RPA Generative AI Action Suggestions for Faster Workflow Creation

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Solution Overview

Problem

Existing automation systems require significant time and effort for creating automation programs, even for users with limited software development experience, necessitating improved methods for accelerating the development process.

Innovation Solution

A system utilizing generative AI to generate personalized, context-aware suggestions for automation actions based on previous user actions, enhancing the automation program creation process by predicting user needs and providing intelligent recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If automation programs are created using traditional methods, then users can build automation programs, but it requires significant time and effort even for users with limited software development experience

Engineering Contradiction:

Improveease of automation program creationVSAvoidtime required for automation program creation

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system enables automation programs to suggest and perform actions autonomously based on detected patterns and user goals, reducing the manual effort required from users. The automation system serves itself by generating recommendations and executing tasks without extensive user intervention.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Traditional manual programming methods are replaced with machine learning algorithms that automatically analyze user interactions, detect patterns, and generate automation programs. This substitutes the mechanical process of manual coding with an automated AI-based system.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If automation programs are created manually, then users can customize their workflows, but it requires at least a moderate level of software development experience

Engineering Contradiction:

Improvecustomization capabilityVSAvoiddevelopment experience requirement

Core Design Contradiction:

Adaptability or versatilityVSEase of manufacture

Solution Approach 1:

The system automatically adapts to user needs by detecting patterns in user interactions and generating customized automation programs tailored to individual workflows, eliminating the need for users to have formal software development expertise.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system changes the complexity parameters by transforming complex programming tasks into simplified pattern-matching problems. It converts detailed customization requirements into high-level goal specifications that the ML system handles automatically.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If traditional automation systems are used, then automation can be achieved, but the development process lacks acceleration and efficiency

Engineering Contradiction:

Improveautomation development efficiencyVSAvoidtime for automation program creation

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of user goals and historical data before generating automation programs. It pre-processes information about user workflows, detected patterns, and recommended actions to accelerate the subsequent program generation process.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Manual programming processes are replaced with automated machine learning systems that rapidly generate, refine, and optimize automation programs, dramatically increasing development efficiency and reducing creation time.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250245568A1Robotic process automation utilizing machine learning to suggest actions for automating processes

Publication Date: 2025.07.31 AUTOMATION ANYWHERE INC

AI summary

A system, method or computer readable medium for generating next suggested actions using generative Artificial Intelligence (AI). The system, method or computer readable medium can leverage the power of artificial intelligence algorithms to generate personalized, context-aware suggestions for user actions based on input data. By employing generative AI models, the system, method or computer readable medium can effectively anticipate user needs and provide intelligent recommendations to enhance user experiences across various domains.