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
Claims 69-88 were previously pending and subject to a non-final Office Action having a notification date of May 18, 2026 (“non-final Office Action”). Following the non-final Office Action, Applicant filed an amendment on August 18, 2026 (the “Amendment”), amending claims 69, 77, and 83.
The present Final Office Action addresses pending claims 69-88 in the Amendment.
Response to Arguments
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §112
While these claim rejections are withdrawn in view of the Amendment, new rejections under 35 USC 112(b) are presented below in view of the Amendment.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §101
Starting on page 12 of the Amendment, Applicant asserts:
The present amendments further clarify that the claimed ensemble ML model is not recited merely as a black-box tool or generic instruction to "apply" machine learning. The independent claims now recite that metabolite profiles are obtained from a training set of samples and used to identify the most discriminative combination of metabolites, that a plurality of models comprising different numbers of discriminating metabolites are generated, that performance of each model is evaluated by a cross-validation process, and that at least one model satisfying a threshold for a performance metric contributes to the corresponding score for each related classification. These limitations define a particular machine-learning classification architecture applied to biological expression data, not a process that a medical professional could practically perform mentally or with pen and paper.
The Examiner initially notes that a medical professional could obtain/review training metabolite profiles (e.g., that each include various metadata/parameters describing metabolites) to identify a “most discriminative” combination of metabolites for each of a plurality diseases/disorders (e.g., based on p-values or the like). Furthermore, while the claims also now recite how a plurality of models including different numbers of discriminating metabolites are generated, a performance of each model of the plurality of models is evaluated by a cross-validation process, and at least one of the models that achieves a performance metric threshold contributes to the corresponding score for each related classification, these limitations just amount to defining a high level technical environment or field of use in which the abstract idea is implemented (see MPEP § 2106.05(h)) and/or recite the end result/idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). More specifically, there are no details regarding how the plurality of models are generated, no details regarding how the cross-validation process actually proceeds, and no details regarding how the model that achieves the performance metric “contributes to the corresponding score for each related classification” such that these limitations just recite the “idea of a solution.”
At pages 12-13 of the Amendment, Applicant then recites:
The Examiner analogizes the claims to collecting, analyzing, and displaying information. But the amended claims do not end at generic data review or presentation of results. Rather, they recite a specific sequence for constructing and using an ensemble model to generate disease-classification scores from biological data, and then using those scores within a spectrum of related disease classifications to determine a confidence or likelihood for a selected classification. The claims therefore should not be characterized at a high level of abstraction divorced from their recited machine-learning and bioinformatics operations.
The Examiner initially notes that the claims never recite that the newly added limitations are directed to “constructing” the ensemble ML model and in fact do not even reference the ensemble ML model in the first place. Even if such limitations did make reference to constructing the ensemble ML model, identifying a most discriminative combination of metabolites from metabolite profiles is practically performable in the human mind at such high level of generality and the subsequent three limitations just relate to a particular technology/field of use (see MPEP § 2106.05(h)) and/or amount to reciting the “idea of a solution” (see MPEP § 2106.05(f)) as discussed herein. Furthermore, the claims provide absolutely no details regarding how the ensemble ML model produces the spectrum of related classifications (other than to say “analyzing…via an ensemble machine learning (ML) model, the expression data, to produce a spectrum…”) which thus just amounts to reciting the “idea of a solution” (see MPEP § 2106.05(f)) and limiting use of the abstract idea to a particular technology/field of use (see MPEP § 2106.05(h)) and/or amount to as discussed herein. Still further, using the scores within a spectrum of related disease classifications to determine a confidence or likelihood for a selected classification is practically performable in the human mind at such high level of generality.
On page 13 of the Amendment, then takes the position that [0107], [0109], and [0142] of the present specification allegedly explains the technical significance of the newly added limitations directed to the metabolite profiles and generation/analysis of corresponding models. In relation to how [0107] discloses how the ensemble model can classify samples in a manner subject to less variation than the individual models that make up the ensemble, this statement is just defining what an ensemble ML model is at a high level such that incorporating generic high level of an ensemble ML model into the claims just amounts to reciting the “idea of a solution” (see MPEP § 2106.05(f)) and limiting use of the abstract idea to a particular technology/field of use (see MPEP § 2106.05(h)) as discussed herein. In relation to how [0109] discloses generating multiple models with different numbers of discriminating metabolites and evaluating their performance via cross-validation to “guard against overfitting,” neither the claims nor this paragraph provides any details regarding how the models are generated which just amounts to reciting the “idea of a solution” (see MPEP § 2106.05(f)). Also, while evaluating model performance is practically performable in the mind with pen and paper (e.g., via comparing predicted outputs to expected outputs, etc.), generic use of “cross-validation” to perform such model validation just amounts to limiting use of the abstract idea to a particular technology/field of use (see MPEP § 2106.05(h)) and/or reciting the “idea of a solution” (see MPEP § 2106.05(f)). Finally, in relation to how [0142] discloses an example in which only models with an AUC > 0.8 contributed to the final score, a person could practically in their mind compare performance metrics of models to a threshold to determine which models should contribute to a final score.
On pages 13-14 of the Amendment, Applicant takes the position that generating a spectrum of related classifications and corresponding scores can help identify/resolve/mitigate misclassifications between related diseases/disorders/conditions (e.g., MS and ALS) which allegedly is tied to a “practical classification problem.” Assuming Applicant is implying that such “practical classification problem” is related to a “practical application” of the abstract idea, the Examiner disagrees because producing a spectrum of related classifications and corresponding scores based on expression data to mitigate misclassifications between related diseases/disorders/conditions is practically performable in the human mind with pen and paper as set forth in the rejection below.
Applicant then asserts:
Indeed, the amended independent claims recite limitations directed to how the ensemble ML model is generated and filtered: training-set metabolite profiles are used to identify discriminative metabolites, multiple candidate models using different numbers of discriminating metabolites are generated, each candidate model is evaluated by cross-validation, and only model(s) satisfying the recited performance threshold contribute to the classification score. These are not merely statements of the desired classification result. They constrain the model-generation and model-contribution process that produces the score used in the claimed disease-spectrum classification.
The Examiner initially reiterates that the present claims never recite that the newly added limitations directed to how metabolite profiles are obtained and used to identify most discriminative metabolites, models with different numbers of discriminating metabolites are generated, etc. define filtering/generation of the ensemble ML model. Even if they did, many of the limitations (e.g., obtaining metabolite profiles and identifying most discriminative metabolites, evaluating model performance, determining whether model performance metrics achieve thresholds) are part of the abstract idea while the remaining additional limitations still just amount to reciting the “idea of a solution” (see MPEP § 2106.05(f)) and/or limiting use of the abstract idea to a particular technology/field of use (see MPEP § 2106.05(h)) as discussed herein.
At pages 14-15 of the Amendment, takes the position that the amended claims recite “significantly more” than the abstract idea for reasons similar to those discussed above. The Examiner respectfully disagrees for at least reasons similar to those regarding why the additional limitations do not provide a “practical application” of the at least one abstract idea as thoroughly discussed herein. The Examiner also disagrees with Applicant’s assertion that the present claims now provide the “how” regarding a particular implementation for producing disease-classifications cores using an ensemble model rather than a desired result as also thoroughly discussed herein.
Accordingly, the claims continue to be rejected under 35 USC 101.
Claim Objections
Claim 69 is objected to because of the following informalities:
In claim 69, lines 14-15, it appears that “discrimination metabolites” should be changed to --the discriminating metabolites--. Similar changes should be made to claims 77 and 83.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 69-88 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.
Regarding each of independent claims 69, 77, and 83, it is initially not understood how the newly added “plurality of models” relates to the “ensemble” ML model. While the top of page 14 of the Amendment discusses how the independent claims recite “how the ensemble ML model is generated and filtered: … multiple candidate models using different numbers of discriminating metabolites are generated…,” the present claims never recite that the plurality of models are used to develop/generate the ensemble ML model, that the plurality of models are “candidate” models, etc. It is therefore recommended that Applicant amends the claims to clarify how the newly added limitations are related to training/generation/filtering of the ensemble ML model.
Furthermore, it is not understood to what the “most discriminative combination” of metabolites is in relation. Mores specifically, most discriminative in relation to differentiating among a plurality of related classifications of diseases/disorders/etc.? Clarification is required.
Still further, the difference between the “metabolite profiles” and the “profile associated with the disease” recited in claims 69, 77, and 83 is not understood as the supporting paragraphs cited by Applicant at pages 10-11 of the Amendment (i.e., [0045]-[0046]) only reference “metabolite profiles.” Assuming the “profile associated with the disease” is for instance referring to a “disease, disorder, or condition-associated profile” discussed in [0043] of the present specification, then it is not understood how the progression of the disease is determined based on the ensemble ML analysis of the profile because the specification discusses ([0014], [0045]) how the determined scores are used to determine the disease/disorder progression rather than the “profile associated with the disease.” Clarification is required.
The remaining claims are rejected based on their dependency from the above claims.
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 69-88 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more:
Subject Matter Eligibility Criteria - Step 1:
Claims 69-76 are directed to a method (i.e., a process), claims 77-82 are directed to a non-transitory computer-readable storage medium (i.e., a manufacture), and claims 83-88 are directed to a device (i.e., a machine). Accordingly, claims 69-88 are all within at least one of the four statutory categories. 35 USC §101.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 and July 2024 updates issued by the USPTO as incorporated into the MPEP, as supported by relevant case law), the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 83 includes limitations that recite at least one abstract idea. Specifically, independent claim 83 recites:
A device comprising:
a processor configured to:
receive user data comprising biometric data corresponding to a category of biological traits of a user;
generate expression data based on the user data, the expression data generation comprising analyzing the user data via a sequencing algorithm, and determining the expression data as corresponding to at least a portion of the biological traits;
analyze, via an ensemble machine learning (ML) model, the expression data, to produce a spectrum of a plurality of related classifications of a plurality of related diseases for the user and a corresponding score for each related classification, the spectrum of the plurality of related classifications corresponding to a current status of a disease and development of the disease as per the biological traits of the user,
wherein metabolite profiles are obtained from a training set of samples, which are used to identify a most discriminative combination of metabolites;
wherein a plurality of models comprising different numbers of discriminating metabolites are generated;
wherein a performance of each model of the plurality of models is evaluated by a cross-validation process;
wherein at least one model of the plurality of models that achieves at least one threshold for at least one performance metric contributes to the corresponding score for each related classification; and
wherein the spectrum mitigates misclassifications between related diseases, disorders, or conditions;
determine, based on the spectrum of the plurality of related classifications and the corresponding score for each related classification, a confidence or likelihood that the user is positive for a selected classification of the plurality of related classifications;
perform, via the ensemble ML model, an ensemble ML analysis of a profile associated with the disease based on the selected classification, the ensemble ML analysis of the disease profile comprising analyzing parameters of the disease as identified within the disease profile that correspond to the classification of the user;
determine, based on the ensemble ML analysis of the disease profile, a progression of the disease; and
communicate, for display, an electronic report, the electronic report comprising a recommendation for treatment of the disease based on the determined progression of the disease and the current classification of the user.
The Examiner submits that the foregoing underlined limitations constitute “mental processes” because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). As an example, a medical professional (e.g., laboratory geneticist) could practically in their mind analyze biological expression data of a current patient with that of known healthy and diseased patients having various diseases to determine a probability/likelihood/score/status for each of a "spectrum" of related classifications (e.g., MLS and ALS) as well as development of the disease as per the biological traits of the user (e.g., based on sex, age, ethnicity, etc., the medical professional could determine that MLS in the user will develop at a typical rate) in a manner that "[mitigates] misclassifications between related diseases, disorders, or conditions.”
The medical professional could also practically in their mind with pen and paper obtain/review training metabolite profiles (e.g., that each include various metadata/parameters describing metabolites) to identify a “most discriminative” combination of metabolites for each of a plurality diseases/disorders (e.g., based on p-values or the like), evaluate performance of a plurality of models (e.g., by comparing predicted outputs to expected outputs), and determine one or more of the models that achieve some performance metric threshold (e.g., identifying one or more of the models that achieve a threshold number of “correct” predictions).
The medical professional could also practically in their mind with pen and paper determine, based on the spectrum of the plurality of related classifications and the corresponding score for each related classification, a confidence or likelihood that the user is positive for a selected classification of the plurality of related classifications. For instance, a higher score could correspond to a high confidence/likelihood that the user is positive for a selected one of the classifications.
The medical professional could also practically in their mind with pen and paper analyze/review parameters (e.g., metabolite levels) of a disease profile associated with the corresponding disease (e.g., comparing the disease profile to a metabolite profile of the current patient), determine disease progression based on the analysis (e.g., based on scoring trends over time), and display a report with treatment recommendations based on the disease progression and patient classification. These recitations, under their broadest reasonable interpretation, 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 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
The Examiner also submits that the foregoing underlined limitations constitute “certain methods of organizing human activity” because they relates to managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions). These limitations are similar to a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). MPEP 2106.04(a)(2)(II)(C).
Accordingly, the claim recites at least one abstract idea.
Furthermore, dependent claims 70, 78, and 84 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) because they merely call for generating a "visualization related to the spectrum of the plurality of related classifications" which, at such high level of generality, could be performed in the human mind with pen and paper.
Also, dependent claims 75, 81, and 87 call for selecting a certain type of ML model based on identification of the type of biological traits which could be performed in the human mind.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A device comprising (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)):
a processor configured to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)):
receive user data comprising biometric data corresponding to a category of biological traits of a user;
generate expression data based on the user data, the expression data generation comprising analyzing the user data via a sequencing algorithm, and determining the expression data as corresponding to at least a portion of the biological traits (extra-solution activity (data gathering; e.g., determining level of biomarker in blood) as noted below, see MPEP § 2106.05(g));
analyze, via an ensemble machine learning (ML) model (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f); mere field of use limitation as noted below, see MPEP § 2106.05(h)), the expression data, to produce a spectrum of a plurality of related classifications of a plurality of related diseases for the user and a corresponding score for each related classification, the spectrum of the plurality of related classifications corresponding to a current status of a disease and development of the disease as per the biological traits of the user,
wherein metabolite profiles are obtained from a training set of samples, which are used to identify a most discriminative combination of metabolites;
wherein a plurality of models comprising different numbers of discriminating metabolites are generated (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f); mere field of use limitation as noted below, see MPEP § 2106.05(h));
wherein a performance of each model of the plurality of models is evaluated by a cross-validation process (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f); mere field of use limitation as noted below, see MPEP § 2106.05(h));
wherein at least one model of the plurality of models that achieves at least one threshold for at least one performance metric contributes to the corresponding score for each related classification (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f); mere field of use limitation as noted below, see MPEP § 2106.05(h)); and
wherein the spectrum mitigates misclassifications between related diseases, disorders, or conditions;
determine, based on the spectrum of the plurality of related classifications and the corresponding score for each related classification, a confidence or likelihood that the user is positive for a selected classification of the plurality of related classifications;
perform, via the ensemble ML model (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f); mere field of use limitation as noted below, see MPEP § 2106.05(h)), an ensemble ML (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f); mere field of use limitation as noted below, see MPEP § 2106.05(h)) analysis of a profile associated with the disease based on the selected classification, the ensemble ML (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f); mere field of use limitation as noted below, see MPEP § 2106.05(h)) analysis of the disease profile comprising analyzing parameters of the disease as identified within the disease profile that correspond to the classification of the user;
determine, based on the ensemble ML (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)) analysis of the disease profile, a progression of the disease; and
communicate, for display, an electronic report, the electronic (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) report comprising a recommendation for treatment of the disease based on the determined progression of the disease and the current classification of the user.
For the following reasons, the Examiner submits that the above-identified additional limitations, when considered as a whole with the limitations reciting the at least one abstract idea, do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the user device and processor and the report being electronic and communicated, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitation of generating expression data based on the user data, via analyzing the user data with a sequencing algorithm and determining the expression data as corresponding to at least a portion of the biological traits, the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (e.g., determining level of biomarker in blood, Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 79 (2012)) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)).
Regarding the additional limitations of how the (mentally performable) expression analysis, profile analysis, and disease progression determination are all generically performed using or based on the ensemble AI/ML model, the Examiner submits that these limitations amount to merely defining a high level technical environment or field of use in which the abstract idea is implemented (see MPEP § 2106.05(h)). Additionally or alternatively, these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves those results, are almost always found to be ineligible for patenting under Section 101.” Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1356 (Fed. Cir. 2024).
While the claims also now recite how a plurality of models including different numbers of discriminating metabolites are generated, generic use of a cross-validation process, and the at least one model achieving the performance metric threshold contributes to the corresponding score for each related classification, these limitations still just amount to defining a high level technical environment or field of use in which the abstract idea is implemented (see MPEP § 2106.05(h)) and/or recite the end result/idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). More specifically, there are no details regarding how the plurality of models are generated, no details regarding how the cross-validation process actually proceeds, and no details regarding how the model that achieves the performance metric “contributes to the corresponding score for each related classification” such that these limitations just recite the “idea of a solution.”
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Furthermore, looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 83 and analogous independent claims 69 and 77 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 83 and analogous independent claims 69 and 77 are directed to at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 71, 79, and 85: These claims recite how the electronic report includes functionality to display recommended treatment disease status updates which just amounts to using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Claims 72-74, 80, and 86: These claims generically recite how the ensemble ML model includes at least one NN model, or a series of ML models, or at least three ML models which again amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
Claims 75, 81, and 87: These claims recite how a type of the ensemble ML model corresponds to a type of the user biological traits which just amounts to reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
Claims 76, 82, and 88: These claims recite how the sequencing algorithm includes RNA sequencing technology and thus do no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
When the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 83 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations of the user device and processor and the report being electronic and communicated, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of how the (mentally performable) expression analysis, profile analysis, and disease progression determination are all generically performed using or based on the ensemble AI/ML model, the Examiner submits that these limitations amount to merely defining a high level technical environment or field of use in which the abstract idea is implemented (see MPEP § 2106.05(h)). Additionally or alternatively, these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves those results, are almost always found to be ineligible for patenting under Section 101.” Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1356 (Fed. Cir. 2024).
While the claims also now recite how a plurality of models including different numbers of discriminating metabolites are generated, generic use of a cross-validation process, and the at least one model achieving the performance metric threshold contributes to the corresponding score for each related classification, these limitations still just amount to defining a high level technical environment or field of use in which the abstract idea is implemented (see MPEP § 2106.05(h)) and/or recite the end result/idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). More specifically, there are no details regarding how the plurality of models are generated, no details regarding how the cross-validation process actually proceeds, and no details regarding how the model that achieves the performance metric “contributes to the corresponding score for each related classification” such that these limitations just recite the “idea of a solution.”
Regarding the additional limitations directed to generating expression data based on the user data, via analyzing the user data with a sequencing algorithm and determining the expression data as corresponding to at least a portion of the biological traits which the Examiner submits merely adds insignificant extra-solution activity to the abstract idea (see MPEP § 2106.05(g)), the Examiner has reevaluated such limitations and determined such limitations to not be unconventional as they merely consist of determining the level of a biomarker in blood by any means (Mayo, 566 U.S. at 79, 101 USPQ2d at 1968) and analyzing DNA to provide sequence information or detect allelic variants (Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016)).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Claims 71, 79, and 85: These claims recite how the electronic report includes functionality to display recommended treatment disease status updates which just amounts to using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Claims 72-74, 80, and 86: These claims generically recite how the ensemble ML model includes at least one NN model, or a series of ML models, or at least three ML models which again amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
Claims 75, 81, and 87: These claims recite how a type of the ensemble ML model corresponds to a type of the user biological traits which just amounts to reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
Claims 76, 82, and 88: These claims recite how the sequencing algorithm includes RNA sequencing technology and thus do no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
Therefore, claims 69-88 are ineligible under 35 USC §101.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHON A. SZUMNY whose telephone number is (303) 297-4376. The examiner can normally be reached Monday-Friday 7-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Dunham, can be reached on 571-272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
Prosecution Insights