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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 August 28, 2026 has been entered.
Response to Amendment
The following Office action in response to communications received August 28, 2026. Claims 1 and 16-17 have been amended. Therefore, claims 1-18 are pending and addressed below.
Applicants’ amendments to the claims are not sufficient to overcome the rejections set forth in the previous office action dated May 29, 2026.
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:
Claims 1–15 and 18 recite a “computer-implemented method” and are directed to a process. Claim 16 recites “a system comprising one or more data processors and a non-transitory computer readable storage medium.” Claim 17 recites “a computer-program product tangibly embodied in a non-transitory machine-readable storage medium.” Accordingly, the claims are directed to a process, machine, or manufacture and fall within a statutory category.
Independent claims 1, 16, and 17 are directed to an abstract idea consisting of accessing blood analyte omics data and anthropomorphic body index information, applying a trained machine learning model to generate and classify an omics body index, comparing an omics body index with a subsequent omics body index, and identifying and outputting information indicative of a metabolic health response.
Claim 1 recites, in part, “generating an omics body index for the subject by applying a trained machine learning model to the blood analyte omics data of the subject,” “classifying the subject by an omics body index class from the different anthropomorphic body index classes according to anthropomorphic body index class boundaries,” “generating a subsequent omics body index for the subject by applying the trained machine learning model to the subsequent blood analyte omics data of the subject,” and “identifying a change indicative of a metabolic health response to the health intervention based on a reduction in the subsequent omics body index relative to the omics body index, wherein the subsequent anthropomorphic body index does not exhibit a corresponding reduction relative to the baseline anthropomorphic body index.”
The limitations of claim 1, as drafted and under their broadest reasonable interpretation, recite mathematical concepts. In particular, the claimed limitations that recite “applying a trained machine learning model,” “generating an omics body index,” “classifying the subject, according to anthropomorphic body index class boundaries,” “a reduction in the subsequent omics body index relative to the omics body index,” and “a corresponding reduction relative to the baseline anthropomorphic body index.” These limitations recite model-based computations and comparison of information.
The limitations of claim 1 also recite mental processes. The claim at least encompasses evaluating “blood analyte omics data,” “a baseline anthropomorphic body index,” “subsequent blood analyte omics data,” and “a subsequent anthropomorphic body index” and to generate “an omics body index.” The claimed output is information identifying a change. Claim 1 does not recite for example administering the health intervention, controlling an instrument or medical device, performing a laboratory measurement and/or changing a physical condition of the subject.
Claims 7-10 additionally recite certain methods of organizing human activity. Claim 7 recites “outputting feedback on the omics body index class” comprising “health intervention potential,” “recommended health intervention,” and “feedback on efficacy.” Claim 8 recites “the recommended health intervention is a lifestyle intervention.” Claim 10 recites “the recommended health intervention is a lifestyle change, such as regular exercise, prebiotics, probiotics, supplements, and prescribed medical treatment compliance.” These limitations encompass managing personal behavior by providing information concerning a recommended health intervention or a lifestyle change. The claims recite outputting recommendations and feedback.
Claims 2-6 and 18 further specify “body mass index, waist circumference (cm), and waist-to-height ratio,” class boundaries, and classifications based on “underweight, normal, and overweight.” These limitations specify the information and numerical boundaries used in the claimed classification. Claims 11-15 further specify “metabolomic data,” “proteomic data,” “clinical labs data,” “MetBMI model,” “MetWHtR,” “ProBMI model,” “ChemBMI model,” “CombiBMI model,” and “imputation by random forest regression or k-nearest neighbors (kNN).” These limitations specify information and forms of model-based processing used in the claimed generating and classifying operations, but for the recitation of generic computer components, the claim steps are simply accessing information, applying a trained machine learning model to information, generating an omics body index, classifying the subject according to anthropomorphic body index class boundaries, comparing the generated information with subsequent information, identifying information indicative of a metabolic health response, and outputting information.
The claims recite additional elements including “a computer comprising one or more processors programmed to perform a series of steps, one or more data processors, a non-transitory computer readable storage medium containing instruction, a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, and instructions configured to cause one or more data processors to perform a set of actions.”
These elements are recited at a high level of generality. They are used to access, apply, generate, classify and identify a change indicative of a metabolic health response. The claims do not recite for example a particular processor configuration, a particular storage configuration, a particular data transfer technique or a particular improvement to the operation of the recited computer components.
The additional elements do not integrate the abstract idea into a practical application. Claims 1, 16, and 17 use “blood analyte omics data, a baseline anthropomorphic body index, subsequent blood analyte omics data, and a subsequent anthropomorphic body index” as inputs to the claimed generating, classifying, comparison, and identifying operations. The claims do not recite obtaining a blood, performing a blood-analyte measurement, operating or improving an instrument, improving a measurement technique, controlling a device, administering a health intervention, or changing a physical state.
The claims do not recite an improvement to computer functioning, a network, the trained machine learning model, a model architecture, or another technology. Although claim 1 recites “a trained machine learning model fitted to blood analyte omics data and anthropomorphic body index data of a reference population,” claim 1 does not recite how the trained machine learning model is fitted, how the trained machine learning model applies the blood analyte omics data, how the model produces the omics body index, or a particular model architecture that improves computer or model operation. The limitation “identifying a change indicative of a metabolic health response to the health intervention” does not recite a treatment or another physical application. It identifies information based on “a reduction in the subsequent omics body index relative to the omics body index” and the absence of “a corresponding reduction relative to the baseline anthropomorphic body index.” The identified result remains information.
Claims 7-10 do not integrate the exception into a practical application because “outputting feedback, recommended health intervention, a lifestyle intervention, a longitudinal trajectory, and a lifestyle change” merely provide information. The claims do not recite performing the recited lifestyle change or otherwise carrying out a health intervention.
The ordered combination of claim elements adds nothing significantly more than the abstract idea itself. “A computer comprising one or more processors,” “one or more data processors,” “a non-transitory computer readable storage medium,” and “a non-transitory machine-readable storage medium” are used to store instructions and perform the recited accessing, generating, classifying, identifying, and outputting functions. The claims do not recite a non-conventional arrangement of those components or a technological improvement produced by those components.
The limitation “accessing blood analyte omics data of the subject and a baseline anthropomorphic body index of the subject” is insignificant extra-solution activity because it merely accesses information used by the recited model-based processing. The limitation “after the health intervention, accessing subsequent blood analyte omics data of the subject and a subsequent anthropomorphic body index of the subject” is insignificant extra-solution activity because it merely accesses subsequent information used by the recited comparison. The limitation “outputting the omics body index class for the subject” is insignificant extra-solution activity because it merely outputs information produced by the recited generating and classifying operations. For claims 7-10, “outputting feedback on the omics body index class” is insignificant extra-solution activity because it merely outputs information produced by the recited abstract analysis. “The feedback is a longitudinal trajectory” identifies a form of output and remains insignificant extra-solution activity. For claim 12, “accessing clinical labs data of the subject” merely accesses additional information for application of “the machine learning model.”
Accordingly, claims 1-18 are directed to an abstract idea without significantly more and therefore are not patent eligible under 35 U.S.C. §101.
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 August 28, 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.
Applicant argues that claim 1 does not recite mathematical concepts because claim 1 does not set forth a mathematical relationship, formula, equation, or calculation performed by the trained machine learning model. This argument is not persuasive. The rejection does not rest on an assertion that claim 1 expressly recites a particular mathematical formula or equation. Rather, claim 1 recites the model-based generation of “an omics body index,” classification “according to anthropomorphic body index class boundaries,” and identification based on “a reduction in the subsequent omics body index relative to the omics body index” and “a corresponding reduction relative to the baseline anthropomorphic body index.” The claim language thereby recites applying a trained machine learning model, classification according to stated boundaries, and comparing results. The absence of a formula does not remove the recited model-based computations, classification or comparison from the abstract idea analysis.
Applicant argues that the model-derived operations cannot practically be performed in the human mind because “the trained machine-learning model must be applied to the longitudinal omics data.” This argument is not persuasive. The rejection does not maintain that a person performs all claimed operations in the human mind. Claim 1 nevertheless encompasses evaluating information to generate a classification and identify a change indicative of a metabolic health response. Further, claim 1 recites the computer and trained machine learning model only as a result-oriented level. Claim 1 does not recite a particular machine-learning-model architecture, a particular model-training operation, a particular model-application operation, or an improvement to the functioning of the computer or trained machine learning model.
Applicant argues that claim 1 does not recite a certain method of organizing human activity because it does not recite recommending, administering, or managing the health intervention. This argument is persuasive as to claim 1. Accordingly, claim 1 is not maintained as reciting a certain method of organizing human activity. The rejection of claim 1 is maintained because claim 1 recites mathematical concepts and mental processes. Claims 7-10 remain directed, in part, to managing personal behavior because they expressly recite “recommended health intervention,” “a lifestyle intervention,” and “a lifestyle change, such as regular exercise, prebiotics, probiotics, supplements, and prescribed medical treatment compliance.”
Applicant argues that claim 1 improves longitudinal metabolic-health monitoring because the claimed process identifies a model-derived change when “the subsequent anthropomorphic body index does not exhibit a corresponding reduction relative to the baseline anthropomorphic body index.” This argument is not persuasive because the asserted improvement is an improvement in the information generated by the claim, namely, information “indicative of a metabolic health response,” rather than an improvement to a computer, a laboratory instrument, a blood-analyte measurement, a processor, a memory, or the trained machine learning model.
The specification states that BMI “can undervalue metabolic improvements in health following exercise” and is “too crude to capture a variety of heterogeneous metabolic health states.” The specification also states that advantages include “the ability to determine an earlier response to changes in metabolic health.” Those statements describe the value or content of the resulting metabolic-health information. They do not establish that claim 1 recites an improvement to the operation of the computer components or to the technological operation of the trained machine learning model.
Applicant’s reliance on McRO is not persuasive. Claim 1 does not recite specific rules that improve a technological process. Claim 1 recites the desired informational result, “identifying a change indicative of a metabolic health response to the health intervention,” using a trained machine learning model without reciting how the model is configured or operates to produce that result. The claims do not recite an asserted technological improvement comparable to a claimed improvement in a computer-implemented technological process.
Applicant further argues that the temporal sequence, repeated application of the trained machine learning model, and the stated discordance meaningfully limit the claim. The limitations confine the data inputs and the conditions under which the claimed information is identified, but they do not integrate the recited abstract analysis into a practical application. The claim still recites accessing information, applying a trained machine learning model, generating and classifying information, comparing information, identifying an informational result, and outputting information. The claim does not require action on the subject, or a technological improvement in the operation of the recited computer components or model.
For the reasons above, Applicant’s arguments have been considered but are not persuasive. 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.
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
