DETAILED ACTION
Applicant's response, filed 10/17/2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1, 3, 7-11 are pending.
Claims 2, and 4-6 are cancelled.
Claims 1, 3, 7-11 are rejected.
Priority
The instant application claims the benefit of priority to Japanese Patent Application No. 2020-205213, filed 12/10/2020. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 and the claim to foreign priority is acknowledged. As such, the effective filing date of claims 1, 3, 7-11 is 12/10/2020.
Claim Objections
Response to Amendment
In view of applicant’s amendments to the claims previous objections to claims 4 and 7 have been withdrawn.
Claim Rejections - 35 USC § 112
Response to Amendment
In view of applicant’s cancellation of claims 5 and 6, previous rejections under 35 U.S.C. 112(b) are rendered moot.
Claim Rejections - 35 USC § 101
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 101 have been reviewed, updated, and provided below.
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 7-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite an apparatus and method for generating trait prediction models based on summary statistics and inter-polymorphism correlated information. The judicial exception is not integrated into a practical application because while claims 1, 3, 7-11 attempt to integrate the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea or it is insignificant extra solution activity and simply implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well-understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to stator subject matter, specifically apparatus (Claims 1, 3, 7-10) and methods (Claim 11).
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [see MPEP § 2106.04(a)]
The claims herein recite abstract ideas, mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claim 1: Generating a plurality of first trait prediction models is a process that can be performed in a person’s mind and is therefore a mental process. Generating a second trait prediction model based on regularized regression of the first trait prediction model, dividing using correlations a genomic region, and determining a weighted average parameter are merely verbal articulations of mathematical processes and are therefore, mathematical concepts.
Claim 3: The second trait prediction model being defined by a total sum of integrated values of an output value of each of the first trait prediction models and a weighted average parameter, and the processing circuit determining a value of weight to minimize an objective function, are merely a verbal articulation of mathematical processes and is therefore a mathematical concept.
Claim 9: Applying SNP data to each of a plurality of trait prediction models to calculate trait values, and calculating a second trait value based on the first trait values and weighted averages are merely a verbal articulation of mathematical processes and is therefore a mathematical concept.
Claim 10: The processing circuit dividing a single genome region into a plurality of genome regions are steps of evaluating data and making judgements about data that can be practically performed in the human mind and is therefore a mental process. The processing circuit applying the trait prediction models to calculate the first trait values for each region and output a second trait value based on the first set along with weighted average parameters is merely a verbal articulation of mathematical processes and is therefore a mathematical concept.
Claim 11: Generating a plurality of first trait prediction models is a process that can be performed in a person’s mind and is therefore a mental process. Generating a second trait prediction model based on regularized regression of the first trait prediction model, dividing using correlations a genomic region, and determining a weighted average parameter are merely verbal articulations of mathematical processes and are therefore, mathematical concepts.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [see MPEP § 2106.04(d) and MPEP § 2106.05(a)-(c) & (e)-(h)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of non-abstract elements:
Claim 1: An apparatus is generic and nonspecific, and a processing circuit is a general, nonspecific computer element.
Claim 7: Acquiring GWAS statistics as summary statistics is merely collecting data from other research and is therefore mere data gathering.
Claim 8: Acquiring a linkage disequilibrium coefficient is merely collecting data from other research and is therefore mere data gathering.
Claim 9: An apparatus is generic and nonspecific, and a processing circuit is a general, nonspecific computer element. Acquiring SNP data for an individual is merely collecting data from other research and is therefore mere data gathering. Outputting the second trait value is merely collecting data from the process performed and is therefore mere data gathering.
Claim 10: Providing the prediction models and weighted parameters is merely collecting data from other research and is therefore mere data gathering.
Claim 11: Performing the method with a processing circuit is is a general, nonspecific computer element.
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional, nonspecific or insignificant extra solution activities. These additional elements include:
The additional elements of an apparatus and a processing circuit generic and nonspecific, or a general, nonspecific computer element, respectively that are well understood and conventional within the art [see MPEP § 2106.5(d), 2106.05(f) and 2106.05(g)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of acquiring GWAS statistics, acquiring linkage disequilibrium coefficients, outputting the second trait value, and providing the prediction models and weighted parameters, are insignificant extra solution activities, specifically mere data gathering (Gathering and analyzing information using conventional techniques - TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48) [see MPEP § 2106.5(g)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1, 3, 7-11 when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 10/17/2025 have been fully considered but they are not persuasive. Applicant asserts on page 9 of the Remarks filed 10/17/2025 that independent claim 1, and therefore claims 3, and 7-11, are directed to concrete technical improvements and as such is significantly more than the just the abstract idea itself, pointing to paragraph [0025] of the specification “it is possible to generate a prediction model with higher prediction accuracy than the prediction models generated from the results of genome-wide association studies of different race populations”. However, examiner directs applicant to MPEP 2106.05(a) It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements - Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)). Here applicant asserts the improvement to be the prediction accuracy, but that is merely an abstract idea, a mental process, which on its own cannot be the basis for the improvement to a technological field or computer.
Claim Rejections - 35 USC § 103
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 103 have been reviewed, updated, and provided below.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 7-11 are rejected under 35 U.S.C. 103 as being unpatentable over Milton et al. (Frontiers in Genetics (2015) 1-6; previously cited), Iglesias et al. (Computational Geosciences (2015) 177-212; previously cited), Banfield et al. (IEEE Transactions on Pattern Analysis and Machine Intelligence (2007) 173-180; previously cited), Al-Azani et al. (Procedia Computer Science (2017) 359-366; previously cited), Gonzalez-Recio et al. (Livestock Science (2014) 217-231; previously cited), Fan et al. (International Conference on Computer Science and Network Technology (2015) 579-583; previously cited), Seldin et al. (Nature Reviews Genetics (2011) 523-528; previously cited), and Wientjes et al. (Genomic Selection (2013) 621-631; previously cited).
Claim 1 is directed to an apparatus which is configured to generate multiple trait prediction models using summary statistic information, and subsequently from that generate a second trait prediction model based upon the regularization of the ensemble of the previous models.
Claim 7 is directed to the method of claim 1 but further specifies the acquiring of GWAS statistics as summary statistics.
Claim 9 is directed to an apparatus which is configured to generate multiple trait prediction models using summary statistic information, and subsequently from that generate a second trait prediction model based upon the regularization of the ensemble of the previous models.
Claim 11 is directed to a method to generate multiple trait prediction models using summary statistic information, and subsequently from that generate a second trait prediction model based upon the regularization of the ensemble of the previous models.
Milton et al. teaches on page 2, column 1, paragraph 3 “The idea of the ensemble methodology is to build a predictive model by combining predictions from multiple models. Here, we propose an ensemble of M cumulative genetic models in which the predicted value of a phenotype is computed as the average prediction from M genetic models”, on page 3, column 1, subparagraph 4 “The 1000 individuals in each simulated data set were randomly separated into a set of 900 individuals (discovery dataset) and 100 individuals (test dataset) that were used for model building and testing”, in paragraph 1 of the same page and column “This simulation procedure was used to generate 1000 datasets for each combination of heritability and number of causal SNPs” and further in subparagraph 2 of page 2, column 2 “The phenotype was generated from a linear regression model with m = 5, 10,and30causalSNPs(out of S = 1000) with a total variability σ2 Total = 1. Here we define a causal SNP to be a SNP truly associated with the phenotype (a true positive). We chose three different levels of heritability: low (h2 = 0.20), medium (h2 = 0.40), and high (h2= 0.60), and for each h2 we defined the effect size ak for each causal SNP, under a strictly additive model” and on page 1, column 2, paragraph 2 “The method assumes that there is a list of S SNPs ordered by decreasing statistical significance that result from a GWAS”. Further Milton et al. teaches on page 2, column 1, paragraph 1 “This genetic score GSi,N based on N SNPs is used as a covariate in the linear regression model where yi,N is the phenotype of the ith individual and the regression coefficients β0,N, β1,N can be estimated using the Maximum Likelihood (ML) method”.. While Milton et al. does not explicitly recite a computer and thus processing circuit, it is obvious to automate a process by implementing it on a generic computer in order to increase the speed at which the analysis can be performed (see MPEP 2144.04(III)).
Milton et al. does not teach the use of regularization within the ensemble method.
Iglesias et al. teaches in the abstract “We propose the application of iterative regularization for the development of ensemble methods for solving Bayesian inverse problems… In this work, we are interested in the application of the proposed ensemble methods for the solution of Bayesian inverse problems”.
Banfield et al. teaches on page 1, column 2, paragraph 2 “an approach based on average algorithm rank was argued as the best way to evaluate multiple algorithms on multiple data sets”.
Banfield et al. does not teach a reference value for the summary statistic information.
Al-Azani et al. teaches on in the abstract “The purpose of this study is to compare the performance of different classifiers for polarity determination in highly imbalanced short text datasets using features learned by word embedding rather than hand-crafted features”.
Gonzalez-Recio et al. teaches in section 2.1 Reproducing Kernel Hilbert Space Regressions (paragraph 2), 2.2 Support Vector Machines (paragraph 1) and section 2.4 Boosting (paragraph 1), the direct use of loss functions and in section 2.3 Artificial Neural Networks (paragraph 4), the concept of error back-propagation, all of which are loss functions to iteratively reduce error.
Gonzalez-Recio et al. does not teach that the regularization contains the sum of the L1 and L2 term.
Fan et al. teaches on page 580, column 2, equation 5 the concept of the elastic net, which is a penalized regression function that takes into account the sum of the L1 and L2 norms.
Seldin et al. teaches on page 523, column 2, paragraph 2 “Importantly, admixture between different continental populations also creates mosaic chromosomes containing segments of distinct ancestry, which we refer to as local ancestry. A causal risk allele with large allele frequency differences between ancestral populations — such as those that may exist for a disease with varying prevalence among populations — leads to deviations in local ancestry at the causal locus. Thus, local ancestry estimates can be used for admixture mapping, in which disease cases from an admixed population are scanned for loci with unusual deviations in local ancestry… the advent of genome-wide association studies (GWASs) has led to new techniques for local ancestry inference and raises some additional challenges, such as combining SNP and admixture association signals, optimizing genotype imputation and fine-mapping causal variants”.
Wientjes et al. teaches in the abstract “In this study, we investigated the effects of LD and family relationships on reliability of genomic predictions and the potential of deterministic formulas to predict reliability using population parameters in populations with complex family structures”, reading on the inter-polymorphism correlated information representing linkage disequilibrium.
It would have been obvious at the time of invention to a person skilled in the art to combine the teachings of Milton et al. for the ensemble method of trait prediction, with the teachings of Iglesias et al. for the use of regularization within the ensemble method. This is because while Milton does not expressly teach the use of summary statistic data nor the use of inter-polymorphism correlated information, it does teach the use of “a list of S SNPs ordered by decreasing statistical significance that result from a GWAS”, which would be summary statistics, which in turn would contain p-values and effect size estimates that inherently contain inter-polymorphism correlated information. Further, Milton et al. describes the model they are using from the perspective of a genetic score for an individual that can calculate the maximum likelihood of their trait which would assume the use of SNP information for an individual, which would also assume outputting said information.
Additionally, inverse problems are merely problems for which you are calculating or determining unknown parameters based on observed data, such as the optimal weights for model combination, and as Iglesias et al. points out on page 1, column 2, paragraph 1 “Our numerical experiments showcase the advantage of using iterative regularization for obtaining more robust and stable approximation of the posterior than unregularized methods”.
Furthermore, it would have been obvious at the time of invention to a person skilled in the art to combine the teachings of Milton et al. and Iglesias et al., with the teachings of Banfield et al. for the use of multiple algorithms in an ensemble, and the teachings of Al-Azani et al. for the use of embeddings. This is because as Banfield suggests on page 1, column 2, paragraph 2 the use of an average algorithm rank “allows for a summary decision to be made on statistically significant performance differences over the whole group of data sets”. Additionally, through the use of embeddings Al-Azani et al. found that “applying word embedding with ensemble and SMOTE can achieve more than 15% improvement on average in F1 score over the baseline”, meaning the use of embeddings can allow for higher precision-recall. While the papers are very different in their datasets they are working with, the overall theme here is the improvement of machine learning models, irrespective of the data they work with.
It would have been additionally obvious at the time of invention to a person skilled in the art to combine the teachings of Milton et al. and Iglesias et al., with the teachings of Gonzalez-Recio et al. for the use of a loss function and the teachings of Fan et al. for the use of the elastic net penalty as the former is specifically employed to iteratively train and update models based upon a mathematical formula, which is the whole point of the second trait prediction model, and the latter specifically states on page 579, column 2, paragraph 2 “Ridge regression was a biased estimate prompted by Hotel and Kennard in 1970, coming from OLS”, and on page 580, column 2, paragraph 2 “Lasso is not robust to extreme correlations among the predictors… Elastic net[10] combines Lasso and ridge regression, that is, it uses a mixture of L1-penalty and L2-penalty”.
Additionally, it would have been obvious at the time of invention to a person skilled in the art to combine the teachings of Milton et al. and Iglesias et al. for the apparatus of claims 1, 9, and 11, with the teachings of Seldin et al. for the use of local ancestry, or local regions, to effectively break the genome into segments based upon ancestry (correlation) and perform the method taught by Milton et al. and Iglesias et al. for claims 1, 9 and 11. This is because as Seldin et al. points out on page 1, column 1, paragraph 2 “Although genetic differences between populations represent only a small fraction of genetic variation, many simple and complex diseases have a prevalence that varies substantially with genetic ancestry owing to genetic and/or environmental factors…Studies of natural selection suggest that many genetic variants have been positively selected over the past several thousand years and that many of these are unique to particular continental groups. Thus, recently admixed populations are likely to harbor a larger number of genetic variants that have functional effects”.
Finally, it would have been obvious at the time of invention to a person skilled in the art to combine the teachings of Milton et al., Iglesias et al., Banfield et al., Al-Azani et al., Gonzalez-Recio et al., Fan et al., and Seldin et al., with the teachings of Wientjes et al. for the incorporation of LD into the apparatus, as the latter states “Compared to within-breed genomic prediction, reliability of across-breed predictions may be lower due to differences in allele frequencies, LD pattern, and haplotypes among breeds”, therefore, suggesting the need for such information capture within the model to accurately predict across populations. One would have had a reasonable expectation of success given that such information is routinely included in such methods and as Wientjes et al. points out on page 1, column 2, paragraph 1-2 “Many studies demonstrated higher reliabilities for direct genomic breeding values compared to breeding values based on pedigree information only, especially for juvenile individuals without phenotypic information. The response to genomic selection relies on linkage disequilibrium (LD) between specific alleles of SNPs and quantitative trait loci (QTL)”.
One would have had a reasonable expectation of success given that these types of machine learning techniques fit together much the same as any building block, combining and chaining them together in multiple ways, four of the papers are merely review papers of current methods within machine learning that can be fit into any model architecture, and Seldin et al. not only insinuates the use as such but also provides reference to such studies on page 523, column 3, paragraph 1. Therefore, it would be obvious to a person with ordinary skill in the art to incorporate the teachings of each and to be successful.
Claim 3 is directed to the apparatus of claim 1 but further specifies the use of a weighted average and a regularization term
Milton et al. and Iglesias et al. teach the apparatus of claims 1, 9 and 11 as previously described.
Milton et al. and Iglesias et al. do not teach the use of a regularization term nor specifically the use of the sum of the L1 and L2 terms.
Gonzalez-Recio et al. teaches in section 2.1 Reproducing Kernel Hilbert Space Regressions (paragraph 2), 2.2 Support Vector Machines (paragraph 1) and section 2.4 Boosting (paragraph 1), the direct use of loss functions and in section 2.3 Artificial Neural Networks (paragraph 4), the concept of error back-propagation, all of which are loss functions to iteratively reduce error.
Gonzalez-Recio et al. does not teach that the regularization contains the sum of the L1 and L2 term.
Fan et al. teaches on page 580, column 2, equation 5 the concept of the elastic net, which is a penalized regression function that takes into account the sum of the L1 and L2 norms.
Claim 8 is directed to the method of claim 1 but further specifies the acquiring of a linkage disequilibrium coefficient.
Milton et al. and Iglesias et al. teach the apparatus of claims 1, 9 and 11 as previously described.
Milton et al. and Iglesias et al. do not teach the acquiring of a linkage disequilibrium coefficient.
Wientjes et al. teaches in the abstract “In this study, we investigated the effects of LD and family relationships on reliability of genomic predictions and the potential of deterministic formulas to predict reliability using population parameters in populations with complex family structures”.
Claim 10 is directed to the apparatus of claim 9 but further specifies breaking the genome and data into corresponding regions, proving the prediction models and parameters and then calculating the first set models and from that outputting the second model.
Milton et al. and Iglesias et al. teach the apparatus of claims 1, 9 and 11 as previously described.
Milton et al. and Iglesias et al. do not teach the genome being broken into regions or the models being applied to said regions.
Seldin et al. teaches on page 523, column 2, paragraph 2 “Importantly, admixture between different continental populations also creates mosaic chromosomes containing segments of distinct ancestry, which we refer to as local ancestry. A causal risk allele with large allele frequency differences between ancestral populations — such as those that may exist for a disease with varying prevalence among populations — leads to deviations in local ancestry at the causal locus. Thus, local ancestry estimates can be used for admixture mapping, in which disease cases from an admixed population are scanned for loci with unusual deviations in local ancestry… the advent of genome-wide association studies (GWASs) has led to new techniques for local ancestry inference and raises some additional challenges, such as combining SNP and admixture association signals, optimizing genotype imputation and fine-mapping causal variants”.
Response to Arguments
Applicant's arguments filed 10/17/2025 have been fully considered but they are not persuasive. Applicant asserts on page 5 of the Remarks filed 10/17/2025 that the previously cited prior art does not teach the amended claims 1 and 11 respective limitations. However, claims 1 and 11 have been amended with the limitations of previously rejected claims 2, and 4-6. As such examiner has restructured the rejections such that amended claims stand rejected. In regards to applicant claims that Gonzalez-Recio et al. and Fan et al. do not teach the described methods in regards to GWAS, the paper is directed to machine-learning methods for genome-wide prediction of complex traits (and titled as such). Furthermore, applicant claims that Banfield et al. and Al-Azani et al. do not use mutually different algorithms for summary statistics representing LD patterns, to which applicant is correct but the methods are merely shown as use of and comparison between algorithms within the state of the art, not for this direct application, rather as methods currently available for use that are well-understood and routine within the art. Applicant further claims Seldin et al. merely teaches local-ancestry segmentation but not dividing based on the correlation between populations a genomic region. However, this is the definition of local-ancestry segmentation. Finally, applicant asserts that neither Milton et al. nor Iglesias et al. teach generating models for multiple populations. However, this point is moot is view of previously cited, and newly mapped art Seldin et al. which does showcase modeling in admixed populations.
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.
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/K.N.A./Examiner, Art Unit 1687
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685