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 the Claims
Claims 1, 4-13 have been amended. Claims 1-13 are pending.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims at a high level recite classifying and marching documents.
Step 1: Does the Claim Fall within a Statutory Category?
Yes. Claims 1-13 recite a method and a system and therefore, are directed to the statutory class of machine and a product.
The USPTO Guidance recites:
(1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes) (Step 2A, Prong 1); and
(2) additional elements that integrate the judicial exception into a practical application (Step 2A, Prong 2). MPEP §§ 2106.04(a), (d).
Only if the claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look in Step 2B to whether the claim:
(3) adds a specific limitation beyond the judicial exception that is not “well-understood, routine, conventional” in the field; or
(4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. MPEP § 2106.05(d).
Step 2A, Prong One: Is a Judicial Exception Recited?
First, determine whether the claims recite any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity, or mental processes). MPEP § 2106.04(a).
Claim 1 recites –
▪ a weight calculation circuit configured to obtain weight information for minimizing an objective function of a quantum approximate support vector machine (QASVM) algorithm (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a mathematical evaluation, which performs the determination, thereby further defining the abstract idea. A human being may use this mathematical calculation to facilitate the mental evaluation in order to arrive at the necessary determination. This claim limitation appears to recite both a mathematical formula and mental process);
▪ (i) calculating the objective function using a quantum computing circuit (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a mathematical evaluation, which performs the determination, thereby further defining the abstract idea. A human being may use this mathematical calculation to facilitate the mental evaluation in order to arrive at the necessary determination by means of using any computing tools),
▪ (ii) updating optimization parameters through heuristic optimization using a classical computing circuit based on the calculated objective function to obtain the weight information (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a mathematical evaluation, which performs the determination, thereby further defining the abstract idea. A human being may use this mathematical calculation to facilitate the mental evaluation in order to arrive at the necessary determination. This claim limitation appears to recite both a mathematical formula and mental process);
▪ a data classification circuit configured to calculate a classification score of the QASVM algorithm using the weight information obtained from the weight calculation circuit, and classify a class of input data based on the calculated classification score (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a mathematical evaluation, which performs the determination, thereby further defining the abstract idea. A human being may use this mathematical calculation to facilitate the mental evaluation in order to arrive at the necessary determination. This claim limitation appears to recite both a mathematical formula and mental process);
These limitations, based on their broadest reasonable interpretation, recite a mental process, i.e. a judicial exception. For these reasons, the independent claim 1, as well as independent claim 12, which include limitations commensurate in scope with claim 1, recite a judicial exception.
A method, like the claimed method, “a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent eligible.” See Digitech Image Techs, LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014). See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016) where collecting information, analyzing it, and displaying results from certain results of the collection and analysis was held to be an abstract idea. See In re Meyer, 688 F.2d 789, 795—96 (CCPA 1982), which held that “a mental process that a neurologist should follow” when testing a patient for nervous system malfunctions was not patentable.
Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
Next determine whether the claims recite additional elements that integrate the judicial exception into a practical application (see MPEP §§ 2106.05(a)-(c), (e)-(h)). To integrate the exception into a practical application, the additional claim elements must, for example, improve the functioning of a computer or any other technology or technical field (see MPEP § 2106.05(a)), apply the judicial exception with a particular machine (see MPEP § 2106.05(b)), or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP § 2106.05(e)).
Additional elements:
▪ data classification apparatus configured to perform data classification on a noisy intermediate scale quantum (NISQ) computer (Amount to “Apply it”. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note: the data is received from the decentralized network, thus, using the network profile data amount to merely invoking a computer (to receive the data) component to apply the exception);
▪ a weight calculation circuit, using a classical computing circuit and a data classification circuit (Amount to “Apply it”. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note: high level application of using routine computer hardware to merely invoking a computer component to apply the exception).
The term “additional elements” for claim features, limitations, or steps that the claim recites beyond the identified judicial exception. The claims do not recite any improvements to these additional elements, nor does the claims recite any particularly programmed or configured computer system, device, or machine learning. Rather, the additional elements in claims 1 and 12 serve merely to automate the abstract idea. See Int’l Bus. Machs. Corp. v. Zillow Group, Inc., 50 F. 4" 1371, 1382 (Fed. Cir. 2022) (“[A] patent that ‘automate[s] “pen and paper methodologies” to conserve human resources and minimize errors’ is a ‘quintessential “do it on a computer” patent’ directed to an abstract idea.”) (quoting Univ. of Fla. Rsch. Found., Inc. v. Gen. Elec. Co., 916 F.3d 1363, 1367 (Fed. Cir. 2019)). Therefore, none of these recited additional elements, whether considered individually or in combination, integrates the judicial exception into a practical application.
The additional elements listed above that relate to computing components are recited at a high level of generality (i.e., as generic components performing generic computer functions such as communicating and processing known data) such that they amount to no more than mere instructions to apply the exception using generic computing components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, the claims do not purport to improve the functioning of the computer itself. There is no technological problem that the claimed invention solves. Rather, the computer system is invoked merely as a tool. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, these claims are directed to an abstract idea.
For these reasons, independent claim 1, as well as independent claim 12, which include similar additional elements as claim 1, are directed to an abstract idea.
Step 2B: Does the Claim Provide an Inventive Concept?
Next, determine whether the claims recite an “inventive concept” that “must be significantly more than the abstract idea itself, and cannot simply be an instruction to implement or apply the abstract idea on a computer.” BASCOM Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016); see MPEP § 2106.05(d). There must be more than “computer functions [that] are “well-understood, routine, conventional activit[ies]’ previously known to the industry.” Alice Corp. v. CLS Bank Int'l, 573 U.S. 208, 225 (2014) (second alteration in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 73 (2012)); see MPEP § 2106.05(d).
Step 2B: The additional elements are not sufficient to amount to significantly more than the judicial exception.
Additional elements: (see MPEP 2106.05(d)(Il). Taking the claim elements separately, the function performed by the computer at each step of the process is purely conventional. Using a computer and associated computer network to obtain data, use data to identify other data, and comparing data, are some of the most basic functions of a computer. All of these computer functions are well-understood, routine, conventional activities previously known to the industry. The method claims do not, for example, purport to improve the functioning of the computer itself. Nor do they effect an improvement in any other technology or technical field. Instead, the claims at issue amount to nothing significantly more than an instruction to apply the abstract idea of displaying, processing and storing data using some unspecified, generic computer).
No “inventive concept” sufficient to transform the abstract method of organizing human activity into a patent-eligible application. See MPEP § 2106.05. Rather, the additional elements identified above are merely well-understood, conventional computer components, as confirmed by the Specification. See MPEP § 2106.05(d)(1). For example, the Specification refers to the additional elements in generic terms.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements relating to computing components amount to no more than applying the exception using a generic computing components. Mere instructions to apply an exception using a generic computing component cannot provide an inventive concept. Furthermore, the broadest reasonable interpretation of the claimed computer components (i.e., additional elements) includes any generic computing components that are capable of being programmed to communicate and process known data.
Additionally, the computer components are used for performing insignificant extra-solution activity and well understood, routine, and conventional functions. For example, the claimed processor and machine learning merely communicates and processes known data. Activities such as these are insignificant extra-solution activity and, therefore, well understood, routine, and conventional. See MPEP 2106.05(d); see also, e.g., OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price); CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (Obtaining information about transactions using the Internet to verify credit card transactions); Ultramercial, Inc. v. Hulu, LLC, 772 F.3d at 715, 112 USPQ2d at 1754 (Consulting and updating an activity log); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) (Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display); Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016) (Recording a customer’s order); Return Mail, Inc. v. U.S. Postal Service, -- F.3d --, -- USPQ2d --, slip op. at 32 (Fed. Cir. August 28, 2017) (Identifying undeliverable mail items, decoding data on those mail items, and creating output data); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015) (Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price). Furthermore, limitations such as integrating account details are well-understood, routine, and conventional activity. See Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log).
Independent system claims 1 and 12 contain the identified abstract ideas, with the additional elements of a processor, hardware and the media, which is a generic computer component, and thus not significantly more for the same reasons and rationale above.
Dependent claims further describe the abstract idea. The additional elements of the dependent claims fail to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea.
With respect to claims 2-5, 13:
Step 2A Prong 1: the claims recite a judicial exception (an abstract idea)
▪ claims further recite data analyzing data and applying further mathematical reasonings, such as SVM, VQA algorithms and mathematical expressions(i.e. engagement marker measures, set of weights includes positive and negative weights, receive a numerical weight value, using acyclic graph calculation and a formula) (Abstract Idea of a mental process. Under the broadest reasonable interpretation, the obtaining/determining probability distribution and divergence, as drafted, is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a mathematical evaluation, which performs the determination, thereby further defining the abstract idea. A human being may use this mathematical calculation to facilitate the mental evaluation in order to arrive at the necessary determination. This claim limitation appears to recite both a mathematical formula and mental process).
Step 2A Prong 2: the additional elements that are not sufficient to integrate the judicial exception into a practical application. Additional elements: no additional elements recited. Step 2B: the additional element is not sufficient to amount to significantly more than the judicial exception.
With respect to claims 6-9, 10:
Dependent claims 6-9 recite a judicial exception by applying additional mathematical reasoning and logical processing by using a classical heuristic optimization technique. The claims disclose assigning weights to profiles, either numerically or by a slider (Abstract Idea of a mental process. Under the broadest reasonable interpretation, the obtaining/determining probability distribution and divergence, as drafted, is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — This claim limitation appears to recite both a mathematical formula and mental process.
Step 2A Prong 2: the additional elements that are not sufficient to integrate the judicial exception into a practical application.
Additional elements: first and quantum circuits (However, without any explicitly recited hardware, quantum circuits are fundamentally mathematical in the context of quantum circuit theory, representing an abstract vector or density matrix, and conventional activities previously known to the industry. Generic computer implementation does not provide significantly more than the abstract idea).
Step 2A Prong 2: the additional elements that are not sufficient to integrate the judicial exception into a practical application.
With respect to claim 11:
Step 2A Prong 1: the claims recite a judicial exception (an abstract idea)
▪ generate an input state required for a classification protocol, and a binary classification circuit configured to perform binary classification by performing a swap test on qubits of an input state (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — Performing binary classification using a swap test on qubits is fundamentally a mathematical operation and conceptual framework- see MPEP 2106.05(f))).
Additional elements: generation unit (a generic computer functions of receiving and processing that are well-understood, routine, and conventional activities previously known to the industry. Extracting caption data and natural text processing are merely extra-solution activities and does not meaningfully limit the independent claims. Generic computer implementation does not provide significantly more than the abstract idea. Amount to no more than mere instructions to apply the abstract idea using a generic computer component- see MPEP 2106.05(f))).
Step 2B: the additional element is not sufficient to amount to significantly more than the judicial exception. Therefore, the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
In summary, the present claims recite the abstract idea of performing mathematical calculations associated with optimization of a QASVM objective function to determine weights and calculating classification score using the determined weights. The recited quantum and classical computing circuits merely apply the mathematical calculations using computing components and do not integrate the mathematical concept into a practical application because the claims do not recite any improvements to the functionating of the NISQ computer, quantum computing technology pr another technological field.
As such, the claims are not patent eligible.
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, 4-6, 9-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Havlíček et al. “Supervised learning with quantum-enhanced feature space” in view of Willsch et al. “Support vector machines on the D-Wave quantum annealer” and in further view of Vall-llosera et al. (US 2022/0366314).
NOTE - there are two references Havlíček et al. “Supervised learning with quantum-enhanced feature space” (2019), hereafter Havlíček and Havlíček et al. “Supervised learning with quantum-enhanced feature space” (2018), which construed to be a single reference. Hereafter Havlíček and Havlíček 2018 respectively.
Regarding claim 1, Havlíček / Havlíček 2018 teaches a data classification apparatus configured to perform data classification on a noisy intermediate scale quantum (NISQ) computer (Havlíček p.209 C1), the apparatus comprising:
a weight calculation circuit configured to obtain weight information for minimizing an objective function of a quantum approximate support vector machine (QASVM) (Havlíček p.210 C1 -“During the training of the classifier we optimize the parameters (θ, b). For the optimization, we need to define a cost function. For a single training sample we use the error probability … First, we train the classifier and optimize (θ, b). We have found that Spall’s simultaneous perturbation stochastic approximation algorithm performs well in the noisy experimental setting. We can use the circuit as a classifier after the parameters have converged to (θ∗, b∗)”, wherein the weight calculation unit represent weights (parameters) to minimize the objective) (see NOTE) algorithm by:
(i) calculating the objective function using a quantum computing circuit (Havlíček p.210 C1 starting at 2nd par. Havlíček 2018 p.4 see “classification rule can be restated in the familiar SVM form …”) (see NOTE~), and
(ii) updating optimization parameters through heuristic optimization using a classical computing circuit based on the calculated objective function to obtain the weight information (Havlíček p.210 starting at “we train the classifier and optimize (θ, b)”, p.211 C1, F3a ) (see NOTE~); and
a data classification circuit configured to calculate a classification score of the QASVM algorithm using the weight information obtained from the weight calculation circuit, and classify a class of input data based on the calculated classification score (Havlíček p.210 C1
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, “We can use the circuit as a classifier after the parameters have converged”, which describes data classification – after obtaining optimized parameter θ (weights), the classification circuit computes a classification score / probability
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(or empirical
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) and assigns a class label based on the comparison with a bias b).
NOTE Havlíček teaches weights (parameters as weights) are optimized to minimize cost that approximates SVM-style classification, however, Havlíček doesn’t explicitly teach a quantum approximate support vector machine (QASVM). Instead, Havlíček calls it “quantum variation classifier” in direct analogy to SVM, not QASVM). However, it is obvious and reasonable to conclude that the “variational circuit classifier corresponds to a separating hyperplane in the quantum feature space and implements a linear threshold function as used in a conventional SVM” (p.211 C1) is an obvious variation of the QASVM.
However, to further obviate such reasoning, Willsch discloses a weight calculation circuit configured to obtain weight information [-as the trained dual weights of an SVM/ QASVM-] for minimizing an objective function of a quantum approximate support vector machine (QASVM) (QASVM) (p.2 ¶2.1 Eqs. (3)–(5) is the so-called dual formulation of an SVM” and ¶2.2 wherein quantum annealer formulated as a QUBO, the solution is the weight information see “train SVMs on a D-Wave 2000Q quantum annealer”).
NOTE~ - Further, Havlíček teaches (i) calculating the objective function using a quantum computing circuit (i.e. calculating its VQS cost Remp using a quantum circuit). Havlíček does not explicitly teach, however Willsch discloses (i) calculating the objective function [-of a QASVM-] using a quantum computing circuit (p.1 C1L1-20 “A QUBO problem is defined as the minimization of the energy function and ¶2.1 Eqs. (3)).
Analogously, Havlíček does not explicitly teach, however Willsch discloses (ii) … obtain the weight information [-of a QASVM-] (¶2.1 Eqs. (3)-(5) and Fig.1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Havlíček to include minimizing an objective function of a quantum approximate support vector machine (QASVM) as disclosed by Willsch. Doing so provides an exponential speedup quantum algorithm (Willsch Abstract / Conclusion).
Further, if Havlíček as modified does not explicitly teach, however Vall-llosera discloses a data classification circuit (that is separate from the trainer) configured to calculate a classification score of the QASVM algorithm using the weight information obtained from the weight calculation circuit, and classify a class of input data based on the calculated classification score ([0049] “quantum circuit for each test vector to be classified, parameters ( e.g. the parameters of the classification boundary) determined after executing the matrix inversion and training parts once may be used”, [0079]-[0084]).
I.e. Vall-llosera teaches a system architecture of an “apparatus … comprising” the two separate circuits.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Havlíček to include apparatus with two separate circuits as disclosed by Vall-llosera. Doing so improves both the kernel matrix computation part and the classification part of a support vector machine (Vall-llosera [0049]).
Regarding claim 2, Havlíček as modified teaches the apparatus of claim 1, wherein the QASVM algorithm is an algorithm that approximates an optimization problem of a support vector machine (SVM) algorithm (Havlíček p.209 C1, p.211 C1, Havlíček 2018 p.9).
Regarding claim 4, Havlíček as modified teaches the apparatus of claim 1, wherein the weight calculation circuit is configured to calculate the weight information by using variational quantum algorithms (VQA) (Havlíček p.209 C1, p.210 C1, p.211 C1, Havlíček 2018 p.11).
Regarding claim 5, Havlíček as modified teaches the apparatus of claim 1, wherein the weight calculation circuit comprises an objective function calculation circuit configured to calculate an objective function of the QASVM algorithm (Willsch ¶2.1 Eqs. (3)-(5) and Fig.1), and a parameter update circuit configured to update the optimization parameters (Havlíček pp.210-211, F1, see variational optimization loop where parameters are updated iteratively based on cost (objective function and pp.209-210 quantum circuits for kernel estimation or scoring combined classically), Vall-llosera [0049] [0079]-[0084]).
Regarding claim 6, Havlíček as modified teaches the apparatus of claim 5, wherein the objective function calculation circuit comprises a first quantum circuit configured to calculate a first part of the objective function of the QASVM algorithm (Willsch ¶2.1 Eqs. (3)-(5) and Fig.1), and a second quantum circuit configured to calculate a second part of the objective function of the QASVM algorithm (Havlíček pp.210-211 see “feature map circuit” prepares the encoded state (part one) and “Variational circuit used for our optimization” (part two), the total objective is combined from outputs of the two units, which obviously describes objective (risk/cost) being evaluated by combining outputs from separate circuit parts, Vall-llosera [0049], [0079]-[0084]).
Regarding claim 9, Havlíček as modified teaches the apparatus of claim 5, wherein the parameter update circuit is configured to update the optimization parameters by using a classical heuristic optimization technique (Havlíček p.210 C1 see SPSA, F3, Willsch ¶2.1-2.2).
Regarding claim 10, Havlíček as modified teaches the apparatus of claim 1, wherein the data classification circuit is configured to calculate a classification score of the QASVM algorithm by using a predetermined quantum circuit (Havlíček 2018 p.3 C1, p.15-16, Willsch ¶2.1-2.2,. Vall-llosera [0049], [0079]-[0084]).
Regarding claim 11, Havlíček as modified teaches the apparatus of claim 1, wherein the data classification circuit comprises an input state generation circuit configured to generate an input state required for a classification protocol, and a binary classification circuit configured to perform binary classification by performing a swap test on qubits of an input state (Havlíček 2018 p.9, p.18-19, Fig.S5).
NOTE Rebentrost as previously cited likewise discloses performing a swap test on qubits of an input state on p.2 C2 last par., p.3 C1 and further obviates the teachings of Havlíček as modified.
Regarding claim 13, Havlíček as modified teaches the apparatus of claim 4, wherein the VQA comprises of a parameterized quantum circuit controlled with an optimizer algorithm (Havlíček p.209 C1, p.210 C1, p.211 C1, Havlíček 2018 p.11-12, p.15 see “classical optimizer”, Willsch ¶2.1-2.2).
Regarding claim 12, Havlíček teaches a data classification method feasible on a noisy intermediate scale quantum (NISQ) computer, the method comprising: obtaining weight information for minimizing an objective function of a quantum approximate support vector machine (QASVM) algorithm by (i) calculating the objective function using a quantum computing circuit; and (ii) updating optimization parameters for minimizing the objective function through heuristic optimization using a classical computing circuit based on the calculated objective function to obtain the weight information; calculating a classification score of the QASVM algorithm using the obtained weight information; and classifying a class of input data based on the calculated classification score.
Claim 12 recites substantially the same limitations as claim 1, and is rejected for substantially the same reasons.
Claim 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Havlíček as modified and in further view of Rebentrost et al. “Quantum Support Vector Machine for Big Data Classification”.
Regarding claim 3, Havlíček as modified does not explicitly teach, however Rebentrost disclose the apparatus of claim 1, wherein an objective function
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of the QASVM algorithm is defined by a mathematical expression,
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where x is data, y is a class of data, a is a weight of data, C is a hyperparameter, and
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is a kernel function (Rebentrost p.1 Eq (1), Havlíček pp.211-212).
NOTE the claimed form (minimization with
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is a well-known reformulation of the soft-margin dual (via Lagrangian techniques). Rebentrost explicitly teaches quantum algorithm to solve this optimization for weights
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on a quantum computer. Havlíček likewise teaches p.211 C1“implements a linear threshold function as used in a conventional SVM,” while not exact minimization form with the
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term, it teaches approximating SVM objective (including regularization C) in a quantum variational setting on the NISQ. See specifically Havlíček 2018 p.5 C1, p.10 Eq (4, 6) see dual Lagrangian.
Thus, while the exact formula is not explicitly disclosed, the particular elements are obvious and are known in the art, and any particular equation would be an obvious to try combination of elements in order to achieve a predictable results. See MPEP 2143.
Such formula is also obvious in view of the applicant’s admitted prior art Park et al. “The theory of the quantum kernel-based binary classifier” (IDS 12/28/2023) see p.2 ¶2.1 eq.2-3.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Havlíček to include quantum approximate support vector machine (QASVM) as disclosed by Rebentrost. Doing so provides an exponential speedup quantum algorithm (Rebentrost Abstract / Conclusion).
Also see analogous art - Koudai Shiba et al. "Variational Quantum Support Vector Machine based on Deutsch- Jozsa Ranking" likewise disclosed claim 3 on p.64 eq (3) and further obviated the teachings of Havlíček and Rebentrost.
Claims 7-8, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Havlíček as modified and in further view of the applicant’s admitted prior art - Park et al. “The theory of the quantum kernel-based binary classifier” (IDS 12/28/2023) or Park et al. “Circuit-Based Quantum Random Access Memory for Classical Data”, hereafter Park I and Park II respectively.
Regarding claim 7, Havlíček as modified teaches the apparatus of claim 6, wherein the first quantum circuit comprises an input state generation circuit configured to convert classical data into data in a quantum state by using function calculation unit configured to calculate the first part of the objective function by performing a swap test on qubits of an input state (Havlíček 2018 p.18-19, Fig.S5).
Havlíček as modified does not explicitly teach, however Park discloses a quantum state by using amplitude encoding (Park I p.6 see Discussion), and an objective function calculation unit configured to calculate the first part of the objective function by performing a swap test on qubits of an input state (Park I Fig.4, p.6 lines 1-15, Park II p.2 ¶2, p.5 ¶3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Havlíček as modified to include amplitude encoding as disclosed by Park. Doing so provides an efficient procedure to encode classical data in quantum superposition states (Park Abstract).
Regarding claim 8, Havlíček as modified teaches the apparatus of claim 6, wherein the second quantum circuit comprises an input state generation circuit configured to convert classical data into data in a quantum state by using
Havlíček as modified does not explicitly teach, however Park discloses using amplitude encoding (Park I p.6 see Discussion), and an objective function calculation unit configured to calculate the second part of the objective function by performing a CNOT operation on qubits of the input state (Park I p.7, Fig.5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Havlíček as modified to include amplitude encoding as disclosed by Park. Doing so provides an efficient procedure to encode classical data in quantum superposition states (Park Abstract).
Regarding claim 11, Havlíček as modified teaches the apparatus of claim 1 as disclosed above, Park additionally discloses, wherein the data classification circuit comprises an input state generation circuit configured to generate an input state required for a classification protocol, and a binary classification circuit configured to perform binary classification by performing a swap test on qubits of an input state (Park I Fig.4, p.6 lines 1-15, Park II p.2 ¶2, p.5 ¶3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Havlíček as modified to include a swap test on qubits of an input state as disclosed by Park. Doing so provides an efficient procedure to encode classical data in quantum superposition states (Park Abstract).
Claims 4, 13 is/are additionally rejected under 35 U.S.C. 103 as being unpatentable over Havlíček as modified and in further view of Cerezo et al. “Variational quantum algorithms”.
Regarding claim 4, Havlíček as modified teaches the apparatus of claim 1 as disclosed above, Cerezo additionally discloses wherein the weight calculation circuit is configured to calculate the weight information by using variational quantum algorithms (VQA)(p.1 C2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Havlíček as modified to include variational quantum algorithms (VQA) as disclosed by Cerezo. Doing so provides an efficiency and reliability (Cerezo p.6 D.).
Regarding claim 13, Havlíček as modified teaches the apparatus of claim 4, wherein the VQA comprises a parameterized quantum circuit controlled with an optimizer algorithm (Cerezo p.1 C2).
◊ Claims 1 and 12 is/are additionally and/or alternatively rejected under 35 U.S.C. 103 as being unpatentable over Vall-llosera et al. (US 2022/0366314) in view of Havlíček et al. “Supervised learning with quantum-enhanced feature space”, NOTE - there are two references Havlíček et al. “Supervised learning with quantum-enhanced feature spaces” (2019), hereafter Havlíček and Havlíček et al. “Supervised learning with quantum-enhanced feature spaces” (2018), which construed to be a single reference. Hereafter Havlíček and Havlíček 2018 respectively and in further view of and in further view of Vall-llosera et al. (US 2022/0366314).
Regarding claim 1, Vall-llosera teaches a data classification apparatus configured to perform data classification
a weight calculation circuit configured to obtain weight information for minimizing an objective function of a quantum approximate support vector machine (QASVM) algorithm ([0049])(see NOTE) by:
(i) calculating the objective function using a quantum computing circuit ([0022]), and
(ii)
a data classification circuit configured to calculate a classification score of the QASVM algorithm ([0021], [0049]-[0050]) using the weight information obtained from the weight calculation circuit ([0059], [0079]), and classify a class of input data based on the calculated classification score ([0074]-[0075], [0077]-[0079], [0084]).
Vall-llosera does not explicitly teach, however Havlíček / Havlíček 2018 discloses data classification on a noisy intermediate scale quantum (NISQ) computer (p.209 C1),
weight information for minimizing an objective function of a quantum approximate support vector machine (QASVM) algorithm (Havlíček p.210 C1 -“During the training of the classifier we optimize the parameters (θ, b). For the optimization, we need to define a cost function. For a single training sample we use the error probability … First, we train the classifier and optimize (θ, b). We have found that Spall’s simultaneous perturbation stochastic approximation algorithm performs well in the noisy experimental setting. We can use the circuit as a classifier after the parameters have converged to (θ∗, b∗)”, wherein the weight calculation unit represent weights (parameters) to minimize the objective).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Vall-llosera to perform data classification on a noisy intermediate scale quantum (NISQ) computer as disclosed by Havlíček. Doing so would allow a use of quantum-enhanced feature space that is efficiently accessible on a quantum computer and provides a possible path to quantum advantage (Havlíček Abstract).
NOTE if Vall-llosera does not explicitly teach the name or construction of a quantum approximate support vector machine (QASVM). However, to further obviate such reasoning, Willsch discloses a weight calculation circuit configured to obtain weight information [-as the trained dual weights of an SVM/ QASVM-] for minimizing an objective function of a quantum approximate support vector machine (QASVM) (QASVM) (p.2 ¶2.1 Eqs. (3)–(5) is the so-called dual formulation of an SVM” and ¶2.2 wherein quantum annealer formulated as a QUBO, the solution is the weight information see “train SVMs on a D-Wave 2000Q quantum annealer”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Vall-llosera to include minimizing an objective function of a quantum approximate support vector machine (QASVM) as disclosed by Willsch. Doing so provides an exponential speedup quantum algorithm (Willsch Abstract / Conclusion).
Further, if Vall-llosera does not explicitly teach, however Havlíček and Willsch disclose -
(i) calculating the objective function using a quantum computing circuit (Havlíček p.210 C1 starting at 2nd par. Havlíček 2018 p.4 see “classification rule can be restated in the familiar SVM form …”, Fig.3, Willsch p.2 ¶2.1-2.2) and
(ii) updating optimization parameters through heuristic optimization (Havlíček p.210 starting at “we train the classifier and optimize (θ, b)”, p.211 C1, F3a, Willsch p.2 ¶2.1-2.2)
Further, Havlíček and Willsch teach a classification score of the QASVM algorithm (Havlíček p.211 C1, Willsch p.2 ¶2.1-2.2)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Vall-llosera to include (i) and (ii) as disclosed by Havlíček and Willsch. Doing so would allow for optimization of training objective on a quantum circuit i.e. use the SVM dual that Willsch minimizes heuristically to obtain α) which is already implemented by Vall-llosera.
Response to Arguments
◊ Applicant's arguments filed 09/04/2026, with respect to the rejection under 35 USC 101 have been fully considered but they are not persuasive.
The claims are directed to applying fundamental mathematical calculations, such as mathematical optimization of a QASVM objective function to obtain weight and using the resulting weights to calculate a classification score. The claim requires QASVM implemented using a quantum computing circuit on a NISQ computer with a particular hybrid quantum / classical architecture.
However, under the current USPTO guidance merely putting a mathematical calculation into a particular computing environment does not automatically ingrate the mathematical concept into a practical application. The present claims do not impose meaningful technological limitation or demonstrate an improvement to the technology itself. The present claims, as currently filed do not particularly state that the QASVM improves the NOSQ computer.
Under the 2019 Revised Guidance, the claims are evaluated to determine if additional elements that integrate the judicial exception into a practical application (see Manual of Patent Examining Procedure ("MPEP") §§ 2106.05(a)-(c), (e)-(h)). See 2019 Revised Guidance, 84 Fed. Reg. at 51-52, 55. Acclaim that integrates a judicial exception into a practical application applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. See 2019 Revised Guidance, 84 Fed. Reg. at 54.
For example, limitations that are indicative of "integration into a practical application" include:
- Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP § 2106.05(a);
- Applying the judicial exception with, or by use of, a particular machine - see MPEP § 2106.05(b);
- Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP §2106.05(c); and
- Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP § 2106.05(e).
In contrast, limitations that are not indicative of "integration into a practical application" include:
- Adding the words "apply it" (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP § 2106.05(+);
- Adding insignificant extra-solution activity to the judicial exception- see MPEP § 2106.05(g); and
- Generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h).
See 2019 Revised Guidance, 84 Fed. Reg. at 54-55 ("Prong Two’).
In view of the 2019 Revised Guidance, one must consider whether there are additional elements set forth in the claims that integrate the judicial exception into a practical application. The identified additional non-abstract element recited in the only independent claim is: scoring model, exploration model and a processor and a memory (claim 12). These generic computer hardware merely perform generic computer functions of receiving, processing and transmitting data and represent a purely conventional implementation of applicant's determining of an event timeline and do not represent significantly more than the abstract idea. See at least MPEP § 2106.05(a) ("Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field").
The claims do present any other issues as set forth in the 2019 Revised Guidance regarding a determination of whether the additional generic elements integrate the judicial exception into a practical application. See Revised Guidance, 84 Fed. Reg. at 55. Rather, the claims on appeal merely use instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea. The claims do not recite improvements to the functioning of a computer or any other technology field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, the claims to do apply the abstract idea with a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (e.g. data remains data even after processing; MPEP 2106.05(c)), the claims no not apply or use the abstract idea in some other meaningful way beyond generally linking the user of the abstract idea to a particular technological environment (i.e. a generic computer) such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea (MPEP 2106.05(e)). The recited generic computing elements are no more than mere instructions to apply the exception using a generic computer component.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, under Step 2A, Prong Two (MPEP §§ 2106.05(a)-(c) and (e) (h)), the claims do not integrate the judicial exception into a practical application.
The present claims recite the abstract idea of performing mathematical calculations associated with optimization of a QASVM objective function to determine weights and calculating classification score using the determined weights. The recited quantum and classical computing circuits merely apply the mathematical calculations using computing components and do not integrate the mathematical concept into a practical application because the claims do not recite any improvements to the functionating of the NISQ computer, quantum computing technology pr another technological field.
The applicant is advised, in view of the specification to particularly establish that the present hybrid QASVM architecture produces a technological improvement in the NISQ computing or specifically solves a NISQ specific technical problem (i.e. noise, limited qubit count, depth, measurement overhead, optimization instability, etc.), instead of merely using QASVM to classify data on NISQ computer without demonstrating a technological improvement to quantum computing.
Based on the above, the rejection is maintained.
◊ Havlíček With respect to the rejection under 35 USC 103 and the reference of Havlíček, the arguments (see page 13 of the Remarks) are not persuasive.
Applicant attacks Havlíček’s quantum-kernel estimator, in which a classical SVM solver operates on a precomputed kernel. The rejection relies on Havlíček’s variational quantum classifier, wherein Havlíček calculates a classification objective on a quantum circuit, updates variational parameters with a classical heuristic (SPSA) and uses the converged parameters as the trained weights of an SVM-like decision rule, which is analogous to the processing sequence of claim 1.
The applicant’s remaining distinction is that Havlíček’s cost is not denominates the “QASVM objective function” and thus, the distinction is mostly a renaming of the Havlíček’s iteration (loop) However, the claim 1 does not recites or requires a particular formula. Havlíček treats the VQC as the SVM analogue –
“variational quantum circuit to classify the data in a way similar to the method of conventional SVMs” (Havlíček); “variational quantum circuit to classify a training set in direct analogy to conventional SVMs” (Havlíček 2018). The empirical risk Remp(θ) is evaluated from quantum shots and “is fed to the classical optimizer” SPSA -250 iterations (Fig 3) and then classify with (θ∗, b∗=0). Wherein QKE is in a different embodiment. However, distinguishing QKE does not distinguish VQC.
The applicant argues – “the VQC does not teach or suggest the claimed processing sequence.” However, that is exactly Havlíček VQC. The applicant admits the only missing word is QA0SVM, i.e. the cost in snot “the QASVM objective”. That is a labeling argument, not a sequence argument.
Still, Havlíček clearly teaches the required sequence – Quantum circuit evaluate the classifier / cost decision expectation and Classical circuit – heuristic update of the parameters that define the separator.
The applicant is correct, Havlíček does not teach “objective function using a quantum computing circuit”. However, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. Havlíček already says VQC is an SVM hyperplane in features space and that the optimal w can instead be found from the “Wolfe-dual of the SVM.” Thus, replacing Emerical risk with the SVM dual / least-square SVM objective while keeping the same quantum evaluation and SPSA loop is the obvious use of the SVM dual as a known convex objective.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/POLINA G PEACH/ Primary Examiner, Art Unit 2165 September 15, 2026
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