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 .
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 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ravi et al. – hereinafter Ravi (US 2024/0378085) in view of Bhat et al. – hereinafter Bhat (US 2023/0110527)/ Tao Xin, Shilin Huang, Sirui Lu, Keren Li, Zhihuang Luo, Zhangqi Yin, Jun Li, Dawei Lu, Guilu Long, Bei Zeng, NMRCloudQ: a quantum cloud experience on a nuclear magnetic resonance quantum computer, Science Bulletin, Volume 63, Issue 1 - hereinafter Xin
As per claim 1, Ravi discloses a system for analyzing and executing incoming multi-channel network requests based on pre-generated channel weightages, comprising:
at least one processing device; ([0037]; The quantum computing device 132 includes a quantum processor 230 having multiple qubits 232 upon which the compute job 202 is executed. In some embodiments, the quantum processor 230 may include 50 or 100 qubits 232, but it should be understood that the present disclosure is envisioned to be operable and beneficial for quantum processors with many tens, hundreds, or more qubits 232.)
at least one memory device; and ([0023]; Example classical computing devices include conventional personal computers, servers, tablets, smartphones, x86-based processors, random access memory (“RAM”) modules, and so forth)
a module stored in the at least one memory device comprising executable instructions that when executed by the at least one processing device, cause the at least one processing device to: ([0009]; at least one classical processor and storing instructions that, when executed by the at least one classical processor)
receive a network request from at least one network channel of a plurality of network channels; ([0009]; (i) create a first job queue that includes a plurality of jobs configured to be executed on the first quantum computing device; (ii) receive, from a client device, a request for execution of a quantum program)
determine a weightage inductor for the network request via a quantum machine learning model executed via a quantum machine learning optimizer; assign the weightage inductor to the network request ([0103] Further, the associated coefficients a; for each feature may be configured from the set [−1, 0, or 1] or may be statically or dynamically configured based on, for example, past performance, current system conditions, or the like. For example, in situations with low overall queuing times (e.g., where average queuing times for the selected subset of QCs 132 are below a predetermined threshold), the short wait times are less significant, and thus higher weight may be placed on high fidelity (e.g., on the QCs 132 with higher fidelity scores). [0104]; In some embodiments, some factors may be dynamically configured (e.g., a machine learning model trained for a particular feature using historical performance data or performance characteristics 330, as supervised or unsupervised training, or the like)
and store the weightage inductor in a data repository; and ([0039]; In other words, an exponential number of correlated logical states can be stored and processed simultaneously by the quantum computing device 132 with a linear number of qubits 232.)
wherein the weightage inductor determines a reliability of the network request ([0103]; For example, in situations with low overall queuing times (e.g., where average queuing times for the selected subset of QCs 132 are below a predetermined threshold), the short wait times are less significant, and thus higher weight may be placed on high fidelity (e.g., on the QCs 132 with higher fidelity scores). In such situations, higher-predicted fidelity QCs 132 may tend to be routinely targeted, and thus may cause those QCs 132 to field more job executions.)
process the network request based on the weightage inductor by initiating a first set of processes.( [0089] In-queue optimizations can include those that are cognizant of dynamic variation characteristics. Effect of variations can be controlled by reduced micro-architectural activity which can be achieved by reducing resources allocated to all jobs or by intelligent reorganization of resources according to system optimality. These optimizations can be performed as late as possible so that the latest possible effect of variations can be incorporated in the job optimization.)
Ravi fails to disclose wherein the quantum machine learning optimizer comprises nuclear magnetic resonance quantum computing and determine, via a middleware application and request controller, that the network request is a first time request.
Bhat discloses determine, via a middleware application and request controller, that the network request is a first time request; [0056] At 802, application manager 210 receives a login request from a user dashboard service 212. A user may enter their credentials into the dashboard service 212. In some embodiments, application manager 210 may invoke the authorization endpoint of authentication service 220. The authorize endpoint may be used to request tokens or authorization codes through applications 104.; [0077] If the application manager 210 determines the request received is a first request from the user to access the application, method 900 may proceed to 912. If the application manager 210 determines the request received is not the first request from the user to access the application, method 900 may proceed to 918.; Fig. 2: items 210, 220)
It would have been obvious before the earliest effective filing date for the teachings of Ravi to be modified so that the middleware and request controller to be implemented by the application manager and authorize endpoint and determine whether the request is a first request. This would have implemented role-based access control (RBAC) to restrict system access to authorized users in multi-tenant environments using cloud-native objects. (Bhatt, [0002])
The combined teachings of Ravi / Bhat fail to disclose wherein the quantum machine learning optimizer comprises nuclear magnetic resonance quantum computing
Xin discloses wherein the quantum machine learning optimizer comprises nuclear magnetic resonance quantum computing. (1. Introduction: In this work, we provide online availability of another actual quantum hardware, which is based on a nuclear magnetic resonance (NMR) spectrometer. NMR spectroscopy is arguably one of the most versatile analytic methods for investigating quantum computation and quantum control [3–5].)
It would have been obvious before the effective filing date for the combined teachings of
Ravi / Bhat to be further modified so that the quantum machine learning optimizer user nuclear magnetic resonance quantum computing. This would have
As per claim 2, Ravi / Bhat / Xin disclose the system according to claim 1. Ravi discloses wherein the executable instructions cause the at least one processing device to determine the weightage inductor based on one or more customizable parameters. ([0103] Further, the associated coefficients a; for each feature may be configured from the set [−1, 0, or 1] or may be statically or dynamically configured based on, for example, past performance, current system conditions, or the like. For example, in situations with low overall queuing times (e.g., where average queuing times for the selected subset of QCs 132 are below a predetermined threshold), the short wait times are less significant, and thus higher weight may be placed on high fidelity (e.g., on the QCs 132 with higher fidelity scores))
As per claim 3, Ravi / Bhat / Xin disclose the system according to claim 2. Ravi discloses wherein the one or more customizable parameters comprise at least a type of the at least one network channel used to initiate the network request, type of the network request, type of a user computing system used to initiate the network request, type of software associated with the user computing system, type of hardware associated with the user computing system, type of data associated with the network request, amount of the data associated with the network request, historical data associated with the at least one network channel, and type of users associated with the network request. ([0103] Further, the associated coefficients a; for each feature may be configured from the set [−1, 0, or 1] or may be statically or dynamically configured based on, for example, past performance, current system conditions, or the like. For example, in situations with low overall queuing times (e.g., where average queuing times for the selected subset of QCs 132 are below a predetermined threshold), the short wait times are less significant, and thus higher weight may be placed on high fidelity (e.g., on the QCs 132 with higher fidelity scores).
As per claim 4, Ravi / Bhat / Xin disclose the system according to claim 1. Ravi discloses wherein the executable instructions cause the at least one processing device to:
receive a second network request from the at least one network channel; (([0036]; As such, jobs 122 specifying or otherwise assigned to execute on the first quantum computing device 132 can be placed on a first job queue 120 and jobs 122 specifying or otherwise assigned to execute on the second computing device 132 can be placed on a second job queue 12)
determine that the second network request is a repetitive request, wherein the second network request has same parameters as the network request; ([0105]; Second, some jobs 122 assigned to a particular QC 132 may “cross over” a particular calibration cycle of that QC 132 (e.g., having been compiled prior to a recalibration of the QC 132, but then executing after the recalibration of the QC 132). The QaO server 110 may be configured to address each of these situations.)
extract the weightage inductor associated with the network request from the data repository; and ([0103] Further, the associated coefficients a; for each feature may be configured from the set [−1, 0, or 1] or may be statically or dynamically configured based on, for example, past performance, current system conditions, or the like. For example, in situations with low overall queuing times (e.g., where average queuing times for the selected subset of QCs 132 are below a predetermined threshold), the short wait times are less significant, and thus higher weight may be placed on high fidelity (e.g., on the QCs 132 with higher fidelity scores). [0104]; In some embodiments, some factors may be dynamically configured (e.g., a machine learning model trained for a particular feature using historical performance data or performance characteristics 330, as supervised or unsupervised training, or the like)
process the second network request based on the weightage inductor. ( [0089] In-queue optimizations can include those that are cognizant of dynamic variation characteristics. Effect of variations can be controlled by reduced micro-architectural activity which can be achieved by reducing resources allocated to all jobs or by intelligent reorganization of resources according to system optimality. These optimizations can be performed as late as possible so that the latest possible effect of variations can be incorporated in the job optimization.)
As per claim 5, Ravi / Bhat / Xin disclose the system according to claim 4. Ravi discloses wherein processing the second network request based on the weightage inductor comprises bypassing at least one process from the first set of processes. )
As per claim 6, Ravi / Bhat / Xin disclose the system according to claim 1. Ravi discloses wherein the executable instructions cause the at least one processing device to train the machine learning models to calculate weightage inductors for incoming network requests. ; ([0105]; Second, some jobs 122 assigned to a particular QC 132 may “cross over” a particular calibration cycle of that QC 132 (e.g., having been compiled prior to a recalibration of the QC 132, but then executing after the recalibration of the QC 132). The QaO server 110 may be configured to address each of these situations.)
As per claim 7, Ravi / Bhat / Xin disclose the system according to claim 1. Ravi discloses wherein processing the network request based on the weightage inductor by initiating the first set of processes comprises processing the first set of processes in parallel, via the quantum machine learning optimizer. ([0044]; Such compilation and optimization processes may include, for example, breaking up the logical operations of the quantum program into subsets, or blocks of qubits 232 (and their associated operations) such that the QaO server 110 is able to generate adequate optimization solutions for the subset of instructions, addressing parallelism problems inherent in breaking up the logical operations into blocks, and optimizing the logical operations based on the strengths and weaknesses of the underlying physical hardware.)
As per claims 8 and 15, please see the discussion under claim 1 as similar logic applies.
As per claims 9 and 16, please see the discussion under claim 2 as similar logic applies.
As per claims 10 and 17, please see the discussion under claim 3 as similar logic applies.
As per claims 11 and 18, please see the discussion under claim 4 as similar logic applies.
As per claims 12 and 19, please see the discussion under claim 5 as similar logic applies.
As per claims 13 and 20, please see the discussion under claim 2 as similar logic applies
As per claim 14, please see the discussion under claim 7 as similar logic applies.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 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
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 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.
Any inquiry concerning this communication or earlier communications from theexaminer should be directed to Chirag R Patel whose telephone number is (571)272-7966. The examiner can normally be reached on Monday to Friday from 9:00AM to 6:00PM. If attempts to reach the examiner by telephone are unsuccessful, theexaminer's supervisor, Glenton Burgess, can be reached on 571-272-3949. The fax phone number for the organization where this application or proceedingis assigned is 571-273-8300.
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/Chirag R Patel/
Primary Examiner, Art Unit 2454
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