DETAILED ACTION
This action is in response to the Applicant Response filed 22 January 2026 for application 17/621,499 filed 21 December 2021.
Claim(s) 37, 44 is/are currently amended.
Claim(s) 33-34 is/are cancelled.
Claim(s) 31-32, 35-44 is/are pending.
Claim(s) 37-42, 44 is/are rejected.
Claims 31-32, 35-36, 43 are allowed.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 22 January 2026 has been entered.
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 103 rejections of claims 37-42, 44 have been fully considered but are not persuasive.
Applicant argues that Li does not teach inversion of the kernel matrix. Examiner respectfully disagrees. Examiner first notes that refences to Li are based on the Experimental Realization of a Quantum Support Vector Machine reference as cited in the specification not the Experimental Realization of Quantum Artificial Intelligence reference, which appear to be the basis of some of applicant’s arguments. The specification states that the quantum circuit includes matrix inversion and the operation of the quantum circuit is taught by Li (Specification, p. 2). Moreover, the matrix inversion described in Figure 8 of the instant application is also demonstrated in Figure 3 of Li. The specification of the instant application goes on to state that the QSVM classification circuit shown in Li comprises the matrix inversion part. Therefore, Li teaches the matrix inversion.
The remainder of applicant’s arguments are based on the newly amended subject matter or references not cited by Examiner. All arguments are addressed in the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims below.
Therefore claims 37-42, 44 stand rejected under 35 U.S.C. 103.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 37-42, 44 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 37, 44 recite wherein the measurement comprises a vector of classification parameters. However, the specification do does not provide support for the measurement comprising a vector of classification parameters. While the specification provides for determining a measurement of a status of the at least one qubit and classifying a vector using the classical computer based on the measurement (Specification, p. 14; Figure 9), the specification does not provide support for measurement comprising a vector of classification parameters. Additionally, claims 37, 44 recite an inversion of the kernel matrix. While the specification discusses matrix inversion which is describe in the Li reference (which is cited as [2] in the specification list of references), applicant argues that the claimed kernel inversion is different than the Li reference. Therefore, the specification does not provide support for inversion of the kernel matrix. Clarification or correction is required.
Examiner’s Note: For the purposes of examination, the limitations will be interpreted as the measurement providing a basis for the classification of vectors and the matrix inversion as described in Li.
Claims 38-42 are rejected under 35 U.S.C. 112(a) due to their dependence, either directly or indirectly, on claims 37, 44.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 37-40, 44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Havlicek et al. (Supervised Learning with Quantum Enhanced Feature Spaces, hereinafter referred to as “Havlicek”) in view of Li et al. (Experimental Realization of a Quantum Support Vector Machine, hereinafter referred to as “Li”).
Regarding claim 37 (Currently Amended), Havlicek teaches a method of classifying a test vector using a support vector machine algorithm, the method comprising:
determining a kernel matrix of the support vector machine algorithm based on a plurality of training vectors, each training vector representing a respective classification (Havlicek, p. 5, Quantum Kernel Estimation – teaches determining the kernel matrix based on training vectors representing classifications);
executing, using a quantum computing device, a linear equation solution algorithm on qubits of the quantum computing device based on … the kernel matrix (Havlicek, p. 5, Quantum Kernel Estimation – teaches applying two consecutive feature map circuits; see also Havlicek, p. 2, Quantum Feature Map);
manipulating at least one of the qubits of the quantum computing device based on the plurality of training vectors (Havlicek, p. 5, Quantum Kernel Estimation – teaches manipulating qubit states based on training vectors representing classifications);
determining a measurement of a status of the at least one qubit (Havlicek, p. 5, Quantum Kernel Estimation – teaches performing measurements and recording results), wherein the measurement comprises a vector of classification parameters (Havlicek, p. 5, Quantum Kernel Estimation – teaches classifying vectors based on the measurements); and
classifying a test vector using a classical computing device based on the measurement (Havlicek, p. 5, Quantum Kernel Estimation – teaches classifying 10 different test sets on a classical computing device).
However, Havlicek does not teach the inverse of the kernel matrix.
Li teaches executing, using a quantum computing device, a linear equation solution algorithm on qubits of the quantum computing device based on an inversion of the kernel matrix (Li, p. 2 – teaches using the inverse of the kernel matrix to execute a linear equation solution; see also Li, Figure 3 [same image as Fig. 3 of the instant application]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Havlicek with the teachings of Li in order to provide quantum speedup in the field of quantum SVMs (Li, Abstract – “The fundamental principle of artificial intelligence is the ability of machines to learn from previous experience and do future work accordingly. In the age of big data, classical learning machines often require huge computational resources in many practical cases. Quantum machine learning algorithms, on the other hand, could be exponentially faster than their classical counterparts by utilizing quantum parallelism. Here, we demonstrate a quantum machine learning algorithm to implement handwriting recognition on a four qubit NMR test bench. The quantum machine learns standard character fonts and then recognizes handwritten characters from a set with two candidates. Because of the wide spread importance of artificial intelligence and its tremendous consumption of computational resources, quantum speedup would be extremely attractive against the challenges of big data.”).
Regarding claim 38 (Previously Presented), Havlicek in view of Li teaches all of the limitations of the method of claim 37 as noted above. Havlicek further teaches classifying at least one further test vector based on the measurement (Havlicek, p. 5, Quantum Kernel Estimation – teaches classifying 10 different test sets).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Havlicek and Li for the same reasons as disclosed in claim 37 above.
Regarding claim 39 (Previously Presented), Havlicek in view of Li teaches all of the limitations of the method of claim 38 as noted above. Havlicek further teaches classifying the at least one further test vector based on the measurement without repeating the executing and manipulating steps (Havlicek, p. 5, Quantum Kernel Estimation – teaches classifying 10 different test sets without repeating the executing and manipulating steps).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Havlicek and Li for the same reasons as disclosed in claim 38 above.
Regarding claim 40 (Previously Presented), Havlicek in view of Li teaches all of the limitations of the method of claim 37 as noted above. Havlicek further teaches wherein the determining the measurement of the status of the at least one qubit comprises determining a measurement of the status of four qubits of the quantum computing device (Havlicek, Figure 2 – teaches that experiments are performed using two qubits, but the processor has 5 qubits meaning 4 qubits could be used in the experiment).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Havlicek and Li for the same reasons as disclosed in claim 37 above.
Regarding claim 44 (Currently Amended), it is the apparatus embodiment of claim 37 with similar limitations to claim 37 and is rejected using the same reasoning found in claim 1. Havlicek further teaches an apparatus for classifying a test vector using a support vector machine algorithm (Havlicek, p. 5, Quantum Kernel Estimation – teaches using quantum and classical computing to determine the kernel matrix and classify data), the apparatus comprising:
processing circuitry (Havlicek, p. 5, Quantum Kernel Estimation – teaches using quantum and classical computing);
memory containing instructions executable by the processing circuitry (Havlicek, p. 5, Quantum Kernel Estimation – teaches using quantum and classical computing) whereby the apparatus is operative to …
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Havlicek and Li for the same reasons as disclosed in claim 37 above.
Claim(s) 41 is/are rejected under 35 U.S.C. 103 as being unpatentable over Havlicek in view of Li and further in view of Wright, John (Lecture 2: Quantum Math Basics, hereinafter referred to as “Wright”).
Regarding claim 41 (Previously Presented), Havlicek in view of Li teaches all of the limitations of the method of claim 40 as noted above. However, Havlicek in view of Li does not explicitly teach wherein the determining the measurement of the status of the four qubits of the quantum computing device comprises determining estimates of
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, wherein
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comprise coefficients of quantum orthonormal bases
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of the four qubits.
Wright teaches wherein the determining the measurement of the status of the four qubits of the quantum computing device comprises determining estimates of
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, wherein
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comprise coefficients of quantum orthonormal bases
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of the four qubits (Wright, section 4 – teaches measurement of a two qubit system [It would be obvious that this can be extended to a 4-qubit system]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Havlicek in view of Li with the teachings of Wright in order to understand basic underlying concepts of quantum computing in the field of quantum SVMs (Wright, section 1 – “From last lecture, we have seen some of the essentials of the quantum circuit model of computation, as well as their strong connections with classical randomized model of computation. Today, we will characterize the quantum model in a more formal way. Let's get started with the very basics.”).
Allowable Subject Matter
Claims 31-32, 35-36, 43 are allowed.
Regarding the limitations of claims 31, 43 which do not appear to be taught by the prior art: Corcoles-Gonazales teaches estimating a kernel associated with a feature map using a quantum circuit and using the estimated kernel to perform SVM classification. Gambetta teaches applying quantum feature maps corresponding to quantum kernels to objects with a quantum processor. Havlicek teaches a quantum variational classifier that uses a variational quantum circuit to classify a training set similar to conventional SVMs. Li (both references) teaches quantum machine learning algorithms to implement handwriting recognition. Rebentrost teaches implementing a SVM on a quantum computer.
However, the claims in the application are deemed to be directed to a nonobvious improvement over the prior art of record. As noted above, the independent claims comprise quantum support vector machine used for classification in which a quantum circuit is used to computer a kernel matrix.
Therefore, the cited/applied prior art fails to teach or suggest each and every feature of each of the combination of features recited in the independent claims 31, 43.
When taken into context, the claims as a whole were not uncovered in the prior art; i.e., all dependent claims which depend from claims 31, 43 are allowed as they depend upon an allowable independent claim.
Conclusion
Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125