Automation Program Generation With Role-Guided Machine Learning

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Solution Overview

Problem

Existing automation systems require substantial time and effort for creating automation programs, even with low-code development tools, posing a challenge for users with varying levels of software experience.

Innovation Solution

Utilizing machine learning models to produce automation programs by receiving user requests, providing role definitions, domain knowledge, and functional instructions, and combining outputs from multiple models to form the automation program.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If low-code software development systems are used to create automation programs, then automation program creation becomes accessible to users with lesser development experience, but substantial time and effort are still required

Engineering Contradiction:

Improveautomation program creation accessibilityVSAvoidtime and effort for program creation

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system enables self-service automation program generation through AI agents that automatically create automation programs from user descriptions. The AI agents analyze natural language inputs, identify required tasks and workflows, and generate functional automation programs without requiring user intervention in traditional coding or even low-code assembly processes.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical process of manual program assembly (even with low-code tools) with an intelligent system based on AI agents. These agents use machine learning models to automatically generate automation programs, substituting human cognitive and manual efforts with automated intelligent processing that understands natural language and generates functional code.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If traditional automation program development methods are used, then programs can be created with a moderate level of development experience, but the development process requires substantial time and effort

Engineering Contradiction:

Improveautomation program functionalityVSAvoidautomation program development speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent introduces AI agents as intermediaries between user requirements and automation program implementation. These agents serve as intelligent mediators that translate natural language descriptions into functional automation programs, bridging the gap between user intent and technical execution without requiring users to have development expertise.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary analysis and program generation actions automatically before user deployment. AI agents pre-process user descriptions, identify all necessary components and workflows, and generate complete automation programs in advance, eliminating the need for users to perform time-consuming assembly and configuration tasks.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If automation programs are created manually even with assistance tools, then programs can be customized to user needs, but the process requires substantial time and effort

Engineering Contradiction:

Improveautomation program customizationVSAvoidprogram development time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent implements dynamic automation program generation where AI agents adaptively create programs based on real-time analysis of user descriptions. The system dynamically adjusts program structure, task sequences, and workflow configurations according to specific user needs expressed in natural language, providing customized solutions without manual configuration.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20250244969A1Systems and methods for using machine learning models to produce automation programs and processes

Publication Date: 2025.07.31 AUTOMATION ANYWHERE INC

AI summary

Systems and methods for producing automation programs that are suitable for performing business and personal tasks using software application programs. The methods and systems involve can identify or receive a user request for the production of an automation program and then utilizing one or more machine learning models, where each of the machine learning models can produce an aspect of the requested automation program. Each of the machine learning models are provided with inputs such as a specific user's request for an automation program to automate tasks, the definition of a role that the model should take on, domain knowledge specific to an aspect of the automation program being requested, and functional instructions for each of the machine learning models to produce a desired output. The outputs of each of the machine learning models can be combined to form the user-requested automation program.