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 .
Claims Status
Claims 1-20 are currently pending and under examination herein.
Claims 1-20 are rejected.
Priority
The instant application claims the benefit of priority to Provisional Application No. 63410086, filed 26 Sep 2022. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.78. As such, the effective filing date of claims 1-20 is 26 Sep 2022.
Information Disclosure Statement
The Information Disclosure Statements filed on 16 Nov 2023, 04 Dec 2023, 08 Jan 2024 and 26 Aug 2024 are compliant with the provisions of 37 CFR 1.97 and has/have been considered. A signed copy of list of references cited from each IDS is included with this Office Action.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. 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. Hyperlinks can be found in ¶’s 51, 173, 352 (Table 8) 359, 363, 364 of the submitted document.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea/law of nature/natural phenomenon without significantly more.
Step 2A, Prong 1 Considerations
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1 : YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea/law of nature/natural phenomenon:
Claim 1 recites a method by one or more computing systems for determining a set of segmented annotation data, receiving unannotated genomic data, comparing genomic data, determining a variable value set, selecting a search region, searching within a subset of annotation data, and annotating the identified variant locus.
Claim 5 recites providing a risk score for the disease of interest.
Claim 7 recites selecting a second search region, searching within a subset of annotation data, and annotating the identified variant locus.
Claim 12 recites receiving annotations, receiving unannotated genomic data, comparing genomic data to a reference genome, determining a variable value set, and annotating identified variant loci.
Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. While claims 1 and 12 recite performing some aspects of the analysis “by one or more computing systems,” there are no additional limitations that indicate that this analysis engine requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1, 5, 7 and 12 recite an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 1 : YES).
Step 2A, Prong 2 Considerations
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to affect a particular condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a computer medium and/or use of a hardware processor. Specifically, the claims recite the following additional elements:
Claim 1 recites generating annotated variant loci and displaying the annotated variant loci.
Claim 12 recites generating annotation data and displaying the annotated variant loci.
There are no limitations that indicate that the claimed system, method or storage medium require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The steps for processing genomic data are routine and conventional functions such as performing repetitive calculations and mere data extracting (see MPEP 2106.05(d)(2)). As such, claims 1, 5, 7 and 12 are directed to an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 2 : NO).
Step 2B (MPEP 2106.05.A i-vi)
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional limitations to indicate that the claimed system and method requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The limitation for use of a computer-implemented system, one or more hardware processes, or use of a non-transitory computer-readable storage system amount to mere instructions to apply the judicial exception in a generic way because the executable steps are so generically recited. MPEP 2106.05(d) discloses that 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 are not enough to qualify as “significantly more” when recited in a claim with a judicial exception. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B : No). As such, claims 1-20 are not patent eligible.
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)(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.
Claims 1-5, 7-17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hu (Hu et al. 2017 PLOS Computational Biology 13(6):e1005589, IDS entered 04 Dec 2023).
Claim 1 recites a method of genomic annotation by receiving unannotated genomic data and comparing it to a reference genome to identify variant loci and then displaying the annotated variant loci. Hu discloses AnnoPred, a principled framework that integrates GWAS summary statistics with various types of annotation data to improve risk prediction accuracy (see §Introduction and §Methods. Annotation data and §Data access. GWAS summary statistics and validation data).
Claim 2 recites the method of claim 1 wherein each search region comprises a loci range within a chromosome. Hu discloses the search region comprising a loci range within a chromosome (see §Simulation settings last ¶ on pg. 12).
Claim 3 recites the method of Claim 2, wherein, for each search region, a length of the loci range is determined based on whether the search region includes a coding sequence. Hu discloses the search region including a coding sequence (see §Methods. Annotation data).
Claim 4 recites the method of Claim 2, wherein, when an annotation is mapped to variable value sets associated with multiple search regions within a chromosome, segmenting the annotation data comprises sorting the annotation into a subset of annotation data for a single search region. Hu discloses simulated traits using SNPs of chromosome 1, chromosomes 1 and 2, chromosomes 1 to 4 and the whole genome while keeping the same proportion of causal variants and heritability to mimic the situation of increasing sample size (see §Simulation settings last ¶ on pg. 12).
Claim 5 recites the method of Claim 1, wherein the set of search regions is determined based on a disease of interest, the method further comprising providing a risk score for the disease of interest based on the annotated variant loci, using a risk model. Hu discloses a polygenic risk score for risk prediction performance using both simulations and real data of multiple complex diseases (see §Introduction and §Methods. Calculation of PRS).
Claim 7 recites the method of claim 1 wherein generating the annotated variant loci further comprises: selecting a second search region from the set of search regions based on the variable value set, wherein the second search region corresponds to a second range of loci adjacent to the first range of loci; searching within a subset of annotation data corresponding to the selected second search region to identify annotations associated with a matching variable value set; and annotating the identified variant locus with the identified annotations. Hu discloses GenoCanyon, a statistical framework to predict functional regions in the human genome through integrative analysis of ENCODE epigenomic data and multiple conservation metrics (see §Methods. Annotation data).
Claim 8 recites the method of Claim 1, wherein variables comprise at least one of: chromosome number, overall chromosome location, variant start position, variant stop position, variant identification number, variant type, reference allele, present allele, or reference assembly number. Hu discloses an additional simulation to demonstrate this using WTCCC (Wellcome Trust Case Control Consortium) genotype data with ~15K individuals and ~330K variants (see §Methods. Comparison with existing methods).
Claim 9 recites the method of Claim 1, wherein each subset of annotation data is independently stored in a multi-dimensional array data structure, wherein generating annotated variant loci comprises searching multiple subsets of annotation data in parallel. Hu discloses multiple subsets of annotation data being analyzed in parallel (see §Data access. GWAS summary statistics and validation data).
Claim 10 recites the method of Claim 1, wherein the annotation data are used to determine priors for training a risk model. Hu discloses a polygenic risk score (see §Methods. Calculation of PRS).
Claim 11 recites The method of Claim 1, wherein determining a variable value set for each identified variant locus in the unannotated genomic data comprises converting the unannotated genomic data into a standardized format. Hu discloses a standardized format for the variable value set (see §Methods. Annotation data).
Claim 12 recites receiving annotations from a plurality of data sources comprising different data types; generating annotation data by mapping the annotations to variable value sets, wherein each variable value set corresponds to a genomic variant, wherein the annotation for at least one variable value set comprises a weighted aggregation of multiple annotations, from different data sources, associated with the respective variable value set; receiving unannotated genomic data for a subject; comparing the unannotated genomic data to a reference genome to identify variant loci; determining a variable value set for each identified variant locus in the unannotated genomic data; annotating each identified variant locus using annotations, from the annotation data, associated with a variable value set matching the variable value set for the identified variant locus; and displaying the annotated variant loci. Hu discloses training AnnoPred using publicly accessible GWAS summary statistics and evaluated risk prediction performance using individual-level genotype and phenotype data from cohorts independent from the training samples (see §Introduction and §Methods. Annotation data and §Data access. GWAS summary statistics and validation data).
Claim 13 recites the method of Claim 12, wherein the weighted aggregation is performed based on a weight metric for each of the different data sources. Hu discloses weighted aggregation via summary statistics for various datasets (see §Data access GWAS summary statistics and validation data).
Claim 14 recite the method of Claim 13, wherein the weight metric for a data source is determined based on at least one of: a number of published annotations associated with the data source, whether annotations from the data source are clinical grade, or whether annotations from the data source are based on expert panel review presence. Hu discloses using summary statistics from a number of published annotations; for example, for Crohn's disease, Hu discloses a model using summary statistics from the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC, see §Data access GWAS summary statistics and validation data).
Claim 15 recites the method of Claim 13, wherein the weight metric comprises a predictive weight determined using a supervised learning model. Hu discloses samples from the Wellcome Trust Case Control Consortium as a validation learning set (WTCCC, see §Data access GWAS summary statistics and validation data).
Claim 16 recites the method of Claim 12, wherein each annotation of the multiple annotations is associated with a weight metric, wherein performing the weighted aggregation of the multiple annotations comprises identifying conflicting annotations in the multiple annotations and selecting an annotation from the conflicting annotations with a higher weight metric. Hu discloses removing samples from the WTCCC from the meta-analysis and using it as a validation dataset (see § Data access GWAS summary statistics and validation data).
Claim 17 recites the method of Claim 12, wherein each annotation of the multiple annotations is associated with a weight metric, wherein performing the weighted aggregation of the multiple annotations comprises filtering out annotations with a weight metric below a threshold. Hu discloses only using SNPs with p-values below 5 × 10−8 in the training set (see §Comparison with existing methods).
Claim 20 recites the method of Claim 12, further comprising providing, based on the annotated variant loci, at least one of: a recommendation for further clinical testing, a disease diagnosis, a recommended therapeutic regimen, or a recommended modification to an existing therapeutic regimen. Hu discloses their disease risk prediction model as a critical component of precision medicine (see §Introduction). The objective of precision medicine is to deliver tailored medical treatments for patients according to their genetic characteristics (see Ho et al. 2019 Frontiers in genetic medicine 10(267) pgs. 1-10).
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 6, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hu as applied to claims 1 and 12 under 35 U.S.C. 102(a)(1) above, and further in view of Lu (Lu et al. 2015 Scientific Reports 5:10576).
Claims 6 and 18 recite the methods of Claim 1 and 12 respectively, wherein the annotation data comprises annotations mapped to variable value sets for non-coding DNA variants. Hu does not disclose annotations mapped to variable value sets for non-coding DNA variants. Lu teaches annotations mapped to variable value sets for non-coding DNA variants (see §Introduction).
Claim 19 recites the method of Claim 18, wherein at least a portion of the annotations mapped to variable value sets for non-coding DNA variants are determined using at least one of: genome-wide association studies (GWAS), CRISPR-based functional screens, or by activity-by-contact models. Both Hu and Lu disclose annotations mapped using GWAS.
Taken together, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Hu and Lu to include annotations mapped to variable values sets for non-coding DNA variants since there are a significant amount of variants associated with human diseases outside of coding DNA regions, and a large fraction of the non-coding regions of the human genome are functionally relevant. It would also have been obvious to use GWAS for both coding and non-coding variants as it is a standard research tool used to identify genomic variants that are statistically associated with a risk for a disease or a particular trait.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Cano-Gamez and Trynka 2020 Frontiers in genetics 11(424) pgs. 1-21; Pain et al. 2021 Human molecular genetics 30(8) pgs. 727-738.
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/M.S.C./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
