DETAILED ACTION
The Applicant’s filing, received 27 June 2023, has been fully considered. The following rejections and/or objections 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 the Claims
Claims 1-20 are pending.
Claims 1-20 are rejected.
Priority
This application claims benefit of 63/367,075, filed 27 June 2022.
Information Disclosure Statement
The information disclosure statements (IDS) received 25 April 2024 and 19 July 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, these information disclosure statements have been considered by the examiner.
Drawings
The drawings received 27 June 2023 are accepted.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-8 recite a system comprising at least one processor and a non-transitory computer readable medium (i.e., a machine and/or a manufacture); claims 9-16 recite a non-transitory computer readable medium comprising instructions (i.e., a machine and/or a manufacture); and claims 17-20 recite a method (i.e., a process).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
identify candidate structural variants that satisfy a threshold quantity of occurrences within a genomic sample database (i.e., mental processes, e.g., observation, evaluation, and judgment; and mathematical concepts, e.g., a quantitative statistical value representing a threshold);
select, from the candidate structural variants, structural variant haplotypes (i.e., mental processes, e.g., decision-making);
identify, from a linear reference genome, reference haplotypes corresponding to the structural variant haplotypes (i.e., mental processes, e.g., observation, evaluation, and judgment); and
generate a structural variation graph genome comprising alternate contiguous sequences representing the structural variant haplotypes and reference sequences representing the reference haplotypes (i.e., mental processes, e.g., observation, evaluation, and judgment).
Independent claim 9 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
identify candidate structural variants that satisfy a threshold quantity of occurrences within a genomic sample database;
select, from the candidate structural variants, structural variant haplotypes;
identify, from a linear reference genome, reference haplotypes corresponding to the structural variant haplotypes; and
generate a structural variation graph genome comprising alternate contiguous sequences representing the structural variant haplotypes and reference sequences representing the reference haplotypes.
Independent claim 17 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
aligning a subset of nucleotide reads with an alternate contiguous sequence representing a structural variant haplotype within a structural variation graph genome (i.e., mental processes, e.g., observation, evaluation, and judgment); and
generating one or more nucleobase calls for the genomic sample based on the aligned subset of nucleotide reads (i.e., mental processes, e.g., observation, evaluation, and judgment).
Dependent claims 2-8, 10-15, and 18-20 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 2 further recites:
selecting, from the candidate structural variants, a first structural variant haplotype that satisfies an additional threshold quantity of occurrences at a first genomic region (i.e., mental processes, e.g., observation, evaluation, and judgment; and mathematical concepts, e.g., a quantitative statistical value representing a threshold); and
selecting, from the candidate structural variants, a second structural variant haplotype that satisfies the additional threshold quantity of occurrences at a second genomic region (i.e., mental processes, e.g., observation, evaluation, and judgment; and mathematical concepts, e.g., a quantitative statistical value representing a threshold).
Dependent claim 3 further recites:
selecting a first structural variant haplotype adjacent to a first flanking variant within a first nucleotide sequence of the genomic sample database (i.e., mental processes, e.g., observation, evaluation, and judgment); and
selecting a second structural variant haplotype adjacent to a second flanking variant within a second nucleotide sequence of the genomic sample database (i.e., mental processes, e.g., observation, evaluation, and judgment).
Dependent claim 4 further recites:
identify the candidate structural variants by selecting structural variants representing one or more of a deletion of more than a threshold number of base pairs, an insertion of more than the threshold number of base pairs, a duplication of more than the threshold number of base pairs, an inversion, a translocation, or a copy number variation (CNV) (i.e., mental processes, e.g., observation, evaluation, and judgment; and mathematical concepts, e.g., a quantitative statistical value representing a threshold).
Dependent claim 5 further recites:
generate the structural variation graph genome comprising (i.e., mental processes, e.g., observation, evaluation, and judgment):
a first alternate contiguous sequence representing a first structural variant haplotype and a first flanking variant; and
a second alternate contiguous sequence representing a second structural variant haplotype and a second flanking variant.
Dependent claim 6 further recites:
generate an alignment file that maps the structural variant haplotypes to genomic coordinates of the reference haplotypes within the linear reference genome (i.e., mental processes, e.g., observation, evaluation, and judgment); and
generate the structural variation graph genome by associating, within an organization structure, the alternate contiguous sequences representing the structural variant haplotypes with identifiers for the genomic coordinates of the reference haplotypes (i.e., mental processes, e.g., observation, evaluation, and judgment).
Dependent claim 7 further recites:
generate the alignment file by generating a Sequence Alignment/Map (SAM) liftover file that maps the structural variant haplotypes to the genomic coordinates of the reference haplotypes (i.e., mental processes, e.g., observation, evaluation, and judgment); and
generate the structural variation graph genome utilizing the organization structure by associating, within a hash table, nucleobase identifiers for nucleobases from the alternate contiguous sequences with values representing the genomic coordinates of the reference haplotypes (i.e., mental processes, e.g., observation, evaluation, and judgment).
Dependent claim 8 further recites:
generate the structural variation graph genome by ordering a subset of alternate contiguous sequences corresponding to a genomic region according to frequency within the genomic sample database (i.e., mental processes, e.g., observation, evaluation, and judgment).
Dependent claim 10 further recites:
select the structural variant haplotypes by selecting, from the candidate structural variants, particular structural variant haplotypes that satisfy an additional threshold quantity of occurrences at particular genomic regions (i.e., mental processes, e.g., observation, evaluation, and judgment; and mathematical concepts, e.g., a quantitative statistical value representing a threshold).
Dependent claim 11 further recites:
select the structural variant haplotypes by selecting particular structural variant haplotypes adjacent to particular flanking variants within nucleotide sequences of the genomic sample database (i.e., mental processes, e.g., observation, evaluation, and judgment).
Dependent claim 12 further recites:
select the particular structural variant haplotypes by (i.e., mental processes, e.g., observation, evaluation, and judgment):
selecting a first structural variant haplotype in phase with a first flanking variant within a first nucleotide sequence of the genomic sample database; and
selecting a second structural variant haplotype in phase with a second flanking variant within a second nucleotide sequence of the genomic sample database.
Dependent claim 13 further recites:
generate the structural variation graph genome comprising particular alternate contiguous sequences representing the particular structural variant haplotypes and the particular flanking variants (i.e., mental processes, e.g., observation, evaluation, and judgment).
Dependent claim 14 further recites:
identify, from the genomic sample database, alternate haplotypes comprising one or more of a single nucleotide polymorphism (SNP), a deletion of less than fifty base pairs, or an insertion of less than fifty base pairs (i.e., mental processes, e.g., observation, evaluation, and judgment); and
generate the structural variation graph genome further comprising alternate nucleobases or additional alternate contiguous sequences representing the alternate haplotypes (i.e., mental processes, e.g., observation, evaluation, and judgment).
Dependent claim 15 further recites:
identify the candidate structural variants by selecting structural variants representing one or more of a deletion of more than fifty base pairs, an insertion of more than fifty base pairs, a duplication of more than fifty base pairs, an inversion, a translocation, or a copy number variation (CNV) (i.e., mental processes, e.g., observation, evaluation, and judgment).
Dependent claim 18 further recites:
generating an alignment file or a variant call file comprising one or more of an annotation indicating the structural variant haplotype corresponding to the one or more nucleobase calls, an annotation indicating a frequency within a genomic sample database of the structural variant haplotype corresponding to the one or more nucleobase calls, or genomic coordinates of a linear reference genome that is part of the structural variation graph genome and that corresponds to the one or more nucleobase calls (i.e., mental processes, e.g., observation, evaluation, and judgment).
Dependent claim 19 further recites:
determining that the subset of nucleotide reads overlap with a breakpoint of the alternate contiguous sequence representing the structural variant haplotype (i.e., mental processes, e.g., observation, evaluation, and judgment); and
generating an alignment file or a variant call file comprising an annotation indicating an alignment reflecting the structural variant haplotype within the genomic sample (i.e., mental processes, e.g., observation, evaluation, and judgment).
Dependent claim 20 further recites:
determining that an alignment score for the subset of nucleotide reads does not satisfy a threshold alignment score for a candidate alignment between the subset of nucleotide reads and a primary-assembly region of a linear reference genome (i.e., mental processes, e.g., observation, evaluation, and judgment; and mathematical concepts, e.g., a quantitative statistical value representing a threshold); and
generating a variant call file or an alignment file with the one or more nucleobase calls for the genomic sample based on the aligned subset of nucleotide reads with the alternate contiguous sequence and without nucleobase calls for the genomic sample based on the candidate alignment that does not satisfy the threshold alignment score (i.e., mental processes, e.g., observation, evaluation, and judgment).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., select, from the candidate structural variants, structural variant haplotypes), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., identify candidate structural variants that satisfy a threshold quantity of occurrences within a genomic sample database) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-20 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
In the instant application, the claims provide additional elements to analyze data and generate a structural variation graph genome, however once the graph is generated, the claims do not recite any limitations to which the final data analysis result is practically applied.
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
Dependent claims 2-8, 10-15, and 18-20 do not further recite any elements in addition to the judicial exceptions noted above.
The additional elements in independent claim 1 include:
at least one processor; and
a non-transitory computer readable medium.
The additional elements in independent claim 9 include:
a non-transitory computer readable medium;
at least one processor; and
a computing device.
The additional elements in independent claim 17 include:
identifying nucleotide reads from a genomic sample.
Dependent claim 16 further recites additional elements that include:
the at least one processor comprises a configurable processor and executing the at least one processor comprises configuring the configurable processor.
The additional elements of at least one processor (claims 1 and 9); a non-transitory computer readable medium (claims 1 and 9); and a computing device (claim 9); invoke a computer and/or computer-related components merely as tools for use in the claimed process, such that they amount to no more than mere instructions to apply the exceptions using a generic computer (MPEP 2106.05(f)), and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional element of the at least one processor comprises a configurable processor and executing the at least one processor comprises configuring the configurable processor (claim 16) invokes a computer and/or computer-related components merely as a tool for use in the claimed process, such that it amounts to no more than mere instructions to apply the exceptions using a generic computer (MPEP 2106.05(f)), and therefore is not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, does not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional element of identifying nucleotide reads from a genomic sample (claim 17) is merely a pre-solution activity of gathering data for use in the claimed process – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools; and as such, when all limitations in claims 1-20 have been considered as a whole (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-20 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 2-8, 10-15, and 18-20 do not further recite any elements in addition to the judicial exceptions noted above.
The additional elements recited in independent claims 1, 9, and 17 and dependent claim 16 are identified above, and carried over from Step 2A Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of at least one processor (claims 1 & 9); a non-transitory computer readable medium (claims 1 & 9); and a computing device (claim 9); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
The additional element of the at least one processor comprises a configurable processor and executing the at least one processor comprises configuring the configurable processor (claim 16) is conventional. Evidence of conventionality is shown by:
Dutt et al. ("Configurable Processors for Embedded Computing." Computer, 2003, vol. 36, no. 1, pp. 120-123).
Dutt et al. reviews configurable processors for embedded computing (Title) and shows that configurable processors fill the gap in the center of a design continuum that starts with a pure hardware implementation and ends with a full software realization on general-purpose processors (page 121, col. 1, para. 4; and Figure 1).
The additional element of identifying nucleotide reads from a genomic sample (claim 17) is conventional. Evidence of conventionality is shown by:
McCombie et al. (“Next-Generation Sequencing Technologies.” Cold Spring Harbor Perspectives in Medicine, 2019, vol. 9(11), a036798, pp. 1-9).
McCombie et al. reviews next-generation sequencing (NGS) technologies (Title) and shows that NGS has largely been driven by short-read generation (150 bp) but new platforms have emerged and are now capable of generating long multi-kilobase reads, and further shows that rapid DNA and RNA sequencing is now mainstream and will continue to have an increasing impact on biology and medicine (Abstract).
Therefore, when taken alone (i.e., individually), all additional elements in claims 1-20 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as an ordered combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-20 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 17 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al. (“Paragraph: a graph-based structural variant genotyper for short-read sequence data.” Genome Biology, 2019, vol. 20:291, pp. 1-13).
Independent claim 17 sets forth three steps comprising: identifying reads, aligning some of the reads, i.e., ‘a subset’ of reads, to a reference representing possible haplotypes, and ‘generating’ base calls based on the alignment. These steps are generic and depend upon the data being analyzed, and broadly encompass aligning reads to a reference and noting homology and any variants that are observed. With respect to an ‘alternate contiguous sequence representing a structural variant haplotype’, this can reasonably be interpreted as any reference, and based on the read data being analyzed, the difference in the reads could then represent the haplotype seen in the variant of the read.
Chen et al. teaches an accurate genotyper that models structural variations (SVs) using sequence graphs and annotations of structural variations (Abstract), and further teaches that the use of a graph for each variant makes it possible to systematically evaluate how reads align across breakpoints of candidate variants (page 2, col. 1, para. 2).
Regarding independent claim 17, Chen et al. shows long-read data was generated on a Pacific Biosciences (PacBio) Sequel system using the Circular Consensus Sequencing (CCS) technology (sometimes called “HiFi” reads) (page 2, col. 2, para. 2) and provides for ‘identifying nucleotide reads’, which are subsequently aligned to a reference and thus provide for ‘aligning a subset’ with a ‘sequence representing a structural variant’, and finally based on the alignment, the differences, (i.e., the variants), are indicated based on the alignment. Chen et al. further shows graph-based genotyping of structural variations where, for each structural variant (SV) defined in an input file, constructing a directed acyclic graph containing paths representing the reference sequence and possible alternative alleles for each region where a variant is reported (page 2, col. 1, para. 4; and Fig. 1).
Thus, given the breadth and interpretation of the claim, Chen et al. anticipates claim 17, as it provides for identifying, aligning and genotyping reads on a local sequence graph constructed for each targeted structural variant (SV) (page 8, para. 4).
Claim Rejections - 35 USC § 103
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.
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.
Claims 1-6, 8-15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (“Paragraph: a graph-based structural variant genotyper for short-read sequence data.” Genome Biology, 2019, vol. 20:291, pp. 1-13, as cited above) and Garrison et al. (“Variation graph toolkit improves read mapping by representing genetic variation in the reference.” Nature Biotechnology, 2018, vol. 36(9), pp. 875-879).
Independent claims 1 and 9 encompass a system and a computer-readable medium comprising instructions for performing steps to identify candidate structural variants that satisfy a threshold quantity of occurrences within a genomic sample database; select, from the candidate structural variants, structural variant haplotypes; identify, from a linear reference genome, reference haplotypes corresponding to the structural variant haplotypes; and generate a structural variation graph genome comprising alternate contiguous sequences representing the structural variant haplotypes and reference sequences representing the reference haplotypes.
Dependent claims 2-6 and 8-15 further define characteristics of how structural variants are selected and how the structural variation graph is generated.
Dependent claims 18-20 further define characteristics of how the alignment file is generated.
As noted above under 35 U.S.C. 102, Chen et al. teaches an accurate genotyper that models structural variations (SVs) using sequence graphs and annotations of structural variations (Abstract), and further teaches that the use of a graph for each variant makes it possible to systematically evaluate how reads align across breakpoints of candidate variants (page 2, col. 1, para. 2); and provides for the limitations of independent claim 17, as discussed in the above rejection. While Chen et al. does not explicitly teach a system comprising at least one processor and a non-transitory computer-readable medium comprising instructions as required by independent claims 1 and 9, it is clear that Chen et al. provides the use of software, which makes the use of a processor and memory, (i.e. the use of a computer), obvious, and that the instructions for performing the analysis can be stored in the memory or computer-readable medium (CRM).
Garrison et al. teaches that variation graphs are bidirected DNA sequence graphs that compactly represent genetic variation across a population, including large scale structural variation such as inversions and duplications (Abstract).
Regarding independent claims 1 and 9, Chen et al. shows that the increasing availability and completeness of a reference database of known structural variants (SVs), established through long-read sequencing and deep coverage short-read sequencing, makes it possible to develop methods that use prior knowledge to genotype these variants, and that population studies that involve detection of common or other previously known variants will be greatly enhanced by genotyping using a reference database that is continually updated with newly discovered variants (page 1, col. 2, para. 2); construction of a long read-based ground truth dataset from structural variants (SVs) called in three samples included in the Genome in a Bottle (GIAB) project data (page 2, col. 2, paras. 2-3); and graph-based genotyping of structural variations, where for each structural variation (SV) defined in an input VCF file, a directed acyclic graph is constructed containing paths representing the reference sequence and possible alternative alleles for each region where a variant is reported (page 2, col. 1, Result; and Fig. 1).
Regarding independent claims 1 and 9, Chen et al. does not show generating a structural variation graph genome.
Regarding independent claims 1 and 9, Garrison et al. shows that reference genomes guide our interpretation of DNA sequence data, however conventional linear references represent only one version of each locus, ignoring variation in the population, and further shows that variation graphs are bidirected DNA sequence graphs that compactly represent genetic variation across a population, including large scale structural variation such as inversions and duplications, and further shows a toolkit of computational methods for creating, manipulating, and utilizing these structures as references at the scale of the human genome (Abstract).
Regarding dependent claims 2, 8, and 10, Garrison et al. further shows the final phase of the 1000 Genomes Project (1000GP) produced a data set of approximately 80 million variants in 2504 humans, and further shows making a series of graphs containing all variants or those minor allele frequency thresholds at 0.1%, 1%, or 10%, as well as a graph corresponding to the standard GRCH37 linear reference sequence without any variation (page 3, para. 3) and therefore, by showing the different thresholds and different regions containing different possible structural variants, Garrison et al. provides the basis for identifying or selecting variants representing possible haplotypes from two or more genomic regions as required of the claims.
Regarding dependent claims 3 and 11, Chen et al. further shows extracting reads, as well as their mates (for paired-end reads), from the flanking region of each targeted structural variant (SV) in a Binary Alignment Map (BAM) or CRAM file, where the default target region is one read length upstream of the variant starting position to one read length downstream of the variant ending position, although this can be adjusted at runtime (page 9, col. 2, para. 2; and Figure 1).
Regarding dependent claim 4, Garrison et al. further shows demonstrating the ability of the computation tool (i.e., vg) to work with arbitrary graphs that include duplications, inversions, and translocations, by showing its use with multiple yeast strains independently assembled de novo using long read data, because these assemblies manifest large scale structural variation and novel sequences not detected in reference-based sequencing, including extensive rearrangement and reordering in subtelomeric regions (page 5, para. 2; and Figure 1).
Regarding dependent claim 5, Chen et al. further shows constructing a sequence graph containing all alleles as paths of the graph, with flanking, alternative, and reference sequences represented as nodes, and solid arrows connecting these nodes are edges of the graph (Figure 1).
Regarding dependent claim 6, Chen et al. further shows a method for graph construction, where in a sequence graph, each node represents a sequence that is at least one nucleotide long and directed edges define how the node sequences can be connected together to form complete haplotypes, and labels on edges are used to identify individual alleles or haplotypes through the graph, and each path represents an allele, either the reference allele, or one of the alternative alleles, and further shows that the method supports three types of structural variation graphs: deletion, insertion, and block-wise sequence swaps (page 9, col. 1, para. 4).
Regarding dependent claim 12, Garrison et al. further shows aligning ten million 150 bp paired end reads simulated with errors from the parentally phased haplotypes of an Ashkenazi Jewish male NA24385, sequenced by the Genome in a Bottle (GIAB) Consortium and not included in the 1000GP sample set, to a series of variation graphs (vg graphs) as well as to a linear reference (page 3, paras. 3-4).
Regarding dependent claim 13, Garrison et al. further shows that to illustrate the consequences of mapping to a reference graph rather than a linear reference, Garrison et al. stratified the sites independently called as heterozygous in NA24385 by deletion or insertion length (0 for single nucleotide variants) and by whether the site was present in 1000GP, and measured the fraction of reads mapped to the alternate allele for each category, with the results showing that mapping with vg to the population graph when the variant is present in 1000GP (95.4% of sites) gives nearly balanced coverage of the alternate and reference alleles independent of variant size, whereas mapping to the linear reference either with vg or bwa mem leads to a progressively increasing bias with increasing deletion and (especially) insertion length (Figure 2b), so that for insertions around 30bp a majority of insertion containing reads are missing (there are over twice as many reference reads as alternate reads) (page 4, col. 3).
Regarding dependent claim 14, Chen et al. further shows that once a reference database of insertions is constructed, they can be genotyped with high accuracy in the population (page 8, col. 2, para. 2); and further shows a graph-based genotyping of structural variations including possible alternative alleles (page 2, col. 1, para. 4; and Figure 1).
Regarding dependent claim 15, Chen et al. further shows for each sample, calling structural variations (SVs) that are 50 base pairs or more (bp+) (page 2, col. 2, para. 3; and Methods, page 11, col. 2, para. 1).
Regarding dependent claim 18, Chen et al. further shows population genotyping and annotation (page 11, col. 2, para. 3) and using the annotation tracks from the UCSC Genome Browser to annotate structural variants (SVs) (page 11, col. 2, para. 5).
Regarding dependent claim 19, Chen et al. further shows a graph-based genotyper that is capable of genotyping structural variants (SVs) in a large population of samples sequenced with short reads, and further shows that the use of a graph for each variant makes it possible to systematically evaluate how reads align across breakpoints of the candidate variant, and further shows that the tool can be universally applied to genotype insertions and deletions represented in a variant call format (VCF) file, independent of how they were initially discovered (page 2, col. 1, para. 2).
Regarding dependent claim 20, Chen et al. further shows that with graph alignment, the algorithm extends the recurrence relation and the corresponding dynamic programming score matrices across junctions of the graph, and that for each node, edge, and graph path, alignment statistics such as mismatch rates and graph alignment scores are generated (page 9, col. 2, paras. 2-3).
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 modified the method shown by Chen et al. by incorporating methods for using variation graphs as references for DNA sequencing, as shown by Garrison et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Chen et al. with the methods of Garrison et al., because Garrison et al. shows that variation graphs as bidirected DNA sequence graphs can completely represent genetic variation across a population, including large scale structural variation such as inversions and duplications. This modification would have had a reasonable expectation of success given that both Chen et al. and Garrison et al. disclose graph-based methods for genotyping structural variations.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. and Garrison et al. as applied to claims 1-6, 8-15, and 18-20 above, and Rautiainen et al. (“GraphAligner: rapid and versatile sequence-to-graph alignment.” Genome Biology, 2020, vol. 21:253, pp. 1-28).
Dependent claim 7 further defines characteristics of generating the alignment file and generating the structural variation graph genome.
Chen et al. and Garrison et al. as applied to claims 1-6, 8-15, and 18-20 above, does not show generate the alignment file by generating a Sequence Alignment/Map (SAM) liftover file that maps the structural variant haplotypes to the genomic coordinates of the reference haplotypes; and generate the structural variation graph genome utilizing the organization structure by associating, within a hash table, nucleobase identifiers for nucleobases from the alternate contiguous sequences with values representing the genomic coordinates of the reference haplotypes.
Rautiainen et al. teaches a tool for rapid and versatile sequence-to-graph alignment.
Regarding dependent claim 7, Rautiainen et al. shows that genome graphs can represent genetic variation and sequence uncertainty, and that aligning sequences to genome graphs is key to many applications, including error correction, genome assembly, and genotyping of variants in a pangenome graph (Abstract). Rautiainen et al. further shows that in order to evaluate the alignment accuracy of reads simulated from de novo assembled contigs, their coordinates were lifted over to the GRCH37 reference chromosome 22 using the algorithm “paftools.js liftover” from minimap2 (page 24, para. 4); and further shows that the alignment tool uses a dynamic programming (DP) algorithm to calculate a DP matrix (page 18, paras. 1-2), however in sequence-to-graph alignment (unlike sequence-to-sequence alignment), the matrix cannot be stored contiguously due to the non-linear nature of graphs (page 22, para. 1), and therefore the implementation stores the matrix as a hash table from node IDs to a sparse representation of the alignment between a substring of the read and the sequence of a node (page 22, 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 modified the method shown by Chen et al. and Garrison et al. as applied to claims 1-6, 8-15, and 18-20 above, by incorporating methods for using a liftover file to convert genomic coordinates and annotations from one alignment reference (e.g., an assembly version of a reference genome) to another alignment reference, as shown by Rautiainen et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Chen et al. and Garrison et al. as applied to claims 1-6, 8-15, and 18-20 above, with the methods of Rautiainen et al., because Rautiainen et al. shows using the algorithm “paftools.js liftover” from minimap2 to liftover the coordinates of simulated reads to a reference genome. This modification would have had a reasonable expectation of success given that both Chen et al. and Garrison et al. as applied to claims 1-6, 8-15, and 18-20 above, and Rautiainen et al. disclose methods for aligning sequences to graphs.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. and Garrison et al. as applied to claims 1-6, 8-15, and 18-20 above, and Dutt et al. ("Configurable Processors for Embedded Computing." Computer, 2003, vol. 36, no. 1, pp. 120-123, as cited above).
Dependent claim 16 further defines the type of processor.
Chen et al. and Garrison et al. as applied to claims 1-6, 8-15, and 18-20 above, does not show the at least one processor comprises a configurable processor and executing the at least one processor comprises configuring the configurable processor.
Dutt et al. teaches configurable processors for embedded computing.
Regarding dependent claim 16, Dutt et al. shows that configurable processors fill the gap in the center of a design continuum that starts with a pure hardware implementation and ends with a full software realization on general-purpose processors (page 121, col. 1, para. 4; and Figure 1); and further shows that configurable processors give embedded system designers virtually unlimited choices in processor core architectures, allowing them to customize several features to suit the application and design constraints at hand, e.g., designers can tune the processor’s instruction set architecture to the application’s characteristics (page 120, col. 3, paras. 3-4).
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 modified the method shown by Chen et al. and Garrison et al. as applied to claims 1-6, 8-15, and 18-20 above, by incorporating a configurable processor, as shown by Dutt et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Chen et al. and Garrison et al. as applied to claims 1-6, 8-15, and 18-20 above, with the methods of Dutt et al., because Dutt et al. shows that configurable processor technology empowers application designers to create customized processors that can run their application codes efficiently while eliminating the complex chain of the processor design flow (page 123, col. 2, para. 3). This modification would have had a reasonable expectation of success given that both Chen et al. and Garrison et al. as applied to claims 1-6, 8-15, and 18-20 above, discusses the computational complexities associated with aligning sequences to variation graphs, and Dutt et al. discusses how configurable processors can run application codes efficiently.
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this application.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN W. BAILEY whose telephone number is (571)272-8170. The examiner can normally be reached Mon - Fri. 1000 - 1800.
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, KARLHEINZ SKOWRONEK can be reached at (571) 272-9047. 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.
/STEVEN W. BAILEY/Examiner, Art Unit 1687
Prosecution Insights