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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Vietnam on 5/10/2023. It is noted, however, that applicant has not filed a certified copy of the Vietnamese Patent application as required by 37 CFR 1.55. However, in an effort to execute efficient compact prosecution, all claims will be examined under an effective filing date of 5/10/2023.
Claim Status
Claims 1-8 are pending.
Claims 1-8 are rejected.
Drawings
Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
The patent or application file contains at least one drawing executed in color, specifically Figures 1, 3, and 4. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code in paragraph [0077]. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
The use of the term American Heart Association, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “a pairwise imputation score module performing imputation”, “a functional score computation module computing functional scores”, and “a tag SNP selection module selecting a tag SNP” in claim 1.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 describes a SNP chip whose limitations include three specified modules a pairwise imputation score module, a functional score computation module, and a tag SNP selection module, that are not described within the specification other than by the name of the modules, i.e. “a tag SNP selection module that selects tag SNPs”.
Claim 6 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Specifically, claim 6 describes a final classifier, however within the claims there is not discussed a first classifier, only the final one, for which in the specification, there is only described the final classifier. However, it is impossible to arrive at a final classifier without first having an initial or previous classifiers.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 6 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 recites the limitation "the criteria" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "the sections" in line 10. There is insufficient antecedent basis for this limitation in the claim.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a device and method for computing polygenic risk scores. The judicial exception is not integrated into a practical application because while claims 1-8 attempt to integrate the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea or it is insignificant extra solution activity and merely implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well-understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of stator subject matter (a process, machine, manufacture, or composition of matter)? [See MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically a device (Claim 1), and a method (Claims 2-8).
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [See MPEP § 2106.04(a)]
The claims herein recite abstract ideas, specifically mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claim 1: Pairwise imputation score computation, imputing as a linear model, harmonizing information, functional score computation, tag SNP selection based on CADD score and sum of squared correlation are processes of calculating, comparing/contrasting, and selecting information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes. Imputing as a linear model is a verbal articulation of a mathematical process and is therefore an abstract idea, specifically a mathematical concept.
Claim 2: Computing PRS based on a gene database, genotype calling, performing imputation, normalization, and annotation, converting to a binary file and from that PRS, converting a bfile to compute PRS, harmonizing the data, adding new PRS samples to existing sample set, and generated data is harmonized, are processes of calculating, comparing/contrasting, translating, and selecting information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process. Imputation being performed using a given population genomic dataset as reference is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 3: Computing the VCF file using the specified data is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 4: Controlling the pre-imputation quality of variants, performing imputation, controlling the post-imputation quality, harmonizing the datasets, and performing re-imputation are processes of selecting, calculating, and comparing/contrasting that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 5: Controlling the pre-imputation quality of variants, filtering individuals, performing imputation, controlling the imputation quality via filtering, merging data, and performing re-imputation are processes of selecting, calculating, and comparing/contrasting that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes. Filtering using the specified methods and controlling the quality via the specified methods is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 6: Selecting, classifying, and increasing the number of reference samples, using cross-validation to select, train and evaluate, ranking based on contribution, summing up the sections, and ranking based on ROC curve are processes of identifying, comparing/contrasting, and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental process.
Claim 7: The data of the model comprising the specified data is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 8: Performing random cross validation, performing grouped cross validation, performing leave-one-out cross validation are processes of selecting, classifying, and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [See MPEP § 2106.04(d) and MPEP § 2106.05(a)-(c) & (e)-(h)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of non-abstract elements:
Claim 1: A single-nucleotide polymorphism chip is a generic and nonspecific elements of a computer that are well-understood, routine and conventional within the art and therefore do not improve the functioning of any computer or technology described therein (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.05(d)(II)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [See MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional or nonspecific. These additional elements include:
The additional elements of a single-nucleotide polymorphism chip (Conventional: Voisey et al 2008 – Page 14, Paragraph 1) is a generic and nonspecific elements of a computer that are well-understood, routine and conventional within the art and therefore do not improve the functioning of any computer or technology described therein (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.05(d)(II)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1-8, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
(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.
Claims 1-3 are rejected under 35 U.S.C. 102(a)(I) as being anticipated by Nguyen et al. (Briefings in Bioinformatics (2022) 1-12).
Claim 1 is directed to a device, a SNP chip, which uses an LmTag algorithm to compute pairwise imputation scores, functional scores, and from them a polygenic risk score.
Nguyen et al. teaches in the abstract “we propose LmTag, a novel method for tag SNP selection that not only improves imputation performance but also prioritizes highly functional SNP markers. We apply LmTag on a wide range of populations using both public and in-house whole-genome sequencing databases. Our results show that LmTag improved both functional marker prioritization and genome-wide imputation accuracy compared to existing methods”, on page 2, column 2, paragraph 2 “A linear model is then employed to assess imputation accuracy scores of tagged SNPs based on pairwise LD r2…SNPs are functionally scored based on public databases including the GWAS catalog, the ClinVar, and the Combined Annotation-Dependent Depletion (CADD)… Our aim is to combine systematically information from both pairwise LDr2, MAF and genomic distance to improve imputation accuracy of tag SNP selection”, on page 4, column 1, paragraph 3 “In general, markers are functionally ranked based on biological evidence and genome-wide predicted functional scores simultaneously”, on page 10 column 2, paragraph 1 “Besides genome-wide imputation capability, the inclusion of likely functional variants can enhance the value of genotyping SNP arrays by producing key information on potential causal SNPs underlying phenotypes. For example, the UK Biobank Axiom Array, Japonica NEO Arrays, and the Axiom Korean Chip applied various selection criteria to include likely functional markers in their array designs”, on page 4, column 2, paragraph 2 “we employ a greedy approach for computational efficiency. However, there are two main differences in our algorithm. Firstly, we use estimated pairwise imputation r2 scores for ranking SNP candidates instead of using pairwise LD r2 like conventional methods. Specifically, for each pair of SNPs, imputation score r2 for each SNP is estimated independently by using coefficients derived from the established linear model and the corresponding LD r2, it’s MAF, mate’s MAF, and genomic distance between the two SNPs…we introduce a tuning parameter K in the algorithm to select tag SNPs with high functional scores”, reading on a self-designed single-nucleotide polymorphism (SNP) chip using an LmTag algorithm, comprising: a pairwise imputation score computation module performing imputation as a linear model, harmonizing information from the linkage disequilibrium squared correlation (LD r2), minor allele frequency (MAF), and physical distance between variants to give an imputation squared correlation value between an imputed genotype and a true genotype of the SNP (Imputation r2); a functional score computation module computing functional scores for SNPs based on biological evidence from data GWAS catalog, Clinvar, and Combined Annotation Dependent Depletion (CADD) Score to assess the biological function for SNP which did not have evidence; a tag SNP selection module selecting a tag SNP based on: having a largest CADD score among tag SNPs; and having a highest sum of a squared correlation between an imputed genotype and an true genotype of the SNP.
Claim 2 is directed to a method for computing the polygenic risk score using the chip from claim 1, for a given population.
Nguyen et al. teaches on page 5, column 1, paragraph 3 “We evaluate the performance of LmTag in both in house generated and public datasets, including data from the 1000 Vietnamese Genomes Project (1KVG) pilot phase and data of three super populations from the 1000 Genomes Project samples re-sequenced by New York Genome Center (1KGP-NYGC). The genomic data of the 1KVG pilot phase were obtained from 504 unrelated Vietnamese population individuals (VNP), including 208 males and 296 females. Their genomes were sequenced at coverage 30x with 150 bp paired end reads using an Illumina Nova Seq 6000 system. Variant calling was performed using the DRAGEN pipeline with the GRCh38 patch release 13 reference genome. Quality check and filtering were performed with bcftools v1.10.2, and phasing was performed with SHAPEIT v4.1.3 to obtain the phased genotypes in Variant Call Format (VCF). Phased genotype data in VCF format of 1KGP NYGC high coverage are obtained from The International Genome Sample Resource (IGSR) data portal. We include only unrelated samples belonging to East Asian (EAS), European (EUR), and South Asian (SAS) in the analysis. These samples are assigned to their super population according to IGSR’s annotation. All genomic data are reprocessed with bcftools v1.10.2 to keep only biallelic SNP with MAF>1%”, and on page 4, column 2, paragraph 1 “CADD integrates more than 60 genomic features based on DNA sequence, for examples gene model annotations, evolutionary constraint, epigenetic measurements and functional predictors into a single score by a machine learning model”, reading on a method of computing polygenic risk score (PRS) for a given population using the self- designed SNP chip according to claim 1, comprising steps that are divided into two flows: a first flow computing PRS based on a disease-/trait-related gene database collected from open sources and provided by parties, comprising: using data from the disease-/trait-related gene database collected from open sources and provided by parties; genotypic calling to generate a Variant Call Format (VCF) file; performing imputation, normalization, and annotation on the VCF file to generate a post-processed VCF file; converting the post-processed VCF file to a binary file (bfile), then computing PRS for all samples in the database; and a second flow computing PRS based on test samples, comprising: using genetic data obtained from the test samples; genotype calling to generate a VCF file; performing imputation, normalization, and annotation on the VCF file to generate a post-processed VCF file; converting the post-processed VCF file to a bfile to compute PRS for the test samples; wherein the self-designed SNP chip is used in the VCF file generation stage of both flows; wherein the VCF file is to be subjected to imputation using a given population genomic dataset as a reference and harmonized by using a data harmonization process; wherein the new PRS-computed samples in the second flow are to be added to the existing sample set in the database; data generated from these two computation flows is to be harmonized and input into a machine learning model to form a single computation process to generate PRS for a group of new test samples.
Claim 3 is directed to the method of claim 2 and thus claim 1, but further specifies that the VCF file is to be computed with the 1KVG and 1KGP as reference.
Nguyen et al. teaches on page 5, column 1, paragraph 3 “We evaluate the performance of LmTag in both in house generated and public datasets, including data from the 1000 Vietnamese Genomes Project (1KVG) pilot phase and data of three super populations from the 1000 Genomes Project samples re-sequenced by New York Genome Center (1KGP-NYGC)”, reading on wherein the VCF file is to be computed using 1KVG (also known as VN1K - 1000 Vietnamese Genome Sequencing Project) and 1KGP (1000 Human Genome Project) datasets as a reference.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen et al. (Briefings in Bioinformatics (2022) 1-12) as applied to claims 1-3 above, and further in view of Chatzinakos et al. (American Journal of Medical Genetics (2021) 16-27).
Claim 4 is directed to the method of claim 3 and thus claim 1, but further specifies the data harmonization comprising the specified steps.
Nguyen et al. teaches on page 5, column 1, paragraph 3 “Quality check and filtering were performed with bcftools v1.10.2, and phasing was performed with SHAPEIT v4.1.3 to obtain the phased genotypes in Variant Call Format (VCF)”, in the abstract “we propose LmTag, a novel method for tag SNP selection that not only improves imputation performance but also prioritizes highly functional SNP markers. We apply LmTag on a wide range of populations using both public and in-house whole-genome sequencing databases. Our results show that LmTag improved both functional marker prioritization and genome-wide imputation accuracy compared to existing methods”, on page 2, column 2, paragraph 2 “A linear model is then employed to assess imputation accuracy scores of tagged SNPs based on pairwise LD r2…SNPs are functionally scored based on public databases including the GWAS catalog, the ClinVar, and the Combined Annotation-Dependent Depletion (CADD)… Our aim is to combine systematically information from both pairwise LDr2, MAF and genomic distance to improve imputation accuracy of tag SNP selection”, on page 5, column 1, paragraph 3 “We evaluate the performance of LmTag in both in house generated and public datasets, including data from the 1000 Vietnamese Genomes Project (1KVG) pilot phase and data of three super populations from the 1000 Genomes Project samples re-sequenced by New York Genome Center (1KGP-NYGC)”, it would be inherent to any method using multiple datasets to “harmonize” the data for inputting, reading on wherein the data harmonization process comprises the following main steps: controlling the pre-imputation quality (Pre-imputation Quality Controls) of individuals and variants, performing imputation for unknown variants (Imputation), controlling the post-imputation quality (Post-imputation Quality Controls), and harmonizing the datasets.
Nguyen et al. does not teach performing re-imputation.
Chatzinakos et al. teaches on page 25, column 1, paragraph 3 “Finally, we recommend users to re-impute summary statistics using the latest reference panels before employing most omics-based prediction tools, for example, TWAS. The re-imputation of GWAS summary statistics is likely to increase the number of gene signals, especially for studies that employed older and smaller imputation panels”, reading on performing imputation (Re-imputation).
It would have been obvious at the time of first filing to have modified the teachings of Nguyen et al. for the device and method of claims 1-3, with the teachings of Chatzinakos et al. for the use of re-imputation as the latter specifically describes “The re-imputation of GWAS summary statistics is likely to increase the number of gene signals, especially for studies that employed older and smaller imputation panels” and in the abstract “DISTMIX2 provides a robust and fast (re)imputation approach for most psychiatric GWAS-studies”. One would have had a reasonable expectation of success givne that both methods are employing similar data, and the inclusion of a re-imputation step would merely be performing a step already claimed in method twice instead of once, while as Chatzinakos et al. states, potentially improving the number of signals. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 5 is directed to the method of claim 4 and thus claim 1, but further specifies the filtering of individuals, generating VCF files via Minimac4, removal of variants with low MAF/small Hardy-Weinberg or square correlation, harmonizing via inner join, and performing re-imputation.
Nguyen et al. teaches on page 4, column 1, paragraph 2 “Specifically, imputation is performed individually for each sample with the exclusion of itself from the reference panel with Mini-mac4 v1.0.2”, on page 5, column 1, paragraph 3 “We evaluate the performance of LmTag in both in house generated and public datasets, including data from the 1000 Vietnamese Genomes Project (1KVG) pilot phase and data of three super populations from the 1000 Genomes Project samples re-sequenced by New York Genome Center (1KGP-NYGC)…Their genomes were sequenced at coverage 30x with 150 bp paired end reads using an Illumina Nova Seq 6000 system. Variant calling was performed using the DRAGEN pipeline with the GRCh38 patch release 13 reference genome. Quality check and filtering were performed with bcftools v1.10.2, and phasing was performed with SHAPEIT v4.1.3 to obtain the phased genotypes in Variant Call Format (VCF). Phased genotype data in VCF format of 1KGP NYGC high coverage are obtained from The International Genome Sample Resource (IGSR) data portal”, on page 5, column 1, paragraph 1 “Then, the search branching is extended to functional scores, i.e. the SNP with the highest functional score in this list is subsequently chosen as a tag SNP t. This SNP is subsequently moved from the candidate set A into tag SNP set T, and SNP t’s neighboring SNPs (satisfying pairwise LD r2 cut-off) are moved into tagged SNP set G. Overall, both the selected tag SNP and its associated tagged SNPs are removed from the candidate set A”, reading on wherein the data harmonization process comprises the following steps: controlling the pre-imputation quality of individuals and variants, comprising: filtering individuals by the heterozygosity, error-rate, missing rate, and Hardy-Weinberg p-value of the genotypic calling of various chip arrays in order to reduce laboratory errors and improve the quality of genotypic data, performing imputation for unknown variants, wherein the VCF file of each flow is to be computed by Minimac4 with 1KVG and 1KGP datasets as reference, controlling the post-imputation quality, comprising further removal of variants with low MAF and/or small Hardy-Weinberg p (p-value) and/or low estimated square correlation score between the imputed genotype and the true genotype of the sample (f'). Furthermore, it would be obvious to a person skilled in the art to use inner join in harmonizing the data as this takes only the data that is in common with both datasets, i.e. variants that are appearing in all ancestries, as these are the potentially causal variants, thereby reading on harmonizing the datasets: all data after the post-imputation quality control is to be merged and shared components of the datasets are to be removed (also known as the "Inner Join" method) to form a dataset without batch effects and low quality variants.
Nguyen et al. does not teach performing re-imputation.
Chatzinakos et al. teaches on page 25, column 1, paragraph 3 “Finally, we recommend users to re-impute summary statistics using the latest reference panels before employing most omics-based prediction tools, for example, TWAS. The re-imputation of GWAS summary statistics is likely to increase the number of gene signals, especially for studies that employed older and smaller imputation panels”, reading on performing imputation (Re-imputation).
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen et al. (Briefings in Bioinformatics (2022) 1-12), and Chatzinakos et al. (American Journal of Medical Genetics (2021) 16-27) as applied to claims 1-5 above, and further in view of Gao et al. (BMC research notes (2020) 1-6).
Claim 6 is directed to the method of claim 5 but further specifies the selecting, classifying and increasing of the number of reference samples, training and evaluating by cross-validation, and ranking according to ROC curve.
Nguyen et al. and Chatzinakos et al. teach the method of claims 1-5 as previously described.
Nguyen et al. and Chatzinakos et al. do not teach the ranking according to ROC curve.
Nguyen et al. teaches on page 4, column 1, paragraph 2 “Specifically, imputation is performed individually for each sample with the exclusion of itself from the reference panel with Mini-mac4 v1.0.2”, on page 4, column 2, paragraph 2 “Firstly, we use estimated pairwise imputation r2 scores for ranking SNP candidates instead of using pairwise LD r2 like conventional methods”, on page 5, column 1, paragraph 3 “We evaluate the performance of LmTag in both in house generated and public datasets, including data from the 1000 Vietnamese Genomes Project (1KVG) pilot phase and data of three super populations from the 1000 Genomes Project samples re-sequenced by New York Genome Center (1KGP-NYGC)…Their genomes were sequenced at coverage 30x with 150 bp paired end reads using an Illumina Nova Seq 6000 system. Variant calling was performed using the DRAGEN pipeline with the GRCh38 patch release 13 reference genome. Quality check and filtering were performed with bcftools v1.10.2, and phasing was performed with SHAPEIT v4.1.3 to obtain the phased genotypes in Variant Call Format (VCF). Phased genotype data in VCF format of 1KGP NYGC high coverage are obtained from The International Genome Sample Resource (IGSR) data portal”, on page 5, column 1, paragraph 1 “Then, the search branching is extended to functional scores, i.e. the SNP with the highest functional score in this list is subsequently chosen as a tag SNP t. This SNP is subsequently moved from the candidate set A into tag SNP set T, and SNP t’s neighboring SNPs (satisfying pairwise LD r2 cut-off) are moved into tagged SNP set G. Overall, both the selected tag SNP and its associated tagged SNPs are removed from the candidate set A”, on page 9, column 1, paragraph 1 “Imputation performances are estimated through leave-one-out cross-validation as described previously”, reading on wherein the machine learning model performs the following: selecting, classifying, and increasing the number of reference samples for each trait from different data sources, wherein each new sample that meets the criteria, such as sample quality, race, age, sex, index body mass (BMI), etc., is to be classified and added to the reference sample set, wherein the data is to be selected, trained, and evaluated by different cross-validation methods.
Gao et al. teaches in Figure 2 “ROC curves for the breast cancer diagnosis dataset. b PR curves for the breast cancer diagnosis dataset. c ROC curve for the cardiovascular disease dataset. d PR curves for the cardiovascular disease dataset. e Feature importance rankings for the breast cancer dataset. f Feature importance rankings for the cardiovascular disease dataset”, in Table 1 “Performance indicators of different classifiers on the breast cancer diagnostic and cardiovascular disease datasets”, and on page 5, column 1, paragraph 2 “To improve the performance of the classifier, we compared grid search, random search, and Bayesian hyperparameter optimization methods. Unlike traditional grid search and random search methods, Bayesian parameter optimization algorithms based on Gaussian processes can find stable hyperparameters, and they are widely used in machine learning”, reading on wherein the ROC curve (Area Under the Receiver Operating Characteristic Curve (AUROC) ranking results are used to find the best fitting and hyperparameter harmonization, and forming the final classifier of the training course.
It would have been obvious at the time of first filing to have modified the teachings of Nguyen et al. and Chatzinakos et al. for the method of claims 1-5 with the teachings of Gao et al. for the use of ROC curves for ranking trait contributions in forming a final classifier as the latter teaches in the abstract “hyperparameter optimization method was more stable than the grid search and random search methods. In a BC diagnosis dataset, the Extreme Gradient Boosting (XGBoost) model had an accuracy of 94.74% and a sensitivity of 93.69%. The mean value of the cell nucleus in the Fine Needle Puncture (FNA) digital image of breast lump was identified as the most important predictive feature for BC. In a CVD dataset, the XGBoost model had an accuracy of 73.50% and a sensitivity of 69.54%. Systolic blood pressure was identified as the most important feature for CVD prediction”. One would have had a reasonable expectation of success given that it would merely be the addition of one known method in analyzing the ROC curves for the construction of the classifier in obtaining the optimized hyperparameters. Therefore, it would have been obvious to a person skilled in the art to have modified the teachings of each and to be successful.
Claim 7 is directed to the method of claim 6 and thus claim 1, but further specifies the data of the machine learning model being selected from the specified model components.
Nguyen et al. and Chatzinakos et al. teach the method of claims 1-5 as previously described.
Nguyen et al. teaches on page 2, column 2, paragraph 2 “A linear model is then employed to assess imputation accuracy scores of tagged SNPs based on pairwise LD r2…SNPs are functionally scored based on public databases including the GWAS catalog, the ClinVar, and the Combined Annotation-Dependent Depletion (CADD)… Our aim is to combine systematically information from both pairwise LDr2, MAF and genomic distance to improve imputation accuracy of tag SNP selection”, reading on wherein the data of the machine learning model is selected from: a Genome-Wide Association Studies (GWAS) dataset, and a reference sample dataset in the database.
Chatzinakos et al. teaches on page 19, column 1, paragraph 2 “To increase bias power, we chose the parameter s, such as to maximize the variance of the within-panel ethnic group correlations”, reading on wherein the data of the machine learning model is selected from: a PRS parameter.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Nguyen et al. (Briefings in Bioinformatics (2022) 1-12), Chatzinakos et al. (American Journal of Medical Genetics (2021) 16-27), and Gao et al. (BMC research notes (2020) 1-6) as applied to claims 1-7 above, and further in view of St. Pierre et al. (Frontiers in genetics (2022) 1-14).
Claim 8 is directed to the method of claim 7 and thus claim 1, but further specifies the methods of cross-validation.
Nguyen et al., Chatzinakos et al., and Gao et al. teach the method of claims 1-7 as previously described.
Nguyen et al. teaches on page 9, column 1, paragraph 1 “Imputation performances are estimated through leave-one-outcross-validation as described previously”, reading on wherein the cross-validation method of the machine learning model comprises: performing leave-one-out cross validation.
Gao et al. teaches on page 2, column 1, paragraph 1 “fivefold cross-validation was used to measure the performance of the model under the corresponding parameters”, reading on wherein the cross-validation method of the machine learning model comprises: performing grouped cross validation.
Nguyen et al., Chatzinakos et al., and Gao et al. do not teach the use of random cross-validation.
St. Pierre et al. teaches on page 5, column 1, paragraph 3 “All analyses were repeated over 10 random cross-validation splits of the data, and results are summarized by medians and interquartile ranges of performance metrics”, reading on wherein the cross-validation method of the machine learning model comprises: performing random cross validation.
It would have been obvious at the time of first filing to have modified the teachings of Nguyen et al., Chatzinakos et al., and Gao et al. for the method of claims 1-7, with the teachings of St Pierre et al. for the use of random cross-validation as the latter states in the abstract “we contrast performance associated with several ways of selecting single nucleotide polymorphisms (SNPs) for inclusion in these scores. By considering GRS and PRS as predictors that are measured with error, insights into their strengths and weaknesses may be obtained, and SNP selection approaches play an important role in defining such errors”. One would have had a reasonable expectation of success given that it is merely a substitution of one known method, cross-validation, for another known method, random cross-validation, that is merely a sub category of the first. Therefore, it would have been obvious at the time of first filing to a person skilled in the art to have modified the teachings of each and to be successful.
Conclusion
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/K.N.A./Examiner, Art Unit 1687
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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