Bot Logic Adaptation for RPA Process Scenario Detection

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Solution Overview

Problem

Robotic process automation (RPA) platforms face challenges in scaling due to the need for extensive human intervention to create, tune, and adapt bots, as well as in calibrating skill levels of human and bot users to perform user-executed processes consistently, especially with changes in user interfaces and processes.

Innovation Solution

A system for real-time process monitoring and skill calibration that initiates monitoring of user interface activity in both human and bot environments, detects new process scenarios, determines new bot logic, and implements it to ensure consistent execution of user-executed processes across different environments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If extensive human intervention is used to create, tune, and adapt bots, then bot functionality and reliability are improved, but scalability and productivity deteriorate

Engineering Contradiction:

Improvebot functionalityVSAvoidscalability

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system enables bots to automatically learn and adapt to new process scenarios through self-service mechanisms. The bot observes user interface activity in multiple environments, automatically detects new process scenarios, and generates its own logic updates without requiring extensive human intervention, thereby maintaining reliability while improving scalability

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements continuous feedback loops where bot performance and user interface changes are monitored across multiple environments. This feedback mechanism allows the bot to automatically adjust and adapt to new scenarios, reducing the need for manual tuning while maintaining high functionality and reliability

Inventive Principle:

Principle #23Feedback

2Manufacturing precision

If bot logic is manually tuned and adapted, then process execution accuracy is improved, but time consumption and complexity increase

Engineering Contradiction:

Improveprocess execution accuracyVSAvoidtime consumption

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by continuously monitoring user interface activity and pre-detecting new process scenarios before they require manual intervention. The bot proactively learns from observed activities and prepares logic updates in advance, ensuring accurate process execution without time-consuming manual adjustments

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces manual mechanical tuning processes with automated electronic monitoring and learning mechanisms. The bot automatically detects process scenarios and generates logic updates through software-based observation and analysis, eliminating time-consuming manual intervention while maintaining high execution accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If monitoring is performed across multiple user environments, then detection accuracy of new process scenarios is improved, but system complexity and resource usage increase

Engineering Contradiction:

Improvedetection accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system implements a universal monitoring framework that can operate across multiple user environments using the same core architecture. The bot employs multi-functional capabilities to observe, detect, and learn from diverse environments without requiring separate complex systems for each environment, thereby improving detection accuracy while managing system complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The monitoring system is segmented into modular components that can independently observe and analyze specific user environments. This segmentation allows the system to scale across multiple environments by adding independent monitoring modules rather than increasing overall system complexity, maintaining high detection accuracy through distributed observation

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11537416B1Detecting and handling new process scenarios for robotic processes

Publication Date: 2022.12.27 NTT DATA SERVICES LLC

AI summary

In an embodiment, a method of real-time process monitoring includes initiating monitoring of user interface (UI) activity in a plurality of user environments in which a user-executed process is performed. The user-executed process is defined in a stored instruction set that identifies a plurality of steps of the user-executed process. The plurality of user environments include a first environment operated by a human worker and a second environment operated by a bot. The method also includes, responsive to the initiating, detecting a new process scenario for the user-executed process. The method also includes determining new bot logic for the new process scenario. The method also includes causing the bot to implement the new bot logic.