AI Center Hosting for RPA ML Model Monitoring and Retraining
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Solution Overview
Problem
Current robotic process automation (RPA) systems lack a seamless integration with machine learning (ML) models due to operational and technological barriers, leading to disconnected processes and a lack of effective model management and deployment.
Innovation Solution
An AI center is introduced to host, monitor, and retrain ML models, allowing RPA robots to call these models, with features like secure storage, retraining based on data conditions, and low-code deployment options, ensuring seamless integration and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ML models are integrated into RPA workflows, then automation capabilities are enhanced, but system complexity increases
Solution Approach 1:
The patent introduces an AI center as an intermediary component that mediates between RPA robots and ML models. The AI center provides standardized interfaces for model deployment, monitoring, and retraining, thereby enhancing automation capabilities while managing system complexity through a centralized coordination layer.
Solution Approach 2:
The AI center serves multiple functions including model deployment, monitoring, retraining management, and version control within a single unified platform. This multi-functional approach allows the system to handle diverse ML model operations without proportionally increasing complexity.
2Ease of operation
If ML models are deployed through traditional API platforms, then model accessibility is improved, but integration difficulty increases
Solution Approach 1:
The patent segments the ML model deployment process into distinct functional modules within the AI center, including model registration, version management, and standardized API interfaces. This segmentation allows RPA robots to interact with ML models through simplified, well-defined interfaces while the complex management functions are handled separately.
3Reliability
If ML models are monitored and retrained separately, then model performance is maintained, but operational efficiency decreases
Solution Approach 1:
The patent merges model monitoring, performance evaluation, and retraining initiation into a unified workflow managed by the AI center. When performance thresholds are breached, the system automatically triggers retraining processes without requiring separate manual interventions, thereby maintaining model reliability while improving operational efficiency.
Solution Approach 2:
The system implements continuous feedback loops where model performance is monitored in real-time, and automatic retraining is triggered when performance degradation is detected. This closed-loop control ensures model performance is maintained while reducing manual operational overhead.
4Ease of operation
If data science teams and RPA teams work independently, then team autonomy is preserved, but collaboration effectiveness decreases
Solution Approach 1:
The AI center acts as an intermediary platform that enables collaboration between data science teams and RPA teams while preserving their respective autonomies. The platform provides standardized interfaces and workflows that allow both teams to work independently on their respective tasks while ensuring effective information exchange and coordination.
Data Source
AI summary
Robotic process automation (RPA) architectures and processes for hosting, monitoring, and retraining ML machine learning (ML) models are disclosed. Retraining is an important part of the ML model lifecycle. The retraining may depend on the type of the ML model and the data on which the ML model will be trained. A secure storage layer may be used to store data from RPA robots for retraining. This retraining may be performed automatically, remotely, and without user involvement.