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 .
Response to Amendment
The following Office action in response to communications received March 2, 2026. Claims 1-2, 12-13 and 16-17 have been amended. Claim 18 has been added. Therefore, claims 1-18 are pending and addressed below.
Applicant’s amendments to the claims are sufficient to overcome the Claim Objections, 35 USC § 103, rejections set forth in the previous office action dated October 6, 2025.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Independent Claim(s) 1 and 16-17 are directed to an abstract idea consisting of using a computer to predict a person’s body type by analyzing their blood data and correlating biomarker data with a physical characteristic to classify an individual.
Independent Claims 1, 16 and 17 recite, in substance: “accessing blood analyte omics data of the subject, generating an omics body index for the subject by applying a trained machine learning model to the blood analyte omics data of the subject, the trained machine learning model fitted to blood analyte omic and anthropomorphic body index data of a reference population comprising a heterogeneous mixture of individuals classified by different anthropomorphic body index classes, classifying the subject by an omics body index class from the different anthropomorphic body index classes according to anthropomorphic body index class boundaries, and outputting the omics body index class for the subject.”
The limitations of Claims 1 and 16-17, as drafted, under its broadest reasonable interpretation, covers the performance of: Mental processes, including concepts performed in the human mind such as observation, evaluation, judgment, and opinion, because the claims use blood analyte omics data, anthropomorphic body index data, and omics body index classes to determine how a subject is classified based on underweight, normal, overweight, obese, or other anthropomorphic body index classes, and how health intervention potential, recommended health intervention, and feedback on efficacy are provided. Certain methods of organizing human activity, including managing personal behavior and clinical workflows, because the claims classify the subject by anthropomorphic body index classes such as World Health Organization (WHO) standards, Asian-Pacific standards, and United Kingdom National Institute for Health and Care Excellence (NICE) standards, and provide lifestyle intervention, lifestyle change, regular exercise, prebiotics, probiotics, supplements, prescribed medical treatment compliance, and longitudinal trajectory as feedback on efficacy of a health intervention. Mathematical concepts, because the claim language includes applying a trained machine learning model to blood analyte omics data and clinical labs data, fitting the machine learning model to blood analyte omic and anthropomorphic body index data of a reference population, generating an omics body index, using anthropomorphic body mass index (BMI), waist circumference, and waist-to-height ratio (WHtR), and using class boundaries and classes such as MetBMI, MetWHtR, ProBMI, ChemBMI, and CombiBMI to classify the subject.
. That is, other than reciting, “computer, processor, non-transitory computer readable storage medium, machine learning model” nothing in the claim element precludes the step from practically being performed in the mind and/or with concepts that encompasses mathematical formulas. For example, but for the “computer” language, “accessing” in the context of this claim encompasses the user manually retrieving blood analyte data. Similarly, the generating an omics body index for a subject, under its broadest reasonable interpretation, covers concepts performed in the mind and/or with concepts that encompass mathematical formulas, but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers concepts performed in the mind or with concepts that encompass mathematical formulas, but for the recitation of generic computer components, then it falls within the “Mental Process, Certain methods of organizing human activity and/or Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of using a “computer, processor, non-transitory computer readable storage medium, machine learning model” to perform all of the “accessing, generating, classifying and outputting” steps. The “computer, processor, non-transitory computer readable storage medium, machine learning model” is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) of executing computer-executable instructions for implementing the specified logical function(s) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 has the following additional elements (i.e., computer, processor, machine learning model). Claim 16 has the following additional elements (i.e., processor, non-transitory computer readable storage medium, machine learning model). Claim 17 has the following additional elements (i.e., processor, non-transitory computer readable storage medium, machine learning model). Looking to the specification, these components are described at a high level of generality (¶ 38; The present invention include: a computer-implemented method of determining an omics-inferred anthropomorphic body index of a subject, the computer comprising one or more processors programmed to perform a series of steps…). The use of a general-purpose computer, taken alone, does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-15 and 18). Particularly, each of the dependent claims also fails to amount to “significantly more’ than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea. Accordingly, none of the current claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology). These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Mental Process, Certain methods of organizing human activity and/or Mathematical Concepts” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Claims 1-18 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Subject Matter Free of Prior Art
Examiner notates below the reasons why the claims overcome the prior art. The limitations most likely to distinguish over the cited combination of Buckler et al. in view of GYLLENSTEN et al. are:
-- generating an omics body index for the subject by applying a trained machine learning model to the blood analyte omics data of the subject, the trained machine learning model fitted to blood analyte omics data and anthropomorphic body index data of a reference population, the reference population comprising a heterogeneous mixture of individuals classified by different anthropomorphic body index classes, wherein the blood analyte omics data of the subject are excluded from data used to train a machine learning model, and the machine learning model is applied to the blood analyte omics data as the trained machine learning model.
Response to Arguments
Applicant’s arguments filed March 2, 2026 have been fully considered but they are not persuasive. In the remarks applicant argues (1) Claims 1-17 were rejected under 35 U.S.C. § 101 as allegedly being directed is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In response to argument (1), Examiner respectfully disagrees. Applicant’s arguments have been fully considered but are not persuasive. In view of the record and the prior 101 analysis, the claims remain directed to an abstract idea implemented on generic computer components and do not recite additional elements that integrate the abstract idea into a practical application or amount to significantly more than the judicial exception.
Applicant asserts under Step 2A, Prong 1, that claim 1 “includes a specific computer-implemented method” and that operations such as “accessing blood analyte omics data,” “generating an omics body index … by applying a trained machine learning model,” and “excluding” the subject’s data from training are “technical operations performed by computer systems.” However, the recited limitations describe the content and flow of information (blood analyte omics data, anthropomorphic body index data, omics body index, classes and class boundaries) and the mathematical and logical operations performed on that information (fitting and applying a trained machine learning model, classifying by classes, outputting results), rather than any specific improvement to computer architecture, memory, processing circuitry, or other technology. As previously explained, these actions fall squarely within the categories of mental processes and mathematical concepts—namely, using mathematical models to compute an index from data and comparing that index to thresholds for classification—and therefore recite a judicial exception under Step 2A, Prong 1. The mere fact that a computer is recited as performing these operations does not, by itself, transform the abstract idea into something other than a judicial exception.
Applicant relies heavily on discussion in the specification regarding “technical limitations of conventional body mass index calculations,” such as misclassification of individuals with a high muscle-to-fat ratio and undervaluation of metabolic improvements, and on specification paragraphs describing combinations of lipids and “species-level lipid biomarkers” and “molecular signature” of obesity. These statements may describe a scientific or clinical problem, but the claims do not recite any particular solution at the level of computer technology; instead they generically require applying a trained machine learning model to blood analyte omics data and classifying the subject using anthropomorphic body index classes. The claimed “technical solution” is expressed as the idea of using an “omics body index” and omics-based BMI models in place of a standard BMI, rather than as a particular improvement in how the computer or machine learning model operates (for example, a specific training procedure, data structure, or algorithmic mechanism that changes how the computer functions). Under MPEP 2106.05(a), an improvement to a technology requires that the claim cover a particular way of achieving the result, not merely the idea of using a model-based substitution; here the claims recite a result-oriented use of “a trained machine learning model” and generic “blood analyte omics data,” without specifying a particular improvement in computer or model operation.
Applicant further argues under Step 2A, Prong 2, that claim 1 integrates any abstract idea into a practical application by improving the functioning of a computer or improving another technology or technical field, and cites MPEP 2106.04(d) and 2106.05(a), with particular emphasis on new examples concerning improvements to machine learning models (e.g., Ex parte Desjardins). Those examples address claims that recite specific ways of training a machine learning model (e.g., protecting knowledge about previous tasks while learning new tasks, or adjusting parameters in a particular manner to improve performance) and thus improve computer component or system performance by changing how the model or computer operates. In contrast, the present claims recite only that a “trained machine learning model” is fitted to blood analyte omic and anthropomorphic body index data of a reference population and applied to blood analyte omics data of a subject, without reciting any particular training algorithm, parameter update rule, architecture change, or other technical details that affect how the model is trained or executed. The assertions in the specification that “multiomic BMI prediction models explain a larger portion of the variation in BMI than any single analyte” or that “each omics-based model demonstrated significantly higher performance” describe benefits of the mathematical model at the application level, but those benefits arise from the choice of features and target, not from any recited change in the underlying computer technology. Accordingly, the claims still merely use a generic machine learning model in its ordinary capacity to map input data to an output value and do not recite a particular improvement to the functioning of the computer, network, or model architecture itself.
Applicant’s reliance on omics-based BMI estimates “capturing the variation in BMI better than any single analyte” and “improved classification of both obesity and metabolic health” does not demonstrate eligibility under Step 2A, Prong 2, because these are improvements in how accurately the mathematical model reflects clinical phenomena, not improvements in the operation of a computer or in any other technology as defined under the USPTO’s subject-matter-eligibility framework. Under MPEP 2106.05(a), improvements to data accuracy or to a business or medical outcome alone do not suffice if the computer is merely being used as a tool to execute the abstract calculation. The claims here do not recite, for example, a specific memory organization, a novel pipeline for data access and model evaluation, or a particular mechanism to protect prior tasks while learning new tasks, as in the Desjardins example. Instead, they recite accessing omics data, generating an index with a trained model, classifying the subject with class boundaries, and outputting feedback—steps that implement the abstract idea on generic computing hardware.
Under Step 2B, Applicant’s arguments that omics-based BMI models “explain a larger portion of the variation in BMI” or achieve “higher performance in BMI prediction” likewise do not identify an “inventive concept” in the claim elements beyond the abstract idea itself. The presence of a trained machine learning model and the use of blood analyte omics data and anthropomorphic body index data are, as claimed, generic data-processing and model-application features that constitute the abstract idea; they are not additional elements that transform the nature of the claim into a patent-eligible application. The claims neither recite unconventional computer components nor recite any non-routine or unconventional way of using the computer components. Rather, they use a computer, one or more data processors, and a non-transitory computer-readable storage medium in their ordinary capacities to store data, execute software instructions, run a model, and output results. Under MPEP 2106.05(f), such “mere instructions to implement an abstract idea on a computer” do not provide “significantly more” than the judicial exception.
For these reasons, applicant’s arguments do not overcome the previously articulated 101 rejection. The claims continue to be directed to an abstract idea—namely, using blood analyte omics data and anthropomorphic body index data in a trained machine learning model to generate an omics body index, classify the subject by classes and class boundaries, and output the omics body index class and related feedback—and the additional elements, considered individually and in combination, do not integrate the exception into a practical application or add an inventive concept. Therefore, the rejection of claims 1–18 under 35 U.S.C. 101 is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20080215362 A1; A health care assessment system and associated methods are provided for providing increased access to individuals for assessing whether a need exists for health care and then also facilitating an individual's access to health care as needed. Embodiments of the system include a health care assessment system for receiving input from a patient and generating an output reflecting an individual’s risk of one or more disease states. The health care assessment system is further capable of providing resources for health care and visual tools for collecting patient history.
CA 3167253 A1; Methods, systems, and software are provided for validating a copy number variation, validating a somatic sequence variant, and/or determining circulating tumor fraction estimates using on-target and off-target sequence reads in a test subject. A copy number status annotation for a genomic segment is validated by applying a first dataset to a plurality of filters comprising a measure of central tendency bin-level sequence ratio filter, a confidence filter, and a measure of central tendency-plus-deviation bin-level sequence ratio filter. A somatic sequence variant is validated by comparing a variant allele fragment count for a candidate somatic sequence variant for a respective locus, against a dynamic variant count threshold for the locus in a respective reference sequence. A circulating tumor fraction is estimated based on a measure of fit between genomic segment-level coverage ratios and integer copy states across a plurality of simulated circulated tumor fractions.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD B WINSTON III whose telephone number is (571)270-7780. The examiner can normally be reached M-F 1030 to 1830.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.B.W/Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683
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