Learning Bot Tuning for Scalable Bot Process Auditing
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Solution Overview
Problem
Automated systems with numerous bots face challenges in optimizing performance and ensuring compliance with security policies, as manual review is time-consuming and inefficient, especially in large-scale deployments, leading to prolonged resolution times for performance or compliance issues.
Innovation Solution
Implementing a system with machine learning bots to autonomously manage and optimize bot operations, including a machine learning bot to monitor and adjust automated processes and a compliance bot to ensure security compliance, reducing the need for manual intervention and enhancing productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual auditing is used to evaluate automated processes for performance optimization and compliance, then auditing can be performed with human judgment, but the process becomes time-consuming and inefficient especially in large-scale deployments
Solution Approach 1:
The system enables automated self-auditing where bots monitor and evaluate themselves and other bots in the system. The learning bot automatically analyzes process data, identifies optimization opportunities, and makes adjustments without human intervention. The compliance bot continuously monitors for security policy violations, replacing manual auditing with autonomous automated assessment.
Solution Approach 2:
The patent replaces the mechanical human auditing process with automated software-based monitoring and analysis systems. Sensors and data collection mechanisms capture process information, which is then processed by learning algorithms and compliance checking software, substituting human physical auditing activities with automated electronic systems.
2Reliability
If manual review processes are used to ensure compliance with security policies, then compliance can be verified with human expertise, but the resolution time for compliance issues increases in large-scale bot deployments
Solution Approach 1:
The compliance bot operates continuously and automatically to monitor all bot activities for security policy compliance. Rather than periodic manual reviews, the system maintains constant surveillance of automated processes, immediately detecting and reporting compliance issues as they occur, ensuring uninterrupted security oversight across the entire bot ecosystem.
Solution Approach 2:
The system implements continuous feedback loops where the compliance bot monitors bot actions, compares them against security policies, and provides immediate feedback on compliance status. This real-time feedback mechanism enables rapid identification and correction of compliance issues, maintaining security standards without manual intervention delays.
3Productivity
If multiple separate projects are implemented to improve automated process performance, then comprehensive optimization can be achieved, but the overall project time and complexity increase
Solution Approach 1:
The patent combines performance optimization and compliance monitoring functions into a single integrated bot management system. The learning bot and compliance bot work together within one unified platform, consolidating multiple optimization projects into a single coordinated system that simultaneously improves performance and ensures compliance without requiring separate project management tracks.
Solution Approach 2:
The learning bot is designed with multi-functionality, capable of both optimizing automated process performance and monitoring compliance with security policies. This universal bot can adapt to different optimization goals and compliance requirements, replacing the need for specialized separate projects with a single versatile automated system.
4Productivity
If voluminous numbers of automated processes are deployed, then system capability and productivity increase, but auditing and compliance monitoring becomes increasingly difficult
Solution Approach 1:
Each bot in the system is equipped with self-monitoring capabilities that automatically track their own performance metrics and compliance status. Bots report their activities and status information to the central learning and compliance bots, enabling autonomous self-assessment at scale without requiring external manual auditing resources proportional to the number of bots deployed.
Solution Approach 2:
The system implements automated feedback mechanisms where bots continuously report performance and compliance data to monitoring systems. This creates a scalable feedback loop that automatically handles monitoring for any number of bots, with the feedback infrastructure scaling proportionally to maintain monitoring effectiveness regardless of system size.
Data Source
AI summary
In an embodiment, a method includes deploying a learning bot onto a system of bots, where the learning bot monitors a first bot of the system of bots, the first bot executing a first automated process. The method further includes determining a learning phase of the learning bot. The learning bot utilizes a plurality of learning phases including a first learning phase, a second learning phase and a third learning phase. The method also includes, responsive to a determination that the learning bot is in the third learning phase, the learning bot: monitoring activity related to the first automated process; collecting data related to the monitored activity; analyzing at least a portion of the collected data; identifying an automatic tuning adjustment responsive to the analyzing; and automatically making the automatic tuning adjustment to the first automated process.