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 .
Claim Status
Claims 1-15, 30-36, 47-48, and 51 are pending and under examination.
Claims 1-15, 30-36, 47-48, and 51 are rejected.
Claims 1 and 51 are independent.
Claims 16-29, 37-46, 49-50, and 52 are canceled.
No claims are allowed, new, or withdrawn.
Claim 1, 10, 15, 32, 34, 36, and 51 are objected to.
Office Action Outline
Rejections applied
Abbreviations
x
112/b Indefiniteness
PHOSITA
"a Person Having Ordinary Skill In The Art before the effective filing date of the claimed invention"
112/b "Means for"
BRI
Broadest Reasonable Interpretation
112/a Enablement,
Written description
CRM
"Computer-Readable Media" and equivalent language
112 Other
IDS
Information Disclosure Statement
x
102, 103
JE
Judicial Exception
x
101 JE(s)
112/a
35 USC 112(a) and similarly for 112/b, etc.
101 Other
N:N
page:line
Double Patenting
MM/DD/YYYY
date format
Priority
As detailed in the 05/24/2023 filing receipt, this application claims priority to as early as 02/17/2022, the filing date of U.S. Provisional Application 63/311,402. At this point in examination, all claims have been interpreted as being accorded this priority date.
Claim Objections
Claims 1, 10, 32-34, 36, and 51 are objected to because of the following informalities:
Claims 1, 10, 32, and 51 each recite a grammatical error in the phrase "a count ...that include..." which should be corrected to "a count ...that includes..."
Similar to above, claims 34 and 36 each recite a grammatical error in the phrase "a second count ...that include..." which should be corrected to "a second count ...that includes..."
Claim 33 appears to have a grammatical issue and should be amended to :"...wherein the cfDNA sample is used [[to]] in performing..."
Appropriate correction is required.
Claim Interpretation
Claim 34 is interpreted to recite a contingency clause in the limitation: "in response to determining that the confidence score is below a confidence threshold."
It is noted claim 36 recites somewhat similar elements as claim 34, however, claim 36 is not interpreted to include a contingency clause because there is a step in claim 36 in which the tumor fraction prediction of the liquid biopsy sample has been determined to be below a threshold signal.
There are embodiments of claim 34, and its dependent claim 35, in which the contingent limitations are not performed, therefore, those steps are not required by the claims (MPEP 2111.04(II) pertains). However, with compact prosecution in mind, the claims have been examined with the understanding that the claims may be amended to change the contingent claim language.
Claim Interpretation – 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are as follows:
Instant claim limitations interpreted as PROPERLY invoking 112(f):
The following claim limitation are interpreted as properly invoking 112(f):
• "methylation sub-model is configured to," in claim 15, recites means (or an equivalent nonce term, here a "model") and function and/or result (here "to calculate a likelihood of observing the count of methylation sequence reads based on the count of methylation sequence reads") without reciting steps or structure to prevent invoking. The specification does disclose sufficient structure, material, or acts, and not just desired results, as exemplified at [0184-0185], and as such, is definite under 112(b) as properly invoking 112(f). MPEP 2181.III-IV pertain.
• The "model is configured to," in claim 32, recites means (or an equivalent nonce term, here a "model") and function and/or result (here "to predict a tumor fraction prediction based on counts of reads for the filtered variants in a given sample") without reciting steps or structure to prevent invoking. The specification does disclose sufficient structure, material, or acts, and not just desired results, as exemplified at [0182], and as such, is definite under 112(b) as properly invoking 112(f). MPEP 2181.III-IV pertain.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15, 30-36, 47-48, and 51 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims depending from rejected claims are rejected similarly, unless otherwise noted, and any amendments in response to the following rejections should be applied throughout the claims, as appropriate.
The "filtering" step of claim 1, includes the recitation "the bank comprising reads generated from non-cancer cfDNA samples and biopsy samples..." It is not clear if the term "non-cancer" should apply to both cfDNA samples and biopsy samples. For examination purposes, the recitation will be interpreted as including reads generated from non-cancer cfDNA samples and from any type (cancer or non-cancer) of biopsy sample. Amending to recite "the bank comprising reads generated from non-cancer cfDNA samples and from biopsy samples..." would likely clarify the claim language.
In claim 11, the relationship is unclear between "a first binary value," "a second binary value," and a/the "particular methylation site." The claim is ambiguous regarding if one or both binary values encode the methylation status at a particular CpG site. Possibly amending the following limitation to recite "; wherein a first binary value at a particular CpG site represents methylation is observed[[,]]; wherein a second binary value at the particular CPG site represents unmethylation is observed" may help overcome the rejection. For examination purposes, the limitation will be interpreted as suggested to amend.
In claim 48, the relationship is unclear between the three elements of: "evaluating treatment comprises one or more of," "determining the treatment to be effective...," and "determining the treatment to be ineffective..." (emphasis added). This is because it is considered the treatment would either be effective or ineffective, but not both, which would be included in "or more of," bolded above. Possibly amending to recite "evaluating treatment comprises one " would help in overcoming the rejection. For compact prosecution, the claim will be interpreted as suggested to amend, with the expectation Applicant will explain or amend appropriately.
In the second element of claim 51, (in which treatment is determined to be ineffective in response to determining the tumor fraction prediction is substantially equal to or greater than an initial tumor fraction prediction) the recited "substantially" in the phrase "substantially equal to" is a term of relative or vague degree or form of association, neither defined in the specification ([0059]) nor having a well-known and sufficiently particular definition in the art and in the instant context. The first element of claim 51 determines treatment to be effective in response to determining the tumor fraction prediction is smaller than an initial tumor fraction prediction; taking the first element into consideration adds to the indefiniteness issue, as the term "substantially" in "substantially equal to" includes embodiments where the tumor fraction prediction is lower (smaller) than the initial tumor fraction prediction. MPEP 2173.05(b) pertains. (Emphasis added.)
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 1-15, 30-36, 47-48, and 51 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 details the following framework to analyze Subject Matter Eligibility:
• Step 1: Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? (see MPEP § 2106.03)
• Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. an abstract idea, a law of nature, or a natural phenomenon? (see MPEP § 2106.04(a), 2106.04(a)(2) & 2106.04(b)).
• Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application? (see MPEP § 2106.04(d))
• Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? (see MPEP § 2106.05)
Step 1:
Claims 1-15, 30-36, 47-48 are directed to a 101 process, here a method. Claim 51 is directed to a 101 machine or manufacture, here a non-transitory computer-readable medium (CRM). As such, claims 1-15, 30-36, 47-48, and 51 are directed to a related method and CRM, which fall under categories of statutory subject matter. (See MPEP § 2106.03). (Step 1: Yes.)
Step 2A, Prong One:
The claims are found to recite abstract ideas in the form of mental processes and mathematical concepts, as well as a law of nature, as follows:
Mental processes and mathematical concepts recited in the claims:
• dividing a dataset into variants (claims 1, 34, 36, and 51)
• filtering variants based on a bank of reference sequence reads to generate filtered subsets of variants (claims 1, 34, 36, and 51)
• determining, for each variant, a methylation read count that include the variant (claims 1, 34, 36, and 51)
• inputting the counts into a model trained on recurrence rates of the variants (claims 1, 34, 36, and 51)
• generating, using the model, a tumor fraction prediction (claims 1, 34, 36, and 51)
• determining recurrence rates based on the reference sequence reads (claim 2)
• filtering out variants whose presence rate in non-cancer samples exceeds a threshold (claim 3)
• the recurrence rate corresponds to rate of observation of the variant among bank reads (claim 4)
• the tumor fraction prediction is respectively a distribution and a fraction (claims 5 and 6)
• the model is a probabilistic model comprising a Poisson distribution (claim 7)
• the model comprises probabilistic distributions (claims 8 and 9)
• the count for each variant of the filtered subset (claim 10)
• a particular variant that comprises contiguous CpG sites is encoded by a series of binary values, each value corresponding to observed or unobserved methylation (claim 11)
• the tumor fraction prediction comprises fractions for a subset of tissues, and each fraction represent a percentage of fragments from each tissue in the subset (claims 12 and 13)
• the model is a binomial mixture model, comprising methylation sub-models *claims 14 and 15)
• the model is a machine learned model; which is a constant model, a binomial model, an independent site model, a neural network model, or a Markov model (claim 30 and 31)
• identifying, for each variant, a count of reads that include the variant (claim 32)
• determining a recurrence rate for cancer and non-cancer based on variant counts (claim 32)
• training the model with the recurrence rates for cancer and non cancer (claim 32)
• determining a confidence score of the tumor fraction prediction (claim 34)
• determining the confidence score is below a threshold (claim 34)
• determining the tumor fraction prediction is below a threshold signal (claim 36)
• evaluating the treatment based on tumor fraction prediction (claim 47)
• determining the treatment to be effective, or ineffective, in response to determining that the tumor fraction prediction of the cfDNA sample collected after beginning the treatment is respectively smaller, or equal to or greater, than an initial tumor fraction prediction of an initial cfDNA sample collected before beginning the treatment (claim 48)
Law of nature recited in the claims:
• Claim 32 recites a law of nature in the correlation between the naturally occurring variants in the subject and cancer in the subject.
Step 2A Prong One Summary: When considering the broadest reasonable interpretation (BRI) of the claims, the mental processes recited in independent claims 1 and 51 (e.g., "dividing a dataset;" "filtering variants;" "determining a count of methylation sequence reads;" etc.) are directed to processes that may be performed in the human mind, or with pen and paper, as there are no particular limitations recited in claims 1 and 51 which would prevent the mental processes from being performed in the human mind or with pen and paper. The claims recite inherent mathematical processes in e.g., filtering variants based on the bank, determining methylation counts, determining recurrence rates, the probabilistic model comprising a Poisson distribution; training the machine learning model, etc. While details of the mathematical concepts are not explicitly shown in the claims, they are discussed throughout the Specification, e.g., at [0168, 0175-0187], etc. Such analysis performed mentally, or with paper and pencil, may take considerable time and effort, and although a general-purpose computer can perform these calculations at a rate and accuracy that can far exceed the mental performance of a skilled artisan, the nature of the activity is essentially the same, and therefore constitutes an abstract idea. Additionally, the claims recite a law of nature in the correlation between the naturally occurring variants in the subject and cancer in the subject. Therefore, the claims recite elements that constitute a judicial exception in the form of an abstract idea(s) and law of nature. (Step 2A, Prong One: Yes.)
Step 2A, Prong Two:
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs). Here at Step 2A, Prong Two, any remaining steps and/or elements not identified as JEs are therefore in addition to the identified JE(s), and are considered additional elements. Because the claims have been interpreted as being directed to judicial exceptions (abstract ideas in this instance) then Step 2A, Prong Two provides that the claims be examined further to determine whether the judicial exception is integrated into a practical application [see MPEP § 2106.04(d)]. A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
MPEP § 2106.04(d)(I) lists the following five example considerations for evaluating whether a judicial exception is integrated into a practical application:
(1) An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a).
(2) Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2).
(3) Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b).
(4) Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c).
(5) Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
The claims recite additional elements as follows:
Additional elements of data gathering, inputting, and outputting steps: Receiving data (claims 1, 34, 36, and 51); the cfDNA liquid biopsy sample (claims 1, 33-34, 36, 47, and 51); collecting the cfDNA sample (claim 47); the cfDNA sample is used in cancer surveillance and early cancer screening (claim 33); sequencing a tissue sample (claims 34 and 36); returning data (interpreted as outputting data, claim 35). Data gathering steps are additional elements which perform functions of inputting, collecting, and outputting the data needed to carry out the abstract idea. These steps are considered insignificant extra-solution activity, and are not sufficient to integrate an abstract idea into a practical application as they do not impose any meaningful limitation on the abstract idea or how it is performed, nor do they provide an improvement to technology (see MPEP § 2106.04(d)(I)).
Additional elements of computer components: Claim 1 recites a computer. Claim 51 recites a non-transitory computer readable medium and processors. The claims require only generic computer components, which do not improve computer technology, and do not integrate the recited judicial exception into a practical application (see MPEP § 2106.04(d)(1) and MPEP § 2106.05(f)).
Step 2A Prong Two summary: The claims have been further analyzed with respect to Step 2A, Prong Two, and no additional elements have been found, alone or in combination, that would integrate the judicial exception into a practical application. At this point in examination, it is not yet the case that any of the Step 2A Prong Two considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of: (1) an improvement, (2) a treatment, (3) a particular machine, or (4) a transformation is clear in the record. For example, regarding the first consideration for improvement at MPEP 2106.04(d)(1), the record, including the Specification, does not yet clearly disclose an explanation of improvement over the previous state of the technology field, and the claims do not yet clearly result in such an improvement. (Step 2A, Prong Two: No). (Step 2A, Prong Two: No).
Step 2B analysis:
Because the additional claim elements do not integrate the abstract idea or law of nature into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept. An inventive concept is furnished by an element or combination of elements that is recited in the claim in addition to the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself (see MPEP § 2106.05).
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 are well-understood, routine, and conventional. Those additional elements are as follows:
Additional elements of data gathering, inputting, and outputting steps: The additional elements of receiving data (claims 1, 34, 36, and 51); the cfDNA liquid biopsy sample (claims 1, 33-34, 36, 47, and 51); collecting the cfDNA sample (claim 47); the cfDNA sample is used in cancer surveillance and early cancer screening (claim 33); sequencing a tissue sample (claims 34 and 36); and returning data (interpreted as outputting data, claim 35) do not cause the claims to rise to the level of significantly more than the judicial exception. The courts have recognized receiving or transmitting data over a network; storing and retrieving information in memory; determining the level of a biomarker in blood by any means; using polymerase chain reaction to amplify and detect DNA; detecting DNA or enzymes in a sample; analyzing DNA to provide sequence information or detect allelic variants; and amplifying and sequencing nucleic acid sequences, [see MPEP§2106.05(d)(II)], as well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as extra-solution activity.
Additionally, Elazezy (Computational and structural biotechnology journal, vol. 16, pages 370-378 (2018); cited on the attached form PTO-892) shows the elements of cfDNA used in cancer surveillance and management, as well as sequencing of tissue samples, to be well-understood, routine, and conventional as follows: Elazezy presents a review on use of circulating tumor DNA as a liquid biopsy component in cancer management. Elazezy shows cfDNA liquid biopsy (entire document), the collecting of cfDNA liquid biopsy samples (p.375, col.2, ¶ 4); the use of cfDNA samples in cancer monitoring (surveillance) and early cancer screening (p.372-374); and the sequencing of a tissue sample (p.371, col.1).
Additional elements of computer components: The additional elements of a computer (claim 1), a non-transitory computer readable medium and processors (claim 51).do not cause the claims to rise to the level of significantly more than the judicial exception, and as such do not provide an inventive concept; these are conventional computer components.
All limitations of claims 1-15, 30-36, 47-48, and 51 have been analyzed with respect to Step 2B, and none provides a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception, and thus do not transform the judicial exception into a patent eligible application of the exceptions. Step2B: NO.
Therefore, the claims, when the limitations are considered individually and as a whole, are rejected under 35 U.S.C. § 101 as being directed to non patent-eligible subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1-10, 12-15, 30-33, and 51 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kang (Genome biology, vol. 18(1):53, pages 1-12 (2017); cited on the 08/21/2026 IDS).
The claims include the following elements for generating a tumor fraction prediction from a cfDNA sample of a subject:
• receiving, and dividing into variants comprising a methylation pattern over CpG site(s), a dataset of methylation reads from the cfDNA subject sample (claims 1 and 51)
• filtering the variants based on a bank of reference sequence reads to generate a filtered subset of variants (claims 1 and 51)
• the bank comprises reads generated from non-cancer cfDNA samples and biopsy samples (claims 1 and 51)
• for each variant, determining a count of methylation sequence reads that includes the variant (claims 1 and 51)
• inputting the counts to a model trained based on recurrence rates of the variants (claims 1 and 51)
• generating the tumor fraction prediction (claims 1 and 51)
• the recurrence rates of the plurality of variants are determined based on, or corresponding to, the reference sequence reads in the bank (claims 2 and 4)
• filtering out one or more variants whose rates of presence in the non-cancer samples exceeds a threshold (claim 3)
• a count of methylation sequence reads of the cfDNA sample includes the methylation pattern over the one or more CpG sites of the variant (claim 10)
Regarding the elements listed directly above, Kang teaches a probabilistic method, CancerLocator, which infers the proportion and tissue of origin of ctDNA in a blood sample using genome-wide DNA methylation data (entire document). Kang teaches measuring the cfDNA methylation profile from a patient's plasma cfDNA, and selecting features for the model using data (i.e., a bank) from healthy plasma (cfDNA) data and TCGA (The Cancer Genome Atlas) tumor data (p.2, fig.1, and entire document). Kang teaches randomly choosing 75% of solid tumor samples and healthy plasma cfDNA samples as a training set to learn features (p.5, col.1, ¶ 2). Kang teaches sequencing to obtain reads from the patient cfDNA (bridging p.7-8); grouping the CpG sites into CpG clusters in order to use more mappable reads (p.8, col.1, ¶ 1); aligning reads to HG19 and counting the numbers of methylated and unmethylated cytosines for each CpG site (p.8, col.1, ¶ 3). Kang teaches for each CpG cluster, the methylation range (MR) is used to indicate a feature’s differential power between classes (i.e., healthy plasma or each tumor type); CpG clusters were selected whose MRs were not lower than a threshold (p.8, bridging cols.1-2). Kang teaches model outputs comprise a simulated methylation sequencing profile of a plasma sample, represented by the integer vectors (p.9, col.2, ¶ 5).
Regarding the tumor fraction prediction is a distribution of probability of a fraction, or is a fraction of, fragments in the cfDNA sample that are tumor derived (claims 5 and 6), Kang teaches a distribution of fractions of predicted ctDNA percentage (p.4, fig.3).
Regarding the probabilistic model comprising a Poisson distribution, weighted by recurrence rate (claim 7), Kang teaches the Poisson distribution nk ~ Poisson(ZBk). Bk is the adjusted CpG dinucleotide bias (p.10, col.1, ¶ 4).
Regarding the site specific noise rate of a variant and sequencing depth of the variant (claim 8) and the depth of the cfDNA sample (claim 9), Kang teaches bk is the background probability for a CpG dinucleotide to be aligned to CpG cluster k;...this reflects the read-depth bias introduced during the sequencing process and read alignment and the density of CpG sites in the clusters (p.9, col.2, ¶ 4).
Regarding the tumor fraction prediction comprises a plurality of fractions for a subset of tissues (claim 12), and each fraction represents a percentage derived from each tissue of the subset of tissues (claim 13), Kang teaches identifying informative features of normal plasma and multiple tumor types from the TCGA database; selecting CpG clusters as features if their methylation range (MR) is sufficiently large; then using the selected features and their beta distributions to deconvolute a patient’s plasma cfDNA into the normal plasma cfDNA distribution and (possibly) a solid tumor DNA distribution. (p.2, fig.1; p.3, col.1, ¶ 2-3; and fig.2). Kang teaches relationship between ctDNA burden (in percent) and tumor tissue prediction for each plasma sample of the real data (p.6, fig.5).
Regarding the binomial mixture model (claim 14), and the methylation sub-models to calculate a likelihood of observing the methylation sequence read count based on the count of methylation reads (claim 15), the machine-learned model is a binomial model (claim 30 and 31); Kang shows each CpG cluster k, mk is modeled by a binomial distribution (p.9, col.1, ¶1). Kang shows given the methylation sequencing profile of a patient’s plasma cfDNA sample, the vectors M and N (respectively, the number of methylated cytosines and the total number of cytosines mapped), they found the maximum-likelihood estimate of two model parameters: a sample’s cfDNA tumor burden Θ and its source tumor type t. For integrating the mixture models of multiple markers into the formulation, all features or markers were assumed independent of each other (p.9, col.1, ¶ 2-3).
Regarding training the machine learned model by identifying a count of reads for each variant for each reference sample in the bank including non-cancer and biopsy samples; determining a recurrence rate for both non-cancer and cancer respectively based on counts of reads in the non-cancer samples and in the biopsy samples; and training the model with the recurrence rates (claim 32), Kang shows randomly choosing 75% of solid tumor samples and
healthy plasma cfDNA samples as a training set to learn features (p.2, fig.1; p.5, col.1).
Regarding early cancer screening (claim 33), Kang teaches using CpG clusters to detect cancer; predict ctDNA fraction, tumor type, and location; and differentiate tumor types and healthy plasma samples (p.2, fig.1; and entire document).
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
103-1 (of 2):
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Kang as applied to claims 1-10, 12-15, 30-33, and 51 above, and further in view of Li-2021, (Li et al.; Briefings in bioinformatics, vol. 22(6):bbab250, pages 1-11 (2021); cited on the 08/21/2026 IDS).
Kang does not show the variant comprising contiguous CpG sites is encoded by first and second binary values respectively representing methylation or unmethylation at a CpG site (claim 11). (Taught by Li-2021)
Regarding the variant comprising contiguous CpG sites is encoded by first and second binary values respectively representing methylation or unmethylation at a CpG site (claim 11), Li-2021 shows each base of a unified read was encoded into a one-hot matrix according to the nucleobase, and the methylation state of the base was also encoded, where 1 presents methylated and 0 presents unmethylated (p.4, col.2, ¶ 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of analyzing methylation fractions of cfDNA in cancer prediction of Kang, to include the binary encoding methods of Li-2021. This is because Li-2021 provides motivation to modify by showing binary encoding in converting reads with three or more CpG sites is an element which results in a valid and comprehensive deep learning model (p.4, col.2, ¶ 2). Further, one of ordinary skill would have had a reasonable expectation of success in modifying Kang with Li-2021, as Kang and Li-2021 are generally drawn to related teaching of analysis of methylation in cfDNA concerning cancer detection. As such, the modification would have been obvious.
103-2 (of 2):
Claims 34-36 and 47-48 are rejected under 35 U.S.C. 103 as being unpatentable over Kang as applied to claims 1-10, 12-15, 30-32, and 51 above, and further in view of Li-2018, (Li et al.; Nucleic acids research, vol. 46(15):e89-e89, pages 1-11 (2018); cited on the attached form PTO-892).
Regarding determining, and returning, a confidence score of the tumor fraction prediction (claims 34 and 35), Kang shows the error rate measure was used for assessing the classification performance (p.4, col.2) and the CancerLocator method obtains a low error rate of 0.265 for the six-class prediction problem (p.6, col.1, ¶1).
Claims 34-36 recite the following elements:
• determining the tumor fraction prediction of the liquid biopsy sample is below a threshold (claim 36)
• sequencing a tissue sample (claim 34 and 36)
• receiving and dividing a second dataset into a second plurality of variant (claim 34 and 36)
• filtering the second plurality of variants based on the bank of sequence reads to generate a second filtered subset of variants (claim 34 and 36)
• determining, and inputting to the model, a second count of methylation sequence reads for each variant (claim 34 and 36)
• generating a second tumor fraction prediction of the...sample (claim 34 and 36)
• returning the tumor fraction prediction (claim 35)
Regarding the elements listed directly above, Kang shows a probabilistic method, CancerLocator, which infers the proportion and tissue of origin of ctDNA in a blood sample using genome-wide DNA methylation data (entire document). Kang shows measuring the cfDNA methylation profile from a patient's plasma cfDNA, and selecting features for the model using data (i.e., a bank) from healthy plasma (cfDNA) data and TCGA (The Cancer Genome Atlas) tumor data (p.2, fig.1, and entire document). Kang shows determining second counts and generating second tumor fraction predictions (p,3, fig. 2). Kang shows collecting a large set of public methylation (sequence) data of solid tumors and plasma cfDNA samples taken from both healthy people and cancer patients (p.7, col.2, ¶ 1). Kang shows sequencing to obtain reads from the patient cfDNA (bridging p.7-8); grouping the CpG sites into CpG clusters in order to use more mappable reads (p.8, col.1, ¶ 1); aligning reads to HG19 and counting the numbers of methylated and unmethylated cytosines for each CpG site (p.8, col.1, ¶ 3). Kang shows for each CpG cluster, the methylation range (MR) is used to indicate a feature’s differential power between classes (i.e., healthy plasma or each tumor type); CpG clusters were selected whose MRs were not lower than a threshold (p.8, bridging cols.1-2). Kang shows model outputs comprise a simulated methylation sequencing profile of a plasma sample, represented by the integer vectors (p.9, col.2, ¶ 5)
Kang does not show sequencing a sample after beginning treatment of claims 34 and 36 (shown by Li-2018).
Kang does not show cfDNA sample is collected after beginning treatment, and evaluating treatment based on the tumor fraction prediction of claim 47, and determining the treatment to be effective or ineffective or ineffective in response to determining the tumor fraction prediction of claim 48 (shown by Li-2018 below).
Regarding sequencing a sample after beginning treatment (claims 34 and 36), the cfDNA sample is collected after beginning treatment, and evaluating treatment based on the tumor fraction prediction (claim 47), and determining the treatment to be effective or ineffective in response to determining the tumor fraction prediction of the cfDNA sample collected after beginning treatment is respectively smaller or larger than an initial tumor fraction prediction of an initial cfDNA sample collected before beginning treatment (claim 48), Li-2018 shows the method "CancerDetector" can also be used for monitoring the cancer progression and treatment, using two cancer liver patients whose plasma samples were obtained before surgical tumor resection and at multiple time points after the surgery. The first patient survived beyond 12 months, while the second patient died of metastatic disease after the operation. As shown in Figure 6, the predicted blood tumor fractions are consistent with the treatment effects: the first patient’s tumor fractions quickly fall into the normal range (i.e., effective treatment); while those of the second retain relatively high values after the surgery (i.e., ineffective treatment) (p.8, col.1, ¶ 3; and p.8, fig.6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of analyzing methylation fractions of cfDNA in cancer prediction of Kang, to include the monitoring and evaluating cancer treatment of Li-2018. This is because Li-2018 provides motivation to modify Kang (to include evaluating and monitoring cancer treatment) in that Li-2018 shows analysis of longitudinal data with the CancerDetector method provides valuable information in predicted fractions which are consistent with treatment effectiveness. Further, one of ordinary skill would have had a reasonable expectation of success in modifying Kang with Li-2018, as Kang and Li-2018 are generally drawn to related teaching of analysis of methylation in cfDNA in clinical cancer genomics. As such, the modification would have been obvious.
Conclusion
No claims are allowed.
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/M.A.V./Examiner, Art Unit 1687
/G. STEVEN VANNI/Primary patents examiner, Art Unit 1686
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