DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Claims 10-19 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 19 March 2026.
Claim Status
Claims 10-19 are withdrawn.
Claims 1-9 and 20 are pending and under examination herein.
Priority
This application is a CON of CN2020/072841 filed 17 January, 2020 and retains the effective filing date.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 7/15/2022, 12/28/2023, and 12/02/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings, submitted 07/15/2022, are accepted by the examiner.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
The hyperlink is located at [0039]: “https://www.ncbi...”
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 1-9 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more, as detailed in the analysis below.
Eligibility Step 1: Subject matter eligibility evaluation in accordance with MPEP § 2106:
Claims 1-9 are directed to a statutory category (method).
Claim 20 is non-statutory as it recites “a computer readable medium”. The claim as instantly recited reads on carrier waves, which are transitory propagating signals and therefore are not proper patentable subject matter because they do not fit within any of the four statutory categories of invention (In re Nuijten, Federal Circuit, 2007). It is noted that the recitation of a "non-transitory computer readable medium" would overcome the rejection with respect to claim 20 reading on signals. However, the amendment to only "non-transitory computer readable medium" would not overcome the rejection under 35 U.S.C. 101 since the claims would still be directed to a judicial exception without significantly more (see below).
[Claims 1-9: Eligibility Step 1: YES]
[Claim 20: Eligibility Step 1: NO]
Though claim 20 is not directed to statutory subject matter, in the interest of compact prosecution, Alice/Mayo evaluation in accordance with MPEP 2143 continues below for all claims.
Eligibility Step 2A: This step determines whether a claim is directed to a judicial exception in accordance with MPEP § 2106.
Eligibility Step 2A -- Prong One: Limitations are analyzed to determine if the claims recite any concepts that could equate to a judicial exception (i.e. abstract idea, law of nature, or natural phenomenon).
Recitations of Judicial Exceptions:
Claim 1: selecting a predetermined region on the reference genome sequence, and determining, based on the sequencing data of the first nucleic acid sample of the pregnant woman, mutation information in the predetermined region; (mental process)
determining the concentration of cell-free fetal nucleic acids corresponding to the predetermined region based on the mutation information in the predetermined region. (mental process)
Claim 2: The method according to claim 1, wherein the predetermined region is 50 to 200 kb, preferably 100 kb in length. (mental process)
Claim 5: The method according to claim 1, wherein in step (2), said determining the mutation information in the predetermined region comprises:
aligning the sequencing data with the reference genome sequence to determine mutation loci in the predetermined region and base types for each of the mutation loci;
for each of the mutation loci, determining a specific base type based on numbers of sequencing reads corresponding to respective base types, and determining a frequency of the specific base type, so as to obtain a plurality of frequencies; (mental process)
determining, based on each of the plurality of frequencies, a proportion of mutation loci corresponding to the frequency, the proportion of the mutation loci characterizing a number of the mutation loci corresponding to the frequency. (mathematical concept, mental process)
Claim 6: determining a distribution of the proportion of the mutation loci with respect to the frequency: (mathematical concept)
determining the concentration of cell-free fetal nucleic acids corresponding to the predetermined region based on the distribution. (mental process, mathematical concept)
Claim 7: two-dimensionally plotting the proportion of the mutation loci against the plurality of frequencies within a predetermined frequency range: (mathematical concept)
determining the concentration of cell-free fetal nucleic acids based on frequencies corresponding to peaks and troughs of the two-dimensionally plotted graph. (mathematical concept, mental process)
Claim 8: wherein the specific base type is a minor allele type. and the predetermined frequency range is a range from 0 to 0.25 or a subset thereof, or a range from 0.25 to 0.5 or a subset thereof, wherein (mathematical concept)
when the predetermined frequency range is a range from 0 to 0.25 or a subset thereof, an allele frequency having a value of a corresponding to a second peak of the peaks in a frequency increasing direction is selected, and the concentration of cell-free fetal nucleic acids is 2a; (mental process)
when the predetermined frequency range is a range from 0.25 to 0.5 or a subset thereof, an allele frequency having a value of b corresponding to a first peak of the peaks in a frequency increasing direction is selected. and the concentration of cell-free fetal nucleic acids is 1-2b. (mental process)
Claim 9: The method according to claim 7, wherein the specific base type is a major allele type, and the predetermined frequency range is a range from 0.5 to 0.75 or a subset thereof, or a range from 0.75 to 1 or a subset thereof, wherein (mathematical concept)
when the predetermined frequency range is a range from 0.5 to 0.75 or a subset thereof. an allele frequency having a value of c corresponding to a second peak of the peaks in a frequency increasing direction is selected, and the concentration of cell-free fetal nucleic acids is 2c; (mental process)
when the predetermined frequency range is a range from 0.75 to 1 or a subset thereof, an allele frequency having a value of d corresponding to a first peak of the peaks in a frequency increasing direction is selected, and the concentration of cell-free fetal nucleic acids is 1-2d. (mental process)
Step 2A- Prong One Analysis:
Determining, selecting, or removing data from a larger set can be accomplished through making mental observations of data, while utilizing pen and paper. As such, the noted limitations fall under the mental process grouping of abstract ideas.
Calculating proportions, and the recitation of mathematical equations are directed to analysis techniques that use mathematical formulas, calculations, and relationships which can similarly be executed by hand or using a pen and paper. As such, limitations that involve activities of this manner fall into the mental process and/or mathematical concept grouping of abstract ideas.
[Step 2A – Prong One: YES]
Eligibility Step 2A – Prong Two: 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. If the claim contains no additional claim elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)). Additional elements are recited, categorized, and analyzed below.
Data Gathering/Outputting:
Claim 1: acquiring sequencing data of a first nucleic acid sample of a pregnant woman and a reference genome sequence, the first nucleic acid sample of the pregnant woman containing cell-free fetal nucleic acids, and the sequencing data being composed of a plurality of sequencing reads;
Claim 3: The method according to claim 1, wherein a sequencing depth of the sequencing data is smaller than or equal to 100X, preferably 60X to 100X.
Claim 4: The method according to claim 1, wherein the first nucleic acid sample of the pregnant woman is derived from peripheral blood of the pregnant woman and the mutation information comprises at least one of SNP, Indel, or SV.
Computer Components:
Claim 20: computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor
Step 2A- Prong Two Analysis:
Limitations that complete necessary data gathering activities for the claimed invention and do not place necessary limits on or integrate the abstract ideas represent insignificant extra solution activity per MPEP 2106.05(g).
Generic computer components or implementations of a method onto a generic computing environment provide mere instructions to implement abstract ideas per Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d 1984.
As such, the additional elements, when viewed separately and in the context of a whole claimed invention, do not integrate the judicial exceptions into practical application.
[Step 2A – Prong Two: NO]
Eligibility Step 2B: Claim elements are probed for inventive concept equating to significantly more than the judicial exception (MPEP 2106.04(II)).
Step 2B Analysis:
Data gathering activities that assess and measure data from prior processing to be used in a diagnosis are further considered well-known and conventional within the art, as exemplified by Mayo, 566 U.S. at 79, 101 USPQ2d at 1968 and PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012).
Generic computing environments that store and retrieve information in memory are further found to be well-understood, routine, and conventional per Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 for and Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984. See also Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015)
[Eligibility Step 2B: NO]
As such claims 1-9 and 20 are directed to judicial exceptions and rejected under 35 U.S.C 101, in accordance with Alice/Mayo, MPEP 2143 evaluation.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jiang et al. (Bioinformatics; Vol. 28: 22; 2012).
Jiang et al. describes FetalQuant, a statistical mixture model which deduces fractional fetal DNA concentration from massively parallel sequencing of DNA in maternal plasma.
Claims 1 and 20 are directed to methods and computer readable storage mediums with computer programs that when executed by a processor: obtain sequencing data of cell-free fetal nucleic acids from a pregnant woman and a reference genome; compare a selected region of the reference genome to a sample sequence data to determine mutations; and use the mutation information to determine a concentration of cell-free fetal nucleic acids in the selected region.
Jiang et al. teaches collecting and sequencing maternal plasma samples from four pregnant women (page 4, column 2); after sequencing, aligning the sequenced reads to a reference genome (page 3, fig. 2); and deducing the fractional fetal DNA concentration directly from targeted MPS data, in which the allelic counts are inferred from aligned plasma DNA reads at each single-nucleotide polymorphism (SNP) sites annotated in the dbSNP 130 (UCSC Hg18) (page 2, column 1).
Jiang et al. further teaches FetalQuant is implemented in C++ which is available for non-commercial users (page 2, column 1); and providing the executables running on x86_64 GNU/Linux platform and Intel Xeon 2.80 GHz CPU (page 6, column 2).
Claim 2 is directed to the predetermined region being 50kb-200kb long.
Jiang et al. teaches the probes covered a total region of ~5.5 Mb on chromosomes 7 (945 kb), 11 (389 kb), 13 (1.1 Mb), 18 (1.2 Mb), 21 (1.3 Mb) and X (181 kb), which were targeted regions that covered a total of ~27 000 SNPs in the dbSNP130 database (page 4, column 2); and after the sequencing, all the sequenced reads were aligned to the human reference genome Hg18, because the capture probes were designed according to reference genome Hg18 (page 4, column 2).
Claim 3 is directed to sequencing depth of the sequencing data being ≤ 100X, preferably within the range of 60 - 100X.
Jiang et al. teaches it takes 17 s to analyze 20197 SNPs with 72.8-fold coverage (page 6, column 2).
Claim 4 is directed to the sequence data being derived from a peripheral blood sample; and the mutations include at least one of the following: single nucleotide polymorphisms, indels, or structural variation mutations.
Jiang et al. teaches collecting maternal plasma samples (page 4, column 2); and using this method to distinguish the informative single-nucleotide polymorphism loci (page 1, column 1).
Claim 5 is directed to locating mutations by aligning the sample sequence to the reference genome to determine the loci and base type change for each mutation; counting the base types for each located mutation; and calculating proportions for the frequency of each base type compared to the total number of mutations.
Jiang et al. teaches inferring the allelic counts from aligned plasma DNA reads at each single-nucleotide polymorphism (SNP) site annotated in the dbSNP 130 (page 2, column 1); calculating the allelic counts from the pileup results, such as for SNP [C/G] site, the A allele (C) has three counts and the B allele (G) has two counts (page 2, fig. 1); and calculating allele counts in the maternal plasma as b/(a + b) (page 2, fig. 1).
Claim 6 is directed to distributing the proportion of base changes as a function of mutation frequency; and using the distribution to determine the concentration of cell- free fetal nucleic acids in the sequence sample.
Jiang et al. teaches distributing the allelic counts for a given SNP locus position i, using a linear combination of the conditional binomial distribution (page 2 column 2), where T is the total number of SNP loci used for estimating the fractional fetal DNA concentration (page 3, column 1).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Jiang et al. (Bioinformatics; Vol. 28: 22; 2012), as applied to claims 1-6 and 20 above.
Jiang et al. describes FetalQuant, a statistical mixture model which deduces fractional fetal DNA concentration from massively parallel sequencing of DNA in maternal plasma.
Jiang et al. teaches a method of determining the concentration of cell-free fetal nucleic acids, based on mutation information of the sample of a pregnant woman, as described above.
Claim 7 is directed to plotting the proportion distribution of mutation loci against the frequencies within a predetermined range; and determining the concentration of cell-free nucleic acids based on the peaks and troughs of the plotted distribution.
Jiang et al. teaches FetalQuant computes the maximum log-likelihood based on the binomial mixture model (page 3, fig. 2), where the B allele fraction can be expected to vary between 0 and 0.5 (page 2, column 1); and determines the fractional fetal DNA concentration by the corresponding f-value illustrated by the vertical dash line in the bottom curve plot (page 3, fig. 2),
Jiang et al. shows the vertical dash line in the bottom curve plot corresponds to the peak of the plotted distribution (page 3, fig. 2).
Jiang et al. does not explicitly teach using the troughs of the plotted distribution to determine the concentration of cell-free nucleic acids.
Jiang et al. further teaches it is important to know the minimum number of SNP loci needed for accurate fractional fetal DNA concentration deduction (page 5, column 2).
Therefore Jiang et al. teaches using the peak of the proportion distribution to determine the concentration of cell-free nucleic acids; and provides further motivation for one of ordinary skill in the art to further use the trough of the distribution, corresponding with the lowest mutation frequency, in order to arrive at a more accurate concentration calculation.
Claims 8 is rejected under 35 U.S.C. 103 as being unpatentable over Jiang et al. (Bioinformatics; Vol. 28: 22; 2012), as applied to claims 1-7 above, in view of Lanman et al. (Plos One; Vol. 10 (10); 2015).
Claim 8 is directed to the specific base type being that of a minor allele, and the predetermined frequency range being within the ranges of 0.0 – 0.25 or 0.25-0.50. It is further directed to having the second peak in the distribution represent the allele frequency, (a) and the concentration of cell-free fetal nucleic acids equal to 2a, when the range is at or between 0.0 – 0.25; and having the first peak in the distribution represents the allele frequency (b) and the concentration of cell-free fetal nucleic acids equal to (1-2b), when the range is at or between 0.25-0.50.
Jiang et al. teaches the expected fractional fetal DNA concentration (f) can be calculated based upon the non-targeted sequencing data and the genotype information from both the mother and fetus according to the equation: f = 2p/(p+q) x 100 in which p is the count of DNA molecules carrying the fetal-specific allele, and q is the count of DNA molecules carrying the allele shared by the fetus and mother (page 5, column 1); where the B allele fraction can be expected to vary between 0 and 0.5 (page 2, column 1).
Jiang et al. teaches the fractional feta DNA concentration can be determined by the observed allelic counts at each SNP locus i for the maternal–fetal genotype combinations
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and
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(page 2, column 2).
Jiang et al. further teaches parameters are mainly determined by the fractional fetal DNA concentration (f) in plasma, where
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(page 3, column 2); and since the fractional fetal DNA concentration is unlikely to be greater than 0.5 in maternal plasma, computing the fractional fetal DNA concentration iteratively from 0 to 0.5, progressing with 0.001 increment per iteration until the log-likelihood achieves the maxima (page 3, column 2); and the determining the four possible maternal–fetal genotype combinations have with the following B allele fractions: 0, f/2, 0.5-f/2, and 0.5 (page 2, fig. 1), exemplified when the B allele fraction is 0.4 (2/5) (page 3, column 1).
Therefore Jiang et al. teaches calculating fetal concentration using minor allele frequencies in a method analogous to the claimed invention. Though Jiang et al. does not explicitly teach subtracting twice the allele frequency that is between 0.25 and 0.5 from 1, it teaches that the concentration must be less than one and derived from the analogous frequency variable. As such, multiplying the final result by 100 and subtracting from one would be obvious to one of ordinary skill in the art before the time of filing, based on the equations and limits taught by Jiang et al.
Jiang et al. does not teach having the first or second peak in the distribution represent allele frequency (claims 8-9).
Lanman et al. describes an analytical and clinical validation of a digital sequencing panel for quantitative, highly accurate evaluations of cell-free circulating tumor DNA.
Lanman et al. teaches reporting mutant allele fractions quantitatively for somatic single nucleotide variants of clinical significance (page 5, fig. 1d); distinguishing them from germline single nucleotide variants (SNVs) by reference to the COSMIC and dbSNP databases, as well as their concentrations (page 5, fig. 1d); and plotting the MAFs for the 24 SNVs in the paired ten samples in a correlation plot (page 8, column 1).
Lanman et al. further teaches the three peaks represent kernel distribution density plots of the frequencies of somatic and germline mutant allele fractions (page 11, fig. 6); in which somatic mutations of the far-left peak can be generally distinguished from heterozygous germline SNVs around 50% MAF of the middle peak (page 11, fig. 6); and completing the digital sequencing process, enabling post-sequencing removal of the false positives associated with detecting somatic variants present in cfDNA at low concentrations, and quantifying the fractional concentration or mutant allele fraction for a given mutation via (b) identifying all germline single nucleotide polymorphisms (SNPs) and somatic single nucleotide variants (SNVs), and (c) calling the somatic SNVs (page 19, column 1).
Lanman et al. further teaches similar biological mechanisms play in circulating peripheral blood in vivo occur (page 18, column 1); digital sequencing relies on massively parallel sequencing of circulating tumor DNA fragments combined with algorithms that utilize multiple analytic inputs to accurately compute quantitative SNV concentrations in plasma (page 17, column 1); and DNA isolated from cancer tissue or fetal DNA isolated from maternal blood are often available in relatively high mutant allele fractions (MAFs) (page 3, column 1).
Therefore Lanman et al. teaches a method of analyzing sequenced and aligned DNA samples, plotting the proportion of SNP mutation loci against allele frequencies derived, and applying an algorithm in order to quantify the concentration of circulating tumor DNA, a subset of cell-free DNA. It further teaches such techniques and determination may also be similar to that of fetal cell-free DNA, derived from peripheral blood sample of a pregnant woman.
As Jiang et al. also teaches informative single-nucleotide polymorphism loci is where the mother is homozygous and the fetus is heterozygous (page 1, column 1), it would be obvious to one of ordinary skill in the art to combine the use the first and second peaks, representing such allele fractions, based on the teachings of Jiang et al. for the purpose of determining the concentration of cell-free fetal DNA; with the combination yielding predictable results.
Claims 9 is rejected under 35 U.S.C. 103 as being unpatentable over Jiang et al. (Bioinformatics; Vol. 28: 22; 2012) in view of Lanman et al. (Plos One; Vol. 10 (10); 2015), as applied to claim 8 above, and in further view of Kang et al. (Plos One; Vol. 11(9); 2016).
Claim 9 is directed to the specific base type being that of a major allele, and the predetermined frequency range being within the range of 0.50 – 0.75 or 0.75- 1.0. It is further directed to having the second peak in the distribution represent the allele frequency, (c) and the concentration of cell-free fetal nucleic acids equal to 2c, when the range is at or between 0.5-0.75; and having the first peak in the distribution represents the allele frequency, (d) and the concentration of cell-free fetal nucleic acids equal to (1-2d), when the range is at or between 0.75- 1.0.
Jiang et al. in view of Lanman et al. teach a method of calculating the concentration of cell-free fetal DNA using the allele frequency, represented by the first and second peaks in a distribution, when the base type is that of a minor allele within the range of 0.0 – 0.25 or 0.25-0.50.
Jiang et al. and Lanman et al. do not teach determining the concentration of cell-free fetal nucleic acids with a major allele frequency within the range of 0.50-0.75 or 0.75-1.0.
Kang et al. describes an advanced model to precisely estimate Cell-Free Fetal DNA concentration in maternal plasma.
Kang et al. teaches calculating the counts of the major and minor alleles at each locus, (page 7, column 1); distributing the SNPs in maternal plasma, by representing each SNP by a specific point; in which the x-axis and y-axis represent the counts of the major alleles (A/a) and the second most common alleles (B/b), respectively (page 5, column 1); and using only the effective SNPs were to estimate the cell-free fetal DNA concentration (f) at each effective SNP locus with Formula 1: f = 2y (x +y) (page 5, column 1), where the major and minor allelic counts at the SNP locus i (denoted xi and yi, respectively) followed the normal distribution (page 7, column 1).
Kang et al. further teaches setting the ratios of the maternal-fetal genotype combinations to 0.7, 0.1, 0.1 and 0.1 for the x-axis (page 7, column 1); and using, Formula 1 and Type 2 (AAab), where the mother was homozygous but the fetus was heterozygous (page 4, column 1); to calculate the cffDNA fraction (page 5, column 1).
Therefore Kang et al. teaches calculating the concentration of fetal cell-free DNA using an identical algorithm and informative SNP type to that of Jiang et al. and Lanman et al., even when the base type is that of a major allele. As such, Kang et al. provides a finding that one of ordinary skill in the art could apply the known quantification technique to both major and minor allele types and frequency ranges yielding predictable results and a reasonable expectation of success.
Conclusion
No claims are currently allowed.
Correspondence
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/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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