RPA Resource Evaluation Model for Reliable Bot Execution
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Solution Overview
Problem
Robotic process automation (RPA) bots often execute slower than expected or fail during tasks involving large file transfers or complex functions, due to issues like network bandwidth, server performance, and resource utilization.
Innovation Solution
A computing platform that trains an intelligent resource evaluation model using historical parameter information from RPA machines and servers, allowing it to determine whether a given RPA machine is sufficient for executing a process automation by analyzing current parameter information and adjusting thresholds dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If RPA bots are deployed to execute process automations without resource evaluation, then productivity is improved through automation, but reliability deteriorates due to bot failures during large file transfers or complex functions
Solution Approach 1:
The system performs preliminary resource evaluation before deploying RPA bots to execute process automations. The intelligent resource evaluation model assesses machine resources (CPU, memory, network bandwidth) and server conditions in advance, predicting whether the bot will succeed or fail. This preliminary action prevents bot failures during resource-intensive tasks by identifying suitable machines beforehand, thus improving reliability without reducing productivity.
Solution Approach 2:
The system implements feedback through the intelligent resource evaluation model that continuously monitors machine and server parameters, compares them against historical data, and predicts bot execution outcomes. This feedback mechanism allows the system to adjust resource allocation and machine selection dynamically, ensuring high reliability while maintaining productivity through informed automation deployment.
2Reliability
If resource evaluation is performed for every bot deployment, then reliability is improved through better machine selection, but device complexity increases due to monitoring and model training requirements
Solution Approach 1:
The intelligent resource evaluation model operates autonomously by automatically collecting machine and server parameters, comparing them with historical data, and generating predictions without manual intervention. The system self-manages the complexity of monitoring and evaluation, reducing the burden on operators while maintaining high machine selection accuracy. This self-service approach handles the computational complexity internally, presenting a simplified interface for deployment.
3Measurement precision
If historical parameter information is collected and stored for training the evaluation model, then measurement precision is improved for predicting bot success, but loss of time occurs during data collection and processing
Solution Approach 1:
The system performs preliminary data collection and model training in advance, building the intelligent resource evaluation model before actual bot deployments. Historical parameter information is collected and processed beforehand to establish baseline predictions. This preliminary action reduces real-time decision delays, as the model is already trained and ready to evaluate new bot deployment scenarios quickly, thus minimizing time loss while maintaining high prediction accuracy.
Data Source
AI summary
Aspects of the disclosure relate to an intelligent resource evaluation engine. A computing platform may monitor the plurality of RPA machines to detect parameter information. The computing platform may store the parameter information along with corresponding RPA machines as a key value pairs in a database. The computing platform may identify first current parameter information for a first RPA machine using the key value pairs. The computing platform may input the first current parameter information into an intelligent resource evaluation model, which may output first machine selection information for the first RPA machine. Based on identifying that the first RPA machine is sufficient to execute the first robotic automation process, the computing platform may send direct the first RPA machine to execute the first robotic automation process.