CTNF 18/239,416 CTNF 98837 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Drawings The drawings have been received on 08/23/2023. These drawings are accepted. Claim Objections 07-29-01 AIA Claim s 13-20 are objected to because of the following informalities: In claims 13-20, “one or more storage media” should read “one or more non-transitory storage media” . Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding Claim 1, Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 1 is directed to a method, i.e., a process, one of the statutory categories. Step 2A Prong One Analysis: The limitations: “for each ML model of a plurality of ML models: generating, based on input data to said each ML model, output data;” “adding the output data to a set of output data;” “wherein the set of output data includes the output data of each ML model in the plurality of ML models;” “identifying a plurality of pairs of ML models, wherein each ML model in the plurality of pairs of ML models is from the plurality of ML models;” “for each pair of ML models in the plurality of pairs of ML models: identifying, from the set of output data, first output data that was generated by a first ML model in said each pair;” “identifying, from the set of output data, second output data that was generated by a second ML model in said each pair;” “generating a diversity value that is based on the first output data and the second output data;” “adding the diversity value to a set of diversity values;” “selecting a subset of the plurality of ML models based on the set of diversity values;” As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., generating, adding, identifying, selecting). The above limitations in the context of this claim encompass, inter alia, generating output data, adding the output data, identifying a plurality of pairs of ML models, identifying first output data, identifying second output data, generating a diversity value, adding the diversity value, selecting a subset of the plurality of ML models (corresponding to mental processes which can be done mentally or by pen and paper). Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. The limitations: “wherein the method is performed by one or more computing devices.” As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a computing device (e.g., by using these elements as tools). Step 2A Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations: “wherein the method is performed by one or more computing devices.” As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a computing device (e.g., by using these elements as tools). The claim is not patent eligible. Regarding Claim 2, Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 2 is directed to a method, i.e., a process, one of the statutory categories. Step 2A Prong One Analysis: The limitations: “for each ML model in the plurality of ML models, generating a correctness value of said each ML model;” “wherein selecting the subset is further based on the correctness value of each ML model of the plurality of ML models.” As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., generating, selecting). The above limitations in the context of this claim encompass, inter alia, generating a correctness value, selecting the subset (corresponding to mental processes which can be done mentally or by pen and paper). Step 2A Prong Two Analysis: Please see the corresponding analysis of Claim 1. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 3, Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 3 is directed to a method, i.e., a process, one of the statutory categories. Step 2A Prong One Analysis: The limitations: “for each ML model in the plurality of ML models, generating a performance value of said each ML model that is based on the correctness value of said each ML model and a time value of said each ML model, the time value being one of (a) a time to train said each ML model or (b) a time to generate an output based on an input to said each ML model;” “wherein selecting the subset is further based on the performance value of each ML model of the plurality of ML models.” As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., generating, selecting). The above limitations in the context of this claim encompass, inter alia, generating a performance value and selecting the subset (corresponding to mental processes which can be done mentally or by pen and paper). Step 2A Prong Two Analysis: Please see the corresponding analysis of Claim 1. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 4, Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 4 is directed to a method, i.e., a process, one of the statutory categories. Step 2A Prong One Analysis: The limitations: “for each machine-learned model in the plurality of ML models, generating a second performance value of said each ML model that is based on the correctness value of said each ML model and a second time value of said each ML model, the second time value being the time to generate one or more outputs based on one or more inputs to said each ML model;” “wherein selecting the subset is further based on the performance value of each ML model of the plurality of ML models.” As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., generating, selecting). The above limitations in the context of this claim encompass, inter alia, generating a second performance value and selecting the subset (corresponding to mental processes which can be done mentally or by pen and paper). Step 2A Prong Two Analysis: Please see the corresponding analysis of Claim 1. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 5, Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 5 is directed to a method, i.e., a process, one of the statutory categories. Step 2A Prong One Analysis: The limitations: “for each ML model of the plurality of ML models, generating a correctness value or a performance value of said each ML model;” “prior to selecting the subset, for each pair of ML models of the plurality of ML models: applying the first weight to each instance of the diversity value to generate modified diversity values;” “applying the second weight to each instance of the correctness value or the performance value to generate modified correctness values or modified performance values;” “wherein selecting the subset is further based on the modified diversity values and the modified correctness values or the modified performance values.” As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., generating, applying, selecting). The above limitations in the context of this claim encompass, inter alia, generating a correctness value, applying the first weight, applying the second weight, selecting the subset (corresponding to mental processes which can be done mentally or by pen and paper). Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. The limitations: “storing a plurality of weights that includes a first weight and a second weight that is different than the first weight, wherein the first weight is associated with the diversity value and the second weight is associated with the correctness value or the performance value;” As drafted, amount to insignificant extra-solution activities, which do not integrate a judicial exception into a practical application. For example, the additional elements of "storing a plurality of weights" amount to mere data gathering and data storage, respectively, which are insignificant extra-solution activities that do not integrate a judicial exception into a practical application. See MPEP 2106.05(g). Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are insignificant extra-solution activities or mere instructions to apply an exception. (i.e., the additional element describes a unit for applying the abstract ideas). Insignificant extra-solution activities and mere instructions to apply an exception cannot provide an inventive concept. Moreover, receiving, communicating, and storing data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP 2106.05(d)(II) ("The courts have recognized the following computer functions as well-understood, routine, and conventional functions ... iv. Storing and retrieving information in memory") (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). The claim is not patent eligible. Regarding Claim 6, Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 6 is directed to a method, i.e., a process, one of the statutory categories. Step 2A Prong One Analysis: Please see the corresponding analysis of Claim 1. Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. The limitations: “further comprising receiving user input that specifies the plurality of weights.” As drafted, amount to insignificant extra-solution activities, which do not integrate a judicial exception into a practical application. For example, the additional elements of "receiving user input data" amount to mere data gathering and data storage, respectively, which are insignificant extra-solution activities that do not integrate a judicial exception into a practical application. See MPEP 2106.05(g). Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are insignificant extra-solution activities or mere instructions to apply an exception. (i.e., the additional element describes a unit for applying the abstract ideas). Insignificant extra-solution activities and mere instructions to apply an exception cannot provide an inventive concept. Moreover, receiving, communicating, and storing data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP 2106.05(d)(II) ("The courts have recognized the following computer functions as well-understood, routine, and conventional functions ... i. Receiving or transmitting data over a network ... iv. Storing and retrieving information in memory") (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). The claim is not patent eligible. Regarding Claim 7, Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 7 is directed to a method, i.e., a process, one of the statutory categories. Step 2A Prong One Analysis: The limitations: “adding the diversity value to the set of diversity values comprising adding the diversity value to a cell in a matrix, of a plurality of cells, whose dimension is the number of ML models in the plurality of ML models;” “the coordinates of the cell in the matrix is determined based on a first number assigned to the first ML model and a second number assigned to the second ML model;” “selecting the subset [comprising executing a Quadratic Unconstrained Binary Optimization (QUBO) solver] relative to the matrix.” As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., adding, determining, selecting). The above limitations in the context of this claim encompass, inter alia, adding the diversity value, determining the coordinates of the cell, selecting the subset (corresponding to mental processes which can be done mentally or by pen and paper). Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. “[selecting the subset] comprising executing a Quadratic Unconstrained Binary Optimization (QUBO) solver [relative to the matrix.” As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a Quadratic Unconstrained Binary Optimization (QUBO) solver (e.g., by using these elements as tools). Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. “[selecting the subset] comprising executing a Quadratic Unconstrained Binary Optimization (QUBO) solver [relative to the matrix.” As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a Quadratic Unconstrained Binary Optimization (QUBO) solver (e.g., by using these elements as tools). The claim is not patent eligible. Regarding Claim 8, Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 8 is directed to a method, i.e., a process, one of the statutory categories. Step 2A Prong One Analysis: The limitations: “the number of ML models in the subset does not exceed a first number;” “the number of ML models in the subset is not less than a second number;” “the number of ML models, in the subset, of a particular type does not exceed a third number;” “the number of ML models, in the subset, of the particular type is not less than a fourth number;” “the number of types of ML models in the subset is not less than a fifth number; or” “the number of ML models in the subset equals an odd number.” As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., identifying). The above limitations in the context of this claim encompass, inter alia, identifying a plurality of pairs of ML models (corresponding to mental processes which can be done mentally or by pen and paper). Step 2A Prong Two Analysis: Please see the corresponding analysis of Claim 1. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 9, Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 9 is directed to a method, i.e., a process, one of the statutory categories. Step 2A Prong One Analysis: The limitations: “generating a z-score for each diversity value in the set of diversity values;” “wherein selecting the subset is further based on the z-score of each diversity value in the set of diversity values.” As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., generating, selecting). The above limitations in the context of this claim encompass, inter alia, generating a z-score, selecting the subset (corresponding to mental processes which can be done mentally or by pen and paper). Step 2A Prong Two Analysis: Please see the corresponding analysis of Claim 1. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 10, Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 10 is directed to a method, i.e., a process, one of the statutory categories. Step 2A Prong One Analysis: Please see the corresponding analysis of Claim 1. Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. The limitations: “using a first machine learning training technique to train a first particular ML model in the plurality of ML models;” “wherein training the first particular ML model comprises training the first particular ML model based on a first set of hyperparameters;” “using the first machine learning training technique to train a second particular ML model in the plurality of ML models;” “wherein training the second particular ML model comprises training the second particular ML model based on a second set of hyperparameters that is different than the first set of hyperparameters.” As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a machine-learning based model (e.g., by using these elements as tools). Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations: “using a first machine learning training technique to train a first particular ML model in the plurality of ML models;” “wherein training the first particular ML model comprises training the first particular ML model based on a first set of hyperparameters;” “using the first machine learning training technique to train a second particular ML model in the plurality of ML models;” “wherein training the second particular ML model comprises training the second particular ML model based on a second set of hyperparameters that is different than the first set of hyperparameters.” As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a machine-learning based model (e.g., by using these elements as tools). The claim is not patent eligible. Regarding Claim 11, Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 11 is directed to a method, i.e., a process, one of the statutory categories. Step 2A Prong One Analysis: Please see the corresponding analysis of Claim 1. Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. The limitations: “using a first machine learning training technique to train a first particular ML model in the plurality of ML models;” “wherein the first particular ML model is a first type of ML model;” “using a second machine learning training technique, that is different than the first machine learning training technique, to train a second particular ML model in the plurality of ML models;” “wherein the second particular ML model is a second type, of ML model, that is different than the first type.” As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a machine-learning based model (e.g., by using these elements as tools). Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations: “using a first machine learning training technique to train a first particular ML model in the plurality of ML models;” “wherein the first particular ML model is a first type of ML model;” “using a second machine learning training technique, that is different than the first machine learning training technique, to train a second particular ML model in the plurality of ML models;” “wherein the second particular ML model is a second type, of ML model, that is different than the first type.” As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a machine-learning based model (e.g., by using these elements as tools). The claim is not patent eligible. Regarding Claim 12, Claim 12 recites a non-transitory storage media for performing steps similar of claim 1 and is rejected with the same rationale, mutatis mutandis , in view of the following additional elements, considered individually and as an ordered combination with the additional elements identified above, failing to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea: “One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause:” This is a recitation of generic computing components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f). Regarding Claim 13, Claim 13 recites a non-transitory storage media for performing steps substantially similar to those of claim 2 and is rejected with the same rationale, mutatis mutandis. Regarding Claim 14, Claim 14 recites a non-transitory storage media for performing steps substantially similar to those of claim 3 and is rejected with the same rationale, mutatis mutandis. Regarding Claim 15, Claim 15 recites a non-transitory storage media for performing steps substantially similar to those of claim 4 and is rejected with the same rationale, mutatis mutandis. Regarding Claim 16, Claim 16 recites a non-transitory storage media for performing steps substantially similar to those of claim 5 and is rejected with the same rationale, mutatis mutandis. Regarding Claim 17, Claim 17 recites a non-transitory storage media for performing steps substantially similar to those of claim 6 and is rejected with the same rationale, mutatis mutandis. Regarding Claim 18, Claim 18 recites a non-transitory storage media for performing steps substantially similar to those of claim 7 and is rejected with the same rationale, mutatis mutandis. Regarding Claim 19, Claim 19 recites a non-transitory storage media for performing steps substantially similar to those of claim 8 and is rejected with the same rationale, mutatis mutandis. Regarding Claim 20, Claim 20 recites a non-transitory storage media for performing steps substantially similar to those of claim 9 and is rejected with the same rationale, mutatis mutandis. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1, 10, 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kuncheva et al. (Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy); hereinafter Kuncheva in view of Caruana et al. (Ensemble selection from libraries of models); hereinafter Caruana Claim 1 is rejected over Kuncheva and Caruana. Regarding claim 1, Kuncheva teaches a method comprising: for each ML model of a plurality of ML models: (Kuncheva [page 2, 2. Diversity in classifier ensembles]: “Let D = {D1, . . . , DL } be a set (pool, committee, mixture, team, ensemble) of classifiers,”) generating, based on input data to said each ML model, output data; (Kuncheva [page 2, 2. Diversity in classifier ensembles]: “for each input x we have L c-dimensional vectors of support … The output D i (x) is 1 if x is recognized correctly by D i , and 0, otherwise.”) identifying a plurality of pairs of ML models, wherein each ML model in the plurality of pairs of ML models is from the plurality of ML models; (Kuncheva [page 5, 3.4. The double-fault measure]: “This measure was used by Giacinto and Roli (2001) to form a pairwise diversity matrix for a classifier pool and subsequently to select classifiers that are least related. It is defined as the proportion of the cases that have been misclassified by both classifiers, i.e.,”; and [page 4]: “There are various statistics to assess the similarity of two classifier outputs (Afifi&Azen, 1979). Yule’s Q statistic (1900) for two classifiers, D i and D k , is”) for each pair of ML models in the plurality of pairs of ML models: identifying, from the set of output data, first output data that was generated by a first ML model in said each pair; ( Kuncheva [page 3-4, 3 Pairwise diversity measures]: “Let Z = {z 1 , . . . , z N } be a labeled data set, z j ∈ R n coming from the classification problem in question. We can represent the output of a classifier Di as an N-dimensional binary vector y i = [ y 1 , i , . . . , y N , i ] T , such that y j , i = 1, if D i recognizes correctly z j , and 0, otherwise, i = 1 , . . . , L .”; Note: See Table 1 of Kuncheva to see the 2x2 table of the relationship between a pair of classifiers.) identifying, from the set of output data, second output data that was generated by a second ML model in said each pair; (Kuncheva [page 4]: “There are various statistics to assess the similarity of two classifier outputs (Afifi & Azen, 1979). Yule’s Q statistic (1900) for two classifiers, D i and D k ,”; Note: See Table 1 of Kuncheva to see the 2x2 table of the relationship between a pair of classifiers. D i is the first ML model and D k is the second ML model.) generating a diversity value that is based on the first output data and the second output data; (Kuncheva [page 4]: “Yule’s Q statistic (1900) for two classifiers, D i and D k , Q i , k = N 11 N 00 - N 01 N 10 N 11 N 00 + N 01 N 10 , (3) where N ab is the number of elements z j of Z for which y j,i = a and y j,k = b (see Table 1).; Note: Q i , k is the diversity value computed using the joint comparison of D i and D k ’s output data. ) adding the diversity value to a set of diversity values; (Kuncheva [page 4]: “For statistically independent classifiers, the expectation of Q i,k is 0. Q varies between −1 and 1. Classifiers that tend to recognize the same objects correctly will have positive values of Q , and those which commit errors on different objects will render Q negative. For a team D of L classifiers, the averaged Q statistics over all pairs of classifiers is, Q a v = 2 L ( L - 1 ) ∑ i = 1 L - 1 ∑ k = i + 1 L Q i , k (4).”; Note: Each Q i,k value is added into a summation.) selecting a subset of the plurality of ML models based on the set of diversity values; (Kuncheva [page 5, 3.4. The double-fault measure]: “This measure was used by Giacinto and Roli (2001) to form a pairwise diversity matrix for a classifier pool and subsequently to select classifiers that are least related . It is defined as the proportion of the cases that have been misclassified by both classifiers, i.e.,”) wherein the method is performed by one or more computing devices. (Kuncheva [page 11, 6.2. Simulation experiment]: “A Matlab program was designed to randomly generate L binary classifier outputs”; Note: A computing device performs the method by running the Matlab program.) Kuncheva does not appear to explicitly teach adding the output data to a set of output data; wherein the set of output data includes the output data of each ML model in the plurality of ML models; However, Caruana teaches adding the output data to a set of output data; (Caruana [page 3]: “Model predictions on the train and hillclimbing sets are cached. This simplifies working with the library and makes model selection faster because the models do not have to be executed during selection.”) wherein the set of output data includes the output data of each ML model in the plurality of ML models; (Caruana [page 3]: “ All models are added to a library no matter how good or bad they are. Model predictions on the train and hillclimbing sets are cached. This simplifies working with the library and makes model selection faster because the models do not have to be executed during selection.”) It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the aggregation of output data of Caruana to make model selection faster (Caruana [page 3]). Kuncheva and Caruana are analogous art because they both concern selecting classifiers for an ensemble. Claim 10 is rejected over Kuncheva and Caruana with the incorporation of claim 1. Regarding claim 10, Kuncheva does not appear to explicitly teach using a first machine learning training technique to train a first particular ML model in the plurality of ML models; wherein training the first particular ML model comprises training the first particular ML model based on a first set of hyperparameters; using the first machine learning training technique to train a second particular ML model in the plurality of ML models; wherein training the second particular ML model comprises training the second particular ML model based on a second set of hyperparameters that is different than the first set of hyperparameters. However, Caruana teaches using a first machine learning training technique to train a first particular ML model in the plurality of ML models; (Caruana [1. Introduction]: “Here we generate diverse sets of models by using many different algorithms. We use Support Vector Machines (SVMs), artificial neural nets (ANNs), memory-based learning (KNN), decision trees (DT), bagged decision trees (BAG-DT), boosted decision trees (BST-DT), and boosted stumps (BST-STMP). For each algorithm we train models using many different parameter settings. For example, we train 121 SVMs by varying the margin parameter C, the kernel, and the kernel parameters (e.g. varying gamma with RBF kernels.)”; and [page 9]: “ SVMs: we use most kernels in SVMLight (Joachims, 1999) {linear, polynomial degree 2 & 3, radial with width {0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 2}} and vary the regularization parameter C by factors of ten from 10 -7 to 10 3 .”) wherein training the first particular ML model comprises training the first particular ML model based on a first set of hyperparameters; (Caruana [page 9]: “ SVMs: we use most kernels in SVMLight (Joachims,1999) {linear, polynomial degree 2 & 3, radial with width {0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 2}} and vary the regularization parameter C by factors of ten from 10 -7 to 10 3 .”) using the first machine learning training technique to train a second particular ML model in the plurality of ML models; (Caruana [page 2]: “ we train 121 SVMs by varying the margin parameter C, the kernel, and the kernel parameters (e.g. varying gamma with RBF kernels.)”; and [page 3]: “The parameters we vary for each algorithm to generate 2000 models are described in the Appendix.”) wherein training the second particular ML model comprises training the second particular ML model based on a second set of hyperparameters that is different than the first set of hyperparameters. (Caruana [page 9]: “ SVMs: we use most kernels in SVMLight (Joachims,1999) {linear, polynomial degree 2 & 3, radial with width {0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 2}} and vary the regularization parameter C by factors of ten from 10 -7 to 10 3 .”) It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the diverse sets of models of Caruana to make model selection faster (Caruana [page 3]). Kuncheva and Caruana are analogous art because they both concern selecting classifiers for an ensemble. Claim 11 is rejected over Kuncheva and Caruana with the incorporation of claim 1. Regarding claim 11, Kuncheva teaches wherein the first particular ML model is a first type of ML model; (Kuncheva [page 18]: “Two classifier models were used, linear and neural network (NN).”; Note: The linear classifier model is the first particular ML model.) wherein the second particular ML model is a second type, of ML model, that is different than the first type. (Kuncheva [page 18]: “Two classifier models were used, linear and neural network (NN) .”; Note: The neural network classifier is the second particular ML model.) Kuncheva does not appear to explicitly teach using a first machine learning training technique to train a first particular ML model in the plurality of ML models; using a second machine learning training technique, that is different than the first machine learning training technique, to train a second particular ML model in the plurality of ML models; However, Caruana using a first machine learning training technique to train a first particular ML model in the plurality of ML models; (Caruana [1. Introduction]: “Here we generate diverse sets of models by using many different algorithms. We use Support Vector Machines (SVMs), artificial neural nets (ANNs), memory-based learning (KNN), decision trees (DT), bagged decision trees (BAG-DT), boosted decision trees (BST-DT), and boosted stumps (BST-STMP). For each algorithm we train models using many different parameter settings. For example, we train 121 SVMs by varying the margin parameter C, the kernel, and the kernel parameters (e.g. varying gamma with RBF kernels.)”; and [page 9]: “ SVMs: we use most kernels in SVMLight (Joachims, 1999) {linear, polynomial degree 2 & 3, radial with width {0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 2}} and vary the regularization parameter C by factors of ten from 10 -7 to 10 3 .”) using a second machine learning training technique, that is different than the first machine learning training technique, to train a second particular ML model in the plurality of ML models; (Caruana [1. Introduction]: “Here we generate diverse sets of models by using many different algorithms. We use Support Vector Machines (SVMs), artificial neural nets (ANNs) , memory-based learning (KNN), decision trees (DT), bagged decision trees (BAG-DT), boosted decision trees (BST-DT), and boosted stumps (BST-STMP). For each algorithm we train models using many different parameter settings. For example, we train 121 SVMs by varying the margin parameter C, the kernel, and the kernel parameters (e.g. varying gamma with RBF kernels.)”; and [page 9]: “ ANN: we train nets with gradient descent backprop and vary the number of hidden units {1, 2, 4, 8, 32, 128} and the momentum {0, 0.2, 0.5, 0.9}.”) It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the diverse sets of models of Caruana to make model selection faster (Caruana [page 3]). Kuncheva and Caruana are analogous art because they both concern selecting classifiers for an ensemble. Claim 12 is rejected over Kuncheva and Caruana. Regarding claim 12, Kuncheva teaches one or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause: (Kuncheva [page 11, 6.2. Simulation experiment]: “A Matlab program was designed to randomly generate L binary classifier outputs”; Note: A computing device performs the method by running the Matlab program.) The remainder of claim 12 is claim 1 in the form of a non-transitory storage media and is rejected for the same reasons as claim 1 stated above . 07-21-aia AIA Claim s 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kuncheva and Caruana in further view of Yang et al. (Classifiers selection for ensemble learning based on accuracy and diversity); hereinafter Yang Claim 2 is rejected over Kuncheva, Caruana and Yang with the incorporation of claim 1. Regarding claim 2, Kuncheva does not appear to explicitly teach for each ML model in the plurality of ML models, generating a correctness value of said each ML model; wherein selecting the subset is further based on the correctness value of each ML model of the plurality of ML models. However, Yang teaches for each ML model in the plurality of ML models, generating a correctness value of said each ML model; (Yang [page 4268]: “Step 2. Evaluate each classifier’s accuracy on validation set;”) wherein selecting the subset is further based on the correctness value of each ML model of the plurality of ML models. (Yang [page 4268]: “Step 3. Select one most accurate classifier or several top accurate classifiers;”) It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the classifier selection algorithm based on accuracy and diversity of Yang to improve the performance of ensemble learning (Yang, 4267). Kuncheva and Yang are analogous art because they both concern selecting classifiers for an ensemble. Dependent claim 13 is claim 2 in the form of a non-transitory storage media and is rejected for the same reasons as claim 2 stated above. For the rejection of the limitations specifically pertaining to the non-transitory storage media of claim 12, see the rejection of claim 12 above . 07-21-aia AIA Claim s 3, 4, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kuncheva and Caruana in further view of Fan et al. (Pruning and Dynamic Scheduling of Cost-sensitive Ensembles); hereinafter Fan Claim 3 is rejected over Kuncheva, Caruana and Fan with the incorporation of claim 1. Regarding claim 3, Kuncheva does not appear to explicitly teach for each ML model in the plurality of ML models, generating a performance value of said each ML model that is based on the correctness value of said each ML model and a time value of said each ML model, the time value being one of (a) a time to train said each ML model or However, Fan teaches for each ML model in the plurality of ML models, generating a performance value of said each ML model that is based on the correctness value of said each ML model and a time value of said each ML model, the time value being one of (a) a time to train said each ML model or (Fan [Abstract]: “we propose several approaches to reduce the number of base classifiers. Among various methods explored, our empirical studies have shown that the benefit-based greedy approach can safely remove more than 90% of the base models while maintaining or even exceeding the prediction accuracy of the original ensemble. Assuming that each base classifier consumes one unit of prediction time , the removal of 90% of base classifiers translates to a prediction speedup of 10 times.”; and [page 6]: “the “overfitting” curve validates the necessity of pruning for both accuracy and efficiency reasons.” It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the pruning and dynamic scheduling of ensembles of Fan to improve accuracy and efficiency (Fan, page 2). Kuncheva and Fan are analogous art because they both concern selecting classifiers for an ensemble. Kuncheva does not appear to explicitly teach (b) a time to generate an output based on an input to said each ML model; wherein selecting the subset is further based on the performance value of each ML model of the plurality of ML models. However, Caruana teaches (b) a time to generate an output based on an input to said each ML model; (Caruana [page 8]: “Adding a model to an ensemble only requires averaging a model's predictions with the ensemble's predictions, which is O(D) for D the size of the hillclimbing set.”) wherein selecting the subset is further based on the performance value of each ML model of the plurality of ML models. (Caruana [page 3, 2.2. Sorted Ensemble Initialization]: “sort the models in the library by their performance, and put the best N models in the ensemble.”) It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the aggregation of output data of Caruana to make model selection faster (Caruana [page 3]). Kuncheva and Caruana are analogous art because they both concern selecting classifiers for an ensemble. Claim 4 is rejected over Kuncheva, Caruana and Fan with the incorporation of claim 1. Regarding claim 4, Kuncheva does not appear to explicitly teach wherein the performance value of said each ML model is a first performance value, wherein the time value is a first time value and is the time to train said each ML model, the method further comprising: wherein selecting the subset is further based on the performance value of each ML model of the plurality of ML models. However, Caruana teaches wherein the performance value of said each ML model is a first performance value, wherein the time value is a first time value and is the time to train said each ML model, the method further comprising: (Caruana [page 7, 7.3. Computational Cost]: “it takes about 48 hours to train the 2000 models using a cluster of ten Linux machines.”) wherein selecting the subset is further based on the performance value of each ML model of the plurality of ML models. (Caruana [page 3, 2.2. Sorted Ensemble Initialization]: “sort the models in the library by their performance, and put the best N models in the ensemble.”) It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the aggregation of output data of Caruana to make model selection faster (Caruana [page 3]). Kuncheva and Caruana are analogous art because they both concern selecting classifiers for an ensemble. Kuncheva does not appear to explicitly teach for each machine-learned model in the plurality of ML models, generating a second performance value of said each ML model that is based on the correctness value of said each ML model and a second time value of said each ML model, the second time value being the time to generate one or more outputs based on one or more inputs to said each ML model; However, Fan teaches for each machine-learned model in the plurality of ML models, generating a second performance value of said each ML model that is based on the correctness value of said each ML model and (Fan [Abstract]: “the benefit-based greedy approach can safely remove more than 90% of the base models while maintaining or even exceeding the prediction accuracy of the original ensemble. Assuming that each base classifier consumes one unit of prediction time, the removal of 90% of base classifiers translates to a prediction speedup of 10 times.”) a second time value of said each ML model, the second time value being the time to generate one or more outputs based on one or more inputs to said each ML model; (Fan [Abstract]: “Assuming that each base classifier consumes one unit of prediction time , the removal of 90% of base classifiers translates to a prediction speedup of 10 times.”) It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the pruning and dynamic scheduling of ensembles of Fan to improve accuracy and efficiency (Fan, page 2). Kuncheva and Fan are analogous art because they both concern selecting classifiers for an ensemble. Dependent claim 14 is claim 3 in the form of a non-transitory storage media and is rejected for the same reasons as claim 3 stated above. For the rejection of the limitations specifically pertaining to the non-transitory storage media of claim 12, see the rejection of claim 12 above. Dependent claim 15 is claim 4 in the form of a non-transitory storage media and is rejected for the same reasons as claim 4 stated above. For the rejection of the limitations specifically pertaining to the non-transitory storage media of claim 12, see the rejection of claim 12 above . 07-21-aia AIA Claim s 5, 6, 8, 16, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kuncheva and Caruana in further view of Brito et al. (US 20240095496 A1); hereinafter Brito Claim 5 is rejected over Kuncheva, Caruana and Brito with the incorporation of claim 1. Regarding claim 5, Kuncheva does not appear to explicitly teach for each ML model of the plurality of ML models, generating a correctness value or a performance value of said each ML model; However, Caruana teaches for each ML model of the plurality of ML models, generating a correctness value or a performance value of said each ML model; (Caruana [page 4, 4. Performance Metrics]: “ We use ten performance metrics: accuracy (ACC), root-mean-squared-error (RMS), mean cross-entropy (MXE), lift (LFT), precision/recall break-even point (BEP), precision/recall F-score (FSC), average precision (APR), ROC Area (ROC), and a measure of probability calibration (CAL). The tenth metric is SAR = (ACC + ROC + (1 - RMS))/3. SAR is a robust metric to use when the correct metric is unknown. An attractive feature of ensemble selection is that it can optimize to metrics such as SAR. We compare performance using ten metrics because different metrics are appropriate in different settings and because learning methods that perform well on one metric do not always perform well on other metrics.”; and [page 8]: “selecting an ensemble from a library of M = 2000 models,”) It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the aggregation of output data of Caruana to make model selection faster (Caruana [page 3]). Kuncheva and Caruana are analogous art because they both concern selecting classifiers for an ensemble. Kuncheva does not appear to explicitly teach storing a plurality of weights that includes a first weight and a second weight that is different than the first weight, wherein the first weight is associated with the diversity value and the second weight is associated with the correctness value or the performance value; prior to selecting the subset, for each pair of ML models of the plurality of ML models: applying the first weight to each instance of the diversity value to generate modified diversity values; applying the second weight to each instance of the correctness value or the performance value to generate modified correctness values or modified performance values; wherein selecting the subset is further based on the modified diversity values and the modified correctness values or the modified performance values. However, Brito teaches storing a plurality of weights that includes a first weight and a second weight that is different than the first weight, wherein the first weight is associated with the diversity value and the second weight is associated with the correctness value or the performance value; (Brito [0058]: “For an ensemble of N classifiers, there may be N loss functions and ( N 2 ). pairwise diversity terms, each with its weight λ i , where i=1, 2, . . . , ( N 2 ). In some embodiments a single weight λ can be used for all classifiers.”; Note: The first weight is with the diversity term and the second weight is associated with the loss function (correctness value).) prior to selecting the subset, for each pair of ML models of the plurality of ML models: applying the first weight to each instance of the diversity value to generate modified diversity values; (Brito [0055]: “For a two-classifier ensemble, this optimization problem can have a cost function which is composed of the loss functions incurred by each classifier and the pairwise diversity metric, as shown in the following equation: Eq. (2)”; and [0057]: “The system can accomplish this by including additional loss functions for the component classifiers in the ensemble, as well as the additional pairwise diversity terms.”) applying the second weight to each instance of the correctness value or the performance value to generate modified correctness values or modified performance values; (Brito [0061]: “the first term can be referred to as the loss term and the second term can be referred to as the regularization term. For example, given an ensemble of two classifiers where i ∈ 2, the classifiers' loss terms can be expressed as … and the regularization term can be expressed as … ”; and [0061]: “Both the first classifier and the second classifier can take into account the regularization term which can enforce orthogonality among its parameters (e.g., the parameters of the first classifier) and the parameters of the other classifiers in the ensemble”) wherein selecting the subset is further based on the modified diversity values and the modified correctness values or the modified performance values. (Brito [0056]: “That is, the desired optimization problem for generating a diverse ensemble of two classifiers can be represented as: Eq. (2)”) It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the weighting of Brito to provide increased overall robustness (Brito [0053]). Kuncheva and Brito are analogous art because they both concern creating an ensemble of classifiers based on diversity. Claim 6 is rejected over Kuncheva, Caruana and Brito with the incorporation of claim 1. Regarding claim 6, Kuncheva does not appear to explicitly teach further comprising receiving user input that specifies the plurality of weights. However, Brito teaches further comprising receiving user input that specifies the plurality of weights. (Brito [0048]: “user 112 can change a configuration or setting related to, e.g., the type of data (180), the type of attack (182), the ensemble size (184), the type of approach (186), and the type of classifier (188). While not depicted in FIG. 1 , user 112 can also change a setting related to the regularization term .”; Note: Changing the λ (weight) would also change the regularization.) It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the weighting of Brito to provide increased overall robustness (Brito [0053]). Kuncheva and Brito are analogous art because they both concern creating an ensemble of classifiers based on diversity. Claim 8 is rejected over Kuncheva, Caruana and Brito with the incorporation of claim 1. Regarding claim 8, Kuncheva does not appear to explicitly teach wherein selecting the subset comprises selecting the subset such that: the number of ML models in the subset does not exceed a first number; However, Caruana teaches the number of ML models in the subset does not exceed a first number; (Caruana [page 2, 2.2. Sorted Ensemble Initialization]: “Instead of starting with an empty ensemble, sort the models in the library by their performance, and put the best N models in the ensemble. N is chosen by looking at performance on the hillclimbing set.”) It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the aggregation of output data of Caruana to make model selection faster (Caruana [page 3]). Kuncheva and Caruana are analogous art because they both concern selecting classifiers for an ensemble. Kuncheva does not appear to explicitly teach the number of ML models in the subset is not less than a second number; the number of ML models, in the subset, of a particular type does not exceed a third number; the number of ML models, in the subset, of the particular type is not less than a fourth number; the number of types of ML models in the subset is not less than a fifth number; or the number of ML models in the subset equals an odd number. However, Brito teaches the number of ML models in the subset is not less than a second number; the number of ML models, in the subset, of a particular type does not exceed a third number; the number of ML models, in the subset, of the particular type is not less than a fourth number; the number of types of ML models in the subset is not less than a fifth number; or the number of ML models in the subset equals an odd number. (Brito [0037]: To make neural network-based models amenable to a diversity metric, the described embodiments replace the final classification layer of the neural network with the three or more affine classifiers of an odd number . As a result, the system can compute the angle between various classifiers, and specifically, between each pair of classifiers. Thus, for each class label, the system can construct an ensemble of one-versus-all affine classifiers using the diversity metric, as described below.”) It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the combining the classification decisions into an ensemble decision rule of Brito to provide increased overall robustness (Brito [0053]). Kuncheva and Brito are analogous art because they both concern creating an ensemble of classifiers based on diversity. Dependent claim 16 is claim 5 in the form of a non-transitory storage media and is rejected for the same reasons as claim 5 stated above. For the rejection of the limitations specifically pertaining to the non-transitory storage media of claim 12, see the rejection of claim 12 above. Dependent claim 17 is claim 6 in the form of a non-transitory storage media and is rejected for the same reasons as claim 6 stated above. For the rejection of the limitations specifically pertaining to the non-transitory storage media of claim 12, see the rejection of claim 12 above. Dependent claim 19 is claim 8 in the form of a non-transitory storage media and is rejected for the same reasons as claim 8 stated above. For the rejection of the limitations specifically pertaining to the non-transitory storage media of claim 12, see the rejection of claim 12 above . 07-21-aia AIA Claim s 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kuncheva and Caruana in further view of Mucke et al. (Feature selection on quantum computers); hereinafter Mucke Claim 7 is rejected over Kuncheva, Caruana and Mucke with the incorporation of claim 1. Regarding claim 7, Kuncheva teaches adding the diversity value to the set of diversity values comprising adding the diversity value to a cell in a matrix, of a plurality of cells, (Kuncheva [page 5, 3.4. The double-fault measure]: “This measure was used by Giacinto and Roli (2001) to form a pairwise diversity matrix for a classifier pool and subsequently to select classifiers that are least related. It is defined as the proportion of the cases that have been misclassified by both classifiers, i.e.,”) whose dimension is the number of ML models in the plurality of ML models; (Kuncheva [page 4]: “For a team D of L classifiers, the averaged Q statistics over all pairs of classifiers is … (4)”) the coordinates of the cell in the matrix is determined based on a first number assigned to the first ML model and a second number assigned to the second ML model; (Kuncheva [page 4]: “Yule’s Q statistic (1900) for two classifiers, D i and D k , Q i , k = N 11 N 00 - N 01 N 10 N 11 N 00 + N 01 N 10 , (3) where N ab is the number of elements z j of Z for which y j,i = a and y j,k = b (see Table 1).; Note: Q i , k is the diversity value computed using the joint comparison of D i and D k ’s output data. ) Kuncheva does not appear to explicitly teach selecting the subset comprising executing a Quadratic Unconstrained Binary Optimization (QUBO) solver relative to the matrix. However, Mucke teaches selecting the subset comprising executing a Quadratic Unconstrained Binary Optimization (QUBO) solver relative to the matrix. (Mucke [page 3, Fig. 1]: “Our proposed Quantum Feature Selection pipeline: From a given data set, the redundancy matrix R and the importance vector I are calculated, Eqs. 3 and 4. They are combined by interpolation with a factor α after the sign of I has been flipped, which results in a QUBO matrix, Eq. 2. The corresponding QUBO problem, Eq. 1, is solved either through quantum computing or classical solvers. The resulting binary solution vector x ∗ is a bit mask that indicates the selected features”) It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the Quantum Feature Selection pipeline of Mucke to reduce model complexity and improve ML interpretability (Mucke, 1 Introduction). Kuncheva and Mucke are analogous art because they both concern selecting the best subset for machine learning. Dependent claim 18 is claim 7 in the form of a non-transitory storage media and is rejected for the same reasons as claim 7 stated above. For the rejection of the limitations specifically pertaining to the non-transitory storage media of claim 12, see the rejection of claim 12 above . 07-21-aia AIA Claim s 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kuncheva and Caruana in further view of Morales-Hernández et al. (Feature selection on quantum computers); hereinafter Morales-Hernández Claim 9 is rejected over Kuncheva, Caruana and Morales-Hernández with the incorporation of claim 1. Regarding claim 9, Kuncheva does not appear to explicitly teach generating a z-score for each diversity value in the set of diversity values; wherein selecting the subset is further based on the z-score of each diversity value in the set of diversity values. However, Morales-Hernández generating a z-score for each diversity value in the set of diversity values; (Morales-Hernández [page 580, 5.2 Experimental Design]: “it takes into account the similarity with the best individual classifier. In the second case, it takes into account the similarity with the average starting from the individual classifiers. 4. We executed 50 iterations and we conserve all the obtained values to analyze them statistically. All diversity measures were standardized according to [46].”) wherein selecting the subset is further based on the z-score of each diversity value in the set of diversity values. (Morales-Hernández [page 595]: “Starting from the best individual classifier (best accuracy), we realize an in-depth search of classifiers that improve one of the following criteria when they are incorporated into the ensemble: – Diversity/Accuracy using CoP, – Diversity/Accuracy using SimBest, – Diversity/Accuracy using the DIF literature measure.”) It would have been obvious before the effective filing date to combine the diversity measure of the classifier ensembles of Kuncheva with the standardization of diversity measures of Morales-Hernández to improve accuracy of the classifier ensembles (Morales-Hernández, page 596). Kuncheva and Morales-Hernández are analogous art because they both concern selecting the best classifiers for an ensemble using a diversity measure. Dependent claim 20 is claim 9 in the form of a non-transitory storage media and is rejected for the same reasons as claim 9 stated above. For the rejection of the limitations specifically pertaining to the non-transitory storage media of claim 12, see the rejection of claim 12 above. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID H TRAN whose telephone number is (703)756-1525. The examiner can normally be reached M-F 9:30 am - 5:30 pm. 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, Viker Lamardo can be reached at (571) 270-5871. 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. /DAVID H TRAN/Examiner, Art Unit 2147 /VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147 Application/Control Number: 18/239,416 Page 2 Art Unit: 2147 Application/Control Number: 18/239,416 Page 3 Art Unit: 2147 Application/Control Number: 18/239,416 Page 4 Art Unit: 2147 Application/Control Number: 18/239,416 Page 5 Art Unit: 2147 Application/Control Number: 18/239,416 Page 6 Art Unit: 2147 Application/Control Number: 18/239,416 Page 7 Art Unit: 2147 Application/Control Number: 18/239,416 Page 8 Art Unit: 2147 Application/Control Number: 18/239,416 Page 9 Art Unit: 2147 Application/Control Number: 18/239,416 Page 10 Art Unit: 2147 Application/Control Number: 18/239,416 Page 11 Art Unit: 2147 Application/Control Number: 18/239,416 Page 12 Art Unit: 2147 Application/Control Number: 18/239,416 Page 13 Art Unit: 2147 Application/Control Number: 18/239,416 Page 14 Art Unit: 2147 Application/Control Number: 18/239,416 Page 15 Art Unit: 2147 Application/Control Number: 18/239,416 Page 16 Art Unit: 2147 Application/Control Number: 18/239,416 Page 17 Art Unit: 2147 Application/Control Number: 18/239,416 Page 18 Art Unit: 2147 Application/Control Number: 18/239,416 Page 19 Art Unit: 2147 Application/Control Number: 18/239,416 Page 20 Art Unit: 2147 Application/Control Number: 18/239,416 Page 21 Art Unit: 2147 Application/Control Number: 18/239,416 Page 22 Art Unit: 2147 Application/Control Number: 18/239,416 Page 23 Art Unit: 2147 Application/Control Number: 18/239,416 Page 24 Art Unit: 2147 Application/Control Number: 18/239,416 Page 25 Art Unit: 2147 Application/Control Number: 18/239,416 Page 26 Art Unit: 2147 Application/Control Number: 18/239,416 Page 27 Art Unit: 2147 Application/Control Number: 18/239,416 Page 28 Art Unit: 2147 Application/Control Number: 18/239,416 Page 29 Art Unit: 2147 Application/Control Number: 18/239,416 Page 30 Art Unit: 2147 Application/Control Number: 18/239,416 Page 31 Art Unit: 2147 Application/Control Number: 18/239,416 Page 32 Art Unit: 2147 Application/Control Number: 18/239,416 Page 33 Art Unit: 2147 Application/Control Number: 18/239,416 Page 34 Art Unit: 2147 Application/Control Number: 18/239,416 Page 35 Art Unit: 2147 Application/Control Number: 18/239,416 Page 36 Art Unit: 2147 Application/Control Number: 18/239,416 Page 37 Art Unit: 2147 Application/Control Number: 18/239,416 Page 38 Art Unit: 2147 Application/Control Number: 18/239,416 Page 39 Art Unit: 2147 Application/Control Number: 18/239,416 Page 40 Art Unit: 2147 Application/Control Number: 18/239,416 Page 41 Art Unit: 2147 Application/Control Number: 18/239,416 Page 42 Art Unit: 2147
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