DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Applicant's submission filed on 03/19/2025 has been entered.
Priority
The present disclosure is a CIP of the prior-filed application 16/430,400, which has a priority date of 06/03/2019. However, the prior-filed application 16/430,400 does not provide a support for the limitation “receiving, by the at least a server, at least a user entry containing the alimentary self-fulfillment action, wherein the user entry comprises a digital reproduction from a user device” of claims 1 and 11. Further, the prior-filed application 16/430,400 does not provide a support for the claims 6-7, 9, 16-17 and 19. Therefore, the limitation above of claims 1 and 11 and the claims 5-7, 9, 15-17 and 19 are given priority date of the present application - 11/03/2021.
Status of the Claims
Claims 1, 8, 11, 16, 18 have been amended. Claims 2, 12 are canceled. Claims 1, 3-11, 13-20 are pending.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3-8, 10, 13-18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tran et al. (US 20180001184) in view of Amin (US 20190062813).
Regarding claim 1, Tran teaches a system for self-fulfillment of an alimentary instruction set based on vibrant constitutional guidance, the system comprising: at least a server ([0516] “include one or more computers or servers that facilitate the storing and retrieval”);
a wearable sensor configured to receive a biological extraction ([0491] “wearable watches/clothing/shoes that monitor activity, heart rate, ECG”) comprising a plurality of biomarkers including at least a pulse rhythm ([0395] “biological parameters … include … heart rate … arrhythmia … pulse”);
a diagnostic engine operating on the at least a server, the diagnostic engine configured to generate a diagnostic output for a user based on the biological extraction ([0410] “probability model can be applied to recover the correct gene information for diagnosis”, [0411], [0420]-[0437], [0543] “systems as disclosed herein may comprise … predicting, diagnosing, and/or prognosing a status or outcome of a disease or condition in a subject based on one or more biomedical outputs”),
wherein the diagnostic output comprises a prognostic label ([0437], [0534] “prognostic biomarkers (predicting future disease course, including recurrence and response to therapy, and monitoring efficacy of therapy)”) and an ameliorative process label ([0412], [0492], [0543]) and wherein generating the diagnostic output comprises:
generating, via a prognostic label learner, the prognostic label as a function of a first training set ([0504], [0509], [0509], [0512]-[0513]), wherein the first training set comprises physiological state data inputs correlated ([0196]) to prognostic label outputs ([0438] “obtains user's physiological … data via one or more sensors … and use one or more machine learning techniques”, [0543] “prognosing a status or outcome of a disease or condition in a subject based on one or more biomedical outputs” [0559] “statistical analyzer is trained with training data … value outside of this range is flagged [-aka correlated-] by the statistical analyzer as a dangerous condition [-prognostic label output-] … as an event … that can cause physiological … damage to the patient”, [0660]),
wherein the prognostic label learner comprises a first machine learning model configured to generate at least a portion of the prognostic label ([0398]); and
generating, via an ameliorative process label learner, the ameliorative process label as a function of a second training set ([0511]-[0512], [0394], [0329]-[0330]),
wherein the second training set comprises the prognostic label outputs generated by the first machine learning model correlated to ameliorative process label outputs ([0543] “prognosing a status or outcome of a disease or condition in a subject based on one or more biomedical outputs”, [0559] “network which has been trained to flag potentially dangerous conditions … the statistical analyzer is trained with training data”, [0504] “neural networks may be trained using all the health related characteristics of the members having a particular condition … provide a weighted answer indicative of the likelihood the person will acquire the condition … predict an incidence of the health condition”, [0509] “"trained" neural network may then be able to receive the health related characteristics of a member to predict whether they will acquire the health condition … resulting likelihood of occurrence may be used to rank the population in terms of likelihood of acquiring the condition” - aka machine learning generates prognostic label output; [0505] “An intervention may be recommended in response to the likelihood of developing the health condition”, [0509] “ranking [prognostic label output] may then be used to prioritize intervention strategies” - aka correlate ameliorative process label outputs with prognostic label),
wherein the ameliorative process label learner comprises a second machine learning model configured to generate at least a portion of the ameliorative process label ([0491], [0508]-[0509], [0513], [0648], [0504], [0509], [0512]-[0513], [0559]);
an alimentary instruction set generator module operating in the at least a server configured to:
generate at least an alimentary instruction set as a function of the diagnostic output ([0393] “each data unit can be linked to a matching therapy”, [0474], [0506]-[0507]); and
update the at least an alimentary instruction set as a function of an alimentary self- fulfillment action ([0245]-[0246], [0248], [0266], [0298]-[0299]); and
a fulfillment module ([0451] “communication message include self-monitoring of both eating habits and physical activity”) operating on the at least a server ([0466]) configured to:
receive, from a user device, at least a user entry containing the alimentary self- fulfillment action ([0287], [0451], [0493], [0507]-[0508], [0513]-[0514]), wherein the user entry comprises a digital reproduction from the user device ([0595] “To monitor progress, the process takes user entered calorie data and optionally captures images of meals using a mobile device such as a mobile camera”, “camera captures images of the food being served to the patient”).
Tran teaches tracking user interactions, self-reporting and obtaining user feedback to optimize desired outcome [0266]. See specifically “monitor a patient's status between appointments to timely initiate, modify, or terminate a treatment plan as necessary. For example, a patient's reported medication use may convey whether the patient is taking too little or too much medication … in comparison to a prescribed treatment plan … determine whether a given treatment plan adequately addresses a patient's needs based on review of the patient's reported” [0298]. Which construed to be analogous to the limitation “update the at least an alimentary instruction set as a function of an alimentary self- fulfillment action.”
However, to further obviate such reasoning Amin discloses update ([0153]) the at least an alimentary instruction set as a function of an alimentary self- fulfillment action ([0042] “recommendations can be at any level of specificity/granularity, including how much of a recommended food to eat … how to prepare a recommended food … how to perform a recommended exercise and/or workout routine/regime … and so on”, [0085] “recommend various courses of action accordingly ( e.g., dietary changes, exercise changes”, [0088]).
NOTE I Amin further teaches –
a prognostic label learner, the prognostic label as a function of a first training set, wherein the prognostic label learner comprises a first machine learning model configured to generate at least a portion of the prognostic label ([0049] “system can be explicitly and/or implicitly trained to provide proper/appropriate diagnoses”, [0116]-[0117]); and generating, via an ameliorative process label learner, the ameliorative process label as a function of a second training set ([0014] “the diagnostic component can be trained (e.g., explicitly and/or implicitly) with known waste samples and/or known recommended treatments”, [0046], [0144]).
NOTE II Amin further teaches –
the first training set comprises physiological state data inputs correlated to prognostic label outputs ([0046] “Lactobacillus bacteria (e.g. a type of beneficial microorganism) in his/her gastrointestinal tract”, which is the physiological data and is used “via explicit/implicit training”, [0144] “monitoring the user's microflora profile over time to learn what recommendations work best for the user, explicit or implicit training, running simulations to test potential recommended courses of action”, [0146] “employ various classification (explicitly trained (e.g., via training data) as well as implicitly trained ( e.g., via observing behavior, preferences, historical information, receiving extrinsic information, and so on)”, [0015] “diagnostic component can be trained (e.g., explicitly and/or implicitly) with known waste samples and/or known recommended treatments”) and
wherein the second training set comprises the prognostic label outputs generated by the first machine learning model correlated to ameliorative process label outputs ([0116] “diagnostic component can be explicitly and/or implicitly trained (e.g., shown which recommendations best resolve which infirmities/conditions”, [0049] “system can be explicitly and/or implicitly trained to provide proper/appropriate diagnoses and/or recommendations (e.g., given known samples to analyze, shown which recommended treatments work best for given samples”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tran to update alimentary instruction set as a function of an alimentary self- fulfillment action as disclosed by Amin. Doing so provides more convenient, automated, and real-time (or near real-time) analysis and corresponding diagnoses and/or recommendations (Amin [0004]).
Claim 11 recites substantially the same limitations as claim 1, and is rejected for substantially the same reasons.
Regarding claims 3 and 13, Tran as modified teaches the system and the method, wherein the fulfillment module is further configured to:
generate a list of suggested self-fulfillment actions (Amin [0042], Tran [0274], [0276], [0492]-[0493], [0514]), wherein generating comprises:
receiving self-fulfillment action training data, wherein the training data correlates the user entry to alimentary instruction set (Amin [0045], Tran [0504], [0509]);
training a self-fulfillment action classifier as a function of the training data (Amin [0014] “the diagnostic component can be trained (e.g., explicitly and/or implicitly) with … known recommended treatments”, Tran [0266], [0283]);
classifying the alimentary instruction set to a list of self-fulfillment actions as a function of the self-fulfillment action classifier (Amin [0146]-[0147], Tran [0204], [0253], [0398]); and
output the list of self-fulfillment actions to the user device (Amin [0015] “notification component can also inform the user of the determined diagnoses and/or recommendations ( e.g., likely illnesses, suggested changes to diet, suggested exercises, suggested medicines”, “notification component can include an internet connection such that recommended foods, medicines, and/or commercial products”, Tran [0299], [0305]).
Regarding claims 4 and 14, Tran as modified teaches the system and the method, wherein the classifier comprises a natural language processing algorithm (Tran [0491] see “the natural language processing of one or more clinical assessments and/or clinical narratives”, [0514] “data obtained from the natural language processing”, [0518]).
Regarding claims 5 and 15, Tran as modified teaches the system and the method, wherein the classifier comprises a fuzzy logic-based classifier (Amin [0146]-[0147], Tran [0559]).
Regarding claims 6 and 16, Tran as modified teaches the system and the method, wherein the fulfillment module is further configured to:
generate a first objective function of the list of self-fulfillment actions (Tran [0508]-[0509], [0511]); and
rank the list of self-fulfillment actions as a function of an optimization of the first objective function (Tran [0203], [0222], [0509]-[0510]).
Regarding claims 7 and 17, Tran as modified teaches the system and the method, wherein the first objective function further comprises a linear objective function (Amin [0147], Tran [0624], [0196], [0398]).
Regarding claims 8 and 18, Tran as modified teaches the system and the method, wherein the alimentary instruction set is generated as a function of a location of the user (Amin [0045], Tran [0203] “set of criteria may include demographic information of one or more patients, such as … location of residence”, [0398], [0493], [0558]).
Regarding claims 10 and 20, Tran as modified teaches the system and the method, wherein the at least a server is configured to receive a constitutional restriction from the user (Tran [0491]-[0493] see “allergies”, “Lifestyle characteristic may include a specific member's behavior characteristics .. (e.g., what types of food does the member eat, and how often … whether the member drinks alcohol”, [0559] “training data where certain signals are determined to be undesirable for the patient”, Amin [0016], [0045]-[0046] “if the user is allergic to almonds … system can refrain from recommending that the user increase his/her consumption of almonds”).
Claims 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tran as modified and in further view of Martinez et al. (US 20200303046) or WU (US 20200322439).
Regarding claims 9 and 19, Tran as modified does not explicitly teach, however Martinez discloses the system and the method, wherein the location of the user is determined as a function of a strength of a network ([0024], [0028]). WU analogously discloses the same in Figure 4:S22.
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Tran as modified to determine location as a function of the strength of a WI-FI network as disclosed by Martinez or WU. Doing so provides patient location and movements based upon a device-free indoor positioning technology that can monitor patients in a monitored space based on passively observing changes in the environment (Martinez [0035]).
Claims 6-7 and 16-17 is/are alternatively rejected under 35 U.S.C. 103 as being unpatentable over Tran as modified and in further view of Mohiuddin et al. (US 20220157458) or Bailey et al. (US 20160335568).
Regarding claims 6 and 16, Tran does not explicitly teach, however Mohiuddin discloses the system and the method, wherein the fulfillment module is further configured to:
generate a first objective function of the list of self-fulfillment actions ([0036]); and rank the list of self-fulfillment actions as a function of an optimization of the first objective function ([0035]-[0036]).
Bailey discloses the same in [0023].
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Tran as modified to include a linear objective function as disclosed by Mohiuddin or Bailey. Doing so provides various embodiments for data correlation between categories of data elements (Mohiuddin [0023]) and obtains a high quality solutions (Bailey [0022]).
Regarding claims 7 and 17, Tran as modified teaches the system and the method, wherein the first objective function further comprises a linear objective function (Mohiuddin [0036], Bailey [0023]).
Claims 3 and 13 is/are alternatively rejected under 35 U.S.C. 103 as being unpatentable over Tran as modified and in further view of Gilutz et al. (US 20220230731).
Regarding claims 3 and 13, Tran as modified teaches the system and the method, wherein the fulfillment module is further configured to:
generate a list of suggested self-fulfillment actions (Amin [0042], Tran [0274], [0276], [0492]-[0493], [0514]), wherein generating comprises:
receiving self-fulfillment action training data, wherein the training data correlates the user entry to alimentary instruction set (Amin [0045], Tran [0504], [0509]);
training a self-fulfillment action classifier as a function of the training data (Amin [0014] “the diagnostic component can be trained (e.g., explicitly and/or implicitly) with … known recommended treatments”, Tran [0266], [0283]);
classifying the alimentary instruction set to a list of self-fulfillment actions as a function of the self-fulfillment action classifier (Amin [0146]-[0147], Tran [0204], [0253], [0398]); and
output the list of self-fulfillment actions to the user device (Amin [0015] “notification component can also inform the user of the determined diagnoses and/or recommendations ( e.g., likely illnesses, suggested changes to diet, suggested exercises, suggested medicines”, “notification component can include an internet connection such that recommended foods, medicines, and/or commercial products”, Tran [0299], [0305]).
Tran as modified teaches receiving user feedback and applying a plurality of machine learning algorithms to determine the best treatments (instructions) for the user. The machine learning algorithms include various classifications of the feedback data.
However, to further obviate such reasoning, Gilutz teaches –
training a self-fulfillment action classifier as a function of the training data ([0039], [0041]) and classifying the alimentary instruction set to a list of self-fulfillment actions as a function of the self-fulfillment action classifier ([0048], [0069]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Tran as modified to include action classifier as a function of the training data as disclosed by Gilutz. Doing so provides a constant improvement the machine learning algorithm (Gilutz [0041]).
Response to Arguments
Applicant's arguments filed 03/19/2025, with respect to the rejection under 35 USC 101 are persuasive. The prior rejections under 35 U.S.C. 101, have been withdrawn as necessitated by the amendment.
Applicant's arguments, in regard to the presently amended claims, have been fully considered, but they are not deemed persuasive.
◊ With respect to the first amended limitation –
“wherein the first training set comprises physiological state data inputs correlated to prognostic label outputs”,
Tran explicitly teaches collecting user’s physiological data (“biomarkers”) and tracking such data over time. Tran also teaches collecting blood, urine and saliva samples [0225]-[0244] and other various sensory data, such as heart rate, blood pressure etc. [0395], all of which are physiological data. The collected data is used in diagnoses and prognoses of various diseases and treatments [0420]-[0437] by “use one or more machine learning techniques ( e.g., SVM technique)” [0438], “analysis may include the use of statistical analysis techniques such as classical, Bayesian, and/or machine learning analysis techniques … neural networks may be trained using all the health related characteristics” [0504], “The resulting "trained" neural network may then be able to receive the health related characteristics of a member to predict whether they will acquire the health condition … interconnections may be reviewed and correlated with the input health related characteristics” [0509]. See further -
“track omic changes to physiological changes in the subject over time” [0660], “mining the clinical database and health database for one or more markers associated with one or more health conditions” [0663], “identifying one or more corrective actions previously taken in the repository and the result of each corrective action for the one or more health conditions” [0664], “determining dynamic trends related directly to the physiological states of the subject during healthy and diseased states by correlating patterns over time and unusual events” [0724].
“diagnostic confidence indication can be assigned by any of a number of known statistical methods … a statistical correlation of current and prior results can be done. … a hidden Markov model can be built,” [0196].
In summary, Tran teaches collecting and using physiological data as a training data for neural networks and means for continuous learning and updating of the machine learning to provide various recommendations and treatments to the users (i.e. “determining … physiological states … by correlating patterns over time” [0724], resulting in “"trained" neural network may then be able to receive the health related characteristics of a member to predict whether they will acquire the health condition … correlated with the input health related characteristics” [0509]). Which is fully analogous to the limitation - “wherein the first training set comprises physiological state data inputs correlated to prognostic label outputs”.
NOTE the reference of Amin analogously discloses analyzing user’s “Lactobacillus bacteria (e.g. a type of beneficial microorganism) in his/her gastrointestinal tract”, which is the physiological data and “via explicit/implicit training” [0046]. Such data is inputted into machine learning models –
“system can be explicitly and/or implicitly trained to provide proper/appropriate diagnoses and/or recommendations (e.g., given known samples to analyze, shown which recommended treatments work best for given samples” [0049], “monitoring the user's microflora profile over time to learn what recommendations work best for the user, explicit or implicit training, running simulations to test potential recommended courses of action” [0144], “employ various classification (explicitly trained (e.g., via training data) as well as implicitly trained ( e.g., via observing behavior, preferences, historical information, receiving extrinsic information, and so on)” [0146], “diagnostic component can be trained (e.g., explicitly and/or implicitly) with known waste samples and/or known recommended treatments” [0015]. Training machine learning with physiological data, such as gastrointestinal tract samples to provide diagnostic and prognostics is fully analogous to the limitation - “wherein the first training set comprises physiological state data inputs correlated to prognostic label outputs.”
Thus, both references of Tran and Amin disclose the limitation - “wherein the first training set comprises physiological state data inputs correlated to prognostic label outputs,” as required by the independent claims.
◊ With respect to the second amended limitation –
“wherein the second training set comprises the prognostic label outputs generated by the first machine learning model correlated to ameliorative process label outputs,”
Tran once again teaches colleting various biomarkers, such as physiological biomarkers and “prognostic biomarkers”. Such data is used by trained neural networks to provide treatments and recommendations -
“network which has been trained to flag potentially dangerous conditions … the statistical analyzer is trained with training data” [0559], “neural networks may be trained using all the health related characteristics of the members having a particular condition … provide a weighted answer indicative of the likelihood the person will acquire the condition … predict an incidence of the health condition” [0504], “"trained" neural network may then be able to receive the health related characteristics of a member to predict whether they will acquire the health condition … resulting likelihood of occurrence may be used to rank the population in terms of likelihood of acquiring the condition” [0509] (aka machine learning generates prognostic label output);
“An intervention may be recommended in response to the likelihood of developing the health condition” [0505], “ranking [prognostic label output] may then be used to prioritize intervention strategies” [0509] (aka correlate ameliorative process label outputs with prognostic label).
Training machine learning with prognostic output, such as likelihoods and probabilities of developing a disease and a corresponding prioritized intervention (aka ameliorative process) is fully analogous to the limitation – “wherein the second training set comprises the prognostic label outputs generated by the first machine learning model correlated to ameliorative process label outputs”
NOTE Amin analogously discloses - “diagnostic component can be explicitly and/or implicitly trained (e.g., shown which recommendations best resolve which infirmities/conditions” [0116], “AI component can learn via searching medical/clinical databases, and so on), the AI component can determine that a modified course of action is more appropriate” [0143], “monitoring the user's microflora profile over time to learn what recommendations work best for the user, explicit or implicit training, running simulations to test potential recommended courses of action [ameliorative label outputs]” [0144], “artificial intelligence (AI) to … predict, prognose, estimate, derive, forecast” [0145].
Thus, both references of Tran and Amin disclose the limitation - “wherein the second training set comprises the prognostic label outputs generated by the first machine learning model correlated to ameliorative process label outputs,” as required by the independent claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is indicated on PTO-892.
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/POLINA G PEACH/ Primary Examiner, Art Unit 2165 October 20, 2025