ML Update Assignment for Accurate Network Requirement Matching

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Solution Overview

Problem

Existing computer systems require manual and resource-intensive processes for updating and managing technical and operational requirements, leading to inefficiencies and outdated implementations.

Innovation Solution

A data collection technique using a classification model trained with past assignment records, combined with a detection and trigger module, to optimize when and how to prompt users for survey data, and automate the assignment of requirements to computer processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual evaluation and assignment of updates to computer processes is performed, then accuracy in assigning updates can be maintained, but significant resource allocation and time are required

Engineering Contradiction:

Improveaccuracy in assigning updatesVSAvoidspeed of update assignment

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces the manual mechanical evaluation process with an automated machine learning classification system. The classification model automatically evaluates incoming updates and assigns them to relevant computer processes based on trained patterns from historical data, eliminating the need for manual review while maintaining assignment accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system creates a digital copy of the manual assignment process by training a classification model on historical assignment records. This digital twin learns from past expert decisions and replicates the assignment logic automatically, enabling rapid processing without sacrificing the quality of manual assignments.

Inventive Principle:

Principle #26Copying

2Reliability

If manual study of each process is performed to assign updates, then proper update allocation can be ensured, but the pace of implementing requirements cannot be kept up with

Engineering Contradiction:

Improveproper update allocationVSAvoidtime delay in implementing requirements

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary action by pre-training the classification model on historical assignment records before actual update assignment begins. This advance preparation enables the system to rapidly classify new updates without manual intervention, keeping pace with the speed at which requirements arrive while ensuring proper allocation through learned patterns.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The classification system enables self-service by automatically assigning updates to computer processes without requiring human intervention. The model independently evaluates incoming updates, determines relevance to various processes, and allocates them appropriately, eliminating the time bottleneck of manual review while maintaining reliability.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If numerous unique many-to-many relationships between updates and processes are manually evaluated, then accurate assignment can be achieved, but significant resource allocation is required

Engineering Contradiction:

Improveaccuracy in update-process matchingVSAvoidcomplexity of evaluation system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces the complex manual evaluation system with an automated machine learning classification model. The model handles the many-to-many relationships between updates and processes through automated feature extraction and classification algorithms, significantly reducing the complexity of the evaluation system while maintaining or improving assignment accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Ease of operation

If outsourcing computer system management is performed, then organizational burden is reduced, but costs increase and other issues are created

Engineering Contradiction:

Improvereduction of organizational burdenVSAvoidincreased costs

Core Design Contradiction:

Ease of operationVSLoss of energy

Solution Approach 1:

The organization implements self-service by developing an internal automated update assignment system using machine learning. This eliminates the need to outsource management functions while reducing the organizational burden through automation. The system handles update evaluation and assignment internally, avoiding outsourcing costs while maintaining operational ease.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20260037251A1Method and system for computer update requirement assignment using a machine learning technique

Publication Date: 2026.02.05 CAPITAL ONE SERVICES LLC

AI summary

A novel data collection technique is disclosed. This data collection technique gathers data relating to various technical processes implemented in a computer network. A database including past assignment of requirements is also provided. These requirements can be software update requirements relating to a computer network. A classification model is trained using the assignment records fort these requirements and the data collected relating to the technical processes. Using the classification model, new requirements can be classified and implemented on the computer network.