RPA Robot Issue Resolution via ML Log Analysis
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Solution Overview
Problem
Current Robotic Process Automation (RPA) technologies face inefficiencies in identifying and resolving issues that cause worker robots to fail, often requiring manual intervention and extensive time to diagnose and fix problems.
Innovation Solution
A system utilizing a trained machine learning model to analyze job log data, predict corrective actions, and determine confidence scores to automatically resolve issues in RPA robots, with the ability to learn from new issues and adapt without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used to diagnose and fix robot failures, then the system can resolve issues with human expertise, but the resolution time increases significantly
Solution Approach 1:
The system enables self-service through automated root cause analysis and corrective action generation. The ML model analyzes job log data and automatically identifies issues and resolutions without requiring manual developer intervention, allowing the system to fix itself while maintaining high reliability.
Solution Approach 2:
The patent replaces the mechanical human intervention process with an automated ML-based system. The machine learning model substitutes human developers in analyzing logs and determining corrective actions, eliminating the time-consuming manual diagnosis process while maintaining or improving resolution accuracy.
2Productivity
If automated corrective actions are implemented based on ML predictions, then the resolution speed increases, but the risk of incorrect actions may increase
Solution Approach 1:
The system implements feedback mechanisms where the ML model continuously learns from resolved issues and incorrect predictions. By analyzing the outcomes of automated corrective actions, the system refines its predictions over time, improving accuracy while maintaining high resolution speed through automation.
Solution Approach 2:
The system performs preliminary analysis and prediction of corrective actions before implementation. The ML model evaluates multiple potential solutions and selects the most probable correct action based on historical data patterns, reducing the risk of incorrect implementations while maintaining rapid resolution.
3Loss of energy
If ML models are used to automatically resolve issues, then operational costs decrease, but the initial implementation complexity increases
Solution Approach 1:
The ML-based system serves multiple functions: it analyzes job log data, identifies root causes, generates corrective actions, and continuously learns from outcomes. This multi-functional approach consolidates what would otherwise require separate manual processes into a single automated system, justifying the initial complexity through long-term operational savings and reduced human intervention requirements.
Data Source
AI summary
A system for managing one or more robots is provided. The system is configured to resolve the one or more issues or faults that lead to failure of execution of one or more automation processes executed by the one or more robots. The system is configured to receive information of an issue associated with at least one robot of the one or more robots and further configured to obtain job log data, associated with the at least one robot, for the issue. The system is further configured to determine, using a trained machine learning model, a corrective action, and its associated confidence score for resolving the received issue, based on the job log data and an analysis performed by the trained machine learning model. Further, system performs the corrective action based on the confidence score and the analysis, for managing the one or more robots.