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 .
DETAILED ACTION
This action is in reference to the communication filed on 24 OCT 2025.
Amendments to claims 1, 11, have been entered and considered, as has the cancellation of claims 10, 20.
Claims 1-9, 11-19 are present and have been examined.
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 1-9, 11-19 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
With respect to claims 1-9, 11-19 the independent claims 1, 11, each recite a process, which is a statutory category of invention.
With respect to claims 1-9, 11-19 the independent claims (claims 1, 11) are directed, in part, to obtaining a biomarker, wherein the biomarker comprises data from one or more of at last a biological sample, a biological indicator, and a measurement associated with an endocrine system of a user, producing an adrenal enumeration as a function of the biomarker including receiving a homeostatic element, identifying a homeostatic divergence, and producing the adrenal enumeration; determine an origin using an origin machine learning model as a function of the biomarker and homeostatic divergence, wherein the origin machine learning model is an unsupervised machine learning process; determine an adrenal profile as a function of the adrenal enumeration wherein determining the adrenal profile comprises utilizing an adrenal machine learning model and further comprises: determining an adrenal movement, receiving an adrenal training set wherein the adrenal training set comprises a plurality of adrenal movements and a plurality of adrenal enumerations as inputs correlated to a plurality of adrenal profiles as outputs; Identifying the adrenal profile as a function of the adrenal enumeration and the adrenal movement using the trained machine learning mode; determine an intended outcome as a function of the adrenal profile determined by the trained adrenal machine-learning model, wherein the intended outcome comprises a treatment outcome designed at least to reverse or eliminate the adrenal profile and the adrenal movement, and wherein the intended outcome further comprises a prevention outcome designed to at least prevent the adrenal profile and adrenal movement; generate a nourishment program as a function of the trained nourishment machine learning model using the intended outcome as an input into the trained nourishment machine learning model, wherein the nourishment program is comprised of at least an edible. These steps outlined when following the claimed limitations are found to be certain methods of organizing human activity – i.e. managing interactions including teaching, following rules and/or instructions. Examiner further notes that the training and retraining of the machine learning models as identified is a mathematical concept – i.e. calculations, relationships, and formulas/equations as executed iteratively using the training set. If a claim limitation under its broadest reasonable interpretation(s) recites managing interactions including teaching, following rules or instructions, it is found to fall into the certain methods of organizing human activity grouping of abstract ideas. If a claim limitation under its broadest reasonable interpretation is found to recite mathematical relationships, equations or calculations, it is found to fall into the mathematical concepts grouping of abstract idea(s). For these reasons, claims 1, 11 recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites additional element – claims 1, 11 includes a “computing device,” to execute the claimed limitations, as well as “training, iteratively, the adrenal machine learning model using the adrenal training set, wherein training the adrenal machine learning model includes updating…with feedback from previous iterations…; and “…iteratively train a nourishment program machine learning model.” Examiner notes the computing device in Claim 11 is recited as subsequent step of the method. The computing device in these claims is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05f), and further sending and receiving information is insignificant extra solution activity (see MPEP 2106.05g). The process of the algorithm training as claimed is similar at best an application thereof of a general link between the use of an algorithm and a particular technological environment/field of use (see MPEP 2106.05h). 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.
The independent claims are additionally directed to claim elements such as “computing device,” “training, iteratively, the adrenal machine learning model using the adrenal training set, wherein training the adrenal machine learning model includes updating…with feedback from previous iterations…; and “…iteratively train a nourishment program machine learning model.” When considered individually, the “computing device” and model training claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in [fig 1 and related text] “Computing device 104 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. computing device 104 may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. computing device 104 may interface or communicate with one or more additional devices as described below in further detail via a network interface device.”
At [026/027] “In an embodiment and without limitation, origin machine-learning model may include a classifier. A “classifier,” as used in this disclosure is a machine-learning model, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith… Still referring to FIG. 1, computing device 104 may be configured to generate a classifier using a Naïve Bayes classification algorithm. Naïve Bayes classification algorithm generates classifiers by assigning class labels to problem instances, represented as vectors of element values. Class labels are drawn from a finite set. Naïve Bayes classification algorithm may include generating a family of algorithms that assume that the value of a particular element is independent of the value of any other element, given a class variable. Naïve Bayes classification algorithm may be based on Bayes Theorem expressed as P(A/B)=P(B/A) P(A)÷P(B), where P(AB) is the probability of hypothesis A given data B also known as posterior probability; P(B/A) is the probability of data B given that the hypothesis A was true; P(A) is the probability of hypothesis A being true regardless of data also known as prior probability of A; and P(B) is the probability of the data regardless of the hypothesis. A naïve Bayes algorithm may be generated by first transforming training data into a frequency table.”
[0024] Still referring to FIG. 1, computing device 104 may train origin machine-learning process as a function of an origin training set. As used in this disclosure “origin training set” is a training set that correlates a biomarker and/or origin of malfunction to an adrenal enumeration. For example, and without limitation, a biomarker of pregnenolone and an origin of malfunction of the hypothalamus may relate to an adrenal enumeration of 2. As a further non-limiting example, a biomarker of cortisol and an origin of malfunction of the adrenal glands may relate to an adrenal enumeration of 91.
At [028] A “K-nearest neighbors algorithm” as used in this disclosure, includes a classification method that utilizes feature similarity to analyze how closely out-of-sample-features resemble training data to classify input data to one or more clusters and/or categories of features as represented in training data; this may be performed by representing both training data and input data in vector forms, and using one or more measures of vector similarity to identify classifications within training data, and to determine a classification of input data. K-nearest neighbors’ algorithm may include specifying a K-value, or a number directing the classifier to select the k most similar entries training data to a given sample, determining the most common classifier of the entries in the database, and classifying the known sample; this may be performed recursively and/or iteratively to generate a classifier that may be used to classify input data as further samples.
[0032] Still referring to FIG. 1, computing device 104 may train adrenal machine-learning process as a function of an adrenal training set. As used in this disclosure, a “adrenal training set” is a training set that correlates at least an adrenal movement and an adrenal enumeration to an adrenal profile.
[0040] Still referring to FIG. 1, computing device 104 may train probabilistic machine-learning process as a function of a probabilistic training set. As used in this disclosure a “probabilistic training set” is a training set that correlates at least an adrenal enumeration to a probabilistic vector. For example, and without limitation, an adrenal enumeration of 34 for reduced cortisol secretion may relate to a probabilistic vector of 85 for the probability of developing an adrenal gland dysregulation.
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back (See MPEP 2106.05f). The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
Dependent claims 2-9, 12-19 are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as further description of the observation and judgement steps including description of the types of data and conclusions therein. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
Response to Arguments
Applicant’s remarks as filed on 24 OCT 2025 have been fully considered.
Applicant’s remarks regarding the rejection under 35 USC 101 begin on page 8, with an overview of USPTO policy regarding subject matter eligibility, and a reproduction of exemplary claim 1 through page 9.
On page 10 of the remarks, Applicant begins a discussion of step 2A prong one with portions of USPTO guidance. Examiner respectfully submits that the limitation(s) as identified represent means of following rules/instructions in order to arrive at the adrenal dysregulation issue(s)/resolutions. Examiner notes that the amended limitation as referenced by Applicant has been amended in the alternative, with several of the alternatives being more analogous to merely sending/receiving the data rather than “obtaining” in a meaningful sense. Applicant’s discussion regarding the producing limitation on page 11 is noted, and found unpersuasive as discussed above this is found to be analogous to merely using the computer to apply or execute the identified abstract idea(s).
As per the further remarks regarding the machine learning limitations on page 11, Examiner notes that the use of a model is found to be a mathematical limitation. While the input data appears to be specific, nevertheless the abstract idea of a mathematical relationship or function is present without significantly more than the abstract idea(s).
Applicant turns to Step 2A prong 2 on page 12 with a discussion of various USPTO policies regarding subject matter eligibility and the provisions for a technical improvement.
Examiner suggests incorporating language that interrelates the various training processes with one another within the claim. Applicant’s remarks regarding Example 47 are noted, however, Examiner does not find analogy between the technical improvement found in network security and the essentially additional training executed by the pending claims. The improvement in example 47 is specifically technical in nature, while the “improvements” discussed by Applicant in the current claim are instead more to the elements as described with respect to the abstract idea. Similarly with respect to Example 48, Examiner does not find these remarks persuasive. The technical improvement of separated audio signal excluding undesired audio does not have a clear analogy to the claims at hand. Instead these claim limitations provide context to the abstract idea and any “improvement” is to the abstract idea(s) as identified rather than a specifical technical improvement of an audio stream or any other technical element.
Applicant turns to step 2B on page 14 Examiner respectfully disagrees this analysis is now moot in view of the amended limitations above. Applicant restates portions of amended exemplary claim 1, and concludes that the claim itself provides for an improvement per Berkheimer as a “non-conventional and specific arrangement of steps.” Examiner respectfully disagrees – biological modeling is not itself a technical field for the purposes of the analysis as identified above. The functioning of the computer or it’s ability to process is in no way affected or improved by the claimed invention.
Examiner suggests incorporating language that interrelates the various training processes with one another within the claim to the extent supported by the specification.
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 KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on 571-270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687