Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-5, 7-14, 16-20, 22-29, and 31-32 are pending.
Claims 1 and 16 are independent.
Claims 6, 15, 21, and 30 are canceled.
Claims 9 and 24 are withdrawn from consideration as they are drawn to nonelected species.
Claims 1-5, 7-8, 10-14, 16-20, 22-23, 25-29, and 31-32 are examined.
Priority
As detailed on the Application Data Sheet, this application claims priority to as early as 12/23/2022. At this point in examination, all claims have been interpreted as being accorded this priority date.
Withdrawn Rejections/Objections
Rejections and/or objections not reiterated from previous office actions are hereby
withdrawn in view of the amendments filed 07/24/2026.
The 35 U.S.C. 112(b) rejections to claims 10-12 and 25-27 in the office action filed 04/29/2026 has been withdrawn in view of 07/24/2026 amendments (pg. 14, para. 2) by amending claims 1 and 16 to recite “selecting one or more reference samples”, thereby correcting the antecedent basis.
The 35 U.S.C. 103 rejections in the office action filed 04/29/2026 have been withdrawn in view of amendments received 07/24/2026. New rejections are applied.
The following rejections and/or objections are either maintained or newly applied. They constitute the complete set presently being applied to the instant application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7-8, 10-14, 16-20, 22-23, 25-29, and 31-32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claim(s) 1-8, 10-14 being representative) is directed to a method and (claim(s) 16-23, 25-29, and 31-22 being representative) is directed to a kit/system. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claims 1 and 16 recite determining and grouping minor allele frequency MAF; the limitation determining a frequency is considered a mathematical calculation, and as such, falls into mathematical concepts groupings of abstract ideas. furthermore, the limitation grouping MAF is considered a mathematical calculation, as claimed in claims 6-7 and 9-12. As such, said limitation falls into mathematical concepts groupings of abstract ideas.
Claims 1 and 16 further recites reordering the panel of SNPs according to mean or median MAF values; the limitation reordering according to a mean or median is considered organizing information through mathematical correlations (mathematical relationship), and as such, falls into mathematical concepts groupings of abstract ideas. Also, said limitation is considered mental process of reordering data using for example, pen and paper or computer.
Claims 1 and 16 further recites determining the MAF information comprising MAF variation summary statistics in the panel of SNPs (mathematical relationship/mathematical concepts; also, said limitation is considered mental process of determining a statistical analysis).
Claims 1 and 16 further recites selecting one or more reference samples from the plurality of samples based on one or more statistical criteria derived from MAF values; (organizing information and manipulating information through mathematical correlations/mathematical relationship/mathematical concepts; also, said limitation is considered mental process of determining a statistical analysis).
Claims 1 and 16 further recites determining MAF differences by comparing the MAF values of the one or more reference samples with the MAF values of one or more other of the selected reference samples, one or more other samples of the plurality of samples, and/or subsets of the SNPs; (mathematical calculation/mathematical concepts; also, said limitation is considered mental process of determining a difference by comparing).
Claims 1 and 16 further recites determining MAF variation summary statistics by aggregating the determined MAF differences; (mathematical calculation/mathematical concepts; also, said limitation is considered mental process of determining summary statistics by aggregating differences).
Claims 1 and 16 further recites grouping the SNPs of the reordered panel of SNPs according to the MAF variation summary statistics; (organizing information and manipulating information through mathematical correlations/mathematical relationship/mathematical concepts; also, said limitation is considered mental process of grouping data according to summary statistics, for example, mean or median).
Claims 2-3 and 17-18 recite additional information about the determining step.
Claims 5 and 20 recite determining a genotype of one or more contributors; the limitations determining a genotype can be practically performed in human mind (mental process) since human mind is capable of determining based on the result of an analysis (specification [0012], FIG. 4).
Claims 7 and 22 recite determining a separation point in the MAF variation summary statistics by determining a local minimum or maximum in a window (mathematical calculation/mathematical concepts).
Claims 8 and 23 provide more information about the determining step.
Claims 10 and 25 recite selecting the one or more reference samples from the plurality of samples comprising selecting a first sample comprising a highest mean MAF value among a plurality of samples (mathematical relationship/mathematical concepts, also mental process of determining based on the result of an analysis); selecting a second sample comprising a lowest correlation coefficient associated with the first sample (mathematical relationship/mathematical concepts, also mental process of selecting); determining MAF differences by subtracting MAF values of the selected first sample and the selected second sample (mathematical calculation/mathematical concepts).
Claims 11 and 26 recite selecting one or more reference samples comprising selecting a single reference sample comprising highest mean MAF value among a plurality of samples (mathematical relationship/mathematical concepts, also mental process of selecting based on result of a calculation); determining an MAF difference between the index sample and each of the plurality of samples (mathematical calculation/mathematical concepts).
Claims 12 and 27 recite selecting the one or more reference samples from the plurality of samples comprises: partitioning the panel of SNPs into a first subset of reordered SNPs having higher MAF values and a second subset of reordered SNPs having lower MAF values (mathematical relationship/mathematical concepts, also mental process of selecting based on partitioning).
Claims 12 and 27 further recite selecting a first reference sample comprising a highest mean MAF value among a set of high reordered SNPs (mathematical relationship/mathematical concepts, also mental process of selecting); selecting a second reference sample comprising a highest mean MAF among a set of low reordered SNPs (mathematical relationship/mathematical concepts, also mental process of selecting); determining an MAF difference between the first index sample and each of the set of high reordered SNPs (mathematical calculation/mathematical concepts); determining an MAF differences between the second reference sample and the first subset of reordered SNPs and second reference sample (mathematical calculation/mathematical concepts).
Claims 13 and 28 recite generating a waterfall plot for the mixed sample (mathematical calculation/mathematical concepts).
Claims 14 and 29 provide more information about the subject and sample.
Claims 31 and 32 recite determining a separation point in the MAF variation summary statistics (mathematical relationship/mathematical concepts); and grouping the MAF information based on the separation point (mathematical relationship/mathematical concepts).
Additionally, claims 1-5, 7-8, 10-14, 16-20, 22-23, 25-29, and 31-32 recite a correlation between nucleic acid from a pregnant recipient and an amount of contributor-derived nucleic acids, and as such, falls into judicial exception of Laws of nature and natural phenomena. See MPEP 2106(b) I.
The identified claims recite a law of nature, a natural phenomenon (product of nature) and/or fall into one of the groups of abstract ideas of mathematical concepts, mental processes, and/or certain methods of organizing human activity for the reasons set forth above. See MPEP 2106.04 (a)(2) III and MPEP 2106.04 (b) I. Therefore, claims are directed to one or more judicial exception(s) and require further analysis in Prong Two. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
The additional elements of claims 1-5, 7-8, 10-14, 16-20, 22-23, 25-29, and 31-32 include the following.
Claims 1 and 16 recite receiving, via a computer or an input function, nucleic acid sequence data; receiving a genomic relationship; a kit/system comprising instructions; and outputting an amount of contributor-derived nucleic acids.
The additional elements of a kit/system comprising a computer and instructions/software are generic computer components and/or processes. There are no limitations that indicate that the processor, input module, processing module, or output module in the computer-implemented system require anything other than generic computing systems. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Furthermore, the additional elements of receiving and outputting amount to necessary data gathering and outputting. The courts have found the limitations that amount to necessary data gathering and outputting are insignificant extra-solution activity that do not integrate a recited judicial exception into a practical application in Mayo, 566 U.S. at 79, 101 USPQ2d at 1968 and O/P Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (see MPEP 2106.05(g)).
Therefore, the additionally recited elements amount to generic computer components and/or insignificant extra-solution activity and, as such, the claims as a whole do no integrate the abstract idea into practical application. See MPEP 2106.05(g). Thus, claims 1-30 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. An inventive concept cannot be furnished by an abstract idea itself. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception.
The additional elements of claims 1-5, 7-8, 10-14, 16-20, 22-23, 25-29, and 31-32 include the following.
Claims 1 and 16 recite receiving, via a computer or an input function, nucleic acid sequence data; receiving a genomic relationship; a kit/system comprising instructions; and outputting an amount of contributor-derived nucleic acids.
The additional elements of a kit/system comprising a computer and instructions/software are conventional computer components and/or processes. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TU Communications LLC v. AV Auto, LLC, 823 F.3d 607,613,118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Furthermore, the additional elements of receiving and outputting amount to necessary data gathering and outputting. The courts have found the limitations that amount to necessary data gathering and outputting are insignificant extra-solution activity that do not amount to significantly more. See MPEP 2106.05(g).
Therefore, these additional elements are not sufficient to amount to significantly more than the judicial exception.
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Response to Arguments Regarding 101 Rejection
Applicant's arguments filed 07/24/2026 have been considered, but they are not yet persuasive. Applicant states (pg. 15, para. 2; pg. 17, first para., emphasis added):
Claims 1 and 16, as amended, recite a particular, multi-step transformation that improves the technical field of genotype-free computational deconvolution of mixed cell-free DNA samples for noninvasive transplant monitoring in a pregnant transplant recipient.
The claims recite a specific technological solution to a technological problem, implemented through a specific ordered workflow. The claims therefore are directed to a practical application and not to any alleged abstract idea or natural correlation itself.
These arguments are not yet persuasive. Applicant remarks are directed to Step 2A Prong Two of 101 analysis, 1st consideration relating to an improvement over the previous state of the technology field integrating possible judicial exceptions into a practical application.
Taken as a whole, the claims are interpreted as being directed to outputting an amount of contributor-derived nucleic acids in a mixed sample (judicial exception) by performing series of mental and/or mathematical processes (judicial exception). It is important to note, the judicial exception alone cannot provide the improvement (See MPEP 2106.04(d) III). The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)). In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)).
The additional elements of a kit/system comprising a computer and instructions/software are generic computer components and/or processes and the additional element of receiving and outputting amount to necessary data gathering and outputting (insignificant extra solution activities) that do not integrate the recited judicial exceptions into a practical application.
The steps of determining and grouping MAF information by reordering the panel, MAF summary statistics, selecting, determining a difference, and grouping, as noted by the Applicant, are judicial exceptions and as such, cannot provide the improvement. See MPEP 2106.04(d) III.
Whether examined individually or in combination, the additional elements of the claim beyond the mental steps, i.e., data collection and computer implementation, are insufficient to establish that the abstract ideas recited are integrated into a practical application.
An explanation of a technical improvement may help to overcome a 101 rejection, as discussed at Step 2A/2nd Prong, 1st consideration of the 101 analysis in MPEP 2106.04(d) and (d)(1). Such an improvement requires detailed explanation applicable to all embodiments reasonably within the claim scope. A concise statement of the particular improvement, a clear difference from the technology field, and explanation of how the claims deliver the improvement is not yet provided.
Applicant further states (pg. 17, para. 2, emphasis added):
Moreover, claims 1 and 16 are not "directed to" the correlation between nucleic acid from a pregnant transplant recipient and an amount of contributor-derived nucleic acids. Claims 1 and 16, as amended, do not preempt the natural correlation. Instead, they recite one particular process for exploiting it, while leaving open numerous alternative approaches.
These arguments are not yet persuasive. Applicant remarks argue preemption and whether the claims recite a law of nature or a natural phenomenon.
While "[t]he Supreme Court has described the concern driving the judicial exceptions as preemption, ...the courts do not use preemption as a standalone test for eligibility" (see July 2015 Guidance, §VI, 2nd para., citing to Alice, Mayo, buySAFE and Ultramercial). "A claim is not patent eligible merely because it applies an abstract idea in a narrow way. For an application of an abstract idea to satisfy step one, the claim’s focus must be something other than the abstract idea itself." and "While preemption concerns are 'the basis for the judicial exceptions to patentability, the absence of complete preemption does not demonstrate patent eligibility.'" (BSG Tech v. BuySeasons, CAFC 8/15/2018 also citing Ariosa). Specificity alone is insufficient to overcome a 101 rejection (see e.g. id.: "while a preemptive claim may be ineligible, the absence of complete preemption does not guarantee that a claim is eligible;" also Wolf v. Capstone Photography, No. 2:13-cv-09573-CAS-PJW, C.D. Cal., 28 Oct. 2014 at p. 21, which, while not controlling, nonetheless provides a cogent analysis and summary of controlling case law on the topic of preemption: "The most specific piece of technology recited... is still generic [and] insufficient to confer patent eligibility" and further citing to Alice). Thus, specificity is not a standalone test for eligibility, and the underlying issue of preemption is inherent in and resolved by the two-part framework from Alice and Mayo, as analyzed in the 101 rejection. It would advance the argument here to identify and argue according to one or more specific steps in 101 analysis as described in MPEP 2106.
The claims remain interpreted as reciting a law of nature or a natural phenomenon.
Applicant further states (pg. 17, para. 2, emphasis added):
Even assuming, arguendo, that independent Claims 1 and 16 recite a judicial exception, they nevertheless recite "significantly more" than any such alleged judicial exception under Step 2B.
As demonstrated further in at least the § 103 remarks below, none of Tang, Zhu, He, Lashmar, or Mayakonda, teaches or suggests this claimed workflow. The absence of such teachings supports the conclusion that the ordered combination is neither routine nor conventional and provides technical capabilities that were not previously achievable using prior art genotype- dependent approaches.
It is submitted that these arguments are not yet persuasive. These Applicant remarks are directed to Step 2B of 101 analyses, specifically evaluating additional elements to determine whether they amount to an inventive concept by considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself. Emphasis added.
Applicant does not specify which claim elements are not well-understood, routine, and conventional. Additionally, in Step 2B additional elements are evaluated to determine whether they amount to significantly more. Emphasis added.
As discussed in the above rejection, the additional elements of a kit/system comprising a computer and instructions/software are conventional computer components and/or processes (a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry). Furthermore, the additional elements of receiving and outputting amount to necessary data gathering and outputting (insignificant extra-solution activity). These additional elements considered individually are not sufficient to amount to significantly more than the judicial exception. Even considering the additional elements "as an ordered combination”, the computer component, receiving and outputting add nothing that is not already present when the steps are considered separately.
Therefore, the additionally recited elements considered individually or in combination do not amount to significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 14, 16-20, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al. (WO2024076469A1; as previously cited in form 892 dated 04/29/2026) in view of Zhu et al. (cfDNA deconvolution via NIPT of a pregnant woman after bone marrow transplant and donor egg IVF, Human Genomics. 2021 Feb 23; 15:14; as previously cited in form 892 dated 04/29/2026), and further in view of He et al. (Comparing strategies for selection of low-density SNPs for imputation mediated genomic prediction in U. S. Holsteins, published online: 14 December 2017, Genetica (2018) 146:137–149; as previously cited in form 892 dated 04/29/2026).
Regarding claims 1 and 16, Tang discloses methods for preparation and analysis of biological samples of maternal transplant recipients, wherein the methods comprise extracting cell-free DNA from the recipient, wherein the cell-free DNA comprises donor-derived cell-free DNA, recipient-derived cell-free DNA, and fetal-derived cell-free DNA, and measuring amounts of cell-free DNA and donor-derived cell-free DNA and detecting donor-derived cell-free DNA (Abstract);, where determining the amount of dd-cfDNA is determined by using machine learning-artificial intelligence, inherently disclosing g that the method is a computer-implemented method [0015]. Tang further discloses that the quantifying step comprises determining the percentage of dd- cfDNA out of the total of dd-cfDNA, fd-cfDNA, and rd-cfDNA in the biological sample [0120]; reading on limitations of a computer-implemented method of outputting an amount of contributor-derived nucleic acids in a mixed sample, obtained from a pregnant transplant recipient, comprising nucleic acids from at least three genetically distinct contributors.
Tang further discloses that the nucleic acid sequence data is generated by performing high throughput sequencing where at least one polymorphic locus (e.g. SNP loci) of the set of polymorphic loci and wherein each of the polymeric loci of the set is amplified (claim 4) [0173]. Tang further discloses that the quantifying step comprises determining the percentage of dd- cfDNA out of the total of dd-cfDNA, fd-cfDNA, and rd-cfDNA in the biological sample [0120]; reading on limitations of receiving, via a computer or an input function, nucleic acid sequence data from a panel of single nucleotide polymorphisms (SNPs) from the nucleic acids from the at least three genetically distinct contributors, wherein the at least three genetically distinct contributors comprise a maternal genomic contributor, a fetal genomic contributor, and a transplant donor genomic contributor.
Tang further discloses a cutoff threshold value for related vs unrelated donor [0166], for example, identifying a genomic relationship among contributors using genomic relatedness type data.
Tang further discloses performing the disclosed method without prior knowledge of donor and/or recipient genotypes [0012]; reading on limitations of wherein the amount of contributor-derived nucleic acids is determined without prior or predetermined genotype information identifying, for a SNP of the panel of SNPs, which allele belongs to which of the at least three genetically distinct contributors.
Tang further discloses determining the allele frequencies at each target locus [0179] and determining the percentage of dd- cfDNA out of the total of dd-cfDNA, fd-cfDNA, and rd-cfDNA in the biological sample [0120]; reading on limitations of outputting an amount of contributor-derived nucleic acids based on the genomic relationship and the MAF information grouping.
Tang [0180] as evidenced by Rabinowitz (US 20180025109A1) [0336] discloses determining minor allele frequencies from the panel of SNPs; reading on limitations of determining and grouping minor allele frequency (MAF) information from the panel of SNPs.
Further regarding limitations of receiving a genomic relationship among the at least three genetically distinct contributors, Zhu discloses a customized Non-Invasive Prenatal Paternity Test (NIPPT) prototype, which is a SNP-based panel that utilizes populational polymorphism to quantify genetic difference to correctly infer the relative genetic similarity between samples and provide a classification of the degree of relationship (pg. 8, col. 1, para. 2).
Zhu further discloses using BWA tool, a software package designed for bioinformatics, inherently disclosing that the method is computer-implemented (pg. 2, col. 2, sub section: Data Analysis, para. 1).
Zhu further discloses receiving NIPT sample from a pregnant female with a history of transplantation (pg. 2, col. 1, section: Material and Method; subsection: Sample Preparation, para. 1), where samples were plasma cfDNA samples (pg. 2, col. 2, last para.); reading on limitations of a computer-implemented method of outputting an amount of contributor-derived nucleic acids in a mixed sample, obtained from a pregnant transplant recipient, comprising nucleic acids from at least three genetically distinct contributors, the method comprising: receiving, via a computer or an input function, nucleic acid sequence data from a panel of single nucleotide polymorphisms (SNPs) from the nucleic acids from the at least three genetically distinct contributors, wherein the at least three genetically distinct contributors comprise a maternal genomic contributor, a fetal genomic contributor, and a transplant donor genomic contributor;
Zhu discloses using a 739-target region panel, which are selected with fewer repeats, consistent thermodynamic properties like GC content and nearest-neighbor melting temperature, and populational polymorphic locus to distinguish between the donor and maternal genome (pg. 2, col. 1, last para.).
Zhu discloses capturing sequencing data analysis workflow to evaluate the genetic relatedness between individual samples; Using the Burrows-Wheeler alignment (BWA) tool to align to human reference genome; processing germline mutations; using match ratio statistics (mrs), which is defined as the number of identical genotypes of shared loci divided by the total number of common non-reference loci between a pair of samples to infer relatedness.
Zhu further discloses using a deconvolution method for estimating the distribution of MAF in the sample (pg. 5, col. 2, para. 1; Fig. 5). Zhu further discloses estimating relatedness of different contributors in the sample (pg. 3, col. 1, para. 2; Table 2; Fig. 2 and 3; see also, Abstract). Zhu further discloses that matching ratio statistics (mrs) shows rather informative group segregation with respect to their biological relationships ranking (pg. 5, para. 1); reading on limitations of receiving a genomic relationship among the at least three genetically distinct contributors; determining and grouping minor allele frequency (MAF) information from the panel of SNPs.
Zhu further discloses estimate the percentage of the 3 different biological sources A (46, XY, 72.64%), B (46, XX, 18%), and C (46, XY, 9.36%) in the tested cfDNA NIPT sample (pg. 5, col. 1, para. 1). Zhu further discloses that the extremely high chromosome Y dosage of 82% male DNA in the plasma compared to normal pregnancy are assumed largely contributed by the male donor blood white cells with possible addition of a male fetus, while 18% are female cfDNA which most likely originated from the mother also with the possible addition of a female fetus… A seq FF-based in-house prediction method gives a 9.36% estimation of the fetal fraction, inferring cfDNA of placenta origin. It is therefore the plasma cfDNA could be a mixture of 3 different sources: A, 46, XY donor-maternal plasma cfDNA; B, 46, XX not replaced maternal cfDNA; C, fetal/placenta cfDNA of unknown gender (pg. 3, col. 2); reading on limitations of outputting an amount of contributor-derived nucleic acids based on the genomic relationship and the MAF information grouping.
Further regarding the MAF information statistical analysis of reordering the panel, MAF variation summary statistics, selecting reference sample, determining MAF differences, and grouping of SPNs, He compares the performance of SNP panels exploring factors such as evenly-spaced SNPs, increased minor allele frequencies, and SNP-trait associations either for single traits independently or for all the three traits jointly. He further disclosed exploration of the underlying relationships in their prediction, for example, using genomic relatedness data (abstract). He further discloses reordering SNP panels according to mean MAF summary statistics (Tables 2 and 4; Fig 4). He further discloses that SNPs with MAF < 0.05%, and SNPs with > 10% missing genotypes were all removed, for example, the local minimum and maximum window (pg. 138, col. 2, last para.). He further discloses that in order to reduce co-linearity between SNP loci, percentage of genotype sharing was computed on a moving window of 20 neighboring SNPs on each chromosome. For SNPs with > 99% genotype sharing, only the one with the greatest MAF, and closest to the central location of each moving window if there were ties, were kept and all the remaining SNPs were deleted (pg. 139, col. 1, para. 1). He further discloses grouping MAF information and reordering the SNP panel according to MAF (Fig. 2). He further discloses that the average MAF was 0.45 for the SEL6K panel and 0.30 for the UNF6K panel, and it was 0.30 for the 80K SNPs (i.e., 68,748 SNPs with MAF > 0.05) for optimal selection of SNPs (pg. 143, col. 1, last para.); reading on limitations of selecting one or more reference samples from the plurality of samples based on one or more statistical criteria derived from MAF values; determining MAF differences by comparing the MAF values of the one or more reference samples with the MAF values of one or more other of the selected reference samples, one or more other samples of the plurality of samples, and/or subsets of the SNPs.
He further discloses using a multiple-objective, local-optimization (MOLO) algorithm to select LD SNPs for the SEL6K SNP panel (for example, the reference sample), which is capable of selecting SNPs to meet multiple objectives, which included map coverage, minor allele frequency (MAF), map gaps, and many more criteria (pg. 138, col. 1, para. 2; pg. 140-141, subsection: Multiple-objective, local-optimization; Fig. 2); reading on limitations of MAF variation summary statistics by aggregating the determined MAF differences; and grouping the SNPs of the reordered panel of SNPs according to the MAF variation summary statistics.
Further regarding claim 16, Tang discloses a kit is disclosed for determining a transplant status designed to be used with the discloses methods [0165].
Regarding claims 2 and 17, Tang discloses that the method comprises longitudinally collecting a plurality of blood samples from the transplant recipient after transplantation, and measuring the amount of cfDNA and dd-cfDNA to determine a longitudinal change in the amount of cfDNA or a function thereof and a longitudinal change in the amount of dd-cfDNA for the transplant recipient [0138]; reading on limitations of wherein determining and grouping MAF information are based on a set of longitudinal samples.
Regarding claims 3 and 18, Tang discloses repeating the step of quantifying the amount of genomic contributions in the cfDNA longitudinally (claims 5 and 6); reading on limitations of wherein the set of longitudinal samples have the same genotype.
Regarding claims 4 and 19, Tang discloses that the panel of SNPs comprise fewer than 500 SNPs [0173] [0178]; reading on limitations of wherein the panel of SNPs comprises fewer than 500 SNPs.
Regarding claims 5 and 20, Tang discloses determining allele frequencies at each SNP locus [0179]. Tang [ 0180] as evidenced by Rabinowitz (US 20180025109A1) [0336] discloses determining minor allele frequencies from the panel of SNPs. Tang further discloses that the quantifying step comprises determining the percentage of dd- cfDNA out of the total of dd-cfDNA, fd-cfDNA, and rd-cfDNA in the biological sample [0120]; reading on limitations of determining a genotype of one or more of: the pregnant transplant recipient or the maternal genomic contributor, the fetal genomic contributor, and the transplant donor genomic contributor based on the MAF information grouping.
Regarding claims 14 and 29, Tang discloses the pregnant transplant recipient received a kidney transplant [0230]; reading on limitations of wherein the pregnant transplant recipient received a transplant comprising one or more of: a kidney transplant, a heart transplant, a lung transplant, a liver transplant, a pancreas transplant, a vascularized composite transplant, an intestinal transplant, a stomach transplant, a testis transplant, a penis transplant, an ovary transplant, a uterus transplant, a thymus transplant, a face transplant, a hand transplant, a leg transplant, a bone transplant, a cornea transplant, skin transplant, a heart valve transplant, a blood vessel transplant, or any combination thereof.
Rational for combining Tang, Zhu, and He:
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard to Tang, Zhu, and He, Examiner concludes that this combination represents applying known techniques to a known method. Tang, Zhu, and He are directed to assessing genotype of a multiple-contributor genome. Tang disclosed a computer-implemented method of outputting an amount of contributor-derived nucleic acids in a mixed sample, obtained from a pregnant transplant recipient with at least three genetically distinct contributors and determining MAF from SNP panels. In the same field of research, Zhu provided the details of determining MAF from SNP panels and grouping minor allele frequency (MAF) and determining a genomic relationship among genomic contributors. Furthermore, He provided the details of MAF determination and grouping.
One of ordinary skill in the art before the effective filing data of the claimed invention would have been capable of applying this known technique of determining MAF from SNP panels to infer relatedness and genomic contributions, as taught by Zhu, and MAF analysis, as taught by He, to the known method of Tang that was ready for improvement and the results would have been predictable to one of ordinary skill in the art.
One ordinary skilled in the art would have had a reasonable expectation of success at combining these known methods and this combination would have allowed for assembling more informative SNP panels that would increase the accuracy of genomic contribution inference separating contributors more accurately and efficiently. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Claims 7-8, 10-12, 22-23, 25-27, and 31-32 are rejected under 35 U.S.C. 103 as being unpatentable over Tang , in view of Zhu , in view of He, as applied to claims 1-5, 14, 16-20, and 29 above, and further in view of Lashmar (Assessing single-nucleotide polymorphism selection methods for the development of a low-density panel optimized for imputation in South African Drakensberger beef cattle, Journal of Animal Science, 2021, Vol. 99, No. 7, 1–12; as previously cited in form 892 dated 04/29/2026).
Claims 7-8, 10-12, 22-23, 25-27, and 31-32 depend on claims 1 and 16. Limitations of claims 1 and 16 have been taught in the above rejections.
Regarding claims 7 and 22, Tang discloses Identification of SNPs at which both the mother and father were homozygous ensured homozygosity of the fetus and that heterozygosity observed in the maternal cfDNA originated from the transplant [0222]. Tang further discloses identifying contributors in a cohort of transplant recipient pregnant women [0226] [0233] [0240], where selected target loci comprise one or more single nucleotide polymorphisms (SNPs) [0011] to calculate donor fraction estimates [0222].
Zhu discloses grouping (pg. 5, col. 1, last two para.; Fig 3). Zhu discloses MAF distribution by using PLINK to infer relatedness and discloses that homozygous and heterozygous loci in a single gDNA sample should present three horizontal bands in the distribution of MAF, as in Fig. 5, the less exogenous DNA it contains the more these three bands will be centered at 0, 0.5, and 1, for example, distribution of minor allele frequencies (pg. 5, col. 2; Figs. 4 and 5) inherently disclosing clumping and filtering of Minor allele frequencies/counts according to one or more min/max thresholds.
Regarding limitations of grouping the panel of SNPs according to the MAF variation summary statistics; determining a separation point in the MAF variation summary statistics by determining a local minimum or maximum in a window, He compares the performance of SNP panels exploring factors such as evenly-spaced SNPs, increased minor allele frequencies, and SNP-trait associations either for single traits independently or for all the three traits jointly (abstract). He further discloses reordering SNP panels according to mean MAF summary statistics (Tables 2 and 4; Fig 4). He further discloses that SNPs with MAF < 0.05%, and SNPs with > 10% missing genotypes were all removed, for example, the local minimum and maximum window (pg. 138, col. 2, last para.). He further discloses that in order to reduce co-linearity between SNP loci, percentage of genotype sharing was computed on a moving window of 20 neighboring SNPs on each chromosome. For SNPs with > 99% genotype sharing, only the one with the greatest MAF, and closest to the central location of each moving window if there were ties, were kept and all the remaining SNPs were deleted (pg. 139, col. 1, para. 1). He further discloses grouping MAF information and reordering the SNP panel according to MAF (Fig. 2). He further discloses that the average MAF was 0.45 for the SEL6K panel and 0.30 for the UNF6K panel, and it was 0.30 for the 80K SNPs (i.e., 68,748 SNPs with MAF > 0.05) for optimal selection of SNPs (pg. 143, col. 1, last para.); reading on limitations of grouping the panel of SNPs according to the MAF variation summary statistics; determining a separation point in the MAF variation summary statistics by determining a local minimum or maximum in a window.
Regarding claims 8 and 23, Tang discloses a method of genotype calling, using the joint distribution model and the allele count probabilities with relative probabilities of each of the status hypotheses that are calculated using statistical techniques taken from a group consisting of a read count analysis, comparing heterozygosity rates, a statistic that is only available when donor genetic information is used, the probability of normalized genotype signals for certain donor/recipient contexts, a statistic that is calculated using an estimated transplant fraction of the first sample or the prepared sample, and combinations thereof [0163] [0192-0193].
Zhu discloses MAF distribution by using PLINK to infer relatedness and discloses that homozygous and heterozygous loci in a single gDNA sample should present three horizontal bands in the distribution of MAF, as in Fig. 5, the less exogenous DNA it contains the more these three bands will be centered at 0, 0.5, and 1, for example, distribution of minor allele frequencies (pg. 5, col. 2; Figs. 4 and 5). Zhu further discloses presenting homozygous and heterozygous loci in a single gDNA sample in the distribution of MAF (pg. 5, col. 2, first para.); reading on limitations of wherein the separation point is used to group the SNPs into homozygous and heterozygous genotype groups.
Regarding claims 10 and 25, Lashmar discloses selecting SNPs with the highest mean minor allele frequency and linkage disequilibrium, for example, lowest correlation coefficient (abstract). Lashmar further discloses that the SNP with the highest MAF was chosen within the first segment per autosome after which each SNP within subsequent segments was chosen based on the highest index score calculated by subtracting MAF values (pg. 4, col. 1, first and second para.); reading on limitations of selecting a first reference sample comprising a highest mean MAF value among the plurality of samples; and selecting a second reference sample comprising a lowest correlation coefficient relative to the first reference sample, wherein determining the MAF differences comprises determining the MAF differences between the single reference sample and each of the plurality of samples subtracting the MAF values of the selected first reference sample and the selected second reference sample.
Regarding claims 11 and 26, Lashmar discloses that SNP’s within segments of equal size were chosen based on an index that maximized MAF whilst attempting to adhere to the ideal inter-SNP spacing per autosome for each panel density. The SNP with the highest MAF was chosen within the first segment per autosome after which each SNP within subsequent segments was chosen based on the highest index score calculated by calculating a difference (pg. 4, col. 1, para. 1); reading on limitations selecting a single reference sample comprising a highest mean MAF value among the plurality of samples and wherein determining the MAF differences comprises determining the MAF differences between the single reference sample and each of the plurality of samples.
Regarding claims 31 and 32, He discloses that monomorphic SNPs and SNPs with MAF < 0.05%, and SNPs with > 10% missing genotypes were all removed (pg. 138, col. 2, last para., Fig. 3). He further discloses grouping SNP’s based on separation points (Fig. 2-4). He further discloses reordering SNP panels according to mean MAF summary statistics (Tables 2 and 4; Fig 4). He further discloses that SNPs with MAF < 0.05%, and SNPs with > 10% missing genotypes were all removed, for example, the local minimum and maximum window (pg. 138, col. 2, last para.). He further discloses that in order to reduce co-linearity between SNP loci, percentage of genotype sharing was computed on a moving window of 20 neighboring SNPs on each chromosome. For SNPs with > 99% genotype sharing, only the one with the greatest MAF, and closest to the central location of each moving window if there were ties, were kept and all the remaining SNPs were deleted (pg. 139, col. 1, para. 1). He further discloses grouping MAF information and reordering the SNP panel according to MAF (Fig. 2). He further discloses that the average MAF was 0.45 for the SEL6K panel and 0.30 for the UNF6K panel, and it was 0.30 for the 80K SNPs (i.e., 68,748 SNPs with MAF > 0.05) for optimal selection of SNPs (pg. 143, col. 1, last para.); reading on limitations of determining a separation point in the MAF variation summary statistics; and grouping the MAF information SNPs of the reordered panel of SNPs based on the separation point.
Regarding claims 12 and 27, Lashmar discloses that SNP on each autosome were partitioned into a number of clusters based on their proximity in genomic position using the PAM algorithm implemented in R’s “clara” package. The number of clusters was equal to the number of pre-defined SNP to be selected per autosome. The medial SNP within each SNP cluster was chosen.
Rationale for combining to Tang, Zhu, He and Lashmar:
Applying the KSR standard to Tang, Zhu, He and Lashmar, Examiner concludes that this combination represents applying known techniques to a known method. Tang, Zhu, He and Lashmar are directed to providing optimal SNP panels from MAF information. Tang and zhu disclosed a computer-implemented method of outputting an amount of contributor-derived nucleic acids in a mixed sample, obtained from a pregnant transplant recipient with at least three genetically distinct contributors, determining relatedness, and determining MAF from SNP panels. In the same field of research, He and Lashmar provided the details of determining MAF from SNP panels. One of ordinary skill in the art before the effective filing data of the claimed invention would have been capable of applying these known techniques of determining MAF from SNP panels to the known method of Tang and Zhu that was ready for improvement and the results would have been predictable to one of ordinary skill in the art.
One ordinary skilled in the art would have had a reasonable expectation of success at combining the method of Tang, Zhu, He and Lashmar. Combining these known methods would have allowed for assembling more informative SNP panels that would increase the accuracy of genomic contribution inference. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Claims 13 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Tang , in view of Zhu , in view of He, as applied to claims 1-5, 14, 16-20, and 29 above, and further in view of Mayakonda et al. (Maftools: Efficient analysis, visualization and summarization of MAF files from large-scale cohort-based cancer studies, bioRxiv, posted May 11, 2016, pages 1-8; as previously cited in form 892 dated 04/29/2026).
Claims 13 and 28 depend on claims 1 and 16. Limitations of claims 1 and 16 have been taught in the above rejections.
Regarding claims 13 and 28, Tang further discloses determining the allele frequencies at each target locus [0179] and determining the percentage of dd- cfDNA out of the total of dd-cfDNA, fd-cfDNA, and rd-cfDNA in the biological sample [0120]; reading on limitations of outputting an amount of contributor-derived nucleic acids based on the genomic relationship and the MAF information grouping.
Tang [ 0180] as evidenced by Rabinowitz (US 20180025109A1) [0336] discloses determining minor allele frequencies from the panel of SNPs. Zhu further discloses using a deconvolution method for estimating the distribution of MAF in the sample (pg. 5, col. 2, para. 1; Fig. 5). Zhu further discloses estimating relatedness of different contributors in the sample (pg. 3, col. 1, para. 2; Table 2; Fig. 2 and 3; see also, Abstract). Zhu further discloses that matching ratio statistics (mrs) shows rather informative group segregation with respect to their biological relationships ranking (pg. 5, para. 1);
Regarding limitations of generating a waterfall plot of the MAF information, where waterfall plot comprises one or more tiers of stairs having one or more steps Mayakonda discloses a method of visualization and summarization of MAF files (abstract). Mayakonda further discloses that MAF files provides baseline data for many downstream analyses such as driver gene detection, detecting mutually exclusive set of events, mutational signatures and tumor heterogeneity estimation (pg. 1, last para.).
Mayakonda further discloses that oncoplots are essential tools for visualizing the comprehensive mutational landscape, sorting frequently muted genes… Oncoplot function from maftools, uses ComplexHeatmap Bioconductor package to draw such plots, for example, waterfall plot, while providing various customizable options. Oncoplot generated using LAML MAF for top ten mutated genes is shown in Figure 2A. plotmafSummary, for example, tool that provides summary statistics, is another function (Figure 2B) which shows overall summary of the cohort in terms of variants per sample and distribution of variants according to variant classification (pg. 2, section: visualization: para. 1; Figure 2).
Rationale for combining Tang, Zhu, He, and Mayakonda:
Applying the KSR standard to Tang, Zhu, He, and Mayakonda, Examiner concludes that this combination represents applying known techniques to a known method. Tang, Zhu, He and Mayakonda are directed to providing visualization to genomic data. Tang, Zhu, and He disclosed a computer-implemented method of outputting an amount of contributor-derived nucleic acids in a mixed sample, obtained from a pregnant transplant recipient with at least three genetically distinct contributors, determining relatedness, and determining MAF from SNP panels. Mayakonda provided the visualization technique of using a waterfall plot. One of ordinary skill in the art before the effective filing data of the claimed invention would have been capable of applying this known visualization technique of Mahyakonda to the known method of Tang, Zhu, and He that was ready for improvement and the results would have been predictable to one of ordinary skill in the art. One ordinary skilled in the art would have had a reasonable expectation of success at combining the method of Tang, Zhu, He and Lashmar. Combining these known methods would have allowed for better visualization of genomic data. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Response to Arguments Regarding 103 Rejections
Applicant's arguments filed 07/24/2026 have been considered, but they are not yet persuasive. Applicant argues that sample-to-sample MAF-difference and aggregation workflow have no counterpart in any of the cited references. As cited in the rejection, He teaches sample-to-sample MAF-difference and aggregation workflow by teaching optimal selection of SNPs, mean MAF, sample-to-sample comparison of MAF, and aggregation of MAF (pg. 139: Selection of LD SNPs -pg. 140: Multiple-objective, local-optimization; Fig. 2).
Applicant further argues that Tang's quantification expressly relies on prior knowledge of parental genotypes. As cited in the rejection, Tang teaches that their methods are performed without prior knowledge of donor and/or recipient genotypes [0012].
Therefore, the new combination of prior art necessitated by the amendments teaches all the limitations of the claimed invention.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-5, 7-8, 10-14, 16-23, and 25-29 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-8, 10-14, 16-23, 25-29, and 31 and 32 of copending Application No. 19/245,981 in view of Tang et al. (WO2024076469A1; as previously cited in form 892 dated 04/29/2026) in view of Zhu et al. (cfDNA deconvolution via NIPT of a pregnant woman after bone marrow transplant and donor egg IVF, Human Genomics. 2021 Feb 23; 15:14; as previously cited in form 892 dated 04/29/2026), and further in view of He et al. (Comparing strategies for selection of low-density SNPs for imputation mediated genomic prediction in U. S. Holsteins, published online: 14 December 2017, Genetica (2018) 146:137–149; as previously cited in form 892 dated 04/29/2026).
Claim 1 recites outputting amounts of contributor derived nucleic acids from a mixed sample with at least three contributors, including receiving sequence data from SNPs, receiving a genomic relationship, determining and grouping minor allele frequency information, and outputting an amount per contributor. Reference claim 1 also recites these elements.
Claim 1 recites the three contributors are a maternal contributor, fetal contributor, and transplant donor contributor, whereas reference claim 1 recites a transplant recipient contributor and two transplant donor contributors. Tang discloses methods for preparation and analysis of biological samples of maternal transplant recipients, wherein the methods comprise extracting cell-free DNA from the recipient, wherein the cell-free DNA comprises donor-derived cell-free DNA, recipient-derived cell-free DNA, and fetal-derived cell-free DNA, and measuring amounts of cell-free DNA and donor-derived cell-free DNA and detecting donor-derived cell-free DNA (Abstract);, where determining the amount of dd-cfDNA is determined by using machine learning-artificial intelligence, inherently disclosing g that the method is a computer-implemented method [0015]. Tang further discloses that the quantifying step comprises determining the percentage of dd- cfDNA out of the total of dd-cfDNA, fd-cfDNA, and rd-cfDNA in the biological sample [0120].
Tang further discloses the transplant recipient has received one or more transplants selected from kidney, liver, pancreas, intestinal, heart, lung, heart/lung, stomach, testis, penis, ovary, uterus, thymus, face, hand, leg, bone, bone marrow, cornea, skin, pancreas islet cell, heart valve, blood vessel, and blood transfusion (claim 18).
Zhu discloses grouping (pg. 5, col. 1, last two para.; Fig 3). Zhu discloses MAF distribution by using PLINK to infer relatedness and discloses that homozygous and heterozygous loci in a single gDNA sample should present three horizontal bands in the distribution of MAF, as in Fig. 5, the less exogenous DNA it contains the more these three bands will be centered at 0, 0.5, and 1, for example, distribution of minor allele frequencies (pg. 5, col. 2; Figs. 4 and 5) inherently disclosing clumping and filtering of Minor allele frequencies/counts according to one or more min/max thresholds.
Regarding limitations of grouping the panel of SNPs according to the MAF variation summary statistics; determining a separation point in the MAF variation summary statistics by determining a local minimum or maximum in a window, He compares the performance of SNP panels exploring factors such as evenly-spaced SNPs, increased minor allele frequencies, and SNP-trait associations either for single traits independently or for all the three traits jointly (abstract). He further discloses reordering SNP panels according to mean MAF summary statistics (Tables 2 and 4; Fig 4). He further discloses that SNPs with MAF < 0.05%, and SNPs with > 10% missing genotypes were all removed, for example, the local minimum and maximum window (pg. 138, col. 2, last para.). He further discloses that in order to reduce co-linearity between SNP loci, percentage of genotype sharing was computed on a moving window of 20 neighboring SNPs on each chromosome. For SNPs with > 99% genotype sharing, only the one with the greatest MAF, and closest to the central location of each moving window if there were ties, were kept and all the remaining SNPs were deleted (pg. 139, col. 1, para. 1). He further discloses grouping MAF information and reordering the SNP panel according to MAF (Fig. 2). He further discloses that the average MAF was 0.45 for the SEL6K panel and 0.30 for the UNF6K panel, and it was 0.30 for the 80K SNPs (i.e., 68,748 SNPs with MAF > 0.05) for optimal selection of SNPs (pg. 143, col. 1, last para.); reading on limitations of grouping the panel of SNPs according to the MAF variation summary statistics; determining a separation point in the MAF variation summary statistics by determining a local minimum or maximum in a window.
Further regarding reference claims 31 and 32, Tang discloses administrating immunosuppressive therapy to the transplant recipient [0035].
Instant claims 2-8, 10-14, 16-23, and 25-29, mirror those of reference claims 2-8, 10-14, 16-23, and 25-29.
The instant claim teaches three contributors to the DNA found in a blood sample: are a maternal contributor, fetal contributor, and transplant donor contributor. Tang also teaches determining the contributions of multiple contributors, including a mother and fetus where the mother is a transplant recipient that has received one or more transplants (claims 1 and 18). Therefore, it would have been prima facie obvious to one of ordinary skill in the art to replace one of the donor contributors with a second donor contributor because one of ordinary skill in the art would have been able to carry out such a substitution, and the results were reasonably predictable as the analysis involves discerning different contributors agnostic to their source as a fetus or donor.
Response to Arguments Regarding Double Patenting Rejection
Applicant's 7/24/2026 arguments have been considered, but they are not yet persuasive. Applicant argues that the non-statutory double patent rejection has been rendered moot by the amendment.
The new combination of prior art teaches all the limitations of the claimed invention. Therefore, the rejection is maintained.
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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/G.S./Examiner, Art Unit 1686
/G. STEVEN VANNI/Primary patents examiner, Art Unit 1686
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