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 .
Response to Arguments
Applicant’s arguments with respect to claims 1, 3-6, and 8-22 have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
Claim Objections
Claims 16 and 21 are objected to because of the following informalities:
Claims 16 and 21 are objected to as being duplicative (unduly multiplied) because they are identical in scope and both depend from claim 15. Applicant is required to cancel one of the duplicate claims or amend to present distinct subject matter.
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 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 of this title, 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, 4-6, 8, 9, 11-16, and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over Durazzo (US 2024/00012691, hereinafter Durazzo) in view of Abdulaal et al. (US 2022/0138019, hereinafter Abdulaal).
Regarding claim 1, Durazzo discloses
A method for optimizing job scheduling performance in a hybrid cloud environment, comprising (fig. 1-5):
obtaining, by a processor, a user configuration data and a deadline for job execution for a job to be executed (paragraph [0045): When an end-to-end application is started, user intents such as execution deadline, accuracy, confidence, cost, or the like can be defined or determined and submitted to the orchestration engine 406);
querying, by the processor, available queues to determine a wait time corresponding to each of the available queues for the job (paragraph [0032]: The telemetry plane 220 is configured to consolidate the queue status of the job queues 204, 208, and 212; paragraph [0050]: the orchestration engine 406 considers vendor criteria such a QPU machine type, network latency, error rate, queue wait-time, cost, and the like against user intents such as budget, execution deadline, accuracy, QPU, end-to-end wait time, or the like) based on the user configuration data (paragraph [0045): When an end-to-end application is started, user intents such as execution deadline, accuracy, confidence, cost, or the like can be defined or determined and submitted to the orchestration engine 406);
comparing, by the processor, the wait times to the deadline for job execution (paragraph [0050]: the orchestration engine 406 considers vendor criteria such a QPU machine type, network latency, error rate, queue wait-time, cost, and the like against user intents such as budget, execution deadline, accuracy, QPU, end-to-end wait time, or the like. Quantum jobs are placed based on this comparison or based on how the user intents match the vendor criteria).
Durazzo does not disclose wherein the user configuration data includes one or more compute requirements …. one or more compute requirements … suggesting, by the processor, a modification to the one or more compute requirements using a machine learning mode to reduce wait times and meet the deadline in response to determining the deadline cannot be met. Abdulaal discloses wherein the user configuration data includes one or more compute requirements …. one or more compute requirements … suggesting, by the processor, a modification to the one or more compute requirements using a machine learning mode to reduce wait times and meet the deadline in response to determining the deadline cannot be met (fig. 1-5, paragraph [0068]: obtaining workload features and user requirements (including a maximum allowable runtime); paragraph [0036]-[0037]: determining compliant hardware configurations using a first ML model, generating performance predictions for those configurations using a second ML model; paragraphs [0045]-[0048], [0087]: generating a recommendation specifying a hardware configuration that meets the user’s time requirement, thereby modifying compute requirements to meet timing constraints).
It would have been obvious to one of ordinary skill in the art to modify Durazzo’s orchestration engine to incorporate Abdulaal’s ML-based recommendation functionality so that, when Durazzo’s system determines a deadline cannot be met, it would suggest modifications to the job’s compute requirements (e.g., hardware type, resource allocation) to reduce wait times and meet the deadline. The motivation would have been to improve the accuracy of workload performance times, and therefore, continually improve the accuracy of the recommendations (Abdulaal paragraph [0021]).
Regarding claim 8 referring to claim 1, Durazzo discloses A computing system for optimizing job scheduling performance in a hybrid cloud environment, comprising: a processor; a memory device coupled to the processor; and a computer readable storage device coupled to the processor, wherein the storage device contains program code executable by the processor via the memory device to implement a method, the method comprising: … (Fig. 5, paragraph [0083]).
Regarding claim 15 referring to claim 1, Durazzo discloses A computer program product for optimizing job scheduling performance in a hybrid cloud environment, the computer program product comprising a computer readable storage device, and program instructions stored on the computer readable storage device, to: … (Fig. 5, paragraph [0083]).
Regarding claims 4, and 11, Durazzo does not disclose one or more compute requirements includes at least one requirement selected from the group consisting of a central processing unit requirement, a graphic processing unit requirement, memory, storage, and network bandwidth. Abdulaal discloses one or more compute requirements includes at least one requirement (paragraph [0068]: obtaining workload features and user requirements (including a maximum allowable runtime); paragraph [0036]-[0037]: determining compliant hardware configurations using a first ML model, generating performance predictions for those configurations using a second ML model; paragraphs [0045]-[0048], [0087]: generating a recommendation specifying a hardware configuration that meets the user’s time requirement, thereby modifying compute requirements to meet timing constraints) selected from the group consisting of a central processing unit requirement, a graphic processing unit requirement, memory, storage, and network bandwidth (paragraph [0030]: The computing device may include one or more processors, memory (e.g., random access memory), and persistent storage (e.g., disk drives, solid state drives, etc.); paragraph [0060]: Hardware configuration A (130A) may include components A (132A) and hardware configuration N (130N) may include components N (132N). The components (e.g., 132A, 132N) may include central processing units (CPUs), graphical processing units (GPUs), memory, and other and/or additional types of computer hardware components without departing from the invention; paragraph [0065]: The component(s) characteristics (218) may specify performance information of the associated component. The performance information may include, for example, clock speed, memory type, memory size, utilization, number of CPU cores, cache types, utilization, memory clock speed, maximum power limit, and other and/or additional performance information associated with the components without departing from the invention; paragraph [0089]: a communication interface). It would have been obvious to one of ordinary skill in the art to modify Durazzo’s user configuration data taught by Abdulaal such as CPU, GPU, memory, storage, and network bandwidth as selectable/consumable requirements for job placement. The motivation would have been to improve the accuracy of workload performance times, and therefore, continually improve the accuracy of the recommendations (Abdulaal paragraph [0021]).
Regarding claims 5, 12, and 19, Durazzo discloses
wherein the machine learning model is trained using data from previous job executions (paragraph [0034]: The estimator engine 224, which may be a machine learning model, may be configured to predict runtime characteristics of the quantum jobs reflected or referenced in the telemetry plane 220; paragraph [0043]: The quantum jobs of App1 are iterative, which indicates that the execution of a future quantum job depends on the execution of a previous quantum job (and/or results of an intervening computer portion); paragraph [0052]: each of these scores may be associated with a worst case and a best case (which may be determined using historical information).
Regarding claims 6, 13, and 20, Durazzo discloses
wherein the available queues are queried via a queue broker (paragraph [0050]: the orchestration engine 406 considers vendor criteria such a QPU machine type, network latency, error rate, queue wait-time, cost, and the like against user intents such as budget, execution deadline, accuracy, QPU, end-to-end wait time, or the like. Quantum jobs are placed based on this comparison or based on how the user intents match the vendor criteria).
Regarding claims 9, Durazzo discloses
wherein the method further comprises placing the job in a queue that can meet the deadline for job execution if found (Note: the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (MPEP 2111.04 II Contingent Limitations). Accordingly, patentable weight is not given because the placing step is not performed if the disclosed condition is not met).
Regarding claims 14, Durazzo discloses
wherein the method further comprises sending the job to another cloud environment for execution when a queue that can meet the deadline for job execution is not found (Note: the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (MPEP 2111.04 II Contingent Limitations). Accordingly, patentable weight is not given because the sending step is not performed if the disclosed condition is not met).
Regarding claims 16 and 21, Durazzo discloses
further comprising program instructions stored on the computer readable storage medium to place the job in a queue that can meet the deadline for job execution if found (Note: the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (MPEP 2111.04 II Contingent Limitations). Accordingly, patentable weight is not given because the placing step is not performed if the disclosed condition is not met).
Regarding claim 22, Durazzo discloses
further comprising program instructions stored on the computer readable storage medium to send the job to another cloud environment for execution when a queue that can meet the deadline for job execution is not found (Note: the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (MPEP 2111.04 II Contingent Limitations). Accordingly, patentable weight is not given because the sending step is not performed if the disclosed condition is not met).
Claims 3, 10, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Durazzo in view of Abdulaal as applied to claims, 1, 8, and 15, and further in view of Amarnath et al. (US 2023/0012710, hereinafter Amarnath).
Regarding claims 3, 10, and 17, Durazzo in view of Abdulaal does not disclose further comprising obtaining, by the processor, the user configuration data from a user interface. Amarnath discloses further comprising obtaining, by the processor, the user configuration data from a user interface (paragraph [0034]: user-specific application configuration settings; paragraph [0051]: one or more devices that enable a user to interact with computer system/server 12 …. Such communication can occur via Input/Output (I/O) interfaces 22; paragraph [0059]: User portal 83 provides access to the cloud computing environment for consumers and system administrators). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Durazzo in view of Abdulaal by incorporating Amarnath’s enable a user to interact with computer system/server via I/O interface and user portal to provide configuration. The motivation would have been to enable a user to interact with computer system/server (Amarnath paragraph [0051]).
Regarding claim 18, Durazzo does not disclose one or more compute requirements includes at least one requirement selected from the group consisting of a central processing unit requirement, a graphic processing unit requirement, memory, storage, and network bandwidth. Abdulaal discloses one or more compute requirements includes at least one requirement (paragraph [0068]: obtaining workload features and user requirements (including a maximum allowable runtime); paragraph [0036]-[0037]: determining compliant hardware configurations using a first ML model, generating performance predictions for those configurations using a second ML model; paragraphs [0045]-[0048], [0087]: generating a recommendation specifying a hardware configuration that meets the user’s time requirement, thereby modifying compute requirements to meet timing constraints) selected from the group consisting of a central processing unit requirement, a graphic processing unit requirement, memory, storage, and network bandwidth (paragraph [0030]: The computing device may include one or more processors, memory (e.g., random access memory), and persistent storage (e.g., disk drives, solid state drives, etc.); paragraph [0060]: Hardware configuration A (130A) may include components A (132A) and hardware configuration N (130N) may include components N (132N). The components (e.g., 132A, 132N) may include central processing units (CPUs), graphical processing units (GPUs), memory, and other and/or additional types of computer hardware components without departing from the invention; paragraph [0065]: The component(s) characteristics (218) may specify performance information of the associated component. The performance information may include, for example, clock speed, memory type, memory size, utilization, number of CPU cores, cache types, utilization, memory clock speed, maximum power limit, and other and/or additional performance information associated with the components without departing from the invention; paragraph [0089]: a communication interface). It would have been obvious to one of ordinary skill in the art to modify Durazzo’s user configuration data taught by Abdulaal such as CPU, GPU, memory, storage, and network bandwidth as selectable/consumable requirements for job placement. The motivation would have been to improve the accuracy of workload performance times, and therefore, continually improve the accuracy of the recommendations (Abdulaal paragraph [0021]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in [0037] CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to [0037] CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SISLEY N. KIM whose telephone number is (571)270-7832. The examiner can normally be reached M-F 11:30AM -7:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, April Y. Blair can be reached on (571)270-1014. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SISLEY N KIM/Primary Examiner, Art Unit 2196
4/24/2026
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