DETAILED ACTION
Claims 1-12 are pending. Applicant has amended claims 1, 5 and 9.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/3/2025 has been entered.
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.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (CN 105979007 A) in view of Gonzalez et al. (US 2016/0301624 A1) further in view of Jackson (US 7,971,204 B2).
As to claim 1, Huang teaches cloud computing system for use in providing at least one cloud-based service associated with execution of at least one workload, the cloud computing system being for use in association with at least one network communication link (Fig. 1 is a system architecture diagram of the NFV NFV system, used in all kinds of network, such as a data center network, a carrier network or local area network … performing a virtual network function 108 and NFV monitoring and management of the infrastructure layer 130; page 10, last paragraph), the cloud computing system comprising:
compute resources comprising at least one central processing unit and memory circuitry (computer hardware 112, virtual memory 118; page 10, last paragraph, NFV device 102 may also perform a network service; page 11, 2nd paragraph and a computing resource and calculation resource of a computing node; page 14, last paragraph. Inherently, a CPU and memory are included in the computing node.);
accelerator resources comprising graphics processing unit (GPU) accelerator circuitry (acceleration source computing node … encoding/decoding or image processing; page 14, 2nd – 3rd paragraphs);
network fabric for use in communicatively coupling at least certain of the compute resources and/or accelerator resources (Virtual infrastructure manager … communicate with each other … includes computing hardware 112, storage hardware 114, acceleration hardware 115, network hardware 116, virtual network; page 11, 4th – 5th paragraphs); and
management resources for use in allocating, based at least in part upon received request data, the compute resources and the accelerator resources for use in the execution of the at least one workload (When the service time delay of high demand, the service acceleration resource scheduling policy corresponding to the service will reflect acceleration resources and computing resource requirement of the same compute nodes, namely, according to the actual need of the resource scheduling policy service to determine acceleration; page 12, 2nd paragraph);
wherein:
the at least one workload comprises at least one virtual machine workload and/or at least one container workload (application virtual machine for service; page 12, 1st- 3rd paragraphs);
the accelerator resources are configurable to comprise local GPU accelerator circuitry and remote GPU accelerator circuitry (acceleration source computing node … encoding/decoding or image processing; page 14, 2nd – 3rd paragraphs and First, resource scheduling strategy … determining the resource type of the current acceleration … local virtualization acceleration resources … if the current acceleration resource type is remote virtualization acceleration resources or remote hard acceleration resources; page 4, 3rd paragraph and page 7, last paragraph – page 8, 9th paragraph, page 12, last paragraph);
the compute resources and the remote GPU accelerator circuitry are comprised in one or more cloud computing data centers (Virtual infrastructure manager … communicate with each other … includes computing hardware 112, storage hardware 114, acceleration hardware 115, network hardware 116, virtual network; page 11, 4th – 5th paragraphs and Fig. 1 is a system architecture diagram of the NFV system, used in all kids of network, such as a data center network, a carrier network or local area network … performing a virtual network function 108 and NFV monitoring and management of the infrastructure layer 130; page 10, last paragraph);
the local GPU accelerator circuitry is comprised in at least one housing that is remote from the one or more cloud computing data centers (the current acceleration resource type is remote virtualization acceleration resources or remote hard acceleration resources; page 12, last paragraph);
the local GPU accelerator circuitry is to be communicatively coupled to the management resources via the at least one network communication link (Virtual infrastructure manager … communicate with each other … includes computing hardware 112, storage hardware 114, acceleration hardware 115, network hardware 116, virtual network; page 11, 4th – 5th paragraphs);
the management resources are configurable to obtain configuration-related data associated with the local GPU accelerator circuitry for use in management of the accelerator resources (receiving acceleration resource attribute information … configuration attribute; page 17, last paragraph and page 21, last 2 paragraphs).
Huang does not teach quality of service data and service level agreement data are used for allocating resources, and the cloud computing system is configurable to dynamically reallocate, based upon past resource utilization data, resource utilization prediction data, and machine-learning, the compute resources and/or the accelerator resources for use in the execution of the at least one workload.
However, Gonzalez teaches the cloud computing system is configurable to dynamically reallocate, based upon past resource utilization data, resource utilization prediction data, and machine-learning, the compute resources and/or the accelerator resources for use in the execution of the at least one workload (reallocate resources in the cloud environment in order to minimize operating cost. The allocation module reallocates the computing resources based on a least expensive set of resources capable of meeting the current resource requirements of the user device; paragraphs [0095], [0110]-[0111] and The recommendation module receives an instruction … based on predicted demand, current usage, historical models, current trends, and operating cost information … reallocate computing resources; paragraph [0096]-[0099]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teaching of Gonzalez to the system of Huang because Gonzalez teaches a method that efficiently using resources in data centers/clouds based on current and historical usage data to improve the performance of the system.
Jackson teaches allocating/reserving resources to workload/job, based at least in part on received workload/job data, quality of service data, and service level agreement (Job information is provided to the workload manager scheduler from a resource manager. Job attributes include ownership of the job, amount and type of resources required by the job, required criteria (I need this job finished in one hour), preferred criteria (I would like this job to complete in ½ hour), and a wallclock limit, indicating how long the resources are required. A job consists of one or more requirements each which requests a number of resources of a given type; col. 5, lines 35-64). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teaching of Jackson to the system of Huang as modified by Gonzales because Jackson teaches a method that allows resources spanning different compute resource types to be reserved based on job resource requirements and criteria, which would ensure the resources allocated to meet the criteria of requestor.
As to claim 2, Huang as modified by Gonzalez teaches storage resources for use in association with the compute resources and/or the accelerator resources (storage hardware; pages 10, last paragraph and page 11, 5th paragraph); and
the compute resource, the accelerator resources, and storage resources are comprised, at least in part, in one or more pools of resources for being dynamically allocated based upon the resource utilization data (page 11, 4th – 5th paragraph and pages 12, 1st-2nd paragraphs) and (Gonzalez: see abstract).
As to claim 3, Huang teaches wherein the cloud computing system is configurable to implement hardware attestation associated, at least in part, with the compute resources and/or the accelerator resources (page 18, 1st paragraph and page 20, Steps S507-S508).
As to claim 4, Huang teaches wherein the management resources configurable to maintain directory data comprising:
identification data to identify the remote GPU accelerator circuitry (acceleration source computing node … encoding/decoding or image processing; page 14, 2nd – 3rd paragraphs and page 12, last paragraph); and
configuration data comprising accelerator architecture-related data associated with the remote GPU accelerator circuitry (page 17, last paragraph - page 18, 1st paragraph and page 20, Steps S507-S508).
As to claim 5, it is the same as the system claim 1 above except this isa machine-readable storage medium claim, and therefore is rejected under the same ground of rejection.
As to claims 6-8, see rejections of claims 2-4 above, respectively.
As to claim 9, it is the same as the storage medium claim 5 above, except this is a method claim and therefore is rejected under the same ground of rejection.
As to claims 10-12, see rejections of claims 6-8 above, respectively.
Response to Arguments
Applicant’s arguments with respect to claims 1-12 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/DIEM K CAO/Primary Examiner, Art Unit 2196
DC
October 17, 2025
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