DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are currently pending and examined on the merits.
Priority
The instant application claims priority to U.S. Provisional Application 63/339,766 filed on 5/9/2022. At this point in examination, the effective filing date of claims 1-20 is 5/9/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/24/2024 is in compliance with the provisions of 37 CFR 1.97. A signed copy of the corresponding 1449 form has been included with this Office Action.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-14 are directed to a system (machine). Claims 15-17 are directed to a non-transitory computer-readable storage medium (machine). Claims 18-20 are directed to a method (process) for detecting damage of deoxyribonucleic acid (DNA) from a tissue sample. Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A, Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth described in the claim.
Claims 1, 5-6, 10, 14-15, 17-18, and 20 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas, as noted below.
Independent claims 1, 15, and 18 further recite:
determining a symmetric normalized odds ratio based at least in part on the information, wherein the symmetric normalized odds ratio corresponds to damage of the DNA (i.e., mental processes, mathematical concepts);
determining the symmetric normalized odds ratio comprises: computing a first odds ratio (i.e., mental processes, mathematical concepts);
determining the symmetric normalized odds ratio comprises: computing a second odds ratio, wherein a numerator and a denominator in the second odds ratio are reversed relative to the first odds ratio (i.e., mental processes, mathematical concepts);
determining the symmetric normalized odds ratio comprises: summing the first odds ratio and the second odds ratio (i.e., mental processes, mathematical concepts);
determining the symmetric normalized odds ratio comprises: normalizing the summation (i.e., mental processes, mathematical concepts);
calculating a confidence metric of one or more of the molecules based at least in part on the symmetric normalized odds ratio and a threshold, wherein the confidence metric corresponds to a probability that the one or more molecules are identified correctly (i.e., mental processes, mathematical concepts).
Dependent claims 5 and 20 further recite:
wherein the operations comprise calling variants in the DNA based at least in part on the confidence metric (i.e., mental processes).
Dependent claim 6 further recites:
wherein the operations comprise filtering out a subset of the call variants based at least in part on the confidence metric (i.e., mental processes).
Dependent claim 10 further recites:
wherein the operations comprise adjusting one or more sonication parameters for subsequent sonication of the tissue sample based at least in part on the confidence metric (i.e., mental processes).
Dependent claim 14 further recites:
wherein the one or more operations comprise determining a quality metric of the tissue sample by aggregating multiple confidence metrics for the molecules in the tissue sample (i.e., mental processes, mathematical concepts).
Dependent claim 17 further recites:
wherein the operations comprise: calling variants in the DNA based at least in part on the confidence metric (i.e., mental processes);
wherein the operations comprise: adjusting one or more sonication parameters for subsequent sonication of the tissue sample based at least in part on the confidence metric (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. Dependent claims 2-4, 7-9, 11-13, 16, and 19 recite information further limiting the judicial exceptions indicated above.
Therefore, claims 1, 5-6, 10, 14-15, 17-18, and 20 recite an abstract idea.
[Step 2A, Prong One: YES]
Eligibility Step 2A, Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that, when examined as a whole, integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A, Prong One are not integrated into a practical application because of the reasons noted below.
Claims 1, 15, and 18 recite the additional non-abstract elements of data gathering:
receiving information corresponding to identified molecules of deoxyribonucleic acid (DNA) from a tissue sample (claims 1, 15, and 18).
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Claims 1, 15, and 18 recite the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof:
a computer system (claims 1 and 18);
an interface circuit (claim 1);
a computation device coupled to the interface circuit (claim 1);
memory (claim 1);
a non-transitory computer-readable storage medium (claim 15).
The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-20 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application. Claims 1, 15, and 18 contain additional elements that would not integrate a judicial exception into a practical application and are further probed for inventive concept in Step 2B.
[Step 2A, Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
With respect to claims 1, 15, and 18: The limitations identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception. Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to claims 1, 15, and 18: The limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception. These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field (Trading Technologies Int’l v. IBG, TLI Communications). They do not require or set forth a particular machine (Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l), they do not affect a transformation of matter, nor do they provide an unconventional step. Simply appending well understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook, Versata Development Group v. SAP America).
[Step 2B: NO]
Therefore, claims 1-20 are patent ineligible under 35 U.S.C. § 101.
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 1-6, 12-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tellaetxe-Abete et al. (NAR Genomics and Bioinformatics, 2021, 3(4), 1-16), as provided in the IDS filed 5/24/2024, in view of GATK Team (StrandOddsRatio, 2020, 1-4, https://gatk.broadinstitute.org/hc/en-us/articles/360041849111-StrandOddsRatio), Zhang et al. (Bioinformatics, 2019, 36(8), 2328-2336), and Utsuno et al. (Fertility and Sterility, 2013, 99(6), 1573-1580.e1).
With respect to claims 1, 15, and 18:
Regarding the recited receiving information corresponding to identified molecules of deoxyribonucleic acid (DNA) from a tissue sample, Tellaetxe-Abete et al. discloses exome-sequencing data from 27 matched formalin-fixed and paraffin-embedded (FFPE) and fresh-frozen (FF) samples from the European Nucleotide Archive, which samples came from 13 different tumor specimens and were sequenced using Illumina technology (pg. 2, col. 2, para. 3, lines 1-5). This teaches DNA sequences from a tissue sample.
Regarding the recited determining a symmetric normalized odds ratio based at least in part on the information, wherein the symmetric normalized odds ratio corresponds to damage of the DNA, Tellaetxe-Abete et al. discloses evaluating the performance of learning algorithms for the classification of formalin-induced cytosine deamination artefacts and non-deamination variants on sequencing data using variant descriptors, which includes strand bias (pg. 2, col. 2, para. 2, lines 1-6; pg. 5, col. 1, para. 1; Supplementary Material, pg. 3, para. 4). The learning algorithms calculate strand bias using a formula SB-GATK =
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to determine whether this anomaly is associated with formalin-induced artefacts. This teaches determining an odds ratio based on the sequencing data to determine whether strand bias calculated using the odds ratio formula corresponds to damage of the DNA.
Tellaetxe-Abete et al. does not disclose determining the symmetric normalized odds ratio comprises: computing a first odds ratio.
However, GATK Team discloses Odds Ratio R =
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(pg. 1, last line). This teaches computing a first odds ratio.
Tellaetxe-Abete et al. does not disclose determining the symmetric normalized odds ratio comprises: computing a second odds ratio, wherein a numerator and a denominator in the second odds ratio are reversed relative to the first odds ratio.
However, GATK Team discloses Odds Ratio 1/R =
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(pg. 2, lines 1-2). This teaches computing a second odds ratio, where the formula is the inverse of the first odds ratio equation.
Tellaetxe-Abete et al. does not disclose determining the symmetric normalized odds ratio comprises: summing the first odds ratio and the second odds ratio.
However, GATK Team discloses summing Odds Ratios R + 1/R to detect a difference in strand bias between forward and reverse strands for the reference or alternate alleles (pg. 1, para. 1, lines 4-5; pg. 2, line 3). The sum makes the formula symmetric. This teaches summing the first and second odds ratios.
Tellaetxe-Abete et al. does not disclose determining the symmetric normalized odds ratio comprises: normalizing the summation.
However, GATK Team discloses applying a scale factor of refRatio and altRatio to ensure that the symmetric odds ratio is large only (pg. 2, lines 3-9). The final value is given in natural log space. This teaches scaling or normalizing the summation.
Tellaetxe-Abete et al. and GATK Team do not disclose calculating a confidence metric of one or more of the molecules based at least in part on the symmetric normalized odds ratio and a threshold, wherein the confidence metric corresponds to a probability that the one or more molecules are identified correctly.
However, Zhang et al. discloses symmetric odds ratios as a parameter in annotating variants in sequenced genomes, which is a feature used to train Gaussian mixture models (GMMs) for computing VQSLOD, a log odds quality score representing the odds of a variant being correctly called (pg. 2328-2329, col. 2, para. 4; pg. 2333, Table 1). Also, further discloses using a sensitivity threshold to determine a VQSLOD used to filter variants with a smaller VQSLOD score (pg. 2328-2329, col. 2, para. 4). This teaches a metric of variants based on symmetric odds ratios and a threshold, where the metric corresponds to a probability that variants are identified correctly.
Zhang et al. does not disclose a confidence metric of one or more DNA molecules.
However, Utsuno et al. discloses using an odds ratio and its confidence interval to assess DNA fragmentation (pg. 1577, col. 1, Table 2; pg. 1558, col. 1, para. 2, lines 8-10). This teaches a confidence metric of DNA molecules based on odds ratios.
It would have been prima facie obvious to one of ordinary skill in the art to combine DNA from formalin-fixed and paraffin-embedded tissue samples disclosed by Tellaetxe-Abete et al. with determining a symmetric normalized odds ratio disclosed by GATK Team, the metric based on symmetric odds ratios and a threshold disclosed by Zhang et al., and a confidence metric of DNA molecules based on odds ratios disclosed by Utsuno et al. to make a method for detecting DNA damage from a tissue sample. One would be motivated to combine DNA from FFPE tissue samples with a symmetric normalized odds ratio, a metric based on symmetric odds ratios and a threshold, and a confidence metric of DNA molecules based on odds ratios because GATK Team discloses that the symmetric odds ratio formula is best at taking into account large amounts of data in high coverage situations (pg. 1, para. 2, lines 3-4). This means the symmetric odds ratio is efficient on handling large amounts of sequence data. The VEF tool disclosed by Zhang et al. can be applied to VCF files of arbitrary size and offers a significant improvement in running time as compared with VQSR (pg. 2335, col. 2, para. 1, lines 1-4). This means the metric from this VEF tool offers flexibility and speed in detecting DNA damage from a tissue sample. Utsuno et al. discloses odds ratios of shape parameters are useful for developing a model for precise prediction of DNA fragmentation (pg. 1578, col. 1, para. 2, lines 8-10). This means the confidence metric based on the odds ratios could contribute to precision in detecting DNA damage from tissue samples. There is a likelihood of success, since variant classification algorithms, symmetric odds ratios, variant filtering tools, and observing sperm DNA fragmentation associations are well known techniques in the field of biology.
With respect to claim 2:
GATK Team, Zhang et al., and Utsuno et al. do not disclose wherein the DNA damage is associated with formalin fixing and paraffin embedding of the tissue sample.
However, Tellaetxe-Abete et al. discloses exome-sequencing data from 27 matched formalin-fixed and paraffin-embedded (FFPE) and fresh-frozen (FF) samples from the European Nucleotide Archive, which samples came from 13 different tumor specimens and were sequenced using Illumina technology (pg. 2, col. 2, para. 3, lines 1-5). Also, further discloses that storing tissue samples by formalin fixation and paraffin embeddings causes DNA obtained from those samples to suffer from significant levels of fragmentation, denaturation, cross-linking and chemical modifications, contributing to sequence artifacts (pg. 1, col. 2, para. 1, lines 4-13). This teaches DNA damage being associated with formalin fixing and paraffin embedding of tissue samples.
With respect to claim 3:
GATK Team, Zhang et al., and Utsuno et al. do not disclose wherein the DNA damage comprises oxidated degradation of guanine to 8-oxoguanine (oxoG) or formaldehyde-induced DNA and chromatin damage; and wherein the formaldehyde-induced DNA and chromatin damage comprises: deamination, depurination, or histone-DNA crosslinks.
However, Tellaetxe-Abete et al. discloses FDeamC (fraction of cytosine deamination artifacts), which is a metric based on the guanine oxidation measure FoxoG (fraction of guanine to 8-oxoguanine oxidation artifacts) (pg. 3, para. 2). Also, further discloses evaluating the performance of learning algorithms for the classification of formalin-induced cytosine deamination artifacts and non-deamination variants on sequencing data using variant descriptors, which includes FDeamC (pg. 2, col. 2, para. 2, lines 1-6; pg. 4, Table 1). This teaches measuring oxidated degradation of guanine to 8-oxoguanine to classify DNA damage on sequencing data.
With respect to claims 4, 16, and 19:
GATK Team, Zhang et al., and Utsuno et al. do not disclose wherein the information comprises DNA sequences that each correspond to a single strand of DNA from the tissue sample.
However, Tellaetxe-Abete et al. discloses evaluating the performance of learning algorithms for the classification of formalin-induced cytosine deamination artefacts and non-deamination variants on sequencing data using variant descriptors, which includes strand bias (pg. 2, col. 2, para. 2, lines 1-6; pg. 5, col. 1, para. 1; Supplementary Material, pg. 3, para. 4). The learning algorithms calculate strand bias using a formula SB-GATK =
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, which determines a discrepancy between the reference and alternate allele count distributions on the forward and reverse strands of DNA from the FFPE tissue samples (pg. 2, col. 2, para. 3, lines 1-5; Supplementary Material, pg. 3, para. 4). This teaches determining strand bias by looking at DNA sequences that each correspond to a single strand of DNA from tissue samples.
GATK Team, Zhang et al., and Utsuno et al. do not disclose wherein the DNA damage is associated with strand bias.
However, Tellaetxe-Abete et al. discloses evaluating the performance of learning algorithms for the classification of formalin-induced cytosine deamination artefacts and non-deamination variants on sequencing data using variant descriptors, which includes strand bias (pg. 2, col. 2, para. 2, lines 1-6; pg. 5, col. 1, para. 1; Supplementary Material, pg. 3, para. 4). The learning algorithms calculate strand bias using a formula SB-GATK =
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to determine whether this anomaly is associated with formalin-induced artefacts. This teaches determining whether the DNA damage is associated with strand bias.
With respect to claims 5 and 20:
Tellaetxe-Abete et al., GATK Team, and Utsuno et al. do not disclose wherein the operations comprise calling variants in the DNA based at least in part on the confidence metric.
However, Zhang et al., discloses symmetric odds ratios as a parameter in annotating variants in sequenced genomes, which is a feature used to train Gaussian mixture models (GMMs) for computing VQSLOD, a log odds quality score representing the odds of a variant being correctly called (pg. 2328, col. 1, para. 2, lines 1-5; pg. 2328-2329, col. 2, para. 4; pg. 2333, Table 1). Also, further discloses using a sensitivity threshold to determine a VQSLOD used to filter variants with a smaller VQSLOD score (pg. 2328-2329, col. 2, para. 4). This teaches calling variants in the DNA based on a confidence metric.
With respect to claim 6:
Tellaetxe-Abete et al., GATK Team, and Utsuno et al. do not disclose wherein the operations comprise filtering out a subset of the call variants based at least in part on the confidence metric.
However, Zhang et al., discloses symmetric odds ratios as a parameter in annotating variants in sequenced genomes, which is a feature used to train Gaussian mixture models (GMMs) for computing VQSLOD, a log odds quality score representing the odds of a variant being correctly called (pg. 2328-2329, col. 2, para. 4; pg. 2333, Table 1). Also, further discloses using a sensitivity threshold to determine a VQSLOD used to filter variants with a smaller VQSLOD score (pg. 2328-2329, col. 2, para. 4). This teaches filtering out variants based on a confidence metric.
With respect to claim 12:
Tellaetxe-Abete et al., Zhang et al., and Utsuno et al. do not disclose wherein a given odds ratio in the first odds ratio and the second odds ratio is computed based at least in part on: a number of occurrences of a first allele on a first strand in the DNA; a number of occurrences of the first allele on a second strand in the DNA; a number of occurrences of a second allele on the first strand in the DNA; and a number of occurrences of the second allele on the second strand in the DNA.
However, GATK Team discloses a 2x2 contingency table that Odds Ratios R and 1/R are based on, which depicts a count of a reference allele on the forward strand X[0][0], a count of a reference allele on the reverse strand X[0][1], a count of an alternate allele on the forward strand X[1][0], and a count of an alternate allele on the reverse strand X[1][1] (pg. 1, para. 3). This teaches first and second odds ratios computed based on a number of occurrences of a first or second allele on a first or second strand in the DNA.
With respect to claim 13:
Tellaetxe-Abete et al., Zhang et al., and Utsuno et al. do not disclose wherein the first allele has a majority allele frequency and the second allele has a minority allele frequency.
However, GATK Team discloses in an example calculation indicating that there are 1450 reference alleles on the forward strand, 345 reference alleles on the reverse strand, 160 alternate alleles on the forward strand, and 212 alternate alleles on the reverse strand, which shows strand bias for the reference allele, but not the alternate allele (pg. 2, lines 14-17). This teaches a first allele having a majority allele frequency and a second allele having a minority allele frequency.
With respect to claim 17:
Tellaetxe-Abete et al., GATK Team, and Utsuno et al. do not disclose wherein the operations comprise: calling variants in the DNA based at least in part on the confidence metric; or adjusting one or more sonication parameters for subsequent sonication of the tissue sample based at least in part on the confidence metric.
However, Zhang et al., discloses symmetric odds ratios as a parameter in annotating variants in sequenced genomes, which is a feature used to train Gaussian mixture models (GMMs) for computing VQSLOD, a log odds quality score representing the odds of a variant being correctly called (pg. 2328, col. 1, para. 2, lines 1-5; pg. 2328-2329, col. 2, para. 4; pg. 2333, Table 1). Also, further discloses using a sensitivity threshold to determine a VQSLOD used to filter variants with a smaller VQSLOD score (pg. 2328-2329, col. 2, para. 4). This teaches calling variants in the DNA based on a confidence metric.
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Tellaetxe-Abete et al. (NAR Genomics and Bioinformatics, 2021, 3(4), 1-16), GATK (StrandOddsRatio, 2020, 1-4, https://gatk.broadinstitute.org/hc/en-us/articles/360041849111-StrandOddsRatio), Zhang et al. (Bioinformatics, 2019, 36(8), 2328-2336), and Utsuno et al. (Fertility and Sterility, 2013, 99(6), 1573-1580.e1) as applied to claims 1-6, 12-13, 15-16, and 18-20 above, in view of Nachmanson et al. (BMC Medical Genomics, 2020, 13(173), 1-15).
Tellaetxe-Abete et al., GATK Team, Zhang et al., and Utsuno et al. are applied to claims 1-6, 12-13, 15-16, and 18-20 above.
With respect to claim 7:
Tellaetxe-Abete et al., GATK Team, Zhang et al., and Utsuno et al. do not disclose wherein the subset comprises false-positive variant calls in the call variants associated with the DNA damage or that are incorrectly labeled as contamination.
However, Nachmanson et al. discloses benchmarking variant calling in highly degraded FFPE DNA and comparing the results to high quality variants called from DNA of an adjacent frozen specimen (pg. 12, col. 1, para. 2, lines 1-5). Also, further discloses false positive variants due to C to T substitutions as a consequence of formalin fixation, which remained at low allelic fraction and displayed strong strand bias (pg. 12, col. 1, para. 2, lines 16-21). This teaches false positive variant calls in variant calls associated with DNA damage.
With respect to claim 8:
Tellaetxe-Abete et al., GATK Team, Zhang et al., and Utsuno et al. do not disclose wherein the subset comprise the variant calls associated with strand bias.
However, Nachmanson et al. discloses false positive variants due to C to T substitutions as a consequence of formalin fixation, which remained at low allelic fraction and displayed strong strand bias (pg. 12, col. 1, para. 2, lines 16-21). This teaches variant calls associated with strand bias.
With respect to claim 9:
Tellaetxe-Abete et al., GATK Team, Zhang et al., and Utsuno et al. do not disclose wherein the variant calls single-nucleotide variants (SNVs).
However, Nachmanson et al. discloses that single nucleotide variants (SNVs) and short insertions and deletions (indels) were called with VarDictJava and Mutect2 (pg. 4, col. 2, para. 2, lines 1-3). This teaches calling single nucleotide variants.
It would have been prima facie obvious to one of ordinary skill in the art to modify the method of detecting DNA damage from tissue samples disclosed by Tellaetxe-Abete et al., GATK Team, Zhang et al., and Utsuno et al. to incorporate calling single-nucleotide variants disclosed by Nachmanson et al. One would be motivated to incorporate single-nucleotide variants into the DNA damage detection method because Nachmanson et al. discloses demonstrating that blunt-end ligation of the sequencing adapter to the fragmented and damaged DNA was key to increasing the quality of the exome sequencing (pg. 13, col. 1, para. 2, lines 3-7). This means incorporating single-nucleotide variants will increase the quality of detecting DNA damage in tissue samples. There is a likelihood of success, since variant classification algorithms, symmetric odds ratios, variant filtering tools, observing sperm DNA fragmentation associations, and mutational profiling are well known techniques in the field of biology.
Claims 10-11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tellaetxe-Abete et al. (NAR Genomics and Bioinformatics, 2021, 3(4), 1-16), GATK (StrandOddsRatio, 2020, 1-4, https://gatk.broadinstitute.org/hc/en-us/articles/360041849111-StrandOddsRatio), Zhang et al. (Bioinformatics, 2019, 36(8), 2328-2336), and Utsuno et al. (Fertility and Sterility, 2013, 99(6), 1573-1580.e1) as applied to claims 1-6, 12-13, 15-16, and 18-20 above, in view of Sanches-Kuiper et al. [US11512340B2].
Tellaetxe-Abete et al., GATK Team, Zhang et al., and Utsuno et al. are applied to claims 1-6, 12-13, 15-16, and 18-20 above.
With respect to claim 10:
Tellaetxe-Abete et al., GATK Team, Zhang et al., and Utsuno et al. do not disclose wherein the operations comprise adjusting one or more sonication parameters for subsequent sonication of the tissue sample based at least in part on the confidence metric.
However, Sanches-Kuiper et al. discloses performing selective shearing on formalin-fixed and paraffin-embedded (FFPE) DNA samples based on DNA fragmentation levels, which includes optimizing the Covaris sonication settings such as duty cycle and bursts per second (pg. 20, col. 17-18, para. 4). This teaches adjusting sonication parameters for sonication of tissue samples based on DNA fragmentation levels.
Sanches-Kuiper et al. does not disclose a confidence metric.
However, Utsuno et al. discloses a confidence interval based on odds ratios for DNA fragmentation and depicts a correlation between the confidence interval with DNA fragmentation level in Table 2 (pg. 1577, col. 1, Table 2; pg. 1558, col. 1, para. 2, lines 8-10). This teaches a confidence metric for DNA fragmentation.
With respect to claim 11:
Tellaetxe-Abete et al., GATK Team, Zhang et al., and Sanches-Kuiper et al. do not disclose wherein the confidence metric corresponds to a level of DNA fragmentation.
However, Utsuno et al. discloses a confidence interval based on odds ratios for DNA fragmentation and depicts a correlation between the confidence interval with DNA fragmentation level in Table 2 (pg. 1577, col. 1, Table 2; pg. 1558, col. 1, para. 2, lines 8-10). This teaches a confidence metric that corresponds to DNA fragmentation levels.
It would have been prima facie obvious to one of ordinary skill in the art to modify the method of detecting DNA damage from tissue samples disclosed by Tellaetxe-Abete et al., GATK Team, Zhang et al., and Utsuno et al. to incorporate adjusting sonication parameters disclosed by Sanches-Kuiper et al. One would be motivated to incorporate adjusting sonication parameters into the DNA damage detection method because Sanches-Kuiper et al. discloses that selective shearing increases the diversity of nucleic acid species in a prepared nucleic acid library and retains shorter nucleic acids that may be discarded in other methods of preparation (pg. 15, col. 8, lines 1-2; pg. 16, col. 10, para. 4, lines 1-4). This means incorporating sonication parameter adjustment maximizes sample recovery without loss for detecting DNA damage in tissue samples. There is a likelihood of success, since variant classification algorithms, symmetric odds ratios, variant filtering tools, observing sperm DNA fragmentation associations, and nucleic acid library preparation are well known techniques in the field of biology.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Tellaetxe-Abete et al. (NAR Genomics and Bioinformatics, 2021, 3(4), 1-16), GATK (StrandOddsRatio, 2020, 1-4, https://gatk.broadinstitute.org/hc/en-us/articles/360041849111-StrandOddsRatio), Zhang et al. (Bioinformatics, 2019, 36(8), 2328-2336), and Utsuno et al. (Fertility and Sterility, 2013, 99(6), 1573-1580.e1) as applied to claims 1-6, 12-13, 15-16, and 18-20 above, in view of Zhou et al. (Journal of Proteome Research, 2017, 16(12), 4523-4530).
Tellaetxe-Abete et al., GATK Team, Zhang et al., and Utsuno et al. are applied to claims 1-6, 12-13, 15-16, and 18-20 above.
With respect to claim 14:
Tellaetxe-Abete et al., GATK Team, Zhang et al., and Utsuno et al. do not disclose wherein the one or more operations comprise determining a quality metric of the tissue sample by aggregating multiple confidence metrics for the molecules in the tissue sample.
However, Zhou et al. discloses a protein-level quantification confidence score, which is a sum of confidence scores from all constituent peptide-spectrum matches (PSMs) for peptides derived from breast cancer xenograft tissue samples (pg. 4524, col. 1-2, para. 4; pg. 4525, col. 1, para. 2, lines 11-13). This aggregated score is then used for protein-level data filtering to control the false positive rate (FPR) of protein quantification (pg. 4527, col. 1, para. 2, lines 1-2 and 13-16). This teaches aggregating confidence scores for molecules in tissue samples and using it as a quality metric.
It would have been prima facie obvious to one of ordinary skill in the art to modify the method of detecting DNA damage from tissue samples disclosed by Tellaetxe-Abete et al., GATK Team, Zhang et al., and Utsuno et al. to incorporate aggregating confidence scores disclosed by Zhou et al. One would be motivated to incorporate aggregating confidence scores into the DNA damage detection method because Zhou et al. discloses that the application of confidence score filtering significantly improved the quality of the quantification results for samples that were difficult to analyze (pg. 4529, col. 1, para. 2, lines 1-3). This means incorporating aggregating confidence scores will increase the quality of detecting DNA damage in tissue samples. There is a likelihood of success, since variant classification algorithms, symmetric odds ratios, variant filtering tools, observing sperm DNA fragmentation associations, and quality assessments of quantitative proteomic analyses are well known techniques in the field of biology.
Conclusion
No claims are allowed.
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/J.N.L./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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