DETAILED ACTION
Applicant's response, filed 8/27/2025, has been fully considered. The following rejections and/or
objections are either reiterated or newly applied. They constitute the complete set presently being
applied to the instant application.
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 .
Claim Status
Claims 1-3 and 6-10 are pending.
Claims 4 and 5 are cancelled.
Claims 1-3 and 6-10 are rejected.
Specification
Response to Amendment
In view of applicant’s amendments to the specification, previous objections to the specification are withdrawn.
Claim Rejections - 35 USC § 112
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 112 are withdrawn.
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. A 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 data itself which are abstract ideas, specifically a mental processes.
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: Establishing a miRNA expression profile database is merely gathering information to be used as input and is thus mere data gathering.
Claim 2: The miRNA expression profile being determined by qPCR, sequencing, microarray or RNA-DNA hybrid capture, is specifying the data source and type which is merely selecting a particular data source.
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.
Claim 6: The type of early detection of cancer comprises one of the those specified is specifying the data source and type which is merely selecting a particular data source.
Claim 7: The liquid biopsy sample comprising plasma, serum or urine 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]), the type of early detection of cancer comprising one of the those specified, and the liquid biopsy sample comprising plasma, serum or urine (conventional method for collection: specification paragraph [0036]), 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.
The additional element of establishing a miRNA expression profile database (Conventional database: paragraph [0034]) is an insignificant extra solution activity, specifically gathering information to be used as input is mere data gathering [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 8/27/2025 have been fully considered but they are not persuasive. Specifically, applicant asserts on page 9, paragraph 2, of the Remarks that “…it is clear that a disease-related miRNA information database was established based on more than 30,000 articles, and 167 miRNAs highly related to cancer were screened out, which is not a conventional method of data collection…” and “…167 miRNAs highly correlated with cancer were screened out, which enables immediate and efficient screening of early-stage cancers…” which are “not a conventional method of data collection”. However, as currently amended these are merely limiting the data itself, which is itself an abstract idea and a therefore a judicial exception, not an additional element, to which section 2106.05(d) states Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. In this we are not examining the judicial exceptions but rather the additional elements. Additionally, it seems that applicant is making an argument for an improvement to technology and examiner would remind applicant that section 2106.05(a) states It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. Which is yet again directed to the additional elements, not to the judicial exception.
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 withdrawn.
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 1-3and 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), and Tan et al. (EBioMedicine (2019) 82-97; newly 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”.
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.
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” 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, and Tan et al. is actively associating miRNAs with pan-cancer. Therefore, it would be obvious to one with ordinary skill in the art to incorporate the teachings of each and to be successful.
Claim 2 is directed to the method of claim 1 but further specifies that the expression profile be determined by one of the specified methods.
Kahraman et al. teaches in paragraph [0093] “The determination of the level of the at least one miRNA may be carried out by any convenient means for determining the level of a nucleotide sequence such as miRNA. For this purpose, qualitative, semi-quantitative and quantitative detection methods can be used. Quantitative detection methods are preferred. A variety of techniques are well known to the person skilled in the art. It is preferred that the level of the at least one miRNA representative for breast cancer in a blood sample from a patient is determined by nucleic acid hybridization, nucleic acid amplification, polymerase extension, sequencing, mass spectroscopy, or any combination thereof. Nucleic acid amplification, for example, may be performed using real time polymerase chain reaction (RT-PCR) such as real time quantitative PCR (RT qPCR)”, reading on wherein the miRNA expression profile is determined by qPCR, sequencing, microarray, or RNA-DNA hybrid capture technology.
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 8/27/2025 have been fully considered but they are not persuasive. Specifically, applicant asserts that claim 1 patently defines over the cited art as the previous rejection did not reject claim 5 under 35 U.S.C. 103. However, subsequent art search and newly cited art read on said claim limitations.
Double Patenting
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under non-statutory double patenting have been reviewed, updated, and provided below.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-4, and 6-7 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-6 of U.S. Patent No. 18/305,396 in view of Tan et al. (EBioMedicine (2019) 82-97; newly cited). Although the claims at issue are not identical, they are not patentably distinct from each other because both inventions are directed to an early cancer detection method comprising the same steps, using the same type of data, procured in the same fashion, from the same types of tissue.
Application: 17/544,947
Application: 18/305,396
Claim 1: 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; and analyzing a miRNA expression profile of a liquid biopsy sample of a subject which is used as a basis for an initial diagnosis of cancer, wherein the miRNA expression profile comprises an expression level of a plurality of miRNAs, the plurality of miRNAs comprise at least 167 miRNAs..
Claim 1: A cancer early detection method, comprising: establishing a miRNA expression profile database of cancer patient populations and healthy populations; and establishing an analysis model for cancer early detection through following steps: a data quality control, a technical replicate merging, a calculation of normalization factors and data normalization, a biomarker feature selection, and a hyperparameter tuning, and the analysis model for cancer early detection includes normalization factors, a set of biomarkers, model weights and model hyperparameters; wherein a miRNA expression profile in a liquid biopsy sample of a subject is analyzed by the analysis model for cancer early detection to be used as a basis for an early detection of cancer.
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”
Claim 2: The early detection and prediction method of pan-cancer of claim 1, wherein the miRNA expression profile is determined by qPCR, sequencing, microarray, or RNA-DNA hybrid capture technology.
Claim 2: The cancer early detection method of claim 1, wherein the miRNA expression profile is determined by qPCR, sequencing, microarray, or RNA-DNA hybrid capture technology.
Claim 3: The early detection and prediction method of pan-cancer of claim 2, wherein the miRNA expression profile is determined by performing qPCR on a cDNA synthesized from a miRNA in the liquid biopsy sample.
Claim 3: The cancer early detection method of claim 2, wherein the miRNA expression profile is determined by performing qPCR on a cDNA synthesized from a miRNA in the liquid biopsy sample.
Claim 6: The early detection and prediction method of pan-cancer of claim 1, wherein a type of the early detection of cancer comprises head and neck cancer, lung cancer, or breast cancer.
Claim 5: The cancer early detection method of claim 1, wherein a type of the early detection of cancer comprises lung cancer.
Claim 7: The early detection and prediction method of pan-cancer of claim 1, wherein the liquid biopsy sample comprises plasma, serum, or urine.
Claim 6: The cancer early detection method of claim 1, wherein the liquid biopsy sample comprises plasma, serum, or urine, and exosomes further purified from the liquid biopsy sample.
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.
It would have been obvious to a person skilled in the art to combine the teachings of U.S. Patent No. 18/305,396 for the majority of the method claims with the teachings of Tan et al. for including at least 167 miRNAs 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 U.S. Patent No. 18/305,396 is teaching the majority of the method, and Tan et al. is actively associating miRNAs with pan-cancer, making this a mere substitution of methods. 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 8/27/2025 have been fully considered but they are not persuasive. While claim 5 was not rejected under double patenting as it did not have a similar claim within the application, the use of 167 miRNAs was described within the specification and so is obvious.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEENAN NEIL ANDERSON-FEARS whose telephone number is (571)272-0108. The examiner can normally be reached M-Th, alternate F, 8-5.
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 571-272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.N.A./Examiner, Art Unit 1687
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685