DETAILED CORRESPONDENCE
This is a non-final office action on merits in response to the arguments and/or amendments filed on 12/17/2025 and the request for continued examination filed on 12/17/2025.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
Claim 4 is cancelled. Amendments to claims 1-2, 5-7, 9-10, and 13 are acknowledged and have been carefully considered. Claims 1-3 and 5-13 are pending and considered below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/17/2025 has been entered.
Subject Matter Free of Art
Claims 1-3 and 5-13 include subject matter that is free of prior art. The cited prior art of record fails to expressly teach or suggest, either alone or in combination, the features found within independent claims 1, 6, and 7. In particular, the cited prior art fails to expressly teach or suggest the specific combination of elements and ordered operations recited in these claims, including detecting one or more specific Alzheimer’s disease-associated SNP mutations corresponding to the mutations listed in Tables 1-1 to 1-77 and determining a risk of developing Alzheimer’s disease using a machine learning model trained on training datasets labeled with information on the onset of Alzheimer’s disease based on those mutations.
For claims 1, 6, and 7, the cited prior art of record fails to expressly teach or suggest, either alone or in combination, detecting the specific mutation set recited in the claims corresponding to Tables 1-1 to 1-77 and using the detected mutation set as input to a trained machine learning model to determine whether a subject will develop Alzheimer’s disease.
The closest prior art of record includes:
Redei (International Publication No. WO 2019/178167 A1),
Redei (International Publication No. WO 2019/178167 A1), referred to hereinafter as Redei, in view of Spurlock (International Publication No. WO 2019/071098 A1), referred to hereinafter as Spurlock,
Redei (International Publication No. WO 2019/178167 A1), referred to hereinafter as Redei, in view of Knüppel et al. (Knüppel et al., Multi-locus stepwise regression: a haplotype-based algorithm for finding genetic associations applied to atopic dermatitis. 2012. BMC Medical Genetics 13:8, page 4 (Year: 2012)), referred to hereinafter as Knüppel, and
Redei (International Publication No. WO 2019/178167 A1), referred to hereinafter as Redei, in view of Knüppel et al., referred to hereinafter as Knüppel, and Tabernero (International Publication No. ES 2463366 A1), referred to hereinafter as Tabernero.
Redei teaches methods for detecting single nucleotide polymorphisms (SNPs) associated with Alzheimer’s disease and applying predictive analytics or machine learning techniques to genetic or biomarker data in order to estimate disease risk. However, Redei fails to teach or suggest detecting the specific mutations recited in the present claims corresponding to the mutations listed in Tables 1-1 to 1-77 or using those mutations in the particular combination recited in the claims.
Spurlock teaches machine learning systems, including random forest classifiers, for analyzing datasets containing genetic and other patient-related information in order to perform disease classification or prediction. However, Spurlock fails to teach or suggest detecting the specific mutation sets recited in the present claims or applying those mutation sets to determine a risk of developing Alzheimer’s disease as recited.
Knüppel teaches statistical and regression-based approaches for identifying genetic associations with disease using haplotype-based algorithms and SNP data. However, Knüppel fails to teach or suggest detecting the specific mutations recited in Tables 1-1 to 1-77 or using such mutations in a machine learning model to determine Alzheimer’s disease risk as recited in the present claims.
Tabernero teaches genetic variants associated with disease conditions and methods for analyzing such genetic variants. However, Tabernero fails to teach or suggest detecting the specific mutations recited in Tables 1-1 to 1-77 or applying those mutations within the claimed machine learning-based method for determining whether a subject will develop Alzheimer’s disease.
Claim Rejections - 35 USC § 112
The rejection of claim 1 under 35 U.S.C. §112(b) is withdrawn. Applicant has amended claim 1 to incorporate the subject matter of the previously referenced tables into the claim, thereby clarifying the scope of the claimed mutations and resolving the indefiniteness issue.
The rejection of claim 4 under 35 U.S.C. §112(d) is also withdrawn as moot because claim 4 has been cancelled.
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 1-3 and 5-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claim 1 recites the limitations of determining whether or not the subject will develop Alzheimer's disease from the first SNP using datasets labeled with information on the onset of Alzheimer's disease for a second SNP, which is a mutation of the Alzheimer's disease-associated gene, wherein the mutations are two or more mutations shown in Tables 1-1 to 1-77. These limitations, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mind or by using a pen and paper. Even when considering the “using a machine learning model trained on a plurality of training datasets” language, the claim encompasses a user analyzing detected SNP mutation information and evaluating or predicting whether the subject will develop Alzheimer’s disease based on known associations between the mutations and disease onset in their mind or by using a pen and paper. The mere nominal recitation of using a machine learning model trained on a plurality of training datasets merely automates the mental process of evaluating relationships between detected SNP mutation information and disease onset, and therefore does not take the claim limitation out of the mental processes grouping. Additionally, the recitation of the mutations that are selected from Tables 1-1 to 1-77 merely specifies particular data to be analyzed and does not impose any meaningful limit on how the abstract idea is performed. Thus, the claim recites a mental process which is an abstract idea.
Independent claims 6 and 7 recite identical or nearly identical steps with respect to claim 1 (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Under Step 2A Prong Two
The claimed limitations, as per method claim 1, include the steps of:
Step 1 of detecting a first SNP which is a mutation of an Alzheimer's disease-associated gene in a genomic DNA sample derived from a subject; and
Step 2 of determining whether or not the subject will develop Alzheimer's disease from the first SNP using a machine learning model trained on a plurality of training datasets labeled with information on the onset of Alzheimer's disease for a second SNP, which is a mutation of the Alzheimer's disease-associated gene detected in a genomic DNA sample derived from a patient who has developed Alzheimer's disease,
wherein the mutations are two or more mutations shown in Tables 1-1 to 1-77, and
wherein the machine learning model includes at least one of a random forest model, a neural network, a support vector machine, a regression model, and a hidden Markov model.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
The judicial exception expressed in claim 1 is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of analyzing genetic mutation information to determine or predict a likelihood of developing Alzheimer’s disease in a computer environment. The claimed computer components (i.e., using a machine learning model trained on a plurality of training datasets; and wherein the machine learning model includes at least one of a random forest model, a neural network, a support vector machine, a regression model, and a hidden Markov model) are recited at a high level of generality and are merely invoked as tools to perform an existing process of evaluating relationships between SNP mutations and disease onset to reach a diagnostic or predictive determination. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application.
The judicial exception expressed in claim 1 is not integrated into a practical application. The claim recites the additional elements of detecting a first SNP which is a mutation of an Alzheimer's disease-associated gene in a genomic DNA sample derived from a subject; and detected in a genomic DNA sample derived from a patient who has developed Alzheimer's disease. These limitations are recited at a high level of generality (i.e., as a general means of collecting genetic mutation information from a subject and from patients), and amounts to merely data gathering, which is a form of insignificant extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claim as a whole merely describes how to generally “apply” the concept of analyzing genetic mutation information to determine or predict a likelihood of developing Alzheimer’s disease in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
For claim 1, under step 2B, the additional elements of detecting a first SNP which is a mutation of an Alzheimer's disease-associated gene in a genomic DNA sample derived from a subject; and detected in a genomic DNA sample derived from a patient who has developed Alzheimer's disease have been evaluated. The claimed machine learning models and data processing steps are described at a high level of generality and perform their conventional functions of receiving data, analyzing data, and generating a prediction. The claim does not recite any specific improvement to machine learning techniques, any specialized data processing architecture, or any unconventional use of genetic data that would amount to significantly more than the abstract idea. The specification further indicates that the machine learning model is used in its ordinary capacity and does not describe any specific implementation, modification, or improvement to the model or its training process (see [0100]-[0101]), supporting that these elements are well-understood, routine, and conventional. See SAP America, Inc. v InvestPic, LLC, 898 F.3d 1161, 1167-68 (Fed. Cir. 2018); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). The use of the information processing method is no more than collecting information before analyzing the information and making a prediction regarding disease onset and does not integrate the abstract idea into a practical application. Therefore, the claim does not recite an inventive concept and is not patent eligible.
Claims 2-3, 5, 8-13 recite no further additional elements, and only further narrow the abstract idea. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above, and do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above.
Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claim is rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Response to Arguments
Applicant’s arguments and amendments, see Remarks/Amendments submitted 12/17/2025 with respect to the rejection of claims 1-3 and 5-13 have been carefully considered and are addressed below.
Claim Rejections - 35 USC § 112
The rejection of claim 1 under 35 U.S.C. §112(b) is withdrawn. Applicant has amended claim 1 to incorporate the subject matter of the previously referenced tables into the claim, thereby clarifying the scope of the claimed mutations and resolving the indefiniteness issue.
The rejection of claim 4 under 35 U.S.C. §112(d) is also withdrawn as moot because claim 4 has been cancelled.
Claim Rejections - 35 USC § 101
Applicant’s arguments have been fully considered but are not persuasive. Applicant states that the amendment specifying that the machine learning model includes one or more of a random forest model, neural network, support vector machine, regression model, or hidden Markov model renders the claims non-abstract. However, this amendment does not remove the claims from the abstract idea grouping. The recitation of these machine learning models merely amounts to an instruction to apply the abstract idea using generic and well known computational tools. These models are recited at a high level of generality and are used in their ordinary capacity to perform data analysis and generate predictions. Accordingly, this limitation constitutes no more than a generic implementation of the abstract idea on a computer and does not integrate the judicial exception into a practical application (see MPEP § 2106.05(f)).
Additionally, with respect to Step 2A, Prong Two, Applicant’s statement that the claims are integrated into a practical application is not supported. The claims do not recite any specific improvement to machine learning techniques, computer functionality, or data processing architecture. Instead, the claims merely use generic machine learning models to analyze genetic mutation data and generate a prediction regarding disease onset, which does not impose a meaningful limit on the abstract idea. See SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167–68 (Fed. Cir. 2018).
Further, Applicant’s argument under Step 2B, that the claimed limitations are not well-understood, routine, and conventional because they are not taught by the prior art, is also not persuasive. The determination of whether additional elements are well-understood, routine, and conventional is separate from the novelty and non-obviousness inquiries under 35 U.S.C. § 102 and 103. The claimed machine learning models and data processing steps are described at a high level of generality and perform their conventional functions of receiving data, analyzing data, and generating a prediction. The specification further indicates that the model is used in its ordinary capacity and does not describe any specific implementation or improvement (see [0100]–[0101]).
Lastly, the step of detecting SNP mutations from genomic DNA samples amounts to data gathering, which is considered insignificant extra-solution activity. Accordingly, Applicant’s amendments and arguments do not overcome the rejection under 35 U.S.C. § 101. The claims remain directed to an abstract idea without significantly more, and the rejection is therefore maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Karow et al. (U.S. Patent Publication 2020/0027557 A1) teaches multimodal systems and methods that analyze genetic and structural brain features (SNPs and MRI data) to diagnose, prognose, classify, and guide treatment for dementia or dementia risk.
Williams et al. (International Publication WO 2011/001135 A1) teaches specific variants in the CLU/APOJ, PICALM, ABCA7, CR1, BIN1, and MS4A gene loci as novel risk indicators for Alzheimer’s disease, and provides diagnostic methods and screening tools based on assaying these variants.
Bisquertt (International publication WO 2021/142417 A2) teaches methods and devices for conducting bloods tests to determine an individual’s genetic background (e.g., SNP) and microRNA expression levels, along with systems for analyzing the combined data using predictive models. The approaches are applicable to various conditions, especially neurological disorders such as Alzheimer’s disease.
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/K.R.L./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685