Self-Adjusting Build Infrastructure for Scalable Software Builds

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing robotic build infrastructure for software application development requires significant human intervention and struggles to scale effectively, leading to inefficiencies and resource wastage.

Innovation Solution

A self-adjusting robotic build infrastructure system that utilizes machine learning and performance tests to automatically select, validate, and certify infrastructure configurations based on input parameters, reducing the need for human intervention and optimizing resource usage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated builds based on a factory model are used, then productivity is improved, but adaptability deteriorates due to difficulty in scaling to specific environments

Engineering Contradiction:

Improvebuild automation efficiencyVSAvoidenvironmental scalability

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The build infrastructure is transformed from a static factory model to a dynamic self-adjusting system that can adapt its configuration based on environmental parameters. The system automatically modifies build parameters, resource allocation, and infrastructure settings to optimize performance across different scaling environments without requiring manual reconfiguration.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system dynamically changes build parameters, resource constraints, and infrastructure configurations based on the target environment. By automatically adjusting parameters such as compute resources, memory allocation, and network settings, the system maintains high productivity across diverse environments while adapting to specific scalability requirements.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If manual provisioning and installation of robotic infrastructure is performed, then adaptability is improved, but loss of time increases due to significant human intervention required

Engineering Contradiction:

Improveinfrastructure customizationVSAvoidprovisioning time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The build infrastructure performs self-provisioning and self-configuration automatically. The system autonomously detects environmental conditions, selects appropriate infrastructure configurations, allocates resources, and validates deployments without human intervention. This eliminates manual provisioning time while maintaining adaptability through automated environmental assessment and configuration selection.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system pre-configures multiple infrastructure templates and configurations in advance, stored in a build infrastructure database. When deployment is initiated, the system automatically selects and applies the appropriate pre-prepared configuration based on environmental parameters, eliminating the need for manual setup while ensuring adaptability to different environments.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If extensive human intervention is used for infrastructure provisioning, then measurement precision is improved through manual validation, but productivity deteriorates due to resource wastage and inefficiency

Engineering Contradiction:

Improvevalidation accuracyVSAvoidresource utilization efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system implements automated feedback loops where performance metrics, validation results, and environmental data are continuously monitored and fed back to the build infrastructure. This feedback mechanism enables automated adjustment of build parameters and infrastructure configuration, maintaining validation precision through systematic testing while eliminating resource wastage associated with manual trial-and-error approaches.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

Manual validation and provisioning processes are replaced with automated computational systems. The system uses algorithms, performance tests, and validation frameworks to automatically assess infrastructure quality, replacing human manual validation while improving both precision through systematic testing and productivity by eliminating repetitive manual tasks and associated resource wastage.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12561132B2System and method for self-adjusting robotic build infrastructure for software application development

Publication Date: 2026.02.24 BANK OF AMERICA CORP

AI summary

Systems, computer program products, and methods are described herein for self-adjusting robotic build infrastructure for software application development. The present disclosure is configured to receive a set of input parameters associated with the planned build; analyze the set of input parameters associated with the planned build; determine a robotic build infrastructure from a robotic build infrastructure database using the analyzed set of input parameters associated with the planned build; compile an image test of the planned build based on the robotic build infrastructure determined from the robotic build infrastructure database; validate the image test through a performance test; adjust the image test based on the analyzed set of input parameters and the performance test validation; initiate a final build of the robotic build infrastructure based on the adjusted image test; and certify the final build of the adjusted robotic build infrastructure.