DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Applicant's response, filed on 02/10/2026, 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. Any newly recited portions herein are necessitated by claim amendment.
Status of claims
Canceled:
3, 6-7, 10-11, 14, 18, 20-28, 30-31, 34, 37, and 39-137
New:
none
Amended:
1
Pending:
1-2, 4-5, 8-9, 12-13, 15-17, 19, 29, 32-33, 35-36, 38 and 138
Withdrawn:
None
Examined:
1-2, 4-5, 8-9, 12-13, 15-17, 19, 29, 32-33, 35-36, 38 and 138
Independent:
1
Allowable:
None
Priority
As detailed on the 09/03/2020 filing receipt, this application claims priority to as early as 12/18/2018 (provisional application 62/781,549). At this point in examination, all claims have been interpreted as being accorded this priority date.
Withdrawn Rejections/Objections
The rejection of claims 1-2, 4-5, 8-9, 11-12, 15-17, 19, 22, 29, 35-36 and 38 rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Khwaja and Dawson, in the Office action mailed 09/11/2025 is withdrawn in view of the amendments filed 02/10/2026. However, a new ground of rejection is applied in response to claim amendments.
The rejection of claims 32-33 under 35 U.S.C. 103 as being unpatentable over Zhou in view of Khwaja and Dawson as applied to claims 1-2, 4-5, 8-9, 11-13, 15-17, 19, 22, 29, 35-36, and 38 above, and further in view of Hackenberg, in the Office action mailed 09/11/2025 is withdrawn in view of the amendments filed 02/10/2026. However, a new ground of rejection is applied in response to claim amendments.
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 1-2, 4-5, 8-9, 12-13, 15-17, 19, 29, 32-33, 35-36, 38 and 138 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Mental processes recited include:
Claims 1 and 9 recite: "detecting a methylation state for each of more than 1000 nucleic acid fragments in the first biological sample at a first time period…," "determining, for each methylation site in each of the nucleic acid fragments, a count based on (1) a first number of nucleic acid fragments containing the corresponding methylation site and having the non-binary score satisfying a threshold value and (2) a second number of nucleic acid fragments containing the corresponding methylation site and having the non-binary score not satisfying the threshold value, wherein the threshold value represents a minimum required likelihood that a given nucleic acid fragment originated from the first cell source…," “…predict the likelihood that a given nucleic acid fragment is from the first cell source based on the methylation states of the given nucleic acid fragment…,” “determining, for each methylation site, a methylation site-specific first cell source fraction by comparing the count of the methylation site…” and “adjusting, based on output from the statistic model, a prognosis of the test subject…” are acts of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 13 recites: "…determining a stage of the type of cancer in the test subject." Determining is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 15 recites: "…determining a treatment option for the cancer in the test subject." Determining is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Mathematical concepts recited include:
Claims 1 and 9 recite: “classifier,” "the classifier comprises a neural network trained on label information together with a first set of methylation state vectors and a second set of methylation state vectors to predict the likelihood that a given nucleic acid fragment is from the first cell source based on the methylation states of the given nucleic acid fragment..." and “statistic model” The classifier, statistic model and classifier comprising a neural network and predicting the likelihood are mathematical concepts and/or formulas. As disclosed in Paragraph [0033] of the instant specification, “In some embodiments, the first classifier is based on a multinomial logistic regression algorithm. In alternative embodiments, the first classifier is based on 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 convolutional neural network, a decision tree algorithm a mixture model, or a hidden Markov model.”
Claim 5 recites: "the cell source fraction for the type of cancer in the reference biological sample in the corresponding reference subject is at least two percent, at least ten percent, or at least twenty percent." Percent is a mathematical concept and/or formula.
Claim 12 recites: "comprising using a difference of estimated first cell source fractions between the first time period and the second time period." Difference of estimated is a mathematical concept and/or formula.
Claim 29 recites: "wherein the count is a quotient of the first number of nucleic acid fragments containing the corresponding methylation site and having the non-binary score satisfying a threshold value and the second number of nucleic acid fragments containing the corresponding methylation site and having the non-binary score not satisfying the threshold value." Quotient is a mathematical concept and/or formula.
Claim 32 recites: "constructing a Poisson model or a negative binomial distribution assumption using the count of each methylation site and the corresponding reference frequency of each methylation site in the first reference set; using the Poisson model or the negative binomial distribution assumption to form a cumulative density function across a range of calculated first cell source fractions; and deeming a first instance of the first cell source fraction to be a mean of the cumulative density function across the range of calculated first cell source fractions."
Claim 33 recites: "constructing a Poisson model or a negative binomial distribution assumption using the count for each methylation site and the corresponding reference frequency of the methylation site in the first reference set, thereby constructing a plurality of Poisson models or a plurality of negative binomial distribution assumptions; and using each Poisson model or each negative binomial distribution assumption to form a corresponding cumulative density function across a range of calculated first cell source fractions; and deeming a first instance of the first cell source fraction to be the mean of the cumulative density function across the range of calculated first cell source fractions combined across the plurality of Poisson models or the plurality of negative binomial distribution assumptions."
Claims 1, 9, 13 and 15 as indicated above include elements of detecting, determining, comparing, predicting and adjusting which are acts of evaluating, analyzing, observing and judging data. Acts of evaluating and analyzing data could be practically performed in the human mind and/or with pen and paper because they merely require making observations, evaluations, judgments, and opinions (See MPEP 2106.04(a)(2) subsection III). Therefore, under the broadest reasonable interpretation, the indicated claims above can be practically carried out in the human mind or with pen and paper as claimed, which falls under the "Mental processes" grouping of abstract ideas.
Claims 1, 5, 9, 12, 29 and 32-33 recite mathematical concepts and formulas as indicated above. For instance, the classifier, statistic model and Poisson model are mathematical concepts and/or formulas that falls under the “mathematical concepts” grouping of abstract ideas.
As such, claims 1-2, 4-5, 8-9, 12-13, 15-17, 19, 29, 32-33, 35-36, 38 and 138 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The above indicated judicial exceptions are not integrated into a practical application because the claims do not recite an additional element that apply, rely on or use the judicial exception in such a manner to amount to integration into a practical application. For example, there are no limitations that reflect an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that equate to mere instructions to implement an abstract idea or insignificant extra solution activity. Specifically, the instant claims recite the following additional elements:
Claims 1 and 9 recite “A) detecting a methylation state…,” "B) inputting methylation states of each of the nucleic acid fragments to a classifier trained to generate a non-binary score for each nucleic acid fragment, wherein the non-binary score represents a likelihood that the corresponding nucleic acid fragment originated from a first cell source, the methylation states of each nucleic acid fragments comprises multiple elements respectively corresponding to the plurality of methylation sites within the nucleic acid fragment, thereby compounding and concurrently leveraging an informative contribution of the plurality of methylation sites in the nucleic acid fragment in generating the non-binary score that falls into a range between zero and one" and "E) inputting the methylation site-specific first cell source fraction of each of the methylation sites into a statistic model"
claim 4: the recited "…first set of methylation state vectors is derived from a sample of a tumor of the type of cancer obtained from the corresponding reference subject." step/element. Obtaining data is a data gathering step that equates to insignificant extra solutional activity.
The elements of claims 1, 4 and 9 as indicated above equate to insignificant extra solutional activities of data gathering (see MPEP 2106.05(g)). Data gathering serves as input to the recited judicial exception in the claims. An example of data gathering that the courts have found to be insignificant extra solution activity is Determining the level of a biomarker in blood, 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) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis) (MPEP 2106.05(g)). The listed additional elements are mere instructions to apply an exception because they recite no more than an idea of a solution or outcome and does not recite a technological solution to a technological problem. (See MPEP 2106.05(f)(1)). As such, as currently recited, the claims do not appear to recite an improvement to technology or apply or use the recited judicial exception in some other meaningful way. Therefore, claims 1-2, 4-5, 8-9, 12-13, 15-17, 19, 29, 32-33, 35-36, 38 and 138 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional activities, insignificant extra-solution activity or mere instructions to implement the abstract idea on a generic computer. The instant claims recite the following additional elements:
Claims 1 and 9 recite “A) detecting a methylation state…,” "B) inputting methylation states of each of the nucleic acid fragments to a classifier trained to generate a non-binary score for each nucleic acid fragment, wherein the non-binary score represents a likelihood that the corresponding nucleic acid fragment originated from a first cell source, the methylation states of each nucleic acid fragments comprises multiple elements respectively corresponding to the plurality of methylation sites within the nucleic acid fragment, thereby compounding and concurrently leveraging an informative contribution of the plurality of methylation sites in the nucleic acid fragment in generating the non-binary score that falls into a range between zero and one" and "E) inputting the methylation site-specific first cell source fraction of each of the methylation sites into a statistic model"
claim 4: the recited "…first set of methylation state vectors is derived from a sample of a tumor of the type of cancer obtained from the corresponding reference subject." step/element. Obtaining data is a data gathering step that equates to insignificant extra solutional activity.
The additional elements indicated above do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. The limitations equate to mere data gathering activities, which are insignificant extra solutional activities. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. (see MPEP 2106.05(g)). Additionally, obtaining methylation data for use in classifier models are well-known methods as taught by Fraga, Huang and the specification of the instant application. Fraga ("DNA methylation: a profile of methods and applications." Biotechniques 33.3 (2002): 632-649.; as cited on the attached on the 892 form) discloses that the method of obtaining methylation status and methylated CPG sites of DNA fragments are known methods with Figure 4 (Page 640). Huang discloses that using methylation data in classification models are known methods as indicated in Table 1 (Page 48). The specification of the instant application also discloses that assays to detect the "methylation status of nucleic acids in a sample" ([125]) are conventional in the art as evidenced by "Any assay known to a person having ordinary skill in the art can be used to detect any of the properties of nucleic acids mentioned herein" ([125]). Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-2, 4-5, 8-9, 12-13, 15-17, 19, 29, 32-33, 35-36, 38 and 138 are not patent eligible.
Response to 35 USC §101 Arguments filed 02/10/2026
Applicant's arguments filed 02/10/2026 have been fully considered but they are not persuasive.
Applicant amended claim 1.
In Applicant's remarks for Claim Rejections under 35 U.S.C. §101, see pages 12-19, Applicant argues under step 2A, prong 1 of the 101 analysis that the claims do not recite a judicial exception of mental processes or mathematical concepts. Applicant states that the steps of claim 1 rely on machine learning architectures, statistical models, and fragment-level biological input vectors, all of which are computationally intensive processes that cannot be practically performed in the human mind. Applicant also states that the claims do not recite a "mathematical concept" as defined in the 2019 Revised PEG. Applicant states that the claimed subject matter describes the application of machine learning, not the underlying mathematics, and do not recite any formula or algorithm.
Applicant discusses the August 2025 Memo and explains Example 39 where the use of machine learning without reciting specific mathematical formulas is not a mathematical concept. Applicant also discusses the ARP decision, issued under the authority of Director Squires and made precedential to the PTAB, in Desjardins, in which the ARP held that claims involving Al-based classification of data did not recite a mental process or mathematical concept. Applicant asserts that the claimed subject matter is similar to the claims in Desjardins and Example 39.
In response, Applicant’s arguments are not persuasive. It is acknowledged that detecting a methylation state for each of more than 1000 nucleic acid fragments in claim 1 when performed mentally, or with paper and pencil, would take a considerable amount of time and effort. However, the process could still be performed by the human mind. As in Example 47 of the Office Guidance, “detecting” was determined to encompass mental observations or evaluations. Claim 1 also recite other mental processes identified above in the 101 rejection section.
In Example 39, the claim limitation of applying one or more transformations to each digital facial image including mirroring, rotating, smoothing, or contrast reduction to create a modified set of digital facial images cannot be practically performed by the human mind. Also, in Example 39 there was no judicial exception because the limitations were merely based on mathematical concepts and no mathematical concepts were actually recited or required. In the case of the instant claims, the claims are distinct from the fact pattern as in Example 39 of the guidance. In the present claims, at step B, the claims are directed to abstract ideas; these mathematical concepts are not merely nominal to the claimed method, but rather are essential components of the claims to perform the method of estimating. See further Example 47 of the Office guidance, particularly at claim 2, as with example 47 at claim 2, the present claims are training a neural network, this is considered as encompassing mathematical concepts. Referring to the example, using a trained neural network for example, is also considered mere instructions to implement the abstract idea on a generic computer (see MPEP 2106.05(f)).
Regarding Ex Parte Desjardins, Appeal No. 2024-0005676 (PTAB, September 26, 2025, Appeals Review Panel (ARP) Decision), the Appeals Review Panel (ARP) determined that under Step 2A Prong One, the claims recited an abstract idea of mathematical concept. However, ARP determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. The ARP also determined that the technological improvements are associated with reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks that were disclosed in the specification. Therefore, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two.
Applicant asserts that under step 2A, prong 2 of the 101 analysis, the claims integrate the judicial exception into a practical application. Applicant states that the limitations in claim 1 collectively reflect a real-world diagnostic workflow grounded in biological measurement and computational analysis, not a generic algorithm or abstract concept. Applicant further states that the claimed method is neither conventional nor performable by a human and solves a technical problem in a specific field by improving sensitivity and interpretability in cell source tracking. Applicant also states that the claims do not merely recite "a classifier'' in the abstract, but instead implement a structured, multi-step modeling process specifically tailored to biological methylation data to inform longitudinal prognosis adjustments.
In response, Applicant’s arguments are not persuasive. The asserted improvement of improving sensitivity and interpretability in cell source tracking is a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art. The specification does not sufficiently disclose a need or the technical problem and explains the details of an unconventional technical solution expressed in the claim. From the asserted improvement, it is not clear how the claimed invention improves over existing technology and it is also not clear how one would gauge the improvement since there are no metrics for comparison between the claimed technology and previous technology. Overall, one of ordinary skill in the art cannot gauge whether the improvements asserted are delivered by the claims because the details provided in the specification do not provide sufficient details such that the improvement would be apparent, do not explain the details of an unconventional technical solution expressed in the claim, or identify technical improvements realized by the claim over the prior art. As stated in MPEP 2106.05(a) and MPEP 2106.04(d), the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Also, the claim limitation of prognosis adjustments are mental processes and would not integrate the JEs into a practical application.
Applicant argues under step 2B of the 101 analysis, the adjustment is a functional transformation in how the subject's condition is assessed, informed by cfDNA-derived evidence, rather than general practitioner judgment or retrospective review. Applicant states that this step that cannot be performed in the human mind without the underlying predictive modeling and multi-site methylation-based scoring infrastructure.
In response, Applicant’s arguments are not persuasive. The process of analyzing DNA methylation sites an assessing a subject’s condition can be performed by the human mind and would fall under the mental process grouping of abstract ideas.
Applicant argues that the Examiner has not provided the factual evidence required by Berkheimer v. HP Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018), to support the contention that any of the additional elements, including the fragment-level scoring classifier trained on vectorized methylation states, the conversion into methylation-site-specific counts, or the use of a statistical model to derive a global fraction, are well-understood, routine, or conventional in the art.
In response, Applicant’s arguments are not persuasive. As discussed in the 101 rejection above under step 2B, obtaining methylation data for use in classifier models are well-known methods as taught by Fraga, Huang and the specification of the instant application. Fraga ("DNA methylation: a profile of methods and applications." Biotechniques 33.3 (2002): 632-649.; as cited on the 09/11/2025 892 form) discloses that the method of obtaining methylation status and methylated CPG sites of DNA fragments are known methods with Figure 4 (Page 640). Huang discloses that using methylation data in classification models are known methods as indicated in Table 1 (Page 48). The specification of the instant application also discloses that assays to detect the "methylation status of nucleic acids in a sample" ([125]) are conventional in the art as evidenced by "Any assay known to a person having ordinary skill in the art can be used to detect any of the properties of nucleic acids mentioned herein" ([125]).
Claim Rejections - 35 USC § 103
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 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 1-2, 4-5, 8-9, 12-13, 15-17, 19, 29, 35-36, 38 and 138 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou (US 2020/0131582 A1, priority 2016; "Notice of References Cited" form 892 mailed 03/30/2023) in view of Khwaja (A deep autoencoder system for differentiation of cancer types based on DNA methylation state. arXiv preprint arXiv:1810.01243; published 2018 Oct 2; cited on the 09/11/2025 "Notice of References Cited" form 892) and Bisarro dos Reis ("Prognostic classifier based on genome-wide DNA methylation profiling in well-differentiated thyroid tumors." The Journal of Clinical Endocrinology & Metabolism 102.11 (2017): 4089-4099.; cited on the attached "Notice of References Cited" form 892). Any newly recited portions herein are necessitated by claim amendment.
Regarding claim 1, Zhou teaches the recited "method of estimating a first cell source fraction in a first biological sample from a test subject" at least with "methods and systems of utilizing sequencing reads for detecting and quantifying the presence of a tissue type or a disease type in cell-free DNA prepared from blood samples" (Abstract) wherein the method "simultaneously identifies the proportions and the tissue-of-origin of tumor-derived cell-free DNA in a blood sample using genome-wide DNA methylation data" ([30]).
Zhou teaches the recited "(A) detecting a methylation state for each of more than 1000 nucleic acid fragments in the first biological sample at a first time period, wherein each nucleic fragment comprises one or more methylation sites" at least with "Methylation sequencing was performed on the plasma cfDNA sample... Sequencing reads were obtained of those cfDNA fragments that fall into the genomic regions of selected markers" ([234]). Zhou teaches that the methylation sequences is performed on "50 or more nucleic acids" (claim 1), and therefore teaches the 1000 or more nucleic acid fragments. The cfDNA is received "at a first time point" ([26]).
Zhou teaches the recited wherein the first biological sample comprises blood, plasma, serum, urine, fecal, saliva, or other types of bodily fluids at least with "methods and systems of utilizing sequencing reads for detecting and quantifying the presence of a tissue type or a disease type in cell-free DNA prepared from blood samples" (Abstract) wherein the method "simultaneously identifies the proportions and the tissue-of-origin of tumor-derived cell-free DNA in a blood sample using genome-wide DNA methylation data" ([30]).
Zhou teaches the recited (B) inputting methylation states of each of the nucleic acid fragments to a trained classifier at least with "Given the input matrix R derived from methylation sequencing data of a patient plasma cfDNA, we have two assumptions (Para. [0208]): Assumption 1: each cfDNA fragment is released from a tissue cell subpopulation and the composition of T tissues contributing to the plasma cfDNA is denoted as a composition vector... (Para. [0209]), Assumption 2: Proportion of cfDNA fragments from a tissue t in the input matrix R can reveal the cfDNA fraction θt of tissue t in the plasma" (Para. [0210]) and with "In some embodiments, DNA methylation microarrays of solid tumor tissues were used to obtain the data to train the model, due to the scarcity of whole-genome bisulfite sequencing data in the public domain." (Para. [0159]). "A simulated methylation sequencing profile of a plasma sample is generated, represented by the integer vectors M=(m1, m2, . . . , mK) and N=(n1, n2, . . . , nK). The elements mk and nk are the number of methylated cytosines and the total number of cytosines in the reads mapped to CpG cluster k, respectively." (Para. [0170]).
Zhou teaches the recited to generate a non-binary score for each nucleic acid fragment, wherein the non-binary score represents a likelihood that the corresponding nucleic acid fragment originated from a first cell source at least with "FIG. 1A actually reveals an intuitive “phased” methylation analysis method: we can label each cfDNA read which source it is likely to come from (normal plasma or liver tumor), then infer the fraction of those reads most likely from liver tumor among all cfDNA reads. This is indeed a cfDNA reads categorization and two-class composition inference process, as formally illustrated in FIG. 1B." (Para. [0089]) and with "The larger the prediction score λ, the higher the chance that the patient has a cancer tumor of type {circumflex over (t)}. Specifically, if λ>a threshold, the patient is predicted as getting cancer with the ctDNA burden {circumflex over (θ)} and the tumor type {circumflex over (t)}; otherwise, he/she is classified as noncancerous." (Para. [0148]). Zhou also teaches "FIG. 2 depicts an exemplary embodiment, illustrating the likelihood of identifying tissue-of-origin of cfDNA sequencing reads and their use for inferring normal tissue composition of plasma cfDNAs." (Para. [0058]). Figure 2 of Zhou depicts non-binary scores for nucleic fragments and it's likelihood that it originated from it's tissue-of-origin.
Zhou teaches the recited the methylation states of each nucleic acid fragments comprises multiple elements respectively corresponding to the plurality of methylation sites within the nucleic acid fragment, thereby compounding and concurrently leveraging informative contribution of the plurality of methylation sites in the nucleic acid fragment in generating the non-binary score that falls into a range between zero and one with "In some embodiments, when establishing the prediction model, tumor copy number aberration (CNA) events are added at pre-defined probabilities (10%, 30% and 50% across all CpG clusters).” (Para. [0149]) and with “Step 2: Generate a random integer copy number ck, for each CpG cluster k, from the categorical distribution ck˜Cat(6, p0, p1, p2, p3, p4, p5), where Cat refers to a categorical distribution. Here pc denotes the probability of observing copy number c∈{0, 1, 2, 3, 4, 5} in the sequencing data. The probabilities pc satisfy three criteria: (i) their sum is equal to one, Σc=0 5 pc=1; (ii) the average copy number is equal to two, Σc=0 5 c*pc=2; and (iii) extreme copy number alterations are less likely to occur. In some cases, the embodiments may predefine p0=0.005, p1=0.16, p2=0.7, p3=0.105, p4=0.025, p5=0.005. Note that the sum of all these probabilities except p2 (30% in this case) is the probability of any given CpG cluster having a CNA event. The embodiments may have other probability configurations for the simulation with more (50%) or fewer (10%) CNA events, and obtained similar results. No CNA event is considered (i.e. ck is fixed to two) when simulating a normal plasma sample.” (para. [0172]).
Zhou also teaches "FIG. 2 depicts an exemplary embodiment, illustrating the likelihood of identifying tissue-of-origin of cfDNA sequencing reads and their use for inferring normal tissue composition of plasma cfDNAs." (Para. [0058]). Figure 2 of Zhou depicts non-binary scores for nucleic fragments and it's likelihood that it originated from it's tissue-of-origin. The recited non-binary scores correspond to Zhou’s probabilities. Zhou teaches compounding methylation sites with “the sum of all these probabilities except p2 (30% in this case) is the probability of any given CpG cluster having a CNA event” (para. [0172]).
Zhou teaches the recited "each methylation state vector in the first set of methylation state vectors is derived from a first tissue sample or a first cell-free nucleic acid sample of a corresponding reference subject in a first plurality of reference subjects, wherein the first tissue sample or the first cell-free nucleic acid sample corresponds to the first cell source" at least with "cancer-specific methylation signatures can be established. The cancer-specific methylation signatures can be used to detect the presence of a specific cancer type as well as determine the relative composition of normal plasma and a specific cancer type within a particular cfDNA sample" ([95]). The methylation signatures can be taken from "solid tumor" and "normal tissues" as shown in FIG. 3.
Zhou teaches the recited "each methylation state vector in the second set of methylation state vectors is derived from a second tissue sample or a second cell-free nucleic acid sample of a corresponding reference subject in a second plurality of reference subjects, wherein the second tissue sample or the second cell-free nucleic acid sample corresponds to a second cell source, wherein the second cell source is different from the first cell source" at least with "Using sequencing data of normal or non-cancer patients, tissue specific methylation signatures can established. The tissue-specific methylation signatures can be used to detect the presence of a specific tissue type as well as determine the relative composition of different tissue types within a particular cfDNA sample" ([96]). The methylation signatures can be taken from "solid tumor" and "normal tissues" as shown in FIG. 3, wherein the solid tumor and normal tissues are different from each other.
Zhou teaches the recited (C) determining, for each methylation site in each of the nucleic acid fragments, a count based on (1) a first number of nucleic acid fragments containing the corresponding methylation site and having the non-binary score satisfying a threshold value and (2) a second number of nucleic acid fragments containing the corresponding methylation site and having the non-binary score not satisfying the threshold value, wherein the threshold value represents a minimum required likelihood that a given nucleic acid fragment originated from the first cell source at least with "After the removal of PCR duplications, the numbers of methylated and unmethylated cytosines are counted for each CpG site. The methylation level (xk) of a specific CpG cluster (k) is calculated as the ratio between the number of methylated cytosines (mk) and the total number of cytosines (nk) within the cluster." (Para. [0166]) and with Zhou teaches "FIG. 2 depicts an exemplary embodiment, illustrating the likelihood of identifying tissue-of-origin of cfDNA sequencing reads and their use for inferring normal tissue composition of plasma cfDNAs." (Para. [0058]). Figure 2 of Zhou depicts non-binary scores for nucleic fragments and its likelihood that it originated from its tissue-of-origin. Zhou also teaches "...determining the patient as being cancerous, having the potential cancer type t, if λ is greater than a predetermined threshold; and determining the patient as being noncancerous, if λ is smaller than the predetermined threshold." (para. [0031]). The predetermined threshold and greater than a predetermined threshold of Zhou corresponds to the recited "satisfying the threshold value" and smaller than the predetermined threshold of Zhou corresponds to the recited "not satisfying the threshold value."
Zhou teaches the recited "(D) determining, for each methylation site, a methylation site-specific first cell source fraction by comparing the count of the methylation site to a reference frequency for the methylation site for the first cell source" at least by establishing "cancer-specific methylation signatures" ([95]) used to identify specific cell sources. Methylation signatures are characterized by "methylation status" ([103]) for "different genomic resolutions (CpG sites or genomic bins), for each class (i.e., a cancer type, or a tissue type, or normal plasma)" ([102]). In a BRI, the count of methylation site reads on methylation status because both describe the amount of methylation present at a genomic region. The methylation signatures are further used to determine "methylation rate," thereby teaching the frequency of the claim language. The methylation signatures are compared to the reference or cancer-specific methylation signatures as evidenced by FIG. 4 and [103].
Zhou teaches the recited "and (E) inputting the methylation site-specific first cell source fraction of each of the methylation sites into a statistic model to obtain the first cell source fraction for the test subject" at least by "FIG. 11 depicts an exemplary embodiment, illustrating three models for characterizing DNA methylation pattern based on individual value of a tissue sample. Three models can be used for characterizing methylation signature at different resolutions (from high to low): (model 1) epialleles, (model 2) CpG sites, and (model 3) bins." (Para. [0067]) and with "FIG. 13 is a mixture model of methylation level (x) in a patient's plasma cfDNA, for different burdens of ctDNAs from the tumor type t, according to one embodiment." (Para. [0069]). Figure 13 depicts inputting methylation levels into a statistical model.
Zhou does not explicitly teach "the classifier comprises a neural network trained on label information together with a first set of methylation state vectors and a second set of methylation state vectors to predict the likelihood that a given nucleic acid fragment is from the first cell source based on the methylation states of the given nucleic acid fragment" of claim 1. However, this limitation was known in the art at the time of the effective filing date of the invention, as taught by Khwaja.
Zhou also does not explicitly teach (F) adjusting, based on output from the statistic model, a prognosis of the test subject when the first cell source fraction is observed to change by more than a predefined threshold across an epoch comprising a plurality of time points, wherein the adjusting is performed automatically based on a comparison of the first cell source fraction to a prior first cell source fraction previously estimated using the classifier- derived methylation site-specific first cell source fractions, and wherein changing the adjusting comprises: downgrading the prognosis when the first cell source fraction increases by more than the threshold: and upgrading the prognosis when the first cell source fraction decreases by more than the threshold of claim 1. However, this limitation was known in the art at the time of the effective filing date of the invention, as taught by Bisarro dos Reis.
Khwaja teaches the recited "the classifier comprises a neural network trained on label information together with a first set of methylation state vectors and a second set of methylation state vectors to predict the likelihood that a given nucleic acid fragment is from the first cell source based on the methylation states of the given nucleic acid fragment" at least with "Together, these layers form an efficient neural network capable of CpG methylation feature classification and cancer type differentiation. The neural network distinguishes between four cell types, and healthy/cancerous states, thus creating eight output classes to classify into." (page 6, col. 1, para. 2) and with "An eight-category classification method is implemented to determine (i) whether a cell line is healthy or cancerous, and (ii) to distinguish the type of cell (and therefore cancer type and state) it associates with." (page 6, col. 2, para. 3)
Khwaja also teaches Figures 2 and 3 and with "The proposed system was trained with previously reported data derived from four case groups of cancer cell lines, achieving overall Sensitivity of 88.24%, Specificity of 83.33%, Accuracy of 84.75% and Matthews Correlation Coefficient of 0.687." (Abstract). In Figure 2, Khwaja teaches the proposed Deep Autoencoder framework that includes the methylation state prediction system while Figure 3 of Khwaja depicts labeling methylation states with Label Methylated/Unmethylated step and training the neural network through supervised learning. In Figure 3, the label methylated of Khwaja corresponds to the recited first methylation state and the label unmethylated corresponds to the recited second methylation state. Overall, Figure 3 of Khwaja depicts labeling methylation states and training the neural network.
Bisarro dos Reis teaches wherein the adjusting is performed automatically based on a comparison of the first cell source fraction to a prior first cell source fraction previously estimated using the classifier- derived methylation site-specific first cell source fractions with “A prognostic classifier based on the methylation changes in a small number of loci was reported, which could correctly classify a subset of patients with WDTC with poor outcomes.” (page 4098, col. 1, para. 2); “Differentially methylated probes with potential biological significance in more aggressive subtypes of thyroid malignancies were initially selected to design a prognostic classifier. This first step consisted in obtaining probes located in the promoter regions by comparing between PDTC/ATC and NT. WDTC (PTC and FTC/HCC) was grouped in poor prognosis (PP) and good prognosis (GP) cases. Patients with confirmed recurrent loco regional disease (histological analysis) or distant metastasis (conclusive imaging test) in the follow-up were categorized as having WDTC-PP. The WDTC-GP group included only patients without any suspicion of active disease on clinical follow up (normal serum thyroglobulin levels and no evidence of disease in the imaging screening) for ≥ 5 years (WDTC-GP: 43 PTC and 5 FTC).” (page 4092, col. 1, para. 2) and Figure 2, Prognostic evaluation based on the methylation data (page 4096).
Bisarro dos Reis teaches wherein changing the adjusting comprises: downgrading the prognosis when the first cell source fraction increases by more than the threshold; and upgrading the prognosis when the first cell source fraction decreases by more than the threshold with “The ∆ β > |0.1| was used as a criterion for the comparison between WDTC-PP, NT, and BTL, which allowed the filtering of probes exclusively altered in WDTC-PP and ATC/PDTC. The remaining probes were submitted to a diagonal linear discriminant analysis (DLDA) method. The classification performance was calculated with a leave-one-out cross-validation test (BRBArrayToolsversion4.4.0). The predictive model used was based on scores stratified as low (NT range), intermediate (below DLDA threshold), and high (above DLDA threshold).” (page 4092, col. 1, para. 3) and Figure 2, Prognostic evaluation based on the methylation data (page 4096).
Rationale for combining
It would have been prima facia obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Zhou and Khwaja to arrive at the claimed invention. Khwaja’s system is able to achieve an overall Sensitivity of 88.24%, Specificity of 83.33% and Accuracy of 84.75% (Abstract). A person of ordinary skill in the art would have been motivated to modify the method of Zhou to include a neural network as taught by Khwaja to accurately predict cancer and cancer types. Furthermore, there would have been a reasonable expectation of success, since Zhou and Khwaja teach methods that pertain to predicting cancer and cancer types using machine learning algorithms.
It would also have been prima facia obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Zhou and Bisarro dos Reis to arrive at the claimed invention. A person of ordinary skill in the art would have been motivated to modify the method of Zhou to include a step of adjusting the prognosis based on methylation changes of samples using a classifier as taught by Bisarro dos Reis to appropriately determine treatment response for disease. Furthermore, there would have been a reasonable expectation of success, since Zhou and Bisarro dos Reis teach methods that pertain to the analysis of DNA methylation and cancer.
Regarding claim 2, Zhou teaches the recited "each methylation state vector in the first set of methylation state vectors represents the methylation state across the genome of the corresponding reference subject in the first plurality of reference subjects" at least with "Using sequencing data of normal or non-cancer patients, tissue specific methylation signatures can established" ([96]) wherein methylation data can be obtained by "datasets of whole-genome bisulfite sequencing" ([161]) of "normal people" ([161]) e.g. "reference subjects" of the claim language.
Regarding claim 4, Zhou teaches the recited "the first cell source is a type of cancer" at least by with a "potential cancer type t" ([33]). Zhou teaches the recited "a methylation state vector in the first set of methylation state vectors is derived from a sample of a tumor of the type of cancer obtained from the corresponding reference subject" at least by obtaining "a robust measurement of methylation values in the solid tumor sample" ([134]) to create cancer specific methylation patterns for the statistical analysis ([248]).
Regarding claim 5, Zhou teaches the recited "the first cell source is a type of cancer" at least by with a "potential cancer type t" ([33]). Zhou teaches the recited "a methylation state vector in the first set of methylation state vectors is derived from cell-free nucleic acids of a reference biological sample from the corresponding reference subject" at least by using "two datasets of whole genome bisulfite sequencing ( WGBS ) data of plasma samples are taken from 32 normal people, 8 patients infected with HBV, 29 liver cancer patients, 4 lung cancer patients, 5 breast cancer patients, and a number of patients with tumors in organs without a large blood flow" ([161]). The WGBS data includes "cell-free DNA" ([163]) sequence information.
Zhou teaches the recited "the cell source fraction for the type of cancer in the reference biological sample in the corresponding reference subject is at least two percent..." at least with the methylation signature match being above 0.1 (10%) as shown in FIG. 6 and [62].
Regarding claim 8, Zhou teaches the recited "the second cell source is one or more cell types that are cancer-free" at least with "using sequencing data of normal or non-cancer patients, tissue specific methylation signatures can be established" ([96]) .
Regarding claim 9, Zhou teaches the recited "repeating steps (A)-(E) for a second biological sample from the test subject at a second time period" at least with "repeating steps i ) to iv ) for each sequencing read in the second plurality of sequencing reads to quantitate the presence of the biological composition in the cfDNA sample at the second time point; and detecting a change in the biological composition between the first and second time points" ([26]).
Regarding claim 12, Zhou teaches the recited "using a difference of estimated first cell source fractions between the first time period and the second time period as a basis or a partial basis for determining a treatment option for a disease condition associated with the first cell source in the test subject" at least with "CancerDetector can also be used for monitoring the cancer progression and treatment" ([286]) wherein the CancerDetector uses a difference in methylation between two time periods ([26]).
Regarding claim 13, Zhou teaches the recited the first cell source is a type of cancer and the method further comprises using the first cell source fraction as a basis or a partial basis for determining a stage of the type of cancer at least with "In some embodiments, methods involve generating a methylation profile that indicates whether the patient has cancer, and if so, from what organ. In certain embodiments, this is done using a biological sample from the patient that comprises cell free DNA." (Para. [0050]) and with "In some embodiments, cancer that is found is classified in a cancer classification. Cancer classifications may be qualified as any of Stages I, II, III, or IV." (Para. [0051])
Regarding claim 15, Zhou teaches the recited "the first cell source is a type of cancer" at least by with a "potential cancer type t" ([33]). Zhou teaches the recited "using the first cell source fraction as a basis or a partial basis for determining a treatment option for the cancer in the test subject" at least by using the first cell source fraction to identify a potential cancer type t then using the potential cancer type to determine "the point of origin of the cancer" wherein a "treatment is tailored to cancer of that origin" ([36]).
Regarding claim 16, Zhou teaches the recited "the first set of methylation state vectors is a single consensus methylation state vector of a genome of the species formed from a methylation state of nucleic acids in the ... across the first plurality of reference subjects" at least by obtaining a methylation signature as the methylation state vector "based on existing methylation sequencing data" ([11]). Zhou teaches establishing "tissue specific methylation signatures" ([96]) and methylation data from cfDNA samples taken from a plurality of reference subjects ("healthy people and cancer patients" [160]).
Zhou teaches the recited "the second set of methylation state vectors is a single consensus methylation state vector of the genome of the species formed from a methylation state of nucleic acids in ... the second cell-free nucleic acid sample across the second plurality of reference subjects" at least by obtaining cfDNA samples ([163]) across several different pluralities of reference subjects to include "32 normal people, 8 patients infected with HBV, 29 liver cancer patients , 4 lung cancer patients, 5 breast cancer patients, and a number of patients with tumors in organs without a large blood flow" ([161]). This plurality of references subjects are distinctly different than the first plurality described above.
Regarding claim 17, Zhou teaches the recited "the first set of methylation state vectors includes a consensus methylation state vector of a genome of the species for each reference subject in the first plurality of reference subjects formed from a methylation state of nucleic acids in the tissue sample ... of the reference subject" at least by obtaining a methylation signature as the methylation state vector "based on existing methylation sequencing data" ([11]). Zhou teaches establishing "tissue specific methylation signatures" ([96]) and methylation data from cfDNA samples taken from a plurality of reference subjects ("healthy people and cancer patients" [160]).
Zhou teaches the recited "the second set of methylation state vectors includes a consensus methylation state vector of the genome of the species for each reference subject in the second plurality of reference subjects formed from a methylation state of nucleic acids in the ... second cell-free nucleic acid sample of the" at least by obtaining cfDNA samples ([163]) across several different pluralities of reference subjects to include "32 normal people, 8 patients infected with HBV, 29 liver cancer patients , 4 lung cancer patients, 5 breast cancer patients, and a number of patients with tumors in organs without a large blood flow" ([161]). This plurality of references subjects are distinctly different than the first plurality described above.
Regarding claim 19, Zhou teaches the recited "the first plurality of reference subjects comprises at least one hundred reference subjects" at least by using "100 ... or more patients" ([52]) within "a control" ([52]), e.g. healthy patients".
Zhou teaches the recited "the second plurality of reference subjects comprises at least one hundred reference subjects different from the first plurality of reference subjects" at least by using "100 ... or more patients" ([52]) within "a control that is indicative of ... a relevant cancerous tissue" ([52]). These cancerous patients would be different from the healthy patients described above.
Regarding claim 29, Zhou teaches the recited the count is a quotient of the first number of nucleic acid fragments containing the corresponding methylation site and having the non-binary score satisfying a threshold value and the second number of nucleic acid fragments containing the corresponding methylation site and having the non-binary score not satisfying the threshold value at least with a "After the removal of PCR duplications, the numbers of methylated and unmethylated cytosines are counted for each CpG site. The methylation level (xk) of a specific CpG cluster (k) is calculated as the ratio between the number of methylated cytosines (mk) and the total number of cytosines (nk) within the cluster. However, if the total number of cytosines (nk) in the reads aligned to the CpG cluster is less than 30, the methylation level of this cluster is treated as 'NA'." (Para. [0166])." The recited "methylation site" reads on "CpG site" of Zhou. The recited "quotient" reads on "ratio" of Zhou and the recited "threshold value" reads on "CpG cluster is less than 30" of Zhou. The recited "first score not satisfying the threshold value" reads on "CpG cluster is less than 30, the methylation level of this cluster is treated as 'NA'" of Zhou. While the recited "having a score satisfying a threshold value" would be "CpG cluster is greater than 30" as inherently taught by Zhou.
Regarding claim 35, Zhou teaches the recited "the first cell source is ... healthy cells" at least with the cell source of cfDNA being derived from "healthy cells" ([39]).
Regarding claim 36, Zhou teaches the recited "the first biological sample comprises ... plasma" as shown in Figure 1.
Regarding claim 38, Zhou teaches the recited "the first cell source is a plurality of cells of a first cancer type and wherein the first cancer type is breast cancer, lung cancer" at least with the cancer type being breast cancer ([156]) or lung cancer ([16]).
Zhou does not teach labeling each methylation state vector in both the first set of methylation state vectors and the second set of methylation state vectors with the corresponding cell source; inputting the labeled methylation state vectors into the classifier; and training the classifier to distinguish between methylation patterns of methylation state vectors associated with the first and second cell sources of claim 138. However, this limitation was known in the art at the time of the effective filing date of the invention, as taught by Khwaja.
Regarding claim 138, Khwaja teaches the recited "labeling each methylation state vector in both the first set of methylation state vectors and the second set of methylation state vectors with the corresponding cell source; inputting the labeled methylation state vectors into the classifier; and training the classifier to distinguish between methylation patterns of methylation state vectors associated with the first and second cell sources" at least with Figures 2 and 3 and with "The proposed system was trained with previously reported data derived from four case groups of cancer cell lines, achieving overall Sensitivity of 88.24%, Specificity of 83.33%, Accuracy of 84.75% and Matthews Correlation Coefficient of 0.687." (Abstract). In Figure 2, Khwaja teaches the proposed Deep Autoencoder framework that includes the methylation state prediction system while Figure 3 of Khwaja depicts labeling methylation states with Label Methylated/Unmethylated step through supervised learning that corresponds to labeling methylation state and training the neural network through supervised learning. In Figure 3, the label methylated of Khwaja corresponds to the recited first methylation state and the label unmethylated corresponds to the recited second methylation state. Overall, Figure 3 of Khwaja depicts labeling methylation states and training the neural network.
Claims 32-33 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou (US 2020/0131582 A1, priority 2016; "Notice of References Cited" form 892 mailed 03/30/2023) in view of Khwaja (A deep autoencoder system for differentiation of cancer types based on DNA methylation state. arXiv preprint arXiv:1810.01243; published 2018 Oct 2; cited on the 09/11/2025 "Notice of References Cited" form 892) and Bisarro dos Reis ("Prognostic classifier based on genome-wide DNA methylation profiling in well-differentiated thyroid tumors." The Journal of Clinical Endocrinology & Metabolism 102.11 (2017): 4089-4099.; cited on the attached "Notice of References Cited" form 892); as applied to claims 1-2, 4-5, 8-9, 12-13, 15-17, 19, 29, 35-36, 38 and 138 above, and in further view of Hackenberg (BMC Bioinformatics 7, 446, published 2006; "Notice of References Cited" form 892 mailed 3/30/2023).
Zhou, Khwaja and Bisarro dos Reis are applied to claims 1-2, 4-5, 8-9, 12-13, 15-17, 19, 29, 35-36, 38 and 138 as discussed above.
Regarding claim 32, Zhou teaches the method of claim 1. Zhou fails to teach the recited "constructing... using... and deeming..."
Hackenberg teaches the recited "constructing ... a negative binomial distribution assumption using the count of each methylation site and the corresponding reference frequency of each methylation site in the first reference set" at least with "the negative binomial distribution (also known as Pascal or Pólya distribution) can be conveniently tailored to the requirements of CpG clusters" (p. 10, col. 1, last par.). Hackenberg teaches the CpG island is identified from "CpG content" (count) and "frequency" of CpG dinucleotides (page 5, right column, third paragraph), therefore these considerations are used when constructing the negative binomial distribution.
Hackenberg teaches the recited "using ... the negative binomial distribution assumption to form a cumulative density function across a range of calculated first cell source fractions" at least by using the negative binomial distribution to obtain "the cumulative density function of the CpG cluster at point nf" (p. 10, last par.).
Hackenberg teaches the recited "deeming the first instance of the first cell source fraction to be a mean of the cumulative density function across the range of calculated first cell source fractions" at least by determining instances where CpG sites are at or above a "p-value ... threshold" (p. 11, first par.) which would be used to analyze instances of cell source fractions when combined with Zhou.
Regarding claim 33, Zhou teaches the method of claim 1. Zhou fails to teach the recited "constructing... using... and deeming..."
Hackenberg teaches the recited "constructing ... a negative binomial distribution assumption using the count for each methylation site and the corresponding reference frequency of the methylation site in the first reference set" at least with "the negative binomial distribution (also known as Pascal or Pólya distribution) can be conveniently tailored to the requirements of CpG clusters" (p. 10, col. 1, last par.). Hackenberg teaches the CpG island is identified from "CpG content" (count) and "frequency" of CpG dinucleotides (page 5, right column, third paragraph), therefore these considerations are used when constructing the negative binomial distribution. The recited "constructing a plurality of Poisson models or a plurality of negative binomial distribution assumptions" would have been made obvious to a PHOSITA combining the methods of Zhou with the rationale below.
Hackenberg teaches the recited "using each ... negative binomial distribution assumption to form a corresponding cumulative density function across a range of calculated first cell source fractions" at least by using the negative binomial distribution to obtain "the cumulative density function of the CpG cluster at point nf" (p. 10, last par.).
Hackenberg teaches the recited "deeming the first instance of the first cell source fraction to be the mean of the cumulative density function across the range of calculated first cell source fractions combined across ... the plurality of negative binomial distribution assumptions" at least by determining instances where CpG sites are at or above a "p-value ... threshold" (p. 11, first par.) which would be used to analyze instances of cell source fractions when combined with Zhou.
Motivation to Combine References
Zhou teaches identifying CpG islands ([164]) when low sequencing coverage is used when performing whole genome sequencing. CpG islands and CpG clusters make up about one half of all CpG sites of the data gathered from microarrays ([164]) and is used in one embodiment of Zhou’s invention to determine potential cancer types based on estimation of methylation levels for each CpG cluster ([31]). The reliance on low coverage whole genome sequencing to measure sufficient CpG clusters to calculate methylation values requires Zhou to accurately identify said clusters using specified criteria ([164]) to include “the cluster is reasonably sized, so that there are sufficient CpG sites to calculate the methylation values” ([164]). One of ordinary skill in the art would need to inquire from a secondary source what “reasonably sized” is. Hackenberg sets forth criteria and strategies for CpG island identification from sequencing data. One strategy includes calculating median distances between neighboring CpG to precisely define CpG islands (page 3, paragraph 2) by comparing p-value cutoffs between sites gathered from a negative binomial distribution and cumulative density function. It would have been prima facie obvious for a PHOSITA before the effective filing date of the instant application, attempting to implement the method set forth by Zhou to have included the CpG island identification techniques taught by Hackenberg in order to increase CpG cluster identification accuracy, with a reasonable expectation of success.
Response to 35 USC §103 Arguments, filed 02/10/2026
Applicant amended claim 1.
Applicant’s remarks, see pages 19-23, filed 02/10/2026, with respect to the rejection(s) of claim(s) 1-2, 4-5, 8-9, 12-13, 15-17, 19, 29, 32-33, 35-36, 38 and 138 under 35 USC § 103 have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground of rejection is made in view of claim amendments.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.K./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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