Intelligent RPA Control Code Update via ML Exception Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robotic process automation (RPA) systems face challenges in efficiently handling and updating incorrect or inefficient control codes, leading to exceptions that require manual intervention and hinder automation efficiency.
Innovation Solution
An intelligent system that retrieves execution logs from RPA sessions, uses machine learning algorithms to classify exceptions, and deploys automated exception handling subroutines to address these issues, including software and firmware updates as needed, thereby reducing manual intervention and improving automation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used to handle exceptions and update control codes in RPA systems, then accuracy in identifying and fixing issues can be maintained, but productivity and automation efficiency deteriorate due to time-consuming manual processes
Solution Approach 1:
The system enables automated self-service through machine learning algorithms that automatically analyze execution logs, classify exceptions, identify root causes, and generate control code updates without requiring manual human intervention for each exception, thereby maintaining accuracy while significantly improving productivity
Solution Approach 2:
The system implements a feedback mechanism where execution logs from RPA sessions are continuously collected and fed into machine learning algorithms that learn from past exceptions and improve their classification and code generation accuracy over time, enabling the system to become progressively more autonomous and efficient
2Productivity
If automated exception handling is implemented using machine learning, then productivity and automation efficiency improve, but device complexity increases due to the need for ML algorithms and automated subroutines
Solution Approach 1:
The system segments the exception handling process into distinct modular components: execution log collection, machine learning-based exception classification, root cause analysis, automated control code generation, and deployment. This segmentation allows each component to be independently developed, tested, and maintained, reducing overall system complexity while enabling high-speed automated processing
Solution Approach 2:
The machine learning algorithm acts as an intermediary between the execution logs and the exception handling process, automatically translating raw log data into classified exceptions and recommended control code updates. This intermediary layer simplifies the overall system architecture by centralizing the intelligence required for automated decision-making
3Adaptability or versatility
If control codes are updated dynamically based on exception analysis, then adaptability and automation capability improve, but reliability may worsen due to potential introduction of new errors through automated updates
Solution Approach 1:
The system performs preliminary actions by generating and testing control code updates in a virtualized environment before deploying them to production RPA sessions. This preliminary testing phase allows the system to validate the correctness and safety of generated code updates, ensuring adaptability improvements do not compromise reliability
Solution Approach 2:
The system implements beforehand cushioning through comprehensive testing and validation mechanisms that prepare for potential errors before they occur in production. By simulating exception scenarios and verifying control code updates in controlled environments, the system cushions against the introduction of new errors, maintaining reliability while enabling adaptive updates
Data Source
AI summary
Systems, computer program products, and methods are described herein for intelligent control code update for robotic process automation. The present invention is configured to retrieve execution logs associated with robotic process automation (RPA) sessions, wherein the execution logs comprises exceptions. Next, the present invention is configured to initiate machine learning algorithms configured to process the one or more execution logs and classify the exceptions into predetermined classes. Next, the present invention is configured to deploy automated exception handling subroutines to address the exceptions based on at least classifying the exceptions into the predetermined classes.