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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on February 16, 2024, February 25, 2025, and February 26, 2026 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 § 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating multiple instantiations of a machine learning model that has been trained, with a training dataset, to model a function that is operable to generate a prediction regarding a quantum process metric”
“sampling the instantiations of the machine learning model”
“populating an ensemble with the instantiations of the machine learning model obtained as a result of the sampling”
“generating respective predictions, regarding the quantum process metric”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“…with each of the instantiations of the machine learning model in the ensemble”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 2,
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the machine learning model comprises a Bayesian neural network”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein a number of instantiations of the machine learning model in the ensemble is set by a service level agreement”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the quantum process metric comprises a quantum annealing performance metric”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein one of the instantiations of the machine learning model is removed from the ensemble, based on a performance of that instantiation of the machine learning model”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 1.
Step 2B Analysis: See corresponding analysis of claim 1.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the respective predictions generated by the instantiations of the machine learning model in the ensemble collectively define a prediction interval”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the ensemble is modified based on the prediction interval”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 1.
Step 2B Analysis: See corresponding analysis of claim 1.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein a prediction interval, associated with the respective predictions made by the instantiations of the machine learning model in the ensemble, is usable to make a quantum computing job placement decision”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 9,
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the predictions made by the instantiations of the machine learning model in the ensemble comprise extrapolations outside of predictions that can be made by the machine learning model based on the training dataset”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 10,
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein a prediction interval collectively defined by the instantiations of the machine learning model in the ensemble is relatively smaller than a prediction interval associated with a prediction made by the machine learning model”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 11,
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating multiple instantiations of a machine learning model that has been trained, with a training dataset, to model a function that is operable to generate a prediction regarding a quantum process metric”
“sampling the instantiations of the machine learning model”
“populating an ensemble with the instantiations of the machine learning model obtained as a result of the sampling”
“generating respective predictions, regarding the quantum process metric”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations…”
“…with each of the instantiations of the machine learning model in the ensemble”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 12,
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 11.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the machine learning model comprises a Bayesian neural network”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 13,
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 11.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein a number of instantiations of the machine learning model in the ensemble is set by a service level agreement”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 14,
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 11.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the quantum process metric comprises a quantum annealing performance metric”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 15,
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein one of the instantiations of the machine learning model is removed from the ensemble, based on a performance of that instantiation of the machine learning model”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 11.
Step 2B Analysis: See corresponding analysis of claim 11.
Regarding Claim 16,
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 11.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the respective predictions generated by the instantiations of the machine learning model in the ensemble collectively define a prediction interval”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 17,
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
wherein the ensemble is modified based on the prediction interval”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 16.
Step 2B Analysis: See corresponding analysis of claim 16.
Regarding Claim 18,
Claim 18 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 11.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein a prediction interval, associated with the respective predictions made by the instantiations of the machine learning model in the ensemble, is usable to make a quantum computing job placement decision”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 19,
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 11.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the predictions made by the instantiations of the machine learning model in the ensemble comprise extrapolations outside of predictions that can be made by the machine learning model based on the training dataset”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 20,
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 11.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein a prediction interval collectively defined by the instantiations of the machine learning model in the ensemble is relatively smaller than a prediction interval associated with a prediction made by the machine learning model”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
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 1-2, 4, 6, 8, 11-12, 14, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Barbosa et al. (Using Machine Learning for Quantum Annealing Accuracy Prediction) (“Barbosa”) in view of Anderson et al. (U.S. Patent Publication No. 2023/0044102) (“Anderson”).
Regarding claim 1, Barbosa teaches a method, comprising: generating multiple instantiations of a machine learning model that has been trained, with a training dataset (Barbosa Section 4 Discussion “We focus on the MC problem, and train several machine learning models on several thousand randomly generated input problems with the aim to learn features to (a) predict if D-Wave 2000Q will be able to solve an instance of MC to optimality, and (b) predict the size of the clique that the D-Wave 2000Q device will find.” Barbosa provides training several machine learning models on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, corresponding to training multiple model instances with a training dataset.), to model a function that is operable to generate a prediction regarding a quantum process metric (Barbosa Section 2 Methods “We aim to predict both if an instance of MC is solvable by D-Wave 2000Q to optimality, as well as the size of the clique which will be found by the annealer.” Barbosa provides predicting both if an instance of Maximum Clique problem is solvable by a D-Wave 2000Q quantum annealer, as well as the size of the clique which will be found by the annealer, thus generating a prediction regarding a quantum process metric); …and generating respective predictions, regarding the quantum process metric (Barbosa Figure 2 and Figure 4; Section 4 Discussion “We focus on the MC problem, and train several machine learning models on several thousand randomly generated input problems with the aim to learn features to (a) predict if D-Wave 2000Q will be able to solve an instance of MC to optimality, and (b) predict the size of the clique that the D-Wave 2000Q device will find.” Barbosa provides respective predictions regarding the quantum annealer, as shown in Figures 2 and 4, where each box in both figures correspond to the respective predictions for a quantum process, including whether the problems are solvable or not.), with each of the instantiations of the machine learning model in the ensemble (Barbosa Section 2.2 Regression “We chose to predict the clique size returned by D-Wave 2000Q with gradient boosting, a popular machine learning regression model. Whereas random forests build deep independent trees, gradient boosting works by constructing an ensemble of dependent shallow trees, each one improving on the previous one. Although shallow trees by themselves are usually only weak predictive models, combining (or “boosting”) them in an ensemble allows for powerful machine learning models.”; Section 4 Discussion “We focus on the MC problem, and train several machine learning models on several thousand randomly generated input problems with the aim to learn features to (a) predict if D-Wave 2000Q will be able to solve an instance of MC to optimality, and (b) predict the size of the clique that the D-Wave 2000Q device will find.” Barbosa provides utilizing the several machine learning models as an ensemble to determine the quantum predictions, thus using each of the model instances in the ensemble.).
Barbosa fails to explicitly teach sampling the instantiations of the machine learning model; populating an ensemble with the instantiations of the machine learning model obtained as a result of the sampling
However, Anderson teaches sampling the instantiations of the machine learning model (Anderson [0040] “As will be discussed in more detail below, the trust scores for one or more machine learning models of a plurality of models that collectively constitute an ensemble-based machine learning model may be used to define loss-based penalty functions for each individual model, which in turn may be used to determine normalized weights for the one or more machine learning models of the plurality.”; [0072] “In some instances, an ensemble machine learning model may comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more than 100 individual machine learning models.” Anderson provides trust scores for one or more machine learning models, thus providing a plurality of model instantiations, wherein the determination of trust scores for plurality of models corresponds to the sampling of a plurality of model instantiations.); populating an ensemble with the instantiations of the machine learning model obtained as a result of the sampling (Anderson [0040] “As will be discussed in more detail below, the trust scores for one or more machine learning models of a plurality of models that collectively constitute an ensemble-based machine learning model may be used to define loss-based penalty functions for each individual model, which in turn may be used to determine normalized weights for the one or more machine learning models of the plurality.”; [0072] “In some instances, an ensemble machine learning model may comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more than 100 individual machine learning models. In some instances, an ensemble machine learning model may comprise any number of machine learning models within the range of values included in this paragraph.” Anderson provides populating an ensemble with a plurality of machine learning models based on calculated trust scores by determine normalized weights for the one or more machine learning models of the plurality based on the trust score, thus populating an ensemble based on the sampling.)
Barbosa and Anderson are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to quantum annealing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Barbosa with the above teachings of Anderson. Doing so would allow for improvements to the predicted output accuracy of a quantum annealing process (Anderson [0221] “The improvements to the predicted output accuracy may also be greater if the reservoir computer used to perform method 700 is trained according to method 600 to optimize linear parameter weights W.sub.out.”).
Regarding claim 2, Barbosa in view of Anderson teaches wherein the machine learning model comprises a Bayesian neural network (Anderson [0051] “In some instances, the machine learning algorithm(s) employed may comprise, e.g., an artificial neural network algorithm, a Gaussian process regression algorithm, a logistical model tree algorithm, a random forest algorithm, a fuzzy classifier algorithm, a decision tree algorithm, a hierarchical clustering algorithm, a Naïve Bayes algorithm…”; [0101] “Quantum computing platforms: In some instances, all or a portion of the methods described herein, e.g., the training and optimization of an ensemble machine learning model, may be performed on a quantum computing platform.”; [0117] “Some of these platforms, e.g., the D-Wave platform, may comprise qubits that are configured to implement quantum annealing processes rather than a more general approach to quantum computing.” Anderson provides utilizing a Bayesian Neural Network in the use of machine learning models for quantum annealing processes.).
Barbosa and Anderson are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to quantum annealing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Barbosa with the above teachings of Anderson. Doing so would allow for improvements to the predicted output accuracy of a quantum annealing process (Anderson [0221] “The improvements to the predicted output accuracy may also be greater if the reservoir computer used to perform method 700 is trained according to method 600 to optimize linear parameter weights W.sub.out.”).
Regarding claim 4, Barbosa in view of Anderson teaches wherein the quantum process metric comprises a quantum annealing performance metric (Barbosa Abstract “Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form… In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems.” Barbosa provides determining factors related to predicting accuracy of a Quantum annealer (the D-Wave 2000Q), thus comprising a quantum annealing performance metric.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Barbosa in view of Anderson for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 6, Barbosa in view of Anderson teaches wherein the respective predictions generated by the instantiations of the machine learning model in the ensemble (See e.g., Barbosa Section 2.2 Regression; Section 4 Discussion; Figures 2 and 4; Barbosa provides respective predictions by an ensemble regarding the quantum annealer, as shown in Figures 2 and 4, where each box in both figures correspond to the respective predictions for a quantum process.) collectively define a prediction interval (Barbosa Figure 2 and Figure 4; Section 4 Discussion “We focus on the MC problem, and train several machine learning models on several thousand randomly generated input problems with the aim to learn features to (a) predict if D-Wave 2000Q will be able to solve an instance of MC to optimality, and (b) predict the size of the clique that the D-Wave 2000Q device will find.”; Figure 4 “Decision tree for classification of MC instances into solvable and unsolvable, using the features outlined in Section 2. Setting of random annealing time and random UTC prefactor. Left branches denote true bifurcations, right branches denote false bifurcations. Green leaves are solvable cases, red leaves are unsolvable cases, and inner nodes are colored in blue.” Barbosa provides respective predictions regarding the quantum annealer, as shown in Figures 2 and 4, where each box in both figures correspond to the respective predictions for a quantum process, and wherein each respective branch defines a prediction interval (i.e., a prediction path followed by the decision tree)).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Barbosa in view of Anderson for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 8, Barbosa in view of Anderson teaches wherein a prediction interval, associated with the respective predictions made by the instantiations of the machine learning model in the ensemble, is usable to make a quantum computing job placement decision (Barbosa Table 1; Section 3.2 “To see if the aforementioned results generalize, we repeated the same experiment with a random annealing time (sampled uniformly at random within the interval of [1,2000] microseconds), and a random UTC prefactor (sampled uniformly at random within the interval [0.5,3]).” Figure 4 “Decision tree for classification of MC instances into solvable and unsolvable, using the features outlined in Section 2. Setting of random annealing time and random UTC prefactor. Left branches denote true bifurcations, right branches denote false bifurcations. Green leaves are solvable cases, red leaves are unsolvable cases, and inner nodes are colored in blue” Barbosa provides using a decision tree to determine whether a maximum clique (MC) problem is solvable or not, as shown in Figures 2 and 4 and Table 1, wherein the classification of whether an MC problem is solvable or not (as shown in Figures 2 and 4) is usable to make a quantum computing job placement decision (solvable or not solvable), and wherein the decision tree prediction path corresponds to a prediction interval.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Barbosa in view of Anderson for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 11, it is the non-transitory storage medium embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found above in the rejection of claim 1. Further, Anderson teaches a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations (Anderson [0107] “Software 850 can also be stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.”).
Barbosa and Anderson are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to quantum annealing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Barbosa with the above teachings of Anderson. Doing so would allow for improvements to the predicted output accuracy of a quantum annealing process (Anderson [0221] “The improvements to the predicted output accuracy may also be greater if the reservoir computer used to perform method 700 is trained according to method 600 to optimize linear parameter weights W.sub.out.”).
Regarding claim 12, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Barbosa in view of Anderson for the same reasons disclosed above in the rejection of claim 2.
Regarding claim 14, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Barbosa in view of Anderson for the same reasons disclosed above in the rejection of claim 4.
Regarding claim 16, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Barbosa in view of Anderson for the same reasons disclosed above in the rejection of claim 6.
Regarding claim 18, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Barbosa in view of Anderson for the same reasons disclosed above in the rejection of claim 8.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Barbosa et al. (Using Machine Learning for Quantum Annealing Accuracy Prediction) (“Barbosa”) in view of Anderson et al. (U.S. Patent Publication No. 2023/0044102) (“Anderson”) in further view of Gujarati et al. (Swayam: Distributed Autoscaling to Meet SLAs of Machine Learning Inference Services with Resource Efficiency) (“Gujarati”).
Regarding claim 3, Barbosa in view of Anderson teaches the method as recited in claim 1, as discussed above in the rejection of claim 1, but fails to teach wherein a number of instantiations of the machine learning model in the ensemble is set by a service level agreement.
However, Gujarati teaches wherein a number of instantiations of the machine learning model in the ensemble is set by a service level agreement (Gujarati Section 1 Introduction “Such inference requests are stateless and bound by Service Level Agreements (SLAs) such as ‘at least 99% of requests must complete within 500ms.’… Swayam tackles the dual challenge of resource efficiency and SLA compliance for ML inference services in a distributed setting. Given a pool of compute servers for hosting backend instances of each service, Swayam ensures an appropriate number of service instances by predicting load, provisioning new instances as needed, and reclaiming unnecessary instances, returning their hardware resources to the global server pool (see Fig. 1 for an illustration).” Gujarati provides identifying an appropriate number of machine learning service instances to meet SLA compliance for ML inference services, corresponding to the number of ML instances is set by a service level agreement.).
Barbosa, Anderson and Gujarati are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to ensemble machine learning methods. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Barbosa in view of Anderson with the above teachings of Gujarati. Doing so would allow for decreasing resource utilization while meeting service-specific service level agreements (Gujarati Abstract “Swayam decreases resource utilization by up to 27%, while meeting the service-specific SLAs over 96% of the time during a three hour window.”).
Regarding claim 13, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Barbosa in view of Anderson in further view of Gujarati for the same reasons disclosed above in the rejection of claim 3.
Claims 5, 7, 15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Barbosa et al. (Using Machine Learning for Quantum Annealing Accuracy Prediction) (“Barbosa”) in view of Anderson et al. (U.S. Patent Publication No. 2023/0044102) (“Anderson”) in further view of Givental et al. (U.S. Patent Publication No. 2021/0281592) (“Givental”).
Regarding claim 5, Barbosa in view of Anderson teaches the method as recited in claim 1, but fails to teach herein one of the instantiations of the machine learning model is removed from the ensemble, based on a performance of that instantiation of the machine learning model.
However, Givental teaches wherein one of the instantiations of the machine learning model is removed from the ensemble, based on a performance of that instantiation of the machine learning model (Givental [0050] “If an unsupervised ML model in the ensemble 120 has an accumulated performance metric that equals or falls below the predetermined threshold value, then the unsupervised ML model may be removed from the ensemble 120 and/or replaced by another available unsupervised ML model.” Givental provides removing a model from a model ensemble based on a performance of that model falling below a threshold value.).
Barbosa, Anderson and Givental are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to ensemble machine learning methods. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Barbosa in view of Anderson with the above teachings of Givental. Doing so would improve the efficiency and effectiveness of detecting target classifications without having to use explicitly defined rules in a rules engine and/or searches (Givental [0025] “The mechanisms of the illustrative embodiments improve the efficiency and effectiveness of detecting target classifications, e.g., detecting computer system security threats/attacks, unauthorized accesses to computing system resources, etc., without having to use explicitly defined rules in a rules engine and/or searches”).
Regarding claim 7, Barbosa in view of Anderson teaches the method as recited in claim 6 as discussed above in the rejection of claim 6, but fails to teach wherein the ensemble is modified based on the prediction interval.
However, Givental teaches wherein the ensemble is modified based on the prediction interval (Givental [0050] “If an unsupervised ML model in the ensemble 120 has an accumulated performance metric that equals or falls below the predetermined threshold value, then the unsupervised ML model may be removed from the ensemble 120 and/or replaced by another available unsupervised ML model.” Givental provides removing a model from a model ensemble based on a performance of that model falling below a threshold value, corresponding to the ensemble is modified based on the prediction interval, wherein the performance metric of a particular model in the ensemble corresponds to the prediction interval.).
Barbosa, Anderson and Givental are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to ensemble machine learning methods. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Barbosa in view of Anderson with the above teachings of Givental. Doing so would improve the efficiency and effectiveness of detecting target classifications without having to use explicitly defined rules in a rules engine and/or searches (Givental [0025] “The mechanisms of the illustrative embodiments improve the efficiency and effectiveness of detecting target classifications, e.g., detecting computer system security threats/attacks, unauthorized accesses to computing system resources, etc., without having to use explicitly defined rules in a rules engine and/or searches”).
Regarding claim 15, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Barbosa in view of Anderson in further view of Givental for the same reasons disclosed above in the rejection of claim 5.
Regarding claim 17, the rejection of claim 16 is incorporated herein. Further, the limitations in this claim are taught by Barbosa in view of Anderson in further view of Givental for the same reasons disclosed above in the rejection of claim 7.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Barbosa et al. (Using Machine Learning for Quantum Annealing Accuracy Prediction) (“Barbosa”) in view of Anderson et al. (U.S. Patent Publication No. 2023/0044102) (“Anderson”) in further view of Brumby et al. (U.S. Patent Publication No. 2020/0272625) (“Brumby”).
Regarding claim 9, Barbosa in view of Anderson teaches the method as recited in claim 1, as discussed above in the rejection of claim 1, but fails to teach wherein the predictions made by the instantiations of the machine learning model in the ensemble comprise extrapolations outside of predictions that can be made by the machine learning model based on the training dataset.
However, Brumby teaches wherein the predictions made by the instantiations of the machine learning model in the ensemble comprise extrapolations outside of predictions that can be made by the machine learning model based on the training dataset (Brumby [0094] “Similar to the interpolative model, the predictive model may be trained on datasets that are more sufficiently complete, but in both space and in time.”; [0096] “Given the large computational requirements this temporal prediction may encompass, it is further advantageous to use the interpolative model in conjunction with the predictive model. In this manner, the predictive model may be used to extrapolate certain core datasets only, limiting the computational space. These extrapolated core datasets are then used to establish a new data timestep which the interpolative model may then expand as necessary, filling out the missing data.”).
Barbosa, Anderson and Brumby are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to ensemble machine learning methods. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Barbosa in view of Anderson with the above teachings of Brumby. Doing so would provide a more efficient computational process (Brumby [0096] “These extrapolated core datasets are then used to establish a new data timestep which the interpolative model may then expand as necessary, filling out the missing data. Through this cooperation, a more efficient process may be achieved.”).
Regarding claim 19, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Barbosa in view of Anderson in further view of Brumby for the same reasons disclosed above in the rejection of claim 9.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Barbosa et al. (Using Machine Learning for Quantum Annealing Accuracy Prediction) (“Barbosa”) in view of Anderson et al. (U.S. Patent Publication No. 2023/0044102) (“Anderson”) in further view of Tian et al. (U.S. Patent Publication No. 2020/0012948) (“Tian”).
Regarding claim 10, Barbosa in view of Anderson teaches the method as recited in claim 1, as discussed above in the rejection of claim 1, but fails to teach wherein a prediction interval collectively defined by the instantiations of the machine learning model in the ensemble is relatively smaller than a prediction interval associated with a prediction made by the machine learning model.
However, Tian teaches wherein a prediction interval collectively defined by the instantiations of the machine learning model in the ensemble is relatively smaller than a prediction interval associated with a prediction made by the machine learning model (Tian [0016] “Individual models in the ensemble respectively formulate a certain relationship between inputs and outputs in the training data. By combining the individual models formulating the respective relationships, the ensemble formulates the relationships represented in the training data more comprehensively than each individual model, and accordingly, predicts the output more accurately than an individual model in the ensemble. However, because the ensemble needs to run all models participating in the ensemble to produce a prediction, the ensemble scoring has a high demand on resources for computation and storage, respective to each model, as well as resources/mechanism for parallel processing, in order to reduce the time lapse from receiving a request until producing the prediction by concurrently processing all models in the ensemble.” Tian provides reducing the time lapse from receiving a request until producing the prediction by using all the models in the ensemble as opposed to an individual model, wherein the prediction made by the ensemble is output in less time than a prediction from an individual model, thus the ensemble prediction interval (i.e., the output of the ensemble) being relatively smaller (i.e., a shorter amount of time) than a prediction made by an individual model.).
Barbosa, Anderson and Tian are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to ensemble machine learning methods. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Barbosa in view of Anderson with the above teachings of Tian. Doing so would reduce time lapse from receiving a request until producing the prediction (Tian [0016] “However, because the ensemble needs to run all models participating in the ensemble to produce a prediction, the ensemble scoring has a high demand on resources for computation and storage, respective to each model, as well as resources/mechanism for parallel processing, in order to reduce the time lapse from receiving a request until producing the prediction by concurrently processing all models in the ensemble.”).
Regarding claim 20, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Barbosa in view of Anderson in further view of Tian for the same reasons disclosed above in the rejection of claim 10.
Conclusion
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/KURT NICHOLAS PRESSLY/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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