CTNF 18/099,910 CTNF 101313 DETAILED ACTION 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. Claim Status Claims 19-34 are pending. Priority This application is a CON of 16/969,923, filed 08/13/2020, which is a 371 of PCT/US2019/015462, filed 01/28/2019, which claims benefit of application no. 62/636,096, filed 02/27/2018. The instant application has the effective filing date of 27 February 2018. Information Disclosure Statement The information disclosure statement (IDS) submitted on 01/20/2023 and 02/18/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner. Drawings The drawings, submitted on 01/20/2023, are accepted by the examiner. 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 19-34 are rejected under U.S.C 101 because the claimed invention is directed to abstract ideas and natural phenomena without significantly more, as detailed in the analysis below. Eligibility Step 1: Subject matter eligibility evaluation in accordance with MPEP § 2106: Claims 19-34 are directed to a statutory category (method). Therefore, in accordance with MPEP § 2106.03 all claims have patent eligible subject matter. [Eligibility Step 1: YES] Eligibility Step 2A: This step determines whether a claim is directed to a judicial exception in accordance with MPEP § 2106. Eligibility Step 2A -- Prong One: Limitations are analyzed to determine if the claims recite any concepts that could equate to a judicial exception (i.e. abstract idea, law of nature, or natural phenomenon). Possible judicial exceptions are explored below. Recitations of Judicial Exceptions: Claim 19: a) pooling a plurality of gene-expression data sets, wherein each of the plurality of data sets comprises a healthy control; (mental process) b) co-normalizing the gene-expression data of the pooled data set to remove batch effects by a correction factor derived from the healthy control of each dataset, wherein the correction factor removes technical variation across the plurality of data sets; (mathematical concept) c) using a machine learning tool to identify at least two clusters within the co-normalized data: (mental process, mathematical concept) d) assigning genes to the clusters, wherein a gene is assigned to a given cluster based on differential expression in the cluster compared to the other clusters or the healthy controls: (mental process, mathematical concept) e) assessing the biological characteristics of each cluster based on gene ontology classification of the genes assigned to the cluster; (mental process) f) iteratively incorporating the differentially expressed genes into a classifier for one or more of the clusters; wherein the differentially expressed genes are accepted into the classifier if the addition of the gene meets a threshold criteria for classifier performance, thereby selecting the genes to derive the gene-expression based classifier. (mental process, mathematical concept) Claim 20: wherein the classifier is validated in data sets without healthy controls. (mathematical concept) Claim 21: wherein the healthy controls are removed from the data sets after the pooled data is co-normalized. (mental process) Claim 22: wherein the gene-expression data sets comprise diseased samples. (mental process) Claim 23: wherein the gene-expression data sets comprise patients with sepsis. (mental process) Claim 25: wherein the gene-expression comprises RNA expression levels. (mental process) Claim 26: wherein the biological characteristics of each cluster are used to inform on the selection of a therapy. (mental process) Claim 27: selecting a therapy for a patient based on biological characteristics of the patient. wherein the biological characteristics are determined by which cluster the patient is assigned to using a classifier developed according to the method of claim 19; (mental process) Claim 28: a) pooling a plurality of gene-expression data sets, wherein each of the plurality of data sets comprises a healthy control; (mental process) b) co-normalizing the gene-expression data of the pooled data set to remove batch effects by a correction factor derived from the healthy control of each dataset, wherein the correction factor removes technical variation across the plurality of data sets; (mathematical concept) c) identifying a plurality of subgroups among the patients in the pooled data sets; (mental process) d) selecting at least one gene of a classifier for assigning a patient to one of the subgroups based on differential expression of the at least one gene among the plurality of subgroups; (mental process) e) training the classifier for subgroup assignment in the pooled co-normalized gene-expression data using cross-validation among the pooled co-normalized data. ( mental process, mathematical concept) Claim 29: wherein the classifier is validated in datasets external to the plurality of pooled co-normalized datasets. (mental process, mathematical concept) Claim 30: wherein the validation comprises supervised classification. (mental process, mathematical concept) Claim 31: wherein the machine learning tool comprises an unsupervised analysis. (mathematical concept) Step 2A – Prong One Analysis: Analysis techniques such as pooling (grouping), filtering, and identifying data from other groups, requiring nothing more than the human mind and pen/paper, read on observations, evaluations, judgments, and/or opinions that fall under the mental process grouping of abstract ideas. Limitations that merely provide additional information regarding the data being analyzed in this manner are similarly categorized (claim 22, 23). Analysis techniques such as scoring using equations, calculations, weighting, and ratios recite mathematical calculations and relationships that fall under the mathematical concept grouping of abstract ideas. Limitations that merely provide additional information regarding the data being analyzed in this manner are similarly categorized (claims 20). Therefore, the claims are found to recite judicial exceptions. [Eligibility Step 2A – Prong One: YES] Eligibility Step 2A – Prong Two: A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. If the claim contains no additional claim elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)). Additional elements are recited, categorized, and analyzed below. Data Gathering Elements: Claim 24: wherein the gene-expression data sets are obtained from samples comprising whole blood, white blood cells, neutrophils, or buffy coat. Computer Components Elements: Claims 19-34: a computer comprising a processor Treatment Administration: Claim 27: administering the therapy Claim 32: wherein the therapy comprises administration of an innate or adaptive immunity modulator. Claim 33: wherein the therapy comprises administration of one or more drugs that modify the coagulation cascade or platelet activation. Claim 34: wherein the therapy comprises administration of a blood product, heparin, low-molecular- weight heparin, apixaban, dabigatran, rivaroxaban, dalteparin, fondaparinux, warfarin, activated protein C, recombinant coagulation cascade proteins, tranexamic acid, or another coagulation- modifying drug. Step 2A – Prong Two Analysis: The data gathering elements are commensurate in scope with determining the level of a biomarker in blood which qualifies as insignificant, extra solution activity per, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012). Generic computer components and implementations provide mere instructions to implement the abstract ideas onto a technological environment per Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The treatment administration element, as recited in claim 27, is commensurate in scope with administering a suitable medication to a patient, based off the judicial exceptions (mental process, mathematical concepts), which do not equate to a particular treatment per MPEP 2106.04(d)(2). The treatment administration element, as recited in claims 32-34, do not appear to have more than a nominal relationship to the judicial exceptions per MPEP 2106.04(d)(2). As such, the additional elements, when viewed separately and in the context of a whole claimed invention, do not integrate the judicial exceptions into practical application. [Eligibility Step 2A – Prong Two: NO] Eligibility Step 2B: Claim elements are probed for inventive concept equating to significantly more than the judicial exception (MPEP 2106.04(II)). Step 2B Analysis: The data gathering element is commensurate in scope with determining the level of a biomarker in blood by any means is well-understood, routine, and conventional per Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017) The computer components are further found to be well-understood, routine, and conventional per 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 for storing and retrieving information in memory and Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (MPEP 2106.05 (a)). The treatment administration elements are further found conventional per Delano et al. (Immunol Rev; Vol. 274: 1; 2017) and Franks et al. (BMC Immunology; Vol. 16:11; 2015); which review the immune systems role during sepsis and using gene expression and gene ontology analysis to identify possible treatment plans. The references further teach how sepsis clearly alters the innate and adaptive immune responses for sustained periods of time after clinical recovery, with immune suppression, chronic inflammation, and persistence of bacterial representing such alterations (Delano: page 1, column 1); and activated protein C (APC) was one of the most promising therapeutics (Franks: page 1, column 2). As such, the additional elements are further found to lack inventive concept. [Eligibility Step 2B: NO] Therefore, claims 19-34 are directed to judicial exceptions without significantly more and are rejected under 35 U.S.C 101. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 19-25 are rejected under 35 U.S.C. 103 as being unpatentable over Sweeney et al. (Science Translational Medicine; Vol. 8: 346; 2016) in view of Dixit et al. (Cell; Vol. 167; e 7; 2016) . Sweeney et al. describes the robust classification of bacterial and viral infections via integrated host gene expression diagnostics. Claims 19 is directed to a computer implemented methods that pool gene-expression data sets, that each include healthy controls. Sweeney et al. teaches developing a method to co-normalize gene expression data (page 1, column 2) in a pooled analysis of 1057 samples from 20 cohorts (page 1, abstract); with the notable requirement that healthy/control patients are required to be present in a data set for it to be pooled with other available data (page 9, column 2). Claims 19 is further directed to co-normalizing the gene-expression data of the pooled data sets using a correction factor that removes technical variation. Sweeney et al. teaches designing and implementing a modified type of array normalization that uses CO-Normalization Using conTrols empirical Bayes (ComBat) normalization methods on healthy controls to obtain bias-free corrections of disease samples in order to remove inter–data set batch effects while remaining unbiased to the diagnosis of the diseased patients (page 4, column 1). Sweeney et al. further teaches ComBat corrects for location and scale of each gene (page 9, column 2). Claim 19 is further directed to using machine learning to identify at least two clusters within the co-normalized data. Sweeney et al. teaches developing a method, COCONUT, to directly compare our model across a large pool of one-class cohorts that would otherwise be unusable for benchmarking a diagnostic gene set (page 8, column 1); separating the COCONUT conormalized public gene expression data that matched inclusion criteria into three classes (page 6, fig. 3; page 5, column 2); and other machine-learning techniques, such as multiple regression or tree-based methods, are typically used to adequately separate the three classes: noninfected inflammation, bacterial infections, and viral infections (page 7, column 1). Claim 19 is further directed to assigning genes to the clusters, based on differential expression in the cluster compared to the other clusters or healthy controls. Sweeney et al. teaches this process resulted in 72 differentially expressed genes significant at the above thresholds (page 2, column 2); and as expected, a “bacterial/viral metascore” based on these seven genes robustly distinguished viral from bacterial infections in all eight of the discovery cohorts (page 2, column 2). Claim 19 is further directed to assessing the biological characteristics of each cluster based on gene ontology classification of the genes within the clusters; and iteratively incorporating the differentially expressed genes into a classifier for one or more of the clusters; and accepting the differentially expressed genes into the classifier if the addition of the gene meets a threshold criteria for classifier performance. Sweeney et al. teaches to find a set of highly diagnostic genes, the significant genes from the meta-analysis were run through a greedy forward search, in which the algorithm starts with zero gene and adds one gene in each cycle that best improves the AUC for diagnosis in the discovery cohorts, until a new gene cannot improve the discovery AUCs more than some threshold (page 7, column 2). Claim 20 is directed to validating the classifier without healthy controls. Sweeney et al. teaches using targeted quantitative NanoString nCounter gene expression assays to prospectively validate these results in independent whole-blood samples from children with sepsis from the Genomics of Pediatric SIRS and Septic Shock Investigators (GPSSSI) cohort in which total n = 96, with 36 SIRS, 49 bacterial sepsis, and 11 viral sepsis patients: Fig. 4 and table S4 (page 6, column 1). Claim 21 is directed to removing healthy controls from the data set after co-normalizing the pooled data. Sweeney et al. teaches healthy patients were not included as a diagnostic class because they were used in the conormalization procedure (page 10, column 1). Claim 22 is directed to diseased samples being included within the gene expression data sets. Sweeney et al. teaches the eight cohorts were composed of 426 patient samples: 142 viral and 284 bacterial infections (page 2, column 1). Claim 23 is directed to the gene expression data sets including patients with sepsis. Sweeney et al. teaches discovery data set GSE66099 includes septic children in the PICU (page 2, table 1); and validating the 11-gene set in eight additional independent data sets that compared healthy controls to those with bacterial or viral sepsis at admission using whole-blood samples (page 7, column 2). Claim 24 is directed to the gene expression data sets being derived from samples that include at least: whole blood, white blood cells, neutrophils, or buffy coat. Sweeney et al. teaches identifying eight cohorts [both whole blood and peripheral blood mononuclear cells (PBMCs)] that included n > 5 patients with both viral and bacterial infections (page 2, column 1). Sweeney et al. does not teach assessing the biological characteristics of each cluster based on gene ontology classification of the genes within the clusters (Claim 19). Dixit et al. describes Perturb-seq, a protocol for dissecting molecular circuits with scalable single-cell RNA Profiling of pooled genetic screens. Dixit et al. teaches genetic screens can be designed in a pooled format, where their readouts measure cell autonomous phenotypes, such as growth, drug resistance, or marker expression (page 1, column 2); clustering the genes; and then performing Gene ontology (GO) enrichment analysis on each cluster (page 23, column 1). Dixit et al. further teaches at its current scale, Perturb-seq can be readily applied for targeted screens of a subset of genes of interest and their interactions; where in some systems, growth or marker-based screens may first be performed to identify this subset prior to Perturb-seq (page 12, column 1). Claim 25 is directed to the gene-expression data including RNA expression levels. Dixit et al. teaches a significant as-yet latent factor that impacts the empirical null distribution of coefficients, include: (1) the mean expression level of a gene (page 21, column 1); and the experiments and analysis provided here and in Adamson et al. (2016) are starting points for future experiments that combine scRNA-seq and pooled screens (page 13, column 1). Therefore Dixit et al. teaches a technique that includes performing gene ontology analysis on pooled genetic expression data. Dixit et al. further provides motivation for one of ordinary skill in the art to use the technique after the data has undergone additional processing; and highlights the significance of using such techniques to analyze RNA expression levels. As such, it would be obvious for one of ordinary skill in the art to combine the gene expression pooling and analysis technique of Sweeney et al. with the gene ontology analysis technique of Dixit et al. with a reasonable expectation of success and each element merely performing the same function as they do separately . 07-21-aia AIA Claim 26-27 and 33-34 are rejected under 35 U.S.C. 103 as being unpatentable over Sweeney et al. (Science Translational Medicine; Vol. 8: 346; 2016.) in view of Dixit et al. (Cell; Vol. 167; e 7; 2016), as applied to claims 19-25 above, and in further view of Panwar et al. (Journal of Hematology & Oncology; Vol. 2: 43; 2009) . Sweeney et al. in view of Dixit et al. teach a method of pooling, clustering, and analyzing gene expression datasets. Claim 26 is directed to using the biological characteristics of each cluster to inform on the selection a therapy; Claim 27 is directed to selecting a therapy for a patient with the biological characteristics of each cluster, determined by the method of claim 19; and administering the therapy. Sweeney et al. in view of Dixit et al. do not teach using the biological characteristics of each cluster to inform therapy selection; nor administering the therapy. Panwar et al. describes a prospective cohort study on Plasma protein C levels in immunocompromised septic patients. Panwar et al. teaches assessing serum Protein C concentrations in immunocompromised patients as compared to immunocompetent patients during sepsis, severe sepsis, septic shock and recovery (page 1, column 1) before embarking on a trial of APC administration in immunocompromised septic patients is essential (page 1, column 1). Panwar et al. teaches finding that the activation of protein C is augmented by 20 fold in the presence of endothelial protein C receptor (EPCR) which is known to have increased plasma concentrations in septic patients (page 4, column 2); the cytokine response in sepsis may result in decreased expression levels of TM and EPCR on endothelium and thus decreased activation of protein C (page 4, column 2); and these mechanisms may explain the reduction in plasma protein C concentrations in severe sepsis observed in our study (page 4, column 2). Panwar et al. further teaches doxorubicin has been shown to induce dose and time dependent decrease in cell surface EPCR levels and increase in cell surface thrombomodulin in human umbilical vein endothelial cells (HUVEC) with a net effect of impaired capacity of HUVECs to convert protein C to activated protein C (page 5, column 1). Claim 33 is directed the therapy including administration of at least one drug that modifies the coagulation cascade or platelet activation. Panwar et al. teaches the role of Protein C pathway in regulating thrombosis, fibrinolysis and inflammatory cascade in a septic patient is well established (page 2, column 1); and the activated coagulation cascade results in thrombin formation which then binds to thrombomodulin (TM) and causes proteolysis of protein C which convert to activated protein C (APC) and then down-regulates thrombin formation in negative feedback loop (page 4, column 1). Claim 34 is directed to the therapy including administration of: a blood product, heparin, low-molecular-weight heparin, apixaban, dabigatran, rivaroxaban, dalteparin, fondaparinux, warfarin, activated protein C, recombinant coagulation cascade proteins, tranexamic acid, or another coagulation- modifying drug. Panwar et al. teaches since activation of protein C system largely depends on TM and EPCR, it is important to study TM expression and EPCR expression in immunocompromised patients during non-sepsis state and in face of septic challenge in future studies (page 5, column 2); and our pilot study might provide the platform for a future clinical trial designed to study benefits of activated protein C therapy in the immunocompromised septic patients (page 6, column 2). Therefore Panwar et al. teaches using the ontological analysis of EPCR gene expression in different patient subgroups in order to determine if the selection and administration of certain therapies, including activated C protein, is appropriate. As such, it would be obvious to one of ordinary skill in the art to apply the known technique of selecting and applying such therapies, to the applicable method of Sweeney et al. in view of Dixit et al., which teach performing gene ontology analysis, with a reasonable expectation of success . 07-21-aia AIA Claim 31-32 are rejected under 35 U.S.C. 103 as being unpatentable over Sweeney et al. (Science Translational Medicine; Vol. 8: 346; 2016.) in view of Dixit et al. (Cell; Vol. 167; e 7; 2016), as applied to claims 19-27 and 33-34 above, and in further view of Marsh et al. (PNAS; Vol. 113: 9; 2016) . Sweeney et al. in view of Dixit et al. and Panwar et al. teach a method of pooling, clustering, and analyzing gene expression datasets; using the biological characteristics of the clustered genes to inform selection of a therapy; and administering the therapy. Sweeney et al. in view of Dixit et al. and Panwar et al. do not teach the machine learning tool, which clusters the co-normalized gene expression data, to include unsupervised analysis ( Claim 31 ); nor the therapy including the administration of an adaptive or innate immunity modulator ( Claim 32 ). Marsh et al. describes the how the adaptive immune system restrains Alzheimer’s disease pathogenesis by modulating microglial function. Regarding claim 31 , Marsh et al. teaches conducting a hierarchical cluster analysis of these 2,552 differentially expressed genes confirmed that each genotype could be grouped together based on similar gene expression profiles; performing gene ontology (GO) enrichment to identify numerous examples of significantly altered pathways involving adaptive or innate immunity as well as antigen presentation and Ig binding; and based on the GO analysis further examining subsets of microglial-enriched or neuronal-enriched genes via unsupervised hierarchical clustering (page 3, fig. 2). Regarding claim 32 , Marsh et al. teaches the gene expression and ontology analysis reveal significant alterations in both adaptive and innate immunity and microglial-enriched genes (page 3; fig. 2); gene expression analysis of the brain implicates altered innate and adaptive immune pathways, including changes in cytokine/chemokine signaling and decreased Ig-mediated processes (page 1, column 1); and treatment with preimmune IgGs dramatically increases Aβ phagocytosis (page 7, column 1). Therefore Marsh et al. teaches the administration of an adaptive immune modulator, IgG, after a process that includes gene ontology and clustered gene expression analysis. As such, it would be obvious to one of ordinary skill in the art to apply the known methods of such treatments based on unsupervised clustering results, to the applicable method of Sweeney et al. in view of Dixit et al. and Panwar et al., with a reasonable expectation of success . 07-21-aia AIA Claim 28-30 are rejected under 35 U.S.C. 103 as being unpatentable over Sweeney et al. (Science Translational Medicine; Vol. 8: 346; 2016.) in view of Shidham et al. (Science Translational Medicine; Vol.7: 287; 2015) . Claim 28 is directed to a computer implemented method that pools gene-expression data sets, which each include healthy controls. Sweeney et al. teaches developing a method to co-normalize gene expression data (page 1, column 2) in a pooled analysis of 1057 samples from 20 cohorts (page 1, abstract); with the notable requirement that healthy/control patients are required to be present in a data set for it to be pooled with other available data (page 9, column 2). Claim 28 is further directed to co-normalizing the gene-expression data of the pooled data sets using a correction factor that removes technical variation. Sweeney et al. teaches designing and implementing a modified type of array normalization that uses CO-Normalization Using conTrols empirical Bayes (ComBat) normalization methods on healthy controls to obtain bias-free corrections of disease samples in order to remove inter–data set batch effects while remaining unbiased to the diagnosis of the diseased patients (page 4, column 1). Sweeney et al. further teaches ComBat corrects for location and scale of each gene (page 9, column 2). Claim 28 is directed to identifying at least two subgroups among the pooled dataset. Sweeney et al. teaches separating the COCONUT conormalized public gene expression data that matched inclusion criteria into three classes (page 6, fig. 3; page 5, column 2). Claim 28 is further directed to selecting at least one gene of a classifier based on differential expression of the at least one gene among subgroups, for assigning a patient to one of the subgroups Sweeney et al. teaches outputting 72 differentially expressed genes significant at the above thresholds; using a greedy forward search to find a gene set optimized for diagnosis resulting in seven genes [higher in viral infections and higher in bacterial infections; and getting a “bacterial/viral metascore” based on these seven genes robustly distinguished viral from bacterial infections in all eight of the discovery cohorts (page 2, column 2). Claim 28 is further directed to training the classifier for subgroup assignment in the pooled co-normalized gene-expression data using cross-validation among the pooled co-normalized data. Sweeney et al. teaches using COCONUT conormalization, the bacterial/viral metascore has a global AUC of 0.92 in the whole-blood discovery cohorts (figs. S9 to S10); and these data sets included the four direct validation cohorts hat included control patients and an additional 20 cohorts that measured either bacterial or viral infections but not both (page 4, column 1) Claim 29 is directed to validating the classifier in external datasets, not included within the pooled, co-normalized data. Sweeney et al. teaches the GPSSSI cohort used for validation was also used by data set GSE66099, but the children profiled here were never profiled via micro-array and are thus not part of the discovery data sets (page 6, column 1); and two cohorts [GSE60244 (13) and GSE63990 (14)] made public after our metaanalysis was completed were used for validation (page 8, column 1). Claim 30 is directed to the validation including supervised classification. Sweeney et al. teaches for both discovery and validation cohorts, summary ROC curves were constructed according to the method of Kester and Buntinx; briefly, linear-exponential models were made for each ROC curve; and the parameters of these individual curves were summarized using a random-effects model to estimate the overall summary ROC curve parameters (page 9, column 1). Sweeney et al. further teaches applying this method to test the bacterial/viral metascore in all public domain microarray cohorts that matched inclusion criteria and used whole blood, in which these data sets included the four direct validation cohorts that included control patients and an additional 20 cohorts that measured either bacterial or viral infections but not both (page 4, column 1). Sweeney et al. teaches these data sets represent a wide variety of clinical conditions, including a range of infection types (Gram-positive, Gram-negative, atypical bacterial, common respiratory viruses, and dengue) and severities (page 4, column 2). Sweeney et al. does not explicitly teach using cross-validation among the pooled co-normalized data ( claim 28 ). Shidham et al. describes a multicohort gene expression analysis for sepsis patients. Shidham et al. teaches co-normalizing all available data sets comparing SIRS/trauma with sepsis/infection in a single matrix; and labeled principal components analysis (PCA) using 168 genes identified by 10-fold cross-validated Lasso-penalized logistic regression showed that SIRS/trauma patients can be separated from sepsis patients with modest overlap (page 2, column 1). Therefore, it would be obvious to one of ordinary skill in the art to combine the prior art technique of using cross-validation among the pooled co-normalized data to an applicable gene expression analysis method with a reasonable expectation of success. Conclusion No claims are currently allowed. Correspondence Any inquiry concerning this communication or earlier communications from the examiner should be directed to Milana Thompson whose telephone number is (571)272-8740. The examiner can normally be reached Monday - Friday, 9:00-6:00 ET. 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, Karlheinz Skowronek can be reached at (571) 272-1113. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /M.K.T./Examiner, Art Unit 1687 Application/Control Number: 18/099,910 Page 2 Art Unit: 1687 Application/Control Number: 18/099,910 Page 3 Art Unit: 1687 Application/Control Number: 18/099,910 Page 4 Art Unit: 1687 Application/Control Number: 18/099,910 Page 5 Art Unit: 1687 Application/Control Number: 18/099,910 Page 6 Art Unit: 1687 Application/Control Number: 18/099,910 Page 7 Art Unit: 1687 Application/Control Number: 18/099,910 Page 8 Art Unit: 1687 Application/Control Number: 18/099,910 Page 9 Art Unit: 1687 Application/Control Number: 18/099,910 Page 10 Art Unit: 1687 Application/Control Number: 18/099,910 Page 11 Art Unit: 1687 Application/Control Number: 18/099,910 Page 12 Art Unit: 1687 Application/Control Number: 18/099,910 Page 13 Art Unit: 1687 Application/Control Number: 18/099,910 Page 14 Art Unit: 1687 Application/Control Number: 18/099,910 Page 15 Art Unit: 1687 Application/Control Number: 18/099,910 Page 16 Art Unit: 1687 Application/Control Number: 18/099,910 Page 17 Art Unit: 1687 Application/Control Number: 18/099,910 Page 18 Art Unit: 1687 Application/Control Number: 18/099,910 Page 19 Art Unit: 1687 Application/Control Number: 18/099,910 Page 21 Art Unit: 1687 Application/Control Number: 18/099,910 Page 22 Art Unit: 1687 Application/Control Number: 18/099,910 Page 23 Art Unit: 1687 Application/Control Number: 18/099,910 Page 24 Art Unit: 1687
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