DETAILED ACTION
Claim(s) 21,24,33,41,45,34,44,62 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated1 by Baccash et al. (US 2013/0110407 A1):
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) as applied in claim 21,24,33,41,45,34,44,62 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) as applied in claims 21,24,33,41,45,34,44,62 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) as applied in claims 21,24,33,41,45,34,44,62 further in view of CLEARLY et al. (US 2014/0057793 A1):
Claim(s) 26,32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) as applied in claims 21,24,33,41,45,34,44,62 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) as applied in claims 21,24,33,41,45,34,44,62 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) as applied in claim 26 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) as applied in claims 21,24,33,41,45,34,44,62 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) as applied in claim 26 further in view of HALPERN et al. (US 2013/0316915 A1):
Claim(s) 43 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) as applied in claims 21,24,33,41,45,34,44,62 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) as applied in claims 21,24,33,41,45,34,44,62 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) as applied in claims 21,24,33,41,45,34,44,62 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):
Response to Arguments
Claim Objections
Applicant’s arguments, see remarks, page 7, filed 6/8/2026, with respect to the claim objection have been fully considered and are persuasive. The claim objection of claims 33,34 has been withdrawn.
Priority
Examiner acknowledges the moot, remarks, page 7, filed 6/8/2026.
Applicant respectfully traverses the rejection under 35 USC 101
Applicant’s arguments, see remarks pages 7, filed 6/8/2026, with respect to 35 USC 101 have been fully considered and are persuasive. The 35 USC 101 rejection of claims 21,22,23,24,25,26,29,30,31,32,33,34,35,40,41,43,44,45,60,61 in the Office action of 3/19/2026, starting page 6, has been withdrawn.
Rejections under 35 USC 103
Applicant's arguments filed 6/8/2026 have been fully considered but they are not persuasive:
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “using only heterogeneous tumor samples”, remarks, pg, 13, 1st para, 2nd S) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “distinguishing between somatic, germline heterozygous, or germline homozygous”, remarks, pg, 13, 1st para, 3rd S) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “determine somatic verses germline status from tumor-only data”, remarks, pg, 13, 1st para, last S) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Zhang (US 2021/0011086 A1) has been removed as being a redundant teaching of Bayesian as already taught by Baccash et al. (US 2013/0110407 A1). Thus claim 21 and new claim 62 is rejected under 35 USC 102:
Claim(s) 21,24,33,41,45,34,44,62 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated2 by Baccash et al. (US 2013/0110407 A1):
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 21,24,33,41,45,34,44,62 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated3 by Baccash et al. (US 2013/0110407 A1):
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Re 21. (Currently Amended), Baccash discloses A method of detecting a somatic tumor variant and/or a germline variant from at least two tumors samples (or likewise “two genomes from two different samples” [0235] 2nd S) obtained from 4:
a) receiving aligned sequence data (or likewise “constructing the sample genome involves mapping (aligning)…the sequencing data” [0046] 2nd & 4th Ss) from the at least two tumor samples (resulting in “a biological sample is obtained from an organism (e.g., a human)” [0046]: fig. 1:160: “Data Repository”) obtained from the subject, wherein5 67 at least one sample has at least 55% tumor cells (or likewise “percentage of cells from…Sample 401 is 100% tumor I” [0110] 4th S & [0111] 1st S);
b) identifying a (“hypothesis”8) candidate variant (or “variation” [0088]: fig. 1:135: “Variant Calling Logic”) within the aligned sequence data using each sample;
c) partitioning the genome into segments (resulting in a “ ‘reference’…portion” [0031]) using each sample, wherein each segment contains at most one copy number alteration (or “copy number variants (‘CNVs’)” [0040] penult S);
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, and thus “Applying the Bayes’ theorem…gives…20% AF” (20% Allele Fraction) [0225] 1st S & [0026] 1st S) of the candidate variant of each segment using each sample;
e) modeling (via “ a VAF (variable allele fraction) model” [0045] penult S), using each sample, to determine nd S, of CNVs) of the segments 9 rd S: fig. 4: “80% Tumor”) 10 (H) 11 (via “first variant score”-“first set” plus “second variant score”-“second set” [0011] 2nd & 3rd Ss, each variant set/group “based on the top1213 hypothesis” [0008] last S) 14 (I) 15 16 (or 1st & 2nd sets mapped to Markush alternative (H));
f) determining an expected allelic fraction (“and thus hypotheses with different allele fractions would have different likelihoods”, [0045] 3rd S, and thus “Applying the Bayes’ theorem…gives…20% AF” (20% Allele Fraction) [0225] 1st S & [0026] 1st S) of17 the18 (A) candidate (via the “reference” {i.e., “Reference Hypothesis19”} -“germline”-“het-erozygous one-base deletion” [0089] [0090] of calling variants/base-deletions) (&) 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 the20 (C) candidate (D) somatic21 variant based on step (e);
g) determining a posterior22 (score) probability (or likewise “the probability… occurring given that another…is already known to have occurred” via “this Bayesian23 probability model.” [0083] last S) that [[a]] (“top” [0087]) the24 candidate variant (fig. 7:760: “top hypothesis” “genome” “variants”) is2526 (E) somatic, (F) germline heterozygous (via the “reference” {i.e., “Reference Hypothesis27”} -“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) germline homozygous28 (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) to integrate29 the observed allelic fractions in step (d) 30 ; and
h) repeating steps e) through g) until the result converges (“to a value referred to as the ‘calibrated score’ ” [0173] penult S); and
i) determining31 the joint probability (or said likewise “the probability… occurring given that another…is already known to have occurred” via “this Bayesian32 probability model.” [0083] last S) that the candidate variant is somatic variant (or likewise “computer the value Lsom” [0261] last S) 33 across34 (or likewise fig. 5:510: box across letters A,G,T,C) the at least two tumor samples obtained from the subject.
Re 24. (Previously Presented), Baccash discloses The method of claim 21,wherein the step of determining the expected allelic fractions of germline and somatic variants further comprises:
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 fraction35 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 number36 (or “allele”-“specific”-“allele fraction37”: allele: copy two: fig. 3: “C” is copied two times for each group) of38 (A) (fragmental) clonal39 (“DNBs” [0057 2nd S: fig. 1:105,162: “Nucleic Acid Fragments40”: “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”41-“likelihood”, [0006] 4th S: same 2/copy 2) events (i.e., alleles-“likelihood42 ratio…(1)”, [0104]); and
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 “measured43… percentage (allele44 fraction) of DNA” [0043] 3rd & 4th Ss) of45 (C) the main (or essential part46) clonal (“DNBs” [0057] 2nd S: fig. 1:105,162: essential/main “Nucleic Acid Fragments”: “fragment”-“likelihood(s)47” [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 discloses 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 is48 (via a “likelihood” [0007][0008]) (A) a true variant (“being a true somatic mutation” [0007]) or (B) an artifact49.
Re 41. (Previously Presented), Baccash discloses 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. (Previously Presented), Baccash discloses 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”50 [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 discloses 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. (Previously Presented), Baccash discloses 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 built5152 (via provided founding input arrows) after determining53 the (A) somatic or (B) germline (via “reference”-“likelihood”54 [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 62. (New), Baccash discloses The method of claim 21, further comprising, before receiving the aligned sequence data:
sequencing nucleic acids from the at least two tumor samples obtained from the subject to generate sequence reads (or likewise “As the reads of two different sequencing runs can … result… if two different samples are used from the same organism” via [0032] last S:
[0032] "Sample polynucleotide sequence", or simply "sample sequence", refers to a sequence of data values representing a nucleic acid sequence of a biological sample that may encompass a gene, a regulatory element, genomic DNA, cDNA, RNAs (including mRNAs, rRNAs, siRNAs, miRNAs and the like), and/or fragments thereof. A sample polynucleotide sequence may represent a nucleic acid physically present in a biological sample, or may represent a secondary nucleic acid such as a product (e.g., a concatemer) of an amplification reaction obtained during a library construction process. The sample sequences can form a "sample genome". If the cells in the sample have different genomes, then the determined sample genome can be considered a "composite genome" of the genomes of cells in the sample. As the reads of two different sequencing runs can differ, the resultant genomes could differ (even if just by one base), even though a same sample is used, and also if two different samples are used from the same organism.)
; and
aligning the sequence reads to a reference genome to generate the aligned sequence data (or likewise “constructing the sample genome involves mapping (aligning) the sequences to a reference genome” via [0046], 2nd S:
[0046] When a biological sample is obtained from an organism (e.g., a human), the nucleic acids in the sample can be sequenced to determine a genome of the sample. Typically, part of constructing the sample genome involves mapping (aligning) the sequences to a reference genome and identifying variations between the sequences and the reference. However, the process of determining a sequence is not error-free. Thus, determining whether the sequencing data actually indicates a true variant or not can be difficult. This difficulty can be compounded when the sample is actually a composite of various cells, with differences in their genomes. The following pipeline provides various embodiments of methods that can be used to identify variations in the genomes of only some of the cells in the sample and determine a fraction of the cells in which a variant appears. The pipelines can also be used to determine a likelihood of whether somatic variations in a tumor sample relative to a normal genome of the organism are true variations.).
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) as applied in claim 21,24,33,41,45,34,44,62 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. (Previously Presented), Baccash 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 the55
(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 does not teach the difference of claim 21 of:
to the56
(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 the57
(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) exon58
read59 (G60C61: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 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 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) as applied in claims 21,24,33,41,45,34,44,62 further in view of GAASTERLAND et al. (US 2015/0315645 A1):
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Re 23. (Previously Presented), Baccash teaches The method of claim 21, further comprising an initial classification62 (“ 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 frequencies63.
Baccash does not teach the difference of claim 23 of (database) “frequencies” 64.
GAASTERLAND teaches the difference of claim 23:
(database) frequencies (“in any of the comparison databases, leaving 2,235 sites (Constraint 13).” [0214] last S) 65.
Since Baccash teaches a database, one of skill in the art of databases can make Baccash’s 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) as applied in claims 21,24,33,41,45,34,44,62 further in view of CLEARLY et al. (US 2014/0057793 A1):
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Re 25. (Currently Amended), Baccash 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 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 teaches calling one of skill in the art of calling (spotting differences in sequences) can make Baccash’s be as CLEARLY’s seeing the in the change refined66 calls more accurately recognizing variants in sequences.
Claim(s) 26,32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) as applied in claims 21,24,33,41,45,34,44,62 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-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-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-CLEARLY teaches rad depth, one of skill in the art of read depth can make Baccash’s of the combination of Baccash-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.
Re 32. (Currently Amended), Baccash of the combination of Baccash-CLEARLY-AITMAN teaches The method of claim 26 [[25]],wherein the posterior probability is calculated based on a prior probability (said likewise “the probability… occurring given that another…is already known to have occurred” via “this Bayesian67 probability model.” [0083] last S) of a somatic mutation (or likewise “a true somatic mutation” [0007] penult S) 68 69.
Claim(s) 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) as applied in claims 21,24,33,41,45,34,44,62 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) as applied in claim 26 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-CLEARLY-AITMAN teaches The method of claim 26 [[25]], wherein 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-CLEARLY-AITMAN does not teach the difference of claim 29 of:
exon (read depth) is modeled as a Poisson distribution…
the observed exon read depths in 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 (“of70 people”, pg. 4, penult txt blk).
Since Baccash of the combination of Baccash-CLEARLY-AITMAN teaches read depth, one of skill in the art of read depths can make Baccash of the combination of Baccash-CLEARLY-AITMAN 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-CLEARLY-AITMAN-, 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-CLEARLY-AITMAN-PARK teaches sample, one of skill in the art of samples can make PARK’s of the combination of Baccash-CLEARLY-AITMAN-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-CLEARLY-AITMAN-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)71…(via:
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Since Baccash of the combination of Baccash-,CLEARLY-AITMAN-PARK-Williams teaches a read depth, one of skill in the art of read depths can make Baccash’s of the combination of Baccash-CLEARLY-AITMAN-,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) as applied in claims 21,24,33,41,45,34,44,62 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) as applied in claim 26 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-CLEARLY-AITMAN teaches The method of claim 26 [[25]],wherein a likelihood of the72
(A) heterozygous position73 (“(also referred to as a "het")” [0033] 3rd S)
(B) minor allele747576 (“total” [0189])
read counts77 [[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-CLEARLY-AITMAN does not teach the difference of claim 30 of:
a) (heterozygous position78) minor allele798081 read counts82
b) beta-binomial.
HALPERN teaches the difference of claim 30:
a) (heterozygous position83) minor allele848586 read counts87 (“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-CLEARLY-AITMAN 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-CLEARLY-AITMAN 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-, CLEARLY-AITMAN-HALPERN teaches The method of claim 26 [[25]],wherein a likelihood of the somatic position minor allele read counts is modeled as a beta- binomial distribution with an expected allelic fraction of a somatic variant.
Claim(s) 43 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baccash et al. (US 2013/0110407 A1) as applied in claims 21,24,33,41,45,34,44,62 further in view of Chen et al. (US 2009/0281981 A1):
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Re 43. (Previously Presented), Baccash teaches The method of claim 33, wherein applying the classifier comprises fitting (“into one of the three standard hypotheses for a genome” [0107]) a88
(A) quadratic
(B) discriminant
(“Bayesian” [0083] last S) model to the variant.
Baccash 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 teaches classification (categorization or labeling), one of skill in the art of attributes can make Baccash’s 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) as applied in claims 21,24,33,41,45,34,44,62 further in view of Fonte et al. (US 2015/0055085 A1):
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Re 35. (Previously Presented), Baccash teaches The method of claim 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 called89 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 thresholds90; and
f) assigning a candidate variant to a Reject training group if the candidate variant passes one or more Reject thresholds91.
Baccash 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”92-“threshold” [0130] 2nd and 1st to last S) to each selected (or “extracted”93 [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 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) as applied in claims 21,24,33,41,45,34,44,62 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. (Previously Presented), Baccash of the combination of Baccash-Fonte teaches The method of claim 35, wherein the one or more (extracted-image) quality metrics is selected from the group consisting94 of95:
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-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)96.
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)97:
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Since Baccash of the combination of Baccash-Fonte teaches a quality score, one of ordinary skill in quality scores can make Baccash’s of the combination of Baccash-Fonte be as Yin’s seeing the change “filter out low quality regions”, Yin [0126], 1st S.
Suggestions
MPEP 707.07(d) Language To Be Used in Rejecting Claims [R-10.2019], last para:
The examiner should, as a part of the first Office action on the merits, identify any claims which he or she judges, as presently recited, to be allowable and/or should suggest any way (via annotation arrows pointing the way from claim 21 to applicant’s specification’s paragraph [084]: Noise Filtering/Classification/de-duplication/de-copy of “adjacent normal tissue from98 tumor biopsies…for the determination of the presence of somatic mutations” (understood as true positive cancer tissue in contrast to false positive cancer tissue) [084] 3rd S) in which he or she considers that rejected claims (claim 21) may be amended to make them allowable:
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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
De La Vega (US 2019/0050530 A1)
De La Vega teaches “samples…can…comprise about…1%, 5%, 10%, 15%, 20%...tumor cells:
[0123] The one or more samples used in or with the methods, computer systems, and computer readable media provided herein can be any substance containing or presumed to contain nucleic acid. The sample can be a biological sample obtained from a subject. In some embodiments, the biological sample is a liquid sample. The liquid sample can be whole blood, plasma, serum, ascites, cerebrospinal fluid, sweat, urine, tears, saliva, buccal sample, cavity rinse, or organ rinse. The liquid sample can be an essentially cell-free liquid sample, or comprise cell-free nucleic acid (e.g., plasma, serum, sweat, plasma, urine, sweat, tears, saliva, sputum, cerebrospinal fluid). In other embodiments, the biological sample is a solid biological sample, e.g., feces or tissue biopsy. A sample can also comprise in vitro cell culture constituents (including but not limited to conditioned medium resulting from the growth of cells in cell culture medium, recombinant cells and cell components). The sample can comprise a single cell, e.g., a cancer cell, a circulating tumor cell, a cancer stem cell, and the like. A sample can comprise a plurality of cells. In some cases, a sample comprises about, or at least, 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99%, or 100% tumor cells. The subject can be suspected or known to harbor a solid tumor, or can be a subject who previously harbored a solid tumor.
as the closest to the claimed “wherein at least one sample has less than 25% tumor cells” of claim 21.
Servant et al. (Bioinformatics for Precision Medicine in Oncology)
Servant teaches “Variant Calling…of the Bayesian…use of…allele frequencies…is …the posterior probability” via pages 88,89:
Germline Variant Calling
The principle of germline variant detection is simple. At each position on the genome (or covered loci), the genotype is obtained by counting the number of occurrences of each nucleotide among the aligned reads. In case of homozygous variants, all nucleotides should match the same allele, A or B. And in case of het erozygous variants, half of the nucleotide should match an A, when the other half should match a B. Many different softwares have been proposed to detect germline variants. The goal of these tools is to predict the likelihood of a variation for a given locus, based on the quality scores and allele counts of the aligned reads at that locus. Modern methods are mainly based on probabilistic programming and Bayesian modeling. One major advantage of the Bayesian framework is the use of prior information for an SNP at a given position such as allele frequencies and patterns of linkage disequilibrium. These prior probabilities can be derived from databases of known SNPs such as the dbSNP or from the calling of multiple indi viduals at the same time. The variant calling routines implemented in SAMtools and GATK both support the use of multiple samples calling. Briefl y, it is assumed that one can compute the genotype likelihood p ( X | G ), where G is a given genotype and X are the reads covering a given genomic position. Bayes’ formula is used to calculate p ( G | X ), which is the posterior probability of genotype G . The genotype with the highest posterior probability is usually chosen and used to compute a mea sure of confi dence. The result is a variant, a genotype, and an associated measure of uncertainty (which is often described by a score), all of which have a concrete statistical interpretation.
as the closest to the claimed “posterior probability…using a Bayesian model to integrate the observed allelic fractions” of claim 21 or
as the closest to applicant’s disclosure, via [084] 2nd S, of:
“The present subject matter implements a Bayesian tumor-only somatic variant caller that leverages both prior knowledge of population frequencies of germline and cancer mutations, as well as the observed variant allele frequencies.”
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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 2131 Anticipation — Application of 35 U.S.C. 102 [R-08.2017] , 2nd para, 2nd to last S: The elements must be arranged as required by the claim, but this is not an ipsissimis verbis test, i.e., identity of terminology is not required. In re Bond, 910 F.2d 831, 15 USPQ2d 1566 (Fed. Cir. 1990).
2 MPEP 2131 Anticipation — Application of 35 U.S.C. 102 [R-08.2017] , 2nd para, 2nd to last S: The elements must be arranged as required by the claim, but this is not an ipsissimis verbis test, i.e., identity of terminology is not required. In re Bond, 910 F.2d 831, 15 USPQ2d 1566 (Fed. Cir. 1990).
3 MPEP 2131 Anticipation — Application of 35 U.S.C. 102 [R-08.2017] , 2nd para, 2nd to last S: The elements must be arranged as required by the claim, but this is not an ipsissimis verbis test, i.e., identity of terminology is not required. In re Bond, 910 F.2d 831, 15 USPQ2d 1566 (Fed. Cir. 1990).
4 BROAD CLAIM LANGUAGE: -ing (of “comprising”): a suffix of nouns formed from verbs, expressing the action of the verb or its result, product, material, etc. (the art of building; a new building; cotton wadding )., wherein etc. is defined: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted), wherein so is defined: likewise or correspondingly; also; too. (Dictionary.com)
5 BROAD CLAIM LANGUAGE/LONG RANGE CLAIM SCOPE: wherein: in what or in which (Dictionary.com), wherein SCOPE is defined: Linguistics, Logic. the range of words or elements of an expression (claim 21) over which a modifier (e.g., a patent examiner) or operator (e.g., me) has control. (Dictionary.com)
6 and: (used to connect alternatives) (Dictionary.com)
7 The crossed-out text “does not limit” via MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para: As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language [“and”] that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
8 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)
9 and: (used to connect alternatives) (Dictionary.com)
10 Markush element of coordinate-adjective Markush alternatives follows: [(H) and (I)]=[(I) and (H)]
11 main: chief in size, extent, or importance; principal; leading. (Dictionary.com)
12 top: foremost, chief, or principal.
13 Identities (words main & top meaning the same thing): main=top=principal
14 and: (used to connect [Markush] alternatives). (Dictionary.com)
15 Since Markush alternative (H) is taught the Markush element [(H) and (I)] is taught under the broadest reasonable interpretation of claim 1.
16 The crossed-out text “does not limit” via MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para: As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language [“and”] that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
17 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
18 the: (used to mark a noun as being used generically) (Dictionary.com)
19 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)
20 the: (used to mark a noun as being used generically) (Dictionary.com)
21 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.
22 posterior: coming after in time; later; subsequent (sometimes followed byto ). (Dictionary.com)
23 Bayesian: (of a theory) presupposing known a priori probabilities which may be subjectively assessed and which can be revised in the light of experience in accordance with Bayes' theorem. A hypothesis is thus confirmed by an experimental observation which is likely given the hypothesis and unlikely without it Compare maximum likelihood, wherein Bayes’ theorem is defined:
Statistics.
a theorem describing how the conditional probability of each of a set of possible causes, given an observed outcome, can be computed from knowledge of the probability of each cause and of the conditional probability of the outcome, given each cause.
statistics the fundamental result which expresses the conditional probability P ( E/A ) of an event E given an event A as P ( A/E ). P ( E ) /P ( A ); more generally, where En is one of a set of values Ei which partition the sample space, P ( En/A ) = P ( A/En ) P ( En ) / Σ P ( A/Ei ) P ( Ei ). This enables prior estimates of probability to be continually revised in the light of observations, wherein conditional probability is defined: statistics the probability of one event, A, occurring given that another, B, is already known to have occurred: written P ( A|B ) and equal to P ( A and B )| P ( B ) (Dictionary.com)
24 the: (used to mark a noun as being used generically) (Dictionary.com)
25 Markush element of alternatives follows: [(E), (F). or (G)]
26 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)
27 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)
28 Given the Markush alternative (F) is taught the Markush element [€,(F), or (G)] is taught under the broadest reasonable interpretation of claim 1.
29 integrate: to indicate the total amount or the mean value of (Dictionary.com)
30 DOES NOT LIMIT: and: (used to connect alternatives) (Dictionary.com): MPEP 2143.03, 3rd para.
31 CLAIM SCOPE: gerund (noun): verb acting as a noun
32 Bayesian: (of a theory) presupposing known a priori probabilities which may be subjectively assessed and which can be revised in the light of experience in accordance with Bayes' theorem. A hypothesis is thus confirmed by an experimental observation which is likely given the hypothesis and unlikely without it Compare maximum likelihood, wherein Bayes’ theorem is defined:
Statistics.
a theorem describing how the conditional probability of each of a set of possible causes, given an observed outcome, can be computed from knowledge of the probability of each cause and of the conditional probability of the outcome, given each cause.
statistics the fundamental result which expresses the conditional probability P ( E/A ) of an event E given an event A as P ( A/E ). P ( E ) /P ( A ); more generally, where En is one of a set of values Ei which partition the sample space, P ( En/A ) = P ( A/En ) P ( En ) / Σ P ( A/Ei ) P ( Ei ). This enables prior estimates of probability to be continually revised in the light of observations, wherein conditional probability is defined: statistics the probability of one event, A, occurring given that another, B, is already known to have occurred: written P ( A|B ) and equal to P ( A and B )| P ( B ) (Dictionary.com)
33 The crossed-out text “does not limit” via MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para: As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language [“or”] that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
34 SAID CLAIM SCOPE: across: preposition modifies said gerund (noun): verb acting as a noun
35 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)
36 “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)
37 fraction: Mathematics. a number usually expressed in the form a/b. (Dictionary.com)
38 Markush element of coordinate-adjective Markush elements follows: [(A) and (B)]=[(B) and (A)]
39 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)
40 fragment: an isolated, unfinished, or incomplete part. (Dictionary.com)
41 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
42 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)
43 measure: to estimate the relative amount, value, etc., of, by comparison with some standard. (Dictionary.com)
44 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
45 Markush element of coordinate-adjective Markush alternatives follows: [(C) and (D)]=[(D) and (C)]
46 part/fragment: an essential or integral attribute or quality (Dictionary.com)
47 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)
48 Markush element foloows: A or B
49 Given that Markush alternative (A) is taught the Markush element A or B is taught under the broadest reasonable interpretation of claim 33.
50 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)
51 main verb
52BROAD 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)
53 “determining”” is participle participating in the action of “built”
54 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)
55 Markush element follows: [(J) & (K) & (L)] read depth or [(L) & (K) & (J)] read depth
56 Markush element follows: [(J) & (K) & (L)] read depth or [(L) & (K) & (J)] read depth
57 Markush element of coordinate-adjective Markush alternatives follows:
[(J) & (K) & (L)] exon read depth or
[(L) & (K) & (J)] read depth or
[etc.] read depth
58 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
59 cumulative adjective
60 G: Biochemistry. 1 glycine. 2 guanine.
61 C: Biochemistry. 1 cysteine. 2 cytosine.
62 classification: one of the groups or classes into which things may be or have been classified. classify. (Dictionary.com)
63 “frequencies” further limited be “database”
64 “frequencies” further limited be “database”
65 “frequencies” further limited be “database”
66 refine: to make more fine, subtle, or precise. (Dictionary.com)
67 Bayesian: (of a theory) presupposing known a priori probabilities which may be subjectively assessed and which can be revised in the light of experience in accordance with Bayes' theorem. A hypothesis is thus confirmed by an experimental observation which is likely given the hypothesis and unlikely without it Compare maximum likelihood, wherein Bayes’ theorem is defined:
Statistics.
a theorem describing how the conditional probability of each of a set of possible causes, given an observed outcome, can be computed from knowledge of the probability of each cause and of the conditional probability of the outcome, given each cause.
statistics the fundamental result which expresses the conditional probability P ( E/A ) of an event E given an event A as P ( A/E ). P ( E ) /P ( A ); more generally, where En is one of a set of values Ei which partition the sample space, P ( En/A ) = P ( A/En ) P ( En ) / Σ P ( A/Ei ) P ( Ei ). This enables prior estimates of probability to be continually revised in the light of observations, wherein conditional probability is defined: statistics the probability of one event, A, occurring given that another, B, is already known to have occurred: written P ( A|B ) and equal to P ( A and B )| P ( B ) (Dictionary.com)
68 and: (used to connect alternatives). (Dictionary.com)
69 The crossed-out text “does not limit” via MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para: As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language [“or”] that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
70 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)
71 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)
72 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.
73 “heterozygous position” is itself a cumulative adjective and a coordinate adjective
74 “minor allele” is itself a cumulative adjective and a coordinate adjective
75 “minor allele” is further limited by the claimed “heterozygous position”? No: They are coordinate adjectives
76 “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.
77 “read counts” is modified by either Markush alternative (A) or (B)
78 “heterozygous position” is itself a cumulative adjective and a coordinate adjective
79 “minor allele” is itself a cumulative adjective
80 “minor allele” is further limited by the claimed “heterozygous position”
81 “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.
82 “read counts” is modified by either Markush alternative (A) or (B)
83 “heterozygous position” is itself a cumulative adjective and a coordinate adjective
84 “minor allele” is itself a cumulative adjective
85 “minor allele” is further limited by the claimed “heterozygous position”
86 “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.
87 “read counts” is modified by either Markush alternative (A) or (B)
88 Markush element of coordinate-adjective Markush alternatives follows: [(A) or (B)]
89 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)
90 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.
91 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.
92 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)
93 extract: to take or copy out (matter), as from a book ,wherein take is defined: to pick from a number; select. (Dictionary.com)
94 interpreted as closed-ended
95 Markush element of fourteen Markush alternatives follows
96 (italics) represent claim limitations already taguht
97 (italics) represent claim limitations already taught
98 from: (used to express discrimination or distinction). (Dictionary.com)
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