DETAILED ACTION
Claims 1-16 are pending and have been examined.
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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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/18/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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.
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Claims 1-16 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more
Step 1: Claims 1-8 recite a method. Claims 9-16 recite a system comprising an electronic controller. Therefore, claims 1-8 are directed to a process, and claims 9-16 are directed to a machine.
With respect to claims 1 and 9:
2A Prong 1: The claim recites a judicial exception.
selecting a set of variants including a specific variant of interest (mental process – evaluation or judgement)
calculating an empirical prior estimate of disease penetrance based on a number of individuals from the plurality of individuals that have both the disease and at least one variant of the set of variants, and a number of individuals from the plurality of individuals that have at least one variant of the set of variants (mental process – evaluation, or mathematical concepts – mathematical calculations, in light of specification [0036]-[0038] “α_prior and β_prior in equation (1)” [0056]-[0057])
calculating a posterior penetrance estimate for the specific variant of interest based on the empirical prior estimate, a number of individuals from the plurality of individuals that have both the disease and the specific variant of interest, and a number of individuals from the plurality of individuals that have the specific variant of interest (mental process – evaluation, or mathematical concepts – mathematical calculations, in light of specification [0036] “Mean Posterior Penetrance = … (1)”-[0038] [0056]-[0057])
fitting an estimated penetrance for the specific variant of interest to the posterior penetrance estimate (mental process – evaluation, or mathematical concepts – mathematical calculations, in light of specification [0040] “by fitting a regression model of the estimated penetrance based on variant-specific features (e.g., equation (2))… ”)
defining a set of revised variant-specific priors based on the fitting (mental process – evaluation, or mathematical concepts – mathematical calculations, in light of specification [0040] “The variant-specific priors (i.e., the probability correlation of the variant-specific features to the disease) are then revised based on the regression model (step 111) and the posterior penetrance estimate (equation (1)) is recalculated… ”)
recalculating the posterior penetrance estimate based on the set of revised variant-specific priors (mental process – evaluation, or mathematical concepts – mathematical calculations, in light of specification [0040] “the posterior penetrance estimate (equation (1)) is recalculated based on the updated variant-specific priors (step 113)” [0056]-[0057])
terminating the recursive regression modeling in response to determining that a most recent iteration of the recursive regression modeling satisfies one or more defined exit criteria (mental process – evaluation or judgement)
determining the probability of the disease occurring in a patient that has the specific variant of interest based on the posterior penetrance estimate as determined by the recursive regression modeling (mental process – evaluation or judgement)
2A Prong 2: The judicial exception is not integrated into a practical application.
accessing… a database including genetic and disease data for each of a plurality of individuals (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering)
(claim 1) by a computer-based system… by the computer-based system, (claim 9) the system comprising an electronic controller configured to (mere instructions to apply an exception – MPEP 2106.05(f), (2) invoking generic computer components)
applying... a recursive regression modeling to the posterior penetrance estimate, wherein the recursive regression modeling includes (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
accessing… a database including genetic and disease data for each of a plurality of individuals (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering, and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i))
(claim 1) by a computer-based system… by the computer-based system, (claim 9) the system comprising an electronic controller configured to (mere instructions to apply an exception – MPEP 2106.05(f), (2) invoking generic computer components)
applying... a recursive regression modeling to the posterior penetrance estimate, wherein the recursive regression modeling includes (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claims 2 and 10:
2A Prong 1: The claim recites a judicial exception.
wherein calculating the posterior penetrance estimate includes calculating the posterior penetrance estimate as posterior penetrance estimate_i = αi + α_prior / αi + α_prior + βi + β_prior wherein αi is the number of individuals from the plurality of individuals that have both the disease and the specific variant of interest, βi is the number of individuals from the plurality of individuals that have the specific variant of interest and not the disease (mental process – evaluation, or mathematical concepts – mathematical calculation)
α_prior / α_prior + β_prior is a mean disease penetrance observed across all variants of the set of variants, α_prior is a Bayesian prior that biases the posterior penetrance estimate towards the mean disease penetrance observed across all variants of the set of variants, and β_prior is another Bayesian prior that biases the posterior penetrance estimate towards the mean disease penetrance observed across all variants of the set of variants (mental process – evaluation, or mathematical concepts – mathematical calculation)
With respect to claims 3 and 11:
2A Prong 1: The claim recites a judicial exception.
wherein recalculating the posterior penetrance estimate based on the set of revised variant-specific priors includes recalculating the posterior penetrance estimate as posterior penetrance estimate_i = αi + α_i, prior / αi + α_i, prior + βi + β_i, prior wherein α_i,prior is an updated estimate of individuals having both the specific variant in questions and the disease based on the iteration of the recursive regression modeling, and βi,prior is an updated estimate of individuals having the specific variant in question and not the disease based on the iteration of the recursive regression modeling (mental process – evaluation, or mathematical concepts – mathematical calculation)
With respect to claims 4 and 12:
2A Prong 1: The claim recites a judicial exception.
wherein determining that the most recent iteration of the recursive regression modeling satisfies the one or more defined exist criteria includes determining that a mean penetrance calculated by the posterior penetrance estimate has changed by less than 10 % as a result of the most recent iteration (mental process – evaluation or judgement, defining the exist criteria)
With respect to claims 5 and 13:
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein applying, by the computer-based system, the recursive regression modeling to the posterior penetrance estimate includes applying a linear regression modeling with an expectation maximization (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein applying, by the computer-based system, the recursive regression modeling to the posterior penetrance estimate includes applying a linear regression modeling with an expectation maximization (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claims 6 and 14:
2A Prong 1: The claim recites a judicial exception.
wherein determining the probability of the disease occurring in the patient that has the specific variant of interest includes (mental process – evaluation or judgement)
assigning to the specific variant of interest a relative classification of disease probability based on the posterior penetrance estimate (mental process – evaluation or judgement)
assigning to a patient having the specific variant of interest a probability of the disease occurring based on the relative classification of disease probability assigned to the specific variant of interest (mental process – evaluation or judgement)
With respect to claims 7 and 15:
2A Prong 1: The claim recites a judicial exception.
wherein assigning to the specific variant of interest the relative classification of disease probability includes assigning a relative classification selected from a group consisting of benign, mild risk, and pathogenic (mental process – evaluation or judgement)
With respect to claims 8 and 16:
2A Prong 1: The claim recites a judicial exception.
wherein determining the probability of the disease occurring in the patient includes (mental process – evaluation or judgement)
2A Prong 2: The judicial exception is not integrated into a practical application.
automatically searching, by the computer-based system, a set of electronic health records for occurrences of the specific variant of interest (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
notifying a health care provider for each patient with a detected occurrence of the specific variant of interest of the probability of disease occurring for each detected occurrence of the specific variant of interest by at least one selected from a group consisting of updating an electronic health record to include an indication of the determined probability of the disease associated with the specific variant of interest and transmitting a notification to the health care provider including the indication of the determined probability of the disease associated with the specific variant of interest (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
automatically searching, by the computer-based system, a set of electronic health records for occurrences of the specific variant of interest (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
notifying a health care provider for each patient with a detected occurrence of the specific variant of interest of the probability of disease occurring for each detected occurrence of the specific variant of interest by at least one selected from a group consisting of updating an electronic health record to include an indication of the determined probability of the disease associated with the specific variant of interest and transmitting a notification to the health care provider including the indication of the determined probability of the disease associated with the specific variant of interest (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting; , and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i))
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
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 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 1-16 rejected under 35 U.S.C. 103 as being unpatentable over Kroncke ("A Bayesian method to estimate variant-induced disease penetrance" 20200622) in view of Gottesman ("The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future" 20130418)
Note that the publication date of Kroncke is within one year before the effective filing date of the claimed invention. However, the disclosure notes additional persons as joint inventors. See MPEP 2153.01(a). (If, however, the application names fewer joint inventors than a publication (e.g., the application names as joint inventors A and B, and the publication names as authors A, B and C), it would not be readily apparent from the publication that it is an inventor-originated disclosure)
In regard to claims 1 and 9, Kroncke teaches: A method of assessing a probability of a disease occurring in a patient, the method comprising: (Kroncke, p. 1, Abstract "Here we propose a framework to estimate the probability of disease given the presence of a genetic variant conditioned on features of that variant.")
selecting a set of variants including a specific variant of interest; (Kroncke, p. 9, Limitations "Our approach provides a risk score for disease, in this case BrS1, analogous to a diagnostic test... One application of our approach is that we can examine the ratio P(BrS1|SCN5A Variant X)/P(BrS1|wild-type SCN5A) [a set of variants including a specific variant of interest, e.g. SCN5A Variant X and wild-type SCN5A] to see if the data better support that variant X is on the causal pathway to disease."; p. 8, A modified Bayesian approach to estimate BrS penetrance "We put forward a method to estimate the probability that an SCN5A variant will manifest in BrS1 for a given patient (our ‘risk score’), and uncertainty for that score, conditioned on variant attributes."; p. 2, Introduction "By quantitatively integrating multiple features, including in vitro functional experiments, information about the three-dimensional protein structure, and previously published variant-classifiers, such as PolyPhen-2 and PROVEAN, we estimate the BrS1 penetrance attributable to individual SCN5A variants."))
accessing… a database including genetic and disease data for each of a plurality of individuals; (Kroncke, p. 10, Collection of the SCN5A variant dataset "We supplemented this dataset with all SCN5A variants in the gnomAD database of population variation [a database including genetic and disease data] (http://gnomad.broadinstitute.org/; release 2.0) [33].")
calculating an empirical prior estimate of disease penetrance based on (Kroncke, p. 9, Materials and methods "we can estimate BrS1 penetrance for each variant... ... + α_prior / ... + α_prior + β_prior... Eq1... by adding BrS1 cases and controls each variant to the empirical prior, [an empirical prior] ... + α_prior, empirical / ... + α_prior, empirical + β_prior, empirical..."; p. 11, Initial Empirical Bayes beta-binomial prior penetrance calculation "To calculate the empirical BrS1 penetrance prior, [an empirical prior estimate] we calculated α_prior, empirical and β prior, empirical by finding the weighted mean penetrance over all variants in the dataset and estimating the variance."; also see Kroncke_2 figure 1)
a number of individuals from the plurality of individuals that have both the disease and at least one variant of the set of variants, and (Kroncke, p. 3, Precision and accuracy of BrS1 penetrance priors "where α_prior [a number of individuals] and β_prior are the tuning parameters for the beta-binomial distribution and are set equivalent to the number of affected and unaffected individual heterozygotes in the prior"; p. 9, Materials and methods "Where α is the number of variant heterozygotes diagnosed with BrS1 (or BrS1 cases) [both the disease and one variant]...")
a number of individuals from the plurality of individuals that have at least one variant of the set of variants; (Kroncke, p. 3, Precision and accuracy of BrS1 penetrance priors "where α_prior and β_prior [a number of individuals] are the tuning parameters for the beta-binomial distribution and are set equivalent to the number of affected and unaffected individual heterozygotes in the prior"; p. 9, Materials and methods "… β is the number of unaffected heterozygotes of the same variant (or controls) [one variant]")
calculating a posterior penetrance estimate for the specific variant of interest based on the empirical prior estimate, a number of individuals from the plurality of individuals that have both the disease and the specific variant of interest, and a number of individuals from the plurality of individuals that have the specific variant of interest; (Kroncke, p. 3, Precision and accuracy of BrS1 penetrance priors "the BrS1 mean posterior, BrS1 cases + α_prior / total heterozygotes + α_prior + β_prior"; p. 9 Materials and methods "we can estimate BrS1 penetrance for each variant as the average posterior penetrance [a posterior penetrance estimate] denoted as the following: Mean Posterior Penetrance = α + α_prior / α + β + α_prior + β_prior Eq1 Where α [a number of individuals] is the number of variant heterozygotes diagnosed with BrS1 (or BrS1 cases) [both the disease and one variant] and β [a number of individuals] is the number of unaffected heterozygotes of the same variant (or controls) [one variant]"; p. 11, Initial Empirical Bayes beta-binomial prior penetrance calculation "The variant-specific empirical posterior for each variant was then calculated by adding observed heterozygote counts of affected (BrS1 cases) and unaffected to α_prior, empirical and β prior, empirical, [the empirical prior estimate] respectively...")
applying... a recursive regression modeling to the posterior penetrance estimate, wherein the recursive regression modeling includes (Kroncke, p. 9 Materials and methods "we use a variation of the expectation maximization (EM) algorithm [32]. Our modified EM algorithm is an iterative technique [a recursive modeling] composed of three steps: 1) calculate the expected penetrance from an empirical Bayes penetrance model 2) fit a regression model... [regression]... The fitted model is then used to generate an updated prior distribution and, by addition of observed cases and controls for each variant, a subsequent posterior expected penetrance. The updated posterior penetrance is then used to build a new fitted model and further refine the posterior expected penetrance. [to the posterior penetrance estimate]")
fitting an estimated penetrance for the specific variant of interest to the posterior penetrance estimate, (Kroncke, p. 9 Materials and methods "2) fit a regression model of our estimated penetrance [an estimated penetrance] on variant-specific characteristics [the specific variant of interest] by maximum likelihood (Eq 2, below)… The updated posterior penetrance is then used to build a new fitted model and further refine the posterior expected penetrance. [to the posterior penetrance estimate]")
defining a set of revised variant-specific priors based on the fitting, (Kroncke, p. 9 Materials and methods "3) revise our estimate of the BrS1 penetrance prior [a set of revised variant-specific priors] using the fit from step 2 then iterate steps 2–3 until convergence criteria are satisfied (S7 Fig).")
recalculating the posterior penetrance estimate based on the set of revised variant-specific priors, and (Kroncke, p. 9 Materials and methods "3) revise our estimate of the BrS1 penetrance prior [based on the set of revised variant-specific priors] using the fit from step 2 then iterate steps 2–3 until convergence criteria are satisfied (S7 Fig)... The fitted model is then used to generate an updated prior distribution and, by addition of observed cases and controls for each variant, a subsequent posterior expected penetrance. The updated posterior penetrance [recalculating the posterior penetrance estimate] is then used to build a new fitted model and further refine the posterior expected penetrance.")
terminating the recursive regression modeling in response to determining that a most recent iteration of the recursive regression modeling satisfies one or more defined exit criteria; and (Kroncke, p. 9 Materials and methods " 3) revise our estimate of the BrS1 penetrance prior using the fit from step 2 then iterate steps 2–3 until convergence criteria are satisfied [terminating the recursive regression modeling] (S7 Fig).")
determining the probability of the disease occurring in a patient that has the specific variant of interest based on the posterior penetrance estimate as determined by the recursive regression modeling. (Kroncke, p. 9 Materials and methods "These analyses focus on the SCN5A gene, where individual variants are known to influence the clinical presentation of the autosomal dominant arrhythmia Brugada Syndrome (BrS1) [16, 17]… we can estimate BrS1 penetrance for each variant as the average posterior penetrance [the posterior penetrance estimate, the probability of the disease] denoted as the following: Mean Posterior Penetrance = α + α_prior / α + β + α_prior + β_prior Eq1 ")
Kroncke does not teach, but Gottesman teaches: by a computer-based system… by the computer-based system, (Gottesman, p. 762, Figure 2 "Outline of the activities in the eMERGE Network. [a computer-based system] The main activities of the network and how they are integrated together are summarized. See text for details. eMERGE, Electronic Medical Records and Genomics; EMR, electronic medical record"; GWAS, genome-wide association studies." p. 765, Genomics workgroup: genotype imputation to facilitate network-wide genetic studies "The imputation process for eMERGE-II consumed ~1.1 × 106 CPU h."; eMERGE Network/sites and CPU inherently teaches all the computer components)
Claim 9 recites substantially the same limitation as claim 1, therefore the rejection applied to claim 1 also apply to claim 9. In addition, Gottesman teaches: A system for assessing a probability of a disease occurring in a patient, the system comprising an electronic controller configured to: (Gottesman, p. 762, Figure 2 "Outline of the activities in the eMERGE Network. The main activities of the network and how they are integrated together are summarized. See text for details. eMERGE, Electronic Medical Records and Genomics; EMR, electronic medical record"; GWAS, genome-wide association studies." p. 765, Genomics workgroup: genotype imputation to facilitate network-wide genetic studies "The imputation process for eMERGE-II consumed ~1.1 × 106 CPU h."; eMERGE Network/sites and CPU inherently teaches all the computer components)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Kroncke to incorporate the teachings of Gottesman by including a system of eMERGE in the fields of genomics and informatics. Doing so would provide a powerful and cost-effective tool for genomics research and improvements in health care. (Gottesman, p. 768 "eMERGE has made great strides in the fields of genomics and informatics, contributing significantly to the now-established notion that the EMR is a powerful and cost-effective tool for genomics research... It is hoped that this will result in improvements in health care, through safer and more effective prescribing, augmentation of primary and secondary prevention strategies, and enhanced understanding of the biology of disease.")
In regard to claims 2 and 10, Kroncke teaches: wherein calculating the posterior penetrance estimate includes calculating the posterior penetrance estimate as (Kroncke, p. 1, Abstract "Here we propose a framework to estimate the probability of disease given the presence of a genetic variant conditioned on features of that variant.")
posterior penetrance estimate_i = αi + α_prior / αi + α_prior + βi + β_prior (Kroncke, p. 3, Precision and accuracy of BrS1 penetrance priors "the BrS1 mean posterior, BrS1 cases + α_prior / total heterozygotes + α_prior + β_prior"; p. 9 Materials and methods "we can estimate BrS1 penetrance for each variant as the average posterior penetrance [the posterior penetrance estimate] denoted as the following: Mean Posterior Penetrance = α + α_prior / α + β + α_prior + β_prior Eq1")
wherein αi is the number of individuals from the plurality of individuals that have both the disease and the specific variant of interest, βi is the number of individuals from the plurality of individuals that have the specific variant of interest and not the disease, (Kroncke, p. 3, Precision and accuracy of BrS1 penetrance priors "Where α [the number of individuals] is the number of variant heterozygotes diagnosed with BrS1 (or BrS1 cases) [both the disease and the variant] and β [the number of individuals] is the number of unaffected [not the disease] heterozygotes of the same variant (or controls) [the variant]") α_prior / α_prior + β_prior is a mean disease penetrance observed across all variants of the set of variants, (Kroncke, p. 9, Materials and methods "our EM penetrance priors, α_prior,EM / α_prior,EM + β_prior,EM...") α_prior is a Bayesian prior that biases the posterior penetrance estimate towards the mean disease penetrance observed across all variants of the set of variants, and β_prior is another Bayesian prior that biases the posterior penetrance estimate towards the mean disease penetrance observed across all variants of the set of variants. (Kroncke, p. 3, Precision and accuracy of BrS1 penetrance priors "where α_prior and β_prior [a Bayesian prior, another Bayesian prior] are the tuning parameters for the beta-binomial distribution and are set equivalent to the number of affected and unaffected individual heterozygotes in the prior"; p. 11, Initial Empirical Bayes beta-binomial prior penetrance calculation "The variant-specific empirical posterior for each variant was then calculated by adding observed heterozygote counts of affected (BrS1 cases) and unaffected to α_prior, empirical and β prior, empirical, respectively...")
In regard to claims 3 and 11, Kroncke teaches: wherein recalculating the posterior penetrance estimate based on the set of revised variant-specific priors includes recalculating the posterior penetrance estimate as posterior penetrance estimate_i = αi + α_i, prior / αi + α_i, prior + βi + β_i, prior wherein α_i,prior is an updated estimate of individuals having both the specific variant in questions and the disease based on the iteration of the recursive regression modeling, and βi,prior is an updated estimate of individuals having the specific variant in question and not the disease based on the iteration of the recursive regression modeling. (Kroncke, p. 8, A modified Bayesian approach to estimate BrS penetrance "The resulting penetrance and uncertainty estimates yield a posterior that can be re-used as variant-specific prior (interpretable as equivalent to hypothetical observations of affected and unaffected heterozygotes) in a classical Bayesian updating scheme."; p. 9 Materials and methods "The fitted model is then used to generate an updated prior distribution and, by addition of observed cases and controls for each variant, a subsequent posterior expected penetrance. The updated posterior penetrance is then used to build a new fitted model and further refine the posterior expected penetrance."; recalculating posterior mean penetrance and updating parameters iteratively)
In regard to claims 4 and 12, Kroncke teaches: wherein determining that the most recent iteration of the recursive regression modeling satisfies the one or more defined exist criteria includes determining that a mean penetrance calculated by the posterior penetrance estimate has changed by less than 10 % as a result of the most recent iteration. (Kroncke, p. 11, Expectation maximization Bayesian beta-binomial penetrance predictions "The models were built with a linear regression pattern-mixture algorithm, updating posterior mean penetrances iteratively until the resulting estimated mean penetrance..., changed by < 0.01% from the previous iteration. [less than a threshold as a result of the most recent iteration]")
In regard to claims 5 and 13, Kroncke teaches: wherein applying, by the computer-based system, the recursive regression modeling to the posterior penetrance estimate includes applying a linear regression modeling with an expectation maximization. (Kroncke, p. 11, Expectation maximization Bayesian beta-binomial penetrance predictions "The models were built with a linear regression pattern-mixture algorithm, updating posterior mean penetrances iteratively until the resulting estimated mean penetrance... For variant, i, the variance was estimated from this converged EM mean penetrance according to...")
In regard to claims 6 and 14, Kroncke teaches: wherein determining the probability of the disease occurring in the patient that has the specific variant of interest includes (Kroncke, p. 1, Abstract "Here we propose a framework to estimate the probability of disease given the presence of a genetic variant conditioned on features of that variant.")
assigning to the specific variant of interest a relative classification of disease probability based on the posterior penetrance estimate, and (Kroncke, p. 2, Introduction "Given the resulting probabilities, [based on the posterior penetrance estimate] a final classification [assigning a relative classification] can be made into one of the five categories commonly used to distinguish variants—benign, likely benign, variant of uncertain significance, likely pathogenic, or pathogenic.")
assigning to a patient having the specific variant of interest a probability of the disease occurring based on the relative classification of disease probability assigned to the specific variant of interest. (Kroncke, p. 4, Fig 1 "Penetrance priors are informed by variant-specific features. Probability density (y-axis) versus penetrance (x-axis) for three selected SCN5A variants where peak current, penetrance density, and in silico classification are known. Numbers of affected and unaffected individuals reported are presented for each variant. Penetrance priors are low for c.3922C>T (p.Leu1308Phe; Benign according to ClinVar), moderate for c.4978A>G (p.Ile1660Val; VUS), and higher for c.2632C>T (p. Arg878Cys; Pathogenic). When variant-specific data are known, [based on the relative classification] the penetrance estimate is adjusted [assigning a probability] to reflect the penetrance probability consistent with variants with similar features.")
In regard to claims 7 and 15, Kroncke teaches: wherein assigning to the specific variant of interest the relative classification of disease probability includes assigning a relative classification selected from a group consisting of benign, mild risk, and pathogenic. (Kroncke, p. 2, Introduction "Given the resulting probabilities, a final classification can be made into one of the five categories commonly used to distinguish variants—benign, likely benign, variant of uncertain significance, likely pathogenic, or pathogenic. [a group consisting of benign, mild risk, and pathogenic]")
In regard to claims 8 and 16, Kroncke teaches: wherein determining the probability of the disease occurring in the patient includes: (Kroncke, p. 1, Abstract "Here we propose a framework to estimate the probability of disease given the presence of a genetic variant conditioned on features of that variant.")
Kroncke does not teach, but Gottesman teaches: automatically searching, by the computer-based system, a set of electronic health records for occurrences of the specific variant of interest, and (Gottesman, p. 761, Abstract "The Electronic Medical Records and Genomics Network is a National Human Genome Research Institute–funded consortium engaged in the development of methods and best practices for using the electronic medical record as a tool for genomic research. Now in its sixth year and second funding cycle, and comprising nine research groups and a coordinating center, the network has played a major role in validating the concept that clinical data derived from electronic medical records can be used successfully for genomic research."; p. 765, TRANSITION TO PHASE II (eMERGE-II) "In particular, the new, larger network was interested in broadening its scope from using EMR data for discovery of genotype–phenotype associations all the way through to incorporation of genotype data into the EMR ( Figure 2 ). This would allow the network to assess [eMERGE automatically searching] the utility of these results [a set of electronic health records] in clinical decision making such as informing clinicians of relevant pharmacogenomic (PGx) variants [for occurrences of the specific variant of interest] before a drug is prescribed or identifying persons at high genomic risk for a given
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condition. [determined probability of the disease]")
notifying a health care provider for each patient with a detected occurrence of the specific variant of interest of the probability of disease occurring for each detected occurrence of the specific variant of interest by at least one selected from a group consisting of updating an electronic health record to include an indication of the determined probability of the disease associated with the specific variant of interest and transmitting a notification to the health care provider including the indication of the determined probability of the disease associated with the specific variant of interest. (Gottesman, p. 765, TRANSITION TO PHASE II (eMERGE-II) "This new focus required restructuring of the eMERGE-I workgroups for phase II. eMERGE-II introduced workgroups on EMR integration of genomic information, return of genomic results, and PGx, which was designed to address the complexities of linking genetic variation data with EMRs for effective clinical use as well as to address the difficulties in determining which results to use and how to return these results to participants and providers. [notifying a health care provider for each patient, transmitting a notification]; p. 766, EMR integration workgroup: PGx pilot project "The outcomes measured in eMERGE-PGx will be primarily process outcomes (e.g., number of patients identified with an actionable pharmaceutical genotype, [patients' record including an indication of the disease] number of times a CDS rule fires, percentage of clinicians who follow recommendation, and appropriate changes in medication or dose based on recommendation).")
The rationale for combining the teachings of Kroncke and Gottesman is the same as set forth in the rejection of claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kroncke_2 ("A Bayesian method using sparse data to estimate penetrance of disease-associated genetic variants" 20190307) teaches “The fitted model is then used to generate an updated prior distribution and subsequent posterior expected penetrance and this process is iterated until it converges to the maximum likelihood solution (Figure 1).”
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/SU-TING CHUANG/Examiner, Art Unit 2146