DETAILED ACTION
Claims 21,22,23,24,25,26,29,30,31,32,33,34,35,40,41,43,44,45,60,61 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim(s) 21,24,33,41,45,34,44,60 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1):
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claim 21,24,33,41,45,34,44,60 further in view of JOHNSON (US 2016/0186262 A1) and OLSHEN et al. (Circular binary segmentation for the analysis of array-based DNA copy number data):
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of GAASTERLAND et al. (US 2015/0315645 A1):
Claim(s) 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of CLEARLY et al. (US 2014/0057793 A1):
Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of CLEARLY et al. (US 2014/0057793 A1) as applied in claim 25 further in view of AITMANN et al. (WO 2014/181107 A1):
Claim(s) 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of CLEARLY et al. (US 2014/0057793 A1) as applied in claim 25 further in view of PARK et al. (WO 2016/208827 A1) with SEARCH machine translation and Williams et al. (US 2005/0227917 A1) and WEEKS et al. (US 2016/0244818 A1):
Claim(s) 30,31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of CLEARLY et al. (US 2014/0057793 A1) as applied in claim 25 further in view of HALPERN et al. (US 2013/0316915 A1):
Claim(s) 32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of CLEARLY et al. (US 2014/0057793 A1) as applied in claim 25 further in view of Garner, JR. et al. (US 2015/0337388 A1):
Claim(s) 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of Karchin et al. (US 2015/0025861 A1):
Claim(s) 43 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of Chen et al. (US 2009/0281981 A1):
Claim(s) 35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of Fonte et al. (US 2015/0055085 A1):
Claim(s) 61 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of Fonte et al. (US 2015/0055085 A1) as applied in clam 35 further in view of Yin et al. (US 2014/0235461 A1):
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120, 121, 365(c), or 386(c) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. PCT/US2018/016522 has PRO 62/453,492 02/01/2017, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
The claimed “machine learning” of claim 40 is not in Application No. PCT/US2018/016522 has PRO 62/453,492 02/01/2017.
Accordingly, claims are not entitled to the benefit of the prior application
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Claim Objections
Re claims 33,34:
Applicant is advised that should claim 33 be found allowable, claim 34 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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 21,22,23,24,25,26,29,30,31,32,33,34,35,40,41,43,44,45,60,61 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
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Step 0: establish the broadest reasonable interpretation, shown in footnotes in this Office action;
Step 1, claim 21 is a method
Step 2A, prong 1:
The claim(s) recite(s) a mental process and math such as.
“receiving…data…identifying a… variant…observing a… fraction…modeling… determining …repeating”:
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Step 2A, prong 2:
This judicial exception is not integrated into a practical application because claims 21 is not effecting a treatment or prophylaxis1 for a disease or medical condition or improving2 the function-capacity of a computer or technical field3.
Step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements (not boxed in below) considered individually and in combination with abstract such as the claimed (1) “aligned sequence” (BWA-MEM does not need explanation, like 35 USC 112(a), to one of ordinary skill in the art) and (2) “partitioning the genome” (i.e., “exome”-“sequencing”) and (3) “machine learning”4 [00006] in view of applicant’s disclosure and background thereof adheres to the conventional:
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Claim Rejections - 35 USC § 103
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.
Claim(s) 21,24,33,41,45,34,44,60 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1):
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Re 21. (Currently Amended), Baccash teaches A method of detecting a somatic tumor variant and/or a germline variant from a tumor sample of a subject, comprising:
[[a.]] receiving [[an]] aligned sequence data from the tumor sample (resulting in “a biological sample is obtained from an organism (e.g., a human)” [0046]: fig. 1:160: “Data Repository”);
[[b.]] b) identifying a (“hypothesis”5) candidate variant (or “variation” [0088]: fig. 1:135: “Variant Calling Logic”) within the aligned sequence data;
[[c.]] ) partitioning the genome into segments (resulting in a “ ‘reference’…portion” [0031]), wherein each segment contains at most one copy number alteration (or “copy number variants (‘CNVs’)” [0040] penult S);
[[d.]] d) observing an allelic fraction (“being able to detect variants present in a small fraction of the cells in a cancer sample” [0043] last S) of the candidate variant of each segment;
[[e.]] modeling (via “ a VAF (variable allele fraction) model” [0045] penult S) to find a copy number state (i.e., said CNVs) estimate (“of the scores for the discordant loci”, [0173] 2nd S, of CNVs) of the segments and a tumor-cell fraction (or “percentage” [0110] 3rd S: fig. 4: “80% Tumor”) of6
(H) main7 (via “first variant score”-“first set” plus “second variant score”-“second set” [0011] 2nd & 3rd Ss, each variant set/group “based on the top89 hypothesis” [0008] last S) and10
(I) subclonal11
variant (“bucket” [0178]) groups (or 1st & 2nd sets mapped to Markush alternative (H));
[[f.]] fl determining an expected allelic fraction (“and thus hypotheses with different allele fractions would have different likelihoods” [0045] 3rd S) of12 the
(A) candidate (via the “reference” {i.e., “Reference Hypothesis13”} -“germline”-“het-erozygous one-base deletion” [0089] [0090] of calling variants/base-deletions) (&)
(B) germline (via the “reference” {i.e., “Reference Hypothesis”} -“germline”-“het-erozygous one-base deletion” [0089] [0090] of calling variants/deletions)
variant (maps to either Markush alternative (A) or (B) as a called base-deletion) or
the
(C) candidate
(D) somatic14
variant;
[[g.]] g) determining a posterior (score) probability that a (“top” [0087]) candidate variant (fig. 7:760: “top hypothesis” “genome” “variants”) is1516
(E) somatic,
(F) germline heterozygous (via the “reference” {i.e., “Reference Hypothesis17”} -“germline”-“het-erozygous one-base deletion” [0089] [0090] of calling variants/base-deletions with germline serving as reference and of an illustrated germline heterozygous call in [0089]), or
(G) homozygous18 (calls discussed in [0091])
using a Bayesian model (“ to compute a probability ratio for any two hypotheses from the optimization stage, and variant calls are then made based on the most likely hypothesis according to this Bayesian probability model.” [0083] last S); and
[[h.]] h) repeating steps e) through g) until the result converges (“ to a value referred to as the ‘calibrated score’ ” [0173] penult S) .
Baccash does not teach the difference19 of claim 1 of “posterior” (probability)20.
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ZHANG teaches the difference of claim 1 “posterior” (probability)21 via “[0068] Applying a Bayesian-type approach, the posterior probability P(A|r.sub.1,r.sub.2) may be represented as Equation 2:”
Since Baccash teaches Bayesian, one of skill in the art of Bayesian can make Baccash’s be as ZHANG’s seeing the change as “useful for downstream variant detection tools such as DiBayes, Small-InDels, Large-InDels and CNV”, ZHANG [0066]:
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Re 24. (Currently Amended), Baccash of the combination of Baccash,ZHANG teaches The method of claim 21,wherein the step of determining the expected allelic fractions of germline and somatic variants further comprises:
[[a)]] i) estimating (resulting in “a sequence hypothesis for a region (i.e. a hypothesis for the composite genome in the region) can include a specific variable fraction22 for the plurality of alleles that comprise the sequence hypothesis.” [0045] 1st S and “logic can estimate the probability for each DNB” [0122], wherein “ ‘DNB’ refers to the sequence of a nucleic acid fragment from which one or more reads (e.g., such as a mated read) have been sequenced.” [0035] penult S: fig. 1:162: “Mated Reads”) allele-specific copy number23 (or “allele”-“specific”-“allele fraction24”: allele: copy two: fig. 3: “C” is copied two times for each group) of25
(A) (fragmental) clonal26 (“DNBs” [0057 2nd S: fig. 1:105,162: “Nucleic Acid Fragments27”: “Mated Reads”) and
(B) (fragment of a fragment) sub-clonal (“DNBs” [0057] 2nd S: fig. 1:105,162: “Nucleic Acid Fragments”: “fragment”-“part” [0035] 2nd S)
copy number (comprised by “alleles”28-“likelihood”, [0006] 4th S: same 2/copy 2)
events (i.e., alleles-“likelihood29 ratio…(1)”, [0104]); and
[[b)]] ii) estimating (via “initial estimates of the scores for the discordant loci” [0173] 1st S) the (“DNA” [0119] 2nd S) sample fraction (resulting in a “measured30… percentage (allele31 fraction) of DNA” [0043] 3rd & 4th Ss) of32
(C) the main (or essential part33) clonal (“DNBs” [0057] 2nd S: fig. 1:105,162: essential/main “Nucleic Acid Fragments”: “fragment”-“likelihood(s)34” [0165] 2nd S) and
(D) (part-of-a-part) sub-clonal (“DNBs” [0057 2nd S: fig. 1:105,162: “Nucleic Acid Fragments”: “Mated Reads”: part-of-a-part)
populations (randomly drawn mapped to Markush alternative (C) “of alleles, at various genome loci, that are sequenced from the nucleic acid fragments included in a biological sample” [0116] 1st S).
Re 33. (Currently Amended). Baccash of the combination of Baccash,ZHANG teaches The method of claim 21,further comprising:
applying a classifier (or “the replicate calibration logic” [0208]) to determine if the (mis-matching-hypothesis) candidate variant is35 (via a “likelihood” [0007][0008])
(A) a true variant (“being a true somatic mutation” [0007]) or
(B) an artifact36.
Re 41. (Currently Amended), Baccash of the combination of Baccash,ZHANG teaches The method of claim 33,wherein the (loci/locus) classifier is built (“e.g., as embodied in computer system 130)” [0173] 1st S) specifically for (or “especially for” [0077] last S)
(A) SNVs (via “two het SNP loci 510 and 520” [0115], wherein “A het can be a single-nucleotide polymorphism (SNP) if the reference genome location has two alleles that differ by a single base” [0033] 4th S) or
(B) INDELs (“near other variants” [0077]).
Re 45. (Currently Amended), Baccash of the combination of Baccash,ZHANG teaches The method of claim 33,wherein the (loci) classifier is applied (occurs at two places:
(1) fig. 2:270: “Perform replicate calibration to determine likelihood that variation relative to reference is correct”; or
(2) fig. 9:950: “Determine a likelihood of a variant being a false positive for each group”: detailed in fig 10:1000) after determining a
(A) somatic or
(B) germline (via “reference”-“likelihood”37 [0092] wherein “germline sequence is reference” [0090] 3rd S: fig. 2:240: “Identify an initial set of one or more variation calls in the first region based on the optimized list of sequence hypotheses”)
status (mapped to Markush alternative (B)) of the candidate variant.
Claim 34 is rejected like claim 33:
Re 34. (Currently Amended), Baccash of the combination of Baccash,ZHANG teaches The method of claim 21,further comprising:
building a classifier to determine if the candidate variant is a true variant or an artifact.
Claim 44 is rejected like claims 33,34,45:
Re 44. (Currently Amended), Baccash of the combination of Baccash,ZHANG teaches The method of claim 34, wherein the (loci) classifier (
occurs at two places:
(1) fig. 2:270: “Perform replicate calibration to determine likelihood that variation relative to reference is correct”: detailed in fig 10:1000: “Method 1000 may be used to implement block 950 of method 900.” [0194]; or
(2) fig. 9:950: “Determine a likelihood of a variant being a false positive for each group”: detailed in fig 10:1000: “Method 1000 may be used to implement block 950”)
is built3839 (via provided founding input arrows) after determining40 the
(A) somatic or
(B) germline (via “reference”-“likelihood”41 [0092] wherein “germline sequence is reference” [0090] 3rd S: fig. 2:240: “Identify an initial set of one or more variation calls in the first region based on the optimized list of sequence hypotheses”)
status (mapped to Markush alternative (B): the foundation input data, represented as arrows in fig .2) of the candidate variant.
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Re 60. (New), Baccash of the combination of Baccash,ZHANG teaches The method of claim 21, wherein the tumor sample contains42 normal tissue and tumor tissue (or
(1) contaminated normal43 tissue and
(2) contaminated multiple different tumor populations stromal44 tissue
via “normal/stromal tissue contamination within the sample or because of multiple different tumor populations within the same tumor sample” [0038] 4th S).
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claim 21,24,33,41,45,34,44,60 further in view of JOHNSON (US 2016/0186262 A1) and OLSHEN et al. (Circular binary segmentation for the analysis of array-based DNA copy number data):
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Re 22. (Currently Amended), Baccash of the combination of Baccash,ZHANG teaches The method of claim 21, wherein the step of partitioning the genome into segments is performed on the ratio of the tumor (or “genome A” “(e.g., the ‘tumor’ genome)”-“ratios” [0244]) to the45
(J) normal (via “genome B (e.g., the ‘normal’ genome)” [0244])
(K) mean
(L) exon
read (via computer logic) depth (or “quantity of evidence” [0102]) using [[the]] circular binary segmentation.
Baccash of the combination of Baccash,ZHANG does not teach the difference of claim 21 of:
to the46
(J) normal
(K) mean
(L) exon
read depth using [[the]] circular binary segmentation.
JOHNSON, citing to OLSHEN, teaches the difference of claim 22 of:
to the47
(J) normal
(K) (“Bins with high or low GC-content can have lower”) mean (“read depth than bins with medium GC-content (40% to 55% GC).” [0240] 3rd S)
(L) exon48
read49 (G50C51:Guanine Cytosine) depth (mapped to Markush alternative (K)) using [[the]] circular binary segmentation (or CBS “in which the breakpoints can be determined on the basis of a test of hypothesis, with the null hypothesis of no difference in copy number.” [0256] 5th S).
Since Baccash of the combination of Baccash,ZHANG teaches a depth read and contamination resulting various measurements of a copy number, one of skill in the art of depth reads can make Baccash’s of the combination of Baccash,ZHANG be as JOHNSON’s seeing in the change “a method to split the chromo-somes into regions of equal copy number that accounts for the noise in the data”, thus obtaining “the true copy number in the test sample” OLSHEN, pg. 558, penult para, 3rd & 4th Ss, and the true copy number measurement.
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of GAASTERLAND et al. (US 2015/0315645 A1):
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Re 23. (Currently Amended), Baccash of the combination of Baccash,ZHANG teaches The method of claim 21 52 (“ as "homozygous concordant", "heterozygous concordant", or ‘discordant’ ” [0208] 1st S) step (such that “a variant is correct is determined for each discordant loci” [0197] as pre- labelled: "homozygous concordant", "heterozygous concordant", or ‘discordant’ ” [0208] 1st S: fig. 10:1030: “Compute probability P(Het) that variant is correct for each discordant loci”, pre-categorized: "homozygous concordant", "heterozygous concordant", or ‘discordant’ ” [0208] 1st S), wherein the candidate variant is classified (as “varType (snp, ins, del, or sub)” [0207] 1st S) as
(A) somatic (“in a given somatic category such as SNP, insertion, deletion, substitution, etc” [0234]) or
(B) germline
based on (“various genome” [0053]) database frequencies53.
Baccash of the combination of Baccash,ZHANG does not teach the difference of claim 23 of (database) “frequencies” 54.
GAASTERLAND teaches the difference of claim 23:
(database) frequencies (“in any of the comparison databases, leaving 2,235 sites (Constraint 13).” [0214] last S) 55.
Since Baccash of the combination of Baccash,ZHANG teaches a database, one of skill in the art of databases can make Baccash’s of the combination of Baccash,ZHANG be as GAASTERLAND’s seeing the change “minimize false positives”, GAASTERLAND [0214] last S.
Claim(s) 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of CLEARLY et al. (US 2014/0057793 A1):
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Re 25. (Currently Amended), Baccash of the combination of Baccash,ZHANG teaches The method of claim 21,wherein the modeling step uses an expectation maximization approach that maximizes the sum of likelihoods (“by summing the likelihood of generating the DNBs over all possible mappings M” [0122] last S) of two or more data measurements.
Baccash of the combination of Baccash,ZHANG does not teach the difference of claim 25 of:
an expectation maximization approach.
CLEARLY teaches the difference of claim 25:an expectation maximization approach (“to further refine calling” [0135]).
Since Baccash of the combination of Baccash,ZHANG teaches calling one of skill in the art of calling (spotting differences in sequences) can make Baccash’s of the combination of Baccash,ZHANG be as CLEARLY’s seeing the in the change refined56 calls more accurately recognizing variants in sequences.
Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of CLEARLY et al. (US 2014/0057793 A1) as applied in claim 25 further in view of AITMANN et al. (WO 2014/181107 A1):
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Re 26. (Original), Baccash of the combination of Baccash,ZHANG,CLEARLY teaches The method of claim 25, wherein the two or more data measurements are selected from the group consisting of:
a) the exon read depth;
b) the heterozygous variant minor allele read depth;
c) the somatic variant minor allele read depth;
d) the number of heterozygous positions detected in each segment; and
e) the number of somatic calls in known germline variant positions.
Baccash of the combination of Baccash,ZHANG,CLEARLY does not teach the Markush element.
AITMANN teaches Markush alternative a):
a) the exon read depth (“was assessed for each exon in the target region and the ratio of expected and observed read count was obtained, as well as a Bayes factor for the copy number variant calls, as implemented in the method”, pg. 43, ll. 20-25).
Since Baccash of the combination of Baccash,ZHANG,CLEARLY teaches rad depth, one of skill in the art of read depth can make Baccash’s of the combination of Baccash,ZHANG,CLEARLY be as AITMANN’s seeing the change “increase the quality of a reference set for each sample and therefore to maximise the power to detect copy number variants.” AITMANN, pg. 43, ll.19-21.
Claim(s) 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of CLEARLY et al. (US 2014/0057793 A1) as applied in claim 25 further in view of PARK et al. (WO 2016/208827 A1) with SEARCH machine translation and Williams et al. (US 2005/0227917 A1) and WEEKS et al. (US 2016/0244818 A1):
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Re 29. (Currently Amended), Baccash of the combination of Baccash,ZHANG, CLEARLY teaches The method of claim 25,wherein [[the]] a likelihood of the exon read depth is modeled as a Poisson distribution with a (“geometric” [0130]) mean calculated based on the observed exon read depths in [[the]] unmatched control samples.
Baccash of the combination of Baccash,ZHANG, CLEARLY does not teach the difference of claim 29 of:
exon (read depth) is modeled as a Poisson distribution…
the observed exon read depths in [[the]] unmatched control samples.
PARK teaches the difference of claim 29:exon (read depth) (“for each reference gene (exon)”, pg. 7, 6th txt blk) is modeled as a (“exon”-“read-depth”, pg. 2, 7th txt blk) Poisson distribution…
the observed exon read depths (each via “the analyzing step analyzes the depth of the reads aligned with exon sites of the test genes”, pg. 2, 7th txt blk) in (contain: within) [[the]] unmatched control samples (“of57 people”, pg. 4, penult txt blk).
Since Baccash of the combination of Baccash,ZHANG, CLEARLY teaches read depth, one of skill in the art of read depths can make Baccash of the combination of Baccash,ZHANG, CLEARLY be as PARK’s seeing the in change “when it is determined that there is a copy number variation (CNV) gene among the test genes, the determination unit 130 selects a drug (for example, an anticancer agent) corresponding to the detected copy number variation (CNV) gene.”.
The combination of Baccash of the combination of Baccash,ZHANG,CLEARLY, PARK does not teach the remaining difference of claim 29:
a) (exon read depth) modeled as…Poisson (distribution)…
b) unmatched control (samples).
Williams teaches difference b) of claim 29:
b) unmatched control ( “ (i.e., a pooled sample of normal colon from many patients; results shown in column 9, entitled "% Cln Unm Met")” [1169] bullet “4)”) (samples).
Since PARK of the combination of Baccash,ZHANG,CLEARLY, PARK teaches sample, one of skill in the art of samples can make PARK’s of the combination of Baccash,ZHANG,CLEARLY, PARK be as Williams’ seeing the change “that the sequences set forth in the in the sequence listing may be used to detect cancerous cells, particularly, cancerous colon, prostate, breast, and metastasized colon cells.” and treat accordingly.
Baccash of the combination of Baccash,ZHANG,CLEARLY,PARK,Williams does not teach the last difference a) of claim 29:
a) (exon read depth) modeled as…Poisson (distribution)…
WEEKS teaches the last difference of claim 29:
a) (exon read depth) modeled as…Poisson (distribution)58…(via:
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Since Baccash of the combination of Baccash,ZHANG,CLEARLY,PARK,Williams teaches a read depth, one of skill in the art of read depths can make Baccash’s of the combination of Baccash,ZHANG,CLEARLY,PARK,Williams be as WEEKS’s seeing in the change “modeling accuracy improved as read depth increased”, WEEKS [0466] penult S.
Claim(s) 30,31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of CLEARLY et al. (US 2014/0057793 A1) as applied in claim 25 further in view of HALPERN et al. (US 2013/0316915 A1):
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Re 30. (Currently Amended), Baccash of the combination of Baccash,ZHANG,CLEARLY teaches The method of claim 25,wherein [[the]] a likelihood of the59
(A) heterozygous position60 (“(also referred to as a "het")” [0033] 3rd S)
(B) minor allele616263 (“total” [0189])
read counts64 [[are]] is modeled as a beta-binomial (“true variants”-“score” [0217]) distribution with an expected allelic fraction (or “a maximum likelihood allele fraction” [0154]) of a germline variant (“present at 50% allele fraction” [0258]).
Baccash of the combination of Baccash,ZHANG,CLEARLY does not teach the difference of claim 30 of:
a) (heterozygous position65) minor allele666768 read counts69
b) beta-binomial.
HALPERN teaches the difference of claim 30:
a) (heterozygous position70) minor allele717273 read counts74 (“from a tumor sample at heterozygous variant loci in the matched normal sample” [0303])
b) (Deviation from binomial sampling can be handled via a” [0303]) beta-binomial (“model”).
Since Baccash of the combination of Baccash,ZHANG,CLEARLY teaches heterozygous position (loci) and read depth, one of skill in the art of loci and read depth can make Baccash’s of the combination of Baccash,ZHANG,CLEARLY be as HALPERN’s seeing in the change a “model for processing total and allele-specific read depth data that result in an easily-interpretable graphical representation of a tumor sample”, HALPERN [0008] 2nd S.
Claim 31 rejected like claim 30:
Re 31. (Currently Amended), Baccash of the combination of Baccash,ZHANG, CLEARLY,HALPERN teaches The method of claim 25,wherein [[the]] a likelihood of the somatic position minor allele read counts [[are]] is modeled as a beta- binomial distribution with an expected allelic fraction of a somatic variant.
Claim(s) 32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of CLEARLY et al. (US 2014/0057793 A1) as applied in claim 25 further in view of Garner, JR. et al. (US 2015/0337388 A1):
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Re 32. (Currently Amended), ZHANG of the combination of Baccash,ZHANG, CLEARLY teaches The method of claim 25,wherein the posterior probability is calculated based on a prior probability (that “can be used in pairing quality calculations” ZHANG [0071] [0073]) of a somatic mutation and [[the]] a prior probability (that “can be used in pairing quality calculations” ZHANG [0071] [0073]) of the germline genotypes:
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ZHANG of the combination of Baccash,ZHANG, CLEARLY does not teach the difference of claim 32 of (germline) “genotypes”.
Garner teaches the difference of claim 32:
(germline) (“GBM germline” {“glioblastoma multiforme (GBM)”} [0368]) genotypes (figs. 22A,B,C).
Since Baccash of the combination of Baccash,ZHANG, CLEARLY teaches germline75, one of skill in the art of germs76 or lines77 can make Baccash’s of the combination of Baccash,ZHANG,CLEARLY be as Garner’s seeing in the change “tools, including both computer implemented methods and physical reagents, that can be used to analyze microsatellites across populations and can also be applied to analyzing microsatellites in individual subjects as a diagnostic or risk assessment tool or as part of a treatment or monitoring regime.”, Garner [0008] 2nd S.
Claim(s) 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of Karchin et al. (US 2015/0025861 A1):
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Re 40. (Currently Amended), Baccash of the combination of Baccash,ZHANG teaches The method of claim 33, wherein the classifier is a machine learning (“loess” [0202]) algorithm.
Baccash of the combination of Baccash,ZHANG does not teach the difference of claim 40 of:
machine learning.
Karchin teaches the difference of claim 40:
machine learning (“to analyze and predict the demonstration of a phenotype by a subject.” [0033] penult S: fig. 3:249: “PHENOTYPE PREDICTIVE MODELER”).
Since Baccash of the combination of Baccash,ZHANG teaches an algorithm/computer one of skill in the art of computers78 can make Baccash’s of the combination of Baccash,ZHANG be as Karchin’s seeing in the change that “demographics data for the subject may also be received and used as an input to the model. For example, population-level prevalence used as a prior in the model may be adjusted79 based on the actual demographics of the subject under analysis.” and thus accurately “performing screening of an individual, to ascertain how likely the individual is to demonstrate a particular phenotype (e.g., a trait, a disease or disorder, etc.). For example, a particular variation may indicate an increased probability of developing a certain type of cancer.” Karchin [0003].
Claim(s) 43 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of Chen et al. (US 2009/0281981 A1):
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Re 43. (Currently Amended), Baccash of the combination of Baccash,ZHANG teaches The method of claim 33,wherein applying the classifier comprises fitting (“into one of the three standard hypotheses for a genome” [0107]) a80
(A) quadratic
(B) discriminant
(“Bayesian” [0083] last S) model to the variant.
Baccash of the combination of Baccash,ZHANG does not teach the difference of claim 43 of:
quadratic discriminant.
Chen teaches the difference of claim 43 of:
quadratic discriminant (“are frequently used, assuming an underlying multivariate normal data distribution.” [0021] 3rd S.
Since Baccash of the combination of Baccash,ZHANG teaches classification (categorization or labeling), one of skill in the art of attributes can make Baccash’s of the combination of Baccash,ZHANG be as Chen’s seeing in the change “the best possible separation of classes within feature space” , Chen [0021] 2nd S.
Claim(s) 35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of Fonte et al. (US 2015/0055085 A1):
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Re 35. (Currently Amended), Baccash of the combination of Baccash,ZHANG teaches The method of claim [[31]]34, wherein building the classifier comprises:
a) selecting (as “evidence” [0102] ultimately resulting in “scores” [0102]) one or more (mapping/alignment “image processing errors”-“measurement” [0102]:fig. 5 & [0089]: image alignment of letters) quality metrics;
b) assigning a Pass threshold and a Reject threshold to each (image alignment-mapping) selected (error) quality metric;
c) identifying (“top”) candidate variants (ultimately resulting in “One or more variants between the reference genome and the sample genome are called81 for the first region based on the top hypothesis.” [0008] last S based on an “identified” “first region”) from the tumor sample;
d) calculating (as computer-computed scores) the selected quality metrics for each candidate variant;
e) assigning a candidate variant to a Pass training group if the candidate variant passes one or more Pass thresholds82; and
f) assigning a candidate variant to a Reject training group if the candidate variant passes one or more Reject thresholds83.
Baccash of the combination of Baccash,ZHANG does not teach the difference of claim 35 of:
(selecting quality) metrics…
assigning a Pass threshold and a Reject threshold to each selected (quality) metric…
selected (quality) metrics.
Fonte teaches the difference of claim 35:
(selecting quality) (“previously described” [0130]) metrics…
assigning (via “the quality threshold…is based on the previously described metrics” [0130] last S) a Pass threshold (or “quality threshold” [0130] last S, surpassing) and a Reject threshold (or a “filter”84-“threshold” [0130] 2nd and 1st to last S) to each selected (or “extracted”85 [0030]) (quality) metric (or
selected (quality) metrics (or “extracted”-“information” [0030]).
Since Baccash teaches image processing, one of skill in the art of images can make Baccash’s of the combination of Baccash,ZHANG be as Fonte’s seeing in the change quality images for mapping/aligning image reads resulting in quality alignments of hypotheses to a reference hypothesis.
Claim(s) 61 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) in view of ZHANG et al. (US 2012/0011086 A1) as applied in claims 21,24,33,41,45,34,44,60 further in view of Fonte et al. (US 2015/0055085 A1) as applied in clam 35 further in view of Yin et al. (US 2014/0235461 A1):
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Re 61. (New), Baccash of the combination of Baccash,ZHANG,Fonte teaches The method of claim 35, wherein the one or more (extracted-image) quality metrics is selected from the group consisting86 of87:
percentage of bases having minimum base quality,
percentage of bases supporting the major or minor allele,
the minimum percentage of reads from forward or reverse strand,
minimum average mapping quality of reads supporting the major or minor allele,
minimum average base quality of bases supporting the major or minor allele,
maximum average percentage of mismatches in reads supporting the major and minor alleles,
minimum average distance from either end of sequence of the major or minor allele,
difference in average percentage of forward strand between the major and minor alleles,
difference in average base quality between the major and minor alleles,
difference in average mapping quality between the major and minor alleles,
difference in average percentage of mismatches between the major and minor alleles,
difference in average read position between the major and minor alleles,
quality score of position from unmatched controls, and
mean (“base call” Baccash [0102]) quality score in region (“harboring longer variations” Baccash [0072] 3rd S).
Baccash of the combination of Baccash,ZHANG,Fonte does not teach the Markush element of Markush alternatives (emphasis on the last, broadest Markush alternative) of claim 61 of:
mean (quality score in region)88.
Yin teaches the Markush element of Markush alternatives (emphasis on the last, broadest Markush alternative) of claim 61 of:
(via TABLES 1 & 2) mean (quality score in region)89:
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Since Baccash of the combination of Baccash,ZHANG,Fonte teaches a quality score, one of ordinary skill in quality scores can make Baccash’s of the combination of Baccash,ZHANG,Fonte be as Yin’s seeing the change “filter out low quality regions”, Yin [0126], 1st S.
Conclusion
The prior art “nearest to the subject matter defined in the claims” (MPEP 707.05) made of record and not relied upon is considered pertinent to applicant's disclosure.
The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action.
Citation
Relevance
Christoforides et al.: common inventor: David Craig (Identification of somatic mutations in cancer through Bayesian-based analysis of sequenced genome pairs)
Christoforides teaches, page 9, rcol, 2nd full para:
“We then apply Bayes’ theorem to extract a probability for homozygosity. Since we can assume that the normal genome is diploid, the marginal probability of the evidence (P(Dnormal)) can be taken to be the sum of the likelihoods of the evidence given the three possible genotype classes (homozygous matching the reference, homozygous variant to the reference, and heterozygous). The selected default values for the prior probabilities for each genotype (πi), as well as the hyperparameters αι and βι are listed in Table 3.”
as the closest to the claimed “determining a posterior probability that a candidate variant is somatic, germline heterozygous, or homozygous using a Bayesian model” of claim 21.
Wu et al. (US 2014/0364439 A1)
Wu teaches “candidate driver genes” [0080] (i.e..“hypothesis90 testing” “genes” [0208]) and:
[0259] At each iteration of the Gibbs sampler, each mutation is assigned to a unique cluster and the posterior CCF distribution of each cluster is computed using Bayes' rule, as opposed to drawing a sample from the posterior (a uniform prior on CCF from 0.01 to 1 is used). When considering the probability of a mutation to join an existing cluster, the likelihood calculation of the mutation arising from the cluster is integrated over the uncertainty in the cluster CCF. This allows for rapid convergence of the Gibbs sampler to its stationary distribution, which was typically obtained in fewer than 100 iterations for the analysis presented in this study.”
as the closest to the claimed “determining a posterior probability that a candidate variant is somatic, germline heterozygous, or homozygous using a Bayesian model” of claim 21.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS ROSARIO whose telephone number is (571)272-7397. The examiner can normally be reached Monday-Friday, 9AM-5PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DENNIS ROSARIO/Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
1 MPEP 2106.04(d)(2) Particular Treatment and Prophylaxis in Step 2A Prong Two [R-07.2022]: A claim reciting a judicial exception is not directed to the judicial exception if it also recites additional element(s) demonstrating that the claim as a whole integrates the exception into a practical application. One way to demonstrate such integration is when the additional elements apply or use the recited judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition. The application or use of the judicial exception in this manner meaningfully limits the claim by going beyond generally linking the use of the judicial exception to a particular technological environment, and thus transforms a claim into patent-eligible subject matter. Such claims are eligible at Step 2A, because they are not "directed to" the recited judicial exception.
2 Applicant’s disclosure:
[086]The present subject matter takes into account how the number of private germline variants varies with ancestry and uses a strategy to reduce false positives due to private germline variants in tumor-only somatic mutation calling. The present subject matter uses a model of variant allele frequencies to improve classification of somatic versus germline variants. The present subject matter estimates allelic copy number and clonal sample fractions to model the expected allele frequency distributions of somatic and germline variants. The present subject matter uses a Bayesian framework that integrates the allele frequency model with prior probabilities of somatic or germline calculated from 1000 genomes population and cancer mutations counts from COSMIC. The power to detect somatic variants by the present subject matter has been evaluated. In one approach, the effects of tumor content, copy number, and read depth were systematically examined on simulations. In a second approach, in silico dilutions and downsampling were used to examine effects of tumor content, copy number, and read depth on real patient data. In a third approach, tumor samples of different ancestry were used to evaluate the tumor-only calling approach.
[018]In some aspects, the classifier is a machine learning algorithm. In one aspect, the classifier is built specifically for SNVs. In another aspect, the classifier is built specifically for INDELs. In one aspect, applying the classifier comprises fitting a quadratic discriminant model to the variant.
3 Applicant’s disclosure:
TECHNICAL FIELD
[002]This disclosure relates generally to sequencing data processing and benchmarking, and in particular, to detecting somatic and germline variants in tumor samples.
wherein data processing is defined: a sequence of operations performed on data, esp by a computer, in order to extract information, reorder files, etc (Dictionary.com)
4 machine learning: a branch of artificial intelligence in which a “computer” (not claimed) generates rules (i.e., applicant’s disclosed building a classifier: fig. 4: “Bayesian Variant Classification”) underlying or based on raw data (the claimed “aligned sequence data”) that has been fed into it (referring back to either the computer or the classifier” most likely the computer receiving the data since the Bayes classifier is still under construction/training/leaning to be trained) (Dictionary.com)
5 hypothesis: a suggested explanation for a group of facts or phenomena, either accepted as a basis for further verification ( working hypothesis ) or accepted as likely to be true Compare theory, wherein likely is defined: having good possibilities of success, where possibility is defined: a competitor, candidate, etc, who has a moderately good chance of winning, being chosen, etc (Dictionary.com)
6 Markush element of coordinate-adjective Markush alternatives follows: [(H) and (I)]=[(I) and (H)]
7 main: chief in size, extent, or importance; principal; leading. (Dictionary.com)
8 top: foremost, chief, or principal.
9 Identities (words main & top meaning the same thing): main=top=principal
10 and: (used to connect [Markush] alternatives). (Dictionary.com)
11 Since Markush alternative (H) is taught the Markush element [(H) and (I)] is taught under the broadest reasonable interpretation of claim 1.
12 Markush element of coordinate-adjective Markush alternatives follows:
[(A) & (B) or (C) & (D)]=[(B) & (A) or (D) & (C)]: there is no perceptible difference in meaning when swapped
13 hypothesis: a suggested explanation for a group of facts or phenomena, either accepted as a basis for further verification ( working hypothesis ) or accepted as likely to be true Compare theory, wherein likely is defined: having good possibilities of success, where possibility is defined: a competitor, candidate, etc, who has a moderately good chance of winning, being chosen, etc (Dictionary.com)
14 Given that Markush alternative (A) is taught, the Markush element [(A) & (B) or (C) & (D)]=[(B) & (A) or (D) & (C)] is taught under the broadest reasonable interpretation of claim 1.
15 Markush element of alternatives follows: [(E), (F). or (G)]
16 is: 3rd person singular present indicative of be, wherein be is defined: (used as a copula to connect the subject with its predicate adjective, or predicate nominative, in order to describe, identify, or amplify the subject), wherein describe is defined: to represent or delineate by a picture or figure, wherein figure is defined: an instructive or illustrative drawing (as shown in [0089]’s AGTC pattens) or diagram, as found in a book or an owner’s manual. (Dictionary.com)
17 hypothesis: a suggested explanation for a group of facts or phenomena, either accepted as a basis for further verification ( working hypothesis ) or accepted as likely to be true Compare theory, wherein likely is defined: having good possibilities of success, where possibility is defined: a competitor, candidate, etc, who has a moderately good chance of winning, being chosen, etc (Dictionary.com)
18 Given the Markush alternative (F) is taught the Markush element [€,(F), or (G)] is taught under the broadest reasonable interpretation of claim 1.
19 THE CLAIMED INVENTION AS A WHOLE regarding “posterior”:
The (somatic/germline) determination problem is via applicant’s disclosure:
[004]The identification of somatic mutations through next generation sequencing has enabled the identification of cancer driver events in individual patient tumor samples2–4. There is also ongoing effort to discover new cancer driver mutations, particularly in non-coding regions5. Although sequencing of tumor-associated cancer gene panels and exomes is starting to be adopted in clinical practice to personalize therapy, there is much to learn about how mutation status correlates with response to therapy. Archival tissue collections represent a rich resource for identifying new driver mutations and clarifying how genomic features relate to clinical outcomes6,7. However, most archival collections do not contain blood samples or other normal tissue from sites distant to the tumor. Without a normal tissue sample for comparison, it is difficult to determine which variants are somatic and which are germline8. Innovative approaches are needed to identify somatic variants when normal tissue is not available.
The calling strategy solution for the problem is fig. 11 (reproduced below):
[037]Figs. 11A-11L Overview of variant calling strategy of the single-sample approach. After filtering candidate variant positions by quality, an EM approach is used to fit a model of clonal allelic copy number. (Figs. 11A-11L) Example copy number plots for three conditions: with high tumor content and moderate coverage (Figs. 11A-11D), with high tumor content and high coverage (Figs. 11E-11H), and with moderate tumor content and moderate coverage (Figs. 11I-11L). A one-copy loss (the line on the right half of each panel) is detected in the segment (indicated by arrow head). (Figs. 11B-11C, 11F-11G, 11J-11K) The expected somatic and germline allelic fractions: germline variants (grey and "g"), somatic main clone (blue and "sm"), and somatic sub-clones (green, red, and "ss") for diploid regions (Figs. 11B, 11, 11J) and one-copy loss regions (Figs. 11C, 11G, 11K). In high tumor content and moderate coverage, the main clone distribution overlaps with the germline and is difficult to detect in the diploid region, while the red sub-clone is more difficult to detect in the one copy loss region. Increasing the coverage increases the sharpness of the distributions making the somatic variants easier to detect. In the moderate tumor content sample, all clones are easy to differentiate from germline in the diploid region, but the main clone is hard to detect in the one copy loss region. Using these distributions to calculate conditional probabilities, as well as using 1000 genomes population frequencies and COSMIC mutation counts to calculate prior probabilities, somatic and germline variants can be called. (Figs. 11D, 11H, 11L) The allelic fractions of germline and somatic variants colored by a clone. A plus sign indicates a variant and an open circle indicates a false positive. With high tumor content, variants in the main clone are detected better in the deleted region under moderate coverage condition. The number of variants detected increases in the high coverage condition.
Indication of obviousness: Why does applicant’s disclosure have “prior probabilities” in the disclosed solution to the determination problem when claim 1 has “posterior probability”? This absence of “prior probabilities” in claim 1 is an Indication of obviousness when claim 1 is considered as a whole.
20 (italics) represent claims limitations already taught
21 (italics) represent claims limitations already taught
22 fraction: Mathematics. a ratio of algebraic quantities similarly expressed, wherein ratio is defined: proportional relation; rate, wherein rate is defined: a certain quantity or amount of one thing considered in relation to a unit of another thing and used as a standard or measure, wherein measure is defined: any standard of comparison, estimation, or judgment. (Dictionary.com)
23 “number” is a mass noun, wherein mass noun is defined a noun, as sunshine, electricity, or happiness, that typically refers to an indefinitely divisible substance or an abstract notion, and that in English cannot be used, in such a sense, with the indefinite article or in the plural. (Dictionary.com)
24 fraction: Mathematics. a number usually expressed in the form a/b. (Dictionary.com)
25 Markush element of coordinate-adjective Markush elements follows: [(A) and (B)]=[(B) and (A)]
26 clone: Also called: gene clone. a segment of DNA that has been isolated and replicated by laboratory manipulation: used to analyse genes and manufacture their products (proteins) (Dictionary.com)
27 fragment: an isolated, unfinished, or incomplete part. (Dictionary.com)
28 allele: Also called: allelomorph. any of two or more variants of a gene that have the same relative position on homologous chromosomes and are responsible for alternative characteristics, such as smooth or wrinkled seeds in peas See also multiple alleles (Dictionary.com: BRITISH): copy number: same two
29 likelihood/ probability: Statistics. the relative possibility that an event will occur, as expressed by the ratio of the number of actual occurrences to the total number of possible occurrences. (Dictionary.com)
30 measure: to estimate the relative amount, value, etc., of, by comparison with some standard. (Dictionary.com)
31 allele:
1 Also called: allelomorph. any of two or more variants of a gene that have the same relative position on homologous chromosomes and are responsible for alternative characteristics, such as smooth or wrinkled seeds in peas See also multiple alleles (Dictionary.com: BRITISH): copy number: same two
2 Any of the possible forms in which a gene for a specific trait can occur. In almost all animal cells, two alleles for each gene are inherited, one from each parent. Paired alleles (one on each of two paired chromosomes) that are the same are called homozygous, and those that are different are called heterozygous. In heterozygous pairings, one allele is usually dominant, and the other recessive. Complex traits such as height and longevity are usually caused by the interactions of numerous pairs of alleles, while simple traits such as eye color may be caused by just one pair. (Dictionary.com: SCIENTIFIC): copy number: same two
32 Markush element of coordinate-adjective Markush alternatives follows: [(C) and (D)]=[(D) and (C)]
33 part/fragment: an essential or integral attribute or quality (Dictionary.com)
34 likelihood: statistics the probability of a given sample being randomly drawn regarded as a function of the parameters of the population. The likelihood ratio is the ratio of this to the maximized likelihood See also maximum likelihood (Dictionary.com)
35 Markush element foloows: A or B
36 Given that Markush alternative (A) is taught the Markush element A or B is taught under the broadest reasonable interpretation of claim 33.
37 likelihood: the state of being likely or probable; probability, wherein state is defined: 1 the condition of a person or thing, as with respect to circumstances or attributes. 3 status, rank, or position in life; station, wherein condition is defined: a particular mode of being of a person or thing; existing state; situation with respect to circumstances, wherein mode is defined: a designated condition or status, as for performing a task or responding to a problem (Dictionary.com)
38 main verb
39BROAD CLAIM LANGUAGE: built: simple past tense and past participle of build, wherein build (USED WITH OBJECT: classifier) is defined: to base (USED WITH OBJECT: classifier); found (a relationship built on trust.), wherein base (USED WITH OBJECT: classifier) is defined: to make or form a base (NOUN) or foundation for, wherein base (NOUN) is defined: a fundamental principle or groundwork; foundation; basis, wherein basis is defined: a basic fact, amount, standard, etc., used in making computations, reaching conclusions, or the like, wherein found (a relationship built on trust) is defined: to provide a basis or ground for, wherein basis is defined: a basic fact, amount, standard, etc., used in making computations, reaching conclusions, or the like (Dictionary.com)
40 “determining”” is participle participating in the action of “built”
41 likelihood: the state of being likely or probable; probability, wherein state is defined: 1 the condition of a person or thing, as with respect to circumstances or attributes. 3 status, rank, or position in life; station, wherein condition is defined: a particular mode of being of a person or thing; existing state; situation with respect to circumstances, wherein mode is defined: a designated condition or status, as for performing a task or responding to a problem (Dictionary.com)
42 “contains” is interpreted as open-ended via MPEP:
2111.03 Transitional Phrases [R-01.2024]
The transitional phrases "comprising", "consisting essentially of" and "consisting of" define the scope of a claim with respect to what unrecited additional components or steps, if any, are excluded from the scope of the claim. The determination of what is or is not excluded by a transitional phrase must be made on a case-by-case basis in light of the facts of each case.
I. COMPRISING
The transitional term "comprising", which is synonymous with "including," "containing," or "characterized by," is inclusive or open-ended and does not exclude additional, unrecited elements or method steps. See, e.g., Mars Inc. v. H.J. Heinz Co., 377 F.3d 1369, 1376, 71 USPQ2d 1837, 1843 (Fed. Cir. 2004) ("[L]ike the term ‘comprising,’ the terms ‘containing’ and ‘mixture’ are open-ended."). Invitrogen Corp. v. Biocrest Manufacturing, L.P., 327 F.3d 1364, 1368, 66 USPQ2d 1631, 1634 (Fed. Cir. 2003) ("The transition ‘comprising’ in a method claim indicates that the claim is open-ended and allows for additional steps."); Genentech, Inc. v. Chiron Corp., 112 F.3d 495, 501, 42 USPQ2d 1608, 1613 (Fed. Cir. 1997) ("Comprising" is a term of art used in claim language which means that the named elements are essential, but other elements may be added and still form a construct within the scope of the claim.); Moleculon Research Corp. v. CBS, Inc., 793 F.2d 1261, 229 USPQ 805 (Fed. Cir. 1986); In re Baxter, 656 F.2d 679, 686, 210 USPQ 795, 803 (CCPA 1981); Ex parte Davis, 80 USPQ 448, 450 (Bd. App. 1948) ("comprising" leaves "the claim open for the inclusion of unspecified ingredients even in major amounts"). In Gillette Co. v. Energizer Holdings Inc., 405 F.3d 1367, 1371-73, 74 USPQ2d 1586, 1589-91 (Fed. Cir. 2005), the court held that a claim to "a safety razor blade unit comprising a guard, a cap, and a group of first, second, and third blades" encompasses razors with more than three blades because the transitional phrase "comprising" in the preamble and the phrase "group of" are presumptively open-ended. "The word ‘comprising’ transitioning from the preamble to the body signals that the entire claim is presumptively open-ended." Id. In contrast, the court noted the phrase "group consisting of" is a closed term, which is often used in claim drafting to signal a "Markush group" that is by its nature closed. Id. The court also emphasized that reference to "first," "second," and "third" blades in the claim was not used to show a serial or numerical limitation but instead was used to distinguish or identify the various members of the group. Id.
43 normal: Biology, Medicine/Medical.
1 free from any infection or other form of disease or malformation, or from experimental therapy or manipulation.
2 of natural occurrence. (Dictionary.com)
44 stroma: Anatomy. the supporting framework, usually of connective tissue, of an organ, as distinguished from the parenchyma. (Dictionary.com)
45 Markush element follows: [(J) & (K) & (L)] read depth or [(L) & (K) & (J)] read depth
46 Markush element follows: [(J) & (K) & (L)] read depth or [(L) & (K) & (J)] read depth
47 Markush element of coordinate-adjective Markush alternatives follows:
[(J) & (K) & (L)] exon read depth or
[(L) & (K) & (J)] read depth or
[etc.] read depth
48 Since Markush alternative (K) is taught the Markush element is taught:
[(J) & (K) & (L)] read depth or
[(L) & (K) & (J)] read depth or
[etc.] read depth
49 cumulative adjective
50 G: Biochemistry. 1 glycine. 2 guanine.
51 C: Biochemistry. 1 cysteine. 2 cytosine.
52 classification: one of the groups or classes into which things may be or have been classified. classify. (Dictionary.com)
53 “frequencies” further limited be “database”
54 “frequencies” further limited be “database”
55 “frequencies” further limited be “database”
56 refine: to make more fine, subtle, or precise. (Dictionary.com)
57 of: (used to indicate inclusion in a number, class, or whole), wherein inclusion is defined: the act of including, wherein include is defined: to contain, as a whole does parts or any part or element.. (Dictionary.com)
58 Poisson distribution: statistics a distribution that represents the number of events occurring randomly in a fixed time at an average rate λ ; symbol P 0 ( λ ). For large n and small p with np = λ it approximates to the binomial distribution Bi ( n,p ) (Dictionary.com)
59 Markush element of coordinate-adjective Markush alternatives follows: [(A) & (B)]=[(B) & (A) read counts]: I perceive no difference in meaning when the alternatives are swapped.
60 “heterozygous position” is itself a cumulative adjective and a coordinate adjective
61 “minor allele” is itself a cumulative adjective and a coordinate adjective
62 “minor allele” is further limited by the claimed “heterozygous position”? No: They are coordinate adjectives
63 “heterozygous position minor allele” considered as a cumulative adjective, as a compound-cumulative-adjective, is further limiting “read counts”: this interpretation appears as a narrow subset of the broadest reasonable interpretation of claim 30.
64 “read counts” is modified by either Markush alternative (A) or (B)
65 “heterozygous position” is itself a cumulative adjective and a coordinate adjective
66 “minor allele” is itself a cumulative adjective
67 “minor allele” is further limited by the claimed “heterozygous position”
68 “heterozygous position minor allele” considered as a cumulative adjective, as a compound-cumulative-adjective, is further limiting “read counts”: this interpretation appears as a narrow subset of the broadest reasonable interpretation of claim 30.
69 “read counts” is modified by either Markush alternative (A) or (B)
70 “heterozygous position” is itself a cumulative adjective and a coordinate adjective
71 “minor allele” is itself a cumulative adjective
72 “minor allele” is further limited by the claimed “heterozygous position”
73 “heterozygous position minor allele” considered as a cumulative adjective, as a compound-cumulative-adjective, is further limiting “read counts”: this interpretation appears as a narrow subset of the broadest reasonable interpretation of claim 30.
74 “read counts” is modified by either Markush alternative (A) or (B)
75 germline: (initial evolution stage generation series) the initial stage in development or evolution, as a germ cell or ancestral form, series of generations of persons, animals, or plants descended from a common ancestor (Examiner mash-up)
76 germ: the initial stage in development or evolution, as a germ cell or ancestral form.
77 line: a series of generations of persons, animals, or plants descended from a common ancestor. (Dictionary.com)
78 computer: a computer program or algorithm (Dictionary.com)
79 adjust: to put in good working order; regulate; bring to a proper state or position, wherein regulate is defined: to adjust so as to ensure accuracy of operation. (Dictionary.com)
80 Markush element of coordinate-adjective Markush alternatives follows: [(A) or (B)]
81 call: to designate as something specified, wherein designate is defined: to mark or point out; indicate; show; specify, wherein mark is defined: an affixed or impressed device, symbol, inscription, etc., serving to give information, identify, indicate origin or ownership, attest to character or comparative merit, or the like, as a trademark, wherein comma is defined: the punctuation mark(,) indicating a slight pause in the spoken sentence and used where there is a listing of items or to separate a nonrestrictive clause or phrase (“as a trademark”) from a main clause (Dictionary.com)
82 unsatisfied contingent limitation: examiner need not show evidence: MPEP 2111/04 II. CONTINGENT LIMITATIONS: “Therefore "[t]he Examiner did not need to present evidence…of the [ ] method steps of claim” 35.
83 unsatisfied contingent limitation: examiner need not show evidence: Therefore "[t]he Examiner did not need to present evidence…of the [ ] method steps of claim” 35.
84 filter: A computer software program that selectively screens out incoming information, wherein screens is defined: to select, reject, consider, or group (people, objects, ideas, etc.) by examining systematically. (Dictionary.com)
85 extract: to take or copy out (matter), as from a book ,wherein take is defined: to pick from a number; select. (Dictionary.com)
86 interpreted as closed-ended
87 Markush element of fourteen Markush alternatives follows
88 (italics) represent claim limitations already taguht
89 (italics) represent claim limitations already taught
90 hypothesis: a suggested explanation for a group of facts or phenomena, either accepted as a basis for further verification ( working hypothesis ) or accepted as likely to be true Compare theory, wherein likely is defined: having good possibilities of success, where possibility is defined: a competitor, candidate, etc, who has a moderately good chance of winning, being chosen, etc (Dictionary.com)