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 § 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. Claims 1-18 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. Claim 1 and analogous claim 10 are rejected under 35 U.S.C. 112(b) as indefinite because the limitation reciting that “the quantum computer is configured to perform quantum estimation of a Pearson correlation coefficient on the dataset in the quantum accessible data structure to create a weighted dataset” fails to particularly point out and distinctly claim the subject matter regarded as the invention. The defect is the missing connection between the recited Pearson-correlation estimation and the claimed “weighted dataset.” Although a Pearson correlation coefficient has a recognized meaning as a measure of correlation between variables, the claim does not explain what variables are being correlated, whether one coefficient or multiple coefficients are estimated, what part of the dataset is being weighted, or what operation uses the estimated coefficient or coefficients to generate the weighted dataset. Thus, the claim recites a starting point, namely estimation of a Pearson correlation coefficient, and an end result, namely a weighted dataset, but omits the intervening relationship that defines how the latter is produced from the former. Because materially different interpretations remain plausible, including weighting features, weighting training examples, weighting labels, or performing some other correlation-based transformation, one of ordinary skill in the art would not be able to determine with reasonable certainty the scope of the claim. Accordingly, the metes and bounds of the claim are unclear, and the claim is indefinite under 35 U.S.C. 112(b). See attached NPL: Pearson correlation coefficient, Wikipedia. Similarly, Claim 9 and analogous claim 18 recite, “wherein the quantum computer is configured to perform quantum estimation of a new Pearson correlation coefficient on the dataset in the quantum accessible data structure to create a new weighted dataset;” fails to particularly point out and distinctly claim the subject matter regarded as the invention. The defect is the missing connection between the recited Pearson-correlation estimation and the claimed “weighted dataset.” Although a Pearson correlation coefficient has a recognized meaning as a measure of correlation between variables, the claim does not explain what variables are being correlated, whether one coefficient or multiple coefficients are estimated, what part of the dataset is being weighted, or what operation uses the estimated coefficient or coefficients to generate the weighted dataset. Thus, the claim recites a starting point, namely estimation of a Pearson correlation coefficient, and an end result, namely a weighted dataset, but omits the intervening relationship that defines how the latter is produced from the former. Accordingly, the metes and bounds of the claim are unclear, and the claim is indefinite under 35 U.S.C. 112(b). See attached NPL: Pearson correlation coefficient, Wikipedia. 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 1-18 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more. Regarding claim 1: Step 1: is the claim directed to one of the four statutory categories? Yes, the claim is directed to a method. Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? Yes. The limitation: “providing, by the classical computer program, the quantum accessible data structure to a quantum computer, wherein the quantum computer is configured to perform quantum estimation of a Pearson correlation coefficient on the dataset in the quantum accessible data structure to create a weighted dataset;” is directed to a mathematical concept under MPEP 2106.04(a)(2)(I). Further, the limitation: “and selecting, by the classical computer program, a label for each of the plurality of clusters” is directed to a mental process of judgment under MPEP 2106.04(a)(2)(III). Step 2A, prong 2: Do the additional elements integrate into a practical application? No. The limitations: “receiving, by a classical computer program, a dataset comprising a plurality of training examples, each of the plurality of training examples having a plurality of features; loading, by a classical computer program, the dataset into a quantum accessible data structure;” and “clustering, by the classical computer program and the quantum computer, each of the plurality of training examples in the weighted dataset into one of a plurality of clusters” is directed to mere data gathering under MPEP 2106.05(g). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The limitations: “receiving, by a classical computer program, a dataset comprising a plurality of training examples, each of the plurality of training examples having a plurality of features; loading, by a classical computer program, the dataset into a quantum accessible data structure;” and “clustering, by the classical computer program and the quantum computer, each of the plurality of training examples in the weighted dataset into one of a plurality of clusters” is directed to the well-understood, routine, and conventional activity of “Receiving or transmitting data over a network” under MPEP 2106.05(d). Regarding claim 2: Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim is dependent on claim 1. No. The limitation: “wherein the dataset further comprises a plurality of labels” is directed to a field of use under MPEP 2106.05(h). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The limitation: “wherein the dataset further comprises a plurality of labels” is directed to a field of use under MPEP 2106.05(h). Regarding claim 3: Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim is dependent on claim 1. Step 2A, prong 2: Do the additional elements integrate into a practical application? No. The limitation: “wherein the quantum accessible data structure is stored in quantum read-only memory and is accessed via superposition” is directed to field of use under MPEP 2106.05(h). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The limitation: “wherein the quantum accessible data structure is stored in quantum read-only memory and is accessed via superposition” is directed to field of use under MPEP 2106.05(h). Regarding claim 4: Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim is dependent on claim 1. Step 2A, prong 2: Do the additional elements integrate into a practical application? No. The limitation: “wherein the quantum computer stores the training examples in the quantum accessible data structure as amplitude encoded quantum states” is directed to mere data gathering under MPEP 2106.05(g). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The limitation: “wherein the quantum computer stores the training examples in the quantum accessible data structure as amplitude encoded quantum states” is directed to the well-understood, routine, and conventional activity of “storing and retrieving information from memory” under MPEP 2106.05(d). Regarding claim 5: Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim is dependent on claim 1. Further, the limitation: “wherein the Pearson correlation coefficient is estimated between each feature vector in the quantum accessible data structure and a target label, wherein each feature vector comprises the features for each training example” is directed to a mathematical concept under MPEP 2106.04(a)(2)(1). Regarding claim 6: Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim is dependent on claim 1. Further, the limitation: “wherein the quantum computer estimates the Pearson coefficient using the SWAP test and amplitude amplification” is directed to a mathematical concept under MPEP 2106.04(a)(2)(1). Regarding claim 7: Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim is dependent on claim 1. Further, the limitations: “selecting, by the classical computer program, initial centroids for the weighted dataset and storing the centroids in the quantum accessible data structure;” is directed to a mental process of judgment under MPEP 2106.04(a)(2)(III). Further, the limitation: “wherein the quantum computer is configured to estimate a distance of each training example in the weighted dataset to the initial centroids, to assign each training example to one of a plurality of clusters based on the distances, and to update the initial centroids by averaging the training examples in each cluster” is directed to a mathematical concept under MPEP 2106.04(a)(2)(I). Step 2A, prong 2: Do the additional elements integrate into a practical application? No. The limitations: “storing, by the classical computer program, the initial centroids in the quantum accessible data structure; and communicating, by the classical computer program, the quantum accessible data structure with the initial centroids to the quantum computer,” are directed to mere data gathering under MPEP 2106.05(g). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The limitations: “storing, by the classical computer program, the initial centroids in the quantum accessible data structure; and communicating, by the classical computer program, the quantum accessible data structure with the initial centroids to the quantum computer,” are directed to the well-understood, routine, and conventional activities of “storing and retrieving information from memory” and “receiving and transmitting information over a network” under MPEP 2106.05(d). Regarding claim 8: Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim is dependent on claim 1. Further, the limitation: “wherein the quantum computer is configured to repeat the estimating, the assigning, and updating until a maximum number of iterations has occurred or convergence is achieved” is directed to a mental process of evaluation under MPEP 2106.04(a)(2)(III). Regarding claim 9: Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim is dependent on claim 1. Further, the limitations: “wherein the quantum computer is configured to perform quantum estimation of a new Pearson correlation coefficient on the dataset in the quantum accessible data structure to create a new weighted dataset;” is directed to a mathematical concept under MPEP 2106.04(a)(2)(I); “supervised clustering, by the classical computer program and the quantum computer, the training examples and the new training examples in the new weighted dataset into a plurality of clusters; and selecting, by the classical computer program, a new label for each of the plurality of clusters” are directed to a mental process of judgment under MPEP 2106.04(a)(2)(III). Step 2A, prong 2: Do the additional elements integrate into a practical application? No. The limitations: “receiving, by the classical computer program, a new dataset comprising a plurality of new training examples, each of the plurality of new training examples having a plurality of new features; loading, by a classical computer program, the new dataset into the quantum accessible data structure; providing, by the classical computer program, the quantum accessible data structure to a quantum computer,” are directed to mere data gathering under MPEP 2106.05(g). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The limitations: “receiving, by the classical computer program, a new dataset comprising a plurality of new training examples, each of the plurality of new training examples having a plurality of new features; loading, by a classical computer program, the new dataset into the quantum accessible data structure; providing, by the classical computer program, the quantum accessible data structure to a quantum computer,” are directed to the well-understood, routine, and conventional activity of “Receiving or transmitting data over a network” under MPEP 2106.05(d). Claim 10 is rejected with the same rationale as claim 1. Claim 11 is rejected with the same rationale as claim 2. Claim 12 is rejected with the same rationale as claim 3. Claim 13 is rejected with the same rationale as claim 4. Claim 14 is rejected with the same rationale as claim 5. Claim 15 is rejected with the same rationale as claim 6. Claim 16 is rejected with the same rationale as claim 7. Claim 17 is rejected with the same rationale as claim 8. Claim 18 is rejected with the same rationale as claim 9. 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 -5, 7- 9 , 10-14, and 16-1 8 are rejected under 35 U.S.C. 103 as being unpatentable over US Pre-Grant Patent 2020/0265333 (Horesh et al; Horesh) in view of US Pre-Grant Patent 2020/0302234 (Walters et al; Walters). Regarding claim 1 and analogous claim 10 : Horesh teaches: 1. A method for decision tree construction using quantum algorithms, comprising: receiving, by a classical computer program, a dataset comprising a plurality of training examples, each of the plurality of training examples having a plurality of features; loading, by a classical computer program, the dataset into a quantum accessible data structure; ( Horesh, ¶0030) “In one or more embodiments, a classical computer is used to store a large data set associated with classification training data [i.e. receiving, by a classical computer program, a dataset comprising a plurality of training examples, each of the plurality of training examples having a plurality of features;] , and a quantum computer is used to simultaneously evaluate a quality of the feature maps, sampling of the training data, and the approximating functions [i.e. loading, by a classical computer program, the dataset into a quantum accessible data structure;] .” 2. providing, by the classical computer program, the quantum accessible data structure to a quantum computer, ( Horesh , ¶0034) “Accordingly, one or more embodiments provide for a system and method that enables intelligent subsampling of a complex feature space typically requiring a quantum computer using a classical computer [i.e. providing, by the classical computer program, the quantum accessible data structure to a quantum computer,] .” 3. clustering, by the classical computer program and the quantum computer, each of the plurality of training examples in the weighted dataset into one of a plurality of clusters; ( Horesh , ¶0032) “In one or more embodiments, the distance metric may include one or more of a distance between centroids of classes, a distance between elements of each class (e.g., all-to-all), a distance between centroids of class clusters, center of mass distances using density and entropy functions. In particular embodiments, different distance criteria may be used for different data sets [i.e. clustering, by the classical computer program and the quantum computer, each of the plurality of training examples in the weighted dataset into one of a plurality of clusters;] .” 4. and selecting, by the classical computer program, a label for each of the plurality of clusters. ( Horesh , ¶0032) “In one or more embodiments, the distance metric may include one or more of a distance between centroids of classes, a distance between elements of each class (e.g., all-to-all), a distance between centroids of class clusters, center of mass distances using density and entropy function [i.e. and selecting, by the classical computer program, a label for each of the plurality of clusters] .” Horesh does not explicitly teach: 1. wherein the quantum computer is configured to perform quantum estimation of a Pearson correlation coefficient on the dataset in the quantum accessible data structure to create a weighted dataset; Walters teaches: 1. wherein the quantum computer is configured to perform quantum estimation of a Pearson correlation coefficient on the dataset in the quantum accessible data structure to create a weighted dataset; ( Walters , ¶0143) “In addition to determining similarity using the distance between vectors, the similarity score may also be adjusted by evaluating correlations between the centroid and key features or locations of the user dataset. For example, optimization system 105 may determine the correlation between locations and centroids by estimating correlation using Pearson R correlation measures [i.e. wherein the quantum computer is configured to perform quantum estimation of a Pearson correlation coefficient on the dataset] . ” One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Horesh with Walters. The motivation is to improve the system of Walters in a predictable way by using the Pearson correlation estimate to interact with “optimization system 105 may determine correlations between key locations and centroids ( Walters , ¶0143).” Regarding claim 2 and analogous claim 11: Horesh and Walters teach the method of claim 1. Horesh teaches: 1. wherein the dataset further comprises a plurality of labels. ( Horesh , ¶0014) “Another embodiment further includes selecting updated training samples from the training data set, and applying the updated quantum feature map to the updated sampled objects to compute new output vectors. Another embodiment further includes applying the updated quantum feature map to the selected sampled objects to compute new output vectors. Thus, one or more of the embodiments provides for computing of new output vectors to provide improved classification of data [i.e. wherein the dataset further comprises a plurality of labels]. ” One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Horesh with Walters. The motivation is the same as claim 1. Regarding claim 3 and analogous claim 12: Horesh and Walters teach the method of claim 1. Horesh teaches: 1. wherein the quantum accessible data structure is stored in quantum read-only memory and is accessed via superposition. ( Horesh , ¶000 6 ) “ Conventional computers encode information in bits. Each bit can take the value of 1 or 0. These 1s and 0s act as on/off switches that ultimately drive computer functions. Quantum computers, on the other hand, are based on qubits, which operate according to two key principles of quantum physics: superposition and entanglement. Superposition means that each qubit can represent both a 1 and a 0 inference between possible outcomes for an event. Entanglement means that qubits in a superposition can be correlated with each other in a non-classical way; that is, the state of one (whether it is a 1 or a 0 or both) can depend on the state of another, and that there is more information contained within the two qubits when they are entangled than as two individual qubits [i.e. wherein the quantum accessible data structure is stored in quantum read-only memory and is accessed via superposition] .” Examiner notes that superposition is an inherent property of quantum memory through qubits. See attached NPL: Qubits, Wikipedia. One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Horesh with Walters. The motivation is the same as claim 1. Regarding claim 4 and analogous claim 13: Horesh and Walters teach the method of claim 1. Horesh teaches: 1. wherein the quantum computer stores the training examples in the quantum accessible data structure as amplitude encoded quantum states. ( Horesh , ¶0050) “Quantum computers, on the other hand, are based on qubits, which operate according to two key principles of quantum physics: superposition and entanglement.” Examiner notes that “A bit is always completely in either one of its two states, and a set of n bits (e.g. a processor register or some bit array) can only hold a single of its 2n possible states at any time. A quantum state can be in a superposition state, which means that the qubit can have non-zero probability amplitude in both its states simultaneously (popularly expressed as "it can be in both states simultaneously").” See attached NPL: Qubits, Wikipedia. Regarding claim 5 and analogous claim 14: Horesh and Walters teach the method of claim 1. Horesh teaches: 1. [wherein the Pearson correlation coefficient is estimated between each feature vector] in the quantum accessible data structure [and a target label, wherein each feature vector comprises the features for each training example]. ( Horesh, ¶0041) “FIG. 1 depicts a block diagram of a network of data processing systems in which illustrative embodiments may be implemented. Data processing environment 100 is a network of computers in which the illustrative embodiments may be implemented. Data processing environment 100 includes network 102. Network 102 is the medium used to provide communications links between various devices and computers connected together within data processing environment 100 [i.e. in the quantum accessible data structure] .” Walters teaches: 1. wherein the Pearson correlation coefficient is estimated between each feature vector [in the quantum accessible data structure] and a target label, wherein each feature vector comprises the features for each training example. “In addition to determining similarity using the distance between vectors, the similarity score may also be adjusted by evaluating correlations between the centroid and key features or locations of the user dataset. For example, optimization system 105 may determine the correlation between locations and centroids by estimating correlation using Pearson R correlation measures [i.e. wherein the Pearson correlation coefficient is estimated between each feature vector] … Thus, in step 1022 optimization system 105 may determine correlations between key locations and centroids [i.e. and a target label, wherein each feature vector comprises the features for each training example] . One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Horesh with Walters. The motivation is the same as claim 1. Regarding claim 7 and analogous claim 16: Horesh and Walters teach the method of claim 1. Horesh teaches: 1. selecting, by the classical computer program, initial centroids for the weighted dataset and storing the centroids in the quantum accessible data structure; storing, by the classical computer program, the initial centroids in the quantum accessible data structure; ( Horesh, ¶0031) “In particular embodiments, the feature maps may be based upon a circuit description of a quantum circuit. In the embodiment, the quantum distances are used to determine an optimal feature map to provide for an optimal approximating function of the classifier [i.e. selecting, by the classical computer program, initial centroids for the weighted dataset and storing the centroids in the quantum accessible data structure;] . In an embodiment, a classical computer determines, from a quantum feature map and a small number of data points, a classical feature map that provides a best approximation [i.e. storing, by the classical computer program, the initial centroids in the quantum accessible data structure;] .” 2 . and communicating, by the classical computer program, the quantum accessible data structure with the initial centroids to the quantum computer, wherein the quantum computer is configured to estimate a distance of each training example in the weighted dataset to the initial centroids, [ to assign each training example to one of a plurality of clusters based on the distances, and to update the initial centroids by averaging the training examples in each cluster ] . ( Horesh , ¶0032) “In the embodiment, the classical computer searches for an approximating classical feature map based upon a quality measure obtained from distance measurements. In particular embodiments, the distance measurement a linear or non-linear distance measurement [i.e. and communicating, by the classical computer program, the quantum accessible data structure with the initial centroids to the quantum computer, wherein the quantum computer is configured to estimate a distance of each training example in the weighted dataset to the initial centroids ]. ” Walters teaches: 1. [and communicating, by the classical computer program, the quantum accessible data structure with the initial centroids to the quantum computer, wherein the quantum computer is configured to estimate a distance of each training example in the weighted dataset to the initial centroids,] to assign each training example to one of a plurality of clusters based on the distances, and to update the initial centroids by averaging the training examples in each cluster. ( Walters , ¶0138) “In some embodiments, optimization system 105 may find the centroid by computing a weighted average of points in the cluster with weights being assigned based on the intensity of the peak of particularity of the feature [i.e. to assign each training example to one of a plurality of clusters based on the distances, and to update the initial centroids by averaging the training examples in each cluster. ]” One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Horesh with Walters. The motivation is the same as claim 1. Regarding claim 8 and analogous claim 17: Horesh and Walters teach the method of claim 1. Horesh teaches: 1. wherein the quantum computer is configured to repeat the estimating, the assigning, and updating until a maximum number of iterations has occurred or convergence is achieved. ( Horesh, ¶0033) “In the embodiment, the classical computer clusters training samples based upon the approximating function and uses the QSVM to judge a quality of the underlying quantum feature map. In the embodiment, the quantum computer has a set of quantum feature maps and the classical computer may find the best quantum feature map. In the embodiment, the classical computer finds a best approximating classical feature map and iterates with the candidate feature map until an acceptable approximating function for the data set is achieved [i.e. wherein the quantum computer is configured to repeat the estimating, the assigning, and updating until a maximum number of iterations has occurred or convergence is achieved] . One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Horesh with Walters. The motivation is the same as claim 1. Regarding claim 9 and analogous claim 18: Horesh and Walters teach the method of claim 1. Horesh teaches: 1. receiving, by the classical computer program, a new dataset comprising a plurality of new training examples, each of the plurality of new training examples having a plurality of new features; ( Horesh , ¶0008) “In machine learning, a classical support vector machine (SVM) is a supervised learning model associated with learning algorithms that classify data into categories. Typically, a set of training examples are each marked as belonging to a category, and a SVM training algorithm builds a model that assigns new examples to a particular category [i.e. receiving, by the classical computer program, a new dataset comprising a plurality of new training examples, each of the plurality of new training examples having a plurality of new features;] .” 2. loading, by a classical computer program, the new dataset into the quantum accessible data structure; ( Horesh , ¶0042, Figure 1) “Classical processing system 104 couples to network 102. Classical processing system 104 is a classical processing system. Software applications may execute on any quantum data processing system in data processing environment 100. Any software application described as executing in classical processing system 104 in FIG. 1 can be configured to execute in another data processing system in a similar manner [i.e. loading, by a classical computer program, the new dataset into the quantum accessible data structure;] .” Examiner notes that Figure 1 indicates that a cloud service transfers between classical and quantum systems through Network 102. 3. supervised clustering, by the classical computer program and the quantum computer, the training examples and the new training examples in the new weighted dataset into a plurality of clusters; ( Horesh, ¶0033) “In the embodiment, the classical computer clusters training samples based upon the approximating function and uses the QSVM to judge a quality of the underlying quantum feature map. In the embodiment, the quantum computer has a set of quantum feature maps and the classical computer may find the best quantum feature map [i.e. supervised clustering, by the classical computer program and the quantum computer, the training examples and the new training examples in the new weighted dataset into a plurality of clusters;] .” 4. and selecting, by the classical computer program, a new label for each of the plurality of clusters. ( Horesh , ¶0032) “In one or more embodiments, the distance metric may include one or more of a distance between centroids of classes, a distance between elements of each class (e.g., all-to-all), a distance between centroids of class clusters, center of mass distances using density and entropy functions [i.e. and selecting, by the classical computer program, a new label for each of the plurality of clusters] .” Walters teaches: 1. providing, by the classical computer program, the quantum accessible data structure to a quantum computer, wherein the quantum computer is configured to perform quantum estimation of a new Pearson correlation coefficient on the dataset in the quantum accessible data structure to create a new weighted dataset; (Walters, ¶0145) “In step 1030, optimization system 105 may combine the correlation values and the distance values in a single similarity score to assess the similarity between two datasets. For example, optimization system 105 may determine a similarity score with four parts: distances below a threshold distance factored by a first weight, distances above the threshold distance factored by a second weight, correlations below a threshold correlation factored by a third weight, and correlations above a threshold correlation factored by a fourth weight [i.e. providing, by the classical computer program, the quantum accessible data structure to a quantum computer, wherein the quantum computer is configured to perform quantum estimation of a new Pearson correlation coefficient on the dataset in the quantum accessible data structure to create a new weighted dataset;] .“ Examiner notes that Walters 143 assessed that step 1022 “by estimating correlation using Pearson R correlation measures.” In step 1030, the correlation values, which include Pearson R, are used to make a new dataset together with other measures. One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Horesh with Walters. The motivation is the same as claim 1. Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over US Pre-Grant Patent 2020/0265333 (Horesh et al; Horesh) in view of US Pre-Grant Patent 2020/0302234 (Walters et al; Walters), further in view of “Bootstrap Embedding on a Quantum Computer,” arXiv, 2023 (Van Voorhis et al; Van Voorhis). Regarding claim 6 and analogous claim 1 5 : Horesh and Walters teach the method of claim 1. Neither Horesh nor Walters teaches: 1. wherein the quantum computer estimates the Pearson coefficient using the SWAP test and amplitude amplification. Van Voorhis teaches: 1. wherein the quantum computer estimates the Pearson coefficient using the SWAP test and amplitude amplification. ( Van Voorhis , Sect. 3.3, pg. 22) “In quantum information, there is a class of quantum protocols to perform the task of estimating the overlap between two wave functions or RDMs under various assumptions. Among these protocols, the SWAP test is widely used. Such a SWAP test on a quantum computer can also be naturally implemented by simple controlled-SWAP operations as in Fig. 4, showing a SWAP test between two qubits [i.e. wherein the quantum computer estimates the Pearson coefficient using the SWAP test] .” ( Van Voorhis , Sect. 4, pg. 28) “The adaptive sampling changes the number of samples as the optimization proceeds in order to achieve an increasingly better matching conditions. We note that the SWAP test adds only little computational cost to quantum eigensolvers which can be readily performed on current NISQ devices. The amplitude amplified coherent quantum matching requires iterative application of eigensolvers multiple times which are more suitable for small fault-tolerant quantum computers [i.e. and amplitude amplification] .” One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Horesh and Walters with Van Voorhis. The motivation is to use a predictable method to achieve predictable results through the use of the SWAP Test and amplitude amplification to improve the system without explicitly measuring each amplitude. As stated in the paper, “In quantum information, there is a class of quantum protocols to perform the task of estimating the overlap between two wave functions or RDMs under various assumptions. Among these protocols, the SWAP test is widely used…There are well-established ways of performing such amplitude amplification task via coherent quantum algorithms.48 See SI Sec. S7 for the construction of the amplitude amplification and binary search quantum algorithm (Van Voorhis, pgs. 22 and 28).” Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. -Quantification of Correlations in Quantum States, Quantum Computing: A Shift From Bits to Qubits, 30 March 2023, Cristian E. Susa-Quintero -US Pre-Grant Patent 20220382203 -US Pre-Grant Patent 20180349605 Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL JUSTIN BREENE whose telephone number is (571)272-6320. 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, Michael J Huntley can be reached on 303-297-4307. 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. /P.J.B./ Examiner, Art Unit 2129 /MICHAEL J HUNTLEY/ Supervisory Patent Examiner, Art Unit 2129