RPA Robot Self-Healing Through Policy-Based Error Response
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Solution Overview
Problem
Current Robotics Process Automation (RPA) maintenance requires significant manual effort and resources, leading to high operating costs and potential disruptions in business processes, limiting the scalability and reliability of RPA-powered software robots.
Innovation Solution
A computer-implemented method and system that acquire operation metrics of RPA robots during automation workflows and determine optimizing actions based on predefined policies, enabling self-healing capabilities to reduce maintenance effort and enhance reliability and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and maintenance staff are employed to detect and fix errors in RPA robots, then error detection and fixing can be performed, but operating costs increase significantly
Solution Approach 1:
The patent implements self-healing capabilities in RPA robots through automated error detection and resolution mechanisms. The system autonomously monitors its own operation, detects errors using machine learning models, and executes corrective actions without human intervention, allowing the system to serve and maintain itself
Solution Approach 2:
The patent establishes continuous feedback loops where operational data from RPA robots is collected, analyzed by machine learning models, and used to generate corrective actions. This closed-loop feedback system enables real-time error detection and automatic response, improving reliability while eliminating the need for manual monitoring staff
2Reliability
If manual maintenance is performed to fix errors in RPA robots, then problems can be resolved, but business process availability is significantly degraded due to delays
Solution Approach 1:
The patent implements preliminary error detection and automatic corrective actions that prevent errors from escalating into process failures. By continuously monitoring operational metrics and applying machine learning models, the system identifies and resolves issues in real-time, often before they impact business process availability, eliminating the time delay associated with manual intervention
Solution Approach 2:
The self-healing mechanism automatically detects and resolves errors without waiting for human workers, enabling immediate problem resolution that maintains continuous business process availability
3Reliability
If more staff are hired to monitor and maintain RPA robots, then maintenance quality can be improved, but the number of employees required increases
Solution Approach 1:
The patent replaces human maintenance staff with automated self-healing capabilities. The system autonomously performs monitoring, error detection, and corrective actions, eliminating the need to hire additional employees while maintaining or improving maintenance quality through consistent automated processes
Solution Approach 2:
The patent substitutes the mechanical system of human workers with an automated computational system using machine learning models and algorithms. This replacement maintains high maintenance quality through sophisticated automated analysis while reducing employee requirements
4Productivity
If RPA robots are deployed for business process automation, then productivity and repeatability increase, but maintenance effort and costs increase
Solution Approach 1:
The patent addresses maintenance complexity by implementing self-healing capabilities that automatically handle error detection and resolution. This reduces the complexity of maintaining RPA robots at scale, allowing organizations to deploy more robots for productivity gains without proportionally increasing maintenance burdens
Solution Approach 2:
The continuous feedback mechanism collects operational data from RPA robots and uses machine learning models to automatically generate corrective actions. This systematic approach simplifies maintenance complexity by providing automated, data-driven resolution processes that scale with the number of deployed robots
Data Source
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AI summary
There is provided a method (100) for managing a Robotics Process Automation (RPA) robot. The method comprises: acquiring (S110) data associated with an operation metric of the RPA robot during execution of an automation workflow, and determining (S120) an optimising action based on a policy and the acquired data associated with the operation metric of the RPA robot.