Listener Robot Training for Personalized RPA Workflows

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Solution Overview

Problem

Existing RPA robots are not adequately adapted to the specific needs of individual users, leading to inefficiencies and suboptimal performance.

Innovation Solution

Implementing human-in-the-loop robot training, where listener robots monitor user interactions, generate workflows based on machine learning models, and deploy personalized robot versions tailored to individual user behaviors and preferences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If RPA robots are designed based on anticipated general functionality needs, then they can be deployed broadly to assist users, but they are not adapted to the specific needs of individual users

Engineering Contradiction:

ImproveAdaptability to user-specific needsVSAvoidRobot training and personalization complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system enables robots to automatically train themselves by observing user interactions and generating personalized workflows without requiring manual reconfiguration. The robot monitors user actions, learns from them, and autonomously adapts its behavior to match individual user preferences and workflows.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements continuous feedback loops where user interactions with the robot are monitored and fed back into the machine learning model. This feedback mechanism allows the robot to learn from actual user behavior and continuously improve its personalization, resolving the contradiction between adaptability and complexity by making the system self-improving.

Inventive Principle:

Principle #23Feedback

2Productivity

If listener robots monitor user interactions to generate personalized workflows, then robot performance is enhanced, but data processing and model training complexity increases

Engineering Contradiction:

ImproveTask automation efficiencyVSAvoidMachine learning model complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by continuously monitoring and collecting user interaction data in the background. This preliminary data collection and initial processing occur before full workflow generation, allowing the system to prepare personalized workflows in advance based on accumulated observations, thereby improving efficiency without proportionally increasing complexity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The machine learning process is segmented into distinct components: data collection by listener robots, data processing, model training, and workflow generation. This segmentation allows each component to be optimized independently and distributed across different system elements, reducing overall system complexity while maintaining high productivity.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentEP4046113B1Human-in-the-loop robot training for robotic process automation

Publication Date: 2026.03.11 UIPATH INC

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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.