JP7379668B2 - 機械学習ワークロードのためのタスクスケジューリング - Google Patents
機械学習ワークロードのためのタスクスケジューリング Download PDFInfo
- Publication number
- JP7379668B2 JP7379668B2 JP2022514245A JP2022514245A JP7379668B2 JP 7379668 B2 JP7379668 B2 JP 7379668B2 JP 2022514245 A JP2022514245 A JP 2022514245A JP 2022514245 A JP2022514245 A JP 2022514245A JP 7379668 B2 JP7379668 B2 JP 7379668B2
- Authority
- JP
- Japan
- Prior art keywords
- host
- hosts
- resources
- hardware
- task
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/5044—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering hardware capabilities
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
- G06F9/5016—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals the resource being the memory
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
- G06F2209/50—Indexing scheme relating to G06F9/50
- G06F2209/502—Proximity
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2212/00—Indexing scheme relating to accessing, addressing or allocation within memory systems or architectures
- G06F2212/25—Using a specific main memory architecture
- G06F2212/254—Distributed memory
- G06F2212/2542—Non-uniform memory access [NUMA] architecture
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T1/00—General purpose image data processing
- G06T1/20—Processor architectures; Processor configuration, e.g. pipelining
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Neurology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Advance Control (AREA)
- Multi Processors (AREA)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2023187576A JP7637747B2 (ja) | 2019-11-20 | 2023-11-01 | 機械学習ワークロードのためのタスクスケジューリング |
Applications Claiming Priority (5)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201962938304P | 2019-11-20 | 2019-11-20 | |
| US62/938,304 | 2019-11-20 | ||
| US16/720,717 US11544113B2 (en) | 2019-11-20 | 2019-12-19 | Task scheduling for machine-learning workloads |
| US16/720,717 | 2019-12-19 | ||
| PCT/US2020/049648 WO2021101617A1 (en) | 2019-11-20 | 2020-09-08 | Task scheduling for machine-learning workloads |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2023187576A Division JP7637747B2 (ja) | 2019-11-20 | 2023-11-01 | 機械学習ワークロードのためのタスクスケジューリング |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JP2023511467A JP2023511467A (ja) | 2023-03-20 |
| JP7379668B2 true JP7379668B2 (ja) | 2023-11-14 |
Family
ID=75910002
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2022514245A Active JP7379668B2 (ja) | 2019-11-20 | 2020-09-08 | 機械学習ワークロードのためのタスクスケジューリング |
| JP2023187576A Active JP7637747B2 (ja) | 2019-11-20 | 2023-11-01 | 機械学習ワークロードのためのタスクスケジューリング |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2023187576A Active JP7637747B2 (ja) | 2019-11-20 | 2023-11-01 | 機械学習ワークロードのためのタスクスケジューリング |
Country Status (6)
| Country | Link |
|---|---|
| US (2) | US11544113B2 (enExample) |
| EP (1) | EP4062281A1 (enExample) |
| JP (2) | JP7379668B2 (enExample) |
| KR (1) | KR102759334B1 (enExample) |
| CN (1) | CN114503077A (enExample) |
| WO (1) | WO2021101617A1 (enExample) |
Families Citing this family (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11544113B2 (en) * | 2019-11-20 | 2023-01-03 | Google Llc | Task scheduling for machine-learning workloads |
| WO2021195104A1 (en) * | 2020-03-23 | 2021-09-30 | Mentium Technologies Inc. | Digital-imc hybrid system architecture for neural network acceleration |
| CN111930498B (zh) * | 2020-06-29 | 2022-11-29 | 苏州浪潮智能科技有限公司 | 一种高效的gpu资源分配优化方法和系统 |
| KR102871422B1 (ko) * | 2020-10-21 | 2025-10-15 | 삼성전자주식회사 | 데이터 처리 방법 및 장치 및 이를 포함한 전자 장치 및 가속기 시스템 |
| US11847489B2 (en) * | 2021-01-26 | 2023-12-19 | Apple Inc. | United states graphics processor techniques with split between workload distribution control data on shared control bus and corresponding graphics data on memory interfaces |
| US11436054B1 (en) * | 2021-04-05 | 2022-09-06 | Hewlett Packard Enterprise Development Lp | Directing queries to nodes of a cluster of a container orchestration platform distributed across a host system and a hardware accelerator of the host system |
| US11716257B1 (en) * | 2021-05-24 | 2023-08-01 | Neureality Ltd. | Batching of artificial intelligence jobs |
| DE102021213282A1 (de) * | 2021-11-25 | 2023-05-25 | Robert Bosch Gesellschaft mit beschränkter Haftung | Partizipatives Sicherheitsprotokoll für Datenwolken-basierte Funktionen |
| CN114490002B (zh) * | 2022-02-17 | 2025-11-28 | 上海阵量智能科技有限公司 | 数据处理系统、任务调度方法、装置、芯片、及电子设备 |
| US12417047B2 (en) * | 2023-01-10 | 2025-09-16 | Google Llc | Heterogeneous ML accelerator cluster with flexible system resource balance |
| US20250045099A1 (en) * | 2023-08-02 | 2025-02-06 | Samsung Electronics Co., Ltd. | Systems, methods, and apparatus for assigning machine learning tasks to compute devices |
| US12481534B2 (en) * | 2024-01-22 | 2025-11-25 | Dropbox, Inc. | Dynamically selecting artificial intelligence models and hardware environments to execute tasks |
| TWI897553B (zh) * | 2024-06-19 | 2025-09-11 | 聯發科技股份有限公司 | 處理單元的排程方法及非暫態機器可讀介質 |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015132887A (ja) | 2014-01-09 | 2015-07-23 | 富士通株式会社 | 要求分散プログラム、要求分散方法および情報処理装置 |
| US20190114534A1 (en) | 2017-10-17 | 2019-04-18 | Xilinx, Inc. | Neural network processing system having multiple processors and a neural network accelerator |
| US20190312772A1 (en) | 2018-04-04 | 2019-10-10 | EMC IP Holding Company LLC | Topology-aware provisioning of hardware accelerator resources in a distributed environment |
| US20190325302A1 (en) | 2018-04-23 | 2019-10-24 | EMC IP Holding Company LLC | Implementing parameter server in networking infrastructure for high-performance computing |
Family Cites Families (37)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1476834A1 (en) | 2002-02-07 | 2004-11-17 | Thinkdynamics Inc. | Method and system for managing resources in a data center |
| JP5056345B2 (ja) | 2007-10-29 | 2012-10-24 | 富士通株式会社 | データ処理装置およびデータ処理方法 |
| US9652372B2 (en) | 2010-12-15 | 2017-05-16 | At&T Intellectual Property I, L.P. | Method and apparatus for improving non-uniform memory access |
| US20130185729A1 (en) | 2012-01-13 | 2013-07-18 | Rutgers, The State University Of New Jersey | Accelerating resource allocation in virtualized environments using workload classes and/or workload signatures |
| FR2991074B1 (fr) | 2012-05-25 | 2014-06-06 | Bull Sas | Procede, dispositif et programme d'ordinateur de controle dynamique de distances d'acces memoire dans un systeme de type numa |
| JP5949188B2 (ja) | 2012-06-08 | 2016-07-06 | 日本電気株式会社 | 密結合マルチプロセッサシステム |
| US9588804B2 (en) * | 2014-01-21 | 2017-03-07 | Qualcomm Incorporated | System and method for synchronous task dispatch in a portable device |
| US9697045B2 (en) | 2015-03-24 | 2017-07-04 | International Business Machines Corporation | Selecting resource allocation policies and resolving resource conflicts |
| US10241674B2 (en) | 2015-12-11 | 2019-03-26 | Vmware, Inc. | Workload aware NUMA scheduling |
| US11153223B2 (en) * | 2016-04-07 | 2021-10-19 | International Business Machines Corporation | Specifying a disaggregated compute system |
| WO2018067680A1 (en) | 2016-10-05 | 2018-04-12 | Hidden Path Entertainment, Inc. | System and method of capturing and rendering a stereoscopic panorama using a depth buffer |
| US10176550B1 (en) | 2017-03-20 | 2019-01-08 | Nutanix, Inc. | GPU resource usage display and dynamic GPU resource allocation in a networked virtualization system |
| CN107168782A (zh) * | 2017-04-24 | 2017-09-15 | 复旦大学 | 一种基于Spark与GPU的并行计算系统 |
| US11010205B2 (en) | 2017-05-30 | 2021-05-18 | Hewlett Packard Enterprise Development Lp | Virtual network function resource allocation |
| US10686728B2 (en) * | 2017-07-06 | 2020-06-16 | Huawei Technologies Co., Ltd. | Systems and methods for allocating computing resources in distributed computing |
| US10445249B2 (en) | 2017-11-09 | 2019-10-15 | International Business Machines Corporation | Facilitating access to memory locality domain information |
| US10713092B2 (en) | 2018-01-02 | 2020-07-14 | Jpmorgan Chase Bank, N.A. | Dynamic resource management of a pool of resources for multi-tenant applications based on sample exceution, query type or jobs |
| US10942767B2 (en) | 2018-02-27 | 2021-03-09 | Microsoft Technology Licensing, Llc | Deep neural network workload scheduling |
| US10601903B2 (en) * | 2018-05-17 | 2020-03-24 | International Business Machines Corporation | Optimizing dynamical resource allocations based on locality of resources in disaggregated data centers |
| US11030012B2 (en) | 2018-09-28 | 2021-06-08 | Intel Corporation | Methods and apparatus for allocating a workload to an accelerator using machine learning |
| US11216314B2 (en) * | 2018-11-02 | 2022-01-04 | EMC IP Holding Company LLC | Dynamic reallocation of resources in accelerator-as-a-service computing environment |
| US12254526B2 (en) * | 2019-03-15 | 2025-03-18 | Intel Corporation | On chip dense memory for temporal buffering |
| US11184236B2 (en) * | 2019-04-30 | 2021-11-23 | Intel Corporation | Methods and apparatus to control processing of telemetry data at an edge platform |
| US11521042B2 (en) | 2019-05-21 | 2022-12-06 | Anil Ravindranath | System and method to dynamically and automatically sharing resources of coprocessor AI accelerators |
| US11301307B2 (en) * | 2019-07-24 | 2022-04-12 | Red Hat, Inc. | Predictive analysis for migration schedulers |
| US12052260B2 (en) * | 2019-09-30 | 2024-07-30 | International Business Machines Corporation | Scalable and dynamic transfer learning mechanism |
| US20210149677A1 (en) * | 2019-11-15 | 2021-05-20 | Intel Corporation | Enhanced processor functions for calculation |
| US11726793B2 (en) * | 2019-11-15 | 2023-08-15 | Intel Corporation | Data locality enhancement for graphics processing units |
| US11544113B2 (en) * | 2019-11-20 | 2023-01-03 | Google Llc | Task scheduling for machine-learning workloads |
| US11586932B2 (en) * | 2020-03-10 | 2023-02-21 | International Business Machines Corporation | Model training with variable batch sizing and gradient checkpoint segments |
| US11526964B2 (en) * | 2020-06-10 | 2022-12-13 | Intel Corporation | Deep learning based selection of samples for adaptive supersampling |
| US12216738B2 (en) * | 2020-10-12 | 2025-02-04 | International Business Machines Corporation | Predicting performance of machine learning models |
| WO2022118322A1 (en) * | 2020-12-02 | 2022-06-09 | Unifabrix Ltd. | System and method for multimodal computer address space provisioning |
| US20220188691A1 (en) * | 2020-12-11 | 2022-06-16 | International Business Machines Corporation | Machine Learning Pipeline Generation |
| US20220124005A1 (en) * | 2021-11-16 | 2022-04-21 | Kshitij Arun Doshi | Systems and methods for reactive intent-driven end-to-end orchestration |
| US12461781B2 (en) * | 2021-12-22 | 2025-11-04 | Intel Corporation | Low power inference engine pipeline in a graphics processing unit |
| US20230137191A1 (en) * | 2022-11-12 | 2023-05-04 | Adrian C. Hoban | Mechanism to recompose workload packages in a computing environment |
-
2019
- 2019-12-19 US US16/720,717 patent/US11544113B2/en active Active
-
2020
- 2020-09-08 WO PCT/US2020/049648 patent/WO2021101617A1/en not_active Ceased
- 2020-09-08 CN CN202080061569.2A patent/CN114503077A/zh active Pending
- 2020-09-08 EP EP20775537.2A patent/EP4062281A1/en active Pending
- 2020-09-08 JP JP2022514245A patent/JP7379668B2/ja active Active
- 2020-09-08 KR KR1020227007076A patent/KR102759334B1/ko active Active
-
2022
- 2022-12-29 US US18/091,263 patent/US12321781B2/en active Active
-
2023
- 2023-11-01 JP JP2023187576A patent/JP7637747B2/ja active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015132887A (ja) | 2014-01-09 | 2015-07-23 | 富士通株式会社 | 要求分散プログラム、要求分散方法および情報処理装置 |
| US20190114534A1 (en) | 2017-10-17 | 2019-04-18 | Xilinx, Inc. | Neural network processing system having multiple processors and a neural network accelerator |
| US20190312772A1 (en) | 2018-04-04 | 2019-10-10 | EMC IP Holding Company LLC | Topology-aware provisioning of hardware accelerator resources in a distributed environment |
| US20190325302A1 (en) | 2018-04-23 | 2019-10-24 | EMC IP Holding Company LLC | Implementing parameter server in networking infrastructure for high-performance computing |
Also Published As
| Publication number | Publication date |
|---|---|
| KR102759334B1 (ko) | 2025-01-22 |
| JP2024020271A (ja) | 2024-02-14 |
| JP7637747B2 (ja) | 2025-02-28 |
| WO2021101617A1 (en) | 2021-05-27 |
| US11544113B2 (en) | 2023-01-03 |
| KR20220038497A (ko) | 2022-03-28 |
| US20210149729A1 (en) | 2021-05-20 |
| JP2023511467A (ja) | 2023-03-20 |
| CN114503077A (zh) | 2022-05-13 |
| US12321781B2 (en) | 2025-06-03 |
| US20230136661A1 (en) | 2023-05-04 |
| EP4062281A1 (en) | 2022-09-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP7379668B2 (ja) | 機械学習ワークロードのためのタスクスケジューリング | |
| CN111247533B (zh) | 用于神经网络加速的机器学习运行时库 | |
| KR102860886B1 (ko) | 스케줄러, 스케줄러의 동작 방법 및 이를 포함한 가속기 시스템 | |
| CN115269717A (zh) | 存储设备、分布式存储系统以及数据处理方法 | |
| CN114730275A (zh) | 使用张量在分布式计算系统中进行矢量化资源调度的方法和装置 | |
| US12314851B2 (en) | Microservice-based training systems in heterogeneous graphic processor unit (GPU) cluster and operating method thereof | |
| KR102871422B1 (ko) | 데이터 처리 방법 및 장치 및 이를 포함한 전자 장치 및 가속기 시스템 | |
| US11954518B2 (en) | User-defined metered priority queues | |
| CN102929725A (zh) | 信号处理并行计算软件的动态重配置方法 | |
| KR102742714B1 (ko) | 중요 경로 기반 스케줄링 및 후처리를 통한 리소스 활용을 극대화할 수 있는 multi-GPU 기반 딥러닝 모델 추론 기법 | |
| US20220229695A1 (en) | System and method for scheduling in a computing system | |
| KR20230064963A (ko) | 클러스터 컴퓨팅 시스템에서의 리소스 할당 방법 및 장치 | |
| KR20220049294A (ko) | 스케줄러, 스케줄러의 동작 방법 및 이를 포함한 전자 장치 | |
| CN113841132B (zh) | 用于主机设备接口的非对称数据通信 | |
| KR102787376B1 (ko) | 가속기를 분할하는 전자 장치, 배치를 할당하는 전자 장치 및 그 동작 방법 | |
| CN116670649B (zh) | 用于设计具有优化的系统延时的机器人系统架构的方法和系统 | |
| CN116166396A (zh) | 调度模型的训练方法、装置、电子设备及可读存储介质 | |
| US11663465B2 (en) | Method of managing task performance in an artificial neural network, and system executing an artificial neural network | |
| WO2021199396A1 (ja) | 分散処理ノードおよび分散処理システム | |
| KR102831990B1 (ko) | 이기종 그래픽 프로세서 유닛 클러스터를 이용한 마이크로서비스 기반의 트레이닝 시스템 및 그 동작 방법 | |
| HK40071346A (en) | Task scheduling for machine-learning workloads | |
| US12153957B2 (en) | Hierarchical work scheduling | |
| CN120407195B (zh) | 混合异构计算任务的执行方法、设备、介质和程序产品 | |
| CN114461331B (zh) | 一种资源预部署方法、装置、电子设备及可读存储介质 | |
| US20240095083A1 (en) | Parallel workload scheduling based on workload data coherence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20221121 |
|
| A621 | Written request for application examination |
Free format text: JAPANESE INTERMEDIATE CODE: A621 Effective date: 20221121 |
|
| A977 | Report on retrieval |
Free format text: JAPANESE INTERMEDIATE CODE: A971007 Effective date: 20230824 |
|
| TRDD | Decision of grant or rejection written | ||
| A01 | Written decision to grant a patent or to grant a registration (utility model) |
Free format text: JAPANESE INTERMEDIATE CODE: A01 Effective date: 20231003 |
|
| A61 | First payment of annual fees (during grant procedure) |
Free format text: JAPANESE INTERMEDIATE CODE: A61 Effective date: 20231101 |
|
| R150 | Certificate of patent or registration of utility model |
Ref document number: 7379668 Country of ref document: JP Free format text: JAPANESE INTERMEDIATE CODE: R150 |