DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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.
Step 1
Claims 1, 2, 5-13, and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1 and 2 are directed to a method, claims 5-13 and 15 are directed to an apparatus; thus, each of the pending claims are directed to a statutory category of invention.
Step 2A Prong One
Claim 1, representative of the claimed invention, recites the steps of obtaining genetic data from biological samples obtained from individuals in a population, wherein the genetic data comprises sequencing reads obtained from biological samples; storing the genetic data obtained from the population; processing the genetic data to identify genetic variants; wherein the processing of the stored genetic data comprises aligning the genetic data to a reference genome to identify sequence variation, using alignment algorithms, thereby generating aligned sequencing read data mapped to genomic coordinates; and annotating the identified variants with genomic features such as gene annotations, functional domains, and regulatory elements, including annotation information obtained from external genomic databases; identifying genetic variants, including single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variations, by performing variant calling on aligned sequencing reads; filtering identified genetic variants based on quality parameters including read depth, mapping quality, and variant allele frequency; using statistical analysis to identify genetic variants that are associated with the trait or disease of interest wherein the statistical analysis comprises correcting for population stratification and performing genome-wide multiple testing correction; visualizing the results through an interactive graphical interface, wherein the visualization of the results includes generating visual representations of variant data, including frequency plots, genotype-phenotype correlations, and pathway enrichment maps.
The limitations above, as drafted, recite a process that, under its broadest reasonable interpretation, encompass mental processes. The claimed steps recite several steps that include observations, evaluations, judgments and opinions, and “can be performed in the human mind, or by a human using a pen and paper” which have been considered by the courts to be a mental process. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). The courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Apart from the use of generic technology (discussed further below), each of the limitations recited above describes activities that would encompass actions performed in collecting genomic data, performing identification and mapping, and displaying the results.
The recited steps also are considered to be a mental process as methods that can be performed mentally, or which are the equivalent of human mental work. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements of a computer implemented method and graphical user interface. Claim 5 recites modules and assemblies configured for certain functions. The computer, modules and assemblies are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of receiving information, annotating data, performing calculations, and providing/transmitting information) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Likewise, the machine learning model is implemented as a tool to perform an abstract idea. The claim is directed to an abstract idea.
This judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a processor to perform the steps of “obtaining genetic data from biological samples obtained from individuals in a population, wherein the genetic data comprises sequencing reads obtained from biological samples; storing the genetic data obtained from the population; processing the genetic data to identify genetic variants; wherein the processing of the stored genetic data comprises aligning the genetic data to a reference genome to identify sequence variation, using alignment algorithms, thereby generating aligned sequencing read data mapped to genomic coordinates; and annotating the identified variants with genomic features such as gene annotations, functional domains, and regulatory elements, including annotation information obtained from external genomic databases; identifying genetic variants, including single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variations, by performing variant calling on aligned sequencing reads; filtering identified genetic variants based on quality parameters including read depth, mapping quality, and variant allele frequency; using statistical analysis to identify genetic variants that are associated with the trait or disease of interest wherein the statistical analysis comprises correcting for population stratification and performing genome-wide multiple testing correction; visualizing the results through an interactive graphical interface, wherein the visualization of the results includes generating visual representations of variant data, including frequency plots, genotype-phenotype correlations, and pathway enrichment maps” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, even considering the additional elements in combination, the claims do not include elements that are significantly more than the judicial exception.
Step 2B
Limitations that the courts have found to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Improvements to the functioning of a computer, e.g., a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, as discussed in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258-59, 113 USPQ2d 1097, 1106-07 (Fed. Cir. 2014) (see MPEP § 2106.05(a));
ii. Improvements to any other technology or technical field, e.g., a modification of conventional rubber-molding processes to utilize a thermocouple inside the mold to constantly monitor the temperature and thus reduce under- and over-curing problems common in the art, as discussed in Diamond v. Diehr, 450 U.S. 175, 191-92, 209 USPQ 1, 10 (1981) (see MPEP § 2106.05(a));
iii. Applying the judicial exception with, or by use of, a particular machine, e.g., a Fourdrinier machine (which is understood in the art to have a specific structure comprising a headbox, a paper-making wire, and a series of rolls) that is arranged in a particular way to optimize the speed of the machine while maintaining quality of the formed paper web, as discussed in Eibel Process Co. v. Minn. & Ont. Paper Co., 261 U.S. 45, 64-65 (1923) (see MPEP § 2106.05(b));
iv. Effecting a transformation or reduction of a particular article to a different state or thing, e.g., a process that transforms raw, uncured synthetic rubber into precision-molded synthetic rubber products, as discussed in Diehr, 450 U.S. at 184, 209 USPQ at 21 (see MPEP § 2106.05(c));
v. Adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (see MPEP § 2106.05(d)); or
vi. Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, e.g., an immunization step that integrates an abstract idea of data comparison into a specific process of immunizing that lowers the risk that immunized patients will later develop chronic immune-mediated diseases, as discussed in Classen Immunotherapies Inc. v. Biogen IDEC, 659 F.3d 1057, 1066-68, 100 USPQ2d 1492, 1499-1502 (Fed. Cir. 2011) (see MPEP § 2106.05(e)).
Claims 1 and 5 are not similar to any of these limitations.
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
Claims 1 and 5 recite additional elements that are regarded as “apply it” as seen in the Step 2A Prong 2 discussion above. The claims do not set forth a solution to a problem rooted in technology (e.g., technical solution), as performing statistical analysis of data predate the use of computers or machine learning models.
Looking at the limitations of claims 1 and 5 as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, effects a transformation of subject matter to a different state or thing, applies the use of a particular machine, integrate the abstract idea into a practical application or provide any meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Therefore, claims 1 and 5 are not patent eligible.
The dependent claims further describe the abstract idea and do not recite a practical application or significantly more than the judicial exception. None of dependent claims 2-4 or 6-15 recite any further additional elements.
Dependent claims 2, 6-13 and 15 further narrow the scope of the abstract idea in claims 1 and 5 by providing additional information or considerations used in the analysis.
Thus, claims 1, 2, 5-13, and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 5-11 and 15 is/are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by U.S. Patent Application Publication 2021/0395837 to Eltoukhy et al.
As to claims 5, Eltoukhy discloses a computer-implemented system for identifying genetic variants associated with a complex trait or disease, the system comprising:
a data collection module; a data storage module linked to the data collection module, the data storage module configured to store genetic data obtained from a population (Eltoukhy [0012] see “identifying comprises generating a plurality of sequence reads for parent polynucleotides from the sample, and collapsing the sequence reads to generate consensus calls for bases in each parent polynucleotide. In some embodiments, quantifying comprises determining frequency at which the somatic mutations are detected in the population of polynucleotides from the biological sample.”);
a processing assembly linked to the data storage module, the processing assembly further comprises:
a variant identification module linked to the data storage module, the variant identification module is configured to process the genetic data to identify genetic variants in the population; a statistical analysis module linked to the variant identification module, the statistical analysis module configured to analyze the genetic variants identified by the variant identification module and determine which variants are associated with the trait or disease of interest(Etoukhy [0013] see “in some embodiments, the somatic mutations are selected from single nucleotide variations (SNVs), insertions, deletions, inversions, transversions, translocations, copy number variations (CNVs) (e.g., aneuploidy, partial aneuploidy, polyploidy), chromosomal instability, chromosomal structure alterations, gene fusions, chromosome fusions, gene truncations, gene amplification, gene duplications, chromosomal lesions, DNA lesions, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns and abnormal changes in nucleic acid methylation.”); and
wherein the variant identification module performs joint variant calling for multiple samples or populations to improve variant detection sensitivity and accuracy (Etoukhey see [0110]-[0111] see sampling across geographic extent and determining variants in different cell sub-populations), and wherein the variant identification module aligns the genetic data to a reference genome to identify sequence variations, and annotates the identified variants with genomic features such as gene annotations (Etoukhy [0018] see “(b) determining, among said sequence reads, identity of bases that are different than a base of a reference sequence at the locus of the total number of sequence reads mapping to a locus; (c) reporting the identity and relative quantity of the determined bases and their location in the genome”) wherein the statistical analysis module is configured to incorporate functional annotation information (Etoukhy [0018] see “(b) determining, among said sequence reads, identity of bases that are different than a base of a reference sequence at the locus of the total number of sequence reads mapping to a locus; (c) reporting the identity and relative quantity of the determined bases and their location in the genome”);
wherein the visualization module is configured to depict interaction between genes or other biological entities, annotate genes, identify enriched biological pathways, and visualize pathway relationships (Etoukhy [0042]-[0043])
a visualization module linked to the statistical analysis module, the visualization module configured to generate visualizations of the genetic variants identified by the variant identification module and the statistical analysis results generated by the statistical analysis module; a graphical interface linked to the visualization module, the graphical interface configured to visualizing the results obtained (Eltoukhy [0012] see “identifying comprises generating a plurality of sequence reads for parent polynucleotides from the sample, and collapsing the sequence reads to generate consensus calls for bases in each parent polynucleotide. In some embodiments, quantifying comprises determining frequency at which the somatic mutations are detected in the population of polynucleotides from the biological sample” and [0033] see tumor response map).
As to claim 6, see the discussion of claim 5, additionally, Eltoukhy discloses the system wherein the system further comprises a communication network linking the data collection module to the data storage module, the communication network configured to:
enable the transfer of raw genetic data (Eltoukhy [0092]);
facilitate integration with external computing resources (Eltoukhy [0218]).
As to claim 7, see the discussion of claim 5, additionally, Eltoukhy discloses the system wherein the data collection module collects genetic data including genomic data (Eltoukhy [0012]).
As to claim 8, see the discussion of claim 5, additionally, Eltoukhy discloses the system wherein the genetic data variants comprises single nucleotide polymorphisms (SNPs), copy number variations (CNVs), insertions, deletions, or any other type of genetic variation (Etoukhy [0013] see “in some embodiments, the somatic mutations are selected from single nucleotide variations (SNVs), insertions, deletions, inversions, transversions, translocations, copy number variations (CNVs) (e.g., aneuploidy, partial aneuploidy, polyploidy), chromosomal instability, chromosomal structure alterations, gene fusions, chromosome fusions, gene truncations, gene amplification, gene duplications, chromosomal lesions, DNA lesions, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns and abnormal changes in nucleic acid methylation.”)
As to claim 9, see the discussion of claim 5, additionally, Eltoukhy discloses the system. The additional features of claim 9 are directed to an intended use of the data storage module. This is not given patentable weight.
As to claim 10, see the discussion of claim 5, additionally, Eltoukhy discloses the system wherein the variant identification module uses suitable technique to identify genetic variants, such as, read alignment (Etoukhy see mapping alignment to a reference sequence)
As to claim 11, see the discussion of claim 5, additionally, Eltoukhy discloses the system wherein the statistical analysis module uses any suitable technique to perform statistical analysis (Eltoukhy [0160]).
As to claim 15, see the discussion of claim 5, additionally, Eltoukhy discloses the system wherein the visualization module facilitates interpretation of the statistical analysis result and identification of potential functional mechanisms underlying the genetic variants (Eltoukhy [0031]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2021/0395837 to Eltoukhy et al. in view of U.S. Patent Application Publication 2015/0299795 to Lindblad-Toh et al.
As to claims 1, Eltoukhy discloses a computer-implemented method for identifying genetic variants associated with a complex trait or disease, the method comprising:
obtaining genetic data from biological samples obtained from individuals in a population (Eltoukhy [0008] “sequencing polynucleotides from cancer cells from a subject” and [0031] see “In some embodiments, the therapeutic interventions use information of predicted tumor genomic evolution or acquired resistance mechanisms in similar patients in response to treatment.”), wherein the genetic data comprises sequencing reads obtained from the biological samples (Eltoukhy [0095] see “Sequence reads generated from sequencing are subject to analysis including, for example, identifying genetic variants” and [0008] “sequencing polynucleotides from cancer cells from a subject”);
storing the genetic data obtained from the population (Eltoukhy [0217] see “ a database is built in which genetic information from serial samples collected from cancer patients is recorded. This database may also contain intervening treatment and other clinically relevant information, such as, weight, adverse effects, histological testing, blood testing, radiographic information, prior treatments, cancer type, etc. Serial test results can be used to infer efficacy of treatment, especially when used with blood samples, which can give a more unbiased estimate of tumor burden than self-reporting or radiographic reporting by a medical practitioner. Treatment efficacy can be clustered by those with similar genomic profiles and vice versa. Genomic profiles can be organized around, for example, primary genetic alteration, secondary genetic alteration(s), relative amounts of these genetic alterations, and tumor load. This database can be used for decision support for subsequent patients”);
processing the genetic data to identify genetic variants; (Eltoukhy [0012] see “identifying comprises generating a plurality of sequence reads for parent polynucleotides from the sample, and collapsing the sequence reads to generate consensus calls for bases in each parent polynucleotide. In some embodiments, quantifying comprises determining frequency at which the somatic mutations are detected in the population of polynucleotides from the biological sample.”);
wherein the processing of the stored genetic data comprises aligning the genetic data to a reference genome to identify sequence variations, using alignment algorithms, thereby generating aligned sequencing read data mapped to genomic coordinates (Eltoukhy [0172] see “the genomic fragment reads that meet a specified quality score threshold are mapped to a reference genome, or a reference sequence that is known not to contain mutations. After mapping alignment, sequence reads are assigned a mapping score. A mapping score may be a representation or reads mapped back to the reference sequence indicating whether each position is or is not uniquely mappable.” And [0018] see “(b) determining, among said sequence reads, identity of bases that are different than a base of a reference sequence at the locus of the total number of sequence reads mapping to a locus; (c) reporting the identity and relative quantity of the determined bases and their location in the genome”);
annotating the identified variants with genomic features such as gene annotations, including annotation information retrieved from external genomic databases (Etoukhy [0018] see “(b) determining, among said sequence reads, identity of bases that are different than a base of a reference sequence at the locus of the total number of sequence reads mapping to a locus; (c) reporting the identity and relative quantity of the determined bases and their location in the genome” and [0099] see “Genetic variants can be detected by comparing sequences from polynucleotides in a sample to a reference, e.g., to a reference genome sequence, to an index or to a database of known mutations. In one embodiment, the reference sequence is a publicly available reference sequence, such as the human genome sequence HG-19 or NCBI Build 37. In another embodiment, the reference sequence is a sequence in a non-public database. In another embodiment, the reference sequence is a germ line sequence of an organism inferred or determined from sequencing polynucleotides from the organism.”)
identifying genetic variants, including single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variations, by performing variant calling on aligned sequencing reads (Etoukhy [0013] see “in some embodiments, the somatic mutations are selected from single nucleotide variations (SNVs), insertions, deletions, inversions, transversions, translocations, copy number variations (CNVs) (e.g., aneuploidy, partial aneuploidy, polyploidy), chromosomal instability, chromosomal structure alterations, gene fusions, chromosome fusions, gene truncations, gene amplification, gene duplications, chromosomal lesions, DNA lesions, abnormal changes in nucleic acid chemical modifications, abnormal changes in epigenetic patterns and abnormal changes in nucleic acid methylation.” And [0231] see “after fragments are amplified and the sequences of amplified fragments are read and aligned, the fragments are subjected to base calling. Variations in the number of amplified fragments and unseen amplified fragments can introduce errors in base calling. These variations are corrected by calculating the number of unseen amplified fragments.”);
using statistical analysis to identify genetic variants that are associated with the trait or disease of interest (Eltoukhy [0012] see “identifying comprises generating a plurality of sequence reads for parent polynucleotides from the sample, and collapsing the sequence reads to generate consensus calls for bases in each parent polynucleotide. In some embodiments, quantifying comprises determining frequency at which the somatic mutations are detected in the population of polynucleotides from the biological sample.”); and
visualizing the results through an interactive graphical interface, wherein the visualization of the results includes generating visual representations of variant data, including frequency plots (Eltoukhy [0046]-[0049] see “a stacked representation of genetic variants detected at each of a plurality of time points in the subject, wherein a height or width of each layer in the stack that corresponds to a genetic variant represents a quantitative contribution of the genetic variant to the a total quantity of genetic variants at each time point; and b) displaying the stacked representation on a computer monitor or a paper report”), genotype-phenotype correlations (Eltoukhy [0156] see “tumor response map 54 includes a modified streamgraph 56 that shows tumor activities with unique colors for each mutant gene. The graph 56 has accompanying summary explanation textbox 58. More details are provided in a summary of alterations and treatment option section 60.”), and pathway enrichment maps (Eltoukhy [0138] see “generate a tumor response map pathway which may be used by a healthcare practitioner, e.g., physician, for example to make patient care decisions.”)
However, Eltoukhy does not explicitly teach the system wherein the statistical analysis module is configured to correct for population stratification and performing genome-wide multiple testing correction and filtering identified genetic variants based on quality parameters including read depth, mapping quality, and variant allele frequency.
Lindblad-Toh discloses the system wherein the statistical analysis module is configured to correct for population stratification and performing genome-wide multiple testing correction (Lindblad-Toh [0125] and [0168] see at least “to successfully control for the population stratification present in the dataset, analysis approach was taken based on a method described by Price et al. (Price, Zaitlen et al. 2010). The discovery of germ-line was performed by generating 40× whole genome sequencing by Illumina HiSeq of DNA samples from three dogs with that had B-cell lymphoma and had been included in the GWAS. The gene expression levels of twenty-two B-cell lymphoma tumors were profiled by strand-specific RNA-Seq, and analyzed for changes by the germ-line risk alleles at 32.9 Mb and 36.8 Mb loci on chromosome 5.”) and filtering identified genetic variants based on quality parameters including read depth, mapping quality, and variant allele frequency (Lindblad-Toh [0136] see at least “Hard filters were applied for low quality, strand bias, clustering, and excessive read depth.”)
It would have been obvious to one of ordinary skill in the art before the filing of the invention by applicant to perform the processing of Lindblad-Toh in the system of Eltoukhy to improve the accuracy of the results.
As to claim 2, see the discussion of claim 1, additionally, Eltoukhy discloses the method wherein the genetic data is obtained using a technique comprises polymerase chain reaction (PCR) (Eltoukhy [0092]).
As to claims 12-13, However, Eltoukhy does not explicitly teach the system wherein the statistical analysis module is configured to correct for population stratification and performing genome-wide multiple testing correction and filtering identified genetic variants based on quality parameters including read depth, mapping quality, and variant allele frequency.
Lindblad-Toh discloses the system wherein the statistical analysis module is configured to correct for population stratification and performing genome-wide multiple testing correction (Lindblad-Toh [0125] and [0168] see at least “to successfully control for the population stratification present in the dataset, analysis approach was taken based on a method described by Price et al. (Price, Zaitlen et al. 2010). The discovery of germ-line was performed by generating 40× whole genome sequencing by Illumina HiSeq of DNA samples from three dogs with that had B-cell lymphoma and had been included in the GWAS. The gene expression levels of twenty-two B-cell lymphoma tumors were profiled by strand-specific RNA-Seq, and analyzed for changes by the germ-line risk alleles at 32.9 Mb and 36.8 Mb loci on chromosome 5.”)
It would have been obvious to one of ordinary skill in the art before the filing of the invention by applicant to perform the processing of Lindblad-Toh in the system of Eltoukhy to improve the accuracy of the results.
Response to Arguments
Applicant's arguments filed 3/12/26 have been fully considered but they are not persuasive.
Applicant argues that “Alignment of sequencing reads to a reference genome, generation of mapped genomic coordinates, and variant calling across sequencing datasets require computer-based bioinformatics processing and cannot practically be performed in the human mind. Accordingly, under MPEP § 2106.04(a)(2), the claimed operations do not fall within the mental processes grouping of abstract ideas. These steps merely require comparing the data to rules which can be performed mentally. While the specification describes these functions as being performed by a computer, a human would be capable of performing these functions. The claims therefore recite an abstract idea.
Applicant argues that a technical improvement is recited as requiring these specific processing stages in an ordered sequence, the claim provides a technical solution that transforms machine-generated sequencing reads into structured and technically reliable genomic outputs. These features are part of the abstract idea. That is each of the steps argued by applicants amounts to comparing data to rules and displaying the results. Applicant argues that this is similar to Example 48 of the SME update. The example was found elegible because “Further, converting clusters into separate speech waveforms and generating a mixed speech signal from the separate speech waveforms are not insignificant extra-solution activity, mere instructions to apply the exception, or mere field of use limitations. Rather, these steps reflect the improvement described in the disclosure. Accordingly, the claim is directed to an improvement to existing computer technology or to the technology of speech separation, and the claim integrates the abstract idea into a practical application. (Step 2A, Prong Two: YES). The claim is eligible. (Step 2A: NO).” The specification does not describe these particular steps as an improvement to an existing computer technology or to the field of bioinformatics. The claims are therefore do not provide a practical application under 2A prong two.
Applicant argues that the invention provides a practical application of the abstract idea. The claim merely displays the result of a statistical analysis, this does not provide a practical application of the abstract idea. Applicant argues that the combination of elements transforms the data. These steps amount to comparing data to rules and outputting a result. This is well understood, routine and conventional, computer activity.
With respect to claim 1, applicant argues that Eltoukhy does not teach aligning genetic data to a reference genome using alignment algorithms, thereby generating aligned sequencing reads mapped to genomic coordinates. Etoukhy [0018] discloses “(b) determining, among said sequence reads, identity of bases that are different than a base of a reference sequence at the locus of the total number of sequence reads mapping to a locus; (c) reporting the identity and relative quantity of the determined bases and their location in the genome” identifying bases and a location in a genome falls within the broadest reasonable interpretation of aligning genetic data to a reference genome using alignment algorithms, thereby generating aligned sequencing reads mapped to genomic coordinates.
Applicant argues that Eltoukhey does not teach annotation information retrieved from external genomic databases. Eltoukhey discloses at [0099] see “Genetic variants can be detected by comparing sequences from polynucleotides in a sample to a reference, e.g., to a reference genome sequence, to an index or to a database of known mutations. In one embodiment, the reference sequence is a publicly available reference sequence, such as the human genome sequence HG-19 or NCBI Build 37. In another embodiment, the reference sequence is a sequence in a non-public database. In another embodiment, the reference sequence is a germ line sequence of an organism inferred or determined from sequencing polynucleotides from the organism.”
Applicant argues that Eltoukhy does not teach performing variant calling on aligned sequencing reads. The broadest reasonable interpretation of variant calling includes determining differences between sequencing data (reads) and a reference genome. Eltoukhy discloses in [0018] “(b) determining, among said sequence reads, identity of bases that are different than a base of a reference sequence at the locus of the total number of sequence reads mapping to a locus; (c) reporting the identity and relative quantity of the determined bases and their location in the genome”. This falls within the broadest reasonable interpretation of the term.
Applicants arguments regarding quality parameters and statistical analysis are moot in view of new grounds of rejection.
With respect to claim 9 applicant argues that features of claim 9 are technical in nature and not an intended use. Applicant however does not provide any reasoning as to how these features are technical, e.g. what technical implementation would be required to achieve the claimed scalability, reliability, and efficiency. The claim merely describes an intended use of the system.
Applicants arguments regarding claims 12-13 are moot in view of new grounds of rejection.
The rejections are therefore maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Eliza Lam whose telephone number is (571)270-7052. The examiner can normally be reached Monday-Friday 8-4:30PST.
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/ELIZA A LAM/Primary Examiner, Art Unit 3681