DETAILED ACTION
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 .
Status of Claims
The present application is being examined under the claims filed 11/17/2023.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/17/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Computation component
Regularization component
Integration component
Data processing component
Assignment component
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f):
[*Examiner notes: The actual hardware for the various components below is supported by Specification paragraph [0116] “As used in this application, the terms “component,” “system,” “platform” and/or “interface” can refer to and/or can include a computer-related entity or an entity related to an operational machine with one or more specific functionalities. The entities described herein can be either hardware, a combination of hardware and software, software, or software in execution”. Invoking 35 U.S.C. 112(f) necessarily limits the claim to a hardware implementation or a combination of hardware and software (see MPEP 2181 II B).]
Computation component (As recited in claims 1, 4, 5, and 10)
(paragraph [0034]) In an embodiment, as described herein, the computation component 108 can calculate, on logical or physical qubits of the quantum system 301, a plurality of kernels (e.g., base kernels), based on the set of kernel bandwidths, for subsets of features of a dataset and center the plurality of kernels within a feature space of the feature map. As an example, a feature map on n-qubits can be defined by Equation 1.
Regularization component: (claims 1, 8, 9, 11)
(paragraph [0037]) In an embodiment, the regularization component 112 can regularize parameters to combine the plurality of kernels. Various regularization methods can be performed on the combination of base kernels to mitigate overfitting of a kernel learning model by creating a combined kernel that is suited to a given dataset and generalize effectively to different samples of the dataset. The regularization component 112 can, for example, utilize iterative selection, bootstrap resampling, noise-based methods, or Frobenius norm methods to achieve avoidance of overfitting.
Integration component (claims 1, 6, 9)
(paragraph [0044]) In various embodiments, the integration component 302 can combine a plurality of subsampled kernels with a respective kernel of the plurality of kernels. Subsampled kernels (e.g., base kernels) can comprise classical or quantum kernels. In other words, the integration component 302 can combine kernels into a single combined kernel no matter the type of base kernel. Thus, quantum modelling can be combined with classical modeling features after computation of kernel matrices or kernel functions. Furthermore, each base kernel can comprise different feature maps or bandwidths to enable creation of a flexible combined kernel that can represent an arbitrary target kernel function of a dataset.
Data processing component (claims 2, 5)
(paragraph [0042]) In various embodiments, the computation component 108 can engage the data processing component 202 to subsample datapoints of a dataset and define a plurality of subsampled kernels for the subsampled datapoints. Similar to feature subsampling, different kernels can be computed for different subsets of datapoints to mitigate limitations from sample size of a dataset. More specifically, a kernel can be computed on a subset of a dataset instead of the entire dataset for a particular feature mapping kernel function. For example, if N datapoints exist, a subsample of datapoints of size K such that K<N can be used to compute a kernel. Therefore, only K2 circuits are run on the quantum hardware instead of N2 circuits, conserving quantum resources.
Assignment component (claim 10)
(paragraph [0036]) Moreover, the plurality of kernels can be computed with the determined set of kernel bandwidths. In other words, the assignment component 110 can determine a value of bandwidth for each of the plurality of kernels to be computed with. Thus, when combined, the plurality of kernels can be capable of adapting to an arbitrary target kernel.
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 for containing an abstract idea without significantly more.
Regarding Claim 1:
Step 1 – Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is to a machine.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites the abstract ideas of:
a computation component that calculates, […], a plurality of kernels for subsets of features of a feature map and centers the plurality of kernels within a feature space of the feature map; — This limitation is directed to the abstract idea of a mathematical process, and mathematical calculations in particular (MPEP 2106.04(a)(2) I. C.). The claim describes the mathematical operation of calculating a plurality of functions (the broadest reasonable interpretation of calculating “kernels” includes mathematical functions see specification paragraph [0019]).
a regularization component that regularizes parameters to combine the plurality of kernels; and — This limitation is directed to the abstract idea of a mathematical process, and mathematical calculations in particular (MPEP 2106.04(a)(2) I. C.). The claim describes the mathematical operation of calculating a regularization of vectors or numerical values.
an integration component that combines the plurality of kernels into a combined kernel — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating known values to determine how to combine those values.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application. The additional elements:
A system, comprising: a processor that executes computer-executable components stored in a non-transitory computer-readable memory, the computer-executable components comprising: — This limitation is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
on logical or physical qubits of a quantum system — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the judicial exception to a quantum computer environment.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, the claim does not recite additional elements which amount to significantly more than the abstract idea itself. The additional elements as identified in step 2A prong 2:
A system, comprising: a processor that executes computer-executable components stored in a non-transitory computer-readable memory, the computer-executable components comprising: — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
on logical or physical qubits of a quantum system — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
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. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 1:
further comprising a data processing component that performs feature subsampling and data subsampling on a dataset to generate kernels for subsets of the feature subsamples or data subsamples — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating a dataset to choose the most relevant data points and features.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
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. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 1:
wherein a selection of the plurality of features is determined randomly or systematically through classical feature selection — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating a set of features to pick a subset either arbitrarily by random choice, or through a systematic process.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
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. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
wherein the computation component calculates matrices of the plurality of kernels with compute-uncompute tests, SWAP tests, or projected kernel computations — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the judicial exception to the technological environment of classical, quantum computing, or further mathematical calculation.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein the computation component calculates matrices of the plurality of kernels with compute-uncompute tests, SWAP tests, or projected kernel computations — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
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. The claim is dependent on claim 2 which included an abstract idea (see rejection for claim 2). The claim recites the additional limitations:
Step 2A Prong 2:
wherein the computation component engages the data processing component to subsample datapoints of a dataset and define a plurality of subsampled kernels for the subsampled datapoints in calculation of a kernel of the plurality of kernels — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the field of the calculation of functions (kernels) to a particular subset of data.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein the computation component engages the data processing component to subsample datapoints of a dataset and define a plurality of subsampled kernels for the subsampled datapoints in calculation of a kernel of the plurality of kernels — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
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. The claim is dependent on claim 5 which included an abstract idea (see rejection for claim 5). The claim recites the additional limitations:
Step 2A Prong 1:
wherein the integration component combines the plurality of subsampled kernels arising from the respective kernels of the plurality of kernels — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating known values to determine how to combine those values.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
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. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 1:
further comprising selecting distinct kernels using iterative procedures for kernel alignment with a target kernel to enable hardware efficient kernels and a reduction in kernels used during training and testing — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating mathematical functions to make the best choice out of a list of options.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
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. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 1:
wherein the regularization component performs regularization on the parameters to combine the plurality of kernels based on bootstrap resampling, noise, a Frobenius norm method, or the distinct kernels — This limitation is directed to the abstract idea of a mathematical process, and mathematical calculations in particular (MPEP 2106.04(a)(2) I. B.). The claim describes the mathematical operation of regularization using one from a list of particular mathematical methods.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
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. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 1:
wherein the regularization component regularizes weights assigned to the plurality of kernels, and wherein the integration component linearly combines the plurality of kernels based on the regularized weights — This limitation is directed to the abstract idea of a mathematical process, and mathematical calculations in particular (MPEP 2106.04(a)(2) I. C.). The claim describes the mathematical operations of regularization and calculating a linear combination of vectors or values.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
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. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 1:
further comprising an assignment component that determines a set of kernel bandwidths, and wherein the computation component calculates the plurality of kernels based on the kernel bandwidths — This limitation is directed to the abstract idea of a mathematical process, and mathematical calculations in particular (MPEP 2106.04(a)(2) I. C.). The claim describes the mathematical operation of calculating numerical values or vectors and calculating mathematical functions.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
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. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
wherein the regularization component maximizes regularization of the parameters with alignment of the combined kernel to a target kernel — This limitation is directed to mere instructions to apply a judicial exception. Using optimization to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the optimization is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein the regularization component maximizes regularization of the parameters with alignment of the combined kernel to a target kernel — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 12
Independent claim 12 is a computer-implemented method claim corresponding to system claim 1, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 13
Dependent claim 13 is a method claim corresponding to system claim 2, which was directed to an abstract idea, therefore the same rejection and rationale applies.
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. The claim is dependent on claim 9 which included an abstract idea (see rejection for claim 9). The claim recites the additional limitations:
Step 2A Prong 1:
further comprising preparing an input dataset with principal component analysis to reduce dimensionality of the input dataset — This limitation is directed to the abstract idea of a mathematical process, and a mathematical relationship in particular (MPEP 2106.04(a)(2) I. A.). The claim describes the mathematical operations of performing principal component analysis in words.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 15
Dependent claim 15 is a method claim corresponding to system claim 5, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 16
Dependent claim 16 is a system claim corresponding to method claim 6, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 17
Dependent claim 17 is a system claim corresponding to method claim 7, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 18
Dependent claim 18 is a system claim corresponding to method claim 8, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 19
Independent claim 19 is a non-transitory computer-readable medium claim corresponding to system claim 1, which was directed to an abstract idea, therefore the same rejection and rationale applies. The only difference is that claim 1 recites the following additional elements treated under step 2A prong 2 and step 2B:
Step 2A Prong 2:
computer program product comprising a non-transitory computer-readable memory having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to — This limitation is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
computer program product comprising a non-transitory computer-readable memory having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to — Using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
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. The claim is dependent on claim 19 which included an abstract idea (see rejection for claim 19). The claim recites the additional limitations:
Step 2A Prong 1:
wherein the program instructions are further executable to cause the processor to: combine a plurality of subsampled kernels arising from the respective kernels of the plurality of kernels — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating known values to determine how to combine those values.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5-6, 8-9, 12-13, 15-16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over NPL reference Vedaie et al. “Quantum Multiple Kernel Learning” herein referred to as Vedaie in view of NPL reference Ghukasyan et al. “Quantum-Classical Multiple Kernel Learning” herein referred to as Ghukasyan.
Regarding Claim 1:
Vedaie teaches:
A system, comprising:a processor that executes computer-executable components stored in a non-transitory computer-readable memory, the computer-executable components comprising: a computation component that calculates, on logical or physical qubits of a quantum system, a plurality of kernels
(page 1 abstract) “In this work, we propose an MKL method we refer to as quantum MKL, which combines multiple quantum kernels[*Examiner notes: plurality of kernels]. Our method leverages the power of deterministic quantum computing with one qubit (DQC1) to estimate the combined kernel for a set of classically intractable individual quantum kernels.”
for subsets of features of a feature map
(page 1 column 2 paragraph 2) “In principle, each kernel in the combination can represent a unique notion of similarity for a specific subset of data features”
an integration component that combines the plurality of kernels into a combined kernel.
(page 4 above equation 11) “In this work, we focus on two forms of kernel combinations. The first form is a linear combination of M different kernels”
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Vedaie does not explicitly teach:
and centers the plurality of kernels within a feature space of the feature map;
a regularization component that regularizes parameters to combine the plurality of kernels; and
However, Ghukasyan teaches:
and centers the plurality of kernels within a feature space of the feature map;
[*Examiner notes: The broadest reasonable interpretation of centering kernels involves performing a normalization operation related to the kernels]; (page 6 column 1 below equation 17) “For combinations containing unbounded kernels (Linear, Polynomial), we normalize the component Gram matrix (Eq. (15)) before the sums over Kθ,γ (Tab. III) are computed. Normalization additionally helps to stabilize the kernel weighting algorithm (Sec. IIIA2) in general.”
a regularization component that regularizes parameters to combine the plurality of kernels; and
(page 8 column 2 last paragraph) “Prior to optimization, and indeed after, the IQP-containing kernels exhibit the lowest overall accuracy, considering score totals in the same way as above. This improvement can be attributed to regularization via inclusion of the smooth RBF kernel together with IQP.”; (page 4 column 2 paragraph 1) “As a weighted average, this form is both easy to interpret and computationally convenient. Using Eq. (1), we can infer that the resulting kernel,”
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Vedaie, Ghukasyan, and the instant application are analogous because they are all directed to quantum machine learning (QML).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the plurality of kernels taught by Vedaie by applying the regularization, centering, and combination operations to the kernels as taught by Ghukasyan because (Ghukasyan page 6 column 1 below equation 17) “Normalization additionally helps to stabilize the kernel weighting algorithm (Sec. IIIA2) in general” and (Ghukasyan page 1 column 2 last paragraph) “Multiple kernel learning (MKL) aims to enhance performance by combining different kernels into a single, more expressive kernel [16], in order to learn a wider variety of decision functions. Here, tuning combinations in a data-driven way [17] provides an additional means tailoring kernels” and (Ghukasyan page 8 column 2 last paragraph) “Prior to optimization, and indeed after, the IQP-containing kernels exhibit the lowest overall accuracy, considering score totals in the same way as above. This improvement can be attributed to regularization via inclusion of the smooth RBF kernel together with IQP.”
Regarding Claim 2
Vedaie in view of Ghukasyan teaches:The system of claim 1,
(see rejection of claim 1)
Vedaie further teaches:
further comprising a data processing component that performs feature subsampling
[*Examiner notes: The broadest reasonable interpretation of subsampling is choosing a subset]
(page 17 column 1 last paragraph) “The German credit dataset contains 1000 data samples, each with 20 features. In our simulations, we choose only four out of 20 features, namely, “chequing account existence”, “duration”, “credit history”, and “employed since”.”
and data subsampling on a dataset
(page 7 column 1 paragraph 4) “We then randomly split the data samples into training and test datasets, with 75% of the data used for the training dataset and the rest for the test dataset.”
to generate kernels for subsets of the feature subsamples or data subsamples.
(page 7 column 1 paragraph 4) “To restrict the complexity of the optimization in QMKL, we use only 50% of the training dataset. Using the best set of parameters, we then re-train each model on another set of 100 randomly generated instances of the circles dataset with the same training and test split ratio while using only 50% of the training data for optimization.”
Regarding Claim 5
Vedaie in view of Ghukasyan teaches:
The system of claim 2,
(see rejection of claim 2)
And Vedaie further teaches:
wherein the computation component engages the data processing component to subsample datapoints of a dataset and define a plurality of subsampled kernels for the subsampled datapoints in calculation of a kernel of the plurality of kernels.
(page 7 column 1 paragraph 4) “We then randomly split the data samples into training and test datasets, with 75% of the data used for the training dataset and the rest for the test dataset. To restrict the complexity of the optimization in QMKL, we use only 50% of the training dataset. Using the best set of parameters, we then re-train each model on another set of 100 randomly generated instances of the circles dataset with the same training and test split ratio while using only 50% of the training data for optimization.”
Regarding Claim 6
Vedaie in view of Ghukasyan teaches:
The system of claim 5
(see rejection of claim 5)
And Vedaie further teaches:
wherein the integration component combines the plurality of subsampled kernels arising from the respective kernels of the plurality of kernels.
(page 7 column 1 paragraph 4) “We then randomly split the data samples into training and test datasets, with 75% of the data used for the training dataset and the rest for the test dataset. To restrict the complexity of the optimization in QMKL, we use only 50% of the training dataset. Using the best set of parameters, we then re-train each model on another set of 100 randomly generated instances of the circles dataset with the same training and test split ratio while using only 50% of the training data for optimization.”; (page 6 column 2 last paragraph) “We choose to simulate a linear combination of quantum kernels as represented in Eq. (17) as a proof of concept because this choice is largely supported by the success of MKL algorithms that employ a linear sum of kernels in a variety of applications[49,50].”
Regarding Claim 8
Vedaie in view of Ghukasyan teaches:
The system of claim 1
(see rejection of claim 1)
Ghukasyan further teaches:
wherein the regularization component performs regularization on the parameters to combine the plurality of kernels based on bootstrap resampling, noise, a Frobenius norm method, or the distinct kernels.
(page 8 column 2) “Prior to optimization, and indeed after, the IQP-containing kernels exhibit the lowest overall accuracy, considering score totals in the same way as above. This improvement can be attributed to regularization via inclusion of the smooth RBF kernel together with IQP.”
Regarding Claim 9
Vedaie in view of Ghukasyan teaches:
The system of claim 1
(see rejection of claim 1)
Ghukasyan further teaches:
wherein the regularization component regularizes weights assigned to the plurality of kernels,
[*Examiner notes: Ghukasyan regularizes the classical kernel and thus, by extension, the weights assigned to the plurality of kernels]
(page 5 column 1 paragraph 1) “Adding a classical kernel to a complicated (and often periodic) quantum kernel also introduces a natural means of trainable regularization (Fig. 3), which can improve model performance beyond the training set.”
and wherein the integration component linearly combines the plurality of kernels based on the regularized weights.
(page 4 column 1 last paragraph)
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It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to combine Vedaie with Ghukasyan for the same reasons given in claim 1 above.
Regarding Claim 12
Claim 12 is a computer-implemented method claim corresponding to system claim 1. The entirety of claim 12 is taught by the rejection of claim 1 and is thus rejected analogously.
Regarding Claim 13
Claim 13 is a computer-implemented method claim corresponding to system claim 2. The entirety of claim 13 is taught by the rejection of claim 2 and is thus rejected analogously.
Regarding Claim 15
Claim 15 is a computer-implemented method claim corresponding to system claim 5. The entirety of claim 15 is taught by the rejection of claim 5 and is thus rejected analogously.
Regarding Claim 16
Claim 16 is a computer-implemented method claim corresponding to system claim 6. The entirety of claim 16 is taught by the rejection of claim 6 and is thus rejected analogously.
Regarding Claim 18
Claim 18 is a computer-implemented method claim corresponding to system claim 8. The entirety of claim 18 is taught by the rejection of claim 8 and is thus rejected analogously.
Regarding Claim 19
Claim 19 is a computer-readable medium claim corresponding to system claim 1. The entirety of claim 19 is taught by the rejection of claim 1 and is thus rejected analogously.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to combine Vedaie with Ghukasyan for the same reasons given in claim 1 above.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Vedaie in view of Ghukasyan, and further in view of NPL reference Baughman et al. “Study of Feature Importance for Quantum Machine Learning Models” herein referred to as Baughman.
Regarding Claim 3
Vedaie in view of Ghukasyan teaches:
The system of claim 1
(see rejection of claim 1)
Vedaie in view of Ghukasyan does not explicitly teach:
wherein a selection of the plurality of features is determined randomly or systematically through classical feature selection.
However, Baughman teaches:
wherein a selection of the plurality of features is determined randomly or systematically through classical feature selection.
(page 1 abstract) “We developed a hybrid quantum-classical architecture where QML models are trained and feature importance values are calculated from classical algorithms on a real-world dataset.”
Vedaie, Ghukasyan, Baughman and the instant application are analogous because they are all directed to QML.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the plurality of kernels as taught by Vedaie by using classical feature selection to pick the plurality of features as taught by Baughman because (Baughman page 2 paragraph 3) “In CML, feature selection and feature importance are vital components for good performance of machine learning models. Feature selection helps to extract the relevant predictors while providing a quantitative measure for the importance of each feature in the final model. These feature engineering tasks are equally applicable to QML in which the model is built on a large feature space represented by the Hilbert space spanned by the qubits. QML models interact with classical data through feature maps. As a result, we need to have a data processing and feature engineering pipeline for the QML models. In the current literature, there are proposals to implement quantum principal component analysis (QPCA), quantum SVD, and other similar feature engineering methods [2–4].”
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Vedaie in view of Ghukasyan, and further in view of NPL reference Haner et al. “FACTORING USING 2n+2 QUBITS WITH TOFFOLI BASED MODULAR MULTIPLICATION” herein referred to as Haner.
Regarding Claim 4
Vedaie in view of Ghukasyan teaches:
The system of claim 1
(see rejection of claim 1)
Vedaie in view of Ghukasyan does not explicitly teach:
wherein the computation component calculates matrices of the plurality of kernels with compute-uncompute tests, SWAP tests, or projected kernel computations.
However, Haner teaches:
wherein the computation component calculates matrices of the plurality of kernels with compute-uncompute tests, SWAP tests, or projected kernel computations.
(page 9 second to last paragraph) “To test our circuit designs and gate estimates, we simulated our circuits on input sizes of up to 8,192-bit numbers. The scaling results of the Toffoli count Tmult(n) of our controlled modular-multiplier are as expected. Each of the two (controlled) multiplication circuits (namely compute/uncompute) use n (doubly-controlled) modular additions.”
Vedaie, Ghukasyan, Haner, and the instant application are analogous because they are all directed to quantum computing.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the plurality of kernels as taught by Vedaie in view of Ghukasyan by using the compute/uncompute tests as taught by Haner because (Haner page 9 second to last paragraph) “To test our circuit designs and gate estimates, we simulated our circuits on input sizes of up to 8,192-bit numbers. The scaling results of the Toffoli count Tmult(n) of our controlled modular-multiplier are as expected. Each of the two (controlled) multiplication circuits (namely compute/uncompute) use n (doubly-controlled) modular additions”
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Vedaie in view of Ghukasyan, and further in view of NPL reference Hubregtsen et al. “Training quantum embedding kernels on near-term quantum computer” herein referred to as Hubregtsen.
Regarding Claim 7
Vedaie in view of Ghukasyan teaches:
The system of claim 1
(see rejection of claim 1)
Vedaie in view of Ghukasyan does not explicitly teach:
further comprising selecting distinct kernels using iterative procedures for kernel alignment with a target kernel to enable hardware efficient kernels and a reduction in kernels used during training and testing.
However, Hubregtsen teaches:
further comprising selecting distinct kernels using iterative procedures for kernel alignment with a target kernel to enable hardware efficient kernels and a reduction in kernels used during training and testing.
(page 2 column 1 paragraph 1) “To combat the issue of choosing the right kernel function, we port the notion of model optimization from the classical world to the quantum world in the form of trainable quantum embedding kernels (QEKs). To train the parameters of the QEKs, we propose the use of kernel-target alignment, another method ported from the classical domain.”
Vedaie, Ghukasyan, Hubregsten, and the instant application are analogous because they are all directed to QML.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the plurality of kernels as taught by Vedaie in view of Ghukasyan by using kernel alignment as taught by Hubregtsen because (Hubregsten page 1 abstract) “To train the parameters of the QEK, we proposed the use of kernel-target alignment. We verified the feasibility of this method, and showed that for our experimental setup we could reduce the training error significantly.”
Regarding Claim 17
Claim 17 is a computer-implemented method claim corresponding to system claim 7. The entirety of claim 17 is taught by the rejection of claim 7 and is thus rejected analogously.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Vedaie in view of Ghukasyan and further in view of NPL reference Canatar et al. “Bandwidth Enables Generalization in Quantum Kernel Models” herein referred to as Canatar.
Regarding Claim 10
Vedaie in view of Ghukasyan teaches:
The system of claim 1
(see rejection of claim 1)
Vedaie in view of Ghukasyan does not explicitly teach:
further comprising an assignment component that determines a set of kernel bandwidths, and wherein the computation component calculates the plurality of kernels based on the kernel bandwidths.
However, Canatar teaches:
further comprising an assignment component that determines a set of kernel bandwidths,
(page 5 last paragraph) “Our central technique for mitigating this limitation will be to introduce bandwidth to quantum kernels (Shaydulin & Wild, 2021).”
and wherein the computation component calculates the plurality of kernels based on the kernel bandwidths.
(page 5 last paragraph) “We now reconsider the kernel with the feature map of Eq. 3 and introduce a scaling parameter c∈[0,1] that controls the bandwidth of the kernel. The feature map and the kernel become”
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55
531
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Vedaie, Ghukasyan, Canatar, and the instant application are analogous because they are all directed to the instant application.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the plurality of kernels as taught by Vedaie in view of Ghukasyan by incorporating the kernel bandwidth calculations taught by Canatar because (Canatar page 1 abstract) “Specifically, we show that changing the
value of the bandwidth can take a model from provably not being able to generalize to any
target function to good generalization for well-aligned targets. Our analysis shows how the
bandwidth controls the spectrum of the kernel integral operator and thereby the inductive
bias of the model. We demonstrate empirically that our theory correctly predicts how varying
the bandwidth affects generalization of quantum models on challenging datasets, including
those far outside our theoretical assumptions.”
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Vedaie in view of Ghukasyan and further in view of Liu et al. “Multiple Kernel k-Means Clustering with Matrix-Induced Regularization” herein referred to as Liu.
Regarding Claim 11
Vedaie in view of Ghukasyan teaches:
The system of claim 1
(see rejection of claim 1)
Vedaie in view of Ghukasyan does not explicitly teach:
wherein the regularization component maximizes regularization of the parameters with alignment of the combined kernel to a target kernel.
However, Liu teaches:
wherein the regularization component maximizes regularization of the parameters with alignment of the combined kernel to a target kernel.
(page 1888 abstract) “We theoreticcally justify this matrix-induced regularization by revealing its connection with the commonly used kernel alignment criterion. Furthermore, this justification shows that maximizing the kernel alignment for clustering can be viewed as a special case of our approach and indicates the extendability of the proposed matrix-induced regularization for designing better clustering algorithms. As experimentally demonstrated on five challenging MKL benchmark data sets, our algorithm significantly improves existing MKKM and consistently out performs the state-of-the-art ones in the literature, verifying the effectiveness and advantages of incorporating the proposed matrix-induced regularization”
Vedaie, Ghukasyan, Liu, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the plurality of kernels as taught by Vedaie in view of Ghukasyan by using the regularization taught by Liu because (Liu page 188 abstract) “As experimentally demonstrated on five challenging MKL benchmark data sets, our algorithm significantly improves existing MKKM and consistently out performs the state-of-the-art ones in the literature, verifying the effectiveness and advantages of incorporating the proposed matrix-induced regularization”
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Vedaie in view of Ghukasyan and further in view of Arora et al. (PGPUB no US 20200073742 A1) herein referred to as Arora.
Regarding Claim 14
Vedaie in view of Ghukasyan teaches:
The computer-implemented method of claim 9
(see rejection of claim 9)
Vedaie in view of Ghukasyan does not explicitly teach
further comprising preparing an input dataset with principal component analysis to reduce dimensionality of the input dataset.
However, Arora teaches:
further comprising preparing an input dataset with principal component analysis to reduce dimensionality of the input dataset.
(paragraph [0059]) “In some implementations, the machine learning module 155 pre-processes the FVNH prior to providing the FVNH as input to the machine learning model(s). The pre-processing can include reducing the dimensionality of the FVNH, e.g., using a feedforward neural network, principal component analysis (PCA), and/or another appropriate dimensionality reduction technique.”
Vedaie, Ghukasyan, Arora, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the plurality of kernels as taught by Vedaie in view of Ghukasyan by incorporating the input data preparing as taught by Arora because (Arora paragraph [0059]) “By reducing the dimensionality of the FVNHs, the speed at which the machine learning models determine potential causes of a condition of the satellite communication system can be increased and the accuracy of the machine learning models can be increased by preventing overfitting.”
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Vedaie in view of Ghukasyan and further in view of NPL reference Donini et al “Fast Hyperparameter Selection for Graph Kernels via Subsampling and Multiple Kernel Learning”.
Regarding Claim 20
Vedaie in view of Ghukasyan teaches:
The computer program product of claim 19
(see rejection of claim 19)
Vedaie in view of Ghukasyan does not explicitly teach:
wherein the program instructions are further executable to cause the processor to: combine a plurality of subsampled kernels arising from the respective kernels of the plurality of kernels.
However, Donini teaches:
wherein the program instructions are further executable to cause the processor to: combine a plurality of subsampled kernels arising from the respective kernels of the plurality of kernels.
(page 287 abstract) “Multiple Kernel Learning offers a way to approach this computational bottleneck by generating a combination of different kernels under different parametric settings. However, this solution still requires the computation of many large kernel matrices. In this paper we propose a method to efficiently select a small number of kernels on a subset of the original data, gaining a dramatic reduction in the runtime without a significant loss of predictive performance”
Vedaie, Ghukasyan, Donini, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the plurality of kernels taught by Vedaie in view of Ghukasyan by using the combination of subsampled kernels as taught by Donini because (Donini page 287 abstract) “In this paper we propose a method to efficiently select a small number of kernels on a subset of the original data, gaining a dramatic reduction in the runtime without a significant loss of predictive performance”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Grossi et al. “Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection” teaches performing feature selection using quantum techniques
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ezra J Baker whose telephone number is (703)756-1087. The examiner can normally be reached Monday - Friday 10:00 am - 8:00 pm ET.
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/E.J.B./Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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