DETAILED ACTION
The Applicant’s response, received 06 November 2025 has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Applicant's submission filed on 11 September 2025 and supplemental amendment received 06 November 2025 have been entered.
Status of the Claims
Claims 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, 33, and 35-38 are pending.
Claims 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, 33, and 35-38 are rejected.
Priority
The effective filing date of the claimed invention is 24 January 2019.
Claim Objections
The objection to claim 11 in the Office action mailed 11 July 2025 is withdrawn in view of the amendment received 06 November 2025.
Claim Rejections - 35 USC § 112
The rejection of claims 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, and 33 under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, in the Office action mailed 11 July 2025 is withdrawn in view of the amendment received 06 November 2025.
The amendment received 06 November 2025 has been fully considered, however after further consideration, new grounds of rejection are raised under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, in view of the amendment.
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 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, 33, and 35-38 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the reference tissue" in line fifteen. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-4, 6-14, 16, 17, and 36 are indefinite for depending from claim 1 and for failing to remedy the indefiniteness of claim 1.
Claim 19 recites the limitation "the reference tissue" in line twelve. There is insufficient antecedent basis for this limitation in the claim.
Claims 21-23, 25, 26, 37, and 38 are indefinite for depending from claim 19 and for failing to remedy the indefiniteness of claim 19.
Claim 27 recites the limitation "the reference tissue" in line twelve. There is insufficient antecedent basis for this limitation in the claim.
Claims 29-31, 33, and 35 are indefinite for depending from claim 27 and for failing to remedy the indefiniteness of claim 27.
Claim Rejections - 35 USC § 101
The amendment received 06 November 2025 has been fully considered, however after further consideration, the rejection of claims 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, and 33 under 35 U.S.C. 101 in the Office action mailed 11 July 2025 is maintained with modification in view of the amendment.
Claims 35-38 are newly rejected in view of these claims being newly added to the amendment received 06 November 2025.
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 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, 33, and 35-38 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a law of nature without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion); and (c) a law of nature (e.g., naturally occurring relationships).
Claim Interpretation
Independent claims 1, 19, and 27 recite “wherein cfDNA signals from control individuals….” This limitation is interpreted as a product-by-process limitation, with the product being the data of the cfDNA signals, and further interpreted to not require the process of performing the active steps to produce the product.
Independent claims 1, 19, and 27 recite “a set of known genome-wide copy number coverage signals of healthy subjects….” This limitation is interpreted as a product-by-process limitation, with the product being the data of the set of known genome-wide copy number coverage signals of healthy subjects, and further interpreted to not require the process of performing the active steps to produce the product.
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-4, 6-14, 16, 17, and 36 are directed to a method (i.e., a process) of analyzing cell free (cfDNA) in a biological fluid sample; claims 19 and 21-23, 25, 26, 37, and 38 are directed to a method (i.e., a process) of monitoring disease progress in a subject; and claims 27, 29-31, 33, and 35 are directed to a method (i.e., a process) of monitoring tissue and organ health in a subject.
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
generating a genome-wide cfDNA copy number profile based on the WGS assay, wherein the genome-wide cfDNA copy number profile comprises a plurality of copy number coverage signals, each copy number coverage signal corresponding to a cfDNA fragment (i.e., mental processes and mathematical concepts);
generating a plurality of baseline reference profiles using supervised or unsupervised machine learning algorithms, wherein cfDNA signals from control individuals are decomposed and the reference tissue or cell specific profiles are extracted, thereby generating a plurality of baseline reference profiles (i.e., mental processes and mathematical concepts);
removing the plurality of baseline reference profiles from the genome-wide cfDNA copy number profile, wherein each of the plurality of baseline reference profiles corresponds to a particular cell type or tissue type (i.e., mental processes and mathematical concepts); and
determining cell damage, tissue damage, or organ damage based at least in part on a comparison of the genome-wide cfDNA copy number profile to a set of known genome-wide cfDNA signatures (i.e., mental processes and mathematical concepts); and
wherein the comparison is conducted by an unsupervised machine learning model or a supervised machine learning model (i.e., mental processes and mathematical concepts);
wherein the unsupervised machine learning model comprises non-negative matrix factorization (i.e., mental processes and mathematical concepts);
wherein the supervised machine learning model is trained on isolated tissue samples (i.e., mental processes and mathematical concepts); and
wherein a difference of copy number signal in the cfDNA copy number profile based on the WGS assay compared to the known genome-wide cfDNA signatures correlates to a condition in the subject related to cell, tissue, or organ damage (i.e., mental processes and mathematical concepts).
Independent claim 19 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
generating a genome-wide cfDNA profile based on the WGS assay comprising a plurality of copy number coverage signals, each copy number coverage signal corresponding to a cfDNA fragment of a specific cell type or tissue type (i.e., mental processes and mathematical concepts);
generating a plurality of baseline reference profiles using supervised or unsupervised machine learning algorithms, wherein cfDNA signals from control individuals are decomposed and the reference tissue or cell specific profiles are extracted, thereby generating a plurality of baseline reference profiles (i.e., mental processes and mathematical concepts);
removing the plurality of baseline reference profiles from the genome-wide cfDNA profile, wherein each of the plurality of baseline reference profiles corresponds to a particular cell type or tissue type (i.e., mental processes and mathematical concepts);
determining a disease progress in the subject based on a difference of copy number coverage signals in the sample compared to a set of known genome-wide copy number coverage signals of healthy subjects (i.e., mental processes and mathematical concepts);
wherein the comparing the difference of copy number coverage signals in the sample to the set of known genome-wide copy number coverage signals of healthy subjects is conducted by an unsupervised machine learning model or a supervised machine learning model (i.e., mental processes and mathematical concepts);
wherein the unsupervised machine learning model comprises non-negative matrix factorization (i.e., mental processes and mathematical concepts);
wherein the supervised machine learning model comprises a deep neural network (i.e., mental processes and mathematical concepts); and
wherein a difference of copy number signal in the cfDNA copy number profile based on the WGS assay compared to the copy number coverage signals of healthy subjects correlates to a disease progress in the subject (i.e., mental processes and mathematical concepts).
Independent claim 27 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
generating a genome-wide cfDNA profile based on the WGS assay comprising a plurality of copy number coverage signals, each copy number coverage signal corresponding to a cfDNA fragment of a specific cell type or tissue type (i.e., mental processes and mathematical concepts);
generating a plurality of baseline reference profiles using supervised or unsupervised machine learning algorithms, wherein cfDNA signals from control individuals are decomposed and the reference tissue or cell specific profiles are extracted, thereby generating a plurality of baseline reference profiles (i.e., mental processes and mathematical concepts);
removing the plurality of baseline reference profiles from the genome-wide cfDNA profile, wherein each of the plurality of baseline reference profiles corresponds to a particular cell type or tissue type (i.e., mental processes and mathematical concepts);
determining organ health in the subject based on a difference of copy number coverage signal in the sample compared to a set of known genome-wide copy number coverage signals of healthy subjects (i.e., mental processes and mathematical concepts); and
wherein the comparing the difference of copy number coverage signal in the sample compared to a set of known genome-wide copy number coverage signals of healthy subjects is conducted by an unsupervised machine learning model or a supervised machine learning model (i.e., mental processes and mathematical concepts);
wherein the unsupervised machine learning model comprises non-negative matrix factorization (i.e., mental processes and mathematical concepts);
wherein the supervised machine learning model comprises a deep neural network (i.e., mental processes and mathematical concepts); and
wherein a difference of copy number signal in the cfDNA copy number profile based on the WGS assay compared to the known genome-wide copy number coverage signals of healthy subjects correlates to a condition in the subject related to organ health (i.e., mental processes and mathematical concepts).
Independent claim 1 and those claims dependent therefrom, further recite a law of nature by associating genomic data (i.e., a genome-wide cfDNA copy number profile) with phenotypes (i.e., cell damage, tissue damage, or organ damage), i.e., a genotype-phenotype correlation (MPEP 2106.04(b)).
Independent claim 19 and those claims dependent therefrom, further recite a law of nature by associating genomic data (i.e., a genome-wide cfDNA profile) with phenotypes (i.e., disease progress), i.e., a genotype-phenotype correlation (MPEP 2106.04(b)).
Independent claim 27 and those claims dependent therefrom, further recite a law of nature by associating genomic data (i.e., a genome-wide cfDNA copy number profile) with phenotypes (i.e., organ health), i.e., a genotype-phenotype correlation (MPEP 2106.04(b)).
Dependent claims 9, 12, 14, 16, 17, 21, 25, 29, and 35-38 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 9 further recites:
wherein each copy number coverage signal is indicative of a relative contribution of cfDNA from a specific tissue or cell type (i.e., mental processes and mathematical concepts).
Dependent claim 12 further recites:
wherein the genome-wide cfDNA copy number profile is used to quantify amounts of cfDNA from multiple organs for providing an assessment of organ or multi-organ health (i.e., mental processes and mathematical concepts).
Dependent claim 14 further recites:
monitoring organ transplant over time (i.e., mental processes).
Dependent claim 16 further recites:
wherein the genome-wide cfDNA copy number profile is indicative of fetus health (i.e., mental processes).
Dependent claim 17 further recites:
wherein multiple organs are quantified simultaneously by projecting the genome-wide cfDNA copy number profile onto a set of reference cfDNA profiles corresponding to various tissue and cell types (i.e., mental processes and mathematical concepts).
Dependent claim 21 further recites:
comparing the plurality of copy number coverage signals to a profile of known copy number signals of diseased patient samples (i.e., mental processes).
Dependent claim 25 further recites:
wherein the disease is selected from heart failure, lung damage, diabetes, Crohn's disease, or kidney disease (i.e., mental processes).
Dependent claim 29 further recites:
comparing the plurality of copy number coverage signals to a profile of known copy number signals of samples from a patient having poor tissue or organ health (i.e., mental processes).
Dependent claim 35 further recites:
wherein the organ health is multi-organ health (i.e., mental processes).
Dependent claim 36 further recites:
wherein the cell damage, tissue damage, or organ damage comprises liver damage or renal damage (i.e., mental processes).
Dependent claim 37 further recites:
wherein the disease of the disease progress is lupus (i.e., mental processes).
Dependent claim 38 further recites:
wherein the disease of the disease progress is diabetes (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., determining cell damage, tissue damage, or organ damage based at least in part on a comparison of the genome-wide cfDNA copy number profile to a set of known genome-wide cfDNA signatures), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., decomposing data (see para. [0040] in the Specification) and an unsupervised machine learning model comprises non-negative matrix factorization) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. Furthermore, a law of nature correlating a genotype-phenotype association is identified at Eligibility Step 2A Prong One.
Therefore, claims 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, 33, and 35-38 recite an abstract idea and a law of nature.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
Dependent claims 9, 12, 14, 17, 21, 25, 29, and 35-38 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claims 1, 19, and 27 include:
obtaining a biological fluid sample from a subject, the biological fluid sample comprising cfDNA;
extracting cfDNA from the biological fluid sample to provide purified cfDNA, wherein the purified cfDNA comprises a plurality of cfDNA fragments, each fragment corresponding to a specific tissue or cell type;
performing a whole genome sequencing (WGS) assay using the cfDNA fragments; and
a deep neural network.
The limitations that suggest additional elements that are in dependent claims 2, 3, 4, 6, 7, 8, 10, 11, 13, 16, 22, 23, 26, 30, 31, and 33 include:
wherein the biological fluid sample comprises blood, plasma, serum, urine, cerebrospinal fluid, saliva, lymphatic fluid, aqueous humor, vitreous humor, cochlear fluid, tears, milk, sputum, vaginal discharge, or any combination thereof (claims 2, 22, and 30);
wherein performing the WGS assay using the cfDNA fragments comprises sequencing the cfDNA using sequencing-based DNA molecule counting (claims 3, 23, and 31);
enriching cfDNA fragments of interest (claim 4);
extracting comprises size-based enrichment to exclude gDNA and enrich for cfDNA fragments (claim 6);
enriching comprises amplification of the cfDNA (claim 7);
amplification comprises PCR amplification or genome-wide amplification (claim 8);
the tissue type is kidney, muscle, heart, vascular, liver, brain, eye, lung, adipose, gland, bone, bone marrow, cartilage, intestine, stomach, skin, or bladder (claim 10);
displaying an indication of a fraction of cfDNA originating from damaged cells, wherein a cell type of the damaged cells is blood cells, neuron cells, kidney cells, epithelial, extracellular matrix cells, beta cells, or immune cells (claim 11);
the sample is obtained and analyzed periodically from a subject to monitor health over time (claim 13);
the sample is a maternal sample (claim 16); and
amplifying the cfDNA through genome-wide amplification (claims 26 and 33).
The additional elements of obtaining a biological fluid sample from a subject, the biological fluid sample comprising cfDNA (claims 1, 19, and 27); extracting cfDNA from the biological fluid sample to provide purified cfDNA, wherein the purified cfDNA comprises a plurality of cfDNA fragments, each fragment corresponding to a specific tissue or cell type (claims 1, 19, and 27); and performing a whole genome sequencing (WGS) assay using the cfDNA fragments (claims 1, 19, and 27); are insignificant extra-solution activities that are steps for gathering the data used in the judicial exceptions recited by the claimed process, and therefore do not integrate the recited judicial exceptions into a practical application (MPEP 2106.05(g)).
The additional element of a deep neural network (claims 1, 19, and 27) provides nothing more than mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)), and merely confines the use of the abstract idea to the particular technological environment of neural networks (MPEP 2106.05(h)).
The additional elements of performing the WGS assay using the cfDNA fragments comprises sequencing the cfDNA using sequencing-based DNA molecule counting (claims 3, 23, and 31); enriching cfDNA fragments of interest (claim 4), wherein enriching comprises amplification of the cfDNA (claim 7), wherein amplification comprises PCR amplification or genome-wide amplification (claim 8); extracting comprises size-based enrichment to exclude gDNA and enrich for cfDNA fragments (claim 6); and enriching comprises amplifying the cfDNA through targeted amplification or genome-wide amplification (claims 26 and 33); are insignificant extra-solution activities that are steps for gathering the data used in the judicial exceptions recited by the claimed process, and therefore do not integrate the recited judicial exceptions into a practical application (MPEP 2106.05(g)).
The additional element of the biological fluid sample comprises blood, plasma, serum, urine, cerebrospinal fluid, saliva, lymphatic fluid, aqueous humor, vitreous humor, cochlear fluid, tears, milk, sputum, vaginal discharge, or any combination thereof (claims 2, 22, and 30) is an insignificant extra-solution activity that is a step for gathering the data used in the judicial exceptions recited by the claimed process, and therefore does not integrate the recited judicial exceptions into a practical application (MPEP 2106.05(g)).
The additional element of the tissue type is kidney, muscle, heart, vascular, liver, brain, eye, lung, adipose, gland, bone, bone marrow, cartilage, intestine, stomach, skin, or bladder (claim 10) is an insignificant extra-solution activity that is a step for gathering the data used in the judicial exceptions recited by the claimed process, and therefore does not integrate the recited judicial exceptions into a practical application (MPEP 2106.05(g)).
The additional element of displaying an indication of a fraction of cfDNA originating from damaged cells, wherein a cell type of the damaged cells is blood cells, neuron cells, kidney cells, epithelial, extracellular matrix cells, beta cells, or immune cells is an insignificant extra-solution activity that is a data outputting step, and therefore does not integrate the recited judicial exceptions into a practical application (MPEP 2106.05(g)).
The additional element of the sample is a maternal sample (claim 16) is an insignificant extra-solution activity that is a step for gathering the data used in the judicial exceptions recited by the claimed process, and therefore does not integrate the recited judicial exceptions into a practical application (MPEP 2106.05(g)).
The additional element of the sample is obtained and analyzed periodically from a subject to monitor health over time (claim 13) is an insignificant extra-solution activity that is a step for gathering the data used in the judicial exceptions recited by the claimed process, and therefore does not integrate the recited judicial exceptions into a practical application (MPEP 2106.05(g)).
Thus, the additionally recited elements merely amount to insignificant extra-solution data gathering activity, and/or data outputting activity, and/or merely recite instructions to implement a judicial exception on a generic computer, and/or a field of use in which to apply a judicial exception and as such, when all limitations in claims 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, 33, and 35-58 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, 33, and 35-58 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claim recites an abstract idea, and does not integrate that abstract idea into a practical application, the claim is probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 9, 12, 14, 17, 21, 25, 29, and 35-38 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 1, 19, and 27 and dependent claims 2, 3, 4, 6, 7, 8, 10, 11, 13, 16, 22, 23, 26, 30, 31, and 33 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of displaying data (i.e., outputting data) (claim 11); and a deep neural network (claims 1, 19, and 27); are conventional (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
The additional elements of obtaining a biological fluid sample from a subject, the biological fluid sample comprising cfDNA (claims 1, 19, and 27); extracting cfDNA from the biological fluid sample to provide purified cfDNA, wherein the purified cfDNA comprises a plurality of cfDNA fragments, each fragment corresponding to a specific tissue or cell type (claims 1, 19, and 27); and performing a whole genome sequencing (WGS) assay using the cfDNA fragments (claims 1, 19, and 27); are conventional. Evidence for the conventionality is shown by Volckmar et al. (Genes Chromosomes Cancer, 2018, Vol. 57, pp. 123-139; as cited in the Office action mailed 03 March 2025). Volckmar et al. reviews cancer diagnostics using cell-free DNA (cfDNA; Title) and discusses pre-analytical procedures for blood processing, isolation, and quantification of cfDNA, and analytical procedures across different technologies ranging from PCR-based single locus assays to genome-wide approaches (Abstract). Volckmar et al. further shows a liquid biopsy (e.g., blood, urine, cerebrospinal fluid) is taken from cancer patient and can be extracted by various manual or automatic methods, and then subsequently, the cfDNA sample is subjected to different mutation detection methods (e.g., whole exome/genome sequencing; page 124, Figure 1). Volckmar et al. further shows that whole-genome sequencing (WGS) approaches have been implemented to study chromosomal rearrangements and copy number variations (CNVs) in plasma of cancer patients (page 129, col. 2, para. 3) and mutational signatures or copy number signatures derived from WGS may also help more accurately determine the localization of ctDNA origin in cancer of unknown primary patients (page 130, col. 1, para. 1). Volckmar et al. further shows that noninvasive analysis of cfDNA facilitates various clinical applications in the diagnosis and treatment of cancer patients, including evaluation of immediate treatment response and longitudinal therapy monitoring (page 130, col. 2, para. 2). Volckmar et al. further shows primary testing for targeted therapies using liquid biopsy (page 131, Section 7.2) and tracking tumors during therapy via longitudinal testing (page 131, Section 7.3) and further shows a discussion regarding T-cell transfer immunotherapy and chemotherapy (Ibid.).
The additional elements of performing the WGS assay using the cfDNA fragments comprises sequencing the cfDNA using sequencing-based DNA molecule counting (claims 3, 23, and 31); enriching cfDNA fragments of interest (claim 4), wherein enriching comprises amplification of the cfDNA (claim 7), wherein amplification comprises PCR amplification or genome-wide amplification (claim 8); extracting comprises size-based enrichment to exclude gDNA and enrich for cfDNA fragments (claim 6); and amplifying the cfDNA through genome-wide amplification (claims 26 and 33); are conventional. Evidence for the conventionality is shown by Volckmar et al. (Ibid.). Volckmar et al. further shows that sensitive methods are required for accurate cfDNA quantification, and that commonly used technologies include fluorescent dyes, spectrophotometrics, and quantitative PCR (qPCR) analysis (page 127, col. 1, para. 1; and page 129, Section 5.2); targeted and genome-wide sequencing approaches that utilize enrichment of recurrently altered loci by PCR amplification or hybrid capture permitting deep sequencing (page 129, col. 2, para. 1); in contrast to amplicon sequencing, hybridization-based approaches that require less amplification cycles and allow for the detection of rearrangements in ctDNA (Ibid.); different abilities to recover longer or shorter fragments during enrichment (page 126, col. 1, para. 4) and shortening of ctDNA compared to wild type cfDNA might be utilized as a selection factor favoring tumor-derived molecules (page 126, col. 2, para. 3).
The additional elements of the biological fluid sample comprises blood, plasma, serum, urine, cerebrospinal fluid, saliva, lymphatic fluid, aqueous humor, vitreous humor, cochlear fluid, tears, milk, sputum, vaginal discharge, or any combination thereof (claims 2, 22, and 30); and the tissue type is kidney, muscle, heart, vascular, liver, brain, eye, lung, adipose, gland, bone, bone marrow, cartilage, intestine, stomach, skin, or bladder in (claim 10); are conventional. Evidence for the conventionality is shown by Volckmar et al. (Ibid.). Volckmar et al. shows a liquid biopsy sample taken from blood, urine, or cerebrospinal fluid (page 124, Figure 1); and for cfDNA-based analytics of primary and metastatic brain tumors it is important to note that cerebrospinal fluid harbors more ctDNA than blood as the blood-brain barrier reduces molecular exchange with the peripheral circulation (page 126, col. 2, para. 1).
The additional element of the sample is a maternal sample (claim 16) is conventional. Evidence for the conventionality is shown by Ji et al. (Taiwanese Journal of Obstetrics & Gynecology, 2018, Vol. 57, pp. 871-877; as cited in the Office action mailed 03 March 2025). Ji et al. reviews the use of copy number variation profiles in noninvasive prenatal testing (NIPT; Title) and shows findings in the study and the literature review that further validate the effect of maternal malignancy on the copy number variation profile in NIPT data and that strengthen the possibility of detecting malignant tumors with NIPT (Abstract). Ji et al. further shows sample collection of maternal peripheral blood (page 872, col. 2, para. 1).
The additional element of the sample is obtained and analyzed periodically from a subject to monitor health over time (claim 13) is conventional. Evidence for the conventionality is shown by Keating et al. (Transplant International, 2018, Vol. 31, pp. 278-290; as cited in the Office action mailed 03 March 2025). Keating et al. reviews the application of genomics in heart transplantation (Title) and summarizes key advances in genomics which are relevant for heart transplant outcomes (Abstract) such as the use of cell-free circulating DNA approaches in transplantation (cfDNA; page 286, col. 2). Keating et al. shows using donor-derived circulating cfDNA (dd-cfDNA) in monitoring acute rejection in a prospective heart transplant recipient study in which dd-cfDNA was shown to be highly elevated from day 1 post-transplant, followed by a quick decline within a week, and remained low until a rejection event (Ibid.). Keating et al. further shows the use of dd-cfDNA monitoring as a prognostic monitoring assay for rejection as levels of dd-cfDNA were observed to be significantly elevated weeks to months preceding a rejection episode (Ibid.). Keating et al. further shows that since dd-cfDNA can be assessed at defined periods post-transplant, in a minimally invasive manner, and as it is essentially a quantitative read-out of donor versus recipient cfDNA, it therefore can also be used in a prognostic manner to monitor heart allograft status (Ibid.).
Therefore, when taken alone, all additional elements in claims 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, 33, and 35-38 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, 33, and 35-38 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Response to Arguments
The Applicant’s arguments/remarks received 06 November 2025 have been fully considered, but are not persuasive.
The Applicant states on page 9 of the Remarks that the Applicant requests reconsideration of the rejection of the instant claims in view of the 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter the “2019 Guidance”); the October 219 Update: Subject Matter Eligibility (hereinafter the “Update”); and the 2024 Patent Subject Matter Eligibility Guidance Update Including on Artificial Intelligence (hereinafter the “2024 Guidance”); and the August 4, 2025 USPTO Memorandum regarding Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101 (hereinafter the “2025 Memo”).
Regarding the Applicant’s foregoing remarks, it is noted that the above rejection is based on the subject matter eligibility analysis as provided by the MPEP at 2106, with additional consideration given to the documents referenced by the Applicant in the foregoing statement/remarks.
The Applicant further states on page 10 (para. 1) that with regard to the Office action’s analysis of Step 2A Prong 1, the Applicant points to the 2025 Memo, which states that “a claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s).” The Applicant further states that the 2025 Memo states that “[c]laim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall with [the mental process] grouping.” The Applicant further states that the present claims cannot practically be performed in the human mind for reasons previously argued, including, for example, the data sets used for such methods are too large and complex to be practically performed in the human mind, and therefore, proper analysis under Step 2A Prong 1 indicates that the claims do not recite a mental process.
These arguments are not persuasive, because, regarding the Applicant’s argument that the “claims cannot practically be performed in the human mind for reasons previously argued, including, for example, the data sets used for such methods are too large and complex to be practically performed in the human mind,” the amount of data and/or the amount of time to perform the process steps, in and of themselves is not a limitation which takes a process out of the realm of the human mind. It is the process performed on that data which is the mental step, and mental steps identified in the claims do not have to be the fastest, most efficient, or require specialized computing elements. Thus, although the amount of data may be considered to be significantly large and take considerable time and effort to process manually, the use of a computer to perform the claimed method at a rate and accuracy that can far outstrip the mental performance of a skilled artisan does not change the nature of the activity being performed (i.e., an abstract idea), and therefore does not materially alter the patent eligibility of the claimed subject matter.
The Applicant states on page 10 (para. 2) of the Remarks that patent eligibility is also satisfied under the second prong of Step 2A, because the claims integrate any alleged judicial exception into a practical application, for example, claim 1 recites “generating a plurality of baseline reference profiles using supervised or unsupervised machine learning algorithms, wherein cfDNA signals from control individuals are decomposed and the reference tissue or cell specific profiles are extracted, thereby generating a plurality of baseline reference profiles.” The Applicant further states that claims 19 and 27 recite similar “generating” steps, and that these generating steps recite specific processing steps that improve accuracy of the claimed method, thereby improving the technical field.
These arguments are not persuasive, because the “generating” steps in the foregoing argument comprise the judicial exceptions identified at Eligibility Step 2A Prong One in the above rejection, and there are no additional elements identified at Eligibility Step 2A Prong Two that integrate the recited judicial exceptions into a practical application by applying, relying on, or using the judicial exception(s) in a manner that imposes a meaningful limit on the judicial exception(s), as noted and discussed in the above rejection.
The Applicant states on page 10 (para. 3) of the Remarks that the methods include a meaningful limitation on the judicial exception related to determining cell damage, tissue damage, or organ damage with improved sensitivity and specificity. The Applicant further states on page 11 (para. 1) that the methods of the present claims, comprising generating of genome-wide cfDNA copy number profiles based on the WGS assay and generating a plurality of baseline reference provides, using supervised and unsupervised machine learning algorithms, demonstrate an improvement in the technical field of cfDNA analysis, specifically a markedly superior performance in determining condition of a subject related to cell, tissue, or organ damage, and thus, the claim as a whole integrates the judicial exception into a practical application such that the claim is not directed to the judicial exception. The Applicant further states that as indicated in the specification, traditional methods of analyzing cfDNA require sequence specific detection, limiting sensitivity and do not provide accurate, reliable or reproducible determination of a relative contribution of each tissue type in the subject to the total cfDNA in a biological sample, and that traditional methods of organ monitoring or tissue health also require tissue biopsy. The Applicant further states that in contrast, the methods of the present claims are non-invasive and provide immediate determination of dynamics of cell and tissue damage, early detection of indications, and enable quantification and monitoring of multiple organs at once in a single analysis. The Applicant further states that the additional elements integrate the abstract idea into a practical application because the claim improves the functioning of the technical field, and the claimed invention reflects this improvement in the technical field, and that accordingly, at least for the foregoing reasons as well as the additional reasons previously presented of record, the claims are not directed to the judicial exception as recited by the Examiner, and the claims are eligible.
These arguments are not persuasive, because the limitations identified in the foregoing argument as purportedly demonstrating an improvement in the technical field of cfDNA analysis (i.e., “generating of genome-wide cfDNA copy number profiles based on the WGS assay and generating a plurality of baseline reference provides, using supervised and unsupervised machine learning algorithms”) comprise the judicial exceptions identified at Eligibility Step 2A Prong One in the above rejection, and there are no additional elements identified at Eligibility Step 2A Prong Two that integrate the recited judicial exceptions into a practical application by applying, relying on, or using the judicial exception(s) in a manner that imposes a meaningful limit on the judicial exception(s), as noted and discussed in the above rejection. Thus, the instant claimed improvement to cfDNA analysis is a purported improvement to the abstract idea (data analysis), and not an improvement to computer functionality itself, or an improvement to another technology or technical field, as noted in the above rejection and in the responses to arguments previously presented of record.
Claim Rejections - 35 USC § 103
The rejection of claims 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, and 33 under 35 U.S.C. 103 as being unpatentable over Shendure et al. in view of Dharajiya et al. in view of Abdueva in view of Sarwal et al. in the Office action mailed 11 July 2025 is withdrawn in view of the amendment received 06 November 2025.
The amendment received 06 November 2025 has been fully considered, however after further consideration, new grounds of rejection are raised under 35 U.S.C. 103 in view of the amendment.
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.
Claims 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, 33, and 35-38 are rejected under 35 U.S.C. 103 as being unpatentable over Shendure et al. (United States Patent Application Publication No.: US 2017/0211143, as cited in the Office action mailed 08 November 2023) in view of Dharajiya et al. (International Publication Number WO 2018/081465, as cited in the Office action mailed 03 March 2025) in view of Abdueva (International Publication Number WO 2018/009723, as cited in the Office action mailed 03 March 2025) in view of Sarwal et al. (International Publication Number WO 2018/035340, as cited on the Information Disclosure Statement (IDS) received 09 February 2024) in view of Ha et al. (WO 2017/161175, newly cited).
Shendure et al. is directed to methods of determining one or more tissues and/or cell types contributing to cell-free DNA (cfDNA) in a biological sample, and methods of identifying a disease or disorder in a subject as a function of one or more determined tissues and/or cell types contributing to cfDNA in a biological sample from the subject (Abstract).
Dharajiya et al. is directed to systems and methods for characterizing nucleic acid in a biological sample (Title) and further shows a cancer detection method that includes preparing a library of nucleic acids in a biological sample, sequencing the library, measuring a copy number for genes in the sequenced library, and comparing the measured copy numbers with standard copy numbers for the genes to determine variability or similarity between the measured copy numbers and the standard copy numbers (Abstract).
Abdueva is directed to methods for fragmentome profiling of cell-free nucleic acids (Title) and contemplates various uses of cell-free DNA to assess a fragmentome profile that can be representative of a tissue of origin, disease, progression, etc. (Abstract).
Sarwal et al. is directed to an immunoprobe-based method that is directed to assessing organ injury status through a biofluid-based cell-free DNA (cfDNA) assay (Title) that can be used to quantify cfDNA in biofluids using a hybridization approach (Abstract).
Ha et al. is directed to methods for genome characterization using low coverage sequencing to assess the relative fraction of tumor versus normal DNA in a sample, and to assess copy number alterations present in the sample (Abstract).
Regarding independent claims 1, 19, and 27, Shendure et al. shows obtaining a biological sample from the subject, isolating cfDNA from the biological sample, constructing a library, and massively parallel sequencing of cfDNA (para. [0030]); the biological sample comprises, consists essentially of, or consists of whole blood, peripheral blood plasma, urine, or cerebrospinal fluid (para. [0328]); purifying the cfDNA isolated from the biological sample (para. [0358]); PCR amplification (para. [0359]); and displaying clinical diagnoses and cfDNA yields for a cancer panel (page 26, Table 4) and sequencing-related statistics (pages 27-28, Table 5) that show, e.g., a cfDNA samples and contributory cell types or tissues (para. [0205]); generating reference maps from control subjects or subjects with known disease (paras. [0009] & [0010] and throughout); calculating a final adjusted profile by extracting data features, subtracting signal data from the original signal to determine a corrected, i.e., adjusted signal profile (para. [0220]); and determining tissues and/or cell types giving rise to cfDNA in a subject (para. [0010]).
Regarding independent claims 1, 19, and 27, Shendure et al. does not show methods for performing a whole genome sequencing (WGS) assay using the cfDNA fragments (claims 1, 19, and 27); generating a genome-wide cfDNA copy number profile based on the WGS assay, wherein the genome-wide cfDNA copy number profile comprises a plurality of copy number signals, each copy number signal corresponding to a cfDNA fragment (claims 1, 19, and 27); generating a plurality of baseline reference profiles using supervised or unsupervised machine learning algorithms (claims 1, 19, and 27); removing the plurality of baseline reference profiles from the genome-wide cfDNA copy number profile, wherein each of the plurality of baseline reference profiles corresponds to a particular cell type or tissue type (claims 1, 19, and 27); determining cell damage, tissue damage, or organ damage based at least in part on a comparison of the genome-wide cfDNA copy number profile to a set of known genome-wide cfDNA signatures (claims 1, 19, and 27); wherein the comparison is conducted by an unsupervised machine learning model or a supervised machine learning model, wherein the unsupervised machine learning model comprises non-negative matrix factorization, and wherein the supervised machine learning model is trained on isolated tissue samples and comprises a deep neural network (claims 1, 19, and 27); and wherein a difference of copy number signal in the cfDNA copy number profile based on the WGS assay compared to the known genome-wide cfDNA signatures correlates to a condition in the subject related to cell, tissue, or organ damage (claims 1, 19, and 27).
Regarding independent claims 1, 19, and 27, Dharajiya et al. shows a frequency plot depicting genome-wide copy number variation (CNV) profile results (para. [0022]; and FIGS. 1-8); the standard copy number may comprise a copy number of nucleic acid or nucleic acid sequences and/or a copy number profile of nucleic acid or nucleic acid sequences for a wild-type cell or sample, and additionally, or alternatively, the standard copy number may comprise a copy number of nucleic acid or nucleic acid sequences and/or a copy number profile of nucleic acid or nucleic acid sequences for each of one or more cancer or cancerous cells, cell types, or sample (para. [0013]); a subject CNV profile (for breast tissue suspected of being cancerous) can be compared to one or more standard, breast cancer CNV profiles to observe and/or measure similarities and/or differences between the subject and the standard CNVs, and based on the similarities and/or differences, the subject tissue can be diagnosed as being associated with a breast cancer profile, etc. (para. [0082]).
Regarding independent claims 1, 19, and 27, Shendure et al. in view of Dharajiya et al. do not show generating a plurality of baseline reference profiles using supervised or unsupervised machine learning algorithms (claims 1, 19, and 27); removing a plurality of baseline reference profiles from the genome-wide cfDNA copy number profile, wherein each of the plurality of baseline reference profiles corresponds to a particular cell type or tissue type (claims 1, 19, and 27); wherein the comparison is conducted by an unsupervised machine learning model or a supervised machine learning model, wherein the unsupervised machine learning model comprises non-negative matrix factorization, and wherein the supervised machine learning model is trained on isolated tissue samples and comprises a deep neural network (claims 1, 19, and 27); or determining cell damage, tissue damage, or organ damage based at least in part on a comparison of the genome-wide cfDNA copy number profile to a set of known genome-wide cfDNA signatures (claims 1, 19, and 27).
Regarding independent claims 1, 19, and 27, Abdueva shows that by providing a base-by-base view of the genome, next generation sequencing (NGS) may detect small or novel copy number variations (CNVs) that may remain undetected by arrays, and examples of suitable NGS may include whole-genome (WGS; para. [00170]). Abdueva further shows a method for deconvolving a distribution of DNA fragments from cell-free DNA obtained from a subject, thereby generating fractional contributions associated with one or more members selected from the group consisting of a copy number component, a cell clearance component, and a gene expression component (paras. [0045] & [00332]; and Figure 23); subjects with tissue-specific cancer (para. [0016]); a multi-parametric model is derived from a given tissue type selected from a group of different tissue types (para. [0021]); a classifier may be used to determine genetic aberrations in a test subject using DNA from a cell-free sample obtained from the test subject (para. [00280]) and a classifier may be trained by a training set (para. [00281]); machine learning algorithms may be supervised or unsupervised, and learning algorithms include artificial neural networks (para. [00152]).
Regarding independent claims 1, 19, and 27, Shendure et al. in view of Dharajiya et al. in view of Abdueva do not show non-negative matrix factorization (claims 1, 19, and 27); or explicitly determining cell damage, tissue damage, or organ damage based at least in part on a comparison of the genome-wide cfDNA copy number profile (claims 1, 19, and 27).
Regarding independent claims 1, 19, and 27, Sarwal et al. shows a method of detecting organ injury comprising measuring the amount of cfDNA, and amounts of one or more of markers in a biofluid sample obtained from an organ that is suspected of having injury or is likely to develop injury, generating scoring metric, and comparing the scoring metric to the amount of cfDNA hybridized to a probe (para. [0017]). Sarwal et al. further shows a method of quantifying cfDNA for detecting Alu copy number in cell-free DNA (cfDNA) in a biofluid sample from an individual having a lung transplant or lung transplant clinical conditions (para. [0012]; and claim 9); and monitoring organ transplant patients for organ injuries associated with acute rejection episodes by using the cfDNA normalized amounts corresponding to designated time points over a period of time post-transplantation, and patients who are determined to have acute rejection episode can be treated by administering immunosuppressant drugs (i.e., beginning or adjusting a medication regime) (para. [0101]).
Regarding independent claims 1, 19, and 27, Shendure et al. in view of Dharajiya et al. in view of Abdueva in view of Sarwal et al. does not show using non-negative matrix factorization.
Regarding independent claims 1, 19, and 27, Ha et al. shows plots depicting mutational signatures in whole-exome sequencing of cfDNA and tumor biopsies predicted using a Bayesian non-negative matrix factorization (NMF) approach (page 11, lines 9-11; and FIG. 4C).
Regarding claims 2, 22, and 30, Shendure et al. further shows the biological sample comprises, consists essentially of, or consists of whole blood, peripheral blood plasma, urine, or cerebrospinal fluid (para. [085]).
Regarding claims 4 and 33, Shendure et al. further shows PCR amplification was performed to enrich for adaptor-bearing molecules (para. [0180]).
Regarding claim 6, Shendure et al. further shows that mononucleosomal fragments were size selected with 2% agarose gel electrophoresis using standard methods (para. [0161]).
Regarding claims 7 and 8, Shendure et al. further shows the preparation of sequencing libraries comprising amplification for all samples monitored by real-time PCR (para. [0210]).
Regarding claim 10, Shendure et al. further shows clinical diagnoses and cfDNA yield for a cancer panel for various tissue and organ types including kidney, liver, lung, and skin (page 26, Table 4, and para. [0200]).
Regarding claim 11, Shendure et al. further shows sequencing statistics for cfDNA samples with percentages of contributory cell types (pages 27-28, Table 5; and para. [0205]).
Regarding claim 16, Shendure et al. further shows whole blood samples drawn from pregnant women (para. [0158]) and estimating the fetal fraction of cfDNA in pregnancy and/or enhancing detection of chromosomal or other genetic abnormalities (para. 0128]).
Regarding claims 3, 9, 12, 13, 14, 17, 21, 23, 25, 29, 31, and 35-38, Shendure et al. does not show quantifying comprises sequencing the cfDNA using sequencing-based DNA molecule counting (claims 3, 23, and 31); each copy number coverage signal is indicative of a relative contribution of cfDNA from a specific tissue or cell type (claim 9); the genome-wide cfDNA profile is used to quantify amounts of cfDNA from multiple organs for providing an assessment of organ or multi-organ health (claim 12); the sample is obtained and analyzed periodically from a subject to monitor health over time (claim 13); monitoring organ transplant over time (claim 14); or multiple organs are quantified simultaneously by projecting the genome-wide cfDNA profile onto a set of reference cfDNA profiles corresponding to various tissue and cell types (claims 17 and 29); comparing the plurality of copy number signals to a profile of known copy number signals of diseased patient samples (claim 21); the disease is selected from heart failure, lung damage, diabetes, Crohn's disease, or kidney disease (claim 25); the organ health is multi-organ health (claim 35); the cell damage, tissue damage, or organ damage comprises liver damage or renal damage (claim 36); the disease of the disease progress is lupus (claim 37); or the disease of the disease progress is diabetes (claim 38).
Regarding claim 9, Dharajiya et al. further shows comparing the measured number of copies with a standard copy number for the at least one nucleic acid sequence to determine variability or similarity between the measured number of copies and the standard copy number (para. [0012]) and that the comparing step comprises determining a tissue of origin and/or a stage of cancer based on the similarity of the measured copy number of nucleic acid from the biological sample to a standard copy number (para. [0013]).
Regarding claims 17, 21, and 29, Dharajiya et al. further shows that due to genomic instability of neoplastic cells, there are inevitably certain copy number variations that arise, and by producing and analyzing the sequences of a plurality of samples comprising cancer DNA, it is possible to select a locus or multiple loci that, in combination, provide one or more copy number variation plots that are illustrative and/or indicative of one or more cancer phenotypes, e.g., cancer type, tissue of origin, and/or stage/severity of cancer (paras. [0078], [0079], [0080], [0081], [0083] & [0085]; and Figures 3 & 4).
Regarding claims 3, 12, 13, 14, 17, 23, 25, 31, and 35-38, Shendure et al. in view of Dharajiya et al. does not show quantifying comprises sequencing the cfDNA using sequencing-based DNA molecule counting (claims 3, 23, and 31); the genome-wide cfDNA profile is used to quantify amounts of cfDNA from multiple organs for providing an assessment of organ or multi-organ health (claim 12); the sample is obtained and analyzed periodically from a subject to monitor health over time (claim 13); monitoring organ transplant over time (claim 14); multiple organs are quantified simultaneously by projecting the genome-wide cfDNA profile onto a set of reference cfDNA profiles corresponding to various tissue and cell types (claim 17); the disease is selected from heart failure, lung damage, diabetes, Crohn's disease, or kidney disease (claim 25); the organ health is multi-organ health (claim 35); the cell damage, tissue damage, or organ damage comprises liver damage or renal damage (claim 36); the disease of the disease progress is lupus (claim 37); or the disease of the disease progress is diabetes (claim 38).
Regarding claims 3, 23, and 31, Abdueva shows a low-multiplexed PCR panel may be used to perform on cell-free DNA or cell-free RNA molecules an assay selected from the group consisting of: digital PCR, droplet digital PCR, quantitative PCR, and reverse-transcription PCR (para. [00276]); sequencing the cfDNA (para. [0037]); and single molecule sequencing (para. [00143]).
Regarding claim 35, Abdueva further shows that fragmentome profiles (i.e., data) may be incorporated into a classifier to determine the likelihood of information derived from tumor microenvironment (e.g., tissue of origin corresponding to cfDNA fragments). Since a fragmentome profile may comprise a characteristic signal (or signature) from circulating nucleic acids in blood, such a signature may comprise an aggregate signal from tumor cells, leukocytes and other background cells, and a tumor’s microenvironment. A tumor’s cell biology and microenvironment may both play roles in affecting the tumor biology and activity, thus, such information about the likelihood of information derived from tumor microenvironment may be used to identify tissue of origin (e.g., that tumor activity is prevalent in a tissue or organ), and such information may be deconvolved to identify subcomponents (e.g., inflamed organ, leukocytes, tumor, normal apoptotic cells), and such subcomponent information may be used to determine the tissue(s) and/or organ(s) where a tumor is located (para. [00198]).
Regarding claim 36, Abdueva further shows that in some instances, a cohort comprises individuals having a specific type of cancer (e.g., breast, colorectal, pancreatic, prostate, melanoma, lung or liver) (para. [00235]).
Regarding claims 12, 13, 14, 17, 25, 37, and 38, Shendure et al. in view of Dharajiya et al. in view of Abdueva does not show the genome-wide cfDNA profile is used to quantify amounts of cfDNA from multiple organs for providing an assessment of organ or multi-organ health (claim 12); the sample is obtained and analyzed periodically from a subject to monitor health over time (claim 13); monitoring organ transplant over time (claim 14); multiple organs are quantified simultaneously by projecting the genome-wide cfDNA profile onto a set of reference cfDNA profiles corresponding to various tissue and cell types (claim 17); the disease is selected from heart failure, lung damage, diabetes, Crohn's disease, or kidney disease (claim 25); the disease of the disease progress is lupus (claim 37); or the disease of the disease progress is diabetes (claim 38).
Regarding claim 12, Sarwal et al. further shows determining organ health based on the amount of cfDNA, comprising comparing the amount of cfDNA in the biofluid sample to a cutoff value or predictive probability estimate indicative of organ injury status, and that establishing such a cutoff value comprises a group of health individuals, such as a group of individuals who do not have organ injury after an organ transplantation is selected (paras. [0083] – [0084]).
Regarding claims 13 and 14, Sarwal et al. further shows that urine samples can be collected at predetermined time points over a post-transplantation period, and then normalized cfDNA values can be compared to cutoff values for those time points to determine whether the normalized values are predictive of healthy or diseased kidney function (paras. [0094] & [0095]).
Regarding claim 17, Sarwal et al. further shows a metric score can be used to predict or diagnose organ injury, wherein the metric score is generated by including the amount of cfDNA (para. [0090]) and that a cutoff value for the metric score can be established by measuring markers in biofluid samples from the same or similar types of organs from a group of healthy individuals, such as a group of individuals who do not have organ injury after an organ transplantation is selected (para. [0092]).
Regarding claim 25, Sarwal et al. further shows a methodology for quantitative analysis of cell-free DNA in biofluid that can be used to assess kidney health in the context of kidney transplantation and kidney disease (para. [0004]).
Regarding claim 37, Sarwal et al. further shows that kidney injury can also develop in patients having kidney disease, and kidney diseases are diverse, but individuals with kidney disease frequently display characteristic clinical features, and common clinical conditions involving the kidney include but are not limited to the nephritic and nephrotic syndromes, renal cysts, acute kidney injury…lupus… (para. [0047]).
Regarding claim 38, Sarwal et al. further shows that kidney injury can develop in patients who have undergone a kidney transplant, and this can happen because of several immune and non-immune factors such as ischemia reperfusion injury…and problems after a transplant may include: transplant rejection, infections and sepsis…diabetes mellitus type 2… (para. [0046]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Shendure et al. by incorporating a copy number profile of the nucleic sequences according to Dharajiya, as discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Shendure et al. with Dharajiya et al. in order to generate a copy number profile derived from cfDNA that can indicate tissue specificity and/or stage severity of cancer from a non-localized tissue sample such as a bodily fluid. This modification would have had a reasonable expectation of success because both Shendure et al. and Dharajiya et al. show methods of determining tissues and/or cell types from circulating cell-free DNA obtained from a biological fluid sample.
It would have been further obvious to modify Shendure et al. in view of Dharajiya et al. by incorporating whole genome sequencing and methods for deconvoluting cfDNA fragmentation patterns according to Abdueva, as discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Shendure et al. in view of Dharajiya et al. with Abdueva in order to use whole genome sequencing to detect copy number variations, and further use deconvolution methods for deconvolving the distribution of coverage, thereby generating fractional contributions of cfDNA associated with a copy number component, and determining a presence or absence of a genetic aberration in the cfDNA. This modification would have had a reasonable expectation of success because both Shendure et al. in view of Dharajiya et al. and Abdueva show methods for profiling cell-free nucleic acids.
It would have been further obvious to modify Shendure et al. in view of Dharajiya et al. in view of Abdueva by incorporating methods for assessing organ injury status through a biofluid-based cell-free DNA (cfDNA) assay according to Sarwal et al., as discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Shendure et al. in view of Dharajiya et al. in view of Abdueva with Sarwal et al. because Sarwal et al. shows quantifying the amount of cell-free DNA (cfDNA) to detect Alu copy number in cfDNA in a biofluid sample from an individual having a lung transplant or lung transplant clinical conditions. This modification would have had a reasonable expectation of success because both Shendure et al. in view of Dharajiya et al. in view of Abdueva and Sarwal et al. show methods for profiling cell-free DNA obtained from biological samples to assess health statuses.
It would have been further obvious to modify Shendure et al. in view of Dharajiya et al. in view of Abdueva in view of Sarwal et al. by incorporating non-negative matrix factorization, as shown by Ha et al., and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Shendure et al. in view of Dharajiya et al. in view of Abdueva in view of Sarwal et al. with Ha et al., because Ha et al. shows using this technique for decomposing large, complex data matrices into simpler, lower-dimensional components to uncover hidden features, e.g., mutational signatures in whole-exome sequencing of cfDNA and tumor biopsies. This modification would have had a reasonable expectation of success because both Shendure et al. in view of Dharajiya et al. in view of Abdueva in view of Sarwal et al. and Ha et al. are directed to methods for characterizing cell-free DNA with respect to tumor versus normal DNA in a sample.
Response to Arguments
The Applicant's arguments/remarks received 06 November 2025 have been fully considered, but they are not persuasive.
The Applicant states on page 11 (para. 4) of the Remarks that the claims are inventive over the cited art for the reasons discussed in the August 25, 2025 response. In the arguments/remarks received August 25, 2025, the Applicant states on page 11 (para. 2) of the Remarks that amended claim 1 recites an analysis of cfDNA that examines cfDNA fragmentation, which is not an allele-based analysis, and to clarify this difference, claim 1 has been amended to recite “the genome-wide cfDNA copy number profile comprises a plurality of copy number coverage signals” and that contrastingly, allele-based analysis would require “sequencing and detection of single nucleotide variations,” rather than examination of cfDNA fragment length and position. The Applicant further notes that, as claimed, “each copy number coverage signal correspond[s] to a cfDNA fragment.” The Applicant further summarizes the limitations that the Office action (mailed 11 July 2025) noted in the rejection as not being shown by the Shendure reference (e.g., at para. 94 in the Office action mailed 11 July 2025).
These arguments are not persuasive, because first, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Second, Shendure at least shows a method of examining cfDNA fragments to determine tissues and/or cell types giving rise to cell-free DNA (cfDNA) in a subject, the method comprising isolating cfDNA from a biological sample from the subject, the isolated cfDNA comprising a plurality of cfDNA fragments; determining a sequence associated with at least a portion of the plurality of cfDNA fragments; determining a genomic location within a reference genome for at least some cfDNA fragment endpoints of the plurality of cfDNA fragments as a function of the cfDNA fragment sequences; and determining at least some of the tissues and/or cell types giving rise to the cfDNA fragments as a function of the genomic locations of at least some of the cfDNA fragment endpoints (Shendure, para. [0007]).
In the arguments/remarks received August 25, 2025 (page 12, para. 1), the Applicant points to the Office action mailed 11 July 2025 and the Dharajiya reference used in the rejection, and states (page 12, middle) that the Dharajiya reference does not teach or suggest analysis of “copy number coverage signals” and that the reference is silent with respect to such analysis. The Applicant further states (page 12, para. 2) that the Office action fails to establish that Abdueva cures the deficiencies of Shendure and Dharajiya, and provides a summary of various aspects of what Abdueva shows. The Applicant further states (page 12, para. 3) that though Abdueva mentions “coverage of the DNA fragments,” the discussion is in the context of an analysis of fractional contribution of “a copy number (CN) component, a cell clearance component, and a gene expression component,” in other words, Abdueva does not link analysis of cfDNA copy number coverage signal to an analysis to determine an origin cell type or a tissue type for a profile “comprising a plurality of copy number coverage signals.” The Applicant further states on page 13 (para. 2) that Sarwal fails to cure the deficiencies of Shendure, Dharajiya, and Abdueva, and that Sarwal’s approach is completely different from the present claims, because Sarwal does not rely on WGS sequencing – or indeed, sequencing of any kind – but uses immunoprobe binding to quantify cfDNA. The Applicant further states that Sarwal discusses measuring a total amount of cfDNA hybridized to the probe, rather than determination of a cfDNA copy number profile comprising a plurality of copy number coverage signals, and that additionally, this measurement of the amount of cfDNA is paired with a measurement of other markers, including inflammation markers, apoptosis markers, total protein, and/or DNA methylation markers. Finally, the Applicant states (page 13, para. 3) that for at least these reasons, Shendure, Dharajiwa, Abdueva, and/or Sarwal, alone or in any combination, do not establish a prima facie case of obviousness of claim 1, and that claims 19 and 27 are not obvious for similar reasons as discussed with reference to claim 1 above, and further that the dependent claims are patentable over the cited references for at least the same reasons as the independent claims.
These arguments are not persuasive, because first, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Second, the Abdueva reference at least shows various uses of cell-free DNA, using sequence information with or without somatic variant information, to assess a fragmentome profile that can be representative of a tissue of origin, disease, progression, etc. (Abstract). Third, the Sarwal reference is used in the combination of references to show various uses of the data representing the cfDNA in a subject’s sample (i.e., not necessarily the method(s) of obtaining the data), not least the use of the cfDNA data to assess organ injury status (Abstract), as noted at least in the above rejection.
In the arguments/remarks received 06 November 2025, the Applicant states on page 11 (para. 4) that to clarify a point raised in the interview (held on 02 October 2025), a person of ordinary skill in the art would understand that sequencing coverage is different and distinct from sequencing depth, and further states on page 12 (para. 1) that cfDNA coverage-based analysis is distinct from the prior art allele-based analysis discussed in paragraph [0017] of the Applicant’s specification.
These arguments are persuasive in part, to the extent of what a person of ordinary skill in the art would understand, and persuasive in part to the extent that a coverage-based analysis is different than an allele-based analysis (e.g., coverage-based CNV analysis measures total read depth to identify gains or losses in DNA amount, while allele-based (allele-specific) analysis examines the ratio of parental alleles (e.g., B-allele frequency) to detect imbalances, such as loss of heterozygosity (LOH) or copy-neutral events), or put another way, coverage-based analysis shows how much DNA exists, whereas allele-based analysis shows which parental copy is missing or amplified. These arguments are not persuasive in part, to the extent that the argument appears to imply that cfDNA coverage-based analysis is nonobvious over the allele-based analysis discussed in paragraph [0017] of the Applicant’s specification.
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this application.
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/S.W.B./Examiner, Art Unit 1687
/Joseph Woitach/Primary Examiner, Art Unit 1687