RPA Bot Management Framework for AI-Driven Anomaly Resolution
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Solution Overview
Problem
Robotic Process Automation (RPA) systems face challenges in managing software bots deployed across various tasks, as they often encounter anomalies and exceptions that require timely resolution to maintain performance and adhere to service level agreements (SLAs), but existing solutions lack efficient real-time monitoring and automated resolution mechanisms.
Innovation Solution
A bot management framework that utilizes a common data model and trained AI to analyze incident data from RPA systems, determine resolutions for anomalies, and execute these resolutions, while providing real-time monitoring and alerting mechanisms through a unified view, thereby ensuring bot performance and SLA compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual monitoring and resolution methods are used for RPA bot anomalies, then operational control and decision-making remain with human operators, but response time increases, productivity decreases, and operational costs rise
Solution Approach 1:
The system enables automated self-resolution of bot anomalies through AI-driven analysis and execution of resolution actions. The bot management framework autonomously detects anomalies, analyzes incident data, determines resolutions, and executes corrective actions without requiring manual human intervention, allowing the system to serve itself in maintaining operational normality.
Solution Approach 2:
The patent replaces manual mechanical monitoring and resolution processes with an automated digital system. The bot management framework uses software-based anomaly detection, AI model analysis, and automated resolution execution to substitute human operators' manual activities, thereby increasing response speed and reducing operational time loss.
2Reliability
If comprehensive real-time monitoring of all bot incidents is implemented, then anomaly detection capability improves, but system complexity and resource consumption increase
Solution Approach 1:
The bot management framework acts as an intermediary layer between the RPA bot execution environment and the monitoring/analysis systems. It collects incident data from bots, translates it into a common data model, and feeds it to AI models for analysis. This intermediary approach simplifies the overall system architecture by providing a standardized interface and reducing direct complexity between multiple components.
Solution Approach 2:
The framework implements a universal common data model that can handle multiple types of bot incidents and anomalies through a single standardized structure. This multi-functional approach allows the same monitoring and analysis infrastructure to handle diverse incident types (bot overload, SLA breaches, CPU utilization issues, etc.) without requiring separate specialized systems for each anomaly type.
3Extent of automation
If automated AI-driven resolution execution is implemented, then operational costs and manual intervention requirements decrease, but system automation complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-defining resolution actions and their associated criteria within the bot management framework. When anomalies are detected and analyzed by the AI model, the framework can execute pre-planned resolution actions immediately, eliminating the need for complex real-time decision-making logic and reducing automation complexity while maintaining high extent of automation.
4Measurement precision
If multiple data models are used for different bot incident types, then data representation accuracy improves, but data processing complexity and time increase
Solution Approach 1:
The framework implements a universal common data model that can represent multiple types of bot incidents and anomalies through a single standardized structure. This multi-functional data model handles diverse incident types (bot overload, SLA breaches, CPU utilization issues, etc.) uniformly, eliminating the need to switch between multiple specialized data models and thereby reducing data processing time while maintaining representation accuracy.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, a using a bot management framework to provide bot management solutions for software bots deployed in a robotic process automation (RPA) system. In one aspect, a method includes the actions of receiving, from a RPA system, information regarding a software bot deployed within the RPA system to perform an assigned task, the information including incident data from an anomaly in the RPA system while the software bot performed the assigned task; mapping the received incident data according to a common data model; determining a resolution for the anomaly based an analysis through a trained artificial intelligence model of the mapped incident data; and executing the determined resolution.