Batch Processing Control System Using ML Error Remediation
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Solution Overview
Problem
Manual monitoring of batch processing is resource-intensive, prone to human error, and requires significant human effort, leading to inefficiencies and potential breaches of Service Level Agreements (SLAs).
Innovation Solution
An electronic system utilizing machine learning and AI to monitor and automatically control batch processing by determining processing plans, identifying errors, and executing remedial actions, thereby minimizing manual intervention and optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual monitoring of batch processing is performed, then human effort and resource consumption increase, but error detection and response capability are maintained through human judgment
Solution Approach 1:
The system enables self-service automation where the batch processing system monitors itself through automated agents that detect errors, analyze logs, and execute remedial actions without human intervention. The system serves its own monitoring and control needs, eliminating the need for external manual monitoring while maintaining high reliability through automated decision-making algorithms.
Solution Approach 2:
The patent replaces the mechanical system of manual human monitoring with an automated electronic system using software agents, machine learning models, and automated decision engines. This substitution transforms the monitoring process from human-driven mechanical observation to automated computational analysis, improving both automation extent and reliability simultaneously.
2Productivity
If manual monitoring teams are deployed, then response to issues can be made, but resource consumption and operational costs increase significantly
Solution Approach 1:
The system performs self-monitoring and self-correction, eliminating the need for external human resource consumption. Automated agents continuously monitor batch processing operations and execute remedial actions independently, transforming the system into a self-sufficient entity that does not require external energy input in the form of human labor while maintaining high productivity.
Solution Approach 2:
The system performs preliminary automated preparation by pre-configuring monitoring agents, error detection rules, and remedial action protocols before batch processing begins. This preliminary automation setup eliminates the need for ongoing manual resource consumption during processing, as the system is pre-programmed to handle monitoring and response tasks automatically throughout the batch processing lifecycle.
3Loss of energy
If automated systems are implemented, then resource consumption decreases, but system complexity and development requirements increase
Solution Approach 1:
The automated monitoring system is segmented into distinct modular components: batch processing agents that monitor individual processes, centralized decision engines that analyze errors, machine learning models that predict outcomes, and remedial action modules that execute corrections. This segmentation reduces overall system complexity by allowing each component to be developed, tested, and maintained independently while working together as an integrated automated system.
Solution Approach 2:
The system introduces intermediary automated agents that act as mediators between the batch processing operations and the central control system. These agents simplify the interface complexity by handling local monitoring and initial response actions locally, then communicating only essential information to the central system, thereby reducing the overall complexity burden on the main automated monitoring architecture.
4Speed
If manual monitoring is performed, then flexibility in handling unique issues is maintained, but reaction time to incidents is delayed due to human processing
Solution Approach 1:
The system replaces human mechanical processing with automated electronic processing, where software agents instantly detect errors and trigger responses without human reaction delays. The automated decision engines process incident information and execute remedial actions in real-time, achieving speeds impossible for human operators while maintaining operational simplicity through standardized automated workflows that eliminate complex human decision-making processes.
Data Source
AI summary
Systems, computer program products, and methods are described herein for monitoring and automatically controlling batch processing. The present invention may be configured to receive a plurality of data processing requests and determine a processing plan for the plurality of data processing requests. The present invention may be configured to provide, to processing applications and based on the processing plan, actions for performance by the processing applications to complete the plurality of data processing requests. The present invention may be configured to determine a state of the plurality of data processing requests, determine, using an event state decision machine learning model, remedial actions to resolve an error state, and provide instructions to the processing applications to perform the remedial actions.