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 .
Status of Application
This action is in reply to the reply received May 6, 2026 (hereinafter “Reply”) and the accompanying information disclosure statement.
Claims 1, 6, 7, 9-13, and 15-18 are amended.
Claims 4 and 5 are cancelled.
Claims 1-3 and 6-18 are pending.
Information Disclosure Statement
The information disclosure statement submitted May 6, 2026 and its contents have been considered.
Claim Rejections - 35 U.S.C. § 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 1-3 and 6-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Claims 1-18 are directed to an abstract idea without significantly more as required by the Alice test as discussed below.
Step 1
Claims 1-3 and 6-18 are directed to a process, machine, manufacture, or composition of matter.
Step 2A
Claims 1-3 and 6-18 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea; and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
The claims recite the following limitations that are directed to abstract ideas. Claim 15 recites receiving pharmacogenomic data representing at least one pharmacogenomic annotation in association with at least one gene; receiving at least one genomic variation of the at least one gene, searching the pharmacogenomic data for at least one association with each genomic variation, and returning the associated data, the associated data being a haplotype or diplotype and a phenotype; generating at least one report comprising the associated data with the genomic variation associated, predicting at least one genomic variant, wherein at least one of the at least one genomic variation is determined as the at least one genomic variant; and detecting genomic variants leading to altered protein function, by: storing one or more features from an annotated variant dataset of at least one variant; determining one or more validated variants of the annotated variant dataset, each validated variant matching one or more known variants of a known variant dataset, each known variant leading to altered protein function; assigning a classification to one or more predicted variants of variants of the annotated variant dataset not selected as validated variants, each predicted variant leading to altered protein function, the assigning using the machine learning model based on at least one of the one or more features stored in the memory; and determining one or more sequence ontology variants of the variants of the annotated variant dataset not selected as validated variants and not classified as predicted variants, each sequence ontology variant being a loss-of-function variant, the determining being based on at least one of the features stored in the memory; wherein the annotated variant dataset is generated using a Variant Effect Predictor (VEP); each sequence ontology variant is determined by filtering based on sequence ontology data; and the loss-of-function variant is a splice acceptor variant, a splice donor variant, a stop gained variant, a frameshift variant, a stop loss variant, a start loss variant, or a functionally deleterious missense variant. Claims 1 and 18 recite similar features as claim 15. Claims 2, 3, 6-14, 16, and 17 further specify features of the identified abstract ideas or characteristics of the data used thereby.
These limitations describe abstract ideas that correspond to concepts identified as abstract ideas by the courts as mental processes—such as concepts performed in the human mind (including an observation, evaluation, judgment, or opinion)—because the claimed features identified above are concepts performed in the human mind (including an observation, evaluation, judgment, or opinion).
These limitations describe abstract ideas that correspond to concepts identified as abstract ideas by the courts as certain methods of organizing human activity—such as fundamental economic principles or practices (including hedging, insurance, mitigating risk), commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)—because the claimed features identified above manage personal behavior or relationships or interactions between people including following rules or instructions.
Thus, the concepts set forth in claims 1-3 and 6-18 recite abstract ideas.
Prong two of the Step 2A requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. “Integration into a practical application” requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Further, “integration into a practical application” uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application, such as considerations discussed in M.P.E.P. § 2106.05(a)-(h).
The claims recite the following additional elements beyond those identified above as being directed to an abstract idea. Claim 1 recites that its method is computer-implemented, a data processor, a database configuration engine, a machine learning engine, machine learning model, a non-transitory memory, and a display generator configured to generate a display. Claim 15 recites similar features as claim 1. Claim 18 recites similar features as claim 1 and further recites a non-transitory computer readable medium and a processor. Several of the dependent claims further specify additional computer elements (e.g., non-transitory memory and a processor) or particular file types or formats (binary, text, FASTQ, BAM, etc.).
The identified judicial exception(s) are not integrated into a practical application for the following reasons.
First, evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. The additional computer elements identified above—the computer, processors, displays, non-transitory computer readable medium, machine (implementing or characterizing the learning engine and learning model), and non-transitory memory—are recited at a high level of generality. Inclusion of these elements (including the way the machine is recited describe the type of learning engine and learning model) amounts to mere instructions to implement the identified abstract ideas on a computer. See M.P.E.P. § 2106.05(f). The use of conventional computer elements to generate a display or to use particular file types or formats for storing data is the insignificant, extra-solution activity of mere data gathering or outputting in conjunction with a law of nature or abstract idea. See M.P.E.P. § 2106.05(g). To the extent that the claims transform data, the mere manipulation of data is not a transformation. See M.P.E.P. § 2106.05(c). Inclusion of computing system in the claims amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. See M.P.E.P. § 2106.05(h). Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Second, evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. See M.P.E.P. § 2106.05(a). Their collective functions merely provide an implementation of the identified abstract ideas on a computer system in the general field of use of personalized medicine in pharmacogenomics. See M.P.E.P. § 2106.05(h).
Thus, claims 1-3 and 6-18 recite mathematical concepts, mental processes, or certain methods of organizing human activity without including additional elements that integrate the exception into a practical application of the exception.
Accordingly, claims 1-3 and 6-18 are directed to abstract ideas.
Step 2B
Claims 1-3 and 6-18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination. Additional features of these analyses are discussed below.
Evaluated individually, the additional elements do not amount to significantly more than a judicial exception. In addition to the factors discussed regarding Step 2A, prong two, these additional computer elements also provide conventional computer functions that do not add meaningful limits to practicing the abstract idea. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The use of generic computer components to display or to use particular file types or formats for storing data is likewise the well-understood, routine, and conventional computer functions of receiving or transmitting data over a network, e.g., the Internet, and does not impose any meaningful limit on the computer implementation of the identified abstract ideas. See M.P.E.P. § 2106.05(d)(II). Similarly, the use of generic computer components to use particular file types or formats for storing data is likewise the well-understood, routine, and conventional computer functions of receiving, processing, and storing data and does not impose any meaningful limit on the computer implementation of the identified abstract ideas. See M.P.E.P. § 2106.05(d)(II). Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to mere instructions to implement the identified abstract ideas on a computer.
Thus, claims 1-3 and 6-18, taken individually and as an ordered combination of elements, are not directed to eligible subject matter since they are directed to an abstract idea without significantly more.
Statement Regarding the Prior Art
The independent claims recite features that the machine learning engine is configured to detect genomic variants leading to altered protein function, the machine learning engine comprising: a non-transitory memory storing one or more features from an annotated variant dataset of at least one variant; a variant validator configured to determine one or more validated variants of the annotated variant dataset, each validated variant matching one or more known variants of a known variant dataset, each known variant leading to altered protein function; a machine learning model configured to assign a classification to one or more predicted variants of variants of the annotated variant dataset not selected as validated variants, each predicted variant leading to altered protein function, the assigning by the machine learning model based on at least one of the one or more features stored in the memory; and a loss-of-function detector configured to determine one or more sequence ontology variants of the variants of the annotated variant dataset not selected as validated variants and not classified as predicted variants, each sequence ontology variant being a loss-of-function variant, the determining by the loss-of-function detector based on at least one of the features stored in the memory. the annotated variant dataset is generated using a Variant Effect Predictor (VEP), each sequence ontology variant is determined by filtering based on sequence ontology data. the loss-of-function variant is a splice acceptor variant, a splice donor variant, a stop gained variant, a frameshift variant, a stop loss variant, or a start loss variant.
The relevance of Lefkofsky et al. (U.S. Pub. No. 2021/0350904 A1) to the claims is presented in the prosecution history of the instant application.
Ward et al. (U.S. Pub. No. 2021/0115513 A1) teaches techniques for using genetic markers associated with endometriosis, for example via a computer-implemented program to predict risk of developing endometriosis, and methods of preventing or treating endometriosis or a symptom thereof. These techniques can utilize detection of endometriosis associated biomarkers such as single nucleotide polymorphisms (SNPs), insertion deletion polymorphisms (indels), damaging mutation variants, loss of function variants, synonymous mutation variants, nonsynonymous mutation variants, nonsense mutations, recessive markers, splicing/splice-site variants, frameshift mutations, insertions, deletions, genomic rearrangements, stop-gain, stop-loss, Rare Variants (RVs), some of which are identified in its Tables 1-4 (or diagnostically and predicatively functionally comparable biomarkers). In some instances, the method can comprise using a statistical assessment method such as Multi Dimensional Scaling analysis (MDS), logistic regression, or Bayesian analysis. However, Ward does not teach all of the features of the independent claims.
Tymoshenko et al. (U.S. Pub. No. 2021/0256394 A1) teaches methods and systems for identifying variants of a given target protein or target gene that perform the same function and/or improve the phenotypic performance of a host cell transformed with such a variant. To enhance the diversity of identified candidate sequences, the methods may implement the use of a metagenomic database and/or machine learning methods. The methods and systems may be implemented in optimizing a biosynthetic pathway, e.g., to improve the production of a target molecule of interest. However, Tymoshenko does not teach all of the features of the independent claims.
Albertsen et al. (U.S. Pub. No. 2021/0292841 A1) teaches techniques of sequencing one or more genes selected to identify one or more protein damaging or loss of function variants in a human subject suspected of having or developing endometriosis; and administering an endometriosis therapy to the human subject. In these techniques the one or more protein damaging or loss of function variants comprise a stop-gain mutation, a spice-site mutation, a frameshift mutation, a missense mutation, or any combination thereof. However, Albertsen does not teach all of the features of the independent claims.
Hahm et al. (U.S. Pub. No. 2018/0121601 A1) teaches that there are many processing stages for data from DNA (or RNA) sequencing to mapping and aligning to sorting and/or de-duplicating to variant calling, which can vary depending on the primary and/or secondary and/or tertiary processing technologies employed and their applications. However, Hahm does not teach all of the features of the independent claims.
The closest art of record, including the prior art discussed above, each fail to teach, suggest, or render obvious each and every element of the claims as presently arranged in the claims. Further, based on the evidence of record, it appears as though one of ordinary skill in the art at the time of invention would not look to combine these references, or the closest art of record, to arrive at the present claims, without using impermissible hindsight.
Response to Arguments
The arguments submitted with the Reply have been fully considered but are not persuasive.
Applicant argues that claims “recite specific technical implementations that cannot practically be performed in the human mind.” Reply, p. 12. Applicant points to “a machine learning engine, variant validator, machine learning model, and loss-of-function detector” and “variant effect predictor” as the “technical” implementations. Reply, p. 12.
Examiner disagrees. The revised rejections identify features corresponding to the identified abstract idea. These features can be performed by the human mind: for example, an individual could observe the data identified in these limitations, perform the claimed classifications, assignments, or determinations without use of any equipment. To the extent that the claim amendments introduce additional elements, these elements are identified and addressed in the analysis of Step 2A, prong 2 and Step 2B of the Alice test.
Applicant argues that “the claims recite additional elements (such as related to a machine learning engine, variant validator, machine learning model, loss-of-function detector, and Variant Effect Predictor” and that “these additional elements integrate any such alleged abstract ideas into a practical application” because they “can provide an improvement to the technical field of pharmacogenomics and computer systems used in this field.” Reply, p. 14.
Examiner disagrees, because the alleged improvement would be to the science of how a person’s DNA affects their response to medications—not to a particular technology or technological field. In other words, the alleged improvement would be to the abstract idea (i.e., determining biological relationships), not anything relating to the technical aspects of the claimed invention. See SAP Am., Inc. v. InvestPic, LLC, No. 2017-2081, slip op. at 14 (Fed. Cir. Aug. 2, 2018) (“What is needed is an inventive concept in the non-abstract application realm. … [L]imitation of the claims to a particular field of information … does not move the claims out of the realm of abstract ideas.”). Moreover, “[A] claim for a new abstract idea is still an abstract idea.” Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (emphasis added). “[U]nder the Mayo/Alice framework, a claim directed to a newly discovered law of nature (or natural phenomenon or abstract idea) cannot rely on the novelty of that discovery for the inventive concept necessary for patent eligibility ….” Genetic Techs. Ltd. v. Merial L.L.C., 818 F.3d 1369, 1376 (Fed. Cir. 2016) (citations omitted).
Applicant argues that “the claimed subject-matter recites specific computer architecture and components or steps (such as related to a machine learning engine, variant validator, machine learning model, loss-of-function detector, and Variant Effect Predictor (or specific recited computer steps)) that can be used to provide these improvements.” Reply, p. 14.
Examiner disagrees, because as discussed in the rejections, of these elements that recite technical features, they are recited at a high level of generality. Inclusion of these elements (including the way the machine is recited describe the type of learning engine and learning model) amounts to mere instructions to implement the identified abstract ideas on a computer. See M.P.E.P. § 2106.05(f).
Applicant argues that the claims recite significantly more because “the claimed subject-matter is not merely well-understood, routine, or conventional computer activity” and the combination of elements in the claims “represents a specific, unconventional technical arrangement.” Reply, p. 15.
Examiner disagrees. “the relevant inquiry [under step two of the Mayo/Alice framework (i.e., step 2B)] is not whether the claimed invention as a whole is unconventional or non-routine.” BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1290 (Fed. Cir. 2018). Instead, the question is whether the claim includes additional elements, i.e., elements other than the abstract idea itself, that “‘transform the nature of the claim’ into a patent-eligible application.” Alice Corp., 573 U.S. at 217 (quoting Mayo, 566 U.S. at 78). See also Mayo, 566 U.S. at 72-73 (requiring that “a process that focuses upon the use of a natural law also contain other elements or a combination of elements, sometimes referred to as an ‘inventive concept,’ sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the natural law itself” (emphasis added)). The additional elements do not “transform the nature of the claim” as required, because inclusion of the additional elements in the claims (including the way the machine is recited describe the type of learning engine and learning model) amounts to mere instructions to implement the identified abstract ideas on a computer. See M.P.E.P. § 2106.05(f).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. The following references have been cited to further show the state of the art with respect to personalized medicine in pharmacogenetics.
Hatchwell et al. (U.S. Pub. No. 2019/0071726 A1);
Xu et al. (U.S. Pub. No. 2021/0125689 A1);
Lopes et al. (“Targeted genotyping in clinical pharmacogenomics: what is missing?” The Journal of Molecular Diagnostics 24.3 (2022): 253-261).
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/CHRISTOPHER B TOKARCZYK/Primary Examiner, Art Unit 3687
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