DETAILED ACTION-
Applicant’s response filed 01/08/2026 has been fully considered. The following rejections and/or objections are either reiterated or newly applied.
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
Claim 9 is cancelled by Applicant.
Claims 1-8 and 10-15 are currently pending and are herein under examination.
Claims 1-8 and 10-15 are rejected.
Priority
The instant application claims domestic benefit to US Provisional Application 63/166,048 filed 03/25/2021. The claim to domestic benefit is acknowledged for claims 1-8 and 10-15. As such, the effective filing date for claims 1-8 and 10-15 is 03/25/2021.
Abstract
The objection to the abstract is withdrawn in view of abstract filed 01/08/2026.
Withdrawn Rejections
35 USC 112(b)
The rejection of claims 1-6 and 9 under 35 USC 112(b) is withdrawn in view of claim amendments. However, the rejection of claims 7-8 and 10-15 are maintained as discussed below.
35 USC 102
The rejection of claims 1-15 under 35 USC 102 as being anticipate by Johnson et al. (Polygenic Scores for Plasticity: A New Tool for studying Gene-Environment Interplay) is withdrawn in view of claim amendments filed 08 Jan. 2026 and the Declaration under 37 CFR 1.130(A) filed 01/08/2026.
35 USC 103
The rejection of claims 1-8 and 10-14 under 35 U.S.C. 103 as being unpatentable over Conley et al. in view of Choi et al. is withdrawn in view of claim amendments.
Claim Objections
The objections to claims 1 and 10 are withdrawn in view of claim amendments.
Claim Rejections - 35 USC § 112
35 USC 112(b)
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 1-8 and 10-15 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.
These rejections are either maintained from the previous office action or are newly recited as necessitated by claim amendment.
Claim 1, line 10, recites the phrase “the statistical measure of spread” which renders the claim indefinite. It is unclear which statistical measure of spread is being referenced because a statistical measure of spread is calculated “for each outcome”, as recited in lines 8-9. To overcome this rejection, amend the phrase similar to lines 4-5.
Claim 1, line 15, recites the phrase “the vQTL analysis” which renders the claim indefinite. It is unclear which vQTL analysis is being referenced because a vQTL analysis is calculated “for each outcome”, as recited in lines 13-14. To overcome this rejection, amend the phrase similar to lines 4-5.
Furthermore, claims 2-8 are also rejected because they depend on claim 1, which is rejected, and because they do not resolve the issue of indefiniteness.
Claim 7, line 4, recites the phrase “the additional regression” which renders the claim indefinite. It is unclear which additional regression is being referenced because an additional regression is run for each outcome in claim 7, lines 2-3. To overcome this rejection, clarify which additional regression is referenced.
Claim 7, line 5, recites the phrase “the mean level” which lacks antecedent basis. To overcome this rejection, amend the phrase to “a mean level”.
Claim 7, line 5, recites the phrase “the outcome” which renders the claim indefinite because it is unclear which specific outcome is being referenced out of the “one or more outcomes” recited in claim 7, line 3. To overcome this rejection, specify which outcome is being referenced or change the phrase to “an outcome”.
Claim 7, lines 5-6, recites the phrase “the weights of the additional regression” which renders the claim indefinite. It is unclear which weights of which additional regression are being referenced because in claim 7, lines 2-3, weights are generated for each outcome. To overcome this rejection, clarify which weights are being referenced.
Furthermore, claim 8 is also rejected because it depends on claim 7, which is rejected, and because it does not resolve the issue of indefiniteness.
Claim 10, line 9, recites the phrase “the at least one variation polygenic score” which lacks antecedent basis. To overcome this rejection, change the phrase to “the at least one variance polygenic score”.
Claim 10, lines 13, 17-18 and 22, recites the phrase “the at least one variation polygenic score” which render the claim indefinite. Line 9 recites “at least one variation polygenic score”. It is unclear whether the vPGSs in lines 13, 17-18 and 22 are incorporated into the vPGS in line 9. To overcome the rejection, change the phrases to “the at least one variance polygenic score”.
Claim 10, lines 11-12, recites the phrases “the weights of the regressions” which renders the claim indefinite because it is unclear which weights are being referred to because the phrase “the weights of the regressions” also appears in lines 7-8. To overcome this rejection, clarify that the weights from predicting a statistical measure of spread are being referenced.
Furthermore, claims 11-15 are also rejected because they depend on claim 10, which is rejected, and because they do not resolve the issue of indefiniteness.
Claim 14, line 4, recites the phrase “the additional regression” which renders the claim indefinite. It is unclear which additional regression is being referenced because an additional regression is run for each outcome in claim 14, lines 2-4. To overcome this rejection, clarify which additional regression is referenced.
Claim 14, line 5, recites the phrase “the mean level” which lacks antecedent basis. To overcome this rejection, amend the phrase to “a mean level”.
Claim 14, line 5, recites the phrase “the outcome” which renders the claim indefinite because it is unclear which specific outcome out of the “one or more outcomes” recited in claim 7, line 3, is being referenced. To overcome this rejection, specify which outcome is being referenced or change the phrase to “an outcome”.
Claim 14, lines 5-6, recites the phrase “the weights of the additional regression” which renders the claim indefinite. It is unclear which weights of which additional regression are being referenced because in claim 14, lines 2-4, weights are generated for each outcome. To overcome this rejection, clarify which weights are being referenced.
Furthermore, claim 15 is also rejected because it depends on claim 14, which is rejected, and because it does not resolve the issue of indefiniteness.
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 and 10-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a natural phenomenon without significantly more.
Any newly recited portions herein are necessitated by claim amendment.
Step 2A, Prong 1:
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 phenomena (Step 2A, Prong 1). In the instant application, claims 1-9 recite a method and claims 10-15 recite a system. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claim 1 recites “generating at least one variance polygenic score (vPGS) of phenotypic information by: performing a regression to predict a squared Z-score for each outcome of the phenotypic information, and summing weights from prediction of the squared Z-score for each outcome into a single index for scoring individuals' DNA variants into the at least one variance polygenic score; performing a regression to predict a statistical measure of spread within a familial relationship of the phenotypic information, for each outcome, and summing weights from prediction of the statistical measure of spread into a single index for scoring individuals' DNA variants into the at least one variance polygenic score; performing a mean-variance quantitative trait loci (vQTL) analysis for variance heterogeneity of the phenotypic information, for each outcome, and summing weights of regressions of the vQTL analysis into a single index for scoring individuals' DNA variants into the at least one variance polygenic score; performing a vQTL analysis for variance effects of phenotypic information, and summing weights of regressions of the vQTL analysis into a single index for scoring individuals' DNA variants into the at least one variance polygenic score; or a combination thereof; and selecting at least one participant for a clinical trial based on a comparison of one or more of the at least one vPGS scores to a threshold vPGS.”
Claim 2 recites “further comprising selecting or defining the one or more outcomes, the one or more outcomes being present in the phenotypic information.”
Claim 3 recites “wherein the one or more outcomes comprise height, body mass index, systolic blood pressure, diastolic blood pressure, number of alcoholic drinks consumed per a given time period, number of cigarettes consumed per a given time period, depression symptomology score, cognitive test score, educational attainment, and/or number of children born.”
Claims 4 and 12 recite “wherein generating the at least one vPGS comprises calculating a statistical measure of spread within a familial relationship of phenotypic information, and wherein the statistical measure of spread comprises one of standard deviation, Levene's distance, and range.”
Claims 5 and 13 recite “further comprising determining whether the at least one vPGS can capture genetic contributions to variability in an outcome, distinct from genetic contributions to levels of an outcome.”
Claim 6 recites “applying the weights determined from performing the regression to predict a squared Z-score for each outcome or the regression to predict a statistical measure of spread within a familial relationship of the phenotypic information to a different data set.”
Claims 7 and 14 recite “further comprising generating a mean polygenic score (mPGS) by running an additional regression using the phenotypic information to predict an inverse normal transformation for each outcome of one or more outcomes, such that weights of the additional regression reflect a contribution of each genetic locus of a plurality of genetic loci to the mean level of the outcome, and summing the weights of the additional regression into the mPGS.”
Claims 8 and 15 recite “further comprising comparing the mPGS and vPGS scores.”
Claim 10 recites “generate at least one variance polygenic score (vPGS) by: performing a regression to predict a squared Z-score for each outcome of the phenotypic information, and summing the weights of the regressions into a single index for scoring individuals' DNA variants into the at least one variation polygenic score; performing a regression to predict a statistical measure of spread within a familial relationship of the phenotypic information, and summing the weights of the regressions into a single index for scoring individuals' DNA variants into the at least one variation polygenic score; performing a mean-variance quantitative trait loci (vQTL) analysis for variance heterogeneity of the phenotypic information, and summing weights of regressions of the vQTL analysis into a single index for scoring individuals' DNA variants into the at least one variation polygenic score; performing a vQTL analysis for variance effects of phenotypic information, and summing weights of regressions of the vQTL analysis into a single index for scoring individuals' DNA variants into the at least one variation polygenic score; or a combination thereof; and select at least one participant for a clinical trial based on a comparison of one or more of the at least one vPGS scores to a threshold vPGS.”
Limitations reciting a mental process.
The above cited limitations in claims 1-2, 4-8, 10 and 12-15 are recited at such a high level of generality that they equate to a mental process because they are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), which the courts have identified as concepts that can be practically performed in the human mind. The paragraphs below discuss the limitations in these claims that recite a mental process under their broadest reasonable interpretation (BRI).
The BRI of performing a regression to predict a squared Z-score includes performing mathematical calculations which could be performed by pen and paper. The BRI of performing a regression to predict a statistical measure of spread includes performing calculations using a linear regression and a standard deviation. The BRI of performing a mean-variance vQTL analysis for variance heterogeneity includes performing a median-based Levene’s test. The BRI of performing a mean-variance vQTL analysis for variance effects includes performing the calculations of a heteroskedastic linear mixed model. The BRI of summing weight into an index score includes performing mathematical calculations. The BRI of generating a mean polygenic score includes performing the equation recited in specification para. [107]. The BRI of selecting at least one participant for a clinical trial based on vPGS scores includes analyzing data and making a determination, wherein the clinical trial itself is an intended use but is not required to be performed by the claim.
The BRI of selecting or defining an outcome includes determining an outcome as representing height and selecting it. The BRI of determining that the vPGS captures genetic contributions to variability in an outcome requires making a determination through analyzing data. The BRI of applying the weights from the regressions to a different data set includes using the same models but with different data. The BRI of comparing mPGS and vPGS scores includes comparing values.
Limitations reciting a mathematical concept.
The above cited limitations in claims 1, 4, 7-8, 10, 12 and 14-15 equate to a mathematical concept because they are similar to the concepts of organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)), which the courts have identified as mathematical concepts. The BRI of performing the limitations recited in claims 1, 4, 7, 10, 12 and 14 includes using the functions/equations listed in specification para. [65-69] and [107] as well as the equations for linear regressions, standard deviations, Levene’s distance, and a range. The BRI of claim 6 includes using the same models with summed weights on a different dataset, which necessitates performing calculations. The BRI of claims 8 and 15 includes mathematical relationships of comparing numerical scores.
Limitations reciting a natural phenomenon.
The above cited limitations in claims 1, 4-7, 10, and 12-14 equate a natural phenomenon because these limitations are similar to the concept of a correlation between the presence of myeloperoxidase in a bodily sample (such as blood or plasma) and cardiovascular disease risk, Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1361, 123 USPQ2d 1081, 1087 (Fed. Cir. 2017), which the courts have established as a natural phenomenon. These claims predict variance in phenotypic outcomes such as height and body mass index based on DNA variants.
Limitations included in the judicial exception.
The above cited limitations in claim 3 are included in the judicial exception because they limit the one or more outcomes but do not change the fact the outcomes are part of the judicial exception.
As such, claims 1-8 and 10-15 recite an abstract idea and a natural phenomenon (Step 2A, Prong 1: Yes).
Step 2A, Prong 2:
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). Claims 1-8 do not recite any additional elements because all their limitations recite a judicial exception, and thus claims 1-8 cannot be integrated into a practical application (MPEP § 2106(d).III). Claims 10-15 do recite additional elements, but they are not integrated into a practical application for the following reason: The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, claims 10-15 include limitations that equate to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)) and to insignificant extra-solution activity (MPEP § 2106.05(g)). The instant claims recite the following additional elements:
Claim 10 recites “A system, comprising: a processor; and a non-transitory computer readable storage medium containing instructions that, when executed, cause the processor to:”
Claim 11 recites “wherein the processor is further configured to receive information selecting or defining the one or more outcomes, the one or more outcomes being present in the phenotypic information.”Claims 11-15 recite the phrase “The system according to claim 10”.
Claims 13-15 recite “wherein the processor is further configured to”.
Regarding the above cited limitations in claims 10-15 of the system comprising a processor and non-transitory computer readable storage medium, there are no limitations that the system requires anything other than a generic computer. Therefore, these limitations equate to instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
Regarding the above cited limitation in claim 11 of receiving information, this limitation equates to insignificant, extra-solution activity of necessary data gathering because it acquires data necessary to perform to perform the judicial exceptions in claim 10.
As such, claims 1-8 and 10-15 are directed to an abstract idea and a natural phenomenon (Step 2A, Prong 2: No).
Step 2B:
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). Claims 1-8 do not recite any additional elements because all their limitations recite a judicial exception, and thus claims 1-8 cannot amount to significantly more than the judicial exception itself (MPEP § 2106.05.I). Claims 10-15 do recite additional elements, but these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the following reasons: Claims 10-15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The instant claims recite the following additional elements:
Claim 10 recites “A system, comprising: a processor; and a non-transitory computer readable storage medium containing instructions that, when executed, cause the processor to:”
Claim 11 recites “wherein the processor is further configured to receive information selecting or defining the one or more outcomes, the one or more outcomes being present in the phenotypic information.”
Claims 13-15 recite “wherein the processor is further configured to”.
Regarding the above cited limitation in claims 10-15 of the system comprising a processor and non-transitory computer readable storage medium, there are no limitations that the system requires anything other than a generic computer. Therefore, these limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept in Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Regarding the above cited limitation in claim 10 of a non-transitory computer readable storage medium containing instructions, this limitation equates to storing information in memory, which the courts have established as a WURC function of a generic computer in Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Regarding the above cited limitation in claim 11 of receiving information, this limitation equates to receiving/transmitting data over a network because it is performed by a processor, which the courts have established as WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they all equate to WURC functions/components of a generic computer. Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself (Step 2B: No).
As such, claims 1-8 and 10-15 are not patent eligible.
Response to Arguments under 35 USC 101
Applicant's arguments filed 01/08/2026 have been fully considered but they are not persuasive.
Applicant argues that the claims do not recite a natural phenomenon (pg. 8 last para. of Applicant’s remarks). Applicant’s argument is not persuasive for the following reasons:
A polygenic score predicts a quantitative trait such as height, as recited in claim 2, based on DNA variants. The score correlates genetic information with phenotypic outcomes, which is a natural phenomenon. Even if the claims did not recite a natural phenomenon, they still recite both mental processes and mathematical concepts.
Applicant argues that claims 1 and 10 contain a practical application conferred by the following limitation: “selecting/select at least one participant for a clinical trial based on a comparison of one or more of the at least one vPGS scores to a threshold vPGS” (pg. 9, para. 2 of Applicant’s remarks). Applicant’s argument is not persuasive for the following reasons:
The above cited limitation recites an abstract idea. MPEP 2106.04(d).I recites that additional elements are evaluated for whether they integrate a judicial exception into a practical application. Moreover, the phrase “for a clinical trial” recites an intended use and is thus not required by the claim.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8 and 10-15 are rejected under 35 U.S.C. 103 as being unpatentable over Conley et al. (“Conley”; NPL ref. 16 on IDS filed 03/20/2023; PloS one 13, no. 4 (2018): e0194541; previously cited) in view of Choi et al. (“Choi”; Nature protocols 15, no. 9 (2020): 2759-2772; previously cited on PTO892 mailed 10/08/2025) and Damask et al. (“Damask”; Circulation 141, no. 8 (2020): 624-636; newly cited).
This rejection is newly recited and is necessitated by claim amendment.
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
Claims 1 and 10:
A method for calculating variation in a genetic score, comprising:
A system, comprising: a processor; and a non-transitory computer readable storage medium containing instructions that, when executed, cause the processor to:
Conley discloses a sibling method for identifying variance quantitative trait loci (vQTL) (abstract). Software such as VEGAS1 and PASCAL were used (pg. 23, para. 4), indicating that the method is computer-implemented. Computers inherently contain a processor and memory.
generating/generate at least one variance polygenic score (vPGS) of phenotypic information by:
Conley recites “We can extend the polygenic score approach to develop scores that predict variance in a trait (vPGS). Coefficients for a vPGS construction in a prediction sample would be obtained from a vGWAS” (pg. 17, last para.).
performing a regression to predict a statistical measure of spread within a familial relationship of the phenotypic information, for each outcome and
Conley discloses a sibling standard deviation method (pg. 5, last para.). Conley teaches “The model uses sibling pairs as the unit of analysis and regresses the standard deviation of the sibling pair's trait on the pair's count of minor alleles with sibling pair-level controls that include controls for the mean level of the trait in the sibling pair, parental genotype, pair sex (MM or FM or FF), mean pair age, and the within-pair age difference (for the full model specification, as well as alternative specifications tested, see Methods)” (pg. 5, last para. – pg. 6, para. 1). Height an BMI were used as outcomes (abstract).
summing weights from prediction of the statistical measure of spread into a single index for scoring individuals' DNA variants into the at least one variance polygenic score;
Conley teaches using the sibling method to generate “weights for polygenic scores to predict trait variance” (pg. 17, para. 2). Conley teaches “Coefficients for a vPGS construction in a prediction sample would be obtained from a vGWAS done within families with sibling sets” (pg. 17, last para.).
Although Conley generates weights from regressing the standard deviation of the sibling pair's trait to create a vPGS, Conley does not disclose summing the weights into the vPGS.
Choi discloses a review on generating polygenic risk scores (PRS). Choi defines a PRS “as a single value estimate of an individual’s genetic liability to a phenotype, calculated as a sum of their genome-wide genotypes, weighted by corresponding genotype effect size estimates derived from GWAS summary statistic data” (pg. 2760, col. 1, para. 2). Choi states “In calculating PRSs on a binary (e.g., case/control) phenotype, the effect sizes used as weights are typically reported as log Odds Ratios (log(ORs)). Assuming that relative risks on a disease accumulate on a multiplicative rather than an additive scale, then PRSs should be computed as a summation of log(OR)-weighted genotypes” (pg. 2764, col. 2, last para.). Figure 1 shows how PRS are calculated.
When Conley and Choi are taken together, it appears weights from a vGWAS would be summed to calculate a vPGS, similar to the calculation for a classical PGS.
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Conley for using coefficients generated from vGWAS to create a vPGS by summing the weights of the vGWAS in order to derive the vPGS, as suggested by Choi. The motivation for doing so is taught by Conley who teaches extending polygenic scores to predict variance in a trait by using vGWAS (pg. 17, last para.). There would have been a reasonable expectation of success for the combination because Conley suggests using coefficients of vGWAS to create the vPGS, wherein a PRS is typically generated using weighted sums, as taught by Choi.
selecting at least one participant for a clinical trial based on a comparison of one or more of the at least one vPGS scores to a threshold vPGS.
Conley discloses that the propensity of a trait to vary within a population has clinical significance (abstract). Referring to vPGS, Conley recites “Having a polygenic risk score that predicts particular forms of phenotypic variability may be helpful for researchers hoping for non-null results with respect to a given phenotypic measure who are therefore looking to recruit sensitive subjects for experimentation that involves specific environmental exposures” (pg. 18, para. 1). However, Conley does not teach comparing a vPGS score to a threshold vPGS to select participants for a clinical trial.
Damask uses data from ODYSSEY OUTCOMES trial (evaluation of cardiovascular outcomes after an acute coronary syndrome during treatment with alirocumab) to determine whether high PRS for coronary artery disease identifies higher-risk individuals who might derive greater benefits from alirocumab treatment (abstract). Damask recites “There was a relative risk reduction of major adverse cardiovascular events by alirocumab treatment of 37% in the high PRS patients versus a 13% reduction in the lower PRS patients” (pg. 625, bullet 4 on left side). Damask recites “Baseline disease and medical history characteristics were analyzed to assess the distribution of cardiovascular risk factors by genetic risk status: high (>90th percentile threshold) versus lower (≤90th percentile threshold)” (pg. 626, col. 1, para. 5).
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Conley for using vPGS to select sensitive subjects for research experimentation (pg. 18, para. 1) by comparing vPGS scores to a threshold as taught by Damask. The motivation for doing so is taught by Damask who recites that high PRS patients (> 90th percentile) had a relative risk reduction of 37% compared to only 12% in low PRS patients (<90th percentile) (pg. 625, bullet 4 on left side). When combined with Conley, only high risk (>90th percentile) would be selected for experimentation. One of ordinary skill in the art would have had a reasonable expectation of success because Conley states that vPGS can be used to stratify sensitive subjects for experiments (pg. 18, para. 1), wherein Damask provides an example threshold to use for selecting sensitive patients in a trial.
Claims 2-3 and 11:
Conley collects data from databases that contain Framingham Heart Study and Minnesota Twin Family Study data. These studies contain genotypes, height, weight and BMI data (pg. 6, last para.) (pg. 22, sec. Empirical application – pg. 23, para. 2).
Claims 4 and 12:
Conley teaches that the sibling standard deviation method “uses sibling pairs as the unit of analysis and regresses the standard deviation of the sibling pair's trait on the pair's count of minor alleles” (pg. 5, last para. – pg. 6, para. 1). Height and BMI were used as phenotypic outcomes (abstract). The sibling method can be used to generate “weights for polygenic scores to predict trait variance” (pg. 17, para. 2).
Claims 5 and 13:
Conley teaches, referring to the vPGS, “if a vGWAS of MZ twins and DZ twins were conducted, the results could be differenced out to provide a measure of phenotypic capacitance i.e. regulation of internal, genetic variation” (pg. 18, para. 1). Conley recites “Fig D in S1 File shows that the method is adequately powered to detect effects of SNPs that explain fewer than 1% of the variation in traits in samples like the UK Biobank that could be used at this discovery stage” (pg. 17, last para.).
Claim 6:
Conley teaches generating weights for a vPGS using coefficients from vGWAS (pg. 17, para. 2 & last para.). However, Conley does not teach applying the weights to a different dataset. Choi teaches “Once PRSs have been calculated, selecting from the options described above, typically a regression is then performed in the target sample, with the PRS as a predictor of the target trait or experimental outcome” (pg. 2766, col. 1, para. 4).
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Conley for generating a vPGS, which contains associated weights from vGWAS, by applying the vPGS to a target sample, as taught by Choi. The motivation for doing so is that Conley extends the classical PRS to be a vPGS (pg. 17, last para.), wherein Choi teaches that a classical PRS is typically used with the workflow in Figure 1, which includes generating the PRS and using it as a predictor in additional datasets (pg. 2766, col. 1, para. 4). One of ordinary skill in the art would have had a reasonable expectation of success for applying a vPGS to additional data because Choi states that it can be used in additional datasets (pg. 2766, col. 1, para. 4).
Claims 7-8 and 14-15:
Conley teaches that PGS normally predict mean levels of a trait (pg. 17, last para.) and teaches calculating a vPGS. However, Conley does not teach calculating a mPGS to then compare it against a vPGS.
Choi generates a PRS in Figure 1. Choi defines a PRS “as a single value estimate of an individual’s genetic liability to a phenotype, calculated as a sum of their genome-wide genotypes, weighted by corresponding genotype effect size estimates derived from GWAS summary statistic data” (pg. 2760, col. 1, para. 2). In Figure 1 a PRS score is calculated with a LASSO regression. Phenotype values are inverse normalized before GWAS (pg. 2764, col. 2, para. 4).
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Conley for calculating vPGS to also calculate a mPGS, as taught by Choi, in order to compare the two scores. The motivation for doing so is taught by Conley who states that a classical polygenic score can capture environmental sensitivity (pg. 18, para. 1) and states that previous methods suffered from conflation of mean and variance effects (pg. 16, para. 2). Thus, a calculation and comparison of mPGS and vPGS would elucidate the conflation of mean and variance effects. One of ordinary skill in the art would have had a reasonable expectation of success to calculate a mPGS because Conley discusses extending the classical PGS (pg. 17, last para.) and because Choi discloses how to calculate a classical PRS (Figure 1). There also would have been a reasonable expectation of success to compare the scores because it merely requires evaluating data.
Response to Arguments under 35 USC 103
Applicant's arguments filed 01/08/2026 have been fully considered but they do not contain any arguments for the rejection under 35 USC 103.
Conclusion
No claims are allowed.
Notable but not relied upon prior art includes: Lello et al. (Scientific Reports 10, no. 1 (2020): 13190; newly cited) who calculate mean and variance in PRS and PGS (pg. 7, para. 1).
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.
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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