Machine Tool Inference Scheduling System and Method Patent

ChangeBridge: EPO Bulletin - AI & Computing (G06N)

Published March 18th, 2026

Detected March 23rd, 2026

Summary

The European Patent Office has published patent application EP4711869A1 for a multi-task real-time inference scheduling system and method for machine tools. The invention, assigned to DN Solutions Co., Ltd., utilizes machine learning to optimize task scheduling based on real-time operational data.

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What changed

The European Patent Office has published patent application EP4711869A1, detailing a novel system and method for real-time inference scheduling in machine tools. Developed by DN Solutions Co., Ltd., the invention employs machine learning and neural networks to generate multi-task learning models. These models infer and schedule multiple tasks required by individual machine tool control units based on real-time operational contexts and use scenarios.

This patent publication does not impose immediate compliance obligations on regulated entities. However, it signifies advancements in AI and industrial automation that manufacturers, particularly those in the machine tool sector, may wish to monitor for competitive or technological insights. The publication date is March 18, 2026.

Source document (simplified)

← EPO Patent Bulletin

MULTI-TASK REAL-TIME INFERENCE SCHEDULING SYSTEM MACHINE TOOL AND METHOD THEREOF

Publication EP4711869A1 Kind: A1 Mar 18, 2026

Applicants

DN Solutions Co., Ltd.

Inventors

KIM, Donghyun

Abstract

The present invention relates to a multi-task real-time inference scheduling system and real-time inference scheduling method of a machine tool, wherein a central control unit is connected to each of one or more individual control units through a network, receives a use context of each machine tool through each individual control unit, generates a multi-task learning model through a neural network, infers multiple tasks required to be performed by the individual control unit of each machine tool through machine learning by using real-time use contexts collected during operation of the machine tool by a use scenario, and schedules the multiple tasks of the machine tool through machine learning.

IPC Classifications

G05B 19/4069 20060101AFI20241122BHEP G06N 5/04 20230101ALI20241122BHEP G06N 3/08 20230101ALI20241122BHEP G06N 20/00 20190101ALI20241122BHEP G05B 19/418 20060101ALI20241122BHEP B23Q 15/00 20060101ALI20241122BHEP

Designated States

AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LI, LT, LU, LV, MC, ME, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR

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