ML Module for Code Feature Settings

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Solution Overview

Problem

Current methods require manual intervention or reliance on support resources to determine which system features to enable or disable for optimal code performance, leading to errors and performance issues during code deployment.

Innovation Solution

A machine learning module is trained to determine optimal feature settings by analyzing system configuration settings and performance outcomes, enabling or disabling features to optimize code performance and minimize errors.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual feature disabling is implemented by users or support resources, then code performance issues can be addressed, but deployment time and operational complexity increase

Engineering Contradiction:

Improvecode performanceVSAvoiddeployment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of system configurations and automatically determines optimal feature settings before code deployment. The machine learning model pre-calculates which features should be enabled or disabled based on the target system's configuration, eliminating the need for manual intervention after deployment and reducing both deployment time and operational complexity

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The deployment system automatically analyzes system configurations and determines optimal feature settings without requiring user intervention or support resources. The machine learning model autonomously makes decisions about feature enablement/disabling based on configured parameters, allowing the system to self-optimize performance while minimizing deployment time

Inventive Principle:

Principle #25Self-service

2Reliability

If manual feature configuration is required, then code performance can be optimized, but ease of operation decreases

Engineering Contradiction:

Improvecode performanceVSAvoidease of deployment

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system automatically analyzes system configurations and determines optimal feature settings without requiring user intervention. The machine learning model autonomously evaluates the system environment and configures features accordingly, maintaining high code performance while significantly improving ease of operation by eliminating manual configuration steps

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically adjusts feature configuration parameters based on system configuration analysis. The machine learning model changes feature states (enabled/disabled) as output parameters based on input system configuration data, automatically optimizing code performance without requiring manual operational intervention

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If comprehensive feature analysis is performed manually, then accurate feature settings can be determined, but device complexity increases

Engineering Contradiction:

Improvefeature setting accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system replaces manual analytical processes with an automated machine learning-based system. Instead of human operators manually analyzing system configurations and determining feature settings, an AI model automatically performs this analysis, maintaining high measurement precision while reducing system complexity by eliminating manual intervention requirements

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The machine learning model autonomously performs comprehensive feature analysis by automatically analyzing system configurations and determining optimal feature settings. This self-service approach maintains accurate feature setting determination while reducing system complexity by eliminating the need for manual analysis processes

Inventive Principle:

Principle #25Self-service

4Productivity

If automated machine learning-based feature determination is implemented, then deployment efficiency improves, but system complexity increases

Engineering Contradiction:

Improvedeployment efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The machine learning model serves multiple functions: it analyzes system configurations, determines optimal feature settings, and outputs deployment recommendations. This multi-functional approach improves deployment efficiency by consolidating multiple operations into a single automated process while managing system complexity through the use of a unified AI model rather than multiple separate systems

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11461112B2Determining feature settings for code to deploy to a system by training a machine learning module

Publication Date: 2022.10.04 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Provided are a computer program product, system, and method for determining feature settings for code to deploy to a system by training a machine learning module. A determination is made of an outcome of running system code on a system having configuration settings and feature settings of features in the system to enable or disable in response to the outcome. A machine learning module is trained to produce the feature settings indicating to enable or disable the features in response to input comprising the configuration settings of the system.