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 .
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 unit configured to calculate 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);
▪ a data classification unit configured to calculate a classification score of the QASVM algorithm using the weight information obtained from the weight calculation unit, 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 feasible on a quantum computer, such as a noise 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 unit and a data classification unit (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 unit 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. As such, the claims are not patent eligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1 - the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
The dependent claims further carry the same deficiency and likewise rejected.
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 1-6, 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 Rebentrost et al. “Quantum Support Vector Machine for Big Data Classification”.
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 teaches a data classification apparatus feasible on a quantum computer, such as a noise intermediate scale quantum (NISQ) computer (p.209 C1), the apparatus comprising:
a weight calculation unit configured to calculate weight information for minimizing an objective function of a quantum approximate support vector machine (QASVM) (see NOTE) algorithm (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 ); and
a data classification unit configured to calculate a classification score of the QASVM algorithm using the weight information obtained from the weight calculation unit, and classify a class of input data based on the calculated classification score (p.210 C1
PNG
media_image1.png
160
328
media_image1.png
Greyscale
, “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
PNG
media_image2.png
16
28
media_image2.png
Greyscale
(or empirical
PNG
media_image3.png
19
25
media_image3.png
Greyscale
) 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, Rebentrost discloses quantum approximate support vector machine (QASVM)(Abstract).
Rebentrost discloses quantum algorithm, for solving the SVM optimization and using resulting model for classification scoring. Thus, Rebentrost also discloses a data classification unit configured to calculate a classification score of the QASVM algorithm using the weight information obtained from the weight calculation unit (see Abstract, p.210 C1)
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).
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 3, Havlíček as modified teaches the apparatus of claim 1, wherein an objective function
PNG
media_image4.png
29
49
media_image4.png
Greyscale
of the QASVM algorithm is defined by a mathematical expression,
PNG
media_image5.png
48
357
media_image5.png
Greyscale
where x is data, y is a class of data, a is a weight of data, C is a hyperparameter, and
PNG
media_image6.png
13
25
media_image6.png
Greyscale
is a kernel function (Rebentrost p.1 Eq (1), Havlíček pp.211-212).
NOTE the claimed form (minimization with
PNG
media_image7.png
40
64
media_image7.png
Greyscale
is a well-known reformulation of the soft-margin dual (via Lagrangian techniques). Rebentrost explicitly teaches quantum algorithm to solve this optimization for weights
PNG
media_image8.png
13
13
media_image8.png
Greyscale
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
PNG
media_image7.png
40
64
media_image7.png
Greyscale
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
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.
Regarding claim 4, Havlíček as modified teaches the apparatus of claim 1, wherein the weight calculation unit 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 unit comprises an objective function calculation unit configured to calculate an objective function of the QASVM algorithm (Rebentrost p.2 C2), and a parameter update unit configured to update parameters of the objective function (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)).
Regarding claim 6, Havlíček as modified teaches the apparatus of claim 5, wherein the objective function calculation unit comprises a first quantum circuit configured to calculate a first part of the objective function of the QASVM algorithm, 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).
Regarding claim 9, Havlíček as modified teaches the apparatus of claim 5, wherein the parameter update unit is configured to update the parameters of the objective function by using a classical heuristic optimization technique (Havlíček p.210 C1 see SPSA, F3).
Regarding claim 10, Havlíček as modified teaches the apparatus of claim 1, wherein the data classification unit 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).
Regarding claim 11, Havlíček as modified teaches the apparatus of claim 1, wherein the data classification unit comprises an input state generation unit configured to generate an input state required for a classification protocol, and a binary classification unit 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, Rebentrost p.2 C2 last par., p.3 C1).
Regarding claim 13, Havlíček as modified teaches the apparatus of claim 4, 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”).
Regarding claim 12, Havlíček teaches a data classification method feasible on a noise intermediate scale quantum (NISQ) computer, the method comprising: calculating weight information for minimizing an objective function of a quantum approximate support vector machine (QASVM) algorithm; calculating a classification score of the QASVM algorithm using the calculated 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.
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 unit configured to convert classical data into data in a quantum state by using
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 unit 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 unit comprises an input state generation unit configured to generate an input state required for a classification protocol, and a binary classification unit 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 unit 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, the VQA comprises of a parameterized quantum circuit controlled with an optimizer algorithm (Cerezo p.1 C2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is indicated on PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to POLINA G PEACH whose telephone number is (571)270-7646. The examiner can normally be reached Monday-Friday, 9:30 - 5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached at 571-270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/POLINA G PEACH/Primary Examiner, Art Unit 2165 June 1, 2026
Prosecution Insights