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
In the response filed on 27 October 2025, the following has occurred: Claims 1 and 11 have been amended.
Now claims 1-20 are pending.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 11 of U.S. Patent No. 11735310. Although the claims at issue are not identical, they are not patentable distinct from one another because the claims recite generation of parasitic infection programs using machine learning and determination of current position to provide a parasitic nutrition plan in a manner that is an obvious variant.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite system and method for generating a parasitic infection program. The limitations of:
Claim 1, which is representative of claim 11
generate, […], parasitic background training data, wherein the parasitic background training data comprises a plurality of parasitic biomarkers as input correlated to a plurality of parasitic disease as output; [… build …] a parasitic background […] model with the parasitic background training data, wherein the parasitic background […] model is configured to derive individual functions associated with at least a relationship between the parasitic training data for each parasitic biomarker of the plurality of parasitic biomarkers, and […] precondition one or more training examples in the parasitic background training data; generate a parasitic background as a function of the parasitic background […] model and at least a parasitic biomarker, wherein the at least a parasitic biomarker comprises a host factor; generate a parasitic disease assessment as a function of the parasitic background, wherein generating the parasitic disease assessment comprises classifying the parasitic background into the parasitic disease assessment using an assessment classification […] process; generate a parasitic infection program as a function of the parasitic disease assessment using a parasitic program model, wherein the parasitic program model is [… built …] with parasitic program training data comprising a plurality of parasitic disease assessments as input correlated to a plurality of parasitic infection programs as output; determine a current position of a user; and identify, using the determined current position of the user and the parasitic infection assessment, a parasitic intervention, wherein the parasitic intervention comprises at least a first nutrition element intended to be consumed by the user, wherein the at least a first nutrition element intended to be consumed is determined as a function of an identified parasitic infection, and wherein the identified parasitic infection is calculated using a first parasitic infection rate as a function of the determined current position of the user.
, as drafted, is a system, which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via human interaction with generic computer components. That is, by a human user interacting with a computing device using at least a processor, the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, but for the computing device using at least a processor, the claim encompasses collection of data and to learn patterns to be used in making assessments about a user’s location and providing a nutritional program to a user. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally, as drafted is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a computing device using at least a processor, the claim encompasses a user learning patterns and using the learned patterns to assess and make a program for a patient. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a computing device, which implements the abstract idea. The computing device using at least a processor is recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Figure 1, paragraphs [0009]-[0010]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these 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 claim recites the additional elements of “receive…” and “train a… machine-learning model… generate… as a function of the… machine-learning model”. The “receive…” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. The “train a… machine-learning model… generate… as a function of the… machine-learning model” is recited at a high-level of generality (i.e., using training and using a generic off-the shelf model) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does 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 elements of a computing device, to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using a generic hardware component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “receive…” and “train a… machine-learning model… generate… as a function of the… machine-learning model” were considered generally linking the abstract idea to particular technological environment and/or extra-solution activity. The “receive…” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network” is well-understood, routine, and conventional. The “train a… machine-learning model… generate… as a function of the… machine-learning model” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Astarita (20170003268): paragraph [0013]; Singh (20140141983): paragraphs [0163] and [0174]; Randolph (20210303818): paragraphs [0010]-[0014]; training and using a generic machine learning model is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2-10 and 12-20 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claims 2 and 12 describes use of a host body, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 3 and 13 recite organ classification, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 4-5 and 14-15 recite looking at either acute or prospective conditions, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claim 6 and 16 recite nutrition, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 7-8, 10, 17-18 and 20 recite training of models, however generic training of machine learning models was already considered and is incorporated herein.
Claims 9 and 19 recite calculation of a resistance rate, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 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.
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.
Claim(s) 1-2, 4, 6-8, 11-12, 14 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20170003268 (hereafter “Astarita”), in view of U.S. Patent Pub. No. 20140141983 (hereafter “Singh”), in view of U.S. Patent No. 11,056,242 (hereafter “Jain”).
Regarding (Currently Amended) claim 1, Astarita teaches a system for generating a parasitic infection program (Astarita: Figures 1-4, paragraph [0002], “methods and apparatus for profiling the components and associated conditions in a dermatological sample, and preparing personalized cosmetics or treatments”, paragraphs [0083]-[0085], “The dermatological condition can also be related to bacterial, viral, fungal and parasitic infections… The method and apparatus of the present disclosure can distinguish or stratify subjects with… bacterial versus viral versus parasitic versus fungal infection”), the system comprising:
a computing device, wherein the computing device is (Astarita: Figures 1, 3, 7, paragraph [0096], “A portable, small system for real-time analysis of dermatological samples was prepared. The system includes”) configured to:
generate, using at least a processor, parasitic background […] data, wherein the parasitic background […] data comprises a plurality of parasitic biomarkers as input […] (Astarita: paragraphs [0008]-[0010], “a control system… receiving dermatological data from a dermatological analysis of a sample provided by the customer”, paragraphs [0026]-[0028], “The sample can also be tested, screened or analyzed directly, i.e., wherein the samples ions are generated in situ directly from the subject.”, paragraph [0067], “quantification or semiquantification using a panel of selected markers belonging to one or more chemical classes”);
[… utilize …] a parasitic background machine-learning model with the parasitic background […] data, wherein the parasitic background machine learning model is configured to derive individual functions associated with at least a relationship between the parasitic training data for each parasitic biomarker of the plurality of parasitic biomarkers, and wherein the at least a processor is configured to precondition one or more training examples in the parasitic background training data; generate a parasitic background as a function of the parasitic background machine-learning model and at least a parasitic biomarker (Astarita: paragraphs [0006]-[0007], “profile or "fingerprint" dermatological samples, such as skin and hair samples… identifying at least one dermatological related sub-population, group or phenotype in the sample”, paragraphs [0012]-[0013], “assessments can be indicative of health status or the condition of the subject, or the quality of a cosmetic, supplement, food, diet or dosage form, For example, by comparing molecular profiles, patterns of variations between different groups can also be determined, which can be reflective of various conditions… machine learning and pattern-recognition analyses can be used to group observed changes and relate them to molecular groups, genus or species”, paragraph [0018], “Based on preselected markers, the sample can be classified or stratified according to predetermined groups”, paragraph [0026], “”, paragraph [0032], “preparation, such as filtering, extraction, isolation or combinations thereof… sample preparation”, paragraphs [0065]-[0068], “normalize for variation in instrument settings and sampling… normalize for difference between samples… normalize the concentration… converted into cationic derivatives”. The Examiner notes preparation of training samples reads on preconditioning under the broadest reasonable interpretation and in view of Applicant’s specification paragraph [0086]), […];
generate a parasitic disease assessment as a function of the parasitic background, wherein generating the parasitic disease assessment comprises classifying the parasitic background into the parasitic disease assessment using an assessment classification machine-learning process (Astarita: paragraphs [0006]-[0007], “identifying at least one dermatological related sub-population, group or phenotype in the sample, comparing the identified dermatological related sub-population, group or phenotype in the sample to one or more known dermatological profiles, and identifying at least one condition in the dermatological sample”, paragraph [0012], “assess the chemical profile or fingerprint of a dermatological and cosmetic related sample and associate it with a wide range of conditions or pathologies which can then be monitored, reduced, treated or prevented… by comparing molecular profiles, patterns of variations between different groups can also be determined, which can be reflective of various conditions”, paragraph [0013], “Statistical tools such as computational sorting, multivariate, machine learning and pattern-recognition analyses can be used to group observed changes and relate them to molecular groups, genus or species… subjects can be grouped with others having shared skin and hair conditions”);
generate a parasitic infection program as a function of the parasitic disease assessment using a parasitic program model (Astarita: paragraphs [0010], “generating a customized cosmetic product formula using the ingredients and dermatological data, and preparing the customized cosmetic product”, paragraph [0013], “Statistical tools such as computational sorting, multivariate, machine learning and pattern-recognition analyses can be used to group observed changes and relate them to molecular groups, genus or species… subjects can be grouped with others having shared skin and hair conditions… Based on these assessments of the present disclosure, personalized product care or treatment can be provided, Personalized products can also be designed to address the personal features of the customers etc. in real-time, without requiring extensive and expensive lab tests”, paragraph [0067], “quantification or semiquantification using a panel of selected markers belonging to one or more chemical classes… generate a composite biomarker panel associated with predetermined consumer care products, medical treatments and nutritional interventions”), […];
identify, using […] the parasitic infection assessment, a parasitic intervention, wherein the parasitic intervention comprises at least a first nutrition element intended to be consumed by the user, wherein the at least a first nutrition element intended to be consumed is determined as a function of an identified parasitic infection, and wherein the identified parasitic infection is calculated using a first parasitic infection […] (Astarita: Fig. 5, paragraph [0016]-[0019], “The results can be analyzed or compared to standards or other metrics to determine a personalized treatment, nutrition, cosmetic and/or pharmacological product for the patient or customer… determine the composition of the sample and whether the individual suffers from any condition, disorder or imbalance. The individual can be treated with a cosmetic product, a supplement, a food/diet or dosage form to change or correct the condition, disorder or imbalance”, paragraph [0067], “quantification or semi-quantification using a panel of selected markers belonging to one or more chemical classes as disclosed herein… to generate a composite biomarker panel associated with predetermined consumer care products, medical treatments and nutritional interventions”, paragraph [0083], “The dermatological condition can also be related to bacterial, viral, fungal and parasitic infections”, paragraph [0086], “The cosmetic, supplement, food, diet or a dosage form administered can be any that can treat, reduce or eliminate a condition identified using the real-time analysis of the present disclosure. It can be a population-based or a personalized nutritional or topical supplementation”).
Astarita may not explicitly teach (underlined below for clarity):
generate, using at least a processor, parasitic background training data, wherein the parasitic background training data comprises a plurality of parasitic biomarkers as input correlated to a plurality of parasitic disease as output;
train a parasitic background machine-learning model with the parasitic background training data, wherein the parasitic background machine learning model is configured to derive individual functions associated with at least a relationship between the parasitic training data for each parasitic biomarker of the plurality of parasitic biomarkers, and wherein the at least a processor is configured to precondition one or more training examples in the parasitic background training data;
wherein the at least a parasitic biomarker comprises a host factor;
wherein the parasitic program model is trained with parasitic program training data comprising a plurality of parasitic disease assessments as input correlated to a plurality of parasitic infection programs as output.
Singh teaches generate, using at least a processor, parasitic background training data, wherein the parasitic background training data comprises a plurality of parasitic biomarkers as input correlated to a plurality of parasitic disease as output (Singh: paragraph [0403], “The various statistical methods and models described herein can be trained and tested using a cohort of samples (e.g., serological and/or genomic samples) from healthy individuals and patients with a TNF.alpha.-mediated disease or disorder… One skilled in the art will know of additional techniques and diagnostic criteria for obtaining a cohort of patient samples that can be used in training and testing the statistical methods and models of the present invention”);
train a parasitic background machine-learning model with the parasitic background training data, wherein the parasitic background machine learning model is configured to derive individual functions associated with at least a relationship between the parasitic training data for each parasitic biomarker of the plurality of parasitic biomarkers, and wherein the at least a processor is configured to precondition one or more training examples in the parasitic background training data (Singh: paragraph [0101], “an empirically derived index that is based upon an analysis of a plurality of mucosal healing markers. In one aspect, the concentration of markers or their measured concentration values are transformed into an index by an algorithm resident on a computer… the algorithm can be trained with known samples and thereafter validated with samples of known identity”, paragraph [0174], “the algorithm comprises a learning statistical classifier system. In some instances, the learning statistical classifier system is selected from the group consisting of a random forest, classification and regression tree, boosted tree, neural network, support vector machine, general chi-squared automatic interaction detector model, interactive tree, multiadaptive regression spline, machine learning classifier, and combinations thereof”, paragraph [0403], “The various statistical methods and models described herein can be trained and tested using a cohort of samples (e.g., serological and/or genomic samples) from healthy individuals and patients with a TNF.alpha.-mediated disease or disorder”);
wherein the at least a parasitic biomarker comprises a host factor (Singh: paragraph [0008], “the present invention comprises measuring an array of one or a plurality of mucosal healing biomarkers at one or a plurality of time points over the course of therapy with a therapeutic agent to determine a mucosal healing index for selecting therapy, optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment”, paragraph [0223], “Mucosal injury is likely initiated by a combination of endogenous and environmental factors. At first stage, it is believed that food-derived compounds, viral and bacterial-derived factors, as well as host-derived factors, may cause epithelial cell destruction and activation of innate and adaptive immunity”);
wherein the parasitic program model is trained with parasitic program training data comprising a plurality of parasitic disease assessments as input correlated to a plurality of parasitic infection programs as output (Singh: paragraph [0174], “the algorithm comprises a learning statistical classifier system. In some instances, the learning statistical classifier system is selected from the group consisting of a random forest, classification and regression tree, boosted tree, neural network, support vector machine, general chi-squared automatic interaction detector model, interactive tree, multiadaptive regression spline, machine learning classifier, and combinations thereof”, paragraph [0178], “Once the diagnosis or prognosis of a subject receiving anti-TNF drug therapy has been determined or the likelihood of response to an anti-TNF drug has been predicted… the present invention may further comprise recommending a course of therapy based upon the diagnosis, prognosis, or prediction”, paragraph [0261], “The index can be used to determine or make or aid in making a clinical decision… the algorithm can be trained with known samples and thereafter validated with samples of known identity”, paragraph [0403], “The various statistical methods and models described herein can be trained and tested using a cohort of samples (e.g., serological and/or genomic samples) from healthy individuals and patients with a TNF.alpha.-mediated disease or disorder”).
One of ordinary skill in the art before the effective filing date would have found it obvious to train the machine learning models as taught by Singh within the use of machine learning models to predict parasitic infection as taught by Astarita with the motivation of “improve the accuracy of selecting therapy, optimizing therapy, reducing toxicity” (Singh: paragraph [0258]).
Astarita and Singh may not explicitly teach (underlined below for clarity):
determine a current position of a user; and
identify, using the determined current position of the user and the parasitic infection assessment, a parasitic intervention, wherein the parasitic intervention comprises at least a first nutrition element intended to be consumed by the user, wherein the at least a first nutrition element intended to be consumed is determined as a function of an identified parasitic infection, and wherein the identified parasitic infection is calculated using a first parasitic infection rate as a function of the determined current position of the user.
Jain teaches determine a current position of a user (Jain: Figure 22, Column 14, lines 30-40, “location tracking data for at least some of the user devices is based on global positioning system (GPS) data generated using a GPS receiver”, Column 112, line 65-Column 113, line 5, “The process 2200 includes receiving location data for individuals (step 2202). This can include automatic location sensing of the location of a user device of a user, e.g., a smart phone, wearable device, etc. For example, location data can be tracked using GPS data”); and
identify, using the determined current position of the user and the parasitic infection assessment, a parasitic intervention, wherein the parasitic intervention comprises at least a first nutrition element intended to be consumed by the user, wherein the at least a first nutrition element intended to be consumed is determined as a function of an identified parasitic infection, and wherein the identified parasitic infection is calculated using a first parasitic infection rate as a function of the determined current position of the user (Jain: Figures 4, 11-12, 21, Column 6, lines 1-25, “Information about infection rates and other disease measures for the communities where a user has been are used to better predict exposure and likelihood of infection… the system can use monitoring data indicating locations a user has been, amounts of time spent in the locations, occupancy or traffic at the locations, and other data to estimate exposure. The system can progressively adapt the level of monitoring and types of interactions with a user according to the user's predicted exposure level or likelihood of infection.”, Column 49, lines 45-65, “the community data can include infection rates for COVID-19 for different regions of the community, trends of COVID-19 infection rates in the conununity, treatment outcomes in the community (e.g., death rates, hospitalization rates, etc. due to COVID-19)… the computer system 110 may re-evaluate the predictions for the user (e.g., exposure level, susceptibility to the disease, likelihood 60 of infection, etc.) and also re-evaluate the actions that the computer system 110 recommends or carries out for the user 102a.”, Column 115, line 65-Column 116, line 50, “determining disease exposure scores for individuals based on their entry to the geofenced areas (step 2020). The disease exposure score can be a measure of how likely and/or how intensely a person was exposed to COVID-19 or another disease… The computer system 110 can compare the disease exposure measure for a user to reference data to determine notifications, recommendations, and other interventions to provide”. The Examiner notes the intervention may be a nutritional intervention (see, Jain column 46, lines 45 -55)).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using a comparison of the current position of the user to make determinations about interventions with the determination of a nutrition program for parasitic infections as taught by Astarita and Singh with the motivation of “improve the scoring of location tags, ultimately resulting in a more accurate system over time” (Jain: Column 4, lines 40-45).
Regarding (Original) claim 2, Astarita, Singh and Jain teach the limitations of claim 1, and further teach the limitations of wherein the at least a parasitic biomarker comprises a change of parasite as a function of immune response of a host body (Singh: paragraph [0107], “The term "immunosuppressive agent" includes any substance capable of producing an immunosuppressive effect, e.g., the prevention or diminution of the immune response, as by irradiation or by administration of drugs such as anti-metabolites, anti-lymphocyte sera, antibodies, etc.”, paragraph [0223], “Mucosal injury is likely initiated by a combination of endogenous and environmental factors… Specific adaptive immune responses”, paragraph [0304], “body weight, hematopoiesis, angiogenesis, wound healing, insulin resistance, the immune response, and the inflammatory response.”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 4, Astarita, Singh and Jain teach the limitations of claim 1, and further teach the limitations of wherein: the parasitic disease assessment comprises a prospective medical condition predictive classification; and the computing device is further configured to generate the parasitic infection program as a function of the prospective medical condition predictive classification (Astarita: paragraph [0013], “Statistical tools such as computational sorting, multivariate, machine learning and pattern-recognition analyses can be used to group observed changes and relate them to molecular groups, genus or species… subjects can be grouped with others having shared skin and hair conditions… Based on these assessments of the present disclosure, personalized product care or treatment can be provided, Personalized products can also be designed to address the personal features of the customers etc. in real-time, without requiring extensive and expensive lab tests”, paragraph [0084], “dermatological conditions that can be difficult to determine by visual inspection and can benefit from the present disclosure include skin cancer”; Singh: paragraph [0003], “risk of colectomy and colorectal cancer in UC patients”. The Examiner notes these are in agreements with Applicant’s specification paragraph [0026], as examples of prospective conditions).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 6, Astarita, Singh and Jain teach the limitations of claim 1, and further teach the limitations of wherein the parasitic infection program comprises a parasitic infection nutrition program, wherein the parasitic infection nutrition program comprises a frequency of at least a nutrition element (Astarita: paragraph [0003], “a cosmetic treatment, formulation, food/diet, dosage form, etc. can be obtained”, paragraph [0016], “The results can be analyzed or compared to standards or other metrics to determine a personalized treatment, nutrition, cosmetic and/or pharmacological product for the patient or customer”, paragraph [0077], “The amount of each ingredient can also be adjusted to address or treat the condition or disease”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 7, Astarita, Singh and Jain teach the limitations of claim 6, and further teach the limitations of wherein the computing device is further configured to determine the at least a nutrition element (Astarita: paragraph [0003], “a cosmetic treatment, formulation, food/diet, dosage form, etc. can be obtained”, paragraph [0016], “The results can be analyzed or compared to standards or other metrics to determine a personalized treatment, nutrition, cosmetic and/or pharmacological product for the patient or customer”, paragraph [0077], “The amount of each ingredient can also be adjusted to address or treat the condition or disease”),
wherein determining the at least a nutrition element comprises: training a nutrition model using a nutrition machine-learning process and training data, wherein training data includes a plurality of data entries of nutrition amounts correlated to nutrition elements; and determining the at least a nutrition element as a function of the at least a nutrient amount using the nutrition model (Singh: paragraph [0120], “The term "nutrition-based therapy" includes butyrate, probiotics… vitamins, proteins, macromolecules, and/or chemicals that promote mucosal healing such as growth and turnover of intestinal mucosa”, paragraph [0157], “the therapy is a nutrition therapy. In particular embodiments, the nutrition therapy is a special carbohydrate diet”, paragraph [0174], “the algorithm comprises a learning statistical classifier system”, paragraph [0178], “the present invention may further comprise recommending a course of therapy based upon the diagnosis, prognosis, or prediction”, paragraph [0261], “The index can be used to determine or make or aid in making a clinical decision… the algorithm can be trained with known samples and thereafter validated with samples of known identity”, paragraph [0403], “The various statistical methods and models described herein can be trained and tested using a cohort of samples (e.g., serological and/or genomic samples) from healthy individuals and patients with a TNF.alpha.-mediated disease or disorder”. The Examiner notes one of ordinary skill in the art would understand if the treatment is nutrition elements the testing data for treated individuals will correspond with what is required of the claim under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 8, Astarita, Singh and Jain teach the limitations of claim 1, and further teach the limitations of wherein the computing device is further configured to calculate a parasitic infection relapse rate as a function of the parasitic infection program and the host factor of the at least a parasitic biomarker (Singh: paragraph [0005], “We have identified novel markers of mucosal healing that are predictive of the risk of disease relapse and disease remission. A measurement of mucosal healing can be used to periodically assess disease status in patients receiving a therapy regimen”, paragraph [0099], “The present invention provides non-invasive methods for monitoring mucosal healing patients receiving anti-TNF therapy. In addition, the present invention provides methods of predicting therapeutic response, risk of relapse”, paragraph [0220], “predicting risk of relapse. In some embodiments, the methods include detecting, measuring and/or determining the presence and/or levels of markers”), wherein calculating the parasitic infection relapse rate comprises:
training a relapse rate model using a relapse machine-learning process and relapse rate training data, wherein the relapse rate training data comprises a plurality of parasitic infection programs and host factors as input and a plurality of parasitic infection relapse rates as output; and determining the parasitic infection relapse rate as a function of the parasitic infection program and the host factor using the trained relapse rate model (Singh: paragraph [0099], “The present invention provides non-invasive methods for monitoring mucosal healing patients receiving anti-TNF therapy. In addition, the present invention provides methods of predicting therapeutic response, risk of relapse”, paragraph [0174], “the algorithm comprises a learning statistical classifier system”, paragraph [0178], “the present invention may further comprise recommending a course of therapy based upon the diagnosis, prognosis, or prediction”, paragraph [0261], “The index can be used to determine or make or aid in making a clinical decision… the algorithm can be trained with known samples and thereafter validated with samples of known identity”, paragraph [0403], “The various statistical methods and models described herein can be trained and tested using a cohort of samples (e.g., serological and/or genomic samples) from healthy individuals and patients with a TNF.alpha.-mediated disease or disorder”. The Examiner notes one of ordinary skill in the art would understand if the outcome is relapse data the testing data for treated individuals will correspond with what is required of the claim under the broadest reasonable interpretation, Additionally the Examiner notes this is in agreement with paragraphs [0067]-0069] of Applicant’s specification).
The motivation to combine is the same as in claim 1, incorporated herein
REGARDING CLAIM(S) 11-12, 14 and 16-18
Claim(s) 11-12, 14 and 16-18 is/are analogous to Claim(s) 1-2, 4 and 6-8, thus Claim(s) 11-12, 14 and 16-18 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1-2, 4 and 6-8.
Claim(s) 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20170003268 (hereafter “Astarita”), U.S. Patent Pub. No. 20140141983 (hereafter “Singh”) and U.S. Patent No. 11,056,242 (hereafter “Jain”) as applied to claims 1 and 11 above, and further in view of U.S. Patent Pub. No. 20210303818 (hereafter “Randolph”).
Regarding (Original) claim 3, Astarita, Singh and Jain teach the limitations of claim 1, but may not explicitly teach wherein: the parasitic disease assessment comprises an organ type classification; and the computing device is further configured to generate the parasitic infection program as a function of the organ type classification.
Randolph teaches wherein: the parasitic disease assessment comprises an organ type classification; and the computing device is further configured to generate the parasitic infection program as a function of the organ type classification (Randolph: paragraph [0011], “combined FIM with ConvNets to analyze particles, such as protein aggregates in drug products, genetically engineered bacteria cultures, and pathogens in blood among others… ConvNets are a family of neural networks capable of learning relevant properties of an input image that are useful when performing computer vision tasks such as object identification, classification, and statistical representation… ConvNets can be trained using high-throughput FIM images”, paragraph [0028]-[0029], “applying machine learning to detect and analyze cells and microbial pathogens in biological samples in high-throughput systems without labeling individual pathogens… Exemplary biological samples may include:… organ culture… a disease condition may be associated with the type or quantity of the extracted feature of interest or the type and quantity of cells found in the biological sample”. Organ culture type is a classification that is used for disease determination, which teaches what is required under the broadest reasonable interpretation).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using organ classification as taught by Randolph with the classification machine learning model for parasite infection program generation as taught by Astarita, Singh and Jain with the motivation of providing “rapid and accurate methods for identifying infectious pathogens” (Randolph: paragraph [0004]).
REGARDING CLAIM(S) 13
Claim(s) 13 is/are analogous to Claim(s) 3, thus Claim(s) 13 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3.
Claim(s) 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20170003268 (hereafter “Astarita”), U.S. Patent Pub. No. 20140141983 (hereafter “Singh”) and U.S. Patent No. 11,056,242 (hereafter “Jain”) as applied to claims 1 and 11 above, and further in view of U.S. Patent Pub. No. 20210048428 (hereafter “Lake”).
Regarding (Original) claim 5, Astarita, Singh and Jain teach the limitations of claim 1, and further teach the limitations of wherein: the parasitic disease assessment comprises a duration classification, […]; and the computing device is further configured to generate the parasitic infection program as a function of the duration classification (Singh: paragraph [0008], “measuring an array of one or a plurality of mucosal healing biomarkers at one or a plurality of time points over the course of therapy with a therapeutic agent to determine a mucosal healing index for selecting therapy, optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment”, paragraph [0068], “it is possible to predict whether the individual will be responsive to the therapy over the given time period”).
Astarita, Singh and Jain may not explicitly teach (Underlined below for clarity):
wherein the duration classification comprises an acute disease;
Lake teaches wherein the duration classification comprises an acute disease (lake: paragraph [0090], “the infectious disease or disorder is an infectious disease or disorder associated with… gastroenteritis”, paragraph [0143], “VF patients with acute, chronic, or disseminated disease”. Gastroenteritis is an acute condition);
One of ordinary skill in the art before the effective filing date would have found it obvious to include an acute condition as taught by Lake with the conditions as taught by Astarita, Singh and Jain with the motivation of providing “a fast, cost-effective, highly sensitive and specific method” (Lake: paragraph [0004]).
REGARDING CLAIM(S) 15
Claim(s) 15 is/are analogous to Claim(s) 5, thus Claim(s) 15 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 5.
Claim(s) 9-10 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20170003268 (hereafter “Astarita”), U.S. Patent Pub. No. 20140141983 (hereafter “Singh”) and U.S. Patent No. 11,056,242 (hereafter “Jain”) as applied to claims 1 and 11 above, and further in view of U.S. Patent Pub. No. 20220111378 (hereafter “Koelle”).
Regarding (Original) claim 9, Astarita, Singh and Jain teach the limitations of claim 1, and further teach the limitations of wherein the computing device is further configured to: […]; generate a second parasitic infection program as a function of the program resistance rate (Singh: paragraph [0066], “selecting an appropriate anti-TNF therapy for initial treatment, by determining when or how to adjust or modify (e.g., increase or decrease) the subsequent dose of an anti-TNF drug, by determining when or how to combine an anti-TNF drug (e.g., at an initial, increased, decreased, or same dose) with one or more immunosuppressive agents such as methotrexate (MTX) or azathioprine (AZA), and/or by determining when or how to change the current course of therapy”).
Astarita, Singh and Jain may not explicitly teach (underlined below for clarity):
calculate a program resistance rate as a function of the parasitic infection program and the at least a parasitic biomarker;
Koelle teaches calculate a program resistance rate as a function of the parasitic infection program and the at least a parasitic biomarker (Koelle: paragraph [0003], “calculate a program resistance rate as a function of the parasitic infection program and the at least a parasitic biomarker rapidly identify the infectious agents causing disease and to determine their antimicrobial resistance (AMR) profile”, paragraph [0007], “determine the sensitivity of a target infection to a range of treatment choices. This will improve the precision of treatment”);
One of ordinary skill in the art before the effective filing date would have dound it obvious to include using a program resistance rate as taught by Koelle with the modification of treatment plans as taught by Astarita, Singh and Jain with the motivation of “improve the precision of treatment” (Koelle: paragraph [0007]).
Regarding (Original) claim 10, Astarita, Singh, Jain and Koelle teach the limitations of claim 9, and further teach the limitations of wherein calculating the program resistance rate comprises: training a resistance rate model using a resistance machine-learning process and resistance rate training data, wherein the resistance rate training data comprises a plurality of parasitic infection programs and parasitic biomarkers as input correlated to a plurality of program resistance rates as output; and determining the program resistance rate as a function of the parasitic infection program and the at least a parasitic biomarkers using the trained resistance machine-learning process (Koelle: paragraph [0003], “calculate a program resistance rate as a function of the parasitic infection program and the at least a parasitic biomarker rapidly identify the infectious agents causing disease and to determine their antimicrobial resistance (AMR) profile”, paragraph [0007], “determine the sensitivity of a target infection to a range of treatment choices. This will improve the precision of treatment”, paragraph [0094], “Assay results comprising data on the sensitivity and/or resistance of the target to various therapeutic formulas can also be stored to the memory of an adaptive machine learning tool, and the assay results are used by the adaptive machine learning tool in longitudinal and/or geographic analyses of target sensitivities and resistances to particular therapeutic formulas”; Singh: paragraph [0099], “The present invention provides non-invasive methods for monitoring mucosal healing patients receiving anti-TNF therapy. In addition, the present invention provides methods of predicting therapeutic response, risk of relapse”, paragraph [0174], “the algorithm comprises a learning statistical classifier system”, paragraph [0178], “the present invention may further comprise recommending a course of therapy based upon the diagnosis, prognosis, or prediction”, paragraph [0261], “The index can be used to determine or make or aid in making a clinical decision… the algorithm can be trained with known samples and thereafter validated with samples of known identity”, paragraph [0403], “The various statistical methods and models described herein can be trained and tested using a cohort of samples (e.g., serological and/or genomic samples) from healthy individuals and patients with a TNF.alpha.-mediated disease or disorder”).
The motivation to combine is the same as in claim 9, incorporated herein.
REGARDING CLAIM(S) 19 and 20
Claim(s) 19 and 20 is/are analogous to Claim(s) 9 and 10, thus Claim(s) 19 and 20 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 9 and 10.
Response to Arguments
Applicant's arguments filed 27 October 2025 have been fully considered but they are not persuasive. Applicants’ arguments will be addressed herein below in the order in which they appear in the response filed on 27 October 2025.
Double Patenting
Regarding the rejection of claims 1-20, the Examiner notes both the ‘310 Patent and instant case determine and use a current position of a user to provide a nutritional element as a parasitic intervention and are not patentable distinct from one another to one of ordinary skill in the art, as such, the rejection is maintained.
Rejections under 35 U.S.C. § 101
Regarding claims 1-20, the Examiner has considered the Applicant’s arguments but does not find them persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons:
Applicant argues:
Applicant submits that, according to MPEP 2106.04, independent claims 1 and 11, and their dependent claims, are allowable under Step 2A and/or 2B of the eligibility analysis… Claim 1 is directed to a specific system configured to generate a parasitic infection program through machine-learning-based processing of parasitic biomarker data and contextual user location data… These steps are not activities that can be performed mentally or that constitute organizing human behavior. Instead, they involve data processing techniques implemented by a computing device to model biological interactions and generate predictive infection programs in an automated, algorithmic manner… As such, Applicant respectfully submits that amended claim 1 does not recite a method of organizing human activity or any other judicial exception under Step 2A, Prong One. Instead, it is directed to a specific, concrete, and technical application of machine-learning and data analytics for improving the generation of parasitic infection programs… With reference to the July 2024 Subject Matter Eligibility Examples, in Example 47, Claim 3, the eligible claim recites a method using an artificial neural network (ANN) to detect malicious network packets. The claim integrates a judicial exception, such as mathematical operations or mental processes, into a practical application by combining steps that result in improved network security… Just as the training and optimization of the ANN in Example 47 provides a technical improvement in real-time network security, the training and conditioning processes of the present claims provide a technical improvement in generating and classifying parasitic backgrounds for use in infection assessment and intervention. The claimed configuration enhances the system's ability to automatically identify and generate parasitic interventions in real time, based on computed infection rates tied to user location data. This constitutes a direct application of machine learning to a technical field, improving automated disease modeling and intervention systems… With reference to Example 48, Claim 2, the eligible claim involves a method for separating speech signals from multiple sources using a deep neural network (DNN)… This process represents a concrete and practical application of machine learning to biological and geospatial data, improving the precision and personalization of parasitic infection response systems. Just as Example 48's DNN achieves higher-quality results by structuring and applying embeddings to separate complex audio sources, the present claims utilize the trained parasitic background and program models to dynamically generate actionable interventions based on real-world biological and environmental inputs… Accordingly, the present claims integrate any alleged judicial exception into a practical application by reciting specific technological components and data-processing operations that improve the functioning of a machine-learning system in the field of parasitic infection modeling… This nonconventional and specific arrangement of steps provides a technical improvement in the field of parasitic disease modeling and intervention. In particular, the claimed configuration improves the functioning of the computing system by enabling the generation of accurate parasitic disease assessments and context-specific interventions in real time, based on both trained biological models and dynamic geolocation data
The Examiner respectfully disagrees.
It is respectfully submitted, that under the broadest reasonable interpretation the claims are directed toward organization of the interactions between a human user and various generic computer components to collect, organize and provide to the human user a result of the organized data, which as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. The claim is direct toward the certain method of organizing human activity grouping of abstract ideas.
The claims do not recite a technical solution to a technical problem recited in Applicant’s specification, first the Examiner notes no portions of Applicant’s specification are argued for any recitations of technical problems, instead Applicant argues Examples 47 and 48, however these examples claim additional elements that provide technical solutions to technical problems recited in their respective background (i.e., specification), Applicant does not argue any portions of their specification and instead argues the “technical problem” of “parasitic disease detection and intervention”, however this is not a technical problem rooted in computer hardware technology, parasitic disease detection and intervention are public health problems or patient/provider problems (i.e., human activity problems), the claims may improve upon the abstract idea, however an improved abstract idea is still an abstract idea. The claims at best merely train and use an off the shelf machine learning model in a generic manner which is not a practical application and/or significantly more. As no claimed additional element recites a technical solution to a technical problem recited in Applicant’s specification and/or an improvement in the functionality of the computer, the argument is unpersuasive.
Rejections under 35 U.S.C. § 103
Regarding claims 1-20, the Examiner has considered the Applicant's arguments but does not find them persuasive in view of the new grounds of rejection as necessitated by amendment. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
The Office has not asserted that Astarita teaches, suggests, or motivates "determining a current position of a user and identifying, using the determined current position of the user… Singh does not cure the deficiencies of Astarita… Randolph fails to cure the deficiencies of Astarita and Singh… Lake fails to cure the deficiencies of Astarita and Singh… Koelle fails to cure the deficiencies of Astarita and Singh.
The Examiner respectfully disagrees.
It is respectfully submitted, that newly applied Jain (11,056,242), see above but at least Column 6, lines 1-25, in combination with the teachings of Astarita and Sing teach what is required of the amended claims under the broadest reasonable interpretation and would be prima facie obvious to combine with the motivation of “improve the scoring of location tags, ultimately resulting in a more accurate system over time” (Jain: Column 4, lines 40-45). Therefore, in view of the new grounds of rejection as necessitated by amendment the argument is moot.
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 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.
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/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684