AI Minibot Squad Engine for Automated Cloud Infrastructure Delivery

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current cloud data and technology solution delivery processes are costly and time-consuming due to manual design, development, and delivery processes that rely heavily on expert engineering talent, and are prone to human errors and inefficiencies, especially in complex multi-cloud and hybrid cloud environments where legacy services and rapid resource changes complicate infrastructure assessment and migration.

Innovation Solution

The use of machine learning and artificial intelligence modeling, specifically through dynamic minibot squad engines, to analyze current and future architecture state information and generate infrastructure-as-code, enabling automated knowledge engine generation and seamless user interactions for efficient data and technology solution delivery across various cloud platforms.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual design, development, and delivery processes are used with expert engineering talent, then solution quality and reliability are improved, but cost and time consumption increase significantly

Engineering Contradiction:

Improvesolution qualityVSAvoiddelivery time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical processes (expert engineers performing design, development, and delivery tasks) with an automated AI system comprising natural language processing, machine learning models, and programmatic generation capabilities. This substitution maintains solution quality while dramatically reducing delivery time and cost by eliminating human execution from the workflow.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables organizations to perform infrastructure solution delivery themselves through interactive natural language interfaces without requiring external expert engineering talent. The AI assistant autonomously performs gap analysis, generates infrastructure-as-code, and executes delivery tasks based on user requirements, making the organization self-sufficient in cloud transformation tasks.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If expert engineering talent is used for infrastructure assessment and migration, then accuracy and reliability are improved, but cost increases significantly

Engineering Contradiction:

Improveassessment accuracyVSAvoidcost

Core Design Contradiction:

Measurement precisionVSLoss of energy

Solution Approach 1:

The patent replaces expensive human expert assessment processes with automated AI-driven analysis that uses natural language processing and machine learning to evaluate current infrastructure states, identify gaps, and recommend migrations. This maintains high assessment accuracy while eliminating the substantial cost associated with hiring and deploying expert engineering talent.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Ease of operation

If conventional static visualization techniques are used for architecture sharing, then ease of operation is improved, but adaptability and up-to-date information are worsened

Engineering Contradiction:

Improvevisualization usabilityVSAvoiddynamic updating capability

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The patent transforms static visualization techniques into dynamic, automatically updating visual representations of infrastructure architecture. The system continuously synchronizes visualizations with current infrastructure-as-code states, ensuring that displayed information reflects real-time changes without requiring manual updates, thus maintaining ease of operation while achieving adaptability.

Inventive Principle:

Principle #15Dynamics

4Manufacturing precision

If manual processes are used for data curation and management, then data accuracy can be maintained, but time consumption and human error risk increase

Engineering Contradiction:

Improvedata accuracyVSAvoidprocessing time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent replaces manual data curation and management processes with automated programmatic operations that execute data validation, transformation, and migration tasks. This substitution maintains data accuracy through systematic validation rules while eliminating the time consumption and human error risks inherent in manual processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20230186117A1Automated cloud data and technology solution delivery using dynamic minibot squad engine machine learning and artificial intelligence modeling

Publication Date: 2023.06.15 MCKINSEY & CO INC

AI summary

A method includes receiving a computing system future state description in response to prompting a user; determining specific properties; predicting a solution architecture based on the specific properties; and generating infrastructure-as-code. A computing system includes a processor; and a memory having stored thereon instructions that, when executed, cause the computing system to: prompt a user to describe a future state of a computing system; receive a description of the future state; determine specific properties; predict a solution architecture based on the specific properties; and generate infrastructure-as-code. A non-transitory computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause a computer to: prompt a user to describe a future state of a computing system; receive a description of the future state; determine specific properties of the future state; predict a solution architecture based on the specific properties; and generate infrastructure-as-code.