Neural Network Training UI for Local-Cloud Task Allocation

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Solution Overview

Problem

Existing artificial intelligence model training methods face challenges in balancing security and performance, with cloud-based training risking data leakage and on-premise training using low-performance GPUs, while lacking integrated user interfaces for efficient management across environments.

Innovation Solution

A method and user interface for dynamically allocating tasks between local and cloud computing resources, utilizing idle resources and access rights based on user inputs, enabling flexible training across environments with continuous UI/UX updates.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Power

If training is performed in cloud environment, then computing performance is improved, but data security deteriorates

Engineering Contradiction:

Improvecomputing performanceVSAvoiddata security

Core Design Contradiction:

PowerVSReliability

Solution Approach 1:

The system segments computing resources into local and cloud-based components, allowing different tasks to be distributed across these segments based on security and performance requirements

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

A task management server acts as an intermediary between the user terminal and computing nodes, dynamically allocating tasks to appropriate computing resources while managing data flow and access rights

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If training is performed in on-premise environment, then data security is improved, but computing performance deteriorates

Engineering Contradiction:

Improvedata securityVSAvoidcomputing performance

Core Design Contradiction:

ReliabilityVSPower

Solution Approach 1:

The system segments computing resources into local and cloud-based components, allowing different tasks to be distributed across these segments based on security and performance requirements

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Inventive Principle:

Principle #24Intermediary (Mediator)

3Ease of operation

If separate management is performed for each site, then local control is improved, but system complexity deteriorates

Engineering Contradiction:

Improvelocal controlVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system merges multiple computing nodes (local and cloud) into a unified managed environment, where a single task management server handles resource allocation and task scheduling across all sites

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The task management server provides universal functionality for managing tasks across different computing environments, eliminating the need for separate management systems at each site

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20260017513A1Method and user interface for training artificial neural network models in environment including multiple computing nodes

Publication Date: 2026.01.15 AIVEX CO LTD

AI summary

Disclosed is a method training an artificial neural network model performed by a computing device according to an exemplary embodiment of the present disclosure. According to the present disclosure, the computing device identifies a first computing node and a second computing node for training an artificial neural network model, acquires information related to a first task for training the artificial neural network model, based on a user input, and dynamically allocates the first task to at least one of the first computing node or the second computing node, and the first computing node includes one or more local computing resources of the user and the second computing node includes one or more cloud computing resources.