are manually coded for specific tasks, then they can execute precise workflows, but any interface change or application update causes the bot to fail, requiring complete rewriting
Solution Approach 1:
The system implements dynamic adaptability by continuously monitoring application interfaces and automatically adjusting bot workflows when changes are detected. Rather than requiring manual rewriting, the bots dynamically adapt to interface updates by re-learning element locations and relationships through ongoing observation and self-correction mechanisms.
Solution Approach 2:
The patent incorporates feedback loops where the system continuously monitors the success of bot executions and automatically learns from failures. When interface changes cause bot failures, the system receives feedback about the changes, analyzes the impact, and automatically adjusts the bot's element references and workflows to maintain functionality without requiring complete rewriting.
3Adaptability or versatility
If deep understanding of business processes and data extraction capabilities are required, then the RPA bots can handle complex tasks, but the implementation becomes expensive and difficult
Solution Approach 1:
The system provides self-service capabilities by automatically analyzing business processes and extracting necessary data transformation rules without requiring users to have deep domain expertise. The system autonomously observes user interactions, identifies patterns, and generates appropriate data extraction and processing logic, eliminating the need for users to manually program complex business rules.
Solution Approach 2:
The patent replaces manual mechanical analysis of business processes with automated machine learning algorithms. Instead of requiring users to manually understand and code complex business logic, the system uses AI algorithms to automatically analyze process flows, identify decision points, and generate the corresponding automation workflows, substituting human cognitive effort with computational intelligence.
Data Source
AI summary
In variants, a method for robotic process automation (RPA) includes: receiving an automation request; optionally determining an application representation of an application associated with the automation request; generating a set of tasks based on the automation request; optionally classifying each task in the set of tasks; generating a set of instructions for each task in the set of tasks; optionally validating the set of instructions; and optionally executing the set of instructions cooperatively forming an RPA bot.