DETAILED ACTION
This is in response to the application filed on July 3rd 2025, in which claims 1-18 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/3/25 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Cecini et al. US 2017/0264493 A1 in view of Gollapudi et al. US 10,146,439 B2.
Regarding claim 1, Cecini discloses: at least one non-transitory machine-readable storage medium storing instructions to be executed by at least one machine, the at least one machine to be associated with a cloud computing system, the cloud computing system being configurable to provide at least one cloud- based service, the cloud computing system being configurable to comprise management resource circuitry, cloud computing resources, and non-volatile memory … storage resources communicatively coupled together via at least one network (configurable data center with resources including could/network connection and non-volatile memory for executing tasks/cloud-based service – see Figs. 1, 2 and 9, paragraphs 5, 24, 93 and 184), the instructions, when executed by the at least one machine, resulting in the cloud computing system being configured to enable performance of operations comprising:
dynamically allocating and/or dynamically deallocating, by the management resource circuitry, at least one portion of the cloud computing resources to be used in executing at least one container workload and/or at least one virtual machine workload associated with providing of the at least one cloud-based service (dynamically schedule workloads including allocate resources for workload and/or virtual machines – see abstract, paragraphs 5, 7, 72, 189 and 202-203, Figs. 1 and 11); and
receiving, by the management resource circuitry, [non-volatile memory] storage resource access information to be associated with mapping data to permit accessing of the [memory] storage resources (receive resource information for managing workloads on data centers – see paragraphs 87-88, 134, 201, this includes accessing the memory – paragraph 114;non-volatile memory – Fig. 9, paragraph 184);
wherein: the cloud computing resources are configurable to comprise compute resources and/or accelerator resources of multiple data center premises (compute resources across multiple data centers – Figs. 9-10);
the multiple data center premises are configurable to comprise at least one local customer data center premises and at least one cloud service provider data center premises (data centers are associated with cloud – see paragraphs 24, 37; process may be located “locally at one site” or distributed across multiple remote sites – paragraph 183; also see paragraph 34 which teaches that policy may dictate whether public cloud data centers or local resources are optimal for the workload; thus there are multiple data centers including at least one “local customer” and “cloud service provider”);
the dynamically allocating and/or the dynamically deallocating of the at least one portion of the cloud computing resources are configurable to be performed on an as-needed basis, based upon (1) telemetry-based present resource utilization data, (2) future resource utilization prediction data, (3) application programming interface (API) data, (4) resource utilization balancing data, (5) quality of service-related data, and (6) service level agreement data (allocate resources based on utilization, telemetry, predictions, quality of service and SLA – see paragraphs 7, 134, 198, 202-203, 213 and Figs. 10-11; also consider load balance and cost balance – “utilization balancing data”, see paragraphs 5, 25, 79 and 207; and use API to perform allocations – see paragraph 36, claim 13);
the accelerator resources are configurable to comprise graphics processing unit circuits for use in the execution, at least in part, of the at least one container workload and/or the at least one virtual machine workload (GPUs – paragraphs 94, 114); and
the at least one container workload and/or the at least one virtual machine workload are configurable to perform at least one machine learning-related operation (perform machine learning, e.g. “training models” – paragraphs 94 and 194).
Cecini does not explicitly disclose NVMe or the NVMe storage resources to be accessed via NVMe over fabric (NVMe-OF) protocol, but this is taught by Gollapudi as using NVMe in a data center (NVMe over fabric protocol – paragraph 29). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cecini to use NVMe as taught by Gollapudi. Gollapudi teaches that NVMe is merely one type of storage resource protocol known in the data center art (paragraph 4) and that it provides efficient use of resources and high performed (paragraph 29).
Regarding claim 2, Cecini discloses the management resource circuitry is to communicate, at least in part, via at least one API (use API – paragraphs 36, 43, 95).
Regarding claim 3, Cecini discloses the accelerator resources comprise multiple graphics processing units interconnected by at least one accelerator-to-accelerator interconnect (GPUs – paragraphs 94, 114; processing resources are connected – see Fig. 9; data center “interconnects” compute resources – see paragraph 5).
Regarding claim 4, Cecini discloses manage placement of multiple virtual machine workloads and/or multiple container workloads among multiple portions of the cloud computing resources to be used in executing the multiple virtual machine workloads and/or multiple container workloads (multiple VMs/workloads distributed across multiple data centers/resources – paragraphs 5, 78, 191 and Figs. 10-11).
Regarding claim 5, it is a system claim that corresponds to the non-transitory medium of claim 1; therefore it is rejected for the same reasons. As explained above, Cecini also discloses the physical hardware to perform the method including circuitry, resources, etc. (see Figs. 1-2, 9).
Regarding claims 6-8, they correspond to claims 2-4 respectively; thus they are also rejected for the same reasons.
Regarding claims 9-12, they are method claims that directly correspond to the non-transitory medium of claims 1-4 respectively. Therefore, they are rejected for the same reasons.
Regarding claim 13, it is a data center that corresponds to the non-transitory medium of claim 1 as well as the system of claim 5; thus, it is also rejected for the same reasons. Cecini also discloses a “data center” comprising a network (paragraph 5, Figs. 1-2).
Regarding claim 14, Cecini discloses multiple data center premises (abstract, paragraphs 38, 78).
Regarding claim 15, Cecini discloses the multiple data center premises are configurable to comprise at least one local customer data center premises and at least one cloud service provider data center premises (data centers are associated with cloud – see paragraphs 24, 37; process may be located “locally at one site” or distributed across multiple remote sites – paragraph 183; also see paragraph 34 which teaches that policy may dictate whether public cloud data centers or local resources are optimal for the workload; thus there are multiple data centers including at least one “local customer” and “cloud service provider”).
Regarding claims 16-18, they correspond to claims 2-4 respectively; thus they are also rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ganguli et al. US 2014/0095691 A1 discloses dynamically reallocating resources in a cloud data center based on resource utilization and service level agreements (abstract).
Metsch et al. US 2018/0027060 A1 discloses using accelerator resources when allocating resources to execute a workload (abstract, paragraph 46).
Ahuja et al. US 2017/0109205 A1 discloses scheduling a workload on a plurality of data centers (abstract) and resource allocation (paragraph 15).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON D RECEK whose telephone number is (571)270-1975. The examiner can normally be reached Flex M-F 9-5.
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/JASON D RECEK/Primary Examiner, Art Unit 2458
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