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 Rejections - 35 USC § 112
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 123-146 are 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 123 recites “…at least one reference methylation level…” in section (b) lines 3-4. What constitutes a “reference” in this context is unclear. A skilled artisan could interpret this to mean (1) an individual who is not pregnant, (2) an individual who is pregnant and has a complication, (3) an individual who is pregnant and does not have a complication, (4) a sample from the pregnant subject from before they were pregnant, or (5) some other meaning. As such, the metes and bounds of this claim are unclear. For the purposes of prior art, any of these interpretations will be considered.
Claims 128 and 134 recite “molecular subtypes” of different pregnancy-related complications; however, the specification does not provide criteria for distinguishing these subtypes. In the absence of such guidance, a skilled artisan unable to determine what constitutes one subtype versus another. Additionally, recitations of “history of” in claim 134 suggest patient medical history categories, rather than molecularly defined complication subtypes (i.e. “presence or history of gestational hypertension), which adds to the ambiguity.
Claims 132 and 135 make reference to tables present in the specification. However, where possible, claims are to be complete in themselves. Incorporation by reference to a specific figure or table "is permitted only in exceptional circumstances where there is no practical way to define the invention in words and where it is more concise to incorporate by reference than duplicating a drawing or table into the claim. Incorporation by reference is a necessity doctrine, not for applicant’s convenience." Ex parte Fressola, 27 USPQ2d 1608, 1609 (Bd. Pat. App. & Inter. 1993). See MPEP 2173.05(s).
Claims 124-127, 129-131, 133, and 136-146 are indefinite due to their dependence on the aforementioned claim(s).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 123-130 and 132-147 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a natural phenomenon without significantly more.
Subject Matter Eligibility Test (see MPEP § 2106):
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter?
Yes, the claims are directed towards a process.
Step 2A: Are the claims directed to a judicial exception?
Prong 1: Do the claims recite an abstract idea, law of nature, or a natural phenomenon?
Yes, the claims describe a consequence of a natural phenomenon, e.g., the naturally-occurring relationship between the presence or absence of nucleotide methylation in cell-free nucleic acids and the risk/presence of a pregnancy-related complication.
Prong 2: Do the claims recite additional elements that integrate the judicial exception into a practical application?
Claims 123 and 147 involve generating a methylation profile of a pregnant individual at pre-determined genetic locations, processing the profile by computer, and making a determination as to whether or not that individual has, or has a risk of developing, a pregnancy-related complication.
As described by MPEP 2106.05(g), the term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity.
Making a determination with regards to risk based on test results is considered to be insignificant extra-solution activity (i.e., data gathering) because it merely interprets the detected natural correlation and does not impose meaningful limits that apply the judicial exception in a concrete technical or physical manner. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012).
Using a computer to process the data obtained from test results is considered to be mere instructions to apply an exception, as described in MPEP 2106.05(f), because this element does no more than merely invoking a computer as a tool to perform an existing process.
The additional limitations of claims 124-129 and 132-146 include, but are not limited to, specifying a pregnancy-related condition, assay type, genomic locations to be assayed, sample type, and type of computer algorithm used. These limitations either further describe the judicial exception or are themselves insignificant extra-solution activity.
Therefore, the inclusion of these additional elements do not integrate the recited exception into a practical application.
Claim 130 adds a limitation for the administration of a treatment for a pregnancy-related condition and claim 131 specifies treatment options. The inclusion of a treatment or prophylaxis can be sufficient to incorporate a judicial exception into a practical application if the treatment/prophylaxis is sufficiently particular. As such, claim 130 is considered mere instruction to apply due to the generality with which a treatment is prescribed. Therefore, the limitation of claim 130 does not incorporate the judicial exception into a practical application. On the other hand, claim 131 describes particular treatments which can be used to address the pregnancy-related complication identified by the claimed method. Thus, by affirmatively reciting an action that effects a particular treatment for a disease, the judicial exception is integrated into a practical application. As such, claim 131 contains eligible subject matter.
Step 2B: Do the claims recite additional elements that amount to significantly more than the judicial exception?
As discussed in Step 2A, prong B, the additional limitations of claims 123-130 and 132-147 either further describe the judicial exception or are considered to be insignificant extra-solution activity.
Additionally, using a computer, even complex machine learning algorithms, to process data is well-understood, routine, and conventional in the art as evidenced by Peters and Lin who both apply machine learning models to diagnose and prognosticate different medical conditions using different input data (see citations below). In the instance of Peters, methylation data was used to predict the likelihood that an individual had a specific medical condition (such as preeclampsia or risk of preterm birth); whereas Lin used a combination of SNP and clinical data to predict an individual’s response to anti-depressant treatment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, is not considered sufficient to qualify as “significantly more.” See MPEP 2106.05.I.A.
As a result, claims 123-130 and 132-147 do not contain eligible subject matter.
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 123, 124, 126-128, 130, 131, 133, 134, 136, 137, 139, 140, and 142-147 are rejected under 35 U.S.C. 103 as being unpatentable over Choudhury (US 20190055605 A1) in view of Peters (US 20200327959 A1).
Regarding claims 123, 127, 128, 133, 136, 137, and 147, Choudhury identified various classes of biomarkers useful for identifying a subject as having a high risk of developing preeclampsia, as well as noninvasive methods for their use [Choudhury, 0030-0040]. The biomarkers are assessed by comparing test samples obtained from a subject in their first trimester of pregnancy to control samples from individuals either known to have or not have preeclampsia [Choudhury, 0005, 0121]. Both sample types are obtained from a body fluid, such as, and without limitation, amniotic fluid, blood, serum, plasma, and saliva [Choudhury, 0151], which is consistent with the Applicant’s definition of “cell-free biological sample” [specification, 0123].
Three of the classes of identified biomarkers are (1) genomic sites exhibiting differential methylation (2) miRNAs and their expression levels and (3) mRNAs and their expression levels in test versus control samples [Choudhury, 0030, 0109-0115]. Choudhury further teaches that the multiple biomarkers can be assessed simultaneously through the use of a point-of-care diagnostic device which can direct portions of the same sample to different compartments designed to assay different biomarkers [Choudhury, 0142].
Choudhury does not explicitly teach using computer processing to evaluate the results of the biomarker assays.
However, Peters discloses using computational techniques for determining the likelihood that a person has a specified medical condition, such as preeclampsia or an increased risk of preterm birth, based upon methylation profile data using trained machine-learning models [Peters, 0003, 0016, 0058, Fig. 5-7, 0280-0324]. Peters teaches that methylation data sets are often quite large and computationally expensive to process [Peters, 0005]. To address this issue, these computational techniques involve pre-filtering the data before providing it to the trained machine-learning model to enrich for the most informative data. By reducing the data set size in this way, the speed and sensitivity of the machine-learning model is increased, thus improving the predictive power of the system [Peters, 0006].
As both Choudhury and Peters use methylation data for ascertaining preeclampsia risk, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention, to improve Choudhury’s method with Peters’ computational techniques to increase the speed at which data analysis is performed, as well as improving the method’s ability to identify individuals with a high risk of developing preeclampsia. Furthermore, as Peters teaches that their computational method can be used to determine the risk of other conditions using methylation data (i.e., preterm birth), the skilled artisan looking to assess the risk of a variety of pregnancy-related conditions would have been motivated to further expand the method of Choudhury to include a risk assessment of both preeclampsia and preterm birth to achieve a more comprehensive, minimally invasive assessment method. The use of a known technique to improve similar devices (methods or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C.).
Regarding claim 126, Peters describes a computing environment which includes a sample analyzer capable of analyzing the level of cfDNA or other nucleic acids associated with a specified medical condition, and to analyze the methylation patterns of those nucleic acids [Peters, 0089]. These nucleic acids can include DNA, RNA, mRNA, etc. [Peters, 0172].
Regarding claims 130 and 131, Choudhury teaches treating and/or managing preeclampsia symptoms once a risk of preeclampsia development has been identified through the administration of, for example, antihypertensives, corticosteroids, anticonvulsant medications (i.e., magnesium sulfate), low-dose aspirin, or calcium supplements [Choudhury, 0154-0161].
Regarding claims 134 and 140, Choudhury is assessing individuals early in their pregnancy for their risk of developing preeclampsia later in the pregnancy, stating a need for biomarkers that appear before clinical symptoms in order to provide successful intervention [Choudhury, 004-005]. Therefore, a skilled artisan would understand that Choudhury’s ideal subject is an individual in which there is a biological indication of preeclampsia exists but who is clinically asymptomatic.
Regarding claim 139, Peters describes the detection of pregnancy related disease via a computational assessment of DNA methylation signatures in maternal plasma in which an intermediate step includes an estimation of fetal cfDNA vs maternal cfDNA via an amplification technique such as quantitative real-time PCR [Peters, 0584-0590].
Regarding claims 124 and 142-145, Choudhury also teaches kits comprising reagents which can be used to carry out their methods. These kits could include reagents for DNA isolation (implying a need to subject the sample to conditions sufficient to isolate DNA molecule), treatment reagents (i.e. for bisulfite treatment), sequence specific primers, and PCR reagents [Choudhury, 0124]. Different reagents may be required depending on the biomarker being assessed as they may require different analysis techniques. For instance, determining the expression level of miRNA in a sample could be performed via microarray analysis, real-time PCR, Northern blot, or quantitative RT-PCR [Choudhury, 0041]; or bisulfite conversion, digestion by restriction enzymes followed by PCR (COBRA), direct sequencing, pyrosequencing or probe/microarray based assay may be used when assessing differential methylation [Choudhury, 0120].
Regarding claim 146, Peters teaches that their trained machine learning model can be a linear regression model, logistic regression model, decision tree, support vector machine, naïve bayes model, random forest model, or artificial neural network (e.g., feedforward neural network, deep neural network, recurrent neural network, and/or convolution neural network) [Peters, 0057].
Claim 125 is rejected under 35 U.S.C. 103 as being unpatentable over Choudhury and Peters as evidenced by Epigenie.com (Epigenie.com. 5-methylcytosine (5mC). WayBack snapshot from January 31, 2015. Accessed April 29, 2026).
The limitations of claim 123, from which claim 125 depends, have been previously discussed and are made obvious by the combination of Choudhury and Peters. Choudhury discusses assessing genomic sites for differential methylation, defined as whether or not one or more cytosine residues have a methylation group [Choudhury, 0119]. As described by Epigenie.com, 5mC is the normal cytosine nucleotide in DNA which has been modified by the addition of a methyl group to its 5th carbon (Epigenie.com, p1). Therefore, Choudhury teaches assaying 5mC in the cell-free biological sample.
Claim 138 is rejected under 35 U.S.C. 103 as being unpatentable over Choudhury and Peters as evidenced by McQuillan (McQuillan AC et al. International journal of epidemiology. 2008 Apr 1;37(suppl_1):i51-5).
The limitations of claim 123, from which claim 138 depends, have been previously addressed. Choudhury taught that their method could be performed on body fluid samples such as blood, plasma, and serum. A skilled artisan would understand that in order to obtain plasma or serum from a blood sample, the blood sample would need to be separated into its component parts via fractionation, as evidenced by McQuillan who designed an automated blood fractionation system for performing this process on large batches of samples (McQuillan, abstract).
Claim 141 is rejected under 35 U.S.C. 103 as being unpatentable over Choudhury and Peters and in further view of Lin (Lin E et al. Frontiers in psychiatry. 2018 Jul 6;9:290).
The limitations of claim 123, from which claim 141 depends, have been previously discussed. Neither Choudhury or Peters discuss computer processing clinical health data to determine if an individual has an elevated risk of a pregnancy complication.
Lin developed a deep learning model capable of predicting treatment response in individuals with major depressive disorder with a sensitivity of 75% and a specificity of 69%. This model combined analysis of single nucleotide polymorphisms (SNPs) with analysis of clinical factors to optimize the model’s ability to accurately predict treatment response. Clinical factors included age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempts [Lin, abstract, p2]. Lin hypothesized that these results could be generalized within personalized medicine and employed to establish molecular diagnostic and prognostic tools (Lin, p8).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to train the machine learning model of Choudhury and Peters to consider relevant clinical health data, in addition to genetic data, to improve the model’s performance by taking a more holistic approach to the assessment in order to more accurately predict the onset of pregnancy-associated complications.
Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, F.).
Claims 129 and 132 are rejected under 35 U.S.C. 103 as being unpatentable over Choudhury and Peters and in further view of Parets (Parets SE et al. PloS one. 2013 Jun 27;8(6):e67489 ) and Mayne (Mayne BT et al. Epigenomics. 2017 Mar 1;9(3):279-89).
The limitations of claim 123, from which claims 129 and 132 ultimately depend, have been previously addressed. Neither Choudhury or Peters disclose the genomic regions associated with gestational age as claimed by the Applicant, nor specified the type of preterm birth which was being assessed.
However, Parets interrogated the connection between fetal DNA methylation of a variety of genes across a range of gestational ages in samples obtained from the umbilical cord blood of a high risk African American cohort to determine if any patterns exists in regards to the epigenetic profile of fetuses born preterm versus at term (Parets, p2). As a result, 9637 CpG sites associated with gestational age alone were identified with Parets noting that increased gestational age correlated with an increase in DNA methylation; 29 CpG sites were found to be associated with preterm birth independent of gestational age (Parets, abstract, p2).
Additionally, Mayne evaluated data sets (their own and privately available) to determine if epigenetic changes in the human placenta can be used to predict gestational age and if preeclampsia affected the predicted gestational age of the placenta. Mayne hypothesized (and showed) that DNA methylation would be able to accurately estimate gestational age, but that preeclampsia would accelerate the aging of the placenta (Mayne, p280). As a result of this study, Mayne identified 741 CpG sites which were differentially methylated between preeclampsia pregnancies and uncomplicated pregnancies (Mayne, p282, Supplementary Table 1), as well as 62 CpG sites which could be used to accurately predict the gestational age of the placenta (Mayne, p282, Supplementary Table 5).
Pairwise comparisons were performed between the genes claimed by the Applicant and the genes identified by Parets and Mayne. Genes which were found in both the application and the art are listed in Table 1.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention, looking to adapt the method of Choudhury and Peters to investigate gestational age, to use the genes identified by Parets and Mayne as targets for the method. As Parets and Mayne had already performed in-depth studies to determine the correlation between the methylation of these genes and gestational age, the skilled artisan would recognize that using this information would save time and resources by bypassing the need to investigate potential targets, allowing them to focus their efforts on identifying which specific markers were most informative when assessed via cfDNA and thus most useful in the development of minimally invasive diagnostic tests.
Claim 135 is rejected under 35 U.S.C. 103 as being unpatentable over Choudhury and Peters and in further view of Mayne.
The limitations of claims 123 and 127, from which claim 135 depends, have been previously addressed. Neither Choudhury or Peters disclose the genomic regions associated with preeclampsia as claimed by the Applicant. However, as addressed in the rejection of claim 132, Mayne identified 741 CpG sites which were differentially methylated between preeclampsia pregnancies and uncomplicated pregnancies (Mayne, p282, Supplementary Table 1).
A pairwise comparison was performed between the genes claimed by the Applicant and the genes identified by Mayne. Genes which were found in both are listed in Table 1.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention, looking to adapt the method of Choudhury and Peters to investigate preeclampsia, to use the genes identified by Mayne as targets for the method. As Mayne had already performed an in-depth study to determine the correlation between the methylation of these genes and preeclampsia, the skilled artisan would recognize that using this information would save time and resources by bypassing the need to investigate potential targets, allowing them to their efforts on identifying which specific markers were most informative when assessed via cfDNA and thus most useful in the development of minimally invasive diagnostic tests.
Table 1. Results of comparison between sets of identified genes
Applicant
Parets et al (2013)
Supplementary Table 1
Mayne et al (2016)
Supplementary Tables 1 and 5
Table 3
BCAR3, CELSR1, CHST2, EPAS1, ESRRG, GRHL2, ISL1, PALLD, PAX1, PKIB, PLXNA1, PPARD, SLC27A6, SPIRE2, VSX2, ZNF761, ZNF83
CLMP
Table 5
BARX2, CD300A, CD33, CXCR4, CYTH4, DLX4, DLX5, ESRRG, ETS1, FLRT2, GIMAP2, GIMAP8, HLA-A, HLA-DMB, ISL1, LDB2, LILRA4, LILRA6, LILRB1, MICB, MMP9, MNDA, MYO1F, NRP2, PAX1, RASA3, RNF166, SIGLEC9, SLC19A3, TBX20, TMEM229A, TNFSF14, YAP1
BARX2, BMP7, CXCR4, DLX4, DLX5, MSX2, TOR4A
Table 7
ANGPT2, EXPH5, SKIL, TACC1
STAT1, TACC1**
Table 9
ACSL6, ADAMTS19, CAV2, DYNC2LI1, FAM131C, FAM169A, FBXL2, GLIS1, GRIP2, IL20, KLHDC1, LDB3, LRP2, PLEKHA6, PLEKHA7, RAB15, RYR2, SEL1L3, SFRP4, SH3TC2, ST7, WDR63, ZNF862
DMRTB1, DDX31*
Table 11
NCAM2
Table 12
BCAS4, BCHE, BSDC1, CCDC12, CCDC36, CCHCR1, CD96, CLK4, COL13A1, CUTA, DDC, DHRS12, FAM185A, GIMAP4, HOXA9, ITGA11, LGALS3BP, LGALS9C, LGI1, LSM14A, LSM4, NSFL1C, OGG1, PABPC4, RBM19, RINT1, SLC2A1, TPCN2, ZNF483, ZNF785
PARP1, PLD2
Table 13
COL13A1, LGALS9C
From Claim 135
CLDN7, TLE6
CLDN7
Regarding Mayne et al: * = associated with gestational age; ** = associated with both preeclampsia and gestational age.
Conclusion
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/K.N.K./ Examiner, Art Unit 1681
/SAMUEL C WOOLWINE/ Primary Examiner, Art Unit 1681
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