DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 15 October 2025 has been entered.
Status
This First Action Final Office Action is in response to the communication filed on 19 February 2025. Claims 5 and 15 have been cancelled, claims 1 and 11 have been amended, and no new claims have been added. Therefore, claims 1-4, 6-14, and 16-20 are pending and presented for examination.
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
A summary of the Examiner’s Response to Applicant’s amendment:
Applicant’s amendment does not overcome the rejection(s) under 35 USC § 101; therefore, the Examiner maintains the rejection(s) while updating phrasing in keeping with current examination guidelines.
Applicant’s arguments are found to be not persuasive; please see the Response to Arguments below.
Claim Interpretation
The Examiner notes that the claim preambles indicate “reversing a geriatric process”; however, a “geriatric process” is defined by Applicant as “an age-related disease, sickness, illness, infection, ailment, malady, disorder, complaint, affliction, condition, problem and the like” (at Applicant ¶ 0010, as submitted). Therefore, the claims do not necessarily relate to older people (or animals), elder care, etc. – the indication is a relation to any age grouping (e.g., infants, toddlers, teens, middle-aged, seniors, etc.) and related diseases or ailments. Further, the claims do not in any apparent manner actually reverse any affect, impact, measurement, etc. related to aging or an identified disease, ailment, illness, etc. – the claims only identify a nutrient that may, or may not, have some impact (such as slowing an effect, rather than reversing any symptom or ailment) if administered. As such, the preamble of the claims do not limit any structure or result, are merely indicating a hoped or intended purpose or use, and therefore may be granted little if any patentable weight – see MPEP § 2111.02.
Claim Interpretation
The Examiner notes that independent claims 1 and 11 recite identifying a measurement by:
generating a geriatric classifier, wherein the geriatric classifier is configured to input the geriatric process relating to the user and output the measurement as a function of a classification process, wherein the geriatric classifier is trained iteratively to classify input data as further samples;
identifying the measurement using a geriatric classifier, wherein the geriatric classifier comprises a machine learning model configured to receive the geriatric process as an input and generate the measurement as an output
Where it is noted that the “identifying a measurement” is performed by “using a geriatric classifier” – not necessarily the “generat[ed] geriatric classifier” of the prior element, but rather (apparently) using any geriatric classifier. As such, apparently, the classifier of “generating a geriatric classifier” is never required to be used. Further, although both the generated and used “geriatric classifier” are indicated (the machine learning model classifier actually being used), they both merely use the “geriatric process” (i.e., ““an age-related disease, sickness, illness, infection, ailment, malady, disorder, complaint, affliction, condition, problem and the like” as above and per the light of the specification) to output a/the measurement. Neither “geriatric classifier” is ever apparently used for any form of classifying – they both merely output a measurement of some form that should be (or is recommended to be, or could be) considered in relation to the group label.
The Examiner notes, though, that “wherein the geriatric classifier is trained iteratively to classify input data as further samples” does not appear to have any real or applicable meaning. Being trained iteratively is apparently separate from what the intended result would be – any algorithm can/could be trained iteratively regardless of the result. But “input data” is either training data as input (or training), or test data to produce a result. “Further samples” could mean further training data, or more/further data that is to be tested, so all/any input can be regarded as a further sample, and there does not appear to be any basis or indication of classifying input data. The next element – although for a potentially different geriatric classifier – indicates the “geriatric process as an input”, but the geriatric process is received and labeled and either one of the geriatric classifiers is indicated as being to identify a measurement – NOT to classify input data. Therefore, this is interpreted as merely being excess verbiage that may be granted little if any patentable weight.
Then, the claims recite locating a nutrient by “receiving a training data set” (that correlates measurement data inputs to nutrient outputs), and “sorting the training data set into one or more clusters based on a distance metric” (i.e., the distance being a proximity in the training data set), the training data and inputs being in vector forms “and wherein vector similarity is utilized to identify a classification within the training data set”.
So, at this point in the claims, the training data is classified by using vector similarity (i.e., any classifying of any sort), so as to “sort” the training data. Then a neural network is generated, and the classified training data is filtered using the neural network (i.e., any filtering basis a/the neural network may use). Then a “machine learning process” is trained iteratively (using feedback) – the “machine learning process” is apparently a machine learning model, but the term “process” is used so as to distinguish it from the machine learning model that identifies the measurement. Only because the received training data correlates input measurement data to output nutrient data is there an indication of what the iteratively trained machine learning process is to do. The nutrient is then located based on the output of the machine learning model and the machine learning process; although, in reality, the machine learning model is just arriving at the measurement and since that is apparently the input to the machine learning process, the locating of a nutrient is apparently merely based on the machine learning process. However, although there is a measurement identified using the machine learning model, there is no actual or recited/required use of the machine learning process. All the activity indicated for the machine learning process appears to be the generation and training of the process, there is no apparent test information input into the machine learning process so as to produce an output. It is presumed, or assumed, that there is an actual operation or use of the machine learning process, but the claims do not require or recite this – the claim breadth is merely to training the machine learning process and the actual “locating the nutrient” is the expected result if or when the machine learning process is actually used.
The net result is that a measurement is identified using a/the machine learning model, then a neural network is generated and used to filter classified training data such that the machine learning process is iteratively trained using feedback, and if it is presumed or understood that there actually is data input to the machine learning process, then a nutrient may be identified based on using the machine learning process.
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 1-4, 6-14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to a system (claims 1-4 and 6-10) and method (claims 11-14 and 16-20), which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 1 recites a system for reversing a geriatric process, the system comprising: a computing device, wherein the computing device is designed and configured to: receive a geriatric process relating to a user; retrieve a user effective age; assign the geriatric process a geriatric group label as a function of the user effective age; identify a measurement relating to the geriatric group label wherein identifying the measurement comprises: generating a geriatric classifier, wherein the geriatric classifier is configured to input the geriatric process relating to the user and output the measurement as a function of a classification process, wherein the geriatric classifier is trained iteratively to classify input data as further samples; identifying the measurement using a geriatric classifier, wherein the geriatric classifier comprises a machine learning model configured to receive the geriatric process as an input and generate the measurement as an output; and locate a nutrient intended to address the measurement, wherein locating the nutrient further comprises: receiving a training data set, wherein the training data set correlates a plurality of measurement data as inputs to a plurality of nutrient data as outputs: sorting the training data set into one or more clusters based on a distance metric, wherein the distance metric comprises a proximity of data located within the training data set, wherein the training data set and the inputs are represented in vector forms, and wherein vector similarity is utilized to identify a classification within the training data set; generating a neural network by the classification; filtering the classified training data set by utilizing the generated neural network; training, iteratively, a machine learning process as a function of the filtered training data set, wherein training the machine learning process includes retraining the machine learning process with feedback from previous iterations of the machine learning process; and locating the nutrient as a function of the output of the machine learning model and the machine learning process.
Claim 11 is directed to a method with the same, or highly similar, limitations as at claim 1 above, and is therefore analyzed in the same manner as claim 1 above.
The dependent claims (claims 2-4, 6-10, 12-14, and 16-20) appear to be encompassed by the abstract idea of the independent claims since they merely indicate the group label as a function of both the effective age and a chronological age (claims 2 and 12), the group label identifying a body system (claims 3 and 13), including a malady state associated with the user as a basis for the group label (claims 4 and 14), the measurements indicating a deficiency and/or excess (claims 6-7 and 16-17), outputting a remedy based on the nutrient and measurement using an additional machine learning process (claims 8 and 18), locating a plurality of possible remedies and selecting a remedy based on the effective age (claims 9 and 19), and/or determining a nutrient interval (claims 10 and 20).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of identifying a nutrient intended to address a measurement related to a user group label; however, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination).
The computing device cited at claim 1 is described and claimed at a high level of generality (see, e.g., Applicant ¶¶ 0008 and 0046, as submitted). The ensuing three elements of to receive, retrieve, and assign are not limited in how they are performed (aside from performance by the general-purpose computer) within the grouping of mental processes (e.g., observation, evaluation, judgment, and/or opinion) – e.g., an analog example would encompass merely listening to or reading information so as to receive it. The “identify a measurement” element is merely performed by a machine learning model, which is grouped under at least the mathematical calculations grouping of abstract ideas. Under the “locate a nutrient …” portion of claim 1, the receiving is indicated as an additional element (and addressed below), and that the training data set correlates inputs to outputs is the nature or type of data received – the Examiner noting that it appears that any data that could be used for training a model would apparently necessarily correlate inputs and outputs to some degree, otherwise it would appear useless for training. The sorting of the training data into clusters, where clustering would be within the mental process groupings of abstract ideas because they cover concepts (as performed in the human mind, including observation, evaluation, judgment, and opinion). The indication that the clustering is based on a distance metric, the distance metric comprises a proximity of data located within the training data set, and using vector forms and vector similarity is a specific indication of mathematical concepts since at least performing calculations. Filtering the training data set is, similar to elements above, in the mental processes grouping since based on observation, evaluation, judgment, and opinion. The iterative training and retraining of the machine learning process as a function of the filtered data set is, also similar to indications above, an indication of mathematical concepts since at least performing calculations. And locating the nutrient at the final element is also an indication of observation, evaluation, judgment, and opinion that is included in the mental process groupings of abstract ideas. As such, based on the above analysis, the claims combine multiple groupings of abstract ideas.
The Examiner notes, as an example, having had chemotherapy treatment(s) (among other things) well over a decade ago, where, based on the complaint and/or diagnosis, lab blood-work was performed before each treatment, measurements were identified in (or based on) the lab tests, and particular nutrients were recommended or required (i.e., in particular, “horse-sized” pills for potassium, among other nutrients). This reflects receiving a “geriatric process” and effective age, assigning a label, identifying a measurement, locating a nutrient according to the cluster and classification related to the patient treatment, and even recommending or administering the nutrient.
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are the system comprising: a computing device the computing device designed and configured to perform the activities claimed and receiving a training data set (at claims 1 and/or 11). These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
The Examiner notes that Applicant’s specification ¶ 0024 (as submitted, 0032 as published) indicates that “A ‘machine learning process,’ as used in this disclosure, is a process that automatically uses training data to generate an algorithm that will be performed by computing device 104 to produce outputs given data provided as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language.” This appears to literally indicate that the claimed machine learning is distinct from human activity in that the claimed “machine learning process” would be “automatically … generat[ing] an algorithm” instead of the human activity of generating a model algorithm to be run on a computer – i.e., it is described as only distinct in that it is doing the same activity as a human, but doing by a computer (merely applying on or by a computer). The Examiner also notes that the machine learning model being used is indicated by the specification as encompassing the model merely being a mathematical model (see at least Applicant ¶ 0041).
The receiving a training data set is merely designating what is received, where the receiving is part of mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”).
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility. The only description of a computing device is generic in nature, such as “Computing device 104 may include any computing device as described in this disclosure” including one or two, or apparently more, devices in series or parallel (at Applicant ¶ 0008, as submitted) and/or “Examples of a computing device include, but are not limited to, an electronic book reading device, a computer workstation, a terminal computer, a server computer, a handheld device (e.g., a tablet computer, a smartphone, etc.), a web appliance, a network router, a network switch, a network bridge, any machine capable of executing a sequence of instructions that specify an action to be taken by that machine, and any combinations thereof. In one example, a computing device may include and/or be included in a kiosk” (at Applicant ¶ 0046, as submitted).
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore, the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
Allowable Subject Matter
Claims 1-4, 6-14, and 16-20 are indicated as allowable over the prior art.
The following is a statement of reasons for the indication of allowable subject matter:
The closest art of record appears to be Inwald et al. (U.S. Patent Application Publication No. 2022/0028527, hereinafter Inwald), which describes the geriatric process, labeling, identifying a measurement, and locating a nutrient, as recited at the Office Action dated 7 July 2023. Shallenberger (U.S. Patent Application Publication No. 2005/0228239) further teaches the retrieving of a user effective age (as also cited at the Office Action dated 7 July 2023).
However, independent claims 1 and 11 each recite “wherein the training data classifier comprises a neural net generated by a classification algorithm, wherein the classification algorithm sorts the training set into one or more clusters based on a distance metric, wherein the distance metric comprises a proximity of data located within the training set”. The support for this, such as Applicant ¶¶ 0019 and 0021-0022 (as submitted, ¶¶ 0027 and 0029-0030 as published) indicates that the clustering based on distance is apparently performed by a K-nearest neighbor (KNN) algorithm. Where Inwald discloses the use of a KNN and distances used for clustering (see 0045-0046 of Inwald), the Examiner has searched for, but does not find the claimed classification engine as generating a neural net (or network) – the prior art merely uses the KNN. The Examiner notes that the claims require the classification algorithm (e.g., the KNN) be separate from the neural net (also called a neural network) in that the claims indicate the classification engine as generating the neural net/network. The Examiner notes that several references indicate this, such as Neumann (U.S. Patent Application Publication No. 20200321114); however, they are all by the same inventor and/or Applicant and do not qualify as 102(a)(1) art, therefore, they are excepted from being prior art under 102(b)(2). As such, the Examiner indicates the claims as allowable over the prior art.
Response to Arguments
Applicant's arguments filed 15 October 2025 have been fully considered but they are not persuasive.
Applicant argues the 101 rejections (Remarks at 7-17), repeating claim 1 and some mentions of MPEP guidance in arguing the prior rejection (Id. at 7-9).
Applicant then asserts analogy to dicta (i.e., NOT CONTROLLING information, but merely exemplary information) from Synopsis by alleging that generating a classifier and locating a nutrient via a training data set sorted into clusters, with input data being in vector form and using vector similarity “could not conceivably (Id. at 10). The Examiner notes, however, that MPEP § 2106(II) indicates that
With regard to the second criterion for eligibility, the Alice/Mayo test, claim interpretation can affect the first part of the test (whether the claims are directed to a judicial exception). For example, the patentee in Synopsys argued that the claimed methods of logic circuit design were intended to be used in conjunction with computer-based design tools, and were thus not mental processes. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1147-49, 120 USPQ2d 1473, 1480-81 (Fed. Cir. 2016). The court disagreed, because it interpreted the claims as encompassing nothing other than pure mental steps (and thus falling within an abstract idea grouping) because the claims did not include any limitations requiring computer implementation.
And MPEP § 2106.04(a)(2)(III) indicates
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").
These citations indicate that there is no analogy definition to what may possibly be considered incapable of performance in the human mind, other than the specific dicta example at MPEP § 2106.04(a)(2)(III)(A) of “a claim to a specific data encryption method for computer communication involving a several-step manipulation of data” as apparently in Synopsys. Applicant’s claims do not relate to encryption nor computer communication (other than general receiving by a generic computer device); therefore, any analogy to Synopsys appears fruitless or immaterial.
Applicant then argues that the generating a geriatric classifier (which, incidentally, is apparently not actually used) and locating a nutrient by sorting and filtering training data using mathematical concepts (e.g., vector forms and vector similarity) cannot be performed in the human mind (Remarks at 10). However, these elements or functions at the claims are identified as part of, or indicating, the mathematical concepts grouping. The Examiner notes in relation to the secondary or additional groupings indicated above, that MPEP § 2106.04(II)(A)(2) indicates that
Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.").
As such, merely arguing that some elements are in a different grouping does not necessarily indicate eligibility.
Applicant then argues that “In addition, in Synopsys, the Court has set a benchmark …” (Remarks at 11); however, Applicant specifically indicates that this is “an example cited in dicta of Synopsys”. Dicta is NOT a benchmark – dicta is, in this instance, merely an example that would appear to have no precedent value, and especially not since Applicant’s claims are not close to being on-point with the example.
Applicant then argues “the August 2025 Memo [that indicates] examples 39 and 47. Example 39 recites limitation ‘training the neural network in a first stage using the first training set’ does not recite a judicial exception” and that “Therefore, the amended claim recites technical operations that cannot be performed mentally and are therefore not directed to a mental process under Step 2A, Prong One” (Remarks at 11, emphasis omitted). However, Example 39 recites far more than merely training a neural network (“NN”) – the claim of Example 39 is to collecting facial images, transforming them by “mirroring, rotating, smoothing, or contrast reduction to create a modified set of digital facial images”, creating a training set (of collected images, modified images, and non-facial images), training the NN, then creating a second training set (i.e., the first set and also incorrectly detected images), and then “training the neural network in a second stage”. It is the entirety of the process that is considered for eligibility – not the over-simplification of merely training a neural network as Applicant argues. Example 47, claim 2 – issued AFTER Example 39 – indicates that merely training a machine learning algorithm does NOT indicate eligibility. Applicant does not draw specific analogy to either of the indicated examples, but merely alleges the claims should be allowed (since allegedly not performable in the human mind).
Applicant then argues the mathematical concepts grouping, alleging similarity again to Example 39 and that neural network, filtering and training of a machine learning process “do not recite judicial exceptions even if they ‘may involve or rely upon mathematical concepts.’" (Remarks at 12) and this is distinguishable from Example 47 since “claim 2 of Example 47 ‘requires specific mathematical calculations by referring to the mathematical calculations by name, i.e., a backpropagation algorithm and a gradient descent algorithm, and therefore recites a judicial exception” (Remarks at 13, purporting to cite the August 2025 Memo, but emphasis placed by Applicant at Remarks, and NOT present at the August 2025 Memo). Applicant’s argument is apparently that since the claim elements are not entirely in the mathematical concepts grouping, it is alleged that the claims cannot be directed to mathematical concepts. The Examiner notes in relation to the secondary or additional groupings indicated above, that MPEP § 2106.04(II)(A)(2) indicates that
Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.").
Applicant then argues “Step 2A, Prong Two” (Remarks at 13-15), repeating some of the rejection analysis, the August 2025 Memo, and Subject Matter Eligibility Guidelines (Id. at 13-15), then alleging analogy to “Example 47, …[as a] technological improvement that is integrated into a practical application” (Id. at 15). Applicant cites to Applicant ¶ 0021 for support an indication of a practical application; however, this discusses “out-of-sample-features” as part of “A ‘K-nearest neighbors algorithm’ as used in this disclosure”, both of which are not discussed or considered by the claims (KNN has been deleted by amendment). The rest of the features argued via the specification is that the trained model (and/or process) uses vector data and/or vector similarity “’performed recursively and/or iteratively to generate a classifier that may be used to classify input data as further samples’ … to locate a plurality of remedies relating to a nutrient and geriatric process” (Remarks at 15, citing 0021). However, there are no “out-of-sample-features” such that there would or could be “further samples” (as indicated above, this does not appear to mean anything), and the argument indication is that merely using a trained model is itself a practical application. But the abstract idea encompasses the use of a trained model/process and it is elements additional to the abstract idea that are considered as to whether they may transform the abstract idea into a practical application.
Applicant then argues the Step 2B analysis (Remarks at 15-17), alleging that the generating a geriatric classifier, and all the elements that comprise locating a nutrient (until the final element of the claim) have “not been considered in … the Step 2B analysis” (Id. at 16), such that “Claim 1 as amended purports to improve existing computing technology by combining different data manipulation (e.g., filtering and classification mechanisms) and iterative training processes for generating machine learning models (e.g., neural network)”. (Id.). However, as noted above, this is generally included in the abstract idea as encompassing the training and use of machine learning. As above, it is the elements additional to the abstract idea that are considered in Step 2B analysis.
Therefore, the Examiner is not persuaded by Applicant’s arguments.
Conclusion
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zeevi et al., Personalized Nutrition by Prediction of Glycemic Responses, Cell 163, 1079–1094, 19 November 2015, ©2015ElsevierInc., http://dx.doi.org/10.1016/j.cell.2015.11.001, downloaded from https://www.sciencedirect.com/science/article/pii/S0092867415014816 on 28 June 2023, describing that “People eating identical meals present high variability in post-meal blood glucose response. Personalized diets created with the help of an accurate predictor of blood glucose response that integrates parameters such as dietary habits, physical activity, and gut microbiota may successfully lower postmeal blood glucose and its long-term metabolic consequences” (at “In Brief”, cover page), and “We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort” (at “Summary”, 1079)
Öckerman (U.S. Patent Application Publication No. 2014/0161878) discusses that “Various embodiments of the invention relate to compositions comprising vitamins, minerals and trace elements, antioxidants, amino acids, probiotics, and other components and methods for using such compositions to treat or prevent diseases associated with oxidative stress, including cardiovascular disease” (at Abstract).
Morrow et al. (U.S. Patent Application Publication No. 2018/0153915, hereinafter Morrow) discusses patient nutrition, such as for infants (Morrow at 0025), and using clustering with a distance measure (Morrow at 0160).
Neumann, Kenneth (U.S. Patent Application Publication No. 2022/0208339, now U.S. Patent No. 11,854,684) indicates “With continued reference to FIG. 7, at step 720, computing device 104 generates a nourishment program 128. Nourishment program 128 includes any of the nourishment programs as described above in more detail in reference to FIGS. 1-6. Nourishment program 128 may include an eating plan intended to treat, prevent, reverse, and/or cure psychiatric a psychiatric condition and/or a psychiatric disease. Nourishment program 128 may specify recommended and/or assigned meals on specific days of the week. Nourishment program 128 may also include information describing recommended times of the day when certain meals should be consumed. Nourishment program 128 may be generated by training a machine learning process 132 using a training set 136 relating psychiatric markers and nutrient variations to nourishment programs. Computing device 104 may generate a nourishment program as a function of a psychiatric marker 108, nourishment possibilities 124 and a machine learning process 132. This may be performed utilizing any of the methodologies as described above in more detail in reference to FIGS. 1-6.” (Patent Application Publication No. 2022/0208339 at 0054, U.S. Patent 11,854,684 at column:lines 18:48-67). Although the terms may be defined somewhat differently, the instant disclosure defines a nutrient as an eating plan, and where the Patent claims are more specific to psychiatric conditions, the Patent is to the same inventor and Applicant.
Iwendi et al., Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model, IEEE Access, current version dated 17 February 2020, downloaded 28 June 2024 from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8964364, indicating machine learning recommending diet plans, e.g., “This paper proposes a deep learning solution for health base medical dataset that automatically detects which food should be given to which patient base on the disease and other features like age, gender, weight, calories, protein, fat, sodium, fiber, cholesterol.” (At Abstract).
Ramyaa et al., Phenotyping Women Based on Dietary Macronutrients, Physical Activity, and Body Weight Using Machine Learning Tools. Nutrients. 2019 Jul 22;11(7):1681. doi: 10.3390/nu11071681. PMID: 31336626; PMCID: PMC6682952. Downloaded from https://pmc.ncbi.nlm.nih.gov/articles/PMC6682952/ on 15 November 2024, indicating “The primary aim of this study was to use energy balance components, namely input (dietary energy intake and macronutrient composition) and output (physical activity) to predict energy stores (body weight) as a way to evaluate their ability to identify potential phenotypes based on these parameters…. Several machine learning tools were used for this prediction…. [including] k-nearest neighbor (kNN) algorithms …. This study highlights the challenges, limitations, and successes in using machine learning tools on self-reported data to identify determinants of energy balance.” (at Abstract).
Allen et al. (U.S. Patent No. 5563126, hereinafter Allen) indicates “A method for orally administering vitamin preparations is described which combine vitamin B12 (B12, cobalamin) and folic acid (folate), with and without pyridoxine (B6), for preventing and treating elevated serum homocysteine (HC), cystathionine (CT), methylmalonic acid (MMA), or 2-methylcitric acid (2-MCA) levels. These metabolites have been shown to be indicative of B12 and/or folic acid deficiencies. Further, it is likely that a B6 deficiency may be present with a B12 or folate deficiency. The method of the invention is also for use in lowering serum HC, CT, MMA, or 2-MCA in patients with or at risk for neuropsychiatric, vascular, renal or hematologic diseases” (Allen at Abstract), and “These observations, together with the fact that elevated metabolite levels are corrected by parenteral therapy with a combination of vitamins B12, folate, and B6, indicate that a tissue deficiency of one or more of these vitamins occurs commonly in the geriatric population and that measurement of serum vitamin levels alone is an inadequate method for identifying such deficiencies” (Allen at column 10, lines 1-7).
Ahmed et al., Assessment and management of nutrition in older people and its importance to health. Clin Interv Aging. 2010 Aug 9;5:207-16. doi: 10.2147/cia.s9664. PMID: 20711440; PMCID: PMC2920201. Downloaded from https://pmc.ncbi.nlm.nih.gov/articles/PMC2920201/ on 10 April 2025, and indicating various signs and/or symptoms of disease and associated nutrient deficiencies (p. 211 at Table 1) as well as measurement, such as biometric impedence, having prognostic value.
Sak et al., Artificial Intelligence in Nutrients Science Research: A Review. Nutrients. 2021 Jan 22;13(2):322. doi: 10.3390/nu13020322. PMID: 33499405; PMCID: PMC7911928. Downloaded 29 January 2026 from https://pmc.ncbi.nlm.nih.gov/articles/PMC7911928/, and indicating “The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.” (at Abstract).
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/SCOTT D GARTLAND/
Primary Examiner, AU3685