DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-19 and 53 are pending.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 3/30/2017 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
4. The drawings have been reviewed and are accepted as being in compliance with the provisions of 37 CFR 1.121.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-19 and 53 are rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Step 1: Claims 1, 19, and 53 recite “A method for…”; the claim recites a series of steps and therefore is a process, specifically for detecting a methylation of a nucleotide in a
nucleic acid molecule. Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 § I). Abstract ideas include mathematical concepts, and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). The claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea of "using determination based on methylations in analyzing nucleic acid molecules. Embodiments may use kinetic signals produced by a DNA polymerase during single- molecule sequencing. The use of kinetic signals of adaptor sequences may allow for determining the methylation patterns proximal to the ends of a DNA fragment... " (Abstract).
Step 2A Prong One: Claims 1, 19, and 53 recite the limitations "… filtering the first plurality of first data structures through one or more convolutional layers to obtain a plurality of convolutional matrices, applying a transformer layer to the plurality of convolutional matrices to obtain transformer matrices, wherein applying the transformer layer to a convolutional matrix includes generating a plurality of attention scores that quantify a relevance among positions of the convolutional matrix, generating methylation probabilities at respective target positions of the first plurality of first data structures using the transformer matrices, determining outputs using the methylation probabilities, and optimizing, using the plurality of first training samples, parameters of the model based on the outputs of the model matching or not matching corresponding labels of the first labels when the first plurality of first data structures is input to the model, wherein an output of the model specifies whether the nucleotide at the respective target position in the respective window has the methylation, wherein the parameters of the model include the plurality of attention scores, determining, using the model, whether the methylation is present in the nucleotide at the target position within the window in the input data structure.
These limitations are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components.
That is, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “determining” in the context of this claim encompasses a user mentally, and with the aid of pen and paper, grouping and evaluating data, if the token is inexistent then store it. Mathematical concepts recited in the claims include obtaining, from data, values for properties of nucleotides; "creating an input data structure" and "inputting the data structure into a model".
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. The claim recites the additional elements “storing a plurality of first training samples, each including one of the first plurality of first data structures and a first label indicating the first state of the nucleotide at the respective target position…” And The “storing” based on a determination of the existence of the token, this limitation is a mere generic transmission and presentation of collected and analyzed data (MPEP 2106.05(g).
Further “optimizing, using the plurality of first training samples, parameters of the
model based on the outputs of the model matching or not matching corresponding labels of the first labels when the first plurality of first data structures is input to the model, wherein an output of the model specifies whether the nucleotide at the respective target position in the respective window has the methylation, wherein the parameters of the model include the plurality of attention scores…”
A claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they are considered insignificant extra-solution activity. Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activities listed above, including “optimizing” this limitation is a mere generic functionality to presentation of collected and analyzed data. The limitations performed by a “machine learning model” being a tool.; generates a combination of data or “output” based on data and update them based on comparison of data ( it is recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner. (see MPEP 2106.04(a)(2). There are no additional elements that amount to significantly more than the above-identified judicial exception (abstract idea).
Koninklijke KPN N.V. v.Gemalto M2M GmbH, 942 F.3d 1143, 1149 (Fed. Cir.2019) (quoting Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1257 (Fed. Cir. 2016)). In the context of software patents (which includes machine learning patents), the step-one inquiry determines “whether the claims focus on ‘the specific asserted improvement in computer capabilities . . . or, instead, on a process that qualifies as an abstract idea for which computers are invoked merely as a tool.’” Id. (alteration in original) (quoting Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303 (Fed. Cir. 2018)).
As per Claims 2-18 , The claims recite the additional limitations specific to certain data, related to nuclei acid molecules” The claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components, specifically doing comparison of data, from different sources. (See COFFELT V. NVIDIA CORPORATION, The calculations claimed can be done by a human mentally or with a pen and paper.” It further added that “analyzing information by steps people [can] go through in their minds, or by mathematical algorithms, without more . . . [are] mental processes within the abstract-idea category.
Double Patenting
7. 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. See 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); and, 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) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent is shown to be commonly owned with this application. See 37 CFR 1.130(b).
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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claim 1-19 and 53 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-22 of U.S. Patent No. 11,466,308.
Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-19 and 53 of the instant application substantially recite the limitations of claims 1-22 of the cited U.S. Patent No. 11,466,308 for detecting a modification of a nucleotide in a nucleic acid molecule. The claim merely omits certain bolded limitations as shown in comparison table below, and replace them with methylation.
Claim 1 (instant application)
Claim 1 (US 11,466,308)
A method for detecting a methylation of a nucleotide in a nucleic acid molecule, the method comprising: receiving data acquired by sequencing a sample nucleic acid molecule by measuring pulses in a signal corresponding to nucleotides of the sample nucleic acid molecule and obtaining, from the data, values for one or more signal properties;
creating an input data structure, the input data structure comprising a window
around a target position of the nucleotides sequenced in the sample nucleic acid molecule, wherein the input data structure includes, for each nucleotide within the window, one or more values for the one or more signal properties;
inputting the input data structure into a model, wherein the model is a machine
learning model, the model trained by:
receiving a first plurality of first data structures, each first data structure of
the first plurality of first data structures corresponding to a respective window around a respective target position of nucleotides sequenced in a respective nucleic acid molecule of a plurality of first nucleic acid molecules, wherein each of the first nucleic acid molecules is sequenced by measuring pulses in the signal corresponding to the
nucleotides, wherein the methylation has a known first state in a nucleotide at the
respective target position in each window of each first nucleic acid molecule, each first data structure comprising values for the same properties as the input data structure,
storing a plurality of first training samples, each including one of the first
plurality of first data structures and a first label indicating the first state of the nucleotide at the respective target position,
filtering the first plurality of first data structures through one or more
convolutional layers to obtain a plurality of convolutional matrices,
applying a transformer layer to the plurality of convolutional matrices to
obtain transformer matrices, wherein applying the transformer layer to a convolutional
matrix includes generating a plurality of attention scores that quantify a relevance among positions of the convolutional matrix,
generating methylation probabilities at respective target positions of the
first plurality of first data structures using the transformer matrices,
determining outputs using the methylation probabilities, and
optimizing, using the plurality of first training samples, parameters of the
model based on the outputs of the model matching or not matching corresponding labels of the first labels when the first plurality of first data structures is input to the model, wherein an output of the model specifies whether the nucleotide at the respective target position in the respective window has the methylation, wherein the parameters of the model include the plurality of attention scores,
determining, using the model, whether the methylation is present in the nucleotide at the target position within the window in the input data structure.
1. A method for detecting a modification of a nucleotide in a nucleic acid molecule, the method comprising: receiving an input data structure, the input data structure corresponding to a window of nucleotides sequenced in a sample nucleic acid molecule, wherein the sample nucleic acid molecule is sequenced by measuring pulses in an optical signal corresponding to the nucleotides, the input data structure comprising values for the following properties: for each nucleotide within the window: an identity of the nucleotide, a position of the nucleotide with respect to a target position within the respective window, a width of the pulse corresponding to the nucleotide, and an interpulse duration representing a time between the pulse corresponding to the nucleotide and a pulse corresponding to a neighboring nucleotide;
inputting the input data structure into a model, the model trained by: receiving a first plurality of first data structures, each first data structure of the first plurality of data structures corresponding to a respective window of nucleotides sequenced in a respective nucleic acid molecule of a plurality of first nucleic acid molecules, wherein each of the first nucleic acid molecules is sequenced by measuring pulses in the optical signal corresponding to the nucleotides, wherein the modification has a known first state in a nucleotide at a target position in each window of each first nucleic acid molecule, each first data structure comprising values for the same properties as the input data structure,
storing a plurality of first training samples, each including one of the first plurality of first data structures and a first label indicating the first state of the nucleotide at the target position,
and optimizing, using the plurality of first training samples, parameters of the model based on outputs of the model matching or not matching corresponding labels of the first labels when the first plurality of first data structures is input to the model, wherein an output of the model specifies whether the nucleotide at the target position in the respective window has the modification,
determining, using the model, whether the modification is present in a nucleotide at the target position within the window in the input data structure.
Table 1
Therefore, it would have been obvious to one of ordinary skill in the art of data processing at the time the invention was made to modify the invention as claimed in the instance application by substituting methylation with modification, since an omission and addition of a cited limitation would have not changed the process according to which the method and system as claimed; “methylation” refers to a modification of deoxyribonucleic acid (DNA).
Therefore, the use of having a modification would be an obvious variation in the art for the purpose of achieving the same end results having the methylation values based on the comparison and the comparison values and would not interfere with the functionality of the steps previously claimed and would perform the same function.
The dependent claims 2-18 are rejected for fully incorporating the errors of their respective base claims by dependency.
Claim Rejections - 35 USC § 102
8. 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.
9. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
10. Claim(s) 1-19 and 53 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Maher; M. Cyrus (US-20210166813-A1), hereinafter “Maher”.
As per Claim 1, Maher discloses:
A method for detecting a methylation of a nucleotide in a nucleic acid molecule, the method comprising: receiving data acquired by sequencing a sample nucleic acid molecule by measuring pulses in a signal corresponding to nucleotides of the sample nucleic acid molecule and obtaining, from the data, values for one or more signal properties; (Par [0060], “ As used herein, the term “methylation profile” (also called methylation status) can include information related to DNA methylation for a region…” and par [0061], “As used herein, the terms “size profile” and “size distribution” can relate to the sizes of DNA fragments in a biological sample…” and see Figures 2 and 9) creating an input data structure, the input data structure comprising a window around a target position of the nucleotides sequenced in the sample nucleic acid molecule, wherein the input data structure includes, for each nucleotide within the window, one or more values for the one or more signal properties; (Par [0139], “the plurality of genotypic characteristics for the first genotypic data structure (e.g., genotypic data construct 124-1-1) includes a first plurality of bin values (e.g., methylation statuses 138-1)…” par [0254], the second state being the “target” as claimed; see also par [0255], “For example, a machine learning or deep learning model (e.g., a disease classifier)…”)
inputting the input data structure into a model, wherein the model is a machine
learning model, the model trained by: (Par [0147], “includes inputting (314) the first genotypic data construct into a model for the disease condition, thereby generating a first model score set for the disease condition.” And par [0153], “machine learning” and pre-trained ANN…”) receiving a first plurality of first data structures, each first data structure of the first plurality of first data structures corresponding to a respective window around a respective target position of nucleotides sequenced in a respective nucleic acid molecule of a plurality of first nucleic acid molecules, wherein each of the first nucleic acid molecules is sequenced by measuring pulses in the signal corresponding to the nucleotides, (Par [0050], “…or a whole genome, respectively, is independently sequenced. When a mean depth is quoted, the actual depth for different loci included in the dataset can span over a range of values. In some embodiments, deep sequencing can refer to at least 100× in sequencing depth at a locus. In some embodiments, a sequencing depth of 10,000× or higher can be adopted in order to identify rare mutations.” And see par [0254], the second state being the “target” as claimed; see also par [0255], “For example, a machine learning or deep learning model (e.g., a disease classifier)…”) wherein the methylation has a known first state in a nucleotide at the respective target position in each window of each first nucleic acid molecule, each first data structure comprising values for the same properties as the input data structure, (Par [0150], “…on a cohort of subjects in which a first portion of the cohort has the disease condition and a second portion of the cohort is free of the disease condition, e.g., such that it is specifically trained to distinguish between a first state corresponding to not having the disease condition and a second state corresponding to having the disease condition.” And see par [0254], the second state being the “target” as claimed; see also par [0255], “For example, a machine learning or deep learning model (e.g., a disease classifier) can be used to determine a disease state based on values of one or more features determined from one or more cell-free DNA molecules or sequence reads (e.g., derived from one or more cfDNA molecules). In various embodiments, the output of the machine learning or deep learning model is a predictive score or probability of a disease state (e.g., a predictive cancer score).”) storing a plurality of first training samples, each including one of the first plurality of first data structures and a first label indicating the first state of the nucleotide at the respective target position, (Par [0075], “a test genotypic data construct database 120 for storing sets 122 of genotypic data constructs 124 for test subjects, where each genotypic data construct 124 includes genotypic features acquired from sequencing cell-free DNA for the subject, e.g., one or more of genomic copy number data 124, e.g., bin read counts 126 for different regions of the genome of the subject, variant allele data 128, e.g., allele statuses 130 for different alleles within the genome of the subject, allelic ratio data 132, e.g., allele fractions 134 for different alleles within the genome of the subject, and genomic methylation data 136…” includes sets of subject and target genotype data constructs)
filtering the first plurality of first data structures (Par [0344], “For each predicted tissue of origin in the first time point, target methylation variants will be defined from a pre-computed reference database of methylation variants called on that corresponding TOO, filtering variants that are high frequency in the database.”)
through one or more convolutional layers to obtain a plurality of convolutional matrices, (Par [0153], “Convolutional neural networks can be used for classifying methylation patterns in accordance with the present disclosure.” And par [0222])
applying a transformer layer to the plurality of convolutional matrices to
obtain transformer matrices, wherein applying the transformer layer to a convolutional matrix includes generating a plurality of attention scores that quantify a relevance among positions of the convolutional matrix, (Par [0165], “One way to begin a clustering investigation can be to define a distance function and to compute the matrix of distances between all pairs of samples in the training set….” And Par [0340] Fourth, the latent difference in classifier probabilities (or logit-transformed probabilities) will be modeled as a two component mixture distribution, where the first component is a point-mass at zero and the second component is a flexible non-negative distribution…”) generating methylation probabilities at respective target positions of the first plurality of first data structures using the transformer matrices, (par [0254], the second state being the “target” as claimed; see also par [0255], “For example, a machine learning or deep learning model (e.g., a disease classifier) Par [0324-0325], “This likely represents background variance in the methylation signals of these healthy subjects. That is, fluctuations in the genomic methylation pattern over the 12 to 40 month period, for the most part, result in small shifts in the cancer probability output by the classifier.” And see Figure 10; Par [0345], “Each row of this matrix will represent a sample and each column will represent a mixture model feature…”)
determining outputs using the methylation probabilities, and
optimizing, using the plurality of first training samples, (Par [0324-0325], “This likely represents background variance in the methylation signals of these healthy subjects. That is, fluctuations in the genomic methylation pattern over the 12 to 40 month period, for the most part, result in small shifts in the cancer probability output by the classifier.” And see Figure 10; Par [0345]) parameters of the
model based on the outputs of the model matching or not matching corresponding labels of the first labels when the first plurality of first data structures is input to the model, (Par [0173-0174], “Depending on the differences between the scores output by the model-in-training and the output labels of the training data, the weights of the predictive cancer model can be optimized to enable the disease state model to make more accurate predictions. In various embodiments, a disease state model may be a non-parametric model (e.g., k-nearest neighbors) and therefore, the predictive cancer model can be trained to make more accurately make predictions without having to optimize parameters.” And par [0173]; Par [0324-0325] and Par [0345]) wherein an output of the model specifies whether the nucleotide at the respective target position in the respective window has the methylation, wherein the parameters of the model include the plurality of attention scores, determining, using the model, whether the methylation is present in the nucleotide at the target position within the window in the input data structure. (Par [0060], ““DNA methylation” in mammalian genomes can refer to the addition of a methyl group to position 5 of the heterocyclic ring of cytosine (e.g., to produce 5-methylcytosine) among CpG dinucleotides. Methylation of cytosine can occur in cytosines in other sequence contexts, for example, 5′-CHG-3′ and 5′-CHH-3′, where H is adenine, cytosine or thymine. Cytosine methylation can also be in the form of 5-hydroxymethylcytosine. Methylation of DNA can include methylation of non-cytosine nucleotides, such as N6-methyladenine.” And par [0061] As used herein, the terms “size profile” and “size distribution” can relate to the sizes of DNA fragments in a biological sample. A size profile can be a histogram that provides a distribution of an amount of DNA fragments at a variety of sizes. Various statistical parameters (also referred to as size parameters or just parameter) can distinguish one size profile to another. One parameter can be the percentage of DNA fragment of a particular size or range of sizes relative to all DNA fragments or relative to DNA fragments of another size or range.” .” And see par [0254], the second state being the “target” as claimed; see also par [0255], “For example, a machine learning or deep learning model (e.g., a disease classifier)…”).
As per Claim 2, the rejection of Claim1 is incorporated and Maher further discloses: wherein the signal is an optical signal or an electrical signal. (Par [0253], “disease models, because they facilitate identity of changes in the biological signature of a subject over time, even when the biological signal is not yet strong enough for the underlying model to detect. Accordingly, in some embodiments, the model (e.g., the underlying model used to evaluate a genotypic data construct 124 at step 210 of workflow 200) evaluates data from a single time point. That can be samples that evaluate biological features acquired from a single sample from the subject, or from a plurality of samples acquired at a same or similar point in time from the subject (e.g., samples providing different types of biological information, such as genomic and transcriptomic information).”)
As per Claim 3, the rejection of Claim 1 is incorporated and Maher further discloses: wherein the one or more signal properties include: for each nucleotide within the window: an identity of the nucleotide. (Par [0253], “…specificity of existing disease models, because they facilitate identity of changes in the biological signature of a subject over time, even when the biological signal is not yet strong enough for the underlying model to detect. Accordingly, in some embodiments, the model (e.g., the underlying model used to evaluate a genotypic data construct 124 at step 210 of workflow 200)”).
As per Claim 4, the rejection of Claim 3 is incorporated and Maher further discloses: wherein the one or more signal properties further include: for each nucleotide within the window: a position of the nucleotide within the sample nucleic acid molecule, a width of a pulse corresponding to the nucleotide, or an interpulse duration representing a time between the pulse corresponding to the nucleotide and a pulse corresponding to a neighboring nucleotide. (Par [0055], “As used herein, the term “single nucleotide variant” or “SNV” refers to a substitution of one nucleotide at a position (e.g., site) of a nucleotide sequence, e.g., a sequence corresponding to a target nucleic acid molecule from an individual, to a nucleotide that is different from the nucleotide at the corresponding position in a reference genome.”)
As per Claim 5, the rejection of Claim1 is incorporated and Maher further discloses: wherein generating the plurality of attention scores comprises using a plurality of multiple-head self-attentions. (Par [0136], “…entitled “Methylation Fragment Anomaly Detection,” filed Mar. 13, 2018, which is hereby incorporated by reference herein in its entirety. In some embodiments, the methylation state vectors undergo p-value filtration and classification, as described in United States Patent Publication No. US 2019-0287652 A1, the content of which is incorporated herein by reference.” And par [0327], “…each change in cancer probability score was plotted as a function of the time interval between the first and second blood draw. As shown in FIG. 12, no strong relationship is seen between the change in cancer probability scores and the passage of time within a short time-range of the longitudinal dataset.”).
As per Claim 6, the rejection of Claim1 is incorporated and Maher further discloses: wherein generating the methylation probabilities comprises applying one or more neural network layers to the transformer matrices. (Par [0150], “Generally, many different classification algorithms can find use in the systems and methods described herein. For instance, in some embodiments, the model is a neural network algorithm, a support vector machine algorithm, a Naive Bayes algorithm, a nearest neighbor algorithm, a boosted trees algorithm, a random forest algorithm, a decision tree algorithm, a multinomial logistic regression algorithm, a linear model, or a linear regression algorithm (324).
As per Claim 7, the rejection of Claim 6 is incorporated and Maher further discloses: wherein applying the one or more neural network layers comprises performing multiplication by weights or additions by biases. (Par [0142-0143], “the methylation values are normalized to correct for GC bias. For example, in some embodiments, the normalizing includes replacing each respective bin value in the first plurality of bin values (e.g., methylation statuses 138-1 determined from a first biological sample from the subject obtained at a first time) with the respective bin value corrected for a respective first GC bias in the first plurality of bin values, and replacing each respective bin value in the second plurality of bin values (e.g., methylation statuses 138-2 determined from a second biological sample from the subject obtained at a second time) with the respective bin value corrected for a respective second GC bias in the second plurality of bin values.).
As per Claim 8, the rejection of Claim 1 is incorporated and Maher further discloses: wherein the respective convolutional results have lower dimensionality than the respective first data structure. (Par [0081], “… “size and number aberrations in plasma DNA for detecting cancer;” US App. No. 62/642,461 entitled “Method and system for selecting, managing and analyzing data of high dimensionality;…”).
As per Claim 9, the rejection of Claim 1 is incorporated and Maher further discloses: wherein the methylation is 5mC (5-methylcytosine). (Par [0056], “As used herein, the term “methylation” refers to a modification of deoxyribonucleic acid (DNA) where a hydrogen atom on the pyrimidine ring of a cytosine base is converted to a methyl group, forming 5-methylcytosine….”).
As per Claim 10, the rejection of Claim 1 is incorporated and Maher further discloses: wherein the methylation is 6mA (N6-methyladenine). (Par [0060], “…Methylation of DNA can include methylation of non-cytosine nucleotides, such as N6-methyladenine.”).
As per Claim 11, the rejection of Claim 1 is incorporated and Maher further discloses: wherein the window comprises 13 consecutive nucleotides. (Par [0115], “when multiplex sequencing can be used to sequence cfDNA from a plurality of subjects in a single sequencing reaction, a patient-specific index is also added to the nucleic acid molecules. In some embodiments, the patient specific index is a short nucleic acid sequence (e.g., 3-20 nucleotides) that are added to ends of DNA fragments during library construction…”).
As per Claim 12, the rejection of Claim 1 is incorporated and Maher further discloses: wherein the window of the input data structure has a different number of consecutive nucleotides upstream of the nucleotide at the target position than the number of consecutive nucleotides downstream of the nucleotide at the target position. (Par [0115], “when multiplex sequencing can be used to sequence cfDNA from a plurality of subjects in a single sequencing reaction, a patient-specific index is also added to the nucleic acid molecules. In some embodiments, the patient specific index is a short nucleic acid sequence (e.g., 3-20 nucleotides) that are added to ends of DNA fragments during library construction…” and par [0275-0276], “…parameters is normalized for an amount of time between consecutive time points in a time series for the respective subject, and the test trend test parameter is normalized for an amount of time between consecutive time points in a time series for the test subject. Likewise, in some embodiments, each respective reference trend test parameter in the plurality of reference trend test parameters is normalized for an amount of time between consecutive time points in a time series for the respective reference subject by normalizing one or more genotypic…” .” And see par [0254], the second state being the “target” as claimed; see also par [0255], “For example, a machine learning or deep learning model (e.g., a disease classifier)…”).
As per Claim 13, the rejection of Claim 1 is incorporated and Maher further discloses: wherein the window of the input data structure comprises 21 consecutive nucleotides upstream of the nucleotide at the target position and 21 consecutive nucleotides downstream of the nucleotide at the target position. (Par [048], “…A sequence read (or sequencing read) can refer to sequence information corresponding to a nucleic acid molecule (e.g., a string of nucleotides). For example, a sequence read can correspond to a string of nucleotides (e.g., about 20 to about 150) from part of a nucleic acid fragment, can correspond to a string of nucleotides at one or both ends of a nucleic acid fragment, or can correspond to nucleotides of the entire nucleic acid fragment. A sequence read can be obtained in a variety of ways, e.g., using sequencing techniques or using probes, e.g., in hybridization arrays or capture probes, or amplification techniques, such as the polymerase chain reaction (PCR) or linear amplification using a single primer or isothermal amplification.” and see par [0254], the second state being the “target” as claimed; see also par [0255], “For example, a machine learning or deep learning model (e.g., a disease classifier)).
As per Claim 14, the rejection of Claim 1 is incorporated and Maher further discloses: wherein: the data is acquired by sequencing an extended nucleic acid molecule, the extended nucleic acid molecule comprises the sample nucleic acid molecule and an adaptor, the adaptor has a known sequence, and the window of nucleotides includes at least one nucleotide in the adaptor. (Par [0011], “a first plurality of nucleic acid molecules…” and Par [0040], “…such as deoxyribonucleic acid (DNA, e.g., complementary DNA (cDNA), genomic DNA (gDNA) and the like), and/or DNA analogs (e.g., containing base analogs, sugar analogs and/or a non-native backbone and the like), all of which can be in single- or double-stranded form. Unless otherwise limited, a nucleic acid can comprise known analogs of natural nucleotides, some of which can function in a similar manner as naturally occurring nucleotides. A nucleic acid can be in any form useful for conducting processes herein (e.g., linear, circular, supercoiled, single-stranded, double-stranded and the like).” And par [0050], range of values).
As per Claim 15, the rejection of Claim 1 is incorporated and Maher further discloses: wherein determining whether the methylation is present comprises: determining the methylation is present; and determining the methylation is a first type from among a plurality of types. (Par [0056] , “As used herein, the term “methylation” refers to a modification of deoxyribonucleic acid (DNA) where a hydrogen atom on the pyrimidine ring of a cytosine base is converted to a methyl group, forming 5-methylcytosine.” And par [0060], “…a methyl group to position 5 of the heterocyclic ring of cytosine (e.g., to produce 5-methylcytosine) among CpG dinucleotides. Methylation of cytosine can occur in cytosines in other sequence contexts, for example, 5′-CHG-3′ and 5′-CHH-3′, where H is adenine, cytosine or thymine. Cytosine methylation can also be in the form of 5-hydroxymethylcytosine. Methylation of DNA can include methylation of non-cytosine nucleotides, such as N6-methyladenine” And Par [0113], “As described further below biological features (e.g., one or more of read counts 126, allele statuses 130, allelic fractions 134, and methylation statuses 138) can be extracted from sequence reads of the cell-free DNA present in liquid biological samples…”).
As per Claim 16, the rejection of Claim 15 is incorporated and Maher further discloses: wherein each type of the plurality of types is selected from the group consisting of 5mC, 5hmC, and 6mA. (Par [0056] , “As used herein, the term “methylation” refers to a modification of deoxyribonucleic acid (DNA) where a hydrogen atom on the pyrimidine ring of a cytosine base is converted to a methyl group, forming 5-methylcytosine.” And par [0060], “…a methyl group to position 5 of the heterocyclic ring of cytosine (e.g., to produce 5-methylcytosine) among CpG dinucleotides. Methylation of cytosine can occur in cytosines in other sequence contexts, for example, 5′-CHG-3′ and 5′-CHH-3′, where H is adenine, cytosine or thymine. Cytosine methylation can also be in the form of 5-hydroxymethylcytosine. Methylation of DNA can include methylation of non-cytosine nucleotides, such as N6-methyladenine”.).
As per Claim 17, the rejection of Claim 1 is incorporated and Maher further discloses:, wherein the sample nucleic acid molecule is single-stranded. (Par [0047], “For example, sequencing data can include all or a portion of the nucleotide bases in a nucleic acid molecule such as a DNA fragment.” And par [0048], “In some embodiments, sequence reads (e.g., single-end or paired-end reads) can be generated from one or both strands of a targeted nucleic acid fragment.”).
As per Claim 18, the rejection of Claim 1 is incorporated and Maher further discloses: wherein the plurality of first nucleic acid molecules comprises single-stranded nucleic acid molecules. (Par [0047] and par [0048], “In some embodiments, sequence reads (e.g., single-end or paired-end reads) can be generated from one or both strands of a targeted nucleic acid fragment.”).
As per Claim 19, Maher further discloses: A method for detecting a methylation of a nucleotide in a nucleic acid molecule, the method comprising: receiving a first plurality of first data structures, each first data structure of the
first plurality of first data structures corresponding to a respective window of positions around a respective target position of nucleotides sequenced in a respective nucleic acid molecule of a plurality of first nucleic acid molecules, (Par [0060], “ As used herein, the term “methylation profile” (also called methylation status) can include information related to DNA methylation for a region…” and par [0061], “As used herein, the terms “size profile” and “size distribution” can relate to the sizes of DNA fragments in a biological sample…” Par [0139], “the plurality of genotypic characteristics for the first genotypic data structure (e.g., genotypic data construct 124-1-1) includes a first plurality of bin values (e.g., methylation statuses 138-1)…” and see Figures 2 and 9) wherein each of the first nucleic acid molecules is sequenced by measuring pulses in a signal corresponding to the nucleotides, wherein the methylation has a known first state in a nucleotide at the respective target position in each window of each first nucleic acid molecule, each first data structure comprising values for one or more signal properties at positions within the respective window; (Par [0050], “…or a whole genome, respectively, is independently sequenced. When a mean depth is quoted, the actual depth for different loci included in the dataset can span over a range of values. In some embodiments, deep sequencing can refer to at least 100× in sequencing depth at a locus. In some embodiments, a sequencing depth of 10,000× or higher can be adopted in order to identify rare mutations.”)
storing a plurality of first training samples, each first training sample including
one of the first plurality of first data structures and a first label indicating the first state of the nucleotide at the respective target position; (Par [0075], “a test genotypic data construct database 120 for storing sets 122 of genotypic data constructs 124 for test subjects, where each genotypic data construct 124 includes genotypic features acquired from sequencing cell-free DNA for the subject, e.g., one or more of genomic copy number data 124, e.g., bin read counts 126 for different regions of the genome of the subject, variant allele data 128, e.g., allele statuses 130 for different alleles within the genome of the subject, allelic ratio data 132, e.g., allele fractions 134 for different alleles within the genome of the subject, and genomic methylation data 136…” includes sets of subject and target genotype data constructs)and training a model by: filtering the first plurality of first data structures through one or more convolutional layers to obtain a plurality of convolutional matrices, (Par [0344], “For each predicted tissue of origin in the first time point, target methylation variants will be defined from a pre-computed reference database of methylation variants called on that corresponding TOO filtering variants that are high frequency in the database.” And (Par [0153], “…Convolutional neural networks can be used for classifying methylation patterns in accordance with the present disclosure.” And par [0222])
applying a transformer layer to the plurality of convolutional matrices to obtain transformer matrices, wherein applying the transformer layer to a convolutional matrix includes generating a plurality of attention scores that quantify a relevance among positions of the convolutional matrices, (Par [0165], “One way to begin a clustering investigation can be to define a distance function and to compute the matrix of distances between all pairs of samples in the training set….” And Par [0340] Fourth, the latent difference in classifier probabilities (or logit-transformed probabilities) will be modeled as a two component mixture distribution, where the first component is a point-mass at zero and the second component is a flexible non-negative distribution…”) generating methylation probabilities at respective target positions of the first plurality of first data structures using the transformer matrices, (Par [0324-0325], “This likely represents background variance in the methylation signals of these healthy subjects. That is, fluctuations in the genomic methylation pattern over the 12 to 40 month period, for the most part, result in small shifts in the cancer probability output by the classifier.” And see Figure 10; Par [0345], “Each row of this matrix will represent a sample and each column will represent a mixture model feature…”)
determining outputs using the methylation probabilities, and optimizing, using the plurality of first training samples, (Par [0324-0325], “This likely represents background variance in the methylation signals of these healthy subjects. That is, fluctuations in the genomic methylation pattern over the 12 to 40 month period, for the most part, result in small shifts in the cancer probability output by the classifier.” And see Figure 10; Par [0345]) parameters of the model based on the outputs of the model matching or not matching corresponding labels of the first labels when the first plurality of first data structures is input to the model, (Par [0173-0174], “Depending on the differences between the scores output by the model-in-training and the output labels of the training data, the weights of the predictive cancer model can be optimized to enable the disease state model to make more accurate predictions. In various embodiments, a disease state model may be a non-parametric model (e.g., k-nearest neighbors) and therefore, the predictive cancer model can be trained to make more accurately make predictions without having to optimize parameters.” And par [0173]; Par [0324-0325] and Par [0345]) wherein an output of the model specifies whether the nucleotide at the respective target position in the respective window has the methylation, wherein the parameters of the model include the plurality of attention scores. (Par [0060], ““DNA methylation” in mammalian genomes can refer to the addition of a methyl group to position 5 of the heterocyclic ring of cytosine (e.g., to produce 5-methylcytosine) among CpG dinucleotides. Methylation of cytosine can occur in cytosines in other sequence contexts, for example, 5′-CHG-3′ and 5′-CHH-3′, where H is adenine, cytosine or thymine. Cytosine methylation can also be in the form of 5-hydroxymethylcytosine. Methylation of DNA can include methylation of non-cytosine nucleotides, such as N6-methyladenine.” And par [0061] As used herein, the terms “size profile” and “size distribution” can relate to the sizes of DNA fragments in a biological sample. A size profile can be a histogram that provides a distribution of an amount of DNA fragments at a variety of sizes. Various statistical parameters (also referred to as size parameters or just parameter) can distinguish one size profile to another. One parameter can be the percentage of DNA fragment of a particular size or range of sizes relative to all DNA fragments or relative to DNA fragments of another size or range.”).
As per Claim 53, Maher further discloses:
A method for detecting a methylation of a nucleotide in a nucleic acid molecule, the method comprising: receiving a first plurality of first data structures, each first data structure of the first plurality of first data structures corresponding to a respective window around a respective target position of nucleotides sequenced in a respective nucleic acid molecule of a plurality of first nucleic acid molecules, (Par [0060], “ As used herein, the term “methylation profile” (also called methylation status) can include information related to DNA methylation for a region…” and par [0061], “As used herein, the terms “size profile” and “size distribution” can relate to the sizes of DNA fragments in a biological sample…” and Par [0139], “the plurality of genotypic characteristics for the first genotypic data structure (e.g., genotypic data construct 124-1-1) includes a first plurality of bin values (e.g., methylation statuses 138-1)…” and see Figures 2 and 9) wherein each of the first nucleic acid molecules is sequenced by measuring pulses in a signal corresponding to the nucleotides, wherein each first nucleic acid molecule comprises a training sample nucleic acid molecule and a first adaptor having a known sequence, wherein the methylation has a known first state in a nucleotide at the respective target position in a portion of each window of each first nucleic acid molecule corresponding to the training sample nucleic acid molecule, each first data structure comprising values for one or more signal properties; (Par [0050], “…or a whole genome, respectively, is independently sequenced. When a mean depth is quoted, the actual depth for different loci included in the dataset can span over a range of values. In some embodiments, deep sequencing can refer to at least 100× in sequencing depth at a locus. In some embodiments, a sequencing depth of 10,000× or higher can be adopted in order to identify rare mutations.”) storing a plurality of first training samples, each including one of the first plurality of first data structures and a first label indicating the first state of the nucleotide at the respective target position, (Par [0075], “a test genotypic data construct database 120 for storing sets 122 of genotypic data constructs 124 for test subjects, where each genotypic data construct 124 includes genotypic features acquired from sequencing cell-free DNA for the subject, e.g., one or more of genomic copy number data 124, e.g., bin read counts 126 for different regions of the genome of the subject, variant allele data 128, e.g., allele statuses 130 for different alleles within the genome of the subject, allelic ratio data 132, e.g., allele fractions 134 for different alleles within the genome of the subject, and genomic methylation data 136…” includes sets of subject and target genotype data constructs) and training a model by optimizing, using the plurality of first training samples, parameters of the model based on outputs of the model matching or not matching corresponding labels of the first labels when the first plurality of first data structures is input to the model, (Par [0173-0174], “Depending on the differences between the scores output by the model-in-training and the output labels of the training data, the weights of the predictive cancer model can be optimized to enable the disease state model to make more accurate predictions. In various embodiments, a disease state model may be a non-parametric model (e.g., k-nearest neighbors) and therefore, the predictive cancer model can be trained to make more accurately make predictions without having to optimize parameters.” And par [0173]; Par [0324-0325] and Par [0345]) wherein an output of the model specifies whether the nucleotide at the respective target position in the respective window has the methylation. (Par [0060], ““DNA methylation” in mammalian genomes can refer to the addition of a methyl group to position 5 of the heterocyclic ring of cytosine (e.g., to produce 5-methylcytosine) among CpG dinucleotides. Methylation of cytosine can occur in cytosines in other sequence contexts, for example, 5′-CHG-3′ and 5′-CHH-3′, where H is adenine, cytosine or thymine. Cytosine methylation can also be in the form of 5-hydroxymethylcytosine. Methylation of DNA can include methylation of non-cytosine nucleotides, such as N6-methyladenine.” And par [0061] As used herein, the terms “size profile” and “size distribution” can relate to the sizes of DNA fragments in a biological sample. A size profile can be a histogram that provides a distribution of an amount of DNA fragments at a variety of sizes. Various statistical parameters (also referred to as size parameters or just parameter) can distinguish one size profile to another. One parameter can be the percentage of DNA fragment of a particular size or range of sizes relative to all DNA fragments or relative to DNA fragments of another size or range.”).
Conclusion
11. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Cao; Minh Duc (US-20190348152-A1) relates to a system may use machine learning techniques to generate a genome assembly of an organism's DNA, a gene sequence of a portion of an organism's DNA, or an amino acid sequence of a protein. The system may access biological polymer sequences generated by a sequencing device and an assembly generated from the sequences.
Gross; Samuel S. (US-20210313006-A1), relates to a method that enables analyze methylation sequencing data from cell-free DNA for the detection, diagnosis, and/or monitoring of diseases which allows for earlier treatment and thus a greater chance for survival. The neural network architecture improves the efficiency of neural network training and conserves computational power due to the reduced number of layers involved in the training.
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELICA RUIZ whose telephone number is (571)270-3158. The examiner can normally be reached M-F 10:00 am to 6:00 pm.
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/ANGELICA RUIZ/Primary Examiner, Art Unit 2154 June 4, 2026
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