DETAILED ACTION
Claims 1-20 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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 14 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims recited "a computer program product comprising computer program code tangibly embodied in a computer readable medium". The specification (paragraph [0050] and [0190]) is silent on "a computer readable medium" as claimed also excluding carrier wave and/or signal. Therefore, without an explicit definition of "a computer readable medium", an ordinary skill in the art would reasonably interpret "a computer readable medium" includes signal and/or carrier wave. Thus, the recited “a computer readable medium” is not a process, a machine, a manufacture or a composition matter, as defined in 35 U.S.C. 101.
Accordingly, claims 14 and 19-20 fail to recite statutory subject matter under 35 U.S.C. 101.
Examiner suggests amending the above claims to add “non-transitory” to obviate the rejection.
Claim Objections
Claim 18 is objected to because of the following informalities: claim 18 recites “The computer program product according to claim 17”, however, claim 17 is “The apparatus according to claim 16”. Thus, claim 18 should be the apparatus claim, and not the computer program product claim.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 13-15 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nijim et al. (US 11509599 B1).
As to claim 1, Nijim teaches a method for processing a computer job to be executed on a hybrid computer configured with processing resources comprising one or more central processing unit, CPU, processing resources (classical computing device(s); Fig. 2) and at least one quantum processing unit, QPU, processing resources (quantum computing device(s); Fig. 2), the method comprising, wherein the computer job comprises a quantum computation part to be executed on the at least one QPU processing resources (method, and computer readable storage device enabling quantum processing of an application at an edge node of a network as part of the distribution; abstract and Fig. 2):
Processing the computer job by a first scheduler (A machine learning component) of the hybrid computer, wherein the first scheduler is configured for scheduling the processing of computation tasks by processing resources of the hybrid computer among the one or more CPU processing resources and the at least one QPU processing resources, wherein the processing of the computer job by the first scheduler comprises (an edge node of a network closest to an end device running an application may receive an indication to process the application. A machine learning component residing at the edge node may determine whether to process the application at one of the edge nodes in the network or a central cloud, and whether to process the application using classical versus quantum computing. Quantum processing may be enabled by quantum computing device residing within at least one edge node of the network, also referred to herein as the quantum edge node; col. 2, lines 31-40 and Fig. 3, step 306):
Upon receiving a request for quantum processing resources for execution of the computer job, offloading the scheduling of a portion of the computer job comprising the quantum computing part on processing resources of the hybrid computer to a second scheduler (The quantum edge node 200 may also include a scheduler 206 associated with the quantum computing device 204; col. 5, lines 61-62) of the hybrid computer, wherein the second scheduler is configured for scheduling the processing of computation tasks by the at least one QPU processing resources of the hybrid computer (“If at DECISION 306, the machine learning component 118 determines to apply quantum processing to process the application at a quantum edge node 200, the machine learning component 118 may, at OPERATION 312, identify qubits, including a number thereof, to be run in Hibert space at an angle to process the application”; col. 7, lines 10-17 and “At OPERATION 314, a plurality of containers may then be defined based on the identified qubits. The containers may be executable units of software in which application code of the application is packaged, along with its libraries and dependencies, to allow execution of the application code upon deployment by the quantum computing device 204 at the quantum edge node 200.”; col. 7, line 36-42 and “At OPERATION 316, a timing of the deployment of the containers by the quantum computing device 204 on the quantum edge node 200 may be scheduled by the scheduler 206 associated with the quantum computing device 204. For example, the containers may be positioned within a queue at the quantum computing device 204 based on a priority associated with the application.”; col. 7, line 65 – col. 8, line 4); and
Receiving, by the first scheduler, status data regarding execution of the portion of
the computer job comprising the quantum computing part (At OPERATION 320, once the containers reach a head or front of the queue, the containers may be deployed by the quantum computing device 204 at the quantum edge node 200 to process the application. For example, the quantum computation may be run on the containers. As the quantum computation is being run, if the quantum processing error rate is high due to no convergence occurring after a predefined time period has elapsed, the machine learning component 118 may determine, at DECISION 322, to abandon quantum processing and instead switch to and proceed with classical processing of the application by one of the classical computing devices 202 at one of the edge nodes 106. If at DECISION 322, the machine learning component 118 determines to switch to classical processing, the quantum computing device 204 may stop the running of the quantum computation, and the process flow 300 may proceed to OPERATION 308. If at DECISION 322, the machine learning component 118 determines to not switch to classical processing, the quantum computing device 204 may continue running the quantum computation. Once the quantum computation is finished (e.g., has converged), processing results may be returned to the end device 108.; col. 8, lines 26-49).
As to claim 2, Nijim teaches the method according to claim 1, further comprising: scheduling, by the second scheduler, processing of the portion of the computer job comprising the quantum computing part by the at least one QPU processing resources of the hybrid computer (At OPERATION 320, once the containers reach a head or front of the queue, the containers may be deployed by the quantum computing device 204 at the quantum edge node 200 to process the application; col. 8, lines 26-30 and Fig. 3).
As to claim 13, see rejection of claim 1 above.
Nijim teaches an apparatus, the apparatus comprising a processor and a memory operatively coupled to the processor, wherein the apparatus is configured to perform method of claim 1 ( A system enabling quantum computing at an edge node of a network, the system comprising: a plurality of edge nodes, wherein each of the plurality of edge nodes comprises at least one processor, and a memory storage device storing instructions, including instructions for executing a machine learning component residing at each of the plurality of edge nodes, that, when executed by the at least one processor, cause the system to; claim 1).
As to claim 15, see rejection of claim 2 above.
As to claim 14, see rejection of claim 1 above
Nijim teaches a computer program product comprising computer program code tangibly embodied in a computer readable medium, said computer program code comprising instructions to, when provided to a computer system and executed, cause said computer to perform a method of claim 1 (a memory storage device storing instructions, including instructions for executing a machine learning component residing at each of the plurality of edge nodes, that, when executed by the at least one processor, cause the system to; claim 1).
As to claim 19, see rejection of claim 3 above.
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 3-6, 10, 16-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Nijim et al. (US 11509599 B1) in view of Durazzo et al. (US 2023/0419160 A1).
As to claim 3, Nijim does not teach allocating, by the first scheduler, a virtual quantum computing resource for execution of the quantum computation part by a QPU processing resource of the at least one QPU processing resources of the hybrid computer.
However, Durazzo teaches allocating, by the first scheduler, a virtual quantum computing resource for execution of the quantum computation part by a QPU processing resource of the at least one QPU processing resources of the hybrid computer ( running hybrid classical/quantum programs that utilize a split of the work into multiple tiers. Some particular embodiments may employ a total of three execution tiers, or simply ‘tiers,’ namely, two tiers of classical computing resources, and one tier of quantum computing resources. Example embodiments may be employed in connection with both QPUs (quantum processing units) and vQPUs (virtual quantum processing units). In general, a vQPU refers to classical software which emulates a quantum processing unit (QPU); paragraph [0012]).
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 Durazzo to the system of Nijim because Durazzo teaches methods for splitting hybrid quantum/classical workloads among multiple execution tiers, thus provide for efficient use of on-premise computing resources (paragraph [0011]).
As to claim 4, Nijim as modified by Durazzo teaches the method according to claim 3, wherein the virtual quantum computing resource is mapped with the QPU processing resource of the at least one QPU processing resources of the hybrid computer (see Durazzo: a vQPU refers to classical software which emulates a quantum processing unit (QPU); paragraph [0012]).
As to claim 5, Nijim as modified by Durazzo teaches the method according to claim 3, further comprising: executing, by the QPU processing resource mapped with the virtual quantum computing resource, the quantum computing part (see Durazzo: one or more portions of the workload 108 may be orchestrated to a QPU 114 of a hybrid computing system. The QPU 114 may include an execution queue 116 that identifies all the workload portions that have been assigned to the QPU 114 for performance. The workload portions in the execution queue 116 may then be executed using quantum resources of the QPU 114. Particularly, the QPU 114 may provide for quantum circuit execution 118 of the workload portions in the execution queue 116. FIG. 2 discloses example Java code 200 for allocating a portion of a workload to a QPU for execution; paragraph [0022]).
As to claim 6, Nijim as modified by Durazzo teaches the method according to claim 4, wherein executing the quantum computing part by the virtual quantum computing resource comprises: requesting, from the second scheduler, scheduling of processing of the quantum computing part by the at least one QPU processing resources of the hybrid computer (see Durazzo: The QPU 114 may include an execution queue 116 that identifies all the workload portions that have been assigned to the QPU 114 for performance. The workload portions in the execution queue 116 may then be executed using quantum resources of the QPU 114. Particularly, the QPU 114 may provide for quantum circuit execution 118 of the workload portions in the execution queue 116. FIG. 2 discloses example Java code 200 for allocating a portion of a workload to a QPU for execution.; paragraph [0022]).
As to claim 10, Nijim as modified by Durazzo teaches the method according to claim 1, wherein the computer job further comprises a non-quantum computation part to be executed on the one or more CPU processing resources, and wherein one or more of the at least one QPU processing resources have non-quantum processing capabilities, the method further comprising: allocate, by the first scheduler, a virtual quantum computing resource for execution of the quantum computation part by the one or more of the at least one QPU processing resources of the hybrid computer, and a virtual non-quantum processing resources for execution of one or more parts of the non-quantum computation part by the one or more of the at least one QPU processing resources of the hybrid computer (see Nijim: A machine learning component residing at the edge node may determine whether to process the application at one of the edge nodes in the network or a central cloud, and whether to process the application using classical versus quantum computing. Quantum processing may be enabled by quantum computing device residing within at least one edge node of the network, also referred to herein as the quantum edge node; col. 2, lines 31-40) and (see Durazzo: The QPU 114 may include an execution queue 116 that identifies all the workload portions that have been assigned to the QPU 114 for performance. The workload portions in the execution queue 116 may then be executed using quantum resources of the QPU 114. Particularly, the QPU 114 may provide for quantum circuit execution 118 of the workload portions in the execution queue 116. FIG. 2 discloses example Java code 200 for allocating a portion of a workload to a QPU for execution.; paragraph [0022]).
As to claim 16, see rejection of claim 3 above.
As to claim 17, see rejection of claim 10 above.
As to claim 18, see rejection of claim 2 above.
As to claim 20, see rejection of claim 10 above.
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Nijim et al. (US 11509599 B1) in view of Durazzo et al. (US 2024/0012691 A1 – hereinafter Durazzo-2).
As to claim 7, Nijim does not teach, by a quantum resource watch computer job running on the hybrid computer: pinging for status one of the at least one QPU processing resources, and upon determining a failing status of the one of the at least one QPU processing resources further to the pinging of the one of the at least one QPU processing resources, removing the one of the at least one QPU processing resources from a pool of available QPU processing resources for processing the computer job.
However, Nijim teaches the first scheduler determines to abandon quantum processing when the quantum processing error rate is high due to the lack of convergence after a predetermined time period (col. 8, lines 32-37).
Durazzo-2 teaches pinging for status one of the at least one QPU processing resources, and upon determining a failing status of the one of the at least one QPU processing resources further to the pinging of the one of the at least one QPU processing resources, removing the one of the at least one QPU processing resources from a pool of available QPU processing resources for processing the computer job (the quantum job metadata 316 may also include information describing the characteristics of the QPUs 308, 310, and 312. This may include type, qubits, speed, accuracy, or the like.; paragraph [0038] and The orchestration engine 302 can determine runtime characteristics for each of the quantum jobs 314 using the quantum job metadata 316. Using the information available in the telemetry plane 304, the orchestration engine 302 can place the quantum job 306. This may be achieved by matching or comparing characteristics of the quantum job 306 with the characteristics of the QPUs 308, 310, and 312. In this example, the quantum job 306 is placed at the QPU 310; paragraph [0039]. Thus, the QPUs that not met the requirement of the jobs are considered “failed” QPU and are not in the pool of QPUs considered for the jobs).
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 Durazzo-2 to the system of Nijim because Durazzo teaches a global optimization of quantum jobs in a multi-cloud and the quantum jobs are then assigned to the quantum systems of the vendors based on the evaluation of the job requirements and QPU capabilities.
As to claim 8, Nijim as modified by Durazzo-2 teaches the method according to claim 7, further comprising: setting a virtual quantum computing resource corresponding to the one of the at least one QPU processing resources as non-available for processing the computer job (see Durazzo-2: The orchestration engine 302 can determine runtime characteristics for each of the quantum jobs 314 using the quantum job metadata 316. Using the information available in the telemetry plane 304, the orchestration engine 302 can place the quantum job 306. This may be achieved by matching or comparing characteristics of the quantum job 306 with the characteristics of the QPUs 308, 310, and 312. In this example, the quantum job 306 is placed at the QPU 310; paragraph [0039]. Thus, the QPUs that not met the requirement of the jobs are considered “failed” QPU and are not in the pool of QPUs considered for the jobs and “The providers as a whole, or individually, may provide Quantum Processing Units (QPU), virtual QPU (vQPU)”; paragraph [0015]).
As to claim 9, Nijim teaches the method according to claim 7, further comprising: rescheduling, by the second scheduler, running computer jobs scheduled for execution by a classical resource (col. 8, lines 32-37).
Nijim does not teach to reschedule to continue execute the job by the one of the at least one QPU processing resources.
However, Durazzo-2 teaches rescheduling, by the second scheduler, running computer jobs scheduled for execution by the one of the at least one QPU processing resources (Because the orchestration engine 222 may reassign quantum jobs from one quantum processing unit to another quantum processing unit (or from one vendor to another); paragraph [0036] and [0044]).
Allowable Subject Matter
Claims 11-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li et al. (US 20230297401 A1) teaches a hybrid quantum-classical cloud platform and a task execution method. The cloud platform comprises: an SaaS layer for providing a user interface so as to acquire, by means of the user interface, a hybrid quantum-classical programming language corresponding to a task to be executed; a PaaS layer for performing algorithm compilation and task separation on the hybrid quantum-classical programming language to obtain a quantum computing task and a classical computing task corresponding to the task to be executed, and respectively allocating resources to the quantum computing task and the classical computing task; and an IaaS layer for executing the quantum computing task using a quantum virtual machine and executing the classical computing task using a classical server according to the resource allocation condition of the PaaS layer. Therefore, the communication overhead and the data delay can be reduced, the task processing efficiency is improved, and the quantum computing advantage is exerted.
Rivas (US 12,530,222 B1) teaches a cloud-based computer system includes: a quantum computing system comprising a quantum processing unit; a container management and execution system configured to receive a container and execute a program within the container; and a communication channel between the container management and execution system and the quantum computing system for providing program instructions to the quantum computing system. The container management and execution system and the quantum computer system may be co-located in a data center or located in different data centers. The latency of the communication channel may be selected to optimize cost for a required computer performance.
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/DIEM K CAO/Primary Examiner, Art Unit 2196
DC
June 25, 2026
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