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 Status
Claims 1-9 are currently pending and examined on the merits.
Priority
The instant application is a 371 of PCT/IB2021/060403 filed on 11/10/2021 and claims foreign priority under U.S.C. 119 to Application IT102020000027188 filed on 11/13/2020. At this point in examination, the effective filing date of claims 1-9 is 11/13/2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/12/2023 is in compliance with the provisions of 37 CFR 1.97. A signed copy of the corresponding 1449 form has been included with this Office Action.
Drawings
The drawings filed on 5/12/2023 are accepted.
Claim Rejections - 35 USC § 112
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.
Claims 7-9 are 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 7 recites the limitation "said preliminary step" in lines 1-2. There is insufficient antecedent basis for this limitation in the claim. The rejection might be overcome by amending the claim to depend on claim 6. Because dependent claims 8-9 incorporate the limitations of claim 7, they are likewise rejected under 35 U.S.C. 112(b).
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 1-9 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: Claims 1-9 are directed to a method (process) for performing imputation and/or enrichment of genetic data by electronic computation. 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 described in the claim.
Claims 1 and 4-9 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas, as noted below.
Independent claim 1 further recites:
partitioning said genetic data set into a group of genetic data subsets or chunks, mutually disjoint, so that a union of said genetic data subsets corresponds to the genetic data set, wherein said genetic data subsets have a same dimension, corresponding to an amount of genetic data contained, wherein said dimension is a pre-determined minimum dimension, based on a predetermined quality criterion to be complied with by the genetic data imputation and/or enrichment (i.e., mental processes);
determining, as a result of the genetic data imputation and/or enrichment, an enriched version of said genetic data set of the individual, based on said enriched subsets (i.e., mental processes).
Dependent claim 4 further recites:
wherein said step of determining the result of the genetic data imputation and/or enrichment comprises determining the enriched version of said genetic data set of the individual as the union set of said enriched subsets (
S
E
i
) (i.e., mental processes).
Dependent claim 5 further recites:
wherein said genetic data set comprises a set of Single Nucleotide Polymorphisms of the individual, and wherein said subsets or chunks comprise respective subsets of the individual’s Single Nucleotide Polymorphisms, and wherein said dimension of the subsets corresponds to the number of Single Nucleotide Polymorphisms contained (i.e., mental processes; this is further information limiting the judicial exceptions).
Dependent claim 6 further recites:
the further preliminary step of determining the dimension of the subsets as a minimum number of Single Nucleotide Polymorphisms which allow each subset or "chunk" to give rise to an enriched subset which complies with a predetermined quality criterion (i.e., mental processes).
Dependent claim 7 further recites:
defining, based on known data, a genetic reference data (i.e., mental processes);
eliminating from the set of single nucleotide polymorphisms of the genetic data subset to be evaluated the single nucleotide polymorphisms which are not present in the general reference data, thus obtaining a modified set (i.e., mental processes);
partitioning said modified set into chunks having a test dimension (i.e., mental processes);
performing a test imputation determination, by an imputation algorithm selected on said modified set (i.e., mental processes, mathematical concepts);
calculating an imputation quality parameter on the test determination results (i.e., mental processes, mathematical concepts);
varying the test dimension according to a predetermined rule (i.e., mental processes);
iterating said steps of performing a test determination, calculating an imputation quality parameter, and varying the test dimension up to maximizing the imputation quality parameter (i.e., mental processes, mathematical concepts);
determining, as the dimension of the subsets, the resulting test dimension at the end of the iteration (i.e., mental processes).
Dependent claim 8 further recites:
wherein said step of varying the test dimension according to a predetermined rule comprises considering dimensions increased and decreased by an amount equal to one half the test dimension as the next test dimension, wherein the step of calculating comprises calculating an imputation quality parameter on the results of the two further test dimensions equal to the test dimension plus one half the test dimension, and the test dimension minus one half the test dimension; if the imputation quality in the two further test dimensions is similar, having a deviation below a certain threshold, the smaller chunk is chosen; if the deviation between the imputation qualities in the two cases is greater than a certain threshold, the larger chunk is chosen (i.e., mental processes, mathematical concepts).
Dependent claim 9 further recites:
wherein the step of calculating an imputation quality parameter is carried out by a "NON-REF Concordance" technique, which includes finding a percentage of correctly imputed single nucleotide polymorphisms among all the single nucleotide polymorphisms having at least one allele with a variant in ALT, or wherein the step of calculating an imputation quality parameter is carried out based on a comparison of the imputed data with the reference genetic data (i.e., mental processes, mathematical concepts).
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 the claims are currently recited, the imputation method could be performed by writing down simple genetic data subsets and imputation quality parameter calculations with pen and paper, and making decisions with those subsets and equations. Writing out the data and formulas and making decisions on them can be practically performed in the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1 and 4-9 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)).
The judicial exceptions identified in Eligibility Step 2A, Prong One are not integrated into a practical application because of the reasons noted below.
Claim 1 recites processing subsets in parallel, by a first electronic processor capable of performing parallel processing, by applying in parallel, to each of said genetic data subsets, at least one genetic data imputation algorithm, adapted to enrich the genetic information by comparing a partial genetic data set with the complete known genome of one or more reference individuals. Data gathering steps are extra-solution activity as they collect the data needed to carry out the JE. It does not impose any meaningful limitation on the JE or how the JE is performed (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.). Therefore, the claimed additional elements do not integrate the abstract ideas into a practical application.
Claims 1 and 3 recite the additional non-abstract elements of data gathering:
accessing partial information of an individual's genetic patrimony, represented by a genetic data set of the individual, available following a detection by a sequencing technique (claim 1);
obtaining, as results of said parallel processing step, a plurality of enriched genetic data subsets, each enriched subset being an enriched version of a respective genetic data subset (claim 1);
after the step of partitioning, sending digital data corresponding to the subsets or chunks determined by the partition, from a main memory controlled by the second electronic processor to a memory of the first electronic processor (claim 3);
after the step of obtaining a plurality of enriched subsets, sending digital data corresponding to the enriched subsets, from the memory of the first electronic processor to the main memory controlled by the second electronic processor (claim 3).
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Claims 1-3 recite the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof:
a first electronic processor (claim 1);
a first electronic processor capable of performing parallel processing is a Graphical Processing Unit (claim 2);
a second electronic processor being a conventional Control Processing Unit (claim 3);
memory of first and second electronic processors (claim 3).
The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-9 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application. Claims 1-3 contain additional elements that would not integrate a judicial exception into a practical application and are further probed for inventive concept in Step 2B.
[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.
With respect to claims 1 and 3: The courts have recognized that computer functions such as receiving or transmitting data over a network and storing and retrieving information in memory are well-understood, routine, and conventional. This is evidenced by Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); 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).
With respect to claim 1: The limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception. These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field (Trading Technologies Int’l v. IBG, TLI Communications). They do not require or set forth a particular machine (Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l), they do not affect a transformation of matter, nor do they provide an unconventional step. Simply appending well understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook, Versata Development Group v. SAP America).
The additional elements of a first electronic processor capable of performing parallel processing is a Graphical Processing Unit (claim 2), a second electronic processor being a conventional Control Processing Unit (claim 3), and memory of first and second electronic processors (claim 3) is conventional. Evidence for conventionality is shown by Nobile et al. (Briefings in Bioinformatics, 2016, 18(5), 870-885). Nobile et al. reviews GPUs as dedicated co-processors present in every common computer and are proven to be beneficial for genomic sequence analysis (pg. 872, Table 1; pg. 873, col. 1, para. 2, lines 1-2; pg. 873, Table 2). Some GPU-powered tools for sequence alignment listed in Table 2 leverage both GPUs and CPUs. This shows that parallel processing with both GPUs and CPUs are conventional elements in the technical field.
The additional element of processing subsets in parallel, by a first electronic processor capable of performing parallel processing, by applying in parallel, to each of said genetic data subsets, at least one genetic data imputation algorithm, adapted to enrich the genetic information by comparing a partial genetic data set with the complete known genome of one or more reference individuals (claim 1) is conventional. Evidence for conventionality is shown by Das et al. (Nature Genetics, 2016, 48(10), 1284-1287). Das et al. reviews that because imputation is trivially parallelizable, all compared imputation programs can benefit from additional CPUs (for example, by imputing multiple chromosomes, chromosome segments, or samples in a single or multiple parallel invocations of the program) (pg. 1285, col. 2, para. 2). This shows performing imputations on genetic data subsets by parallel processing with multiple electronic processors, which makes it a conventional practice in the art.
[Step 2B: NO]
Therefore, claims 1-20 are patent ineligible under 35 U.S.C. § 101.
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 1-2 and 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Verma et al. (Frontiers in Genetics, 2014, 5(370), 1-15), in view of Zhang et al. (Statistics and Its Interface, 2011, 4(3), 339-352), Howie et al. (IMPUTE2, 2014, 1-29) and Chen et al. (Bioinformatics, 2012, 28(22), 2979-2980).
With respect to claim 1:
Regarding the recited accessing partial information of an individual's genetic patrimony, represented by a genetic data set of the individual, available following a detection by a sequencing technique, Verma et al. discloses study data of the eMERGE network consists of seven adult sites and two pediatric sites, each with DNA databanks linked to EHR. Each site in the network has a set of at least 3000 samples that have been genotyped on one or more genotyping platforms (pg. 2, col. 2, para. 3; pg. 3, Table 1). This teaches genetic datasets of individuals.
Regarding the recited partitioning said genetic data set into a group of genetic data subsets or chunks, mutually disjoint, so that a union of said genetic data subsets corresponds to the genetic data set, wherein said genetic data subsets have a same dimension, corresponding to an amount of genetic data contained, Verma et al. discloses dividing chromosomes into segments including 30,000 SNPs each (referred to as SNPlets) as recommended by the BEAGLE documentation for imputing large datasets (pg. 4, col. 2, para. 2, lines 1-5; pg. 5, col. 1, Figure 1). This teaches partitioning genetic datasets into separate subsets, where the union still makes up the original genetic dataset because the subsets were divided from the original genetic dataset. Each subset has the same number of SNPs and therefore have the same dimension.
Regarding the recited obtaining, as results of said parallel processing step, a plurality of enriched genetic data subsets, each enriched subset being an enriched version of a respective genetic data subset, Verma et al. discloses that in each results file, the data for all SNPs in the buffer regions were removed such that each imputed SNP had results from only one segment (pg. 4, col. 2, para. 2, lines 5-14; pg. 5, col. 1, Figure 1; pg. 6, Figure 3). Buffer regions were added to the end of each SNP before imputation to account for degradation in imputation quality that may occur at the ends of imputed segments. Also, further discloses imputation jobs were run in parallel across several high-performance computing clusters (pg. 5, col. 2, para. 2, lines 1-10). This teaches after a parallel processing step, achieving results files containing enriched genetic data subsets.
Regarding the recited determining, as a result of the genetic data imputation and/or enrichment, an enriched version of said genetic data set of the individual, based on said enriched subsets, Verma et al. discloses that in each results file, the data for all SNPs in the buffer regions were removed such that each imputed SNP had results from only one segment (pg. 4, col. 2, para. 2, lines 5-14; pg. 5, col. 1, Figure 1; pg. 6, Figure 3). Buffer regions were added to the end of each SNP before imputation to account for degradation in imputation quality that may occur at the ends of imputed segments. This teaches achieving results files containing enriched genetic data subsets, which makes up an enriched version of the original genetic dataset because the subsets used for imputation were divided from the original genetic dataset.
Verma et al. does not disclose wherein said dimension is a pre-determined minimum dimension, based on a predetermined quality criterion to be complied with by the genetic data imputation and/or enrichment.
However, Zhang et al. discloses that window sizes of 500 SNPs is recommended for imputation with the best tradeoff between efficacy and accuracy among the partition strategies investigated, where a block of 500 SNPs is equivalent to 1 MB (pg. 10-11, para. 5, lines 8-14). For researchers who need to focus on a shorter region of chromosome than 5 MB as recommended by IMPUTE, a window larger than 50 SNPs is highly recommended. Zhang et al. also discloses that imputation quality was not reliable for regions smaller than 50 SNPs (pg. 10, para. 3, lines 1-4). Therefore, this teaches that a minimum dimension of 50 SNPs is recommended for higher quality or more reliable genetic data imputation.
Verma et al. and Zhang et al. do not disclose processing said subsets in parallel, by a first electronic processor capable of performing parallel processing, by applying in parallel, to each of said genetic data subsets, at least one genetic data imputation algorithm, adapted to enrich the genetic information by comparing a partial genetic data set with the complete known genome of one or more reference individuals.
However, Howie et al. discloses a common scenario where the imputation tool IMPUTE2 could be used: unobserved genotypes in a set of study individuals are imputed or predicted using a set of reference haplotypes and genotypes from a SNP chip (pg. 1, para. 4). Also, further discloses that IMPUTE2 looks for reference haplotypes that share high sequence identity with the haplotypes of a particular study individual (pg. 5, para. 2). The reference haplotypes constitute a “custom” reference panel that can be used to impute missing genotypes in the individual of interest and can be from publicly available reference datasets, such as the 1000 Genomes project and HapMap 3 (pg. 3, Section “Download Reference Data”). Howie et al. discloses that IMPUTE2 can split chromosomes into smaller chunks for imputation (pg. 23-24, Section “Analyzing Whole Chromosomes”). This teaches that IMPUTE2 can perform an imputation algorithm on genetic data subsets by comparing the subsets with panels from complete known genomes of individuals.
Howie et al. does not disclose parallel processing by a first electronic processor.
However, Chen et al. discloses an imputation tool Mendel-GPU that performs genetic data imputation using parallel processing algorithms and GPUs (pg. 2980, col. 2, para. 1, lines 1-9). This teaches parallel processing by a GPU processor.
With respect to claim 2:
Verma et al., Zhang et al., and Howie et al. do not disclose wherein said first electronic processor capable of performing parallel processing is a Graphical Processing Unit.
However, Chen et al. discloses an imputation tool Mendel-GPU that performs genetic data imputation using parallel processing algorithms and GPUs (pg. 2980, col. 2, para. 1, lines 1-9). This teaches parallel processing by a GPU processor.
With respect to claim 4:
Zhang et al., Howie et al., and Chen et al. do not disclose wherein said step of determining the result of the genetic data imputation and/or enrichment comprises determining the enriched version of said genetic data set of the individual as the union set of said enriched subsets (SE_i).
However, Verma et al. discloses that in each results file, the data for all SNPs in the buffer regions were removed such that each imputed SNP had results from only one segment (pg. 4, col. 2, para. 2, lines 5-14; pg. 5, col. 1, Figure 1; pg. 6, Figure 3). Buffer regions were added to the end of each SNP before imputation to account for degradation in imputation quality that may occur at the ends of imputed segments. This teaches achieving results files containing enriched genetic data subsets, where the union of the subsets makes up an enriched version of the original genetic dataset because the subsets used for imputation were divided from the original genetic dataset.
With respect to claim 5:
Zhang et al., Howie et al., and Chen et al. do not disclose wherein said genetic data set comprises a set of Single Nucleotide Polymorphisms of the individual, and wherein said subsets or chunks comprise respective subsets of the individual's Single Nucleotide Polymorphisms, and wherein said dimension of the subsets corresponds to the number of Single Nucleotide Polymorphisms contained.
However, Verma et al. discloses dividing chromosomes into segments including 30,000 SNPs each (referred to as SNPlets) as recommended by the BEAGLE documentation for imputing large datasets (pg. 4, col. 2, para. 2, lines 1-5; pg. 5, col. 1, Figure 1). This teaches partitioning chromosomes comprising of SNPs into separate subsets of SNPs. Each subset has the same number of SNPs and therefore have the same dimension.
With respect to claim 6:
Verma et al., Howie et al., and Chen et al. do not disclose the further preliminary step of determining the dimension of the subsets as a minimum number of Single Nucleotide Polymorphisms which allow each subset or "chunk" to give rise to an enriched subset which complies with a predetermined quality criterion.
However, Zhang et al. discloses that window sizes of 500 SNPs is recommended for imputation with the best tradeoff between efficacy and accuracy among the partition strategies investigated, where a block of 500 SNPs is equivalent to 1 MB (pg. 10-11, para. 5, lines 8-14). For researchers who need to focus on a shorter region of chromosome than 5 MB as recommended by IMPUTE, a window larger than 50 SNPs is highly recommended. Zhang et al. also discloses that imputation quality was not reliable for regions smaller than 50 SNPs (pg. 10, para. 3, lines 1-4). Therefore, this teaches determining a minimum dimension of subsets as a minimum number of 50 SNPs, which is recommended for higher quality or more reliable genetic data imputation.
It would have been prima facie obvious to one of ordinary skill in the art to modify the imputation method disclosed by Verma et al. to incorporate subset dimension requirements disclosed by Zhang et al., performing imputation algorithms on data subsets disclosed by Howie et al., and parallel processing using GPUs disclosed by Chen et al. One would be motivated to modify the imputation method to incorporate subset dimension requirements, applying imputation algorithms on data subsets, and GPU-based parallel processing because Zhang et al. discloses evaluating the influence of untyped rate, sizes of study samples and reference samples, window sizes, and reference choice (for admixed population) on imputation quality, which could be useful in design and analysis of genetic studies (pg. 339, col. 1, Abstract, lines 4-7; pg. 349, col. 1, para. 5). Therefore, one of ordinary skill in the art would be able to modify subset dimensions in the imputation method. Howie et al. discloses IMPUTE2 as a genotype imputation and haplotype phasing program (pg. 1, lines 1-2). One of ordinary skill in the art would be able to incorporate imputation algorithms from a genotype imputation program in the imputation method. Chen et al. discloses that Mendel-GPU is 104 and 144 times faster than BEAGLE and IMPUTE2 (pg. 2980, col. 1, para. 3, lines 10-11). Therefore, incorporating parallel processing by GPUs would increase the speed at which the imputation method is performed. There is a likelihood of success, since imputation methods, data subset dimension requirements, imputation algorithms, and GPU-based parallel processing are all well known techniques in the field of bioinformatics.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Verma et al. (Frontiers in Genetics, 2014, 5(370), 1-15), Zhang et al. (Statistics and Its Interface, 2011, 4(3), 339-352), Howie et al. (IMPUTE2, 2014, 1-29), and Chen et al. (Bioinformatics, 2012, 28(22), 2979-2980) as applied to claims 1-2 and 4-6 above, in view of Nickolls et al. (IEEE Micro, 2010, 30(2), 56-69).
Verma et al., Zhang et al., Howie et al., and Chen et al. are applied to claims 1-2 and 4-6 above.
With respect to claim 3:
Zhang et al., Howie et al., and Chen et al. do not disclose wherein said steps of partitioning the genetic data set and determining the enriched version of the genetic data set are carried out by a second electronic processor, said second electronic processor being a conventional Control Processing Unit.
However, Verma et al. discloses the workflow of both BEAGLE and IMPUTE2 imputation methods, where each job required between 4 and 24 GB RAM and from 4 to 80 CPUs (cores) (pg. 5, col. 2, para. 2, lines 1-10; pg. 6, Figure 3). This teaches that imputation methods performed by BEAGLE and IMPUTE2 includes partitioning genetic datasets and producing an imputed version of the genetic dataset, which are carried out by CPU processors.
Verma et al. does not disclose after the step of partitioning, sending digital data corresponding to the subsets or chunks determined by the partition, from a main memory controlled by the second electronic processor to a memory of the first electronic processor; after the step of obtaining a plurality of enriched subsets, sending digital data corresponding to the enriched subsets, from the memory of the first electronic processor to the main memory controlled by the second electronic processor.
However, Nickolls et al. discloses the PCIe host interface connects the GPU and its DRAM memory with the host CPU and system memory (pg. 61, col. 2, para. 2). The CPU and GPU coprocessing and data transfers use the bidirectional PCIe interface. Also, further discloses that this computing architecture is configured with 16 streaming multiprocessors that can execute up to 1,536 concurrent threads (pg. 60, col. 2, para. 3, lines 4-11; pg. 61, col. 1, para. 2, lines 1-6). This teaches a parallel processing structure where the CPU and GPU are connected so that data transfer can occur between their respective memories.
It would have been prima facie obvious to one of ordinary skill in the art to modify the imputation method disclosed by Verma et al., Zhang et al., Howie et al., and Chen et al. to incorporate data transfer between CPU and GPU electronic processors disclosed by Nickolls et al. One would be motivated to modify the imputation method to incorporate data transfer between processors because Nickolls et al. discloses that GPU performance will continue to scale at Moore’s law rates—about 50 percent per year—giving an order of magnitude increase in performance in five to six years (pg. 67, col. 2, para. 3, lines 1-5). Therefore, incorporating data transfer from GPU architectures would increase performance and efficiency of the imputation method. There is a likelihood of success, since imputation methods and data transfer between processors are well known techniques in the field of bioinformatics.
Claims Free from Prior Art
Claim 7 which recites wherein said preliminary step of determining the dimension of the subsets comprises the following steps: defining, based on known data, a genetic reference data; eliminating from the set of single nucleotide polymorphisms of the genetic data subset to be evaluated the single nucleotide polymorphisms which are not present in the general reference data, thus obtaining a modified set; partitioning said modified set into chunks having a test dimension; performing a test imputation determination, by an imputation algorithm selected on said modified set; calculating an imputation quality parameter on the test determination results; varying the test dimension according to a predetermined rule; iterating said steps of performing a test determination, calculating an imputation quality parameter, and varying the test dimension up to maximizing the imputation quality parameter; determining, as the dimension of the subsets, the resulting test dimension at the end of the iteration is free of the art.
Claim 8 which recites wherein said step of varying the test dimension according to a predetermined rule comprises considering dimensions increased and decreased by an amount equal to one half the test dimension as the next test dimensions, wherein the step of calculating comprises calculating an imputation quality parameter on the results of the two further test dimensions equal to the test dimension plus one half the test dimension, and the test dimension minus one half the test dimension; if the imputation quality in the two further test dimensions is similar, having a deviation below a certain threshold, the smaller chunk is chosen; if the deviation between the imputation qualities in the two cases is greater than a certain threshold, the larger chunk is chosen is free of the art.
Claim 9 which recites wherein the step of calculating an imputation quality parameter is carried out by a "NON-REF Concordance" technique, which includes finding a percentage of correctly imputed single nucleotide polymorphisms among all the single nucleotide polymorphisms having at least one allele with a variant in ALT, or wherein the step of calculating an imputation quality parameter is carried out based on a comparison of the imputed data with the reference genetic data is free of the art.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jammy Luo whose telephone number is (571)272-2358. The examiner can normally be reached Monday - Friday, 9:00 AM - 5:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D Riggs can be reached at (571)270-3062. 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.
/J.N.L./Examiner, Art Unit 1686
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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