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 .
Election/Restrictions
Applicant’s election without traverse of Group II, claims 30-36, drawn to a method for training a machine learning classifier to be capable of distinguishing a population of subjects having a cell proliferative disorder from subjects not having the cell proliferative disorder in the reply filed on 04/23/2026 is acknowledged.
Claims 1-11 and 37 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Groups I and III, drawn to a methylation signature panel characteristic of at least two cell proliferative disorders and drawn to a method for determining a methylation profile of a cell- free deoxyribonucleic acid (cfDNA) sample from a subject, respectively, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 04/23/2026.
Claims Status
Claims 1-11 and 30-37 are pending.
Claims 1-11 and 37 are withdrawn.
Claims 12-29 are canceled.
Claims 30-36 are currently under examination.
Priority
This application is a continuation of International Patent Application No. PCT/US2022/021662, filed March 24, 2022, which claims the benefit of U.S. Provisional Patent Application No. 63/166,641, filed on March 26, 2021. Accordingly, the priority date of claim set filed on April 23, 2026, is determined to be March 26, 2021.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 30-36 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 30 recites “obtaining sets of measured values representative of differentially-methylated genomic regions of the group consisting of differentially-methylated genomic regions in Tables 1-17". It is unclear what “Tables 1-17” is referring to as the claim should be complete and clear by limitations recited in the claims without referring to any tables or drawings disclosed in the specification. Claims 31-36 depend from claim 30. See MPEP § 2173.05(s).
Claim 30 is indefinite over the claim reference or dependence upon a claim that references “Tables 1-17”. The claim is unclear as to which database, build, version, etc. the Gene(s), Biomarker name(s) and chromosome location(s) are referring to, which are critical or essential to the practice of the invention but not included in the claims. Claims 31-36 depend on claim 30. See In re Mayhew, 527 F.2d 1229, 188 USPQ 356 (CCPA 1976).
Improper Markush Rejection
Claims 30-36 are rejected on the basis that it contains an improper Markush grouping of alternatives. See In re Harnisch, 631 F.2d 716, 721-22 (CCPA 1980) and Ex parte Hozumi, 3 USPQ2d 1059, 1060 (Bd. Pat. App. & Int. 1984).
A Markush grouping is proper if the alternatives defined by the Markush group (i.e., alternatives from which a selection is to be made in the context of a combination or process, or alternative chemical compounds as a whole) share a “single structural similarity” and a common use. A Markush grouping meets these requirements in two situations. First, a Markush grouping is proper if the alternatives are all members of the same recognized physical or chemical class or the same art-recognized class, and are disclosed in the specification or known in the art to be functionally equivalent and have a common use. Second, where a Markush grouping describes alternative chemical compounds, whether by words or chemical formulas, and the alternatives do not belong to a recognized class as set forth above, the members of the Markush grouping may be considered to share a “single structural similarity” and common use where the alternatives share both a substantial structural feature and a common use that flows from the substantial structural feature. See MPEP § 2117.
A Markush claim contains an “improper Markush grouping” if:
(1) the species of the Markush group do not share a “single structural similarity,” or (2) the species do not share a common use. Members of a Markush group share a “single structural similarity” when they belong to the same recognized physical or chemical class or to the same art-recognized class. Members of a Markush group share a common use when they are disclosed in the specification or known in the art to be functionally equivalent. See MPEP § 2117.
The Markush grouping of genes listed in Table 1 or Tables 1-17 is improper because the alternatives defined by the Markush grouping do not share both a single structural similarity and a common use for the following reasons:
The recited alternative species in the groups set forth here do not share a single structural similarity, as each different gene that could be detected is itself located in a separate region of the genome and has its own structure. The genes recited in the instant claims, do not share a single structural similarity since each consists of a different nucleotide sequences with different expression patterns. The only structural similarity present is that all detected positions are part of nucleic acid molecules. The fact that the markers comprise nucleotides per se does not support a conclusion that they have a common single structural similarity because the structure of comprising a nucleotide alone is not essential to the common activity of being correlated with differentially-methylated genomic regions. Accordingly, while the different markers are asserted to have the property of being differentially-methylated in at least two proliferative disorders compared to healthy subjects, they do not share a single structural similarity.
MPEP 2117 (II)(A) provides the following guidance as to what constitutes a physical, chemical, or art recognized class:
A recognized physical class, a recognized chemical class, or an art-recognized class is a class wherein “there is an expectation from the knowledge in the art that members of the class will behave in the same way in the context of the claimed invention. In other words, each member could be substituted one for the other, with the expectation that the same intended result would be achieved”
The recited genes do not belong to a recognized chemical class because there is no expectation from the knowledge in the art that the genes will behave in the same manner and can be substituted for one another with the same intended result achieved. In other words, there is no expectation from the knowledge in the art that each of the recited genes would function in the same way in the claimed method; it is only in the context of this specification that it was disclosed that all members of this group may behave in the same way in the context of the claimed invention. Further there is no evidence of record to establish that it is clear from their very nature that each of the recited genes possess the common property of being associated with being differentially-methylated in at least two proliferative disorders compared to healthy subjects.
MPEP 2117 (II) further states the following:
Where a Markush grouping describes alternative chemical compounds, whether by words or chemical formulas, and the compounds do not appear to be members of a recognized physical or chemical class or members of an art-recognized class, the members are considered to share a "single structural similarity" and common use when the alternatively usable compounds share a substantial structural feature that is essential to a common use. Ex parte Hozumi, 3 USPQ2d 1059, 1060 (Bd. Pat. App. & Int. 1984).
The recited alternative species do not share a substantial common structure just because they all have a sugar phosphate backbone. The sugar phosphate backbone of a nucleic acid chain is not considered to be a substantial common structural feature to the group of genes being claimed because it is shared by ALL nucleic acids. Further, the fact that the genes all have a sugar phosphate backbone does not support a conclusion that they have a common single structural similarity because the structure of comprising a sugar phosphate backbone alone is not essential to the asserted common use of being associated with being differentially-methylated in at least two proliferative disorders compared to healthy subjects.
To overcome this rejection, Applicant may set forth each alternative (or grouping of patentably indistinct alternatives) within an improper Markush grouping in a series of independent or dependent claims and/or present convincing arguments that the group members recited in the alternative within a single claim in fact share a single structural similarity as well as a common use.
Following this analysis, the claims are rejected as containing an improper Markush grouping.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 30-36 are rejected under 35 U.S.C. 103 as being unpatentable over Venn et al. (“Venn”; Patent App. Pub. WO 2020154682 A2, July 30, 2020 in view of Tachon et al. (“Tachon”; (2019). Prognostic significance of MEOX2 in gliomas. Modern pathology: an official journal of the United States and Canadian Academy of Pathology, Inc, 32(6), 774–786.) and Cortese et al. (“Cortese”; (2008). Correlative gene expression and DNA methylation profiling in lung development nominate new biomarkers in lung cancer. The international journal of biochemistry & cell biology, 40(8), 1494-1508
Venn discloses “The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel to detect cancer and particular types of cancer.” (Abstract)
Regarding claim 30, Ven teaches FIG. 6A, which is a flowchart describing a process of training a classifier based on hypomethylated and hypermethylated fragments indicative of cancer, according to an embodiment (Para. 53; Fig. 6A- see below). Venn teaches a method comprising “These samples may be processed (e.g., with whole-genome bisulfite sequencing (WGBS)) to determine the methylation status of CpG sites, or the information may be obtained from TCGA. (Para. 117); “whole-genome” reads on MEOX2. Venn teaches a method comprising “With fragments indicative of cancer, the analytics system may train a classifier according to a process 600 illustrated in FIG. 6A, according to an embodiment. The process 600 accesses two training groups of samples - a non-cancer group and a cancer group - and obtains 605 a non-cancer set of methylation state vectors and a cancer set of methylation state vectors comprising anomalously methylated fragments” (Para. 194). “cancer” reads on proliferative
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disorder; “anomalously methylated” reads on differentially methylated.
Thus, Venn suggests a method wherein comprising: a) obtaining sets of measured values representative of differentially-methylated genomic regions of the group consisting of differentially-methylated genomic regions in Tables 1-17, wherein the differentially-methylated genomic regions are associated with at least two cell proliferative disorders, where the measured values are obtained from methylation sequencing data from healthy subjects and subjects having a cell proliferative disorder,
Regarding claim 30, Venn teaches a method wherein “the classifier is trained on converted DNA sequences … the trained classifier determines the presence or absence of cancer or a cancer type by (a) generating a set of features for the sample, wherein each feature in the set of features comprises a numerical value” (Para. 116). Thus, Venn suggests a method wherein comprising: b) using the sets of measured values to generate a set of features corresponding to properties of the differentially-methylated genomic regions,
Regarding claim 30, Venn teaches a method wherein “(b) inputting the set of features into the classifier, wherein the classifier comprises a multinomial classifier; (c) based on the set of features, determining, at the classifier, a set of probability scores, wherein the set of probability scores comprises one probability score per cancer type class and per non-cancer type class; and (d) thresholding the set of probability scores based on one or more values determined during training of the classifier to determine a final cancer classification of the sample” (Para. 116). Thus, Venn suggests a method wherein comprising: c) using the set of features to train the machine learning as a classifier to be capable of distinguishing a population of subjects having a cell proliferative disorder from subjects not having the cell proliferative disorder.
Venn does not explicitly teach obtaining sets of measured values representative of differentially-methylated genomic regions of MEOX2 (elected specie), wherein the differentially-methylated genomic regions are associated with at least two cell proliferative disorders. However, Venn also teaches “While preferred embodiments of the present disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the disclosure. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed in practicing the disclosure” (Para. 313). Thus, one of skill in the art would be motivated to modify the method as taught by Venn to further assess the methylation features of MEOX2 in known cancers as suggested below to incorporate in training a machine learning classifier to distinguish a subject with a proliferative disorder from a subject without proliferative disorder.
Tachon discloses “Gliomas are the most common malignant primary tumors in the central nervous system and have variable predictive clinical courses. Glioblastoma, the most aggressive form of glioma, is a complex disease with unsatisfactory therapeutic solutions and a very poor prognosis. Some processes at stake in gliomagenesis have been discovered but little is known about the role of homeobox genes, even though they are highly expressed in gliomas, particularly in glioblastoma. Among them, the transcription factor Mesenchyme Homeobox 2 (MEOX2) had previously been associated with malignant progression and clinical prognosis in lung cancer and hepatocarcinoma but never studied in glioma. The aim of our study was to investigate the clinical significance of MEOX2 in gliomas. We assessed the expression of MEOX2 according to IDH1/2 molecular profile and patient survival among three different public datasets: The Cancer Genome Atlas (TCGA), The Chinese Glioma Genome Atlas (CGGA) and the US National Cancer Institute Repository for Molecular Brain Neoplasia Data (Rembrandt). We then evaluated the prognostic significance of MEOX2 protein expression on 112 glioma clinical samples including; 56 IDH1 wildtype glioblastomas, 7 IDH1 wild-type lower grade gliomas, 49 IDH1 mutated lower grade gliomas. Survival rates were estimated by the Kaplan-Meier method followed by uni/multivariate analyses. We demonstrated that MEOX2 was one of the transcription factors most closely associated with overall survival in glioma. Moreover, MEOX2 expression was associated with IDH1/2 wildtype molecular subtype and was significantly correlated with overall survival of all gliomas and, more interestingly, in lower grade glioma. To conclude, our results may be the first to provide insight into the clinical significance of MEOX2 in gliomas, which is a factor closely related to patient outcome. MEOX2 could constitute an interesting prognostic biomarker, especially for lower grade glioma.” (Abstract).
Regarding claim 30, Tachon teaches a method comprising “Normalized RSEM gene-level RNAseq data, methylation profile (beta value) and Gistic2 thresholded copy number calls of glioblastoma and lower grade glioma cohort from TCGA were downloaded from Broad GDAC Firehose (gdac.broadinstitue.org). Clinical data were obtained from Table S1 of TCGA publication “Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma”... Normalized FPKM RNAseq data from the Chinese Glioma Genome Atlas (CGGA) were obtained …” (Pg.775, Bioinformatic analysis, Para. 1). Tachon teaches a method comprising “we analyzed copy number alteration of MEOX2 and chr7 as well as methylation status of MEOX2” (Pg. 777, MEOX2 is associated with glioma aggressiveness according to TCGA database, Para. 2) and “Copy number alteration and decrease of methylation prints at MEOX2 locus are two mechanisms that could explain, at least to some extent, the elevated level of MEOX2 mRNA and MEOX2 protein observed in glioblastoma” (Pg. 783, Discussion, Para. 3) “decrease of methylation prints at MEOX2 locus” reads on differentially methylated.
Cortese discloses “Although transcriptional control is key for proper lung development, little is known about the possible accompanying epigenetic modifications. Here, we have used gene expression profiling to identify 99 genes that are upregulated in fetal lung and 354 genes that are upregulated in adult lung. From the differentially expressed genes, we analyzed the accompanying 5′-UTR methylation profiles of 43 genes. Out of these, nine genes (COL11A1, MEOX2, SERPINE2, SOX9, FBN2, MDK, COL1A1, LAPTM5 and MARCO) displayed an inverse correlation of their 5′-UTR methylation and the cognate gene expression, suggesting that these genes are at least partially regulated by DNA methylation. Using the differential gene expression/DNA methylation profiles as a guidepost, we identified four genes (MEOX2, MDK, LAPTM5, FGFR3) aberrantly methylated in lung cancer. MEOX2 was uniformly higher methylated in all lung cancer samples (n = 15), while the methylation of the other three genes was correlated with either the differentiation state of the tumor (MDK, LAPTM5) or the tumor type itself (FGFR3).” (Abstract).
Regarding claim 30, Cortese teaches a method wherein “MEOX2 is differentially methylated in lung cancer. MEOX2 displayed higher methylation (mean methylation 40%) in lung cancer samples when compared to healthy fetal and adult lung” (Fig. 6. DNAmethylation profiles in lung cancer tissues (A) legend) and “The obtained sequencing chromatograms in direct bisulfite sequencing experiments were used to quantify the methylation at a given CpG”(Pg. 1496, 2.5.4. DNA methylation analysis, Para. 1).
Thus, Venn, Tachon and Cortese suggest a method comprising obtaining sets of measured values representative of differentially-methylated genomic regions of MEOX2 (elected specie), wherein the differentially-methylated genomic regions are associated with at least two cell proliferative disorders.
Venn, Tachon and Cortese are considered to be analogous to the claimed invention because they are in the same field of distinguishing proliferative disorders presence in subjects based on features of biomarker. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods of a) obtaining sets of measured values representative of differentially-methylated genomic regions, wherein the differentially-methylated genomic regions are associated with at one or more cell proliferative disorders b) using the sets of measured values to generate a set of features corresponding to properties of the differentially-methylated genomic regions, and c) using the set of features to train the machine learning as a classifier to be capable of distinguishing a population of subjects having a cell proliferative disorder from subjects not having the cell proliferative disorder as taught by Venn to incorporate the method of obtaining sets of measured values representative of differentially-methylated genomic regions of MEOX2 (elected specie), wherein the differentially-methylated genomic regions are associated with a cell proliferative disorder as taught by Tachon and Cortese and provide a method for training a machine learning classifier to be capable of distinguishing a population of subjects having a cell proliferative disorder from subjects not having the cell proliferative disorder. These claim elements were known in the art and one of skill in the art could have combined these elements by known methods with no change in their respective functions, and the combination would have yielded the predictable outcome according to the limitations of claim 1. Doing so would allow for the methylation profile features of MEOX2 to be used to train the machine learning as a classifier to be capable of distinguishing a population of subjects having a cell proliferative disorder from subjects not having the cell proliferative disorder with a reasonable expectation of success.
Regarding claim 31, Venn teaches a method wherein “utilizing methylation information or methylation state vectors. In particular, in some cases, the analytics system determines observed rates of methylation for each CpG site within a sequence read. The rate of methylation represents a fraction or percentage of base pairs that are methylated within a CpG site.” (Para. 230). Thus, Venn, Tachon and Cortese suggest a method wherein the sets of measured values describe characteristics of the methylated regions selected from the group consisting of: base wise methylation percent for CpG, CHG, CHH; the count or rate of observing fragments with different counts or rates of methylated CpGs in a region; conversion efficiency; hypomethylated blocks; methylation levels; number of methylated CpGs per fragment; fraction of CpG methylation to total CpG per fragment; fraction of CpG methylation to total CpG per region; fraction of CpG methylation to total CpG in panel; dinucleotide coverage; evenness of coverage; mean CpG coverage globally; and mean coverage at CpG islands, CGI shelves, and CGI shores.
Regarding claim 32, Venn teaches a method wherein “methods of determining a tissue of origin (TOO) of a cancer” (Para. 23) and “determining methylation status at a plurality of CpG sites; and detecting a health condition of the subject by evaluating the methylation status for the sequence reads, wherein the health condition is (i) a presence or absence of cancer; (ii) a presence or absence of cancer of a tissue of origin (TOO)” (Para. 25). Thus, Venn, Tachon and Cortese suggest a method wherein machine learning classifier is trained to be capable of identifying a tissue of origin of a tumor in the subject.
Regarding claim 33, Venn teaches a method comprising “The methylation state vectors may be stored in temporary or persistent computer memory for later use and processing” (Para. 219). Venn teaches a method comprising “training the model with the training feature vectors from the one or more training subjects without cancer and the training feature vectors from the one or more training subjects with cancer” (Para. 36). Thus, Venn, Tachon and Cortese suggest a method further comprising loading the machine learning classifier into a memory of a computer system, wherein the machine learning classifier is trained using training vectors obtained from training biological samples, a first subset of the training biological samples identified as having the cell proliferative disorder, and a second subset of the training biological samples identified as not having the cell proliferative disorder.
Regarding claim 34, Venn teaches a method comprising “A subject may also be part of a control group known not to have cancer or another disease. A subject may also be part of a cancer or other disease group known to have cancer or another disease. Control and cancer/disease groups may be used to assist in designing or validating the targeted panel.” (Para. 108). Venn teaches a method comprising “For designing the cancer assay panel, an analytics system may collect samples corresponding to various outcomes under consideration, e.g., samples known to have cancer, samples considered to be healthy, samples from a known tissue of origin, etc. The sources of the cfDNA and/or ctDNA used to select target genomic regions can vary depending on the purpose of the assay. For example, different sources may be desirable for an assay intended to detect cancer generally, a specific type of cancer, a cancer stage, or a tissue of origin.” (Para. 115). Venn teaches a method comprising “ at 99% specificity the sensitivity of the method for upper GI tract cancer is at least 62% or at least 68%” (Para. 35). Venn teaches a method comprising “predefined specificity” (Para. 301). “at 99% specificity the sensitivity of the method for upper GI tract cancer is at least 62% or at least 68%” reads on having pre-selected sensitivity and specificity for the different types of cell proliferative disorder to be detected. Thus, Venn, Tachon and Cortese suggest a method further comprising training the machine learning classifier on a panel of predetermined methylated genomic regions associated with at least two cell proliferative disorders, and having pre-selected sensitivity and specificity for the different types of cell proliferative disorder to be detected using the panel.
Regarding claim 35, Venn teaches a method wherein “the information relates to a presence or absence of one or more cancer types, selected from the group consisting of breast cancer, endometrial cancer, cervical cancer, ovarian cancer, bladder cancer, urothelial cancer of renal pelvis, renal cell carcinoma, prostate cancer, anorectal cancer, anal cancer, colorectal cancer, hepatocellular cancer, liver/bile-duct cancer, cholangiocarcinoma and hepatobiliary cancer, pancreatic cancer, upper GI adenocarcinoma, esophageal squamous cell cancer, head and neck cancer, lung cancer, squamous cell lung cancer, lung adenocarcinoma, small cell lung cancer, neuroendocrine cancer, melanoma, thyroid cancer, sarcoma, plasma cell neoplasm, multiple myeloma, myeloid neoplasm, lymphoma, and leukemia. In some embodiments, the information relates to a presence or absence of one or more cancer types, selected from the group consisting of uterine cancer, upper GI squamous cancer, all other upper GI cancers, thyroid cancer, sarcoma, urothelial renal cancer, all other renal cancers, prostate cancer, pancreatic cancer, ovarian cancer, neuroendocrine cancer, multiple myeloma, melanoma, lymphoma, small cell lung cancer, lung adenocarcinoma, all other lung cancers, leukemia, hepatobiliary carcinoma (hcc), hepatobiliary biliary, head and neck cancer, colorectal cancer, cervical cancer, breast cancer, bladder cancer, and anorectal cancer.” (Para. 225). Thus, Venn, Tachon and Cortese suggest a method wherein the at least two cell proliferative disorders are selected from the group consisting of colorectal cancer, breast cancer, ovarian cancer, prostate cancer, lung cancer, pancreatic cancer, uterine cancer, liver cancer, esophagus cancer, stomach cancer, thyroid cancer, and bladder cancer.
Regarding claim 36, Venn teaches a method comprising “In some embodiments, the cancer types are selected from uterine cancer, upper GI squamous cancer, all other upper GI cancers, thyroid cancer, … prostate cancer, pancreatic cancer, ovarian cancer, … small cell lung cancer, lung adenocarcinoma, all other lung cancers, … colorectal cancer, cervical cancer, breast cancer, bladder cancer… In some embodiments, the cancer types are selected from … esophageal cancer, head and neck cancer, liver/bile-duct cancer, lung cancer, … ovarian cancer, pancreatic cancer, … stomach cancer… liver cancer, … pancreatic cancer (Para. 35). Venn teaches a method wherein “In some embodiments, at 99% specificity the sensitivity of the method for liver cancer is at least 82% or at least 85%; at 99% specificity the sensitivity of the method for upper GI tract cancer is at least 62% or at least 68%; …; at 99% specificity the sensitivity of the method for colorectal cancer is at least 60% or at least 65%; at 99% specificity the sensitivity of the method for ovarian cancer is at least 75% or at least 80%; at 99% specificity the sensitivity of the method for lung cancer is at least 60% or at least 65%; … at 99% specificity the sensitivity of the method for anorectal cancer is at least 60% or at least 65%; and at 99% specificity the sensitivity of the method for bladder cancer is at least
40% or at least 44%.” (Para. 35). Venn teaches a method comprising “The sensitivity for detecting 20 different cancer types using the target genomic regions of lists 4-16 or randomly selected portions from lists 4 and 12 is presented in TABLES 25-40. Sensitivity results are presented for a specificity of 0.990 (a 1% false positive rate). Sensitivity is presented for all cancers of the specified cancer type and for cancers at stages I through IV. The sensitivity was generally higher for later stage cancers. For stage IV cancers, the sensitivity was greater than 60% for all cancers with more than one sample and was greater than 90% or breast cancer, ovarian cancer, bladder & urothecal cancer, head & neck cancer, colorectal cancer, liver cancer, pancreas & gallbladder cancer, upper GI cancer, lymphoid neoplasm, and lung cancer. At stage II, sensitivity was best for head & neck cancer, liver cancer, pancreas & gallbladder cancer, upper GI cancer, lymphoid neoplasm, and lung cancer.” (Para. 310; TABLES 25-40). Thus, Venn, Tachon and Cortese suggest a method wherein the machine learning classifier is tailored to provide a pre-selected sensitivity and a pre-selected specificity for each of the at least two cell proliferative disorders, wherein the at least two cell proliferative disorders are selected from the group consisting of colorectal cancer, breast cancer, ovarian cancer, prostate cancer, lung cancer, pancreatic cancer, uterine cancer, liver cancer, esophagus cancer, stomach cancer, thyroid cancer, and bladder cancer, wherein the pre-selected sensitivity for a colorectal cancer associated classification panel is at least 70% sensitivity; the pre-selected specificity for a breast cancer associated classification panel is at least 70% specificity; the pre-selected specificity for an ovarian cancer associated classification panel is at least 90% specificity; the pre-selected specificity for a prostate cancer associated classification panel is at least 70% specificity; the pre-selected specificity for a lung cancer associated classification panel is at least 70% specificity; the pre-selected specificity for a pancreatic cancer associated classification panel is at least 90% specificity; the pre-selected specificity for a uterine cancer associated classification panel is at least 90% specificity; the pre- selected sensitivity for a liver cancer associated classification panel is at least 70% sensitivity; the pre-selected sensitivity for an esophagus cancer associated classification panel is at least 70% sensitivity; the pre-selected sensitivity for a stomach cancer associated classification panel is at least 70% sensitivity; the pre-selected specificity for a thyroid cancer associated classification panel is at least 70% specificity; and the pre-selected sensitivity for a bladder cancer associated classification panel is at least 70% sensitivity selected based on which cancer types are detected by the classification model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Drake et al. US Patent No. US 11681953 B2 (Para. 6-14) Claim 1
No claims are in condition for allowance.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENDRA R VANN-OJUEKAIYE whose telephone number is (571)270-7529. The examiner can normally be reached M-F 9:00 AM- 5:00 PM.
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/KENDRA R VANN-OJUEKAIYE/Examiner, Art Unit 1682
/WU CHENG W SHEN/Supervisory Patent Examiner, Art Unit 1682
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