DETAILED ACTION
This office action is based on the claim set submitted and filed on 06/19/2026.
Claim 1, 6, 11, and 16 have been amended.
Claims 10 and 20 have been canceled.
Claims 1-9 and 11-19 are currently pending and have been examined.
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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 06/19/2026, is/are in accordance with the provisions of 37 CFR 1.97 and are considered by the Examiner.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 1-9 and 11-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
In order to satisfy the written description requirement, the specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See MPEP 2161.01(1). However, generic claim language in the original disclosure does not satisfy the written description requirement if it fails to support the scope of the genus claimed, and even original claims may fail to satisfy the written description requirement when the invention is claimed and described in functional language but the specification does not sufficiently identify how the invention achieves the claimed function, See MPEP 2161.01(1) citing in part Ariad, 598 F.3d at 1349 ("[A]n adequate written description of a claimed genus requires more than a generic statement of an invention's boundaries.").
Specifically, with regard to computer-implemented functional claims, the specification must provide a disclosure of the computer and the algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention, including how to program the disclosed computer to perform the claimed function. MPEP 2161.01(1).
Claim 1 and 11, recite “automatically, by the computing device, dynamically updating the nutritive plan by reclassifying the subsequent second input when the follow-up physiological data reveals an absence of clinical improvement in the at least one disease marker”, for which the subject matter of the limitation was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention. As best understood, it appears that there is no support for the underlined recitation in the original disclosure of the present application for this limitation. As described in applicant’s specification [0066], the description discloses “Nutritive plan 136 generated to treat a urinary tract infection which may include, ...may not reduce the presence of serum antibody immunoglobulin (lg) G, IgM, and IgA which may indicate no improvement in the treatment of a urinary tract infection. Nutritive plan 136 may be updated to, for example, suggest the reduction or removal of carbonated beverages, alcohol, artificial sweeteners, and caffeine from a user's diet”. There is not explicit disclosure as filed describing features the automatically and dynamically updating the plan and there is no description for the step as claimed.
The examiner takes the position that with respect to these limitations or features of the claims, the specification fails to provide an adequate written description of the invention to an extent that would sufficiently show that applicant was in possession of an invention that could operate as claimed. Simply disclosing a vague description, without actually explaining how to perform the function(s) claimed, results in a written description problem under 112(a). The examiner has no idea how applicant actually contemplated doing these steps because nothing is disclosed other than the broad disclosure of the specification as mentioned above.
Therefore, applicant has failed to show the actual subject matter in their possession at the time of the invention in a way sufficient to reasonably convey to one skilled in the relevant art that applicant had possession of the claimed invention at the time the application was filed. Therefore, these limitations of the claims are considered to be new matter. Appropriate correction is required.
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.
Claim 1-9 and 11-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-9 are drawn to an apparatus/system and Claim 11-19 is drawn to a method, and each of which is within the four statutory categories (i.e. a machine and a process). Claims 1-9 and 11-19 are further directed to an abstract idea on the grounds set out in detail below.
Under Step 2A, Prong 1, the steps of the claim for the invention represents an abstract idea of a series of steps that recite a process for generating a health nutrition plan. This abstract idea could have been performed in human mind but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for steps citing a process of collecting health data, classify a condition, and provide a nutrition plan for which both the instant claims and the abstract idea are defined as metal process that can be performed using human mind with the aid of pencil and paper.
Independent Claim 1 recites, and Claim 11 recites similar steps directed to:
“the system comprising a computing device, wherein the computing device is configured to:
receive an input comprising physiological data obtained from a biological fluid sample of a subject;
extract at least one disease marker related to at least one urological disorder from the physiological data;
generate a first machine learning model comprising disease marker classifier,
wherein generating the disease marker classifier comprises:
receiving disease marker training data correlating disease markers related to urological disorders to a urological disorder label;
sort the disease marker training data according to one or more categorizations using a natural language processing algorithm, wherein the one or more categories are generated using at least a correlation algorithm
identifying classifications within the disease marker training data to determine a classification of the input using feature similarity to analyze how closely out-of-sample-features resemble the disease marker training data
training the disease marker classifier using the disease marker training data and the classifications;
classify, using the disease marker classifier, the at least disease marker to a urological disorder label;
generate, by the computing device, a nutritive plan as a function of the urological disorder label by executing a second machine learning model which is trained on nutritive plan training data correlating historical alimentary combinations with historical ameliorative effects for the urological disorder label;
transmitting, by the computing device, the generated nutritive plan to a graphical user interface (GUI) of a remote user device associated with the subject to visually display specific targeted comestibles and specific adverse foods to avoid;
receiving, by the computing device, a subsequent second input comprising follow-up physiological data obtained from a subsequent biological fluid sample from the subject after execution of the nutritive plan;
automatically, by the computing device, dynamically updating the nutritive plan by reclassifying the subsequent second input when the follow-up physiological data reveals an absence of clinical improvement in the at least one disease marker”.
The limitations, as drafted, given the broadest reasonable interpretation, cover performance of the limitations by a human mind with aid of pen and paper constituting a Metal Process along with Certain methods of Organizing Human activity, thus, an abstract idea, but for the recitation of generic computer components. The claimed concept encompasses to performance of the limitations of a mental process that encompasses the user manually the ability to obtain an individual/population health data/information, extract markers of a disease or a disorder, provide labels and correlate the label to a condition subset and generate a nutrition plan for the labeled condition, which are steps reciting mental process that could have been performed by a human mind with aid of pen and paper but other than the mere nominal recitation of the “computing device”, to implement the abstract idea for performing the steps of observing, evaluating, judgment and opinion citing a process for which can be performed using a human mind with the aid of pencil and paper, see MPEP § 2106.04(a)(2)(III). Accordingly, the claim limitations (in BOLD) recite an abstract idea. Any limitations not identified above as part of the Mental Process are deemed "additional elements," and will be discussed in further detail below.
Under Step 2A, Prong 2, this judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas, linking the abstract idea to a particular technological environment. In particular, the claims recite the additional elements such as “computing device, machine learning model, graphical user interface (GUI), natural language processing” that is/are recited at a high - level of generality to perform the steps of the claim, i.e., “training classifier”, ‘transmitting, by the computing device, the generated nutritive plan to a graphical user interface (GUI)...” that it amounts no more than adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 "merely include[ing] instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application. Accordingly, looking at the claim as a whole, individually and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Under step 2B, the claims do not include additional elements that are sufficient to amount to "significantly more" than the judicial exception because as mentioned above, the additional elements amount to no more than generic computing components, recited at a high level of generality, do not present improvements to another technology or technical field, nor do they affect an improvement to the functioning of the computer itself, that amount to no more than mere instruction to perform the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f). There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, See Alice, 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention."). The claims are not patent eligible.
Dependent Claims 2-9 and 12-19 include all of the limitations of claim(s) 1 and 11, and therefore likewise incorporate the above-described abstract idea. While the depending claims add additional limitations, such as
As for claims 2, 6-8, 12, and 16-18 the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper but for, the recitation of the generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible. The claims recite additional elements “machine learning, user device, computing device” that implement the identified abstract idea. These hardware components are recited at a high level of generality (i.e., general purpose computers/components implementing generic computer functions; applicant's specification makes no mention of any specific hardware) to perform the steps, e.g., “train[ing]”, “output[ting]...”, that amounts to no more than the words "apply it" with a computer because it appears to intend to do so, which would still amount to mere instructions to apply the exception using generic computer components, adding insignificant extra-solution activity to the judicial exception, i.e. store[ing], see MPEP 2106.05(d)(g), and OIP Techs, and generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Additionally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements amount to more than mere instruction to apply the exception using generic computer component and have been re-evaluated under the “significantly more” analysis. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
As for claim 3-5, 9, 13-15, and 19, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper but for, the recitation of the generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-9, 11-13, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Bradley et al. (US 2021/0293829 Al -“Bradley”) in view of Kamine (US 2021/0383612 A1) in view of Nankani et al (“Detection Analysis of Various Types of Cancer by Logistic Regression using Machine Learning” “Nankani”) in view of Lisi et al. (US 2021/0034912 A1 – “Lisi”) in view of Avery et al . (US 2021/0241881A1- “Avery”)
Regarding Claim 1 (Currently Amended), Bradley teaches a system for generating a nutritive plan to manage a urological disorder, the system comprising a computing device, wherein the computing device is configured to:
receive an input comprising physiological data obtained from a biological fluid sample of a subject Bradley discloses receiving a sample, e.g., urine, blood, etc., comprising plurality of biomarkers (Bradley: [0009], [0074], [0103], [0326];
extract at least one disease marker related to at least one urological disorder from the physiological data; Bradley discloses the received sample comprising plurality of biomarkers such as urine protein, sodium, urine specific gravity, urine pH, WBC, RBC, etc., associated with a chronic kidney disease (CKD) (Bradley: [0009], [0093], [0103], [0191], [0325];
generate a first machine learning model comprising a disease marker classifier, Bradley discloses using the plurality of biomarkers related to a chronic kidney disease (CKD) a machine learning (ML) technique to generate classification algorithm as a classifier to determine classification of the disease (Bradley: [0005], [0009], [0094-0095], [0101], [0104], [0135]);
wherein generating the disease marker classifier comprises:
receiving disease marker training data correlating disease markers related to urological disorders to a urological disorder label Bradley discloses training data set comprising plurality of biomarkers related to a chronic kidney disease (CKD) and derive classification label of the disease (Bradley: [0009], [0104], [0111])
sort the disease marker training data according to one or more categorizations using a natural language processing algorithm, wherein the one or more categories are generated using at least a correlation algorithm
identifying classifications within the disease marker training data to determine a classification of the input using feature similarity to analyze how closely out-of-sample-features resemble the disease marker training data
training the disease marker classifier using the sorted disease marker training data and the classification Bradley discloses the training dataset comprising plurality of biomarkers related to a chronic kidney disease (CKD) to train the classification algorithm/classifier using a machine learning (ML) algorithm(s) (Bradley: [0009], [0116], [0124-0125], [0132]);
classify, using the disease marker classifier, the at least disease marker to a urological disorder label; Bradley discloses classing biomarkers related to a chronic kidney disease (CKD) (Bradley: [0009], [0105-0106], [0117], [0145])
generate a nutritive plan as a function of the urological disorder label by executing a second machine learning model which is trained on nutritive plan training data correlating historical alimentary combinations with historical ameliorative effects for the urological disorder label Bradley discloses a machine learning (ML) model comprising a training dataset trained to compare a label and predation to refine the ML weights to include associating history of nutrition and its impact on the disease progressing such as increasing or decreasing the risk of developing CDK and customizing a recommendation for a dietary regimen to treat or prevent CKD (Bradley: [Fig. 10A-B], [0010], [0026], [0041], [0063], [0067], [0077], [0107], [0120], [0147], [0156-0157])
transmit the generated nutritive plan to a graphical user interface (GUI) of a remote user device associated with the subject to visually display specific targeted comestibles and specific adverse foods to avoid Bradley discloses displaying the determined customized recommendation of dietary regimen on a graphical user interface where the dietary regimen consisting recommended nutrients (Bradley: [0010-0011], [0019], [0092], [0107-0108])
receive a subsequent second input comprising follow-up physiological data obtained from a subsequent biological fluid sample from the subject after execution of the nutritive plan Bradley discloses receiving a new data comprising biomarkers (Bradley: [0153], [0238], [0253], [0327])
automatically dynamically update the nutritive plan by reclassifying the subsequent second input when the follow-up physiological data reveals an absence of clinical improvement in the at least one disease marker Bradley discloses classing biomarkers related to a chronic kidney disease (CKD) and a new data is used to update the learning and adjusting a supervised neural network (ANN) and re-adjust the model to classify the biomarker such that when every subsequent new measures is available, new measure may indicate increase or decrease of supplement (e.g., protein) in the biomarker which may indicated increase or decrease the risk of disease progression (Bradley: [Fig. 2C], [0077-0081], [0135], [0167], [0249], [0257], [0327]).
Bradley teaches inputting biomarkers training data and classification algorithm classifying biomarkers dividing training dataset into subsets for different prediction models for different categories of disease risk and filtering the training dataset where the classification algorithm comprises an algorithm to include, a logistic regression algorithm, an artificial neural network algorithm (ANN), a recurrent neural network algorithm (RNN), a K-nearest neighbor algorithm (KNN) [0112], [0122-0127], [0131-0133], however Bradley does not expressly disclose the features as undermarked:
sort the disease marker training data according to one or more categorizations using a natural language processing algorithm (NLP), wherein the one or more categories are generated using at least a correlation algorithm
identifying classifications within the disease marker training data to determine a classification of the input using feature similarity to analyze how closely out-of-sample-features resemble the disease marker training data
dynamically update the plan based on the addition or new input indicating no improvement to disease.
Kamine teaches
sort the disease marker training data according to one or more categorizations using a natural language processing algorithm, wherein the one or more categories are generated using at least a correlation algorithm (Kamine: [0031], [0039])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have Bradley disclosing classification of a urological disease biomarkers in a training data to incorporate sorting the training data into categorization or classification using NLP, as taught by Kamine which help modeling relationships between two or more categories of data elements (Kamine: [0035]).
Nankani teaches
identifying classifications within the disease marker training data to determine a classification of the input using feature similarity to analyze how closely out-of-sample-features resemble the disease marker training data Nankani discloses detection and analysis of a disease/cancer and classification of cancer using algorithms such as K nearest Neighbor (KNN) Classification whereas the KNN algorithm works on the concept of feature similarity that is how closely out-of-sample features resemble training set determines how to classify a given data point (Nankani: [p. 100, col 2], [p. 101 col. 1-2]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have Bradley disclosing classification of a urological disease biomarkers in a training data to incorporate determine a classification of the input using feature similarity to analyze how closely out-of-sample-features, as taught by Nankani which help training on a small data set and gives a result very fast (Nankani: [p. 101, col. 1]).
Lisi discloses classifier as a biomarker identifying features relevant to disease label (Lisi: [0062], [0075], [0083], [0162], [0237]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have Bradley disclosing classification of a urological disease biomarkers and labels and incorporate a correlation of biomarker to the label, as taught by Lisi which help improve the efficiency or accuracy of the biomarker (Lisi: [0062]).
Avery teaches
dynamically update the nutritive plan ... when the follow-up physiological data reveals an absence of clinical improvement in the at least one disease marker
Avery discloses automatically updating patient diet and care plan based on adjusted needs or new information provided as an input such as feedback indicating worsening outcomes (Avery: [Fig. 10, 12], [0092], [0094], [0110], [0117-0118], [0121], [0123]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have Bradley disclosing identifying effective treatment and incorporate updating treatment plan, as taught by Avery which help improving patient outcomes (Avery: [0118]).
Regarding Claim 2 (Previously Presented), the combination of Bradley, Kamine, Nankani, Lisi, and Avery teaches the system of claim 1, wherein the computing device is further configured to:
receive disease predictor training data, wherein the disease predictor training data correlates disease markers related to urological disorders and urological disorder labels with disease predictor scores; Bradley discloses receiving a biomarker as predictor for predicting a risk of chronic kidney disease (CKD) to derive probability scores or a classification label (Bradley: [0020-0021], [0092], [0110], [0116])
wherein the disease predictor training data is received from one or more past iterations of a previous predictor training data vectors Bradley discloses the classification algorithm is trained using KNN with dynamic time warping (DTW) and using stratified subsets of a training dataset to create a predictor after various time periods of a visit that predict a risk of developing CKD during which an amount of one or more biomarkers is determined (Bradley: [0098], [0132-0133])
train, using the disease predictor training data, a machine-learning process Bradley discloses the training dataset comprising plurality of biomarkers related to a chronic kidney disease (CKD) to train the classification algorithm/classifier using a machine learning algorithm(s) (Bradley: [0111]);
generate, for each disease marker of a plurality of disease markers, a disease predictor score as a function of the machine-learning process and each respective disease marker of the plurality of disease markers (Bradley: [0009], [0022], [0116]).
Regarding Claim 3 (Original), the combination of Bradley, Kamine, Nankani, Lisi, and Avery teaches the system of claim 2, wherein the computing device is further configured to:
identify a disease marker of the plurality of disease markers having a highest disease predictor score Bradley discloses using range level of biomarkers as reference values and identifying upper limit of each biomarker, i.e., identifying creatinine level (Bradley: [0010], [0030], [0062], [0075]) ;
generate the nutritive plan as a function of the identification Bradley discloses generating a dietary regimen based on monitored biomarker output (Bradley: [0010], [0071], [0107]).
Regarding Claim 5 (Original), the combination of Bradley, Kamine, Nankani, Lisi, and Avery teaches the system of claim 1, wherein the at least one disease marker comprises a diagnostic disease marker Bradley discloses the received sample comprising plurality of biomarkers such as urine protein, sodium, urine specific gravity, urine pH, WBC, RBC, etc., associated with a chronic kidney disease (CKD) (Bradley: [0009], [0093], [0103], [0191], [0325]).
Regarding Claim 6 (Currently Amended), the combination of Bradley, Kamine, Nankani, Lisi, and Avery teaches the system of claim 1, wherein generating the nutritive plan further comprises:
receiving nutritive plan training data, wherein the nutritive plan training data correlates nutritive plans to nutritive plans with the historical ameliorative or preventive effect on urological disorders; Bradley discloses a treatment or regiment with amount of substance that found beneficial and effective to reduce risk of CKD (Bradley: [0063]). Avery discloses a nutrition therapy diet plan for a user and comparing a selected plan to a historical effectiveness of the selected plan (Avery: [0055-0056], [0062], [0088], [0116-0117])
training the second machine-learning model using the nutritive plan training data; (Avery: [0055], [0062], [0118])
outputting the nutritive plan as a function of the urological disorder and the second machine- learning model Avery discloses adjusting and presenting diet plan based on needs of the user (Avery: [0086], [0092], [0121]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have Bradley disclosing identifying effective treatment and incorporate comparing treatment to past outcomes and using the treatment data to train ML and provide output, as taught by Avery which help improving patient outcomes (Avery: [0118]).
Regarding Claim 7 (Original), the combination of Bradley, Kamine, Nankani, Lisi, and Avery teaches the system of claim 6, wherein outputting the nutritive plan further comprises outputting a message independent of a presence of the nutritive plan (Bradly: [0168]).
Regarding Claim 8 (Original), the combination of Bradley, Kamine, Nankani, Lisi, and Avery teaches the system of claim 1, wherein the computing device is further configured to output the nutritive plan to a user device Bradley discloses display the customized recommendation on a graphical user interface (Bradley: [0011], [0092]).
Regarding Claim 9 (Original), the combination of Bradley, Kamine, Nankani, Lisi, and Avery teaches the system of claim 1, wherein the nutritive plan manages a plurality of disorders Bradley discloses a dietary regimen for treatment of CKD diseases (Bradley: [0063], [0071], [0120]).
Regarding Claim 11 (Currently Amended), Bradley teaches a method for generating a nutritive plan to manage a urological disorder, the method comprising:
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
Regarding Claim 12-13, 15-19, the claims recite substantially similar limitations to claim 2-3, and 5-9, as such, are rejected for similar reasons as given above.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Bradley et al. (US 2021/0293829 Al -“Bradley”) in view of Kamine (US 2021/0383612 A1) in view of Nankani et al (“Detection Analysis of Various Types of Cancer by Logistic Regression using Machine Learning” “Nankani”) in view of Lisi et al. (US 2021/0034912 A1 – “Lisi”) in view of Avery et al . (US 2021/0241881A1- “Avery”) in view of Brewer et al . (US 2021/0233611 A1- “Brewer”)
Regarding Claim 4 (Original), the combination of Bradley, Kamine, Nankani, Lisi, and Avery teaches the system of claim 1, wherein the physiological data includes results of a prostate-specific antigen test
Bradley does not disclose a prostate-specific antigen test.
Brewer discloses testing or screening prostate cancer comprising biomarker such as monitored levels of prostate specific antigen (PSA) levels for detecting prostate cancer (Brewer: [0216-0217], [0256], [0352]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have Bradley disclosing classification of a urological disease biomarkers and labels and incorporate a prostate-specific antigen biomarker, as taught by Brewer which help determining the existence of the different cancer populations and assists in the targeting of therapy and helping avoid treatment-associated morbidity in men with indolent disease (Brewer: [0011], [0256]).
Regarding Claim 14, the claim recites substantially similar limitations to claim 4, as such, are rejected for similar reasons as given above.
Response to Amendment
Applicant's arguments filed 06/19/2026 have been fully considered by the Examiner and addressed as the following:
In the remarks, Applicant argues in substance that:
Applicant's arguments with respect to the 35 U.S.C. § 101 rejection on page 1-6.
On page 3-4 of the remarks, the Applicant argues “Applicant respectfully submits that the amended claims, particularly the limitations directed to the generation of the nutritive plan using a second machine learning model and receiving a second input and automatically updating the nutritive plan ... As pointed out above, claim 1 as amended recites several limitations and elements that do not recite a method of organizing human activity nor a mental process”, Examiner respectfully disagree. Examiner asserts that the claims are given their broadest reasonable interpretation for the purpose of determining whether they encompass a judicial exception. The claim(s) limitations are directed towards diagnosing and generating a nutritive plan reciting, under BRI, a process that can be performed by a human receiving and analyzing a patient information/physiological data to identify an declassify markers contributing to a disease such as urological disorder and generate a nutritive plan which are steps have been analyzed under Step 2A, Prong One reciting a process for obtaining/collecting, extracting, sorting, identifying, classifying and provide/generating, which are steps of observing, evaluating, judgment, and opinion that are citing a process for which can be performed using a human mind with the aid of pencil and paper, see MPEP § 2106.04(a)(2)(III), but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for which both the instant claims and the abstract idea are defined as Mental Process.
On page 3-4 of the remarks, the Applicant argues “Applicant asserts that the processor (and memory), and their specific functionality as claimed, integrate the alleged judicial exception into a practical application because the cited limitations above narrow the function of the devices...”, Examiner respectfully disagree. The step of the claim(s) which is/are a process that can be implemented also using a human mind and using pen and paper to perform the analysis and generation of a plan to be presented to a user as such the user interface which is recited as an additional element to perform displaying results as the court found that “collecting information, analyzing it, and displaying certain results of the collection and analysis”, could practically be performed in the human mind, see MPEP 2106.04(a)(2)(III)(A), and Electric Power Group v. Alstom. As described in the rejection above, the claim(s) does/do not describe a particular improvement of computer’s functionality or a technical field, rather using additional elements, “e.g., computing device, machine learning”, recited at a high level of generality to perform the steps that amounts to no more than the words "apply it" with a computer because it appears to intend to do so, which would still amount to mere instructions to apply the exception using generic computer components to perform the steps abstract idea such as obtaining, analyzing, determining, and applying information through leveraging computing technology in a well understood manner however improving upon an abstract idea does not make the abstract idea any less abstract. Even when considering the claims additional elements (e.g., processor, natural language processor), the claims as a whole, individually and in combination, provide no integration of the abstract ideas into a practical application that no meaningful limits on practicing the abstract idea are introduced, see MPEP 2106. The claims as a whole are therefore directed to an abstract idea.
In light of the Alice decision and the guidance provided in the 2019 PEG, the features listed in the claims, are not considered an improvement to another technology or technical field, or an improvement to the functioning of the computer itself rather describes an improvement to the identified abstract idea, which is solving a health facility and patient administrative problem, using computers. However, an improved abstract idea is still abstract, (SAP America v. Investpic *2-3 ("'We may assume that the techniques claimed are "groundbreaking, innovative, or even brilliant," but that is not enough for eligibility. Association for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013).
On page 5-6 of the remarks, the Applicant argues “Similar to BASCOM, claim 1 as amended recites in part ... This specific ordered combination of physical bio-sampling, specific failure-state triggers, and automated machine learning re-execution provides a technical solution that is "significantly more" than the routine or conventional use of computers in this field”, Examiner respectfully disagree. While in Bascom the inventive concept harnesses the technical feature of network technology in a filtering system by associating individual accounts with their own filtering scheme and elements while locating the filtering system on an ISP server. Furthermore, Bascom achieved other benefits over conventional filtering by providing Internet-content filtering in a manner that can be customized for a person attempting to access such content while avoiding the need for (potentially millions of) local servers or computers to perform such filtering and while being less susceptible to circumvention by the user, and structuring a filtering scheme not just to be effective, but also to make user-level customization administrable as users are added instead of becoming intractably complex. In contrast, the claims of the current invention under BRI recite collection of physiological data, analyzing it and generate a nutritive plan using a well-known in the art computing components as such provides no additional elements to be considered for improving technology. Therefore, it’s respectfully submitted that the claimed invention is not analogous to that of BASCOM.
As described in the above rejection, the claim reciting additional elements “computing components” that are recited at a high level of generality, for example, the machine learning is recited in the claims in a high level of generality and is in described in the specification in an arbitrary form without disclosing a specific process how these elements are implemented to perform a task as such is a mere in instruction(s) that may be performed by human that it amounts no more than adding the words "apply it" (or an equivalent). Similarly, as mentioned above Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “e.g. machine learning, computing device” to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. In addition, by relying on computing devices to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible (See Alice, 134 S. Ct. at 2359 "use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions" is not an inventive concept). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
Therefore, the Examiner has addressed the Applicant argument(s) and found this argument is not found to be persuasive. Hence, Examiner remains the 101 rejections of claims which have been updated to address Applicant's amendments.
Applicant's arguments with respect to the 35 U.S.C. § 103 rejection on page 7-14.
On page 11 of the remarks, the Applicant argues that “Applicant submits that claim I as amended is patentably distinguishable over Bradley, Kamine, Nankani and Lisi, alone or in combination, for at least the reasons discussed above”, Examiner respectfully finds the Applicant Argument(s) is/are directed to a newly added feature and features disclosed in dependent claims. Examiner asserts that the amended independent claim(s) are disclosed by Bradley and Avery as described in the above rejection. The Applicant on page 13 of the remarks argued that Avery fails to cure the deficiencies of Bradley, Kamine, Nankani and Lisi, however, the Applicant fails to distinguish the teaching of Avery of the dependent claims over the amended features disclosed by Avery in the above rejection.
Therefore, Examiner finds the Applicant argument is unpersuasive.
Prior Art Cited but not Applied
The following document(s) were found relevant to the disclosure but not applied:
US 2021/0386291 “Oddo" discloses patient monitoring and collecting physiological data and administer a therapeutic remedy to the patient with therapeutic delivery classifier and to modify the therapeutic remedy as a function of physiological data.
The references are relevant since it discloses analyzing a training data and classification.
Conclusion
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 extension fee 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 date of this final action.
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/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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