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 filed 4/27/2026 have been fully considered but they are not persuasive.
Applicant argues, “These operations are rooted in the simulation of quantum algorithms and the determination of classical infrastructure required to execute such simulations, which inherently involves computer-based processing of quantum-specific parameters (e.g., shots, qubits, circuit complexity).” Remarks 9. A computer is not inherent to any algorithm in Applicant’s claims. Applicant’s claims, and figure 2, are something that could be done on the back of an envelope with by a person with knowledge about quantum simulations. A person could, and scientists often do, guess the number of processors and memory needed for a simulation of a quantum algorithm and then choose from available compute resources to maximize computational efficiency based on their experience.
Applicant argues, “These limitations define a concrete control mechanism for configuring classical computing infrastructure in response to quantum simulation demands and real-world resource constraints. This constitutes a technical application that improves how computing systems allocate and configure resources for quantum simulation workloads, rather than merely performing abstract data analysis.” Remarks 9. This is using a computer to run a method, and the method is merely linked to the field of quantum computing. Nothing about these claims necessarily improve the functioning of a computer. Applicant’s claims to computational efficiency are undefined in the specification and encompass cases where efficiency is judged on the price of the compute, which would not necessarily improve computation at all.
Applicant argues, “Importantly, the claims do not merely recite ‘predicting resources,’ but specify how those predictions are used to control system configuration under constrained conditions. This level of implementation detail constitutes significantly more than an abstract idea.” Remarks 10. The constrained conditions are not claimed. The quantum simulation is never run on the selected computers. A system configuration control is not claimed. Therefore, these unclaimed ideas cannot possibly amount to a claim to significantly more than the abstract idea.
Applicant argues, “In Example 47, the improvement relates to network security and system operation. Here, the improvement relates to how computing systems allocate and configure resources for quantum simulations, particularly under constrained conditions.” Remarks 11. Example 47’s claim filters packets and blocks future traffic, based on their determinations. In the instant claims, a computer is selected to run the quantum algorithm, and then nothing happens. The current claims are more similar to just detecting anomalous data packets and doing nothing with that detection result, or outputting the selection, like claim 2 in Example 47. Claim 2 in Example 47 is not patent eligible.
The prior art arguments are moot in light of new art necessitated by the amendments.
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 1-5, 10-15 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental concept abstract idea without significantly more. The claims recite deriving quantum attributes, generating classical computing resource expectations, determining available compute resources, translating available resources into a resource that most closely meets resource expectations. This judicial exception is not integrated into a practical application because receiving parameter values for quantum simulation and the user interface are insignificant extra solution activity. MPEP 2106.05(g). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the data receiving step is a conventional step for gathering data using a generic computer.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-5, 10, 11-15 and 20 are rejected as indefinite under 112(b).
Claims 1 and 11 recites the limitation "the number" in the clause “the number of the classical physical computing entities is selected so that the number most closely corresponds to the output information despite the deficiency existing”. There is insufficient antecedent basis for this limitation in the claim. There are too many numbers in the claims to refer to one number as the number.
Claims 1 and 11 claim “to avoid over- and under-procurement of the classical computing resources…” This idea is undefined in the art, lacks a definition in the specification, and is contradicted by the last clause of the claim that selects compute resources even where a deficiency exists.
Claim Rejections - 35 USC § 103
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 1-2, 4-5, 10-12, 14-15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US20190340532A1 to Ducore et al, Error Mitigation for Short-Depth Quantum Circuits by Temme et al, US9602426B2 to Das et al, and Oracle® VM Manager User’s Guide Release 2.1 (Oracle).
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over US20190340532A1 to Ducore et al, Error Mitigation for Short-Depth Quantum Circuits by Temme et al, US9602426B2 to Das et al, Oracle® VM Manager User’s Guide Release 2.1 (Oracle) and https://web.archive.org/web/20220527001110/https://www.tekdis.co.uk/insights/post/industrial-computer-product-selection-guide-and-how-to-choose-an-industrial-pc as archived 5/27/2022 (Tekdis).
Ducore teaches claims 1 and 11. (Currently Amended) A method for accurately determining an amount of classical computing resources needed to run a quantum computing simulation (i) to ensure computationally-efficient performance of the quantum computing simulation when the classical computing resources are used to execute the quantum computing simulation and (ii) to avoid over- and under-procurement of the classical computing resources, said method comprising: (Ducore para 60 “Then the quantum computer simulator system 110 (e.g., via the manager 130) can pick or select whichever backend hardware will be faster (or can provide the time it will take to run the fastest backend hardware)…” Faster equals computationally efficient performance of the quantum simulation. This avoids over and under procurement because the system in Ducore selects what is need to do the job. Over- and under-procurement are undefined in the specification. Further, the last clause of this claim says that when there is a deficiency, the method selects a system anyway, so over- and under-procurement is not avoided.)
receiving parameter values relating to execution of a simulation of a quantum algorithm (Ducore para 35 “The quantum computer simulator system 110 can receive a job (i.e., an input circuit, quantum program, or quantum algorithm) through the front end 120 and place the job in the queue 125.” The job is the parameter values relating to a quantum algorithm. Ducore para 60 “when the quantum computer simulator system 110 receives a job (e.g., in the queue 125), it is possible to add the cost (e.g., time) for each of the gates, steps, or operations in the job and that will give the total cost of running the job in the single-core CPU, the multi-core CPU, or the GPU, for example.” The time it will take to run on different parallels/accelerators is the value corresponding to parallelization and acceleration. Multi core CPU is more parallelization.)
deriving quantum attributes from the parameter values, (Ducore para 35 “The quantum computer simulator system 110 can receive a job (i.e., an input circuit, quantum program, or quantum algorithm) through the front end 120 and place the job in the queue 125.” The job is the parameter values relating to a quantum algorithm. Ducore para 60 “when the quantum computer simulator system 110 receives a job (e.g., in the queue 125), it is possible to add the cost (e.g., time) for each of the gates, steps, or operations in the job and that will give the total cost of running the job in the single-core CPU, the multi-core CPU, or the GPU, for example.” The values are derived from the job. The job is the parameter values. The value corresponding to how parallelization and acceleration is the total costs on different accelerators/parallel architectures. Ducore para 41 “In addition to which gate is to be simulated, another aspect is the issue of how many system qubits there are overall in the circuit, quantum program, or quantum algorithm being considered for the simulation…”)
providing the quantum attributes as input to a resource prediction engine; (Ducore para 60 “when the quantum computer simulator system 110 receives a job (e.g., in the queue 125), it is possible to add the cost (e.g., time) for each of the gates, steps, or operations in the job…” Providing the job to the algorithm that adds the costs, is providing the jobs to the prediction engine.)
causing the resource prediction engine to generate, based on the quantum attributes, output information concerning aspects of the classical computing resources expected to be needed to run the simulation, wherein the output information includes a raw amount of memory and a number of central processing units (CPUs) required for the simulation;(Ducore para 60 “when the quantum computer simulator system 110 receives a job (e.g., in the queue 125), it is possible to add the cost (e.g., time) for each of the gates, steps, or operations in the job…” The added cost is the resources expected to be needed. Ducore para 35 says the “characteristic curves[ are] generated by the characterizer 150…” Ducore para 52 then shows that the predicted number of resources includes number of cores and memory size, “evaluation programs may differ in characterization functionality or features, while a hardware configuration may refer to the system resource settings (e.g., processing capabilities, processing cores (e.g., GPU, single core CPU, multi-core CPU, Tensor processor, etc.), memory size, memory configuration) allotted for a specific, separate piece of hardware.”)
providing the output information as input to a sizing function; (Ducore para 60 “Then the quantum computer simulator system 110 (e.g., via the manager 130) can pick or select whichever backend hardware will be faster (or can provide the time it will take to run the fastest backend hardware), or if there are two or more options that have similar timing performances, whichever one will result in the lower cost of simulation.” The manager is the sizing function.)
providing, to the sizing function, a number of computing systems that are actually available to execute the simulation; (Ducore para 60 “Then the quantum computer simulator system 110 (e.g., via the manager 130) can pick or select whichever backend hardware will be faster (or can provide the time it will take to run the fastest backend hardware), or if there are two or more options that have similar timing performances, whichever one will result in the lower cost of simulation.” Manager is the sizing function.)
causing the sizing function to translate the output information into a number of classical physical computing entities that will be used to execute the simulation of the quantum algorithm, wherein the number of the classical physical computing entities is selected so that (Ducore para 60 “Then the quantum computer simulator system 110 (e.g., via the manager 130) can pick or select whichever backend hardware will be faster (or can provide the time it will take to run the fastest backend hardware), or if there are two or more options that have similar timing performances, whichever one will result in the lower cost of simulation.” Manager is the sizing function. Speed is computational efficiency.)
Ducore doesn’t teach quantum algorithm that has inherent noise, wherein the parameter values indicate… (ii) a number of times the simulation is to be executed so that a resulting output signal is obtained despite presence of the inherent noise for the quantum algorithm;… wherein the quantum attributes include a number of execution shots corresponding to the number of times the simulation is to be executed to account for the inherent noise…
However, Temme teaches quantum algorithm that has inherent noise, (Temme p.4 “A noisy version of a k-qubit unitary gate U is defined…”) wherein the parameter values indicate… (ii) a number of times the simulation is to be executed so that a resulting output signal is obtained despite presence of the inherent noise for the quantum algorithm;… wherein the quantum attributes include a number of execution shots corresponding to the number of times the simulation is to be executed to account for the inherent noise… (Temme p. 4-5 “The examples considered above suggest that well-characterized noisy circuits can simulate ideal ones with overhead γ ≈ (1+ cϵ)L, where ϵ is the typical error rate and c is a small constant…. Using Eq. (9), one can estimate the number of noisy circuit runs of length L as M∼exp(2cϵL).” This teaches that a parameter of quantum circuit simulation is the number of noisy runs needed to approximate a true noisy algorithm on a quantum circuit.)
Temme, Ducore and the claims all simulate quantum circuits. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to determine the number of shots to simulate noisy circuits because “Decoherence and gate errors lead to wrong estimates of the expectation values of observables used to evaluate the noisy circuit… method cancels errors by resampling randomized circuits according to a quasi probability distribution.” Temme abs. Said another way, a good simulator needs to account for noise and Temme teaches determining the number of runs needed to account for noise.
Ducore doesn’t teach determining, by the sizing function, that the number of computing systems that are actually available differs from the raw amount of memory and the number of CPUs required for the simulation, such that a deficiency exists; and
wherein the number of the classical physical computing entities is selected so that the number most closely corresponds to the output information despite the deficiency existing…
However, Das teaches determining, by the sizing function, that the number of computing systems that are actually available differs from the raw amount of memory and the number of CPUs required for the simulation, such that a deficiency exists; and (Das 11:65 “a deficit determiner component 306 that can compute a fractional deficit (di) for Ti over a predefined interval, which measures the deficit between the utilization of CPU by Ti and the resource reservation of Ti,…”
wherein the number of the classical physical computing entities is selected (Das 8:35 “it is possible that the provider can allocate less CPU to a tenant than what is indicated in the resource reservations 220, particularly when resources of the cloud server 200 are overbooked.”)
Das, Ducore and the claims all allocate jobs. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to determine a deficiency and allocate anyway so that “relatively low response times to bursty and mostly inactive tenant workloads can be provided.” Das 14:43.
Das doesn’t teach selecting the number most close to the output information.
However, Oracle teaches wherein the number of the classical physical computing entities is selected so that the number most closely corresponds to the output information… (Oracle p 4-1 “Server Pool Master chooses the Virtual Machine Server with the maximum resources available (including memory and CPU) to start and run the virtual machine.”
Das, Ducore, Oracle and the claims all allocate jobs. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to assign to the maximum resource even if there is a deficit in order to shrink the deficit to it’s smallest available size.
Ducore teaches claims 2 and 12. (Previously Presented) The method as recited in claim 1, wherein the parameter values relate to any one or more of the following parameters: industry; quantum algorithm; problem space size; robustness of results expected from the execution of the simulation of the quantum algorithm; and a speed of the execution of the simulation of the quantum algorithm using the classical computing entities. (Ducore para 35 “The quantum computer simulator system 110 can receive a job (i.e…. quantum algorithm)” Ducore para 58 teaches that the number of qubits (problem space size) needed is used as an input to build the characteristic curves, “In the case of the single-core CPU (e.g., a hardware with hardware configuration having a single-core CPU), at very small numbers of qubits, it is faster…” The algorithm is related to speed of the execution. Robustness of the results is taught by para 38 “One way in which the characterizer 150 generates or builds these profiles is by building a simple circuit that includes a particular gate or quantum operation in a particular simulator process 145 many times.“ More simulations is equivalent to a more robustness of the result.)
Ducore teaches claims 3 and 13. (Currently Amended) The method as recited in claim 2, wherein the industry and the quantum algorithm collectively determine, at least in part, the quantum circuit complexity. (Ducore para 47 “quantum computer simulator system 110 (e.g., the manager 130) can determine which resource is most efficient for simulating a circuit, quantum program, or quantum algorithm and/or how quickly that circuit, quantum program, or quantum algorithm can be simulated by such a resource.” Ducore para 9 “The process iteratively applies gates on systems of 1, n system qubits until an increasing exponential growth in simulation time is identified to determine the absolute number of supported system qubits.” Number of supported qubits is the circuit complexity.) Ducore doesn’t teach determining based on industry.
However, Tekdis teaches basing compute choices on industry type. (Tekdis “Industrial PCs, embedded systems, all-in-one touch panel PCs, DIN-rail embedded controllers, and IoT gateway devices are feature-rich and designed to meet the challenging requirements for operation in the automation industry.”)
Ducore, Tekdis and the claims are all directed to selecting computing devices. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to choose different devices for different types of industry because “Industrial PCs, embedded systems, all-in-one touch panel PCs, DIN-rail embedded controllers, and IoT gateway devices are feature-rich and designed to meet the challenging requirements for operation in the automation industry.” Tekdis.
Ducore teaches claims 4 and 14. (Currently Amended) The method as recited in claim 2, wherein the problem space size determines, at least in part, the number of qubits associated with execution of the quantum algorithm. (Ducore para 58 “In the case of the single-core CPU (e.g., a hardware with hardware configuration having a single-core CPU), at very small numbers of qubits, it is faster…”)(Ducore para 58 “In the case of the single-core CPU (e.g., a hardware with hardware configuration having a single-core CPU), at very small numbers of qubits, it is faster…”)
Temme teaches claims 5 and 15. (Currently Amended) The method as recited in claim 2, wherein the robustness of results determines, at least in part, the number of execution shots associated with execution of the quantum algorithm. (Temme p. 4-5 “The examples considered above suggest that well-characterized noisy circuits can simulate ideal ones with overhead γ ≈ (1+ cϵ)L, where ϵ is the typical error rate and c is a small constant…. Using Eq. (9), one can estimate the number of noisy circuit runs of length L as M∼exp(2cϵL).” This teaches that a parameter of quantum circuit simulation is the number of noisy runs needed to approximate a true noisy algorithm on a quantum circuit.)
Ducore teaches claims 10 and 20. (Original) The method as recited in claim 1, wherein user-selectable parameters to which the parameter values respectively correspond are presented to a user by way of a user interface. (Ducore para 35 “user that submitted the job.” Ducore para 102 “computer device 700 can also include a user interface component 756 operable to receive inputs from a user “)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 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 mailing date of this final action.
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/AUSTIN HICKS/Primary Examiner, Art Unit 2142
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