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 .
DETAILED ACTION
Claims 1-20 are presented for examination.
Applicant is advised that should claim 14 be found allowable, claim 16 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Objections
Claims 1-3, 6-7 and 11-13 and 17 are objected to because of the following informalities:
As to claim 1, “comparing the values of the service level objective metrics with service level objective metrics provided by the user” should read --comparing the predicted respective values of the service level objective metrics with respective values of service level objective metrics provided by the user-- since it was the “values” of respective service level objective metrics being predicted and user provided that are being compared.
As to claims 3 and 7, these claims are objected to for the same informalities as claim 1 above.
As to claims 11, 13 and 17, these claims are objected to for the same reason as claims 1, 3 and 7 above.
Claim 2 – “the QUBO” should read --the QUBO job--.
Claim 6 – “any one of more of a beta range…” should read --any one or more of a beta range…--.
As to claim 12, this claim is objected for the same reason as claim 2 above.
Appropriate correction is required.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub. 2023/0048306 to Roque et al. (hereafter Roque) in view of “Benchmarking Quantum (-Inspired) Annealing Hardware on Practical Use Cases” to Huang et al. (hereafter Huang).
As to claim 1, Roque teaches the invention substantially as claimed including a method, comprising:
receiving an optimization job [input problems including NP problems for solver such as a simulated-annealing solver type, paragraph 3, lines 7-22; paragraphs 40 and 44];
providing the optimization job and annealer parameters as inputs to a trained machine learning model [input problem type/features and solver types are used by solver selection machine learning model in selecting an optimal solver type including a simulated-annealing solver type, paragraphs 5, 10 and 103-104; paragraph 11, lines 1-5 and 41-44];
performing an inferencing process that comprises using the trained machine learning model to predict, based on the inputs, respective values of service level objective metrics [solver selection machine learning model is configured/trained to determine selected solver types for an input problem such that the determination is based on low resource usage requirement below a resource usage threshold, paragraph 104, lines 1-10 and 17-23];
comparing the values of the service level objective metrics with service level objective metrics provided by a user [performance metrics based in part on success (i.e. multiple optimized solution as well as an optimized solution of best fit from the multiple optimized solution ) or failure (i.e. fail to satisfy a configurable per iteration optimization gain threshold) in determining a solution to the input problem are later used for mapping solver types to problem types by training and configuring a solver selection ML model; input problem and related constraints/features supplied by end user of client computing entity, paragraphs 7, 40-41, 49, 118 and 121]; and
based on the comparing, orchestrating the optimization job to an annealer that is expected to be able to satisfy the service level objective metrics provided by the user [performance metrics based in part on success (i.e. multiple optimized solution as well as an optimized solution of best fit from the multiple optimized solution ) or failure (i.e. satisfy a configurable per iteration optimization gain threshold) in determining a solution to the input problem are later used for mapping solver types to problem types by training and configuring a solver selection ML model, paragraphs 49, 118 and 121; generate container instance(s) corresponding to the selected solver type in executing the input problem, Fig. 4 and corresponding text].
Roque does not specifically teach the optimization job being a QUBO job. However, Huang teaches applying quantum annealing in solving combinatorial optimization problems in QUBO form [Section 1 Introduction, left column, lines 1-12; Section 2 Related Works, subsection 2.1 Annealing-Based Computers]. It would have been obvious before the effective filing date of the claimed invention to have modify Roque with Huang’s teaching of benchmarking quantum annealing hardware because they are both in the same field of endeavor in solving optimization problems [Roque, paragraphs 3 and 40; Huang, abstract].
As to claim 2, Roque and Huang teach the invention substantially as claimed including wherein the QUBO is received by an orchestrator to which the trained machine learning model was deployed [solver selection machine learning model managed by a container management engine of cloud computing server computing entity, Figs. 1 and 5A-5B; paragraph 11, lines 18-20; Huang, Section 1 Introduction, left column, lines 1-12; Section 2 Related Works, subsection 2.1 Annealing-Based Computers].
As to claim 3, Roque and Huang teach the invention substantially as claimed including wherein the service level objective metrics provided by the user comprise execution metrics for the QUBO job [input problem and related constraints/features supplied by end user of client computing entity, paragraphs 40-41; Huang, Section 1 Introduction, left column, lines 1-12; Section 2 Related Works, subsection 2.1 Annealing-Based Computers].
As to claim 4, Roque and Huang teach the invention substantially as claimed including wherein the inferencing process is performed for each annealer in a group of annealers, and the group of annealers includes the annealer to which the QUBO job is orchestrated [determine/select an optimal solver type from the identified set of per-domain solver types corresponding to the solver domain, paragraphs 102-104; multiple container instances of a simulated-annealing solver type, paragraph 10; paragraph 47, lines 14-29; Huang, Section 1 Introduction, left column, lines 1-12; Section 2 Related Works, subsection 2.1 Annealing-Based Computers].
As to claim 5, Roque and Huang teach the invention substantially as claimed including wherein the annealer is one of a group of annealers, and one or more annealers in the group of annealers comprises real hardware and/or simulated hardware [determine/select an optimal solver type from the identified set of per-domain solver types corresponding to the solver domain, paragraphs 102-104; multiple container instances of a simulated-annealing solver type, paragraphs 10 and 44; paragraph 47, lines 14-29].
As to claim 6, Roque and Huang teach the invention substantially as claimed including wherein the annealer parameters comprise any one of more of a beta range, number of sweeps, number of reads, and/or job parameters provided to the trained machine learning model comprise one or both of QUBO matrix size and QUBO matrix density [execution iterations to iteratively determine and improve a solution, paragraph 48].
As to claim 7, Roque and Huang teach the invention substantially as claimed including wherein the orchestrating comprises filtering out one or more annealers that are not expected to be able to satisfy the service level objective metrics provided by the user [determined an optimal solver type from among a set of solver types, paragraph 10; execution of container instance, correspond to different solver types, is halted on cancelled if the per-iteration optimization gain does not satisfy the configurable gain threshold, paragraph 49; performance metrics as part of problem output, inclusive of halted or failure to satisfy a configurable per-iteration optimization gain threshold, are used for training a solver selection machine language model for mapping of solver types to solver problem types, paragraphs 118-121].
As to claim 8, Roque and Huang teach the invention substantially as claimed including wherein receiving the QUBO job comprises receiving parameters of the QUBO job, and identities of one or more annealers that support the parameters of the QUBO job, and the annealer to which the QUBO job is orchestrated is included in the one or more annealers [input problem and related constraints/features supplied by end user of client computing entity, paragraphs 40-41; unique identifiers identifying solver types from set of solver types for the input problem, paragraphs 101 and 103-104; Huang, Section 1 Introduction, left column, lines 1-12; Section 2 Related Works, subsection 2.1 Annealing-Based Computers].
As to claim 9, Roque and Huang teach the invention substantially as claimed including wherein the annealer to which the QUBO job is orchestrated is included in a group of annealers, and the group of annealers collectively defines a heterogeneous annealing infrastructure [per-domain solver types in a cloud-based multi-domain solver system, paragraphs 3, 5 and 101-104; Huang, Section 1 Introduction, left column, lines 1-12; Section 2 Related Works, subsection 2.1 Annealing-Based Computers].
As to claim 10, Roque and Huang teach the invention substantially as claimed including wherein the annealer parameters are selected as inputs to the trained machine learning model based on an extent to which they are expected to be able to be used to predict a value of a service level objective metric [optimal solver type having the predictability or likelihood of success in executing the input problem and generating a problem output without halting execution of a container instance, paragraphs 10, 119 and 121].
As to claims 11-20, Roque and Huang teach the method for estimating execution metrics of different annealers for SLO optimization as recited in claims 1-5 and 7-10 (note: claim 16 recite the same limitations as claim 14, see duplicate claim warning above), therefore Roque and Huang teach the non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform the method.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WO 2023/018599, US PG Pub. 2023/0047692 and 2023/0047230 disclosed optimizing multi-domain processing of input problems using multiple solver types.
US Patent 10,817,337 disclosed selecting a quantum computing resource from a pool of quantum computing resources based at least in part on the input from client indicating a quantum algorithm to run.
Any inquiry concerning this communication or earlier communications from the examiner should a be directed to QING YUAN WU whose telephone number is (571)272-3776. The examiner can normally be reached on M-F 9AM-6PM EST.
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/QING YUAN WU/Primary Examiner, Art Unit 2199
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