DETAILED ACTION
Applicant’s response, filed 08 April 2026 has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 5, 17, 20-21, 26-27, 29-30, 32, 35, and 44-45 are cancelled.
Claims 40-45 are newly added.
Claims 1-4, 6-16, 18-19, 22-25, 28, 31, 33-34, and 36-43 are pending.
Claims 8 and 22-23 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 05 July 2022.
Claims 1-4, 6-7, 9-16, 18-19, 24-25, 28, 31, 33-34, and 36-43 are rejected.
Priority
Applicant’s claim for the benefit of a prior-filed application, U.S. Provisional App. No. 63/134,913 filed 07 Jan. 2021 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. 119(e) or 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. 63/134,913 (hereinafter ‘913), 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 disclosure of App. ‘913 does not disclose “using a panel-targeted sequencing at an average sequencing depth of at least 40X” as recited in claim 1, and claims dependent therefrom. The disclosure of ‘913 discloses targeted sequencing at a depth of 60-80X (para. [0011] and [0020]), at a depth of 100X (para. [0009]), at a depth of 200-300X (para. [0014]). However, there is no disclosure in ‘913 of a claim range of at least 40X, with no upper limit on the depth as claimed.
Accordingly, the effective filing date of the claimed invention is 07 Jan. 2022.
Claim Interpretation
Claim 1 recites “…panel-targeted sequencing”. Applicant’s specification at para. [00303], [00309], and [00319] discloses the panel-targeted sequencing can target one or more genomic regions, one or more chromosomes, or the whole-exome. Accordingly, “panel-targeted sequencing” is interpreted to be any sequencing method that targets less than the whole genome.
Claim 1 recites “E) applying…(i) all or a portion of the first mapped dataset and (ii) all or a portion of the second mapped dataset to a model, wherein the model includes…”. In light of Applicant’s specification at FIG. 4B and [00251], which discloses that applying a model can include applying two separate component models or a joint model, the applying component models of a larger model is considered to read on the claims.
Claim 31 recites “…wherein the first biological sample and the second biological sample are the same biological sample”. In light of Applicant’s specification at para. [00293], the claim is interpreted to mean that the first and second biological samples are two samples from the same biological sample.
Claim Rejections - 35 USC § 112(b)
The rejection of claim 45 under 35 U.S.C. 112(b) in the Office action mailed 08 Jan. 2026 has been withdrawn in view of the cancellation of this claim received 08 April 2026.
Claim Rejections - 35 USC § 103
The rejection of claims 44-45 under 35 U.S.C. 103 in the Office action mailed 08 Jan. 2026 has been withdrawn in view of the cancellation of this claim received 08 April 2026.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 6-7, 9-16, 18, 24-25, 31, 33-34, 36, and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Harris (2015), as evidenced by Ghahramani (2001) in view of Talevich (2016). Any newly recited portion is necessitated by claim amendment.
Cited references:
Harris et al., US 2015/0066824 A1, published 5 March 2015, and effectively filed 29 Aug. 2014 (previously cited);
Ghahramani, An Introduction to Hidden Markov Models and Bayesian Network, 2001, International Journal of Pattern Recognition and Artificial Intelligence, 15(1), pg. 9-42 (previously cited); and
Talevich et al., CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing, PLOS Computational Biology, 2016, pg. 1-18 (previously cited);
Regarding claim 1, Harris discloses the method for determining a copy number variation status of a human subject (Abstract; [0009]; [0197]) comprises the following steps:
Harris discloses A) performing whole-genome sequencing on a first plurality of DNA molecules from a first biological sample of the subject to produce a first plurality of untargeted nucleic acid sequences ([0005], receive untargeted sequencing data from first nucleic acid sample; [0009]; [0056], WGS covers 99.99% of genome; [0085])); [0031], nucleic acids include DNA), at an average sequencing depth of 1X (i.e. from 0.5 to 5X) across 99% of a reference genome ([0009; [0056], WGS covers 99.99% of genome; [0085]), and receiving the first plurality of nucleic acid sequences electronically ([0005], e.g. sequencing data received by computer). Harris et al. further shows the first plurality of sequences comprises at least 100,000 nucleic acid reads (i.e. sequences) ([0098]; [0100]).
Harris discloses B) performing panel-targeted sequencing, including whole-exome sequencing on a second plurality of DNA molecules from a second biological sample of the subject to obtain a second plurality of nucleic acid sequences ([0005]; [0011]; [0031]; [0048]) and receiving the second plurality of nucleic acid sequences electronically ([0005];[0147]), wherein the second plurality of sequencing data comprises at least 10,000 nucleic acid sequences ([0068]);
Harris discloses C) aligning (i.e. mapping), using the computer ([0147]), the first plurality of nucleic acid sequences to positions within a first reference sequence from a human (i.e. mapping to positions within a reference sequencing of the same species as the subject) ([0007]). Harris further discloses copy number variations are detected over the entire genome and the coverage of the first sequencing data can be 99.9999% of the whole genome (i.e. the sequencing data maps to 99.9% of the whole genome) ([0085]; [0340]), which shows the first reference sequence can be a reference genome. Given the reads are whole genome sequencing reads aligned to the genome, this demonstrates this includes a locus of a gene. Furthermore, Harris discloses the panel-targeted sequencing is whole-exome sequencing ([0011]; [0048]), which necessarily would include a gene. Harris discloses the whole genome sequencing data is assigned into genomic bins with measured amounts of sequence reads in each bin (i.e. an identify of a first plurality of bins and a bin value for each bin in the first plurality of bins) ([0009]; [0059]).
Harris discloses D) aligning (i.e. mapping), using the computer ([0147]), the second plurality of nucleic acid sequences from the whole-exome sequencing to positions within a second reference sequence (i.e. a reference construct), including a reference genome ([0010], first and second reference sequences can be the same; [0340], reference sequence can be a reference genome), which includes the plurality of genomic regions targeted by the panel-targeted sequencing, thereby obtaining mapped on-target nucleic acid sequences in the second mapped dataset, including the locus of the gen given the second plurality of sequences were from whole exome sequencing.
Harris discloses E) applying, using the computer ([0147]), each of the first mapped data and the second mapped data set, separately, to one or more statistical models including a Hidden Markov Model (HMM) comprising segmentation (i.e. a first and second segmentation step) (Figure 2B-C, e.g. assay 1 and 2 applied to separate models for separate analyses C; [0010]; [0016] ;[0079]; [0083], e.g. HMM includes segmentation [0340]). Harris discloses each model identifies a polymorphism including a copy number variation indicating the copy number variation status, including a deletion, in regions of the subject ([0016] and [0059], e.g. CNVs detected in targeted data and WGS data; [0075]-[0076], e.g. model output is copy number variations; [0079], e.g. multiple algorithms/models used depending on data inputs to identify variants; [0082]; claim 7, e.g. deletions detected). Harris discloses the genomic regions analyzed can include an exon and intron of a gene (i.e. a locus within a gene) ([0222]-[0223], e.g. region features may be mutually exclusive such as noncoding portion of genome and exome and features may also be overlapping) and also determines genome-wide structural variations with a sensitivity of less than 10 kb ([0060]), demonstrating a first copy number is assigned to a segment to which the locus of the gene maps (i.e. WES data determines CNV for exon) and a second copy number is assigned to a second segment to which the locus of the gene maps (i.e. WGS determines CNV for intron).
Harris further discloses evaluating the detected copy number variations and merging two smaller variations into one larger variation if separated by a small gap ([0083]), and producing a combined output of the analysis indicative of the presence or absence of polymorphisms (i.e. the CNVs) ([0004]-[0005], e.g. polymorphisms of the second and/or first data, combined output provides rapid approach for identifying presence of polymorphisms in subject; [0075]; Figure 2), thereby providing a copy number variation status of a locus in a gene (i.e. the intron and exon).
Regarding claim 1, Harris does not disclose the following:
Regarding claim 1, Harris does not disclose the panel targeted sequencing uses an average sequencing depth of between at least 40X across the genomic regions targeted by the panel-targeted sequencing.
However, Talevich discloses a method for genome-wide copy number detection using targeted reads and nonspecifically captured off-target reads within targeted DNA sequencing data (Abstract), and discloses that performing exome (i.e. targeted-panel) sequencing at a higher coverage increases the sensitivity for calling variants in clinical use (pg. 2, para. 1), and discloses that performing the read depth calculation is the most computationally demanding step, and for exome sequencing data at 100-fold coverage (i.e. at least 40X across the targeted genomic regions) takes on the order of 20 minutes (pg. 3, para. 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have utilized 100X coverage across the regions targeted by the panel-targeted sequencing through routine experimentation of the sequencing coverage within the prior art conditions of increasing coverage to increase the sensitivity for calling variants for clinical use and decreasing coverage to reduce computational run time. See MPEP 2144.05 II. A.
Further regarding D), Harris does not explicitly disclose that the second mapped dataset comprises an identity of a second plurality of bins across the reference construct, and a bin value or a copy number state for each bin in the second plurality of bins.
Instead, Harris discloses assigning data from a combined dataset including the whole genome sequencing data and the targeted sequencing data into bins for a combined analysis ([0010]), and other embodiments in which the whole genome sequencing data alone is assigned into bins prior to copy number variation determination ([0332]).
However, as discussed above, Harris discloses embodiments which involve applying a first and second statistical model, each including a hidden Markov model (HMM), to the first mapped dataset and the second mapped dataset, respectively, in order to identify copy number variations (Figure 2B-C, e.g. assay 1 and 2 applied to separate models for separate analyses C; [0010]; [0016] ; [0059]; [0079]; [0340]). Harris further discloses assessing genomic regions such as copy number variations by assigning data into a plurality of genomic bins, selecting bin size to balance tradeoff between sensitivity and false positive reduction ([0059]), which makes obvious that in the embodiment of analyzing the first and second mapped datasets separately, both datasets would be assigned to bins.
Furthermore, Talevich discloses a method for genome-wide copy number detection using targeted reads and nonspecifically captured off-target reads within targeted DNA sequencing data (Abstract), as discussed above, and further discloses creating coverage values in target bins (i.e. a second plurality of bins) in addition to coverage values in off-target bins (i.e. the first plurality of bins), prior to segmentation (Fig. 1; pg. 3, para. 1; pg. 4, para. 3). Talevich further discloses combining the target bin coverage data and the off-target bin coverage data to form a single coverage data set used in a segmentation algorithm for determining discrete copy numbers (Fig. 1; pg. 4, para. 1-3; pg. 7, para. 4-5). This demonstrates the need for binning coverage values before copy number determination via segmentation. Talevich discloses this combination achieves both exon-level resolution in targeted gene regions and sufficient resolution in the larger intronic regions to identify copy number changes, and successfully inferred copy numbers at 100 kb resolution genome-wide (i.e. louses of genes) (Abstract; pg. 2, para. 1; Fig. 5).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Harris to have further determined bin values for a second plurality of bins for the second mapped dataset, as suggested by each of Harris and Talevich as disclosed above. One of ordinary skill in the art would have been motivated to further modify the teachings of Harris and Talevich in order to combine the prior art element of binning read coverages prior to segmentation to determine CNVs, as shown by each of Harris (for the WGS data) and Talevich above (for both the off-target and on-target data), with the prior art element of processing the second mapped dataset using a hidden Markov model for CNV determination of Harris. One of ordinary skill in the art would recognize that the results of the combination would have predictably resulted in binned values for the second mapped dataset being input into the HMM model for CNV detection, given Harris discloses using binned values for the first mapped dataset in the HMM model and Talevich also discloses using read coverage in target bins for CNV detection, as discussed above.
Regarding claim 2, Harris further discloses the whole-genome sequencing may comprise at least 10,000,000 sequencing reads ([0098];[0100]).
Regarding claim 3¸ Harris further discloses the whole-genome sequencing data (i.e. the first plurality of at least 100,000 nucleic acid sequences) can provide a sequencing depth of 2X or 3X across the genome of the subject ([0061], WGS data can comprise second or third reads; [0332] and FIG. 4, e.g. “single read” WGS refers to 1X sequencing depth).
Regarding claim 4, Harris further discloses the second plurality of at least 10,000 nucleic acid sequences includes at least 100,000 sequence reads ([0068]).
Regarding claim 6, Harris further discloses the panel-targeted sequencing is whole exome sequencing, which includes at least 25 genes ([0011]; [0048]; [0050]).
Regarding claim 7, Harris further discloses the panel-targeted sequencing is whole exome sequencing ([0011]; [0048]).
Regarding claim 9, Harris further discloses the first and second biological samples may be a blood or saliva sample ([0199]), and that the first and second samples can be different samples (i.e. independently selected) ([0006]).
Regarding claim 10¸ Harris further discloses using the measured counts per bin (i.e. the first respective bin values) of the whole-genome sequencing data in the model, wherein the model is a hidden markov model, to detect copy number ([0010], e.g. model is hidden Markov model; [0332]-[0334]; [0340], e.g. hidden markov model uses read bin counts in whole genome sequencing data to predict copy number).
Regarding claim 12, Harris in view of Talevich, as applied to claim 1 above, make obvious determining a respective bin value for each bin in the second plurality of bins. Harris further discloses the process for determining bin values.
Harris discloses, for the targeted sequencing data (i.e. the second mapped dataset), measuring the number of sequence reads (i.e. a respective second bin value) mapped to each of a plurality of genomic bins representing unique segments of the reference construct ([0010]; e.g. data from the combined dataset is set into genomic bins; [0059], e.g. targeted-specific sequencing data assigned into bins; FIG. 5, e.g. bins are non-overlapping).
Harris further discloses using the measured counts per bin (i.e. the second respective bin values) of the targeted sequencing data in the model to detect copy number ([0010], e.g. model used to generate copy number output; FIG. 2).
Regarding claims 11 and 13:
First, regarding claim 13, Harris further discloses, for the targeted sequencing data (i.e. the second mapped dataset), measuring the number of sequence reads mapped to each of a plurality of genomic bins representing unique segments of the reference construct ([0010]; 0059], e.g. targeted-specific sequencing data assigned into bins; FIG. 5, e.g. bins are non-overlapping).
Further regarding claims 11 and 13, Harris does not disclose the following:
Regarding claim 11, Harris, as applied to claim 1 above, does not disclose determining a respective copy number state for each respective bin in the first plurality of bins using the respective first bin value for the respective bin; and the all or the portion of the first mapped dataset inputted into the model in E) comprises the respective copy number state for each respective bin in the first plurality of bins.
Further regarding claim 13¸ Harris, as applied to claim 1 above, does not disclose determining a respective copy number state for each respective bin in the second plurality of bins using the respective second bin value for the respective bin; and the all or the portion of the second mapped dataset inputted into the model in E) comprises the respective copy number state for each respective bin in the second plurality of bins.
However, regarding claims 11, and 13, as discussed above, Talevich discloses a method for genome-wide copy number detection using targeted reads and nonspecifically captured off-target reads within targeted DNA sequencing data (Abstract), which comprises determining coverages in both off-target bins and on-target bins (i.e. determining first and second respective values for a first and second plurality of bins) (figure 1, pg. 3, para. 1). Talevich further discloses the methods includes estimating a copy number ratio using the target and off-target bins (i.e. copy number states for each first and second bin using the respective first and second bin values) (Figure 1, pg. 3, para. 1), and then inputting the copy number ratios for the first and second plurality of bins, which are non-overlapping as discussed above for claim 1 (i.e. the copy number states) into a circular binary segmentation (CBS) model to predict one or more copy number variations in a subject (Figure 1; pg. 7, para. 4-5). Talevich further discloses the off-target reads provide a very low-coverage sequencing of the whole-genome (pg. 2, para. 2). Last, Talevich discloses the model provides highly accurate and reliable copy number estimates genome-wide (pg. 2, para. 3).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Harris in view of Talevich to have determined a copy number state using the first and second respective bin values (e.g. the off-target and on-target bin coverages, respectively) and then inputted the copy number states of the first and second plurality of bins into a segmentation model to determine one or more copy number variations, as shown by Talevich (Figure 1 and 5; pg. 3, para. 1; pg. 4, para. 2; pg. 7, para. 4-5), thus arriving at the invention of claims 11, and 13. One of ordinary skill in the art would have been motivated to combine the methods of Harris and Talevich in order to provide reliable copy number estimates, as shown by Talevich (pg. 2, para. 3). This modification would have had a reasonable expectation of success because Harris also discloses using a segmentation model with bin coverages to estimate copy number in each of a WGS dataset (corresponding to the off-target data of Talevich) and a targeted dataset, such that the copy number ratios determined from bin values of Talevich are applicable as input into each respective HMM model of Harris.
Further regarding claim 14, Harris further discloses the number of bins can include 1000, 1kb bins (i.e. 1000 bins, each less than 5kb) ([0059], can include 1kb bins; FIG. 5, e.g. 1kb bins provide 1000 bins).
Further regarding claim 15, Harris further discloses the bins can include 1000, 1 kb bins ([0059]; FIG. 5), such that the second plurality of bins collectively represents more than 10kb of the reference construct.
Further regarding claim 16, Harris further discloses the bins can be 1 kb bins (i.e. each respective bin corresponds to no more than 1kb of the reference construct ([0059]; FIG. 5).
Regarding claim 18, Harris further discloses the first mapped dataset represents a number of sequence reads mapped to each of a plurality of 10k bins ([0059]), which shows the first mapped dataset collectively represents sequencing depths (i.e. the number of mapped reads) for at least 10kb of the reference genome, given each bin must be at least 1 base pair long.
Harris further discloses the second mapped dataset represents a number of sequence reads mapped to each of a plurality of 10k bins ([0059]), which shows the second mapped dataset collectively represents sequencing depths (i.e. the number of mapped reads) for at least 10kb of the reference construct.
Regarding claim 24, Harris further discloses the model determines copy number variation status of the entire genome through a hidden markov model (i.e. through statistical inference) ([0010]; [0337]; [00340]).
Regarding claim 25, Harris further discloses the model comprises a hidden markov model (([0010]; [0337]; [00340]), which is a type of probabilistic network, as evidenced by Ghahramani. Ghahramani discloses that hidden markov models are a particular kind of Bayesian (i.e. probabilistic) network (pg. 1, para. 1, second bullet). Accordingly, Harris discloses the model comprises a probabilistic network.
Regarding claim 31¸ Harris further discloses the first biological sample and the second biological sample are the same sample ([0006]; [0010]).
Regarding claims 33-34¸ Harris discloses the panel-targeted sequencing targets specific genes (i.e. a gene-panel) or is whole-exome sequencing ([0009]; [0011])
Further regarding claims 33-34¸Harris does not disclose the panel-targeted sequencing is at an average sequencing depth of between 60X and 80X across the genome or an average depth of between 200X and 300X across the targeted genomic regions.
However, Talevich discloses a method for genome-wide copy number detection using targeted reads and nonspecifically captured off-target reads within targeted DNA sequencing data (Abstract), and discloses that performing exome or a gene target panel sequencing at a higher coverage increases the sensitivity for calling variants in clinical use (pg. 2, para. 1), and discloses that performing the read depth calculation is the most computationally demanding step, and for exome sequencing data at 100-fold coverage or a 293-gene target panel at 500-fold coverage takes on the order of 20 minutes (pg. 3, para. 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have utilized lower coverages, such as 60X or 80X or higher coverages at 200X and 300X coverage across the regions targeted by the panel-targeted sequencing through routine experimentation of the sequencing coverage within the prior art conditions of increasing coverage to increase the sensitivity for calling variants for clinical use and decreasing coverage to reduce computational run time. See MPEP 2144.05 II. A.
Regarding claim 36, Harris discloses variants detected in the low coverage whole genome sequencing data (i.e. first mapped dataset) can be used to impute variants over the whole genome ([0055]; [0337]).
Harris further discloses determining a genetic risk of a disease based on the sets of outputs ([0176]-[0177[), which include the imputed variants (([0055]; [0337]).
Regarding claim 38, Harris discloses an analyzed region can include a gene, which includes a locus in the gene [0222]). Harris discloses the analyzed genes may be cancer genes ([0226]), such that such that any CNV detected in the cancer gene provides in indication of cancer in the subject.
Therefore, the invention is prima facie obvious.
Claims 19 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Harris (2015) in view of Talevich, as applied to claim 1 above, further in view of Lachlan (2012), as evidenced by Haugh (2017). This rejection is previously recited.
Cited references:
Lachlan et al, An exome sequencing pipeline for identifying and genotyping common CNVs associated with disease with application to psoriasis, 2012, ECCB, 28, pg. i370-i374 (previously cited); and
Haugh 2017, Machine Learning for OR & FE: Hidden Markov Models, 2017, Columbia University, pg. 1-34; Pub. Date: 2017, Wayback Machine (previously cited).
Regarding claims 19 and 28¸ Harris in view of Talevich disclose the method of claim 1 as applied above.
Further regarding claims 19 and 28¸ Harris in view of Talevich does not explicitly disclose the model comprises at least 500 parameters, as recited in claim 19, or that the model processes the (i) all or the portion of the first mapped dataset and (ii) all or the portion of the second mapped dataset to identify the one or more copy number variations as output of the model in N-dimensional space in the applying E), wherein N is a positive integer of 4 or greater. However, these limitations were obvious to one of ordinary skill in the art for the following reasons.
Harris discloses applying a hidden markov model to separately to the first mapped dataset and second mapped dataset, to identify one or more copy number variations indicating the copy number variation status of the subject ([0010], wherein the output is generated by a statistical model; [014]; [0071]; [0337]; claims 1 and 7). Harris in view of Talevich as applied to claim 1 above, make obvious using first and second mapped datasets including read counts within genomic bins. Harris further discloses the number of bins can include 1000 bins ([0059]; [0337]; [0340]). Harris further discloses that methods that reduce a number of variables such as a principal component analysis (PCA) can be used in the analysis of the data ([0082]), suggesting the application of PCA to the first and second mapped datasets.
Furthermore, Lachlan discloses a method for identifying copy number variations in a DNA sample of a subject (Abstract), which comprises performing principal component analysis on read depth information for each region of N genomic regions (i.e. each bin of a plurality of bins) to calculate the first 50 components (i.e. dimensionality reduction components) (pg. i371, col. 1, para. 7), projecting the first 40 principal components, and modeling the absolute copy numbers of the sample by applying a hidden markov model to the first 40 principal components (i.e. the model is applied in 40-dimensional space on the read depth information, as recited in claim 28), as recited in claim 28 (pg. i371, col. 2, para. 3-5; Figure 1; Table 3). Lachlan further discloses the hidden markov model uses a different emission distribution per hidden copy number genotype per 100-bp window to model the normalized read depths determined by PCA (pg. i371, col. 2, para. 5-6). Given Harris discloses the number of genomic bins can include 1000 bins ([0059]), the hidden markov applied to read depth data of 1000 bins would necessarily comprise at least 1000 parameters (i.e. at least 500 parameters, as recited in claim 19), as evidenced by Haugh. Haugh discloses a hidden markov model is defined by its emission distribution (pg. 5), and that a parameter Bij for the emission distribution is estimated. Accordingly, the hidden markov model generating an emission distribution per copy number state and per 100 bp window, for 1000 windows, would result in a hidden markov model comprising at least 1000 parameters (e.g. at least one per window). Lachlan further discloses that principal components correct for read-depth biases from sequencing data (pg. i373, col. 1, para. 2), and that the principal component algorithm has a very low memory footprint and is highly computationally efficient (pg. i371, col. 1, para. 7).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method made obvious by Harris in view of Talevich, as applied to claim 1 above, to have applied a hidden markov model to the top 40 dimensionality reduction components of the read depth information by generating an emission distribution per copy number state and genomic bin, to estimate a copy number variation status of the subject, according to the method of Lachlan (pg. i371, col. 1, para. 7 and col. 2, para. 3-6; Figure 1), resulting in an application of a hidden markov model to with greater than 500 parameters to read depth information in greater than 4-dimensional space, as discussed above, thus arriving at the inventions of claims 19 and 28. One of ordinary skill in the art would have been motivated to combine the methods of Harris in view of Talevich and Lachlan in order to correct for read-depth biases in the sequencing data using a highly computationally efficient algorithm before predicting copy number variations, as disclosed by Lachlan (pg. i371, col. 1, para. 7; pg. i373, col. 1, para. 2). This modification would have had a reasonable expectation of success because both Lachlan and Harris analyze sequencing depth information in genomic bins, and Harris shows using a hidden markov model to predict copy number (FIG. 4; [0340]) and that a principal component analysis can be used to analyze the sequencing data ([0082]) , such that the method of Lachlan is applicable to the sequencing data of Harris.
Therefore, the invention is prima facie obvious.
Claim 37 is rejected under 35 U.S.C. 103 as being unpatentable over Harris in view of Talevich, as applied to claim 36 above, and further in view of Chen (2020). This rejection is previously recited.
Cited reference: Chen et al., Genotype imputation and variability in polygenic risk score estimation, 2020, Genome Medicine, 12:100, pg. 1-13; previously recited.
Regarding claim 37¸ Harris in view of Talevich disclose the method of claim 36 as applied above.
Further regarding claim 37, Harris in view of Talevich, as applied to claim 36 above, do not disclose the disease risk is a polygenic risk score.
However, Chen discloses a method for estimating polygenic risk scores using imputed genotypes (Abstract), which comprises imputing variants (pg. 3, col. 2, para. 2) and then calculating a polygenic risk score using the imputed variants (pg. 4, col. 1, para. 1 to col. 2, para. 1). Chen further discloses polygenic risk scores can be used to guide healthcare decisions (Abstract).
It would have been prima facie obvious, to one of ordinary skill in the art before the effective filing date of the claimed invention, to have modified the method of Harris in view of Talevich to have calculated a polygenic risk score based on imputed variants, as shown by Chen, discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Harris in view of Talevich and Chen in order to guide healthcare decisions of the subject, as shown by Chen (Abstract). This modification would have had a reasonable expectation of success given Harris discloses determining imputed variants, such that the calculations of Chen are applicable to the data of Harris.
Therefore, the invention is prima facie obvious.
Claim 39 is rejected under 35 U.S.C. 103 as being unpatentable over Harris in view of Talevich, as applied to claim 1 above, and further in view of Azrak (2015). Any newly recited portion is necessitated by claim amendment.
Cited reference: Azrak, upQMPSF, a Method for the Detection of BRCA Exon Copy Number Variants, 2015, Biochem Genet, 53:141-157; previously recited.
Regarding claim 39, Harris in view of Talevich disclose the method of claim 1 as applied above.
Further regarding claim 39, Harris in view of Talevich do not disclose the locus is in BRCA1 or BRCA 2 gene.
However, Azrak discloses a method for detecting a BRCA1 exon copy number variant and discusses that large insertion/deletion mutations are frequently found in genes associated with certain diseases such as cancer (Abstract). Azrak further discloses a high frequency of large genomic rearrangements is reported in BRCA1 in hereditary breast and ovarian cancer patients, including rearrangements spanning one or more exons (pg. 141, 1, para. 1 to pg. 142, para. 1). Azrak discloses these germline mutations in BRCA1 and BRCA2 genes account for most familial breast and ovarian cancer cases (pg. 155, para. 4) and that methods for detecting large copy number variations can be used as a mutation screening assay in genetic testing for hereditary diseases (pg. 155, para. 5-7).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method of Harris in view of Talevich, as applied to claim 1 above, to have detected the copy number variation in a BRCA1 or BRCA2 gene, as shown by Azrak, discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Harris in view of Talevich with Azrak in order to screen for hereditary diseases, given BRCA1 and BRCA2 account for most familial breast and ovarian cancer cases, as shown by Azrak (pg. 155, para. 4-7). This modification would have had a reasonable expectation of success because Harris in view of Talevich disclose a method of generating a whole genome copy number profile, including detecting copy number variants within genes, and thus the method of Azrak is applicable to the copy number detection method of Harris in view of Talevich.
Therefore, the invention is prima facie obvious.
Claims 40-43 are rejected under 35 U.S.C. 103 as being unpatentable over Harris in view of Talevich, as applied to claim 1 above, and further in view of Garvin (2015). This rejection is previously recited.
Cited reference: Garvin et al., Interactive analysis and assessment of single-cell copy-number variations, 2015, Nature Methods, 12(11), pg. 1058-1060 and ONLINE Methods; previously cited
Regarding claim 40, Harris in view of Talevich make obvious the method of claim 1 as applied above.
Further regarding claim 40, Harris in view of Talevich does not disclose the first plurality of bins and the second plurality of bins are each variable-sized bins in which bin size is selected to contain a constant number of mappable positions across the first and second plurality of bins, respectively.
However, Garvin discloses a method for analyzing copy-number variations using sequencing data (Abstract; pg. 1058, col. 1, para. 1), which includes binning aligned reads into variable-length intervals across the genome that contained the same number of uniquely mappable positions (pg. 1059, col. 2, para. 1 to pg. 1060, col. 1, para. 1; pg. 1 of ONLINE Methods: Binning Method). Garvin discloses that copy number analysis begins with binning across mapped reads into fixed or variable length intervals across the genome, which aggregates read-depth information into regions that are more robust, and that fixed length bins are discouraged because they lead to read dropout in regions that span repetitive and complex regions (pg. 1 of ONLINE METHODS, col. 1, para. 2-4, e.g. “Binning method”).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method of Harris in view of Talevich, as applied to claim 1 above, to have used the variable-sized bins of Garvin, discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Harris in view of Talevich with Garvin in order to avoid using fixed length bins that lead to read dropout in regions that span repetitive sequences or complex regions, as shown by Garvin (pg. 1 of ONLINE METHODS, col. 1, para. 2-4, e.g. “Binning method”). This modification would have had a reasonable expectation of success given Harris in view of Talevich bin first and second read sets for copy number analysis, and thus the binning method of Garvin is appliable to Harris in view of Talevich.
Regarding claims 41-43, Harris in view of Talevich make obvious using the first and second mapped datasets containing bin values or copy number states in a model as claimed, as applied to claim 1 above.
Regarding claims 41-42, Talevich further discloses correcting for biases at each bin by correcting for repeat-masked fraction (i.e. predicted mappability) and GC content (pg. 7, para. 3).
Regarding claim 43¸ Talevich further discloses the coverage of each bin is divided by the size of the bin (pg. 4, para. 3).
Regarding claims 41-43, is further noted that the embodiment of the mapped datasets having “bin values” is recited as an alternative (i.e. not required), and Talevich discloses the copy number state (i.e. log2 ratios) of the bins as claimed (Fig. 1, pg. 7, para. 4).
Therefore, the invention is prima facie obvious.
Response to Arguments
Applicant's arguments filed 08 April 2026 regarding 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant remarks amended claim 1 uses a segmentation step to identify copy number variation status of a locus within a gene separately using the first mapped dataset and the second mapped dataset, and that the preserved mapping context for the two datasets allows a model to more accurately perform segmentation for the locus within the gene (Applicant’s remarks at pg. 11, para. 3). Applicant remarks that Harris combines the target-specific sequencing data and the untargeted data into a single combined dataset, and thus does not separately preserve the mapping contexts or the segmentation as recited in claim 1, and further remarks that the Office Action acknowledges this at para. [107] of the previous Office action.
This argument is not persuasive. First, it is noted the above rejection contains newly cited portions in view of Applicant’s claim amendments. Para. [107] of the previous Office action states that Harris does not teach separate datasets of bin values of the first mapped read set and the second mapped read sets. That is, Harris does not explicitly teach determining bin values of the second mapped read set, as discussed in the above rejection of claim 1. Instead, Harris only mentions determining bin values of the first mapped read set prior to input into a model for CNV detection ([0059]; [0332]; Figure 2). However, Harris does not require combining the first dataset and the second dataset into a combined dataset prior to analysis, as alleged by Applicant. Instead, Harris discloses alternative embodiments in which the first and second mapped read sets may be analyzed separately, and then the results combined into a combined output (Figure 2; ([0004]-[0005], e.g. polymorphisms of the second and/or first data [0075]). As discussed in the above rejection, this analysis includes using a HMM to segmentally detect copy number variations in sequence reads.
Furthermore, using a second mapped read set comprising bin values for a second plurality of bins as input into the respective model for copy number variation detection is obvious, as applied in the above rejection in view of the claim amendments. It is also noted that the off-target and on-target bin coverage of Talevich are also for non-overlapping regions and determined from read mapping (Figure 1; pg. 4, para. 1 and 3), and thus any advantages about “preserved mapping contexts” would also be applicable to the method of Talevich. Combining the off-target versus on-target read coverage information does not change where the reads were mapped within a given genome, and thus it is not apparent how analyzing the reads separately versed in a combined format affects the “mapping context”.
Applicant remarks that the combining of Talevich with Harris does not teach separate datasets because Talevich states “The on- and off-target read depths are then combined, normalized to a reference…, corrected…in a final table of log2 copy ratios”, and the segmentation algorithm is thus performed after merging the coverage data and bias correction, which does not preserve the mapping contexts of the first and second datasets, and thus Talevich fails to teach or suggest segmentation as in claim 1 (Applicant’s remarks at pg. 12, para. 2). Applicant further remarks the Office action uses impermissible hindsight to reconstruct Applicant’s claimed method because the proposed combination does not arrive at Applicant’s claimed method given the segmentation process of Talevich merges data form two data sets prior to segmentation (Applicant’s remarks at pg. 12, para. 3 to pg. 13, para. 1). Applicant remarks neither reference teaches separate segmentations (Applicant’s remarks at pg. 13, para. 1).
This argument is not persuasive. First, Harris is relied upon to teach the first and second segmentation steps applied separately to a first mapped dataset and a second mapped dataset, respectively. Harris does disclose separately applying models to the first and second mapped datasets (Figure 2B-C, e.g. assay 1 and 2 applied to same or separate models C; [0010]; [0016] ;[0079]; [0340]), wherein the model is a Hidden Markov Model (HMM) that identifies a copy number variation in regions of the subject ([0075]-[0076], e.g. model output is copy number variations; [0079], e.g. multiple algorithms/models used depending on data inputs to identify variants; [0082]; claim 7, e.g. deletions detected). Talevich is relied upon to demonstrate that it would be obvious to have used bin values for the second mapped read set in the method of Harris that does uses two separate segmentation steps. Therefore, Applicant’s arguments regarding the combined dataset and statement that neither reference teaches separate segmentations are not persuasive.
In the interest of compact prosecution, it is also not persuasive that it would not have been obvious to use the off-target data and on-target data of Talevich as separate inputs into a segmentation model. It is noted that the following explanation is not relied upon in the above rejection, given Harris discloses the separate inputs into a segmentation model, and the explanation is only provided in the interest of compact prosecution. Simply because Talevich combines the log2 ratios of the non-overlapping off-target and on-target bins does not suddenly erase where the reads were mapped such that a “mapping context” is lost. Where the reads of the off-target data and the on-target data were mapped to the reference genome (i.e. in the target regions of the genome or in off-target regions of the genome) is preserved, even in a merged file of the off-target and on-target bins. Therefore, it would have also been considered obvious to input the target bins and off target bins of Talevich separately into a segmentation model rather than together in a merged file, given Harris discloses that separate data can be combined prior to analysis, or the separate data can be analyzed separately and then the results of the analysis combined in an output (Figure 2), wherein the analysis is a copy number analysis as explained in the above rejection. Applying the non-overlapping off-target or on-target data of Talevich together or separately into the segmentation would be considered obvious to try, given the two finite solutions presented by Harris and given the off-target bins and on-target bins of Talevich are non-overlapping (and thus the copy number outputs easily merged to form a continuous copy number output over the genome).
Applicant remarks that Ghahramani does not teach or suggest the segmentation of claim 1 and thus does not supplement the deficiencies of Harris and Talevich, and thus Harris, Ghahramani and Talevich do not teach amended claim 1 (Applicant’s remarks at pg. 13, para. 2-3).
This argument is not persuasive because Ghahramani is not relied upon to teach the segmentation and for the reasons discussed above with respect to Harris and Talevich.
Applicant remarks, regarding claims 19, 28, 37, 39, and 40-43, that each of the references relied upon to make up for the above-identified deficiencies of Harris and Talevich, and therefore, the claims are patentable over the cited references (Applicant’s remarks at pg. 13, para. 4 to pg. 15, para. 2).
This argument is not persuasive for the same reasons discussed above for Harris and Talevich with respect to claim 1.
Conclusion
No claims are allowed.
Claims 1-4, 6-7, 9-16, 18-19, 24-25, 28, 31, 33-34, and 36-43 are patent eligible for the reasons discussed in the Office action mailed 10 Aug. 2023 and 20 Dec. 2022.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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