Admin Change Recommendation System for Enterprise IT

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Solution Overview

Problem

IT administrators in organizations often lack the necessary training, information, and time to efficiently manage enterprise devices, leading to suboptimal utilization of management tools and potential security risks.

Innovation Solution

An admin change recommendation system that analyzes information on admin changes made in various environments, categorizes it based on environmental characteristics, and infers causal relationships between changes and metric values to recommend specific admin changes that improve system performance, reliability, and cost-effectiveness.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If administrators manually manage enterprise devices without automated recommendations, then they have full control over decision-making, but the time and resources required for system management increase significantly

Engineering Contradiction:

Improvesystem management efficiencyVSAvoidtime required for admin changes

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system enables self-service by automatically analyzing device data, inferring causal relationships between admin changes and metric improvements, and generating recommendations without requiring administrator time for manual analysis. The system serves itself by continuously monitoring and learning from enterprise environments to provide actionable insights.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual administrative work with an automated computational system that uses machine learning and data analysis to generate recommendations. This substitution transforms the mechanical process of manual device management into an automated intelligent system that operates independently.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of information

If administrators rely on training and customer support channels, then they can access general guidance, but the information may not be specific to their particular situation and may include unnecessary changes

Engineering Contradiction:

Improveaccessibility of relevant informationVSAvoidcustomization to specific environment

Core Design Contradiction:

Loss of informationVSAdaptability or versatility

Solution Approach 1:

The system applies local quality by tailoring recommendations to specific enterprise environments based on their unique characteristics, device types, and performance metrics. Each recommendation is customized to the local context rather than providing generic advice, ensuring relevance and effectiveness for that particular situation.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring device metrics before and after admin changes, learning from the outcomes, and using this information to improve future recommendations. This closed-loop approach ensures that the information provided is continuously refined based on actual performance data from the specific environment.

Inventive Principle:

Principle #23Feedback

3Reliability

If administrators implement admin changes without data-driven recommendations, then they can act quickly, but the reliability and performance improvement cannot be guaranteed

Engineering Contradiction:

Improveperformance improvement guaranteeVSAvoidspeed of implementation

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary action by analyzing historical data and inferring causal relationships before recommendations are implemented. It pre-evaluates which admin changes are likely to improve specific metrics based on patterns learned from similar environments, allowing administrators to make informed decisions with higher confidence in the expected outcomes.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12199820B2Admin change recommendation in an enterprise

Publication Date: 2025.01.14 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Techniques are described herein that are capable of providing a recommendation of an admin change (i.e., an admin change recommendation) in an enterprise. A type of intended admin change that an administrator is to perform with regard to an enterprise is determined. The type is cross-referenced with information indicating admin changes made by administrator(s) in environment(s) of enterprise(s) and values of metrics resulting therefrom to identify subsets of the information to which the type corresponds. A causal relationship is inferred between admin change(s) made after an admin change of the type and an increase in value(s) of metric(s) that are indicated by information in the subsets. A recommended admin change is recommended to be performed by the administrator based at least in part on a causal relationship between the recommended admin change and an increase in at least one of the value(s) of at least one of the respective metric(s).