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 .
Status of the Claims
Claims 1, 3, 11, 13, 18 have been amended. Claims 1, 3-11, 13-20 are pending.
Claim Objections
Independent claims recite limitation –
“biological samples fail to reflect an increased level associated with that recommended food.”
It is not clear of what the “increased level” is being referred to (I.e. increased level of what, iron?). It seems the complete limitation is based on paragraph [0204] of the specification (PUB version). However, the specification fails to disclose such level of specificity. It seems the applicant is interpreting the specification to achieve such conclusion.
For the purpose of examination, the limitation is examined in view of the paragraph [0204] – “alimentary instruction set that recommends a user to increase intake of iron rich foods for a user with anemia may be updated to recommend consumption of iron rich foods with vitamin c foods to better increase absorption of iron if iron levels do not increase after user entries contain descriptions of foods and/or meals consumed that contain iron rich ingredient.”
However, the applicant should properly clarify the limitation based on the intended meaning. Appropriate clarification is requested.
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) and in further view of KARVELA et al. (US 20170323057).
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 on 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”);
analyze the at least a user entry to determine whether a user actually purchased ([0516] “information may include information associated with grocery store purchases … restaurant purchases, and/or purchases of vitamins and/or supplements”; [0398] “information such as purchases from restaurants and grocery stores can be used to analyze the calorie consumption by the subject”, [0491]) items relating to the alimentary instruction set ([0491] “compliance information refers to a level of compliance of a particular patient with one or more prescribed medical treatments, such as medications, diet”, [0506] “monitored to determine if the recommendation was followed”; “verify they did not follow the recommendation, and determine why the recommendation was not followed”; “monitor characteristics of whether the recommended intervention was performed … collected through self-reported data””; [0507] “monitored to determine if the dietary program was successful in reducing the members saturated fat consumption, and/or whether the dietary program was successful”, [0514]),
wherein the at least a user entry comprises a scan of barcodes pertaining to ingredients (see NOTE I);
match, using a matching database, the user entry to the alimentary instruction set to determine whether the purchased items fulfill recommended nutrient and dietary recommendations contained within the alimentary instruction set ([0595] “The patient's actual diet is then compared to with the recommended diet”; “system determines and looks up a database”; [0393] ‘’ each data unit can be linked to a matching therapy”; [0398] “A comparator module then analyzes each score from the three models to determine if the predictions are in agreement”); and
modify the alimentary instruction set as a function of the user entry ([0287], [0508] “results of the intervention monitoring and associated health related characteristics may be used to further refine the decision process regarding which intervention to recommend”, [0508] “the recommendations may be modified based on any previous engagement by the member in an activity related to an intervention”),
wherein modifying the alimentary instruction set comprises updating the alimentary instruction set ([0398] “if the tests indicate that the treatment is not effective, then the system brings the patient in ahead of planned doctor visit and recommends alternative treatment”, [0471] “Lifestyle modification includes changes to the patient's dieting”, [0507] “intervention recommendation may be a dietary program. The health care characteristics may be monitored to determine if the dietary program was successful in reducing the members saturated fat consumption, and/or whether the dietary program was successful in eliminating or delaying the incidence of the health condition”; [0508] “to tailor the specific recommendation provided … further refine … a recommended intervention”) after user entries indicate consumption of a recommended food and subsequent biological samples ([0552] “physiological data is collected before and after consumption of a food item”, [0597] “monitoring patient food intake”) fail to reflect an increased level ([0559] “value outside of this range is flagged by the statistical analyzer as a dangerous condition”, [0511] “monitor failure/successful characteristic of said intervention”, [0225] “Hemoglobin/hematocrit: … may indicate … anemia”) associated with that recommended food ([0312] p.28 “Notify physician if unable to tolerate food or fluid”, [0552]), by modifying the alimentary instruction set to recommend a food combination that increases absorption of the recommended food (p.27 C1 “Instruct patient about increasing intake of foods/fluids high in potassium (oranges, bananas, figs, dates, tomatoes, potatoes, raisins, apricots, Gatorade, and fruit juices”) (see NOTE I).
NOTE I Tran doesn’t explicitly teach - the at least a user entry comprises a scan of barcodes pertaining to ingredients. However, instead Tran teaches – “Upon receipt by a bar code scanner or an NFC scanner, the product authenticity can be verified” [0379], “product may be a consumable” [0378]; “such as barcodes or NFC/RFID tags, to provide automatic identification of products … use a code scanner to automatically identify the product, and then may collects additional information from operators via … mobile computers” [0380]. Tran also teaches that user entry can be through the mobile phone - [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.” It is well-known that smart phones are a capable of the barcode scanning.
Thus, it would have been obvious to one of ordinary skill in the art to use wither a smart phone or the “bar code scanner or an NFC scanner”, already explicitly disclosed by Tran to scan a “code such as a bar code affixed to a product” to analyze the at least a user entry to determine whether a user actually purchased product by means scanning of barcodes pertaining to ingredients. I.e. Tran teaches verifying diet compliance by analyzing grocery/ restaurant purchases and entering such data by a means of smart phone and in view of the paragraph [0379] such entry can be through the “bar code scanner or an NFC scanner.”
However, to further obviate such reasoning KARVELA discloses analyze the at least a user entry to determine whether a user actually purchased items relating to the alimentary instruction set, wherein the at least a user entry comprises a scan of barcodes pertaining to ingredients ([0159]-[0160], [0164]).
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 analyze the at least a user entry to determine whether a user actually purchased items and a user entry comprises a scan of barcodes pertaining to ingredients as disclosed by KARVELA. Doing so provides immediate, actionable results, that are delivered quickly, reliably, and securely (KARVELA [0018]).
NOTE II Tran doesn’t explicitly teach – “samples fail to reflect an increased level associated with that recommended food, by modifying the alimentary instruction set to recommend a food combination that increases absorption of the recommended food.” However, instead Tran teaches – collect user’s physiological data before and after food consumption, reporting an allergy or a reaction, notifying physician if the user is unable to tolerate food or fluid and recommending various food and fluids intakes for different health concerns.
One of ordinary skill in the art would easily determine based on the collected physiological data (“Metabolic profiling ( especially of urine or blood plasma samples) detects the physiological changes caused by toxic insult of a chemical ( or mixture of chemicals) … which reflects the balance of all these forces on an individual's metabolism” [0524]) obviously would indicate normal or abnormal levels (as shown in [0559]) of any monitored condition, such as food intolerance, which is another form of malabsorption. Recommending various foods for various conditions and based on the metabolic profiling can surely be applicable to the food combination that increases absorption of the recommended food.
Still, Amin discloses - recommended food and subsequent biological samples fail to reflect an increased level associated with that recommended food ([0101] “can indicate hemolytic anemia”, [0097] “can indicate malabsorption, bile deficiency, pancreatic enzyme deficiency,” “indicates how well a user digests their food)”), by modifying the alimentary instruction set to recommend a food combination that increases absorption of the recommended food ([0041] “user's gut microflora, food recently eaten/consumed by the user”, “reveal that a user has an overabundance of undigested vegetable fibers in his/her stool … fibers in stool can indicate insufficient chewing of food”; [0042] “system can determine that eating more of one particular food and/or less of another can help to stimulate healthy levels the bacterium in the user's digestive tract … recommendations can be at any level of specificity/granularity, including how much of a recommended food to eat ( e.g., number of grams and/or calories, and so on), how to prepare a recommended food ( e.g., recipes, refrigeration”, [0043], [0046], [0048]).
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 recommend a food combination that increases absorption of the recommended food as disclosed by Amin. Doing so help diagnose any infirmities suffered by the user and/or to recommend restorative courses of action (Amin [0045]).
◊ Further, 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 III 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 IV 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 the list of suggested self-fulfillment actions 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 04/23/2026, have been fully considered and are addressed in the updated rejections to the claims above.
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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/POLINA G PEACH/ Primary Examiner, Art Unit 2165 May 2, 2026
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