Listener-Based RPA Training for Personalized Workflow Adaptation
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Solution Overview
Problem
Current robotic process automation (RPA) systems lack personalization and adaptability to specific user needs, as they are often designed based on general functionality rather than individual user requirements, leading to inefficiencies and suboptimal performance.
Innovation Solution
A system that includes a user computing device with a listener configured to monitor and log user interactions with RPA robots, transmitting data to a server for analysis. The server determines if modifications are needed to the RPA workflow and inserts activities or sequences of activities to personalize the robot's functionality based on user-specific data, using both global and local machine learning models to adapt to individual user behaviors and exceptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If RPA robots are designed based on general functionality needs, then device complexity is reduced and ease of manufacture is improved, but adaptability to specific user needs deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting user interaction data through listeners during robot operation, analyzing this data to identify personalization opportunities, and proactively generating customized workflows before users manually configure them. This preliminary data collection and analysis enables the robot to adapt to user needs automatically without requiring complex manual configuration.
Solution Approach 2:
The RPA robot implements self-service by automatically analyzing its own interaction logs, identifying patterns in user behavior, and generating personalized workflows autonomously. The system uses machine learning models to self-adapt to user preferences and exceptions, reducing the need for manual intervention while improving adaptability to specific user needs.
2Productivity
If RPA robots are designed based on general functionality needs, then ease of operation is improved, but productivity for specific user tasks deteriorates
Solution Approach 1:
The system implements continuous feedback loops where listeners monitor user interactions with the RPA robot, log data is analyzed to identify exceptions and preferences, and workflows are automatically adjusted based on this feedback. This feedback mechanism enables the robot to improve productivity for user-specific tasks by learning from actual usage patterns while maintaining ease of operation through automatic adaptation rather than manual reconfiguration.
Solution Approach 2:
The RPA workflow transitions from a static, pre-configured sequence to a dynamic system that automatically adapts to user needs. The system dynamically generates and modifies workflows based on real-time analysis of interaction logs, allowing the robot to optimize productivity for specific user tasks while maintaining operational simplicity through automated adjustments.
3Adaptability or versatility
If manual customization of RPA workflows is implemented, then adaptability to user needs is improved, but loss of time for configuration increases
Solution Approach 1:
The system replaces the mechanical process of manual workflow configuration with an automated information processing system. Listeners collect interaction data, machine learning models analyze this data to identify personalization opportunities, and workflows are automatically generated and modified. This substitution eliminates time-consuming manual configuration while maintaining high adaptability to user needs through data-driven automation.
Solution Approach 2:
The system creates copies of workflow patterns from analyzed user interaction data. Instead of manually designing workflows from scratch, the system copies and adapts successful interaction patterns identified in the logs, automatically generating personalized workflows that match user behavior. This copying approach dramatically reduces configuration time while maintaining adaptability.
Data Source
AI summary
Human-in-the-loop robot training using artificial intelligence (AI) for robotic process automation (RPA) is disclosed. This may be accomplished by a listener robot watching interactions of a user or another robot with a computing system. Based on the interactions by the user or robot with the computing system, the robot may be improved and/or personalized for the user or a group of users.