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 Interpretation
Claim 8-14 cites “A computer program product comprising a computer readable storage medium …”, the examiner interprets the computer-readable storage medium to not include transitory signals as para. [0024] cites “A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media.”.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Gambetta et al. (US2020/0320437 A1 – hereinafter Gambetta) and further in view of Chen et al. (“Simple, Fast and Accurate Hyper-parameter Tuning in Gaussian-Kernel SVM” – hereinafter Chen).
Claims 1-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gambetta et al. (US2020/0320437 A1 – hereinafter Gambetta).
In regards to claim 1, Gambetta discloses a method comprising:
controlling, by at least one processor, quantum hardware to transform qubit states associated with a plurality of pairs of data points in a training data set, wherein each pair of data points is transformed using a circuit parameter representing a rotation angle, wherein inner product of transformed qubit states associated with the each pair of data points is computed; (Gambetta abstract, para. [0004-0005] and fig. 10 teaches a processor (classical and quantum in a hybrid system) that controls quantum hardware. Para. [0013] teaches data points in training data wherein it cites “The quantum feature map is constructed by specifying a function called a quantum kernel which computes the inner products between each pair of data points in the quantum feature space.” Para. [0022] teaches applying a parameterized quantum feature map circuit to selected samples (data points) to computer vectors, and para. [0023] teaches the feature maps include qubit rotation angles, thus the rotations angle is applied to data points to transform it. Also see figure 6 and para. [0093] wherein qubit states are transformed using rotation gates.)
minimizing, by the at least one processor, an objective function based on the inner products, wherein the minimizing finds a target circuit parameter representing a target rotation angle that minimizes the objective function, (Gambetta para. [0115] teaches minimizing an objective function wherein it cites “In block 1212, classical processor 122 determines a new set of parameters for the quantum feature map circuit. In block 1214, classical processor 122 reparameterizes the quantum feature map circuit with the new set of parameters. In block 1216, classical processor 122 determines whether the new (updated) quantum feature map circuit produces an acceptable level of accuracy, e.g., a measure of accuracy greater than a predetermined threshold value. In an embodiment, hybrid quantum/classical optimization algorithm 500 optimizes the value of the SVM objective function F. In an embodiment, a classical processor varies a set of parameters of a quantum kernel to minimize the SVM objective function F with respect to the set of quantum kernel parameters.” Also claim 8 cites “…wherein the rotation angle corresponds to the new set of parameters.”. This means the rotation angle is varied to find the minimized objective function of the quantum SVM. Also Gambetta para. [0013] teaches feature map is based on inner products between pairs of data points.) objective function for maximizing the margin (Gambetta para. [0012] cites “Typically, an SVM performs classification by finding a hyperplane that maximizes the margin between two classes. A hyperplane is a subspace whose dimension is one less than that of its ambient space, e.g., a three-dimensional space has two-dimensional hyperplanes.” And para. [0046-0047] teaches:
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, wherein the objective function is a margin-maximizing function, wherein the margin is the distance between between classes.)
building, by the at least one processor, a kernel matrix based on the inner products computed for a sample dataset and the target circuit parameter passed to the quantum hardware. (The examiner interprets the kernel matrix to be a feature map as the instant application in para. [0089] cites “In an embodiment, performing a classification based on the kernel matrix, wherein the kernel matrix represents a feature map…”) (Gambetta para. [0115] teaches generating a kernel matrix wherein it cites “In block 1212, classical processor 122 determines a new set of parameters for the quantum feature map circuit. In block 1214, classical processor 122 reparameterizes the quantum feature map circuit with the new set of parameters. In block 1216, classical processor 122 determines whether the new (updated) quantum feature map circuit produces an acceptable level of accuracy, e.g., a measure of accuracy greater than a predetermined threshold value. In an embodiment, hybrid quantum/classical optimization algorithm 500 optimizes the value of the SVM objective function F. In an embodiment, a classical processor varies a set of parameters of a quantum kernel to minimize the SVM objective function F with respect to the set of quantum kernel parameters.” Also claim 8 cites “…wherein the rotation angle corresponds to the new set of parameters.”. This means the kernel matrix (feature map) is based inner product of data points along with rotation angle.)
However Gambetta does not explicitly disclose wherein a hyperparameter of the objective function controls how much apart the data points of different classes can be distanced.
Chen discloses wherein a hyperparameter of the objective function controls how much apart the data points of different classes can be distanced. (Chen page 349 right column first paragraph teaches and support vector machine (SVM) tuning the margin (variable C, wherein margin in the distance between two classes and the points in the classes. Also see Chen page 351 section B and Algorithm 1 on page 351.
It would have been obvious to one of ordinary skill in the art before earliest effective filing date of the claimed invention to modify the teachings of the Gambetta with that of Chen in order allow for using a hyperparameter that l controls how much apart the data points of different classes can be distanced, which is margin, as both references deal with using support vector machines. Doing so provides the benefit of improving accuracy and performance of the model by reducing overfitting by customizing the margin based on the application and data wherein a wide margin forces the use of simple and smaller margin allows for more complex model.
In regards to claim 2, Gambetta in view of Chen discloses the method of claim 1, further including performing a classification based on the kernel matrix, wherein the kernel matrix represents a feature map of the sample dataset. (Gambetta para. [0115] teaches generating a kernel matrix being a feature map wherein it cites “In block 1212, classical processor 122 determines a new set of parameters for the quantum feature map circuit. In block 1214, classical processor 122 reparameterizes the quantum feature map circuit with the new set of parameters.”.)
In regards to claim 3, Gambetta in view of Chen discloses the method of claim 2, wherein performing a classification includes performing a support vector machine algorithm using the kernel matrix. (Gambetta para. [0116] teaches when the quantum feature map circuit with the right parameters is found to have an acceptable level of accuracy it is used to classify data, specifically it cites “If classical processor 122 determines in block 1216, that an acceptable level of accuracy is obtained by the updated parameters of the current quantum feature map, process 1200 then ends. Accordingly, a trained hybrid classical-quantum classifier is produced. Upon receiving input data that is desired to be classified, the hybrid classical-quantum classifier classifies the received input data to determine a classification of the input data.” Paragraph [0012-0013, 0055 and 0115] teaches the system is a quantum support vector machine and para. [0115] cites “In an embodiment, hybrid quantum/classical optimization algorithm 500 optimizes the value of the SVM objective function F. In an embodiment, a classical processor varies a set of parameters of a quantum kernel to minimize the SVM objective function F with respect to the set of quantum kernel parameters.”)
In regards to claim 4, Gambetta in view of Chen discloses the method of claim 1, wherein the objective function is defined in terms of a sum of inner products of transformed qubit states associated with the plurality of pairs of data points, and in relation to the hyperparameter. (Gambetta para. [0115] teaches minimizing an objective function wherein it cites “In block 1212, classical processor 122 determines a new set of parameters for the quantum feature map circuit. In block 1214, classical processor 122 reparameterizes the quantum feature map circuit with the new set of parameters. In block 1216, classical processor 122 determines whether the new (updated) quantum feature map circuit produces an acceptable level of accuracy, e.g., a measure of accuracy greater than a predetermined threshold value. In an embodiment, hybrid quantum/classical optimization algorithm 500 optimizes the value of the SVM objective function F. In an embodiment, a classical processor varies a set of parameters of a quantum kernel to minimize the SVM objective function F with respect to the set of quantum kernel parameters.” This teaches varying parameters (hyperparameters). Also claim 8 cites “…wherein the rotation angle corresponds to the new set of parameters.”. This means the rotation angle is varied to find the minimized objective function of the quantum SVM. Also Gambetta para. [0013] teaches feature map is based on inner products between pairs of data points.)
In regards to claim 5, Gambetta in view of Chen discloses the method of claim 4, wherein the hyperparameter is configurable. (Gambetta para. [0115] teaches configurable hyperparameters wherein it cites “In block 1212, classical processor 122 determines a new set of parameters for the quantum feature map circuit. In block 1214, classical processor 122 reparameterizes the quantum feature map circuit with the new set of parameters. In block 1216, classical processor 122 determines whether the new (updated) quantum feature map circuit produces an acceptable level of accuracy, e.g., a measure of accuracy greater than a predetermined threshold value. In an embodiment, hybrid quantum/classical optimization algorithm 500 optimizes the value of the SVM objective function F. In an embodiment, a classical processor varies a set of parameters of a quantum kernel to minimize the SVM objective function F with respect to the set of quantum kernel parameters.” This teaches varying parameters (hyperparameters).)
In regards to claim 6, Gambetta in view of Chen discloses the method of claim 1, wherein the at least one processor receives the inner products computed by the quantum hardware. (Gambetta figure 10 shows where both the classical processor and quantum processor operates on the kernels which use the inner products in the feature maps, thus both receive the inner product. )
In regards to claim 7, Gambetta in view of Chen discloses the method of claim 1, wherein the at least one processor receives measurement of transformed qubit states and computes the inner products using the transformed qubit states. (Examiner interprets this mean a processor computes the inner products of vectors, support for this is shown in the instant application in para. [0059] wherein it states transformed qubit states are state vectors associated with data points. Gambetta para. [0018] teaches objects in training data are represented by vectors, para. [0012] teaches the kernels are inner products between pairs of data points in the feature vector. The training data contains data points represented by vectors, and the inner product is computed using these vectors.)
In regards to claim 8, it is a computer program product comprising a computer readable storage medium embodiment of claim 1 with similar limitations, it is therefore rejected using the same reasoning cited for claim 1.
In regards to claim 9, it is a computer program product comprising a computer readable storage medium embodiment of claim 2 with similar limitations, it is therefore rejected using the same reasoning cited for claim 2.
In regards to claim 10, it is a computer program product comprising a computer readable storage medium embodiment of claim 3 with similar limitations, it is therefore rejected using the same reasoning cited for claim 3.
In regards to claim 11, it is a computer program product comprising a computer readable storage medium embodiment of claim 4 with similar limitations, it is therefore rejected using the same reasoning cited for claim 4.
In regards to claim 12, it is a computer program product comprising a computer readable storage medium embodiment of claim 5 with similar limitations, it is therefore rejected using the same reasoning cited for claim 5.
In regards to claim 13, it is a computer program product comprising a computer readable storage medium embodiment of claim 6 with similar limitations, it is therefore rejected using the same reasoning cited for claim 6.
In regards to claim 14, it is a computer program product comprising a computer readable storage medium embodiment of claim 7 with similar limitations, it is therefore rejected using the same reasoning cited for claim 7.
In regards to claim 15, it is a system embodiment of claim 1 with similar limitations, it is therefore rejected using the same reasoning cited for claim 1. The only differences between claim 1 and 15 is the system embodiment includes quantum hardware including at least qubits. Gambetta fig. 10 discloses quantum hardware in element 140 (quantum processing system) and element 142 (quantum processor). Also, para. [0089] cites “With reference to FIG. 3, this figure depicts a qubit for use in a quantum processor (e.g., quantum processor 148 in FIG. 1). Qubit 300 includes capacitor structure 302 and Josephson junction 304. Josephson junction 304 is formed by separating two thin-film superconducting metal layers by a non-superconducting material.”. Thus, Gambetta discloses claim 15.
In regards to claim 16, it is a system embodiment of claim 2 with similar limitations, it is therefore rejected using the same reasoning cited for claim 2.
In regards to claim 17, it is a system embodiment of claim 3 with similar limitations, it is therefore rejected using the same reasoning cited for claim 3.
In regards to claim 18, it is a system embodiment of claim 4 with similar limitations, it is therefore rejected using the same reasoning cited for claim 4.
In regards to claim 19, it is a system embodiment of claim 5 with similar limitations, it is therefore rejected using the same reasoning cited for claim 5.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Gambetta et al. (US2020/0320437 A1 – hereinafter Gambetta) in view of Chen et al. (“Simple, Fast and Accurate Hyper-parameter Tuning in Gaussian-Kernel SVM” – hereinafter Chen) and further in view of Rahman et al. (US 2021/0118550 A1).
In regards to claim 20, Gambetta in view of Chen discloses the system of claim 15, but does not explicitly disclose wherein the sample dataset represents pixel values of an image, and the classification detects possible cancerous cells in the image.
Rahman et al. disclose wherein the sample dataset represents pixel values of an image, and the classification detects possible cancerous cells in the image. (Rahman et al. para. [0055] teaches “ Lesion segmentation training data included the original image, paired with the expert manual tracing of the lesion boundaries in the form of a binary mask, where pixel values of 255 are considered inside the area of the lesion, and pixel values of 0 are outside.”, thus it sample dataset representing pixel values of an image. Rahman et al. para. [0008] teaches “Based on image-based visual queries submitted by dermatologists, the system responds by displaying relevant images of pigmented skin lesions of past cases, as well as classifies the image category as different types of skin cancer.”, which teaches classification of images possible cancerous cells.)
It would have been obvious to one of ordinary skill in the art before the earlies effective filing date of the claimed invention to modify the teachings of Gambetta with that would Rahman et al. in order to allow detecting cancer in images as both references deal with using support vector machines and the benefit of doing so it allow for accurate and efficient detection of cancer in patients that leads to early detection and possibly saving lives.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Elfving et al. (US 2025/0299084 A1) – Elfving discloses a quantum support vector machine (QSVM) that uses kernels (features maps) based on the inner product of pairs of datapoints. It also teaches optimizing an objective function and encoding data via rotational angle/parameter encoding into rotations in a quantum circuit.
Maheshwari et al. – “Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset”- discloses a Quantum Variational classifier that is a quantum support vector machine and it maximizes the margin of a hyperplace 2/||w||, wherein the margin is the distance between data points of the classes.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULINHO E SMITH whose telephone number is (571)270-1358. The examiner can normally be reached Mon-Fri. 10AM-6PM CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Kawsar can be reached at 571-270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127
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