DETAILED ACTION
The Applicant’s response, received 10 July 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
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
This application is a 371 of PCT/US2020/014612, filed 22 January 2020,
which claims benefit of 62/796,481, filed 24 January 2019.
Claim Rejections - 35 USC § 112
The rejection of claims 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, 33, and 35-38 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, in the Office action mailed 13 January 2026 is withdrawn in view of the amendment received 10 July 2026.
Claim Rejections - 35 USC § 101
Response to Amendment
The declaration under 37 CFR 1.132 filed 10 July 2026 is insufficient to overcome the rejection of claims 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, 33, and 35-38 under 35 U.S.C. 101 in the Office action mailed 13 January 2026 because: In view of the foregoing, when all of the assertions are considered, the totality of the rebuttal assertions regarding the volume of data, mathematical operations, and computational resources do not outweigh the assertion that the instant claimed method of analyzing cell free DNA data recites steps of analyzing data, the fundamental nature of which can be practically performed in the human mind with the aid of pen and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Furthermore, the amount of data (e.g., gigabases of base-call data per sample) does not change the fundamental nature of the process of analyzing genomic data, and therefore does not take the process out of the realm of the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., using a supervised machine learning model) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
The amendment received 10 July 2026 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, 33, and 35-38 under 35 U.S.C. 101 in the Office action mailed 13 January 2026 is maintained with modification in view of the amendment.
The rejection has been modified to incorporate the newly amended claims.
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 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 a supervised machine learning model (i.e., mental processes and mathematical concepts);
wherein the supervised machine learning model is trained on isolated tissue samples and comprises a deep neural network (i.e., 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 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 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 a supervised machine learning model (i.e., mental processes and mathematical concepts);
wherein the supervised machine learning model is trained on isolated tissue samples and comprises a deep neural network (i.e., 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 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 a supervised machine learning model (i.e., mental processes and mathematical concepts);
wherein the supervised machine learning model is trained on isolated tissue samples and comprises a deep neural network (i.e., 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.
In the instant application, the independent claims provide additional elements to obtain a biological fluid sample, extract cfDNA from the biological fluid sample, and perform whole genome sequencing to generate cfDNA data for analysis, however once the cfDNA data is generated, the subsequent steps only perform analysis and/or calculations using the cfDNA data. However, the claims do not recite any limitations (i.e., additional elements) to which the result of the data analysis is practically applied.
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; and
performing a whole genome sequencing (WGS) assay using the cfDNA fragments.
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 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 insignificant extra-solution data outputting activity, 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 (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), 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 further 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 element of displaying data (i.e., outputting data) (claim 11) is 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 (i.e., individually), 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 am ordered 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 10 July 2026 have been fully considered, but are not persuasive.
The Applicant states on page 8 (as filed) of the Remarks that the Applicant requests reconsideration in view of the December 4, 2025 Memorandum regarding Subject Matter Eligibility Declarations, and also requests reconsideration 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 request for reconsideration in view of the December 4, 2025 Memorandum regarding Subject Matter Eligibility Declarations, it is noted that this memorandum is consistent with existing USPTO guidance and does not announce any new USPTO practice or procedure. Additionally, the Applicant’s declaration has been considered, as noted above. Further regarding the Applicant’s foregoing remarks, and as noted in the response to arguments in the Office action mailed 13 January 2026, the above rejection and the rejections of record are 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.
Regarding Step 2A Prong One, the Applicant states on page 9 (bottom) of the Remarks that the methods recited in claims 1, 19, and 27 are not abstract ideas, and further states on page 10 (para. 1) that these claims are not mental processes because each claim recites instructions that are necessarily performed on a processor, and further states that the human mind is not equipped to implement any kind of machine learning model. The Applicant further states (para. 2) that the methods also cannot practically be performed mentally, and that steps involving high quantities of sequence data cannot practically be performed mentally in the human mind. The Applicant further points to the December 4, 2025 Memorandum regarding Subject Matter Eligibility Declarations, and further states that the guidance indicates that when a declaration provides evidence showing that the teachings of the specification, as understood by one or ordinary skill in the art, cannot practically be performed in the human mind, this evidence overcomes a prima facie case of ineligibility. The Applicant further states (bottom) that in support of the argument that the claim limitations cannot be practically performed in the human mind, the Applicant submits an Expert Declaration setting forth evidence that the claimed methods cannot be performed in the human mind (page 10 through page 12), in particular, stating that the following claimed steps are not practically performed in the human mind: (a) performing a whole genome sequencing (WGS) assay using the cfDNA fragments; (b) generating a genome-wide cfDNA copy number profile based on the WGS assay, wherein the genome-wide cfDNA profile comprises a plurality of copy number coverage signals, each copy number coverage signal corresponding to a cfDNA fragment; (c) generating a plurality of baseline reference profiles using supervised or unsupervised machine learning algorithms, wherein cfDNA signals from control individuals are decomposed and reference tissue or cell specific profiles are extracted; (d) 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; and (e) determining cell damage, tissue damage, or organ damage (claim 1), disease progress (claim 19), or organ health (claim 27) 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 using a supervised machine learning model comprising a deep neural network trained on isolated tissue samples. The Applicant further states on page 12 (bottom) that thus, the methods of the present invention are not directed to a mental process, and further states that the Office action’s position that the amount of data and/or the amount of time to perform the process steps does not take a process out of the realm of the human mind effectively reads out ‘practically’ from the MPEP’s guidance regarding analysis of potential mental processes, and further states that the ‘practically’ qualifier is substantive and must be considered. The Applicant further states on page 13 that the Office action’s reasoning that the claim remains a mental process even if it takes considerable time and effort to process manually is not supported by the MPEP, and that under such reasoning, any data-analysis claim could be labelled a mental process so long as it can be logically described at a high level, and as demonstrated by the declaration, the present claims are impossible to perform mentally, not just impractical.
These arguments/remarks are not persuasive, because first, claims can recite a mental process even if they are claimed as being performed on a computer (MPEP 2106.04(a)(2)(III)(C)). Second, regarding the Applicant’s assertion that the human mind is not equipped to implement any kind of machine learning model, it is noted that a machine learning model can be calculated manually (i.e., in the human mind with the aid of pen and paper) depending on the type of model, e.g., the simplest type of machine learning model could be a linear regression, which predicts a continuous numerical output by fitting a straight line (y = mx + b) to data, by using an input variable (x) and an output variable (y), making its mathematical relationship easy to calculate and interpret. Third, regarding the Applicant’s assertion that steps involving high quantities of sequence data cannot practically be performed mentally in the human mind, it is noted that the fundamental nature of analyzing genomic data (e.g., concepts from probability, linear algebra, graph theory, and information theory) can be practically performed in the human mind, particularly with the aid of pen and paper. The amount of data (e.g., file size and/or gigabytes of data processing) does not change this fundamental nature, and therefore does not take the process out of the realm of the human mind. Furthermore, recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations can practically be performed in the human mind. Fourth, regarding the Applicant’s statement that “the guidance indicates that when a declaration provides evidence showing that the teachings of the specification, as understood by one or ordinary skill in the art, cannot practically be performed in the human mind, this evidence overcomes a prima facie case of ineligibility”, it is noted that the guidance explicitly states (e.g., at pages 4, 5, 6 and 7) that if the examiner determines that the SMED does not improperly supplement the specification, and that there is a nexus between the claimed invention and the evidence provided, the examiner should weigh all relevant evidence of record to determine if (emphasis added) this objective evidence of eligibility, when taken together with any other evidence of record, overcomes the prima facie case of ineligibility set forth in the previous Office action based on the preponderance of the evidence standard. Fifth, regarding the Applicant’s declaration (which has been acknowledged and addressed above), the declaration has been determined to be insufficient to overcome the rejection under 35 U.S.C. 101 because even though a particular analytical step may involve a large amount of data and/or a high number of iterations of an analytical process, the fundamental nature of the steps used to analyze the genomic data are processes that can practically be performed in the human mind, e.g., considering point (b) in the Applicant’s declaration (and skipping point (a) because point (a) was in fact identified as an additional element in the rejections of record), the fundamental nature of data analysis steps such as tallying data, normalizing data, and binning data are processes that are able to practically be performed in the human mind, particularly with the aid of a pen and paper. Thus, data analysis processes contrast with the fundamental nature of activities such as point (a) of the Applicant’s declaration, i.e., performing a whole genome sequencing (WGS) assay using the cfDNA fragments, which is a process that is not able to practically be performed in the human, and accordingly, is identified as an additional element (i.e., an element in addition to the judicial exception(s)) in the above rejection.
Regarding Step 2A Prong Two, the Applicant states on page 13 (para. 2) of the Remarks that patent eligibility is satisfied under the second prong of Step 2A. The Applicant further states (para. 3) that the claimed machine-learning decomposition and subtraction steps are not generic post-solution activity, and that these steps allow for the claimed type of cfDNA analysis by extracting organ-, tissue-, or cell-specific profiles and quantifying contributions, which are an improvement over the conventional methods. The Applicant points to paragraph [0017] in the specification for a description of the asserted improvement, and states (para. 4) that as such, these non-conventional steps provide an inventive concept that adds significantly more than any alleged judicial exception.
These arguments/remarks are not persuasive, because first, the above rejection does not identify the machine-learning decomposition and subtraction steps as generic post-solution activity, but rather, identifies these limitations as judicial exceptions (i.e., not additional elements). Second, the Applicant’s arguments (e.g., “these non-conventional steps provide an inventive concept that adds significantly more than any alleged judicial exception”) are presented under the heading of Step 2A Prong Two, (i.e., conventionality is not a consideration at Step 2A Prong Two), nonetheless, even at Step 2B (i.e., the search for an inventive concept), when all recited additional elements are considered individually and as an ordered combination, these additional elements fail to transform the judicial exceptions into a patent-eligible application of those exceptions. Third, even at Step 2A Prong Two, the independent claims provide additional elements to obtain a biological fluid sample, extract cfDNA from the biological fluid sample, and perform whole genome sequencing to generate cfDNA data for analysis, however once the cfDNA data is generated, the subsequent steps only perform analysis and/or calculations using the cfDNA data. However, the claims do not recite any limitations (i.e., additional elements) to which the result of the data analysis is practically applied.
Claim Rejections - 35 USC § 103
The rejection of claims 1-4, 6-14, 16, 17, 19, 21-23, 25-27, 29-31, 33, and 35-38 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 view of Ha et al. in the Office action mailed 13 January 2026 is withdrawn in view of the amendment received 10 July 2026.
The amendment received 10 July 2026 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).
Independent claims 1, 19, and 27 encompass steps for processing a biological sample to generate cell-free DNA (cfDNA) data, which is subsequently processed to determine cell damage, tissue damage, or organ damage (claim 1), determine disease progression (claim 19), or determine organ health (claim 27) by generating a genome-wide cfDNA copy number profile based on the whole-genome sequencing assay, generating a plurality of baseline reference profiles using supervised or unsupervised machine learning algorithms, removing the plurality of baseline reference profiles from the genome-wide cfDNA copy number profile, and determining the cell damage, tissue damage, or organ damage (claim 1), disease progression (claim 19), or organ health (claim 27) 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.
Dependent claims 2-4, 6-14, 16, 17, 21-23, 25, 26, 29-31, 33, and 35-38 further define the biological sample and the steps of processing the biological sample to obtain the cfDNA data, and further define the steps of the analysis of the cfDNA data.
Shendure et al. teaches methods for 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. teaches 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 teaches 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. teaches 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).
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 a supervised machine learning model, 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 a supervised machine learning model, 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. [00151]).
Regarding independent claims 1, 19, and 27, Shendure et al. in view of Dharajiya et al. in view of Abdueva do not show 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 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.
Response to Arguments
The Applicant's arguments/remarks received 10 July 2026 have been fully considered, but they are not persuasive.
The Applicant states on page 14 (para. 4) of the Remarks that as amended, claim 1 now recites that “the comparison is conducted by a supervised machine learning model, wherein the supervised machine learning model is trained on isolated tissue samples and comprises a deep neural network.” The Applicant further states that the Office action failed to establish that Shendure, Dharajiya, Abdueva, Sarwal and/or Ha teach or suggest at least this limitation. The Applicant further states (para. 5) that the Office action cited Ha to allege teaching or suggestion of “mutational signatures in whole-exome sequencing of cfDNA and tumor biopsies predicted using a Bayesian non-negative matrix factorization (NMF) approach. The Applicant further states that Ha is silent with respect to “the comparison is conducted by a supervised machine learning model, wherein the supervised machine learning model is trained on isolated tissue samples and comprises a deep neural network.”
These arguments are not persuasive, because the amendment necessitated new grounds of rejection and the new combination of references teach and/or suggest that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to conduct the comparison using a supervised machine learning model, wherein the machine learning model is trained on isolated tissue samples and comprises a deep neural network.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN W. BAILEY whose telephone number is (571)272-8170. The examiner can normally be reached Mon - Fri. 1000 - 1800.
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/S.W.B./Examiner, Art Unit 1687
/Joseph Woitach/Primary Examiner, Art Unit 1687
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