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 .
Applicant's amendments and request for reconsideration filed 4/7/2026, have been acknowledged and entered.
Withdrawn Rejections/Objections
The obviousness double patenting rejection over U.S. Patent No. 18/305,396 in view of Tan et al. in the Office action mailed 1/13/2026 is withdrawn in view of the terminal disclaimer filed 4/1/2026.
Rejections and/or objections not reiterated from previous office actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Claim Status
Claims 1, 3, and 6-10 are pending.
Claim 2, and 4-5 are cancelled.
Claims 1, 3, and 6-10 are rejected.
Claim Rejections - 35 USC § 101
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 101 have been reviewed, updated, and provided below.
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, 3, and 6-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a method of early detection and prediction of pan-cancer via the generation of an miRNA expression profile from a sample, and the use of an SVM for prediction/classification. The judicial exception is not integrated into a practical application because while claims 1, 3, and 6-10 attempt to integrate the exception into a practical application, said application is either generically recited machine learning elements that do not add a meaningful limitation to the abstract idea or it is insignificant extra solution activity and simply implementing the abstract on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the machine learning elements are either directed to mental processes or mathematical concepts that are well-understood, routine and conventional as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically methods (claims 1-10).
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [see MPEP § 2106.04(a)]
The claims herein recite abstract ideas, mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claim 1: Establishing, based on SVM, by the following steps: a data normalization, an imputation, a data scaling, a predictive modeling and a cross-validation, are merely verbal equivalents to mathematical calculations and therefore mathematical concepts. Determining an miRNA expression profile of a liquid biopsy sample of a subject is analyzed by the early detection and prediction method of pan-cancer; analysis of an expression profile is a mental process. The miRNA expression profile comprising an expression level of a plurality of miRNAs, and the plurality of miRNAs comprising at least 167 miRNAs, are merely limiting the expression profile, which is merely limiting the data itself which are abstract ideas, specifically a mental processes.
Claim 6: The type of early detection of cancer comprises one of the those specified is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 7: The liquid biopsy sample comprising plasma, serum or urine is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 8: The normalization being used to make an experimental data distribution of each sample consistent, is merely a verbal equivalent to a mathematical calculation and therefore, is a mathematical concept.
Claim 9: The imputation is used to correct a biomarker without a signal to a maximum value of a cycle threshold, is merely a verbal equivalent to a mathematical calculation and therefore a mathematical concept.
Claim 10: Data scaling is used to normalize a numerical range of data so that the data has zero-mean and unit-variance, is merely a verbal equivalent to mathematical a calculation and therefore a mathematical concept.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [see MPEP § 2106.04(d) and MPEP §
2106.05(a)-(c) & (e)-(h)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of non-abstract elements:
Claim 1: Collecting a liquid biopsy, performing miRNA extraction and cDNA synthesis, establishing a miRNA expression profile database is merely gathering information to be used as input and is thus mere data gathering.
Claim 3: The miRNA expression profile being determined by performing qPCR on a cDNA synthesized from miRNA in a liquid biopsy sample is specifying the data source and type which is merely selecting a particular data source.
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are well-understood, routine, conventional, or insignificant extra solution activities. These additional elements include:
The additional elements of the miRNA expression profile being determined by qPCR, sequencing, microarray or RNA-DNA hybrid capture (conventional method for data generation: specification paragraph [0040]), the miRNA expression profile being determined by performing qPCR on a cDNA synthesized from miRNA in a liquid biopsy sample (conventional method for cDNA synthesis: specification paragraph [0039] – Quarkbio miRNA Universal RT kit), are insignificant extra solution activities, specifically focusing on the data source and type, which is merely selecting a particular data source [see MPEP § 2106.05(g)]. Therefore, taken both individually and as a whole, the additional elements do amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1-3 and 6-10, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 4/7/2026 have been fully considered but they are not persuasive. Specifically, applicant asserts on page 7 of the Remarks filed 4/7/2026 that “…the present application provides a non-invasive early detection and prediction method of pan-cancer, which analyzes the microRNA phenotype in the liquid biopsy sample of the examinee through pan-cancer early screening prediction. Therefore, it is able to screen for early cancer in a timely and efficient manner, and improve the convenience and detection rate of conventional cancer early screening technology, and provide personalized professional cancer detection and monitoring” which are not well-understood, routine or conventional.
However, as currently amended these are merely reciting elements that are well-understood, routine and conventional according to MPEP 2106.05(d) – See Determining the level of a biomarker in blood by any means, 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), Using polymerase chain reaction to amplify and detect DNA, Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016); Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1377, 115 USPQ2d 1152, 1157 (Fed. Cir. 2015), Detecting DNA or enzymes in a sample, Sequenom, 788 F.3d at 1377-78, 115 USPQ2d at 1157); Cleveland Clinic Foundation 859 F.3d at 1362, 123 USPQ2d at 1088 (Fed. Cir. 2017), Analyzing DNA to provide sequence information or detect allelic variants, Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546, and Amplifying and sequencing nucleic acid sequences, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 764, 113 USPQ2d 1241, 1247 (Fed. Cir. 2014). Furthermore, applicant’s use of preexisting kits (See Paragraphs [0039]-[0040]) to perform tasks that the MPEP already cites as well-understood, routine, and conventional cements that no part of the additional elements are unconventional.
Claim Rejections - 35 USC § 103
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 103 have been reviewed, updated, and provided below.
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.
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.
Claims 1, 3, and 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over Kahraman et al. (US 20190226032 A1; previously cited) in view of Luecken et al. (Molecular Systems Biology 2019, vol. 15,6, e8746; previously cited), Raschka et al. ("Model evaluation, model selection, and algorithm selection in machine learning." arXiv preprint arXiv:1811.12808 (2018); previously cited), Visone et al. (The American journal of pathology (2009) 1131-1138), and Tan et al. (EBioMedicine (2019) 82-97; previously cited).
Claim 1 is directed to a method of early detection and prediction of pan-cancer using a miRNA expression profile database and an SVM method to determine an expression profile from a liquid biopsy of an individual that can be the basis of a cancer diagnosis.
Kahraman et al. teaches in paragraph [0193] “Said data carrier may further comprise a reference level of the level of the at least one miRNA determined herein. In case that the data carrier comprises an access code which allows the access to a database, said reference level may be deposited in this database”, in paragraph [0045] “The present inventors analyzed miRNA expression profiles of early stage breast cancer patients compared to healthy controls”, in paragraph [0037] “The term “level”, as used herein, refers to an amount (measured for example in grams, mole, or ion counts) or concentration (e.g. absolute or relative concentration) of the miRNA representative for breast cancer or associated with breast cancer described herein. The term “level”, as used herein, also comprises scaled, normalized, or scaled and normalized amounts or values. Preferably, the level determined herein is the expression level”, in paragraph [0059] “Machine learning approaches may include, but are not limited to, supervised or unsupervised analysis: classification techniques (e.g. naïve Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis Neural Nets, Tree based approaches, Support Vector Machines, Nearest Neighbour Approaches), Regression techniques (e.g. linear Regression, Multiple Regression, logistic regression, probit regression, ordinal logistic regression ordinal probit regression, Poisson Regression, negative binomial Regression, multinomial logistic Regression, truncated regression), Clustering techniques (e.g. k-means clustering, hierarchical clustering, PCA), Adaptations, extensions, and combinations of the previously mentioned approaches.” and in the abstract “The present invention relates to a method for diagnosing breast cancer in a patient or for determining whether a patient will respond to a therapeutic treatment of breast cancer. Further, the present invention relates to the use of at least one polynucleotide for detecting at least one miRNA for diagnosing breast cancer in a blood sample from a patient or for determining whether a patient will respond to a therapeutic treatment of breast cancer in a blood sample from the patient”, which reads on an early detection and prediction method of pan-cancer, comprising: establishing a miRNA expression profile database of cancer patient populations and healthy populations; and establishing, based on SVM, by the following steps: a data normalization, an imputation, a data scaling, a predictive modeling, and a cross-validation, wherein a miRNA expression profile of a liquid biopsy sample of a subject is analyzed by the early detection and prediction method of pan-cancer to be used as a basis for an initial diagnosis of cancer. Kahraman et al. teaches in paragraph [0005] “determining the level of at least one miRNA representative for breast cancer in a blood sample from the patient”, reading on wherein the miRNA expression profile comprises an expression level of a plurality of miRNAs.
Kahraman et al. does not specifically teach imputation or cross validation.
Luecken et al. teaches on page 7 and page 8 under Expression Recovery, the use of imputation in RNA-sequencing analysis.
Luecken et al. does not teach cross validation.
Raschka et al. teaches on page 26 in relation to model building “k-fold cross-validation is more commonly used for model selection or algorithm selection” and on page 34 “this final section will introduce nested cross-validation, which has become a common and recommended a method of choice for algorithm comparisons for small to moderately-sized datasets”.
Raschka et al. does teach the specific 167 miRNAs for cancer association/prediction.
Visone et al. teaches in the abstract “Cancer is the result of a complex multistep process that involves the accumulation of sequential alterations of several genes, including those encoding microRNAs (miRNAs). miRNAs are a class of 17- to 27-nucleotide single-stranded RNA molecules that regulate gene expression post transcriptionally. A large body of evidence implicates aberrant miRNA expression patterns in most, if not all, human malignancies”, on page 1131, column 2, paragraph 3, “The first evidence of aberrant miRNA expression in human cancers was described in B-cell chronic lymphocytic leukemia, wherein hemizygous and/or homozygous chromosomal deletion at the 13q14 locus resulted in the loss or reduction of miR-15 and miR-16 expression”, on page 1132, column 2, paragraph 2 “Specific examples of miRNAs located in instable genomic regions include the miR-15a/16 cluster, embedded into 13q14, and both miR-143 and miR-145, located at 5q33. In fact, B-cell chronic lymphocytic leukemias and pituitary adenomas, which often harbor 13q14 deletions, showed a decreased expression of miR-15a and miR-16,3,7,10 whereas deletion of the 5q33 region observed in lung cancer seems to contribute to the decreased levels of miR-143 and miR-145 in this tumor. Conversely, cluster miR-17-92, located at chromosome 13q31, a region amplified in B-cell lymphomas and lung cancers, has been found to be overexpressed in these malignancies”, on page 1132, column 2, paragraph 3 “They found that the treatment of a human ovarian cancer cell line (OVCAR3) with the demethylating agent 5-aza-2’deoxycytide (5-AZA-CdR) increased the expression levels of nine miRNAs. Interestingly, three of nine miRNAs, miR-21, miR-203, and miR-205, have been found to be also overexpressed in ovarian carcinomas when compared with their normal counterparts, suggesting that hypomethylation could be the mechanism responsible for their overexpression in vivo.8 Moreover, miR-21 and miR-203 are embedded in a region associated with CpG islands, whereby the DNA methylation machinery could directly affect the expression of these miRNAs. Conversely, decreased miR-124a expression was attributed to DNA hypermethylation in colon, breast, and lung carcinomas”, on page 1133, column 2, paragraph 1 “Hu and colleagues, conducted a systematic survey of common pre-miRNA sequences and their surrounding regions and evaluated in detail the association of four selected SNPs in four miRNAs (miR-146a, miR-196a2, miR-499, and miR-149) with the survival of individuals with non-small cell lung cancer”, in Table 1 “Selected miRNAs Aberrantly Expressed in Tumors” include “let-7d, 100, 101, 105, 125a, 125b, 126, 133a, 137, 140, 143, 147, 199a, 199b, 224, 9, 9*, 99a”, and many others. While each and every miRNA may or may not be presented within the review, it would be obvious to a person skilled in the art to optimize the predictive power of the machine learning method for detecting cancer using miRNAs that have been directed/indirectly associated and miRNAs whose family has been associated with cancer progression such as HAS-miR and HAS-let, which would read on the plurality of miRNAs comprise 167 miRNAs, the 167 miRNAs are shown in a table below.
Visone et al. does not teach the collection of a liquid biopsy and from that the sequencing of miRNAs for determining an expression profile.
Tan et al. teaches on page 84, column 1, paragraph 1 “A total of 1046 miRNAs and 20,531 genes (protein coding and noncoding) were included in the TCGA Illumina Hi-seq miRNA Seq and Illumina Hi-Seq RNASeqV2 data”, reading on the plurality of miRNAs comprise at least 167 miRNAs. Furthermore, Tan et al. teaches on page 85, columns 1-2, starting in paragraph 4 “BJ human foreskin fibroblasts were maintained in minimum essential medium supplemented with 10% fetal calf serum, nonessential amino acids, and antibiotics. 293 T, MDA-MB-231 and Huh7 cells were grown in Dulbecco's modified Eagle medium supplemented with 10% fetal calf serum, glutamine, and antibiotics. Hela, Huh7 and T47D cells were cultured in EMEM, DMEM and RPMI-1640, respectively, all basic medium was supplemented with 10% FCS and 1% antibiotics. The expression vectors for miRNAs and 3′UTR dual-luciferase reporter plasmid (pmirGLO) were purchased from Biosettia, Inc. (San Diego, CA). To construct target gene 3′UTR dual-luciferase reporters (pmirGLO-CTLA4–3′UTR, pmirGLO-IGFBP5–3′UTR, pmirGLO-ITK-3’UTR, pmirGLO-PDGFRA-3′UTR, pmirGLO-PIK3CG-3′UTR, pmirGLOTGFBI-3’UTR, pmirGLO-IL7R-3’UTR), target gene 3′UTR exons containing miRNA seed sequences were amplified by PCR from the genomic DNA of 293 T cells. The PCR primers are described in supplemental Materials. All 3’UTR fragments are inserted into pmirGLO by NheI-HF and XhoI. The pGL3-basic plasmid was purchased from Promega Corporation. To construct pGL3-IL7R/PIK3CG-3Kb+3′UTR reporter, a 3 kb promoter sequence from the IL7R and PIK3CG genewas amplified fromthe genomic DNA of 293 T cells and inserted into pGL3-basic plasmid by NheI and XhoI (IL7R promoter), or MluI and BglII (PIK3CG promoter). Then, the PCR product for the 3’UTR of IL7R or PIK3CG (still containing the miRNA seed sequence) was digested by XbaI and inserted into pGL3- IL7R/PIK3CG-3Kb reporter, respectively, downstream of luc+. The PCR primers are described in supplemental Materials. pLV-miR-ctrl, pLV-miR-21, pLV-miR-142, pLV-miR-155, pLV-miR-214 Recombinant lentiviruses were packaged in 293 T cells in the presence of helper plasmids (pMDLg, pRSV-REV, and pVSV-G) using Lipofectamine 2000 (Invitrogen). BJ or MDA-MB-231 cells (1 × 105/well) were seeded into 6-well plates, grown overnight, infected with 300 uL virus in 3 mL fresh medium containing 8 μg/mL polybrene, and spun for 1 h at 1600 to 1800 rpm. Transduced cells were purified with 1.2 μg/mL of puromycin. RNA was isolated from cells using TRIzol (Thermo Fisher Scientific) according to manufacturer's protocol. 500 ng of RNA was reverse transcribed to cDNA with iScript™ Reverse Transcription Supermix(Bio- Rad). Quantitative real-time PCR was performed in triplicate with gene-specific primers and SsoAdvance™ SYBR Green Supermix (Bio- Rad) in a Bio-Rad CFX96 REAL TIME SYSTEM following manufacturer's protocols. GAPDH was used as internal control to normalize the mRNA input for each gene. qPCR primers are described in supplemental Table S7. The target genes 3’UTR activity was analyzed in both 293 T and MDA-MB-231 cells by transient transfection of luciferase reporter constructs. On the 1st day, 6 × 105/well 293 T or 1.5 × 105/well MDA-MB- 231 cells were seeded into 12-well plates. These cells were transfected with 0.17 μg pmirGLO reporter vector, 1.43 μg pLV-miR-ctrl/pLV-miR- 142 vector and 4.0 μl lipo-2000 according tomanufacturer's instruction on the next day. 48 h after transfection, cell lysateswere collected using Passive Lysis Buffer (E1941, Promega). Firefly and Renilla luciferase activity was detected using Dual-Luciferase Reporter Assay System (E1960, Promega) on GloMax®-Multi+ Microplate Multimode Reader (Promega). For gene IL7R and PIK3CG 3 kb promoter +3’UTR reporter analysis, MDA-MB-231 cells were transiently transfected with 0.16 μg pGL3-IL7R/PIK3CG-3Kb+ 3’UTR plasmid, 1.36 μg pLV-miR-ctrl/pLV-miR-142 vector and 0.08 μg of the control Rluc vector driven by β-actin, TK or CMV promoter, using 4 μl lipo-2000 according to manufacturer's instruction. Other procedures are the same with 3’UTR dual-luciferase assay”, reading on collecting a liquid biopsy sample of a subject; performing a miRNA extraction method and a cDNA synthesis to the liquid biopsy sample of the subject; determining a miRNA expression profile of the liquid biopsy sample of the subject by qPCR, sequencing, microarray, or RNA-DNA hybrid capture technology.
It would have been obvious to a person skilled in the art to combine the teachings of Kahraman et al. for the method of claim 1 with the imputation step described in Luecken et al., and the cross-validation step described in Raschka et al. for model tunning and comparisons, as Lueken et al. points out on page 7, column 2, paragraph 2 “A particularly prominent aspect of this noise is dropout. Inferring dropout events, replacing these zeros with appropriate expression values, and reducing the noise in the dataset”, and Raschka et al. points out in the abstract “The correct use of model evaluation, model selection, and algorithm selection techniques is vital in academic machine learning research as well as in many industrial settings […] Common cross-validation techniques such as leave-one out cross-validation and k-fold cross-validation are reviewed, the bias-variance trade-off for choosing k is discussed, and practical tips for the optimal choice of k are given based on empirical evidence […] alternative methods for algorithm selection, such as the combined F-test 5x2 cross-validation and nested cross-validation, are recommended for comparing machine learning algorithms”. Furthermore, it would have been obvious to modify the method to include the specific miRNAs listed using the teachings of Visone et al. which stands as a review of the miRNAs and their families, which have been associated with cancer as Visone et al. teaches in the abstract “This article reviews our current knowledge about miRNAs, focusing on their involvement in cancer and their potential as diagnostic, prognostic, and therapeutic tools”. Additionally, it would have been obvious to modify the method to include at least 167 miRNAs as taught by Tan et al., especially as they teach in the abstract “We found that positive miRNA-gene correlations are surprisingly prevalent and consistent across cancer types”. One would have had a reasonable expectation of success given that Luecken et al. is a review method for best practices in the field as is Raschka et al. just for model building evaluation practices, Tan et al. is actively associating miRNAs with pan-cancer, and Visone et al. is specifying the miRNAs and their families that are associated with cancer in hopes of their use as a prognostic tool. Therefore, it would be obvious to one with ordinary skill in the art to incorporate the teachings of each and to be successful.
Claim 3 is directed to the method of claim 1 but further specifies that the expression profile be determined y performing qPCR on a cDNA synthesized from miRNA in the sample.
Kahraman et al. teaches in paragraph [0093] “The aforesaid real time polymerase chain reaction (RT-PCR) may include the following steps: (i) extracting total RNA from the blood sample isolated from the patient, (ii) obtaining cDNA samples by RNA reverse transcription (RT) reaction using miRNA-specific primers”, reading on wherein the miRNA expression profile is determined by performing qPCR on a cDNA synthesized from a miRNA in the liquid biopsy sample.
Claim 6 is directed to the method of claim 1 but further specifies that the cancer type be one of those specified.
Kahraman et al. teaches in the abstract “The present invention relates to a method for diagnosing breast cancer in a patient…”, reading on wherein a type of the early detection of cancer comprises head and neck cancer, lung cancer, or breast cancer.
Claim 7 is directed to the method of claim 1 but further specifies that sample comprise plasma, serum or urine.
Kahraman et al. teaches in paragraph [0035] “The term “blood sample”, as used herein, encompasses whole blood or a blood fraction such as serum, plasma, or blood cells”, which reads on wherein the liquid biopsy sample comprises plasma, serum, or urine.
Claim 8 is directed to the method of claim 1 but further specifies that the normalization be used make the data distribution of each sample consistent.
Kahraman et al. teaches in paragraph [0037] “The term “level”, as used herein, refers to an amount (measured for example in grams, mole, or ion counts) or concentration (e.g. absolute or relative concentration) of the miRNA representative for breast cancer or associated with breast cancer described herein. The term “level”, as used herein, also comprises scaled, normalized, or scaled and normalized amounts or values. Preferably, the level determined herein is the expression level”, which reads on wherein the normalization is used to make an experimental data distribution of each sample consistent.
Claim 9 is directed to the method of claim 1 but further specifies that the imputation step be used to correct a biomarker without a signal to a maximum value of a cycle threshold of a miRNA biomarker expression in all samples.
Kahraman et al., Luecken et al., and Raschka et al. teach the method of claim 1 as previously described.
Kahraman et al. does not teach the use of an imputation step.
Luecken et al. teaches on page 7 and page 8 under Expression Recovery, the use of imputation in RNA-sequencing analysis.
It would have been obvious to a person skilled in the art that an imputation step, which is well-understood, routine and conventional within the art (Luecken et al. 2019), would inherently correct biomarker signals that are missing or zero expression values in sequencing data as it explains on page 7, column 2, paragraph 2 “A particularly prominent aspect of this noise is dropout. Inferring dropout events, replacing these zeros with appropriate expression values, and reducing the noise in the dataset”. One would have had a reasonable expectation of success given that Lueken et al. is a review paper for the best practices in the field. Therefore, it would be obvious to one with ordinary skill in the art to incorporate the teachings of each and to be successful.
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over Kahraman et al. (US 20190226032 A1; previously cited), Luecken et al. (Molecular Systems Biology 2019, vol. 15,6, e8746; previously cited), Raschka et al. ("Model evaluation, model selection, and algorithm selection in machine learning." arXiv preprint arXiv:1811.12808 (2018); previously cited), and Tan et al. (EBioMedicine (2019) 82-97; newly cited) as applied to claims 1-4 and 6-9 above, and further in view of Ali et al. (Mach Learn Tech Rep 1.1, 2014: 1-6; previously cited).
Claim 10 is directed to the method of claim 1 but further specifies that the data scaling is used to normalize a numerical range of data so that the data has zero-mean and unit-variance.
Kahraman et al., Luecken et al., Raschka et al., and Tan et al. teach the method of claim 1 as previously described.
Kahraman et al., Luecken et al., Raschka et al., and Tan et al. do not teach the normalization of data to have mean equal to zero and standard deviation equal to 1.
Ali et al. teaches on page 1 in the abstract “This paper aims to clarify how and why data are normalized or standardized, these two processes are used in the data preprocessing stage in which the data is prepared to be processed later by one of the data mining and machine learning techniques like support vector machine, neural network, etc”, and on page 5 “Making a data set with mean=0, and standard deviation =1. This scaling method is useful when the data follows a normal distribution (Gaussian distribution), if the data does not follow normal distribution, then this will make problems”.
It would have been obvious to a person skilled in the art to combine the teachings of Kahraman et al. for the method of claim 1, with the teachings of Ali et al. for a data scaling method that would be used to normalize a range of data so that it would have zero-mean and unit-variance, as biological data is normally distributed. One would have had a reasonable expectation of success given that the latter reference teaches you how to use said standardization with your data. Therefore, it would be obvious to one with ordinary skill in the art to incorporate the teachings of each and to be successful.
Response to Arguments
Applicant's arguments filed 4/7/2026 have been fully considered but they are not persuasive. Specifically, applicant asserts that the newly amended limitations of claim 1 are not taught by the previously cited prior. While examiner agrees that the previously cited prior art did not read on all limitations, subsequent art search and newly cited art read on said claim limitations. More specifically, applicant asserts that the 167 miRNAs used in the method are not taught by the previously cited prior art, and while examiner agrees, a newly performed prior art search has provided Visone et al., which in view of the previously cited prior art, specifically Tan et al., the combination cures said deficiency of the newly recited claim limitations.
Furthermore, applicant asserts that there exists no motivation to combine the prior art to arrive at the specified 167 miRNAs used in the method. Examiner agrees that the prior art previously presented did not provide for a motivation to combine that would arrive at the specified 167 miRNAs used in the method. However, the newly performed prior art search has provided Visone et al. which does provide for a rationale to combine that would arrive at the specified 167 miRNAs used in the method.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.N.A./Examiner, Art Unit 1687
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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