AI-Driven IT Asset Provisioning for New Hires

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Solution Overview

Problem

Provisioning the right information handling system for new hires is challenging due to budget constraints and varying user needs, as existing methods rely on HR data that may not accurately predict hardware performance requirements, leading to over or under provisioning issues, especially in remote working conditions.

Innovation Solution

A system and method using a named entity recognition machine learning model to identify keywords from job descriptions and persona information, which filters for similar employees' platforms, selecting devices based on software needs, job duties, and user experiences to determine the most efficient IT asset configuration for new hires.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If HR data is used to provision IT assets for new hires, then the provisioning process is simple and quick, but the accuracy of matching hardware performance requirements to user needs deteriorates

Engineering Contradiction:

Improveprovisioning speedVSAvoidhardware performance prediction accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent introduces an AI model as an intermediary between HR data and IT asset provisioning decisions. The model processes HR data along with job description text and persona information to generate accurate hardware performance predictions, thereby maintaining provisioning speed while improving prediction accuracy

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the manual or rule-based mechanical system of HR data interpretation with an AI-based systematic approach. The AI model automatically analyzes unstructured text data and generates structured predictions about hardware requirements, eliminating the need for manual assessment while improving accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If high-performance IT assets are provisioned to all new hires, then user performance needs are met, but budget constraints are violated due to over-provisioning

Engineering Contradiction:

Improveuser performance satisfactionVSAvoidIT asset expenditure

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The patent applies local quality by tailoring IT asset specifications to each user's specific needs rather than applying a uniform high-performance configuration to all users. The AI model generates personalized hardware recommendations based on individual job requirements, ensuring each user receives appropriately configured assets that match their specific performance needs

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent changes the parameters of IT asset provisioning from fixed high-performance specifications to dynamic, AI-determined configurations. The system adjusts hardware parameters such as processor type, memory size, and storage capacity based on predicted user needs, optimizing the balance between performance satisfaction and cost efficiency

Inventive Principle:

Principle #35Parameter changes

3Quantity of substance

If low-performance IT assets are provisioned to save budget, then budget constraints are satisfied, but user performance needs are not met

Engineering Contradiction:

ImproveIT asset expenditureVSAvoiduser performance satisfaction

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The patent applies preliminary action by using the AI model to predict and determine appropriate hardware specifications before IT assets are provisioned. This advance prediction ensures that users receive assets with sufficient performance capability from the start, preventing the need for later upgrades or replacements that would incur additional costs

Inventive Principle:

Principle #10Preliminary action

4Adaptability or versatility

If IT asset provisioning is done manually based on HR data, then flexibility in handling individual cases is maintained, but the complexity and time required for provisioning increases

Engineering Contradiction:

Improvehandling individual user needsVSAvoidprovisioning process complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements self-service by enabling the AI model to automatically analyze user requirements and generate provisioning recommendations without manual intervention. The system extracts relevant information from job descriptions and persona data, performs analysis, and produces hardware recommendations autonomously, reducing process complexity while maintaining adaptability to individual needs

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20240354316A1System for Manufacturing and Provisioning an Information Handling System

Publication Date: 2024.10.24 DELL PROD LP

AI summary

A system, method, and computer-readable medium for performing an information technology system monitoring and management operation. The information technology system monitoring and management operation includes: identifying IT asset data from a plurality IT asset data sources contained within an IT environment; extracting information from at least some of the IT asset information, the information being extracted via a named entity recognition model; analyzing the information extracted from the at least some of the IT asset information; and, provisioning an IT asset for the new user based upon the analyzing.