RPA Resource Evaluation Using Predictive Machine Selection

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Solution Overview

Problem

Robotic process automations (RPAs) often experience slower execution or failures due to insufficient resource allocation, particularly when handling large files or complex functions, leading to potential workflow disruptions.

Innovation Solution

An intelligent resource evaluation system that uses a computing platform to train an intelligent resource evaluation model based on historical parameter information from RPA machines and servers. This model outputs machine selection information indicating whether an RPA machine is sufficient for executing a process automation, allowing for real-time monitoring and dynamic resource allocation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If RPA machines are assigned to execute complex functions or handle large files without sufficient resource evaluation, then productivity is maintained, but reliability deteriorates due to execution failures or slowdowns

Engineering Contradiction:

ImproveRPA execution reliabilityVSAvoidresource evaluation system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs resource evaluation before RPA execution by training a machine learning model on historical parameter information and using it to predict whether selected machines will succeed or fail in executing given processes, preventing failures before they occur

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors RPA machines during execution, collects real-time parameter information, and uses this feedback to refine machine selections and prevent failures dynamically during the automation process

Inventive Principle:

Principle #23Feedback

2Productivity

If real-time monitoring and evaluation of RPA machines is implemented, then execution efficiency is improved, but device complexity increases

Engineering Contradiction:

ImproveRPA execution efficiencyVSAvoidmonitoring system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system enables RPA machines to self-evaluate their resource adequacy by inputting their own parameter information into the trained machine learning model, which automatically determines whether they are sufficient for the assigned task without requiring complex external evaluation infrastructure

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system transforms physical resource parameters (CPU utilization, memory utilization, disk space, network bandwidth) into predictive insights about execution success by training a machine learning model on historical parameter data, converting complex monitoring into simple success/failure predictions

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250181038A1Intelligent resource evaluator system for robotic process automations

Publication Date: 2025.06.05 BANK OF AMERICA CORP

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.