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 .
The present application was filed on October 2, 2020.
Claims 1-8,11-17, 19-25, and 27 are presented for examination and are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submissions filed on 07/30th/2025 (amendment) and 08/21st/2025 (RCE) have been entered.
Response to Arguments
Applicant’s argument, see REMARKS page 9-10 filed 07/27th/2025, regarding the rejection of claims 1-8,11-17, 19-25, and 27 under 35 U.S.C. §101 as being directed to non-statutory subject matter, have been considered and are not persuasive.
Applicant’s argument #1
In a previous response, dated December 27, 2024, Applicant amended claims 1, 11, and 19 to recite that the prediction models are trained to predict an outcome that indicates "a phenotypic characteristic of the individual," where the phenotypic characteristic includes "one or both of: presence or absence of a disease; or response to a treatment for the disease" and to further recite "providing a treatment to the patient based at least in part on the predicted phenotypic characteristic of the patient." Applicant argued that the amended claims integrate any abstract idea into a practical application, specifically, providing treatment to a patient.
The Office Action asserts that the step of "providing a treatment for the disease ... " "amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h)." (Office Action at p. 10).
Applicant disagrees. The more apt analogy is to Classen Immunotherapies Inc. v. Biogen
IDEC, 659 F.3d 1057, 100 USPQ2d 1492 (Fed. Cir. 2011), discussed at MPEP §21206.05(e). In Classen, the claims recited performing an analysis to determine a low-risk immunization schedule, then immunizing a mammalian subject according to the schedule. The Federal Circuit held that, although the analysis was an abstract mental process, the immunization step was a meaningful limitation. Similarly here, treating a patient for a disease is a meaningful limitation.
In addition, with regard to claim 27, the rejection asserts that the recitation of dep learning classifier that is trained using machine learning "amounts to mere instructions to apply the abstract idea using generic computer components." However, in this case, the alleged abstract idea is "the mental processing grouping of abstract ideas that can be can be performed in the human mind, or by a human with pencil and paper." (Office Action at p. 7). "Machine learning" refers to a type of process that is exclusively performed by computer systems and cannot be performed by a human with pencil and paper. Thus, a claim that recites machine learning cannot be directed to "mental steps."
Examiner’s response #1
The examiner respectfully disagrees. Classen Immunotherapies Inc. v. Biogen
IDEC is directed the treatment to a the specific field of immunization while the instant application recited the treatment limitation at a high level of generality and does not identify the steps of the treatment.
Furthermore, While the amended claims recite that the prediction models are trained to predict an outcome that indicates "a phenotypic characteristic of the individual," where the phenotypic characteristic includes "one or both of: presence or absence of a disease; or response to a treatment for the disease" and to further recite "providing a treatment to the patient based at least in part on the predicted phenotypic characteristic of the patient.", there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of identifying a training set of data samples, segmenting the data samples, assigning the data samples to one of the plurality of strata, predicting whether the patient has the disease, determining, for each stratum, a likelihood of the outcome… for that stratum, determining, for each stratum, a probability that the testing sample belongs to that stratum, or computing a predicted outcome for the testing sample based on the likelihood for each stratum weighted by the probability that the testing sample belongs to that stratum rather than to an improvement on the functioning of a computer or to any other technology. See MPEP 2106.05(a). Thus, even when considering the elements in combination, the claim as a whole does not integrate the recited exception into a practical application.
Applicant’s argument, see REMARKS page 14-15 filed 07/30th/2024, regarding the rejection of claims 1-8,11-17, 19-25, and 27 under 35 U.S.C. §103 have been considered and are not persuasive.
Applicant’s argument #1
Applicant previously argued that "segmenting the data" means that each data sample, including all of its variables, is assigned to one stratum. (Amendment dated December 27, 2024, at pp. 11-12). The present Office Action responds that this feature is not adequately recited in the claims. (Office Action at p. 5).
In the interest of expediting prosecution, independent claims 1, 11, and 19 have been amended to recite that segmenting of the data samples is performed "such that each data sample of the training set is assigned to one of the plurality of strata and each stratum in the plurality of strata includes a different subset of the data samples of the training set." Support can be found in the specification, e.g., at [0019], [0026], and [0027].
As explained in Applicant's previous response, segmenting of data samples as recited in the present claims is different from the segmentation of features of data samples into subsets as taught by the cited references.
Applicant also maintains that the Office Action's plucking of a feature from Ricci, a feature from Hibi, and a feature from Zhao in an attempt to close the gaps between the present claims and the initially cited references (Wang and Fan) appears to be impermissible hindsight reconstruction, using the claims as a guide. The Office Action asserts that the references are "analogous art," but even if that is the case, that does not preclude the possibility of hindsight bias infecting the analysis of such art.
Examiner’s response #1
The examiner respectfully disagrees. The way the amendment is recited does not render the claim narrower. Adding “such that each data sample of the training set is assigned to one of the plurality of strata and each stratum in the plurality of strata includes a different subset of the data samples of the training set” is akin to repeating the already recited limitation of “segmenting the data samples of the training set into a plurality of strata based on a measure of similarity of the data samples”. In other words, segmenting data samples into a plurality of strata already fulfills the newly amended limitation of assigning each data sample to one of the plurality of strata while each stratum in the plurality of strata includes a different subset of the data sample. When segmenting a data set into a plurality of strata, the same data point that is part of the segmented dataset cannot exist in more than one strata of the plurality of stratum.
Furthermore, using multiple secondary references to reject amendments is not impermissible hindsight reconstruction, but rather, is the bases of any 103 rejection.
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 1-8,11-17, 19-25, and 27 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1,
Step 1:
Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One:
Claim 1 recites the following limitations:
identifying a training set of data samples, wherein each data sample in the training set includes a plurality of variables indicating presence or absence of each of a plurality of single nucleotide polymorphisms (SNPs) in an individual and a known outcome indicating a phenotypic characteristic of the individual, the phenotypic characteristic including one or both of: presence or absence of a disease; or response to a treatment for the disease;
segmenting the data samples for the training set into a plurality of strata based on a measure of similarity of the data samples such that each data sample of the training set is assigned to one of the plurality of strata and each stratum in the plurality of strata includes a different subset of the data samples of the training set;
predicting, based on the testing sample, whether the patient has the disease, wherein predicting whether the patient has the disease includes: determining, for each stratum, a likelihood of the outcome… for that stratum;
determining, for each stratum, a probability that the testing sample belongs to that stratum; and
computing a predicted outcome for the testing sample based on the likelihood for each stratum weighted by the probability that the testing sample belongs to that stratum, wherein the predicted outcome predicts the phenotypic characteristic of the patient.
This/these limitations require identifying and segmenting a training set of data, predicting outcomes for test samples (corresponds to evaluation/judgement), determining probabilities, and computing predicted outcomes. This falls within the mental process grouping of abstract ideas that can be performed in the human mind, or by a human with pencil and paper.
Step 2A Prong Two:
The abstract idea of claim 1 is not integrated into a practical application because the additional elements in claim 1 are:
A computer-implemented method for predicting likelihood of an outcome based on a set of variables
Obtaining, for a patient, a testing sample for which the variables are known, and
training a prediction model for each stratum, wherein the prediction model predicts a likelihood of an outcome based on the variables and wherein training of the prediction model is performed independently for each stratum;
using the prediction model
Mere instructions to apply the abstract idea using generic computer components (A computer-implemented method for predicting likelihood of an outcome based on a set of variables) and (training a prediction model for each stratum, wherein the prediction model predicts a likelihood of an outcome based on the variables and wherein training of the prediction model is performed independently for each stratum; using the prediction model) and (using the prediction model) do not represent a practical application of the abstract idea (see MPEP 2106.05(f)).
Further, the recitation of:
obtaining a testing sample for which the variables are known; and
amounts to recitation of insignificant extra-solution activity of data gathering. See MPEP 2106.05(g).
providing a treatment for the disease to the patient based at least in part on the predicted phenotypic characteristic of the patient
is recited at a high level of generality and amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
Finally, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. Using generic computer components to perform the abstract idea (A computer-implemented method for predicting likelihood of an outcome based on a set of variables) and (training a prediction model for each stratum, wherein the prediction model predicts a likelihood of an outcome based on the variables and wherein training of the prediction model is performed independently for each stratum; using the prediction model) amounts to no more than mere instructions to apply the exception using generic computer components which cannot provide an inventive concept, see MPEP 2106.05(f).
Further, the following recitation of insignificant extra-solution activity (obtaining, for a patient, a testing sample for which the variables are known; and) amounts to insignificant extra-solution activity of data gathering, see MPEP 2106.05(g). Further, MPEP 2106(d)(II) notes the following, "The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);… iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. Accordingly, the additional element does not integrate the abstract idea into a practical application because the recitation of insignificant extra solution activity is well-understood, routine, and conventional.
providing a treatment for the disease to the patient based at least in part on the predicted phenotypic characteristic of the patient
is recited at a high level of generality and amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Furthermore, the courts have found limitations directed to linking data to a field of use, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II)).
Claim 2,
Claim 2 is dependent on claim 1 and only includes additional limitations drawn to mathematical concepts:
wherein segmenting the data samples of the training set includes: building a matrix for the training set of data samples;
computing a set of eigenvalues and a set of eigenvectors from the matrix;
sorting the eigenvectors based on respective magnitudes of the eigenvalues; and
using the sorted eigenvectors to segment the data samples of the training set.
This claim does not recite any additional elements beyond those recited in claim 1, and as such do not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject-matter ineligible.
Claim 3,
Claim 3 is dependent on claim 2 and only includes additional limitations drawn to mathematical concepts:
wherein using the sorted eigenvectors to segment the data samples of the training set includes: selecting a subset of the sorted eigenvectors as significant eigenvectors;
computing a weighted average vector of the significant eigenvectors, wherein the average is weighted according to the eigenvalues;
sorting components of the weighted average vector; and
using quantiles of the weighted average vector to assign each data sample from the training set to one of the strata.
This claim does not recite any additional elements beyond those recited in claim 1, and as such do not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject-matter ineligible.
Claim 4,
Claim 4 is dependent on claim 1 and only includes additional limitations drawn to mathematical concepts:
computing a center for each of the plurality of strata.
This claim does not recite any additional elements beyond those recited in claim 1, and as such do not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject-matter ineligible.
Claim 5,
Claim 5 is dependent on claim 4 and only includes additional limitations drawn to mathematical concepts:
wherein determining, for each stratum, a probability that the testing sample belongs to that stratum includes computing a distance metric between the testing sample and the center of that stratum.
This claim does not recite any additional elements beyond those recited in claim 4, and as such do not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject-matter ineligible.
Claim 6,
Claim 6 is dependent on claim 1 and only recites additional elements
wherein the predicted outcome for the testing sample is computed based on a Bayesian model.
that amounts to mere instructions to apply the abstract idea using generic computer components which cannot integrate the abstract idea into a practical application or provide an inventive concept (see MPEP 2106.05(f)). The claim thus remains subject-matter ineligible.
Claim 7,
Claim 7 is dependent on claim 1 and only recites additional elements
wherein the prediction model for each stratum is a linear regression model.
that amounts to mere instructions to apply the abstract idea using generic computer components which cannot integrate the abstract idea into a practical application or provide an inventive concept (see MPEP 2106.05(f)). The claim thus remains subject-matter ineligible.
Claim 8,
Claim 8 is dependent on claim 1 and only recites additional elements
wherein the prediction model for each stratum is a logistic regression model.
that amounts to mere instructions to apply the abstract idea using generic computer components which cannot integrate the abstract idea into a practical application or provide an inventive concept (see MPEP 2106.05(f)). The claim thus remains subject-matter ineligible.
Claim 27,
Claim 8 is dependent on claim 1 and only recites additional elements
wherein the prediction model is a deep learning classifier and training the deep-learning classifier uses machine learning.
that amounts to mere instructions to apply the abstract idea using generic computer components which cannot integrate the abstract idea into a practical application or provide an inventive concept (see MPEP 2106.05(f)). The claim thus remains subject-matter ineligible.
Claim 11,
Claim 11 is directed to a computer system comprising a memory and processor, which is directed to a machine, one of the statutory categories. Claim 11 recites: A computer system comprising: a memory; and a processor coupled to the memory and configured to… which performs a process that has limitations similar to the limitations of claim 1, thus is rejected with the same rationale applied against claim 1. As performing an abstract idea on a generic computer component cannot integrate the abstract idea into a practical application and cannot provide an inventive concept, claim 11 remains subject matter ineligible.
Claim 12,
This claim is dependent on claim 11 and recites limitations that are similar to the limitations recited in claim 2, thus is rejected with the same rationale applied against claim 2. This claim does not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject matter ineligible.
Claim 13,
This claim is dependent on claim 12 and recites limitations that are similar to the limitations recited in claim 3, thus is rejected with the same rationale applied against claim 3. This claim does not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject matter ineligible.
Claim 14,
Claim 14 is dependent on claim 11 and only includes additional limitations drawn to mathematical concepts:
compute a center for each of the plurality of strata, wherein determining, for each stratum, a probability that the testing sample belongs to that stratum includes computing a distance metric between the testing sample and the center of that stratum.
This claim does not recite any additional elements beyond those recited in claim 11, and as such do not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject-matter ineligible.
Claim 15,
This claim is dependent on claim 11 and recites limitations that are similar to the limitations recited in claim 6, thus is rejected with the same rationale applied against claim 6. This claim does not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject matter ineligible.
Claim 16,
This claim is dependent on claim 11 and recites limitations that are similar to the limitations recited in claim 7, thus is rejected with the same rationale applied against claim 7. This claim does not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject matter ineligible.
Claim 17,
This claim is dependent on claim 11 and recites limitations that are similar to the limitations recited in claim 8, thus is rejected with the same rationale applied against claim 8. This claim does not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject matter ineligible.
Claim 19,
Claim 19 is directed to a non-transitory computer-readable storage medium, which is directed to a product, one of the statutory categories. Claim 19 recites: A non-transitory computer-readable storage medium having stored therein program code instructions that, when executed by a processor of a computer system, cause the computer system to perform a method comprising… which performs a process that has limitations similar to the limitations of claim 1, thus is rejected with the same rationale applied against claim 1. As performing an abstract idea on a generic computer component cannot integrate the abstract idea into a practical application and cannot provide an inventive concept, claim 11 remains subject matter ineligible..
Claim 20,
This claim is dependent on claim 19 and recites limitations that are similar to the limitations recited in claim 2, thus is rejected with the same rationale applied against claim 2. This claim does not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject matter ineligible.
Claim 21,
This claim is dependent on claim 20 and recites limitations that are similar to the limitations recited in claim 3, thus is rejected with the same rationale applied against claim 3. This claim does not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject matter ineligible.
Claim 22,
This claim is dependent on claim 19 and recites limitations that are similar to the limitations recited in claim 14, thus is rejected with the same rationale applied against claim 14. This claim does not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject matter ineligible.
Claim 23,
This claim is dependent on claim 19 and recites limitations that are similar to the limitations recited in claim 6, thus is rejected with the same rationale applied against claim 6. This claim does not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject matter ineligible.
Claim 24,
This claim is dependent on claim 19 and recites limitations that are similar to the limitations recited in claim 7, thus is rejected with the same rationale applied against claim 7. This claim does not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject matter ineligible.
Claim 25,
This claim is dependent on claim 19 and recites limitations that are similar to the limitations recited in claim 8, thus is rejected with the same rationale applied against claim 8. This claim does not recite any additional elements which could integrate the abstract idea into a practical application or be significantly more than the abstract idea. The claim thus remains subject matter ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 11, 12, 19, 20, and 27 is /are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“A Novel Feature Subspace Selection Method in Random Forests for High Dimensional Data”) in view of Fan et al. (“Development of PCA-based cluster quantile regression (PCA-CQR) framework for streamflow prediction: Application to the Xiangxi river watershed, China”) in view of Ricci et al. (US10460839) in view of Zhao et al. (US20200051696A1) in view of Hibi et al. (US20100228099A1)
Claim 1,
Wang teaches:
segmenting the data samples of the training set into a plurality of strata based on a measure of similarity of the data samples; (Page 4383, Section 1: “Stratified Sampling (SS) is a sampling method to introduce a stratification variable to divide the data into several subgroups and then randomly sample from each subgroup according to the ratio of target sample size to subgroup size, ensuring to obtain the representative sample of the data [5]. Moreover, in PCA-SS, it is exactly the principal component that serves as the stratification variable to divide the feature set.” teaches using stratified sampling to segment the data into a plurality of strata; Page 4383, Section 1: “According to a specified principal components principle, the features transformed by PCA are divided into two parts, i.e. informative and less informative parts” teaches using principal component analysis to divide the features into two parts, therefore the different strata from stratified sampling are similar to each other)
training a prediction model for each stratum, wherein the prediction model predicts a likelihood of an outcome based on the variables and wherein training of the prediction model is performed independently for each stratum; (Page 4384, Section 3: “Generally, random forests are built by combining several decision trees, each of which is trained in isolation. To be specific, the random forests algorithm is usually described as follows: Given a data set D = {(Xi, Yi), Xi ∈ RD, Yi ∈ Y}N i=1, where Xi is one instance with D dimensional features and Yi is the target, Y∈{1, 2,...,C} with C being the number of classes. N is the number of training instances.” teaches that random forests are built by combining multiple decision trees and that each decision tree is trained in isolation (independently); Page 4386, Section 4C: “The stratified sampling of a subspace with p features can now be accomplished by selecting individual features at random from the two parts. The features are selected in proportion to the relative sizes of the two parts. That is, we randomly selected… where D1 is the number of features in A1, p1 and p2 are the number of samples from the two parts A1 and A2 respectively. These samples are then merged to form a feature subspace for tree construction.” teaches that stratified sampling is used to create subspaces (strata) that are used to train the decision trees; Page 4383: “Random forests are a type of ensemble methods for classification and regression that construct some identically randomized decision trees and make predictions by averaging the results from individual trees” teaches that random forests (prediction model) are used for classification and regression (predict outcomes))
Wang does not teach:
identifying a training set of data samples, wherein each data sample in the training set includes a plurality of variables indicating presence or absence of each of a plurality of single nucleotide polymorphisms (SNPs) in an individual and a known outcome indicating a phenotypic characteristic of the individual, the phenotypic characteristic including one or both of: presence or absence of a disease; or response to a treatment for the disease; and
obtaining, for a patient, a testing sample for which the variables are known; and predicting, based on the testing sample, whether the patient has the disease, wherein predicting whether the patient has the disease includes: determining, for each stratum, a likelihood of the outcome using the prediction model for that stratum; and
determining, for each stratum, a probability that the testing sample belongs to that stratum; and
computing a predicted outcome for the testing sample based on the likelihood for each stratum weighted by the probability that the testing sample belongs to that stratum, and
wherein the predicted outcome predicts the phenotypic characteristic of the patient
providing a treatment for the disease to the patient based at least in part on the predicted phenotypic characteristic of the patient
However, Ricci teaches:
identifying a training set of data samples, wherein each data sample in the training set includes a plurality of variables indicating presence or absence of each of a plurality of single nucleotide polymorphisms (SNPs) in an individual and a known outcome indicating a phenotypic characteristic of the individual, the phenotypic characteristic including one or both of: presence or absence of a disease; or response to a treatment for the disease; (Col. 10, Line 45-56: “The aggregator service 106 may also identify matching elements of the bioinformatics dataset including gene(s), gene identifier, a gene sequence, single nucleotide polymorphism(s), nucleic acid sequence(s), protein sequence(s) (proteomics), annotating genome(s), a shotgun sequence, an associated periodontal disease, a caries susceptibility, an impacted tooth, a tooth loss, an angle's classification of malocclusion, level(s) of immunoglobulin G (IGG) and immunoglobulinA (IGA), and/or diabetes diagnosis, among others by matching the attributes of the correlated dental 55 image information 338 with elements of the bioinformatics dataset” teaches identifying a dataset that includes single nucleotide polymorphism(s), and an associated periodontal disease).
Wang and Ricci are analogous arts because they are directed to employing machine learning in data processing.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Ricci’s identification of a dataset that includes single nucleotide polymorphism(s), and an associated periodontal disease with a motivation to expand the bioinformatics dataset for a future analysis (Ricci, Col. 10, Line 36-37).
Furthermore, Zhao teaches:
obtaining, for a patient, a testing sample for which the variables are known; and predicting, based on the testing sample, whether the patient has the disease, wherein predicting whether the patient has the disease includes: determining, for each stratum, a likelihood of the outcome using the prediction model for that stratum; and
determining, for each stratum, a probability that the testing sample belongs to that stratum ([0039]: “The embodiments described herein relate to a method for identifying the likelihood of a patient getting an infection based on their physiological status as well as the patients surrounding them and possible routes of infection transmission based upon the hospital layout. Conceptually, the method may be divided into three main stages: 1) patient data is transformed into synthetic images (stage 1); 2) a machine learning model such as a convolutional neural network (CNN) is used to predict probability of infection for each individual patient based on the synthetic images generated previously (stage 2); and 3) a graphical model of the layout of the hospital is used to detect possible routes of disease transmission, based on which the probability of infection previously obtained for each patient is adjusted (stage 3)” teaches obtaining patient data and transforming this data into images, predicting using a CNN a probability of infection for each individual patient based on the synthesized images.
Wang and Zhao are analogous arts because they are directed to employing machine learning in data processing.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zhao’s method of obtaining patient data and transforming this data into images, predicting using a CNN a probability of infection for each individual patient based on the synthesized images with a motivation to readily adapt to monitoring the individual patient in each hospital unit (Zhao, [0038]).
Furthermore, Fan teaches:
determining, for each stratum, a probability that the testing sample belongs to that stratum; and (Page 290, Section 4.3: “In detail, the quantile values of 0.05, 0.25, 0.50, 0.75 and 0.95 would be generated from the proposed PCA-CQR models, denoted as Qsim,0.05, Qsim,0.25, Qsim,0.50, Qsim,0.75 and Qsim,0.95. The corresponding probabilities would be respectively assigned to be 0.05, 0.20, 0.25, 0.25, 0.20, 0.05 for the intervals of [Qsim,min, Qsim,0.05, Qsim,0.05, Qsim,0.25, Qsim,0.25, Qsim,0.50, Qsim,0.50, Qsim,0.75, Qsim,0.75, Qsim,0.95] and [Qsim,0.95, Qsim,max].” and Page 286, Section 3.4: “Consider the probabilistic forecasts divided into J categories, the probabilistic forecast at the time period t, Qt,sim, would correspond to a vector {pt,1, pt,2, . . ., pt,J}, where pi,j (j = 1, 2, . . ., J) represents the probability for the forecast Qt,sim falling into category Cj. Similarly, a probability vector for the real observation, Qt,obs, can be defined as {ot,1, ot,2, . . ., ot,J}, with oi,j = 1 if Qt,obs ∈ Cj and oi,j = 0 in the reverse case.” teaches assigning probabilities to each category (stratum))
computing a predicted outcome for the testing sample based on the likelihood for each stratum weighted by the probability that the testing sample belongs to that stratum. (Page 286, Section 3.4: “Eq. (31) indicates the RPS for a single probabilistic forecast” and Equation 31:
PNG
media_image1.png
108
664
media_image1.png
Greyscale
teaches that the ranked probability score (RPS) is calculated by the summation of each individual probabilities of a forecast being in each category, thus the RPS is a summation of each weighted category)
Wang and Fan are analogous arts because they are directed to grouping data with principal component analysis.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Fan’s algorithm of assigning individual probabilities to each cluster and computing a probability score using a summation of probabilities of each cluster with Wang’s algorithm for stratified sampling with a motivation to perform better in both calibration and validation periods than traditional methods while providing satisfactory predictions (Fan, Page 288).
Furthermore, Hibi teaches:
wherein the predicted outcome predicts the phenotypic characteristic of the patient ([0056]: “In the above description, relationship between prediction input factors and prediction output factors of two or more patients stored in the prediction factor data base 12 was patternized using age, sex, the presence or absence of cerebrovascular accidents, the presence or absence of malignant diseases, the presence or absence of deglutition pneumonia before gastrostomy, the presence or absence of dementia, the presence or absence of degenerative diseases, amount of serum total protein, amount of serum albumin and amount of hemoglobin as the prediction input factors and using the number of the number of survival days after PEG as the prediction output factors. Hereinafter, the sigmoid function output from the intermediate layer to the output layer via the above-mentioned process is called prognosis prediction formula”.
Wang and Hibi are analogous arts because they are directed to employing machine learning in data processing.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Hibi’s method of patternizing relationships between prediction input factors and prediction output factors of two or more patients utilizing the presence or absence of diseases with a motivation to allow a doctor to obtain predictive results sufficient to judge whether or not to perform a PEG (percutaneous endoscopic gastrostomy) for the patient (Hibi, [0059]).
Furthermore, Ricci teaches:
providing a treatment for the disease to the patient based at least in part on the predicted phenotypic characteristic of the patient (Col. 14, Line 29-42: “The method may include receiving a dental image of a patient from a dental image provider. The dental image may next be processed with a ML anatomy dataset. An anatomy from the ML anatomy dataset may be identified and matched to the dental image. The dental image may also be matched and identified with a ML pathology and treatment dataset. A pathology and a treatment from the machine leaning pathology and treatment dataset may be matched to the dental image. In addition, a patient dataset of the patient associated with the dental image may be queried and received from a patient data provider. Subsequently, the dental image and the anatomy, the pathology, and the treatment associated with the dental image may be inserted to the patient dataset.” teaches identifying a patient’s phenotypic characteristic based on a dental image and proving a treatment based on the patient’s identified phenotypic characteristic).
Wang and Ricci are analogous arts because they are directed to employing machine learning in data processing.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Ricci’s identification of a dataset that includes single nucleotide polymorphism(s), and an associated periodontal disease with a motivation to compile a diagnostic aid for a user (Ricci, Col. 14, Line 46-47).
Claim 2,
The combination of Wang and Fan teaches:
The method of claim 1,
Wang further teaches:
wherein segmenting the data samples of the training set includes: building a matrix for the training set of data samples; (Page 4385, Section 4B: “In the following, we briefly introduce the procedure of PCA. Given the subset D(l) m with the dimension N ×F(l) whose rows represent instances and columns represent features…” teaches that the subset of the data set is represented by a rows of instances and columns of features, thus the subset of Dm is a matrix)
computing a set of eigenvalues and a set of eigenvectors from the matrix; (Page 4385, Section 4B: “Then, we compute the covariance matrix as follows… where Σ is exactly the covariance matrix of subset D(l) m . After that, we can compute the eigenvectors {u} and eigenvalues {λ} of the Σ.” teaches calculating a eigenvectors and eigenvalues from the covariance matrix)
sorting the eigenvectors based on respective magnitudes of the eigenvalues; and (Page 4385, Section 4B: “Through stacking the eigenvectors in columns in the decreasing order of the corresponding eigenvalues, we obtain the transformation matrix U.” teaches stacking the eigenvectors in decreasing order of their eigenvalues)
using the sorted eigenvectors to segment the data samples of the training set. (Page 4385, Section 4C: “Generally, assuming λ1, λ2,...,λF (l) are the eigenvalues of the covariance matrix Σ which have been sorted in a decrease order, we define a cumulative ratio R of eigenvalues… Therefore, according to the cumulative ratio R, for the transformed subset D (l) m , we assign the former r eigenvalues corresponding features to the informative part A1 and the latter F(l) − r eigenvalues corresponding features to the less informative part A2… The stratified sampling of a subspace with p features can now be accomplished by selecting individual features at random from the two parts.” teaches that the sorted eigenvectors are used by the PCA-SS algorithm to perform stratified sampling and divide the training set into subspaces)
Claim 27,
The combination of Wang and Fan teaches:
The method of claim 1,
The combination of Wang and Fan does not teach:
wherein the prediction model is a deep learning classifier and training the deep-learning classifier uses machine learning.
However, Zhao teaches:
wherein the prediction model is a deep learning classifier and training the deep-learning classifier uses machine learning. ([0039] “Conceptually, the method may be divided into three main stages: 1) patient data is transformed into synthetic images (stage 1); 2) a machine learning model such as a convolutional neural network (CNN) is used to predict probability of infection for each individual patient based on the synthetic images generated previously (stage 2)” teaches using a machine learning model such as a convolutional neural network (CNN) to predict probability of infection)
Wang and Zhao are analogous arts because they are directed to employing machine learning in data processing.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zhao’s method of predicting using a CNN a probability of infection for each individual patient based on the synthesized images with a motivation to readily adapt to monitoring the individual patient in each hospital unit (Zhao, [0038]).
Claim 11,
This claim has limitations similar to the limitations of claim 1, thus is rejected with the same rationale applied against claim 1.
Claim 12,
This claim has limitations similar to the limitations of claim 2, thus is rejected with the same rationale applied against claim 2.
Claim 19,
This claim has limitations similar to the limitations of claim 1, thus is rejected with the same rationale applied against claim 1.
Claim 20,
This claim has limitations similar to the limitations of claim 2, thus is rejected with the same rationale applied against claim 2.
Claims 3, 13, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Fan, further in view of Xiao et al. (“Multivariate sensitivity analysis based on the direction of eigen space through principal component analysis”)
Claim 3,
The combination of Wang and Fan teaches:
The method of claim 2,
Wang further teaches:
wherein using the sorted eigenvectors to segment the data samples of the training set includes: selecting a subset of the sorted eigenvectors as significant eigenvectors; (Page 4386, Section 4C: “Therefore, according to the cumulative ratio R, for the transformed subset D (l) m , we assign the former r eigenvalues corresponding features to the informative part A1 and the latter F(l) − r eigenvalues corresponding features to the less informative part A2.” teaches selecting eigenvalues that correspond to features that are informative to the A1 subgroup, thus this subset of eigenvectors are significant)
Fan further teaches:
using quantiles… to assign each data sample from the training set to one of the strata. (Page 290, Section 4.3: “In detail, the quantile values of 0.05, 0.25, 0.50, 0.75 and 0.95 would be generated from the proposed PCA-CQR models, denoted as Qsim,0.05, Qsim,0.25, Qsim,0.50, Qsim,0.75 and Qsim,0.95. The corresponding probabilities would be respectively assigned to be 0.05, 0.20, 0.25, 0.25, 0.20, 0.05 for the intervals of [Qsim,min, Qsim,0.05, Qsim,0.05, Qsim,0.25, Qsim,0.25, Qsim,0.50, Qsim,0.50, Qsim,0.75, Qsim,0.75, Qsim,0.95] and [Qsim,0.95, Qsim,max].” teaches using quantiles to assign data to a corresponding interval)
The combination of claim 1 has already incorporated the PCA-CQR algorithm, thus incorporating the details of the quantiles required by claim 3.
The combination of Wang and Fan does not teach:
computing a weighted average vector of the significant eigenvectors, wherein the average is weighted according to the eigenvalues;
sorting components of the weighted average vector; and
However, Xiao teaches:
computing a weighted average vector of the significant eigenvectors, wherein the average is weighted according to the eigenvalues; (Page 3, Section 3: “However, since the eigenvectors are correlated with different eigenvalues, that is to say, the different eigenvectors contains different amounts of information of model outputs, a weighted average of δi,k for all the eigenvectors should be better.” teaches computing a weighted average of eigenvectors)
sorting components of the weighted average vector; and (Page 2, Section 1: “In this work, a new kind of sensitivity indices is proposed which measure the importance of model inputs through the influence of the inputs on the direction of the transformed output space obtained by PCA. PCA is multivariate statistical method [34,35], which can transform the original variables into a set of new orthogonal variables which are sorted according to their variance.” teaches that the calculated sensitivity indices measure the importance of model inputs for the direction of the associated eigenvector that is generated from principal component analysis, which is used to obtain orthogonal variables that are sorted by variance)
Wang/Fan/Xiao are analogous arts because they are directed to using principal component analysis.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Xiao’s calculated weighted average of eigenvectors with Wang’s eigenvectors generated from PCA as modified by Fan with a motivation to more accurately determine the effect of input variables on the direction of principal components in the eigen space (Fan, Page 288).
Claim 13,
This claim has limitations similar to the limitations of claim 3, thus is rejected with the same rationale applied against claim 3.
Claim 21,
This claim has limitations similar to the limitations of claim 3, thus is rejected with the same rationale applied against claim 3.
Claims 4, 5, 14, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Fan, further in view of Rokach et al. (“CLUSTERING METHODS”)
Claim 4,
The combination of Wang and Fan teaches:
The method of claim 1,
The combination of Wang and Fan does not teach:
computing a center for each of the plurality of strata.
However, Rokach teaches:
computing a center for each of the plurality of strata. (Page 333: “The simplest and most commonly used algorithm, employing a squared error criterion is the K-means algorithm. This algorithm partitions the data into K clusters (Cl, Cz, . . . , CK), represented by their centers or means. The center of each cluster is calculated as the mean of all the instances belonging to that cluster.” teaches calculating the center of a cluster)
Wang/Fan/Rokach are analogous arts because they are directed to clustering or grouping data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Rokach’s clustering methods with Wang’s PCA-SS algorithm as modified by Fan with a motivation to use a computationally efficient and easily interpretable algorithm for clustering (Rokach, Page 334).
Claim 5,
The combination of Wang, Fan, and Rokach teaches:
The method of claim 4,
Rokach further teaches:
wherein determining, for each stratum, a probability that the testing sample belongs to that stratum includes computing a distance metric between the testing sample and the center of that stratum. (Page 322: “Many clustering methods use distance measures to determine the similarity or dissimilarity between any pair of objects. It is useful to denote the distance between two instances xi and xj as: d(xi,xj).” teaches using a distance metric to determine if an item should be clustered, thus a distance metric between the center of a cluster and an object can be calculated)
The combination of claim 4 has already incorporated the clustering based on a center of a cluster, therefore already incorporating the details of the distance metric required by claim 5.
Claim 14,
The combination of Wang and Fan teaches:
The computer system of claim 11,
The combination of Wang and Fan does not teach:
wherein the processor is further configured to: compute a center for each of the plurality of strata, wherein determining, for each stratum, a probability that the testing sample belongs to that stratum includes computing a distance metric between the testing sample and the center of that stratum.
However, Rokach teaches:
wherein the processor is further configured to: compute a center for each of the plurality of strata, wherein determining, for each stratum, a probability that the testing sample belongs to that stratum includes computing a distance metric between the testing sample and the center of that stratum. (Page 333: “The simplest and most commonly used algorithm, employing a squared error criterion is the K-means algorithm. This algorithm partitions the data into K clusters (Cl, Cz, . . . , CK), represented by their centers or means. The center of each cluster is calculated as the mean of all the instances belonging to that cluster.” teaches calculating the center of a cluster; Page 322: “Many clustering methods use distance measures to determine the similarity or dissimilarity between any pair of objects. It is useful to denote the distance between two instances xi and xj as: d(xi,xj).” teaches using a distance metric to determine if an item should be clustered, thus a distance metric between the center of a cluster and an object can be calculated)
Wang/Fan/Rokach are analogous arts because they are directed to clustering or grouping data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Rokach’s clustering methods with Wang’s PCA-SS algorithm as modified by Fan with a motivation to use a computationally efficient and easily interpretable algorithm for clustering (Rokach, Page 334).
Claim 22,
This claim has limitations similar to the limitations of claim 14, thus is rejected with the same rationale applied against claim 14.
Claims 6, 15, and 23 is /are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Fan, further in view of Kunihama et al. (“Nonparametric Bayes modeling with sample survey weights”)
Claim 6,
The combination of Wang and Fan teaches:
The method of claim 1,
The combination of Wang and Fan does not teach:
wherein the predicted outcome for the testing sample is computed based on a Bayesian model.
However, Kunihama teaches:
wherein the predicted outcome for the testing sample is computed based on a Bayesian model. (Page 43: “We propose a simple Bayesian adjustment method using the result in Theorem 1. Although survey weights are treated as random variables in Bayesian settings, additional modeling of the weights can induce highly complex models. Instead, we avoid such complexity in the model but still incorporate uncertainty by generating mixture weights. We consider a standard Bayesian mixture model… where λ = (λ1, . . . , λH ) ′ with λh ≥ 0 and H h=1 λh = 1, and π (λ) and π (θh) are priors for λ and θh” teaches using a Bayesian mixture model)
Wang/Fan/Kunihama are analogous arts because they are directed to clustering or grouping data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Kunihama’s Bayesian mixture model with Wang’s PCA-SS algorithm as modified by Fan with a motivation to provide a simple and efficient method for applying stratified sampling with Bayesian analysis (Kunihama, Page 42).
Claim 15,
This claim has limitations similar to the limitations of claim 6, thus is rejected with the same rationale applied against claim 6.
Claim 23,
This claim has limitations similar to the limitations of claim 6, thus is rejected with the same rationale applied against claim 6.
Claims 7, 16, and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Fan, further in view of Senn et al. (“Stratification for the propensity score compared with linear regression techniques to assess the effect of treatment or exposure”)
Claim 7,
The combination of Wang and Fan teaches:
The method of claim 1,
The combination of Wang and Fan does not teach:
wherein the prediction model for each stratum is a linear regression model.
However, Senn teaches:
wherein the prediction model for each stratum is a linear regression model. (Page 5530: “In this paper we strive to highlight the mechanisms by which the two strategies operate, with a focus on linear regression.” teaches using a linear regression model)
Wang/Fan/Senn are analogous arts because they are directed to clustering or grouping data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Senn’s linear regression model with Wang’s PCA-SS algorithm as modified by Fan with a motivation to use a model that minimizes residual variance among strata (Senn, Page 5540).
Claim 16,
This claim has limitations similar to the limitations of claim 7, thus is rejected with the same rationale applied against claim 7.
Claim 24,
This claim has limitations similar to the limitations of claim 7, thus is rejected with the same rationale applied against claim 7.
Claims 8-10, 17, 18, 25, and 26 is /are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Fan, further in view of Zhu et al. (“Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques”)
Claim 8,
The combination of Wang and Fan teaches:
The method of claim 1,
The combination of Wang and Fan does not teach:
wherein the prediction model for each stratum is a logistic regression model.
However, Zhu teaches:
wherein the prediction model for each stratum is a logistic regression model. (Page 2, Section 1: “This research work proposes PCA for dimensionality reduction, which helps to define suitable initial centroids for our dataset when the k-means algorithm is applied. K-means is then used to find outliers and to cluster the data into similar groups, with logistic regression as a classifier for the dataset” teaches using logistic regression)
Wang/Fan/Zhu are analogous arts because they are directed to clustering or grouping data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zhu’s logistic regression model with Wang’s PCA-SS algorithm as modified by Fan with a motivation to use an efficient regression predictive analysis algorithm (Zhu, Page 1).
Claim 17,
This claim has limitations similar to the limitations of claim 8, thus is rejected with the same rationale applied against claim 8.
Claim 25,
This claim has limitations similar to the limitations of claim 8, thus is rejected with the same rationale applied against claim 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yuan (US20190167204A1)
“Yuan teaches a method for monitoring patients for risk of worsening heart failure”
An (US20180325466A1)
“An teaches a method for monitoring patient for syncope”
Khammanivong - US 2020/0115762 A1
“Khammanivong teaches a machine learning models to associate a known biological state of an organism with patterns of expression exhibited by the organism of genes of a gene signature associated with a disease state, such as to train the machine learning models to determine unknown biological states associated with the patterns of expression”
Vladimirova - US 2020/0105413 A1
“Vladimirova teaches a method for performing a clinical prediction ”
Choi - US 11,663,715 B2
“Choi teaches a method for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics”
Carroll - US 2011/0123100 Al
“Carroll teaches a method for predicting states of a subject”
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/SHAMCY ALGHAZZY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128