“dynamic automation method and algorithm for optimizing (human, software, hardware) resources in it service development projects”

An integrated system with machine learning and real-time feedback dynamically optimizes IT project resources, addressing inefficiencies in workforce, software, and hardware allocation, thereby reducing costs and improving project quality and stability.

KR1020260112899APending Publication Date: 2026-07-21한상진
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Patent Information

Application Number
KR1020250004419
Authority / Receiving Office
KR · KR
Patent Type
Applications
Current Assignee / Owner
Filing Date
2025-01-10
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing IT project management systems face inefficiencies in workforce reallocation, software license management, and server resource scaling due to manual decision-making, lack of comprehensive machine learning integration, and inadequate real-time monitoring and feedback loops, leading to bottlenecks, cost overruns, and reduced project quality.

Method used

An integrated system comprising a machine learning-based prediction module, decision engine, resource control module, and monitoring module that dynamically reallocates human, software, and hardware resources by predicting future demands, applying rule tables and priorities, and continuously improving through real-time feedback.

Benefits of technology

This system efficiently reduces project schedules, minimizes costs, enhances resource utilization, and maintains project quality by automating resource allocation and adjustment in response to real-time changes, improving machine learning model accuracy over time.

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Abstract

The present invention relates to a technology for optimizing human, software (SW), and hardware (HW) resources generated in IT service development projects through dynamic automation. Specifically, the invention provides an integrated process in which (1) a machine learning prediction module (100) calculates future resource demand based on project management tools, server / network logs, SW license usage history, etc., (2) a decision engine (200) determines resource allocation logic, such as personnel reallocation, license increase / decrease, and server expansion / reduction, by referring to rule tables and priorities, (3) a resource control module (300) executes allocation / release commands for actual resources (personnel, licenses, servers), and (4) a monitoring module (400) collects and analyzes the above results and real-time project status to continuously correct prediction errors and decision logic. This enables the resolution of recurring issues during IT service development projects, such as manpower bottlenecks, license overloads or shortages, and server resource overuse or insufficient resources, effectively mitigating project schedule delays and cost increases. Furthermore, by continuously correcting discrepancies between the machine learning model and actual data during project execution through online or adaptive learning methods, it improves long-term accuracy and efficiently performs dynamic resource optimization. Consequently, various benefits are achieved, including shortened project schedules, cost reductions, and improved resource utilization. At the same time, managers can reduce their workload through automated resource allocation processes and further enhance project quality and stability.
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