Automated RPA Tool Selection Using Decision Trees
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Solution Overview
Problem
The current challenge in process automation is the manual intervention required for selecting appropriate Robotic Process Automation (RPA) tools and Bot (BOT) for process execution, leading to increased processing time and decreased efficiency due to the need for manual selection based on process requirements and availability.
Innovation Solution
A method and system for automatically selecting RPA tools and BOTs using machine learning techniques, specifically a decision tree algorithm for predicting RPA tools and statistical Jaccard index for BOT selection, based on input data and historical process data to reduce processing time and enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used for selecting RPA tool and BOT, then selection accuracy can be maintained, but processing time increases and efficiency decreases
Solution Approach 1:
The system enables self-service automation by allowing the RPA system to automatically select appropriate RPA tools and BOTs based on process requirements and historical data, eliminating the need for manual intervention while maintaining selection quality through algorithmic decision-making
Solution Approach 2:
The patent replaces the mechanical manual selection process with an automated computational system that uses machine learning algorithms, decision trees, and statistical methods to select RPA tools and BOTs, substituting human cognitive work with automated information processing
2Reliability
If manual selection of BOT is performed for new requirements, then appropriate BOT can be identified, but overall cycle time of automation system increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing historical process data, BOT performance data, and tool characteristics in advance, enabling rapid automated matching and selection when new automation requirements arise without manual intervention
Solution Approach 2:
The system incorporates feedback mechanisms by analyzing historical process data and BOT performance outcomes to continuously improve the accuracy of BOT selection algorithms, ensuring reliable identification while reducing cycle time through learned patterns
3Ease of manufacture
If execution waits for BOT availability, then resource allocation can be optimized, but process execution delay increases
Solution Approach 1:
The system applies dynamic principles by continuously monitoring BOT availability status and process execution requirements, automatically adjusting resource allocation and selecting alternative BOTs or devices in real-time to minimize execution delays while optimizing resource utilization
Data Source
AI summary
The present invention discloses a method and a system for automatic selection of process automation Robotic Process Automation (RPA) tool and BOT. The method comprising receiving input data associated with a process to be executed, selecting an RPA tool from a plurality of RPA tools for process execution based on the input data and historical process data, wherein the selection is performed by calculating information gain for each parameter of the input data and computing probability for each type of RPA tool based on the information gain and the historical process data, identifying one or more BOTs from a plurality of BOTs based on the selected RPA tool, historical BOT data and the input data for the process execution, and executing the identified one or more BOTs on one or more devices based on selection of the one or more devices from available plurality of devices.