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 .
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on October 2nd 2025, February 3rd 2025, November 19th 2024, August 8th 2024, and January 26th 2024 were filed. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 7-9, 11-15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Dukatz (CA 2997970 A1) in view of Nimavat (US 20220261688 A1) and Limberg (US 20210097419 A1).
Regarding claim 1,
Dukatz teaches [a] method comprising: receiving a problem to be solved at an orchestration service (Paragraph 14, “…the method comprising: receiving data representing a computational task to be performed by a system including one or more quantum computing resources and one or more classical computing resources”, “A system can receive computational tasks… to be performed.”, Paragraph 65, “…the input data may include… each computational task was routed to. In addition, the input data associated with the multiple computational tasks previously performed by the system…”, Paragraph 41, “the machine learning module may… route a task to a second-choice device.”, Paragraph 44, “The system 100… is configured to receive as input data representing a computational task to be solved…”
Dukatz teaches receiving data representing a computational task to be solved by a system. The system corresponds to the orchestration service which is responsible for routing tasks via the machine learning module.);
inputting the problem to a classifier that is configured to generate an output that includes a list of labels (Paragraph 82, “The machine learning model 204 is a predictive model… e.g., classification tasks.”, Paragraph 88, “The machine learning module 132 is configured to provide data representing the computational task or computational sub tasks to the trained machine learning model 204 . The machine learning model 204 is configured to process the received data and to determine which of the one or more additional computing resources 110 a - 110 d to route the received data representing the computational task or sub tasks to.”
Dukatz teaches inputting data representing the computational task into a machine learning model configured to perform classification tasks and determine which computing resource to route the task to. The determined computing resource outputted from the model corresponds to a classification output label.),
wherein the classifier is trained using telemetry data of multiple quantum computing systems and features of solved historical problems (Paragraph 4, “…obtaining a first set of data, the first set of data… representing multiple computational tasks previously performed by the system; obtaining a second set of data… representing properties associated with using the one or more quantum computing resources…”, Paragraph 9, “ properties… comprise… (i) approximate qualities of solutions generated by the one or more quantum computing resources; (ii) computational times… or (iii) computational costs…”, Paragraph 65, “…store system input data associated with the multiple computational tasks previously performed… include… size of an input data… error tolerance… required level of confidence”
Dukatz teaches training the machine learning model using historical computational task data (features of solved historical problems) and properties associated with quantum computing such as time, quality, cost, number of qubits, which correspond to telemetry data of multiple quantum computing systems.);
and sending the problem to a first quantum computing system associated with a first label… (Paragraph 14, “…processing the received data using a machine learning model to determine which… quantum computing resources… to route the data representing the computational task to… and routing the data representing the computational task to the determined computing resource…”, Paragraph 89, “…the machine learning model 204 may determine that a received optimization task should be routed to a quantum annealer…”
Dukatz teaches that the machine learning model determines the appropriate computing resource and routes the computational task to a quantum annealer to perform the task.)
Dukatz does not teach inputting the problem to a classifier that is configured to generate an output that includes a list of labels, wherein each of the labels corresponds to a quantum computing system and wherein the labels are sorted according to relevance… wherein the first label is a most relevant label in the list of labels.
Nimavat, in the same field of endeavor, teaches inputting… to a classifier that is configured to generate an output that includes a list of labels (Paragraph 20 of Nimavat, “One or more embodiments apply a trained machine learning model trained to filter and rank entities relative to a set of requirements.”, Paragraph 28, “The ML application 104 may use the ranking match score to select an order of a subset of interface elements… within the ranked array displayed in the GUI.”, Paragraph 44, “In some examples, the ML engine 110 may generate a ranking match score (for a subset of entities filtered using the overall match score) corresponding to the extent of matching between a subset of attributes and a corresponding subset of requirements.”
Nimavat teaches inputting into a trained machine learning model that filters and ranks entities and outputs a ranked array of interface elements that are presented to the user.),
and wherein the labels are sorted according to relevance… (Paragraph 20 of Nimavat, “One or more embodiments apply a trained machine learning model trained to filter and rank entities relative to a set of requirements.”, Paragraph 41, “In this way, the system may use the ranking match score to rank entities relative to the subset of requirements.”, Paragraph 44, “In some examples, the ML engine 110 may generate a ranking match score (for a subset of entities filtered using the overall match score) corresponding to the extent of matching between a subset of attributes and a corresponding subset of requirements. The relative importance of various attributes may be represented as corresponding attribute weights...”
Nimavat teaches ranking entities in relation to the requirements using a ranking match score. The ranking match score quantifies the degree of matching between the entity attributes and the requirements, generating sorted labeled entities based on their relevance.)
…wherein the first label is a most relevant label in the list of labels (Paragraph 28, “The ML application 104 may use the ranking match score to select an order of a subset of interface elements (filtered from a set using the overall match scores) within the ranked array displayed in the GUI.”, Paragraph 41, “…the ranking match score may enable this subset of relevant entities to be evaluated against a narrower set of criteria (e.g., fewer requirements) that may be of greater relevance or greater interest to a user. In this way, the system may use the ranking match score to rank entities relative to the subset of requirements.”
Nimavat teaches selecting an order of entities within a ranked array based on the ranking match score. The highest degree of matched entity is presented first.).
Therefore, a person of ordinary skill in the art before the effective filing date would have been motivated to incorporate Dukatz’s quantum task routing system with Nimavat’s ranking machine learning output in order to produce a relevance-ordered list of candidates computing resources to improve the resource selection accuracy in a multi-system quantum computing environment (Paragraph 70 of Nimavat).
Dukatz and Nimavat do not teach wherein each of the labels corresponds to a quantum computing system…
Limberg, in the same field of endeavor, teaches wherein each of the labels corresponds to a quantum computing system… (Paragraph 65, “To facilitate such selection by determination component 110 based on one or more defined run criteria as described above, determination component 110 can rank each quantum platform according to one or more ranking schemes… assigning a value of zero (0) to each quantum platform that can support such component(s) and/or achieve a defined result and a value of one (1) to each quantum platform that cannot…”
Limberg teaches that a rank/label is assigned to every quantum platform (quantum system) in order to select the platform capable of running the instructions.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Dukatz and Nimavat’s teachings with Limberg’s quantum computing system selection process according to ranked schemes in order to improve resource selection accuracy and decision making in a multi-platform quantum computing environment (Paragraph 2 of Limberg).
Regarding claim 2,
Dukatz teaches the quantum computing system comprises a quantum annealing system sending the problem to a second quantum computing system… (Paragraph 16, “…quantum computing resources comprise one or more of (i) quantum gate computers, (ii) adiabatic annealers, or (iii) quantum simulators.” Paragraph 14, “a system including one or more quantum computing resources and one or more classical computing resources”).
Regarding claim 3,
Dukatz does not teach the first quantum computing system cannot solve the problem …associated with a second label, wherein the second label is a second most relevant label.
Limberg, in the same field of endeavor, teaches the first quantum computing system cannot solve the problem (Paragraph 52, “In another example, quantum platform routing system 102 can further facilitate… evaluating one or more defined run criteria of… at least one quantum platform; managing at least one of execution of the one or more components by the at least one quantum platform… and/or learning to select the at least one quantum platform based on… historical execution results of the at least one quantum platform.”, Paragraph 80, “…control component 302 can manage (e.g., address, resolve, etc.) responses from the at least one quantum platform (e.g., requests for additional information, execution failure responses, offline responses, etc.).”
Limberg teaches that the control component manages execution failures and responses from a quantum platform and evaluates historical results when selecting a different platform to use.),
sending the problem to a second quantum computing system… (Paragraph 57, “Determination component 110 can select at least one quantum platform to execute one or more components of a quantum application based on one or more defined run criteria.”, Paragraph 91, “…evaluation component 202 can further evaluate one or more features (e.g., attributes, parameters, etc.) of one or more quantum platforms such as, for instance, quantum platforms (a) and (b)”, Paragraph 92, “determination component 110 can select quantum platform (a) to run algorithm A1 and/or can further select quantum platform (b)”, “…enable routing the one or more components… to one or more quantum platforms… best suited… based on the one or more defined run criteria”, Paragraph 110, “learning… to select the at least one quantum platform based on at least one of historical selections… or historical execution results”
Limberg teaches sending the problem to a second quantum computing system because it discloses evaluating multiple quantum platforms and selecting among platforms such as platform a and b to execute application components.)
…associated with a second label, wherein the second label is a second most relevant label (Paragraph 79, “control component 302 can submit an execution request to the at least one quantum platform (e.g., a scheduling component and/or a queueing component of the at least one quantum platform), where each of such execution request(s) can comprise the one or more components”, Paragraph 103, “selecting… at least one quantum platform… based on a defined run criterion…”, Paragraph 110, “learning… based on at least one of historical selections… or historical execution results”, Paragraph 65, “…determination component 110 can rank each quantum platform according to one or more ranking schemes… select at least one of such quantum platforms based on a ranking value corresponding to such quantum platform(s) (e.g., a highest value relative to other quantum platforms, a lowest value relative to other quantum platforms, etc.).”
Limberg teaches a second label that is the second most relevant label because it discloses ranking each quantum platform and selecting platforms based on their ranked values and the historical execution results.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Dukatz’s machine learning based quantum task routing system with Limberg’s ranked quantum platform selection and failure management mechanisms in order to improve reliability and execution success in multi-platform quantum computing environments (Paragraph 66 of Limberg).
Regarding claim 4,
Dukatz does not teach iterating through quantum computing systems associated with the labels in the list of labels until the problem is solved or the list of labels is exhausted.
Limberg, in the same field of endeavor, teaches iterating through quantum computing systems associated with the labels in the list of labels until the problem is solved or the list of labels is exhausted (Paragraph 65, “…determination component 110 can rank each quantum platform according to one or more ranking schemes… select at least one of such quantum platforms based on a ranking value corresponding to such quantum platform(s) (e.g., a highest value relative to other quantum platforms, a lowest value relative to other quantum platforms…”, Paragraph 92, “determination component 110 can select quantum platform (a) to run algorithm A1 and/or can further select quantum platform (b)”, Paragraph 80, “…control component 302 can manage (e.g., address, resolve, etc.) responses from the at least one quantum platform (e.g., requests for additional information, execution failure responses, offline responses, etc.).”
Limberg teaches ranking multiple quantum platforms, selecting among them, and managing execution failures.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Dukatz’s machine learning based quantum task routing system with Limberg’s ranked quantum platform selection in order to improve reliability and execution robustness in a multi-platform quantum computing environment (Paragraph 66 of Limberg).
Regarding claim 5,
Dukatz does not teach adding a module to the orchestration service wherein the module is configured to consider additional criteria for selecting a quantum computing system.
Limberg, in the same field of endeavor, teaches adding a module to the orchestration service (Paragraph 46, “Quantum platform routing system 102, memory 104, processor 106, dissection component 108, determination component 110, and/or another component of quantum platform routing system…”, Paragraph 84, “Monitor component 402 can monitor one or more components of quantum platform routing system 102 to collect… historical data that can be used by determination component…”
Limberg teaches a modular orchestration service that contains a determination component, evaluation component, and monitor component, which are included in the orchestration service.),
wherein the module is configured to consider additional criteria for selecting a quantum computing system (Paragraph 60, “Determination component 110 can select at least one quantum platform comprising certain quantum hardware… that can execute one or more components of a quantum application based on such one or more defined run criteria described above that can be defined and/or weighted by an entity. For example, determination component 110 can select at least one quantum platform based on one or more of such defined run criteria described above that can be defined and/or weighted (e.g., individually weighted using a percentage (%) assigned to each criterion) by an entity (e.g., a programmer… a human… using an interface component of quantum platform routing system… For instance, such an entity can employ such an interface component to specify execution speed over result accuracy (e.g., via assigning a greater weight to execution speed) and determination component 110 can select at least one quantum platform that can execute one or more components of a quantum application based on such specification.”
Linberg teaches that defined run criteria can be specified and weighted by an entity to influence the platform selection. Thus, the module considers additional selection criteria in order to influence the selection process.),
wherein the additional criteria impact an order of the labels in the list of labels (Paragraph 65, “To facilitate such selection by determination component 110 based on one or more defined run criteria… determination component 110 can rank each quantum platform according to one or more ranking schemes that indicate: a) whether or not each quantum platform can support (e.g., can execute) the one or more components of a quantum application; and/or b) whether or not each quantum platform can achieve a defined result… In these examples, determination component 110 can select at least one of such quantum platforms based on a ranking value corresponding to such quantum platform(s)…”
Limberg teaches ranking each quantum platform based on defined criteria and selecting them based on ranked values. When changes to selection criteria (changing the rank values) occur, the selection criteria are also impacted for which platform next gets selected since the ranking value will also change.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Dukatz’s machine learning based quantum task routing system with Limberg’s routing architecture that applies weighted run criteria in order to improve adaptability and optimize resource selection in a multi-platform quantum environment (Paragraph 66 of Limberg).
Regarding claim 7,
Dukatz teaches adding the problem, once solved, and features of the solved problem to a training dataset (Paragraph 63, “The cache 124 is configured to store different types of data relating to the system 100 and to computational tasks performed by the system 100.”, Paragraph 64, “a previously generated solution may be labelled as a successful solution if the solution was generated within a predetermined acceptable amount of time… Conversely, a previously generated solution may be labelled as an unsuccessful solution… Such information may be provided for input into the machine learning module 132.”, Paragraph 39, “The training data includes data from several sources, as described below, which may be used to generate multiple training examples. Each training example…”, Paragraph 62, “The training data may include labeled training examples, e.g., a machine learning model input paired with a respective known machine learning model output”
Dukatz teaches storing previously performed computational tasks and their labeled outcomes (successful/unsuccessful) and using that data to generate labeled training examples for the machine learning module.).
Regarding claim 8,
Dukatz teaches executing problems in a training data set at multiple quantum annealing systems, wherein features from each of the executions is added to a training dataset (Paragraph 58, “The additional computing resources 110a .Math. 110d may include quantum annealer computing resources, e.g., quantum annealer 110a.”, Paragraph 37, “Asystem can receive computational tasks…”, Paragraph 57, “…outsource one or more computations associated with solving the computational task based on the task objectives 112a and 112b to the additional computing resources 110a .Math. 110d.”, Paragraph 39, “The training data includes data from several sources… which may be used to generate multiple training examples.”, Paragraph 112, “Each training example in the set of training examples may include a machine learning model input paired with a known machine learning model output.”, Paragraph 65, “The cache 124 may also be configured to store system input data associated with the multiple computational tasks previously performed by the system. For example, the input data may include data representing a type of computing resource that each computational task was routed to.”
Dukatz teaches executing computational tasks on multiple quantum annealing resources and storing labeled outcomes to generate training examples, thus corresponding to the added features into the training dataset.).
Regarding claim 9,
Dukatz does not teach adjusting the list of labels based on service level objectives associated with the problem.
Limberg, in the same field of endeavor, teaches adjusting the list of labels based on service level objectives associated with the problem (Paragraph 60 of Limberg, “one or more defined run criteria described above that can be defined and/or weighted by an entity… specify execution speed over result accuracy (e.g., via assigning a greater weight to execution speed)…”, Paragraph 65, “determination component 110 can employ a binary ranking scheme to rank each quantum platform… ranking scheme that can account for one or more weighted values of one or more defined run criteria… select at least one of such quantum platforms based on a ranking value”
Limberg teaches defining and weighting run criteria such as execution speed or accuracy result (which correspond to service level objectives) and ranking platforms based on those criteria. Those specifications/ modifications adjust the ordered list of platforms according to the service objectives associated with the problem.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Dukatz’s machine learning based quantum task routing system with Limberg’s routing architecture in order to improve adjustment of the ranked list of systems based on the objectives in order to improve the selection system in the multi-platform quantum environment (Paragraph 66 of Limberg).
Claims 11-15 and 17-19 are non-transitory computer readable medium claim that recites identical limitations to claims 1-5 and 7-9. Therefore, claims 11-15 and 17-19 are rejected using the same rationale as claims 1-5 and 7-9.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Dukatz (CA 2997970 A1) in view of Nimavat (US 20220261688 A1), Limberg (US 20210097419 A1), Schuetz (US 12447617 B1) and Michuda (US 11527323 B2).
Regarding claim 6,
Dukatz teaches and inputting the… problem to the classifier (Paragraph 82, “The machine learning model 204 is a predictive model that may be perform one or more machine learning tasks, e.g., classification tasks. For machine learning model 204 may be an artificial neural network, e.g., a deep network.”).
Dukatz does not teach further comprising encoding the problem as a quadratic unconstrained binary optimization configuration… the classifier comprises a chain classifier.
Schuetz, in the same field of endeavor, teaches further comprising encoding the problem as a quadratic unconstrained binary optimization configuration (Paragraph 38 of Schuetz, “Conversely, quantum annealers are special-purpose machines designed to solve certain combinatorial optimization problems belonging to the class of Quadratic Unconstrained Optimization (QUBO) problems.”, Paragraph 39, “…and the QUBO matrix Q is a square matrix that encodes the actual problem to solve.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Dukatz’s machine learning based quantum task routing system with Schuetz’s Qubo encoding technique in order to facilitate more accurate classification and downstream results (Paragraph 13 of Schuertz).
Dukatz and Schuetz do not teach the classifier comprises a chain classifier.
Michuda, in the same field of endeavor, teaches the classifier comprises a chain classifier (Paragraph 136, “In some embodiments, each individual classifier in the classifier chains performs a binary classification on a subset of the features of the subject… In some embodiments, an ensemble model—comprising one or more chains of classifiers—classifies subjects by majority vote (e.g., each chain of classifiers gets one vote).”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Dukatz and Schuertz’s teaching with Michuda’s chain classifier in order to leverage the benefits of chain classifiers for handling feature sets (Paragraph 136 of Michuda).
Claim 16 is a non-transitory computer readable medium claim that recites identical limitations to claim 6. Therefore, claim 16 is rejected using the same rationale as claim 6.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dukatz (CA 2997970 A1) in view of Nimavat (US 20220261688 A1), Limberg (US 20210097419 A1), and Krneta (US 11907092 B2).
Regarding claim 10,
Dukatz does not teach generating an alert that the problem cannot be solved at a present time and attempting to solve the problem at a later time using the quantum computing systems associated with the labels in the list of labels
Krneta, in the same field of endeavor, teaches generating an alert that the problem cannot be solved at a present time and attempting to solve the problem at a later time using the quantum computing systems associated with the labels in the list of labels (Paragraph 14 of Krneta, “For example, quantum computing resources may be limited and costly to reserve and use. Thus, when issues like this occur, it is desirable that alerts can be provided to users in real time, such as while the algorithm is executing as opposed to at a later time…”, Paragraph 19, “When the metrics fail to satisfy the threshold, the quantum computing monitoring system may consider that algorithm including the quantum computing portion, has failed to make the desired progress. In response, the quantum computing monitoring system may provide an alert to the user…This may allow a user to abort execution of the algorithm if progress is unsatisfactory prior to incurring costs for further execution of the algorithm.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Dukatz’s machine learning based quantum task routing system with Krneta’s real time alerts in order to improve adaptability and optimize resource selection in a multi-platform quantum environment (Paragraph 1 of Krneta).
Claim 20 is a non-transitory computer readable medium claim that recites identical limitations to claim 10. Therefore, claim 20 is rejected using the same rationale as claim 10.
Conclusion
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/M.M.H./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125