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 and 11 have been amended. Claims 7, 17 have been canceled. Claims 1-6, 8-16 and 18-20 are pending.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities: Claims recite multiple unrelated users without proper anteceding bases. Claims disclose “user entries”, followed by “the user preferences” and “a suggestion of items to consume by a user”, “at least a user constraint.” Thus, it is not clear if the claims require a single user/client or multiple unrelated users.
Appropriate clarification is required.
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 1-6, 8-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hujsak (US 20180240359) in view of Blander et al. (US 20180344239) and in further view of Tran et al. (US 20180001184) and Lantrip et al. (US 20150363860).
Regarding claim 1, Hujsak teaches a system for optimizing dietary levels utilizing artificial intelligence, the system comprising: at least a server (F1), wherein the at least a server is designed and configured to:
receive, from a first graphical user interface, past user entries ([0120], [0148], [0153] “aggregated user information, information from trackers such as accepted recommendations, food trackers, and rankings of foods … behavioral information of users such as analysis of choices, assessment of motivation level, rewards and incentives”, [0154] “take into account the user's culinary and sustainability preferences, and ratings of past meals …user information including historical … health history … diet history”), wherein the past user entries comprise at least a dietary request ([0057]-[0058], [0091] “a request for nutritional information relevant to the subject”, [0141]), user preferences ([0148]) and physiological state data ([0080] “a patient has a defect in their MTTR (Methionine Synthase Reductase) gene which regenerates methyl B12 (methylcobalamin) which is needed to detoxify homocysteine and turn it into methionine, the end result is a B-12 deficiency”, [0094], [0112] “Such profiles are associated with the functioning of physiological systems”, also see [0118], where user data includes “personal genome … diseases, signs, symptoms … psychological factors, nutrition and disease etiology, and other variables specific to an individual”, [0154]) comprising genomic data ([0080], [0113], [0118]) i
receive, from a second graphical user interface, information describing one or more categories of physiological data ([0098] “information from existing and new sources that can be incorporated into the knowledge database”; such as data from “medical research papers, nutritional research papers, biochemical research papers, botanical research papers, ethnobotanical studies, medical references … scientific reference works, nutrient data sets, chemical data sets, raw scientific data sets, news articles and releases, and government reports”; “They may be in document, media, or data formats”, [0099] “performs automatic classification and categorization of newly ingested data and information”, [0101], [0107], [0161] “include a medical decision support application for physicians and nutritionists”, “medical professionals make optimized recommendations”, wherein the medical decision support application for physicians and nutritionists includes GUI – [0125] and is the second graphical user interface)(see NOTE);
identify at least an alimentary process label as a function of the at least a dietary request the user preferences ([0148]) and the physiological data, wherein the at least a dietary request comprises a preferred dietary style of eating ([0053] “histories of consumer eating patterns”, “consumers with a specific profile type and cultural background may be predisposed to consume certain food types”, F11K, see “DIET Ketogenic” and “Things Important to You”, “No Meat”, “Low Carb”, “Organic and Natural”, [0118] “receive dietary choices … from multiple consumers”, [0148]), the user preferences comprise foods a user cannot eat ([0148] “indicates that the user has a ketogenic diet … indicate that the user is allergic to foods … indicates that the user values no meat … no sugar”, [0154] “user information including … preferences on cuisine, flavor, likes or dislikes of food”),
wherein the physiological data comprises information describing lab data diagnostic laboratory tests, general wellness tests”, [0154], and
wherein the first graphical user interface ([0128]) is configured to provide
select a first training data set and a second training data set from a plurality of training data sets, wherein each of the first and the second the training data sets comprises a plurality of data entries ([0118] “stored data to train the machine-learned models”, [0121]), wherein training data includes a plurality of data entries ([0053] where Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task" (see https://en.wikipedia.org/wiki/Machine learning), [0105], [0160]) correlating the past user entries ([0154]) and the physiological data ([0116]) to at least an alimentary process label respectively ([0091]-[0092], [0118], [0121], [0124], [0148], [0075] “provide beneficial options for recipe design that are targeted specifically at a disease, sign, symptom, injury”)(see NOTE I);
continuously generate ([0090], [0116] “Based on the consumer information … generate a dietary plan over a subsequent number of weeks”), as a function of the past user entries ([0117]-[0118], [0152]) and the physiological data ([0042], [0058], [0084], [0091]-[0092]), one or more alimentary instruction set comprising at least a suggestion of items to consume by a user (F11B, see “Helpful Tip”, F11D see “Suggested Recipes”, F11F see “Bonus Tip”), wherein generating the one or more alimentary instruction sets further comprises:
training a first machine-learning model as a function of the first training data set and a second machine-learning model as a function of the second training data set ([0118] “train the machine-learned models used to predict consumers' behavior that will become increasingly more accurate over time”, [0121]); and
updating the first and the second training data sets with input and output results from the first machine learning model and second training data sets respectively ([0105], [0116], [0118]-[0119], [0121]) (see NOTE II);
updating the first and the second machine learning model with the updated first and second training data respectively ([0105], [0118]-[0119], [0121]) (see NOTE II);
generating the one or more alimentary instruction set as a function of the first machine-learning model and the second machine-learning model ([0051]-[0053] “Machine-learning of eating behavior can also be used to determine the emotional state of users and provide recommendations for food”, [0118]);
identify at least a meal as a function of the one or more alimentary instruction set ([0052], [0075], [0114]-[0115]);
select at least a physical performance executor as a function of the one or more alimentary instruction sets ([0137]-[0138], [0142], [0145])(see NOTE III) and at least a user constraint comprising a temporal preference of the user for the at least a physical performance executor ([0141] “request the user to indicate which member … will be eating the meal date and time of the meal”, [0142] “allows the user to plan the meal”) (see NOTE IV); and
an alimentary instruction label learner, wherein the alimentary instruction label learner ([0105]) is configured to:
◊ Hujsak does not explicitly teach, however Blander discloses genomic data including telomere length data ([0030] “a genetic marker or telomere length can be used as a biomarker”, [0002]) and wherein the physiological data comprises information describing red blood cell count of the user ([0030], F7).
◊ NOTE Hujsak teaches - “client devices 116 may be physicians … nurses and nurse practitioners counseling outpatients on nutrition … nutritionists … consumers … chefs … insurance executives … military food designers,” etc. [0058], and “builds and deploys one or more applications 444 supported by the nutritional application platform 110 to client devices 116”, which “include the necessary components … those that generate graphical user interfaces (GUI) at the client devices 116 that users can use to interact with the application platform 110 or view data obtained from the application platform 110” [0125], which provides multiple graphical interfaces where experts, such as nutritionists and experts can input and review documents.
However, if Hujsak does not explicitly teach, however Blander discloses receive, from a second graphical user interface ([0036] “selecting a particular source can launch a different interface (e.g., the interface 275)”), information describing one or more categories of physiological data ([0025], [0033], [0038], [0042]).
Hujsak does not explicitly teach, however Blander discloses wherein the first graphical user interface ([0046]) is configured to provide a field ([0037], [0042]) in which to indicate a reference to a document describing categories of dietary data and relationships between the categories and the alimentary process label ([0034]-[0035], [0037] “present one or more fields that allow a user to specify several attributes associated with the rule … A rule may have one or more supporting sources, the details of which may be retrieved from the rules database”; “recommendations to female users who have high SHBG levels, and take oral contraceptives”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Hujsak to include telomere length, RBC data and first and second graphical user interfaces and providing a field in which to indicate a reference to a document describing categories of dietary data and relationships between the categories and the alimentary process label as disclosed by Blander. Doing so helps in evaluating health, wellness and fitness of individuals (Blander [0002]).
◊ With respect to the last limitation, it is noted that the intended functionality disclose an unsupervised machine-learning process, where a dataset is fed into a machine learning system, and the machine learning system analyses the data based upon clustering of data points. This type of analysis is frequently used to detect similarities or correlations that identify anomalies/outliers, or to detect patterns in a set of data for identifying relationships among dimensions in the training data. Such unsupervised machine-learning is often combined with a semi-supervised learning algorithm, supervised learning algorithm, a deep learning algorithm, or any other types of algorithms.
Hujsak explicitly teaches using supervised and unsupervised learning algorithms ([0105]). However, Hujsak doesn’t go into details of actual functions of the unsupervised learning algorithm. Still, as indicated above, the unsupervised learning algorithm using clustering of data points to discover new relationship among data. Therefore, the claim limitations not explicitly recited in Hujsak are implicit.
Still, merely obviate such reasoning, Tran discloses -
an alimentary instruction label learner, wherein the alimentary instruction label learner ([0203]-[0204]) is configured to:
cluster data of the first training set according to detected relationships between elements of the first training set, wherein the relationships comprise correlations of alimentary labels ([0215], [0499], [0532]-[0533]); and
combine the clustered data ([0203]-[0204] “combine higher and lower levels of information”) to add new criteria ([0160] “combining knowledge of known interactions with structural similarity it is possible to identify new interactions”, [0212], [0282], [0529], [0532]) for the alimentary instruction label learner to apply in relating dietary data to alimentary labels ([0329]-[0335], [0767]-[0768]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Hujsak to expand on clustering performed by the unsupervised learning algorithm as disclosed by Tran. Doing so would help predict disease risks based on the aggregated genetic information, treatment data, and treatment response from a patient population and recommending lifestyle modification to mitigate the disease risks (Tran [0003]).
◊ NOTE I Hujsak teaches machine learning (aka training data) is based on dietary choices from multiple consumers [0118], [0053]. Such training data from different consumers comprise different user profiles, such as “user "Denise Miller" indicates that the user has a ketogenic diet” … the user is associated with health conditions "hyperthyroidism" and "migraines." … indicates that the user values no meat, low carbohydrates, organic and natural, and high protein diets” [0148], “preferences for avoiding specific diseases or for enhancing performances in athletic or mental capabilities” [0150], which is construed to be analogous to the limitation “correlating the past user entries and the physiological data to at least an alimentary process label”.
However, to merely obviate such reasoning, Tran discloses correlating the past user entries and the physiological data to at least an alimentary process label ([0393] “each data unit can be linked to a matching therapy”, [0474], [0506]-[0507].
◊ NOTE II Hujsak teaches machine trained learning – “use the stored data to train the machine-learned models used to predict consumers' behavior that will become increasingly more accurate over time” [0118], “use the data in the data store to train the behavioral model and improve accuracy of the behavioral model” [0121].
Improving accuracy of the machine learning model over time, obviously indicates that the machine learning model is continuously updated. It is also well-known that updating of the machine learning model includes the retraining of the machine learning model with a new information. Therefore, the claim limitations not explicitly recited in Hujsak are implicit.
However, to obviate such well-known functionality of the machine learning, Tran teaches –
“updating the training data with input and output results from the first machine learning model; retraining the first machine learning model with an updated training data” ([0245]-[0246], [0248], [0269], [0278], i.e. the system is initially pre-trained [0280] and then the system is updated based on the feedback loop, [0281]-[0283] “though the negative feedback aspect may predominantly be used for identifying gaps that the system needs to address”, “automatically revising probabilities from the collected information; storing the revised probabilities, wherein the revised probabilities are used to determine the plan”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Hujsak to include correlated alimentary process label and retraining of the machine learning model as disclosed by Tran. Doing so would help predict disease risks based on the aggregated genetic information, treatment data, and treatment response from a patient population and recommending lifestyle modification to mitigate the disease risks (Tran [0003]).
◊ NOTE III Hujsak teaches a plurality of options for ordering a takeout [0145]. Such takeout meals are based on the nutritional recommendation provide by the system [0150], which is construed to be analogous to “select at least a physical performance executor as a function of the alimentary instruction set”. However, to merely obviate such reasoning, Lantrip discloses select at least a physical performance executor as a function of the alimentary instruction set ([0039]-[0041], [0047], [0051], [0053], [0056]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Hujsak to facilitate a delivery of prepared meals to the user as disclosed by Lantrip. Doing so would continuously identify individual food preferences and automatically creating real-time and dynamic personalized food services and serving food items that will satisfy the preferences and dietary requirements of individuals whenever and wherever they are required (Lantrip [0001], [0003]).
◊ NOTE IV Hujsak teaches that user preferences include indicating “date and time of the meal” (aka temporal preference of the user). Hujsak further teaches options for ordering a takeout and “Institutional Food Service Food Delivery” (F12:1210), which include ”Food Retailers”, “Grocery Delivery” and “Automated grocery delivery” (F12F). Given that user can indicate “date and time of the meal”, it is reasonable and obvious to conclude that for the “Automated grocery delivery” or a “takeout”, such information (“date and tie”) is provided with the meal or food orders. See for example “hospital may design meals that are delivered to patients” [0o03], where it’s only obvious that such meals are delivered based on user’s temporal preferences (dinner, breakfast or any other specific time). Such functionality is also obvious in view of F11A – see “Upcoming: Dinner 11/21/2018” next to “Order Takeout.” Therefore, the claim limitations “a user constraint comprising a temporal preference of the user for the at least a physical performance executor” not explicitly recited in Hujsak are implicit and obvious. It would have been obvious to one of ordinary skill in the art at the time of invention to include temporal preferences, already disclosed by Hujsak, with the takeout order or grocery delivery (specifically with the “Automated grocery delivery”). Doing so allows user to “accomplish planning” based on personal preferences (Hujsak [0017]).
Claim 11 recites substantially the same limitations as claim 1, and is rejected for substantially the same reasons.
Regarding claims 2 and 12, Hujsak as modified teaches the system and the method, wherein the at least a dietary request further comprises at least an element of user data (Hujsak [0126], [0148], [0150]).
Regarding claims 3 and 13, Hujsak as modified teaches the system and the method system of claim 1, wherein the alimentary instruction set further comprises at least a supplement to be consumed by a user (Hujsak [0135], F12E “Treatment with Prescriptive Foods and/or Supplements”, Tran [0312] “Instruct patient about increasing intake of foods/fluids high in potassium (oranges, bananas, figs, dates, tomatoes, potatoes, raisins, apricots, Gatorade, and fruit juices and foods/fluids high in calcium such as low-fat milk, yogurt, or calcium supplements”, [0516]).
Regarding claims 4 and 14, Hujsak as modified teaches the system and the method system of claim 1, wherein the at least a server is further configured to generate a machine-learning algorithm, wherein the machine-learning algorithm is configured to generate the alimentary instruction set as a function of a classification of the at least an alimentary process label (Hujsak [0049], [0099], Tran [0398] “A classifier is generated by the learning module”; [0508] “Generic model-fitting or classification algorithms e.g., neural networks (e.g., back propagation, feed-forward networks, etc.), meta-learning techniques such as boost, etc., may be applied for predictive data mining”).
Regarding claims 5 and 15, Hujsak as modified teaches the system and the method, wherein the at least a server is further configured to associate the at least a dietary request with a category (Hujsak [0047], F11K see Diet type “Ketogenic”, [0148], Tran [0253], [0449] as in recommending “low-calorie diet”, [0483], [0487], [0503] “include dietary characteristics”; “recommendations may then be tailored to the particular pre-detectable characteristics exhibited by the particular member”, [0506]-[0507] “classifying a member into a readiness to change category, interventions may be further tailored for the individual member”, [0552]).
Regarding claims 6 and 16, Hujsak as modified teaches the system and the method, wherein the category identifies an impactful condition (Hujsak [0040], [0084], [0087], Tran [0503] “characteristics are characteristics that impact the chance of acquiring risk factors associated with a condition”).
Regarding claims 7 and 17, Hujsak as modified teaches the system and the method system of claim 1, wherein the at least a server further comprises a graphical user interface, wherein the graphical user interface displays a plurality of meals (Hujsak F11D-E, F, H), wherein each of the plurality of meals is ordered as a function of the alimentary instruction set (Hujsak [0137]-[0138], [0142], [0145]).
Regarding claims 8 and 18, Hujsak as modified teaches the system and the method, wherein the alimentary instruction set further comprises an element of narrative language related to the alimentary instruction set (Hujsak [0150], [0152], [0154] “motivational messages, educational/training) to define the level of motivation to align with recommendations”, also see “Bonus Tip”, “Helpful Tip” in F11, D:1108, F, I, Tran [0452] “provide guidance on diet”, [0278]).
Regarding claims 9 and 19, Hujsak as modified teaches the system and the method, wherein the element of narrative language further comprises a text describing a current alimentary instruction set status of a user (Hujsak [0053] “determine the emotional state of users and provide recommendations for food that satisfies emotional eating with healthier choices or that may intentionally alter emotional state”, [0118], [0137], Tran [0149] “information can cover dosage guidance, possible side effects or differences in effectiveness for people with certain genomic variation”, [0278], [0282], [0298] “provide patients with guidance and treatment options”, [0441]).
Regarding claims 10 and 20, Hujsak as modified teaches the system and the method, wherein the at least a server is further configured to generate a physical performance instruction set, wherein the physical performance instruction set comprises a pickup location for the at least a physical performance executor and a delivery address for the at least a meal (Hujsak F12F see Locations, [0145], Lantrip [0039]-[0041], [0047], [0051], [0053], [0056]).
◊ Claims 1-6, 8-16 is/are alternatively rejected under 35 U.S.C. 103 as being unpatentable over Hujsak (US 2018/0240359) in view of APPELBAUM et al. (US 20180315499), in further view of Blander et al. (US 20180344239), in further view of Mirabile (US 20140236759) and in further view of Menon et al. (US 5537488) or Harris et al. (US 11586960).
Regarding claim 1, Hujsak teaches a system as disclosed above.
◊ Hujsak does not explicitly teach, however Blander discloses genomic data including telomere length data ([0030] “a genetic marker or telomere length can be used as a biomarker”, [0002]) and wherein the physiological data comprises information describing red blood cell count of the user ([0030], F7).
◊ NOTE Hujsak teaches - “client devices 116 may be physicians … nurses and nurse practitioners counseling outpatients on nutrition … nutritionists … consumers … chefs … insurance executives … military food designers,” etc. [0058], and “builds and deploys one or more applications 444 supported by the nutritional application platform 110 to client devices 116”, which “include the necessary components … those that generate graphical user interfaces (GUI) at the client devices 116 that users can use to interact with the application platform 110 or view data obtained from the application platform 110” [0125], which provides multiple graphical interfaces where experts, such as nutritionists and experts can input and review documents.
However, if Hujsak does not explicitly teach, however Blander discloses receive, from a second graphical user interface ([0036] “selecting a particular source can launch a different interface (e.g., the interface 275)”), information describing one or more categories of physiological data ([0025], [0033], [0038], [0042]).
Hujsak does not explicitly teach, however Blander discloses wherein the first graphical user interface ([0046]) is configured to provide a field ([0037], [0042]) in which to indicate a reference to a document describing categories of dietary data and relationships between the categories and the alimentary process label ([0034]-[0035], [0037] “present one or more fields that allow a user to specify several attributes associated with the rule … A rule may have one or more supporting sources, the details of which may be retrieved from the rules database”; “recommendations to female users who have high SHBG levels, and take oral contraceptives”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Hujsak to include telomere length, RBC data and first and second graphical user interfaces and providing a field in which to indicate a reference to a document describing categories of dietary data and relationships between the categories and the alimentary process label as disclosed by Blander. Doing so helps in evaluating health, wellness and fitness of individuals (Blander [0002]).
◊ Hujsak does not explicitly teach, however, Mirabile discloses identify at least an alimentary process label as a function of the at least a dietary request, user preferences and a physiological data ([0037], [0039], [0041], [0060]), wherein the at least a dietary request comprises a preferred dietary style of eating, the user preferences comprise foods a user cannot eat, ([0036], [0041] , see diabetic, [0053]), and wherein the physiological data comprises information describing red blood cell count of the user ([0041]).
◊ NOTE However, if Hujsak does not explicitly teach, Mirabile discloses select at least a physical performance executor as a function of the alimentary instruction set ([0027], [0032], [0045]-[0046], [0032], [0045], [0125], [0131], [0137], [0139]) and at least a user constraint comprising a temporal preference of the user for the at least a physical performance executor ([0029] “delivery to geographic location and during date and time of day, and determines which meals best satisfy the person's specified meal requirements and preferences”, [0045], [0033] and [0040]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Hujsak to include blood oxygen data and blood sugar data, RBC test, temporal preference of the user and to facilitate a delivery of prepared meals to the user as disclosed by Mirabile. Doing so would automatically provide a person with satisfactory food options for a specific time and place (Mirabile [0007]).
◊ Hujsak does not explicitly teach, however, APPELBAUM discloses updating the first and the second training data sets with input and output results from the first machine learning model and second training data sets respectively; retraining the first and the second machine learning model with the updated first and second training data respectively ([0112]-[0113], [0139], [0192]).
NOTE I Hujsak teaches machine learning (aka training data) is based on dietary choices from multiple consumers [0118], [0053]. Such training data from different consumers comprise different user profiles, such as “user "Denise Miller" indicates that the user has a ketogenic diet” … the user is associated with health conditions "hyperthyroidism" and "migraines." … indicates that the user values no meat, low carbohydrates, organic and natural, and high protein diets” [0148], “preferences for avoiding specific diseases or for enhancing performances in athletic or mental capabilities” [0150], which is construed to be analogous to the limitation “correlating at least a dietary request data and the physiological data to at least an alimentary process label respectively”. However, to merely obviate such reasoning, APPELBAUM discloses correlating at least a dietary request data and the physiological data to at least an alimentary process label respectively ([0149]-[0150], [0162], [0192]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Hujsak to include retraining the first machine learning model and correlating at least a dietary request data to at least an alimentary process label as disclosed by APPELBAUM. Doing so would provide a measurable improvement in one or more therapeutic milestone in cardiometabolic disorders such as diabetes, and can further lead to an actual reduction in pharmaceutical reliance s (APPELBAUM [0002]).
◊ Hujsak explicitly teaches using supervised and unsupervised learning algorithms ([0105]). However, Hujsak doesn’t go into details of actual functions of the unsupervised learning algorithm. Still, as indicated above, the unsupervised learning algorithm using clustering of data points to discover new relationship among data. Therefore, the claim limitations not explicitly recited in Hujsak are implicit.
Still, merely obviate such reasoning, Menon discloses -
cluster data of the first training set according to detected relationships between elements of the first training set, wherein the relationships comprise correlations of alimentary labels (C1L24-31, where categories are labels as shown in C2L30-31); and combine the clustered data to add new criteria for the alimentary instruction label learner (C1L33-37, C3L).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Hujsak to expand on the unsupervised learning algorithm disclosed by Menon. Doing so provides the user of the system with an indication of the degree of confidence associated with each classification and save considerable processing time when there are many training input patterns (Menon C4L23-25, 42-43).
◊ Harris likewise discloses cluster data of the first training set according to detected relationships between elements of the first training set, wherein the relationships comprise correlations of alimentary labels (C10L47-67, , C11L1-17); and combine the clustered data to add new criteria for the alimentary instruction label learner (C10L47-67, C11L1-17).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Hujsak to expand on the unsupervised learning algorithm disclosed by Harris. Doing so helps detects new features in a dynamically changing environment (Harris C1L39-40).
Regarding claims 2 and 12, Hujsak as modified teaches the system and the method, wherein the at least a dietary request further comprises at least an element of user data (Hujsak [0126], [0148], [0150], APPELBAUM [0108]).
Regarding claims 3 and 13, Hujsak as modified teaches the system and the method system of claim 1, wherein the alimentary instruction set further comprises at least a supplement to be consumed by a user (Hujsak [0135], F12E “Treatment with Prescriptive Foods and/or Supplements”, Tran [0312] “Instruct patient about increasing intake of foods/fluids high in potassium (oranges, bananas, figs, dates, tomatoes, potatoes, raisins, apricots, Gatorade, and fruit juices and foods/fluids high in calcium such as low-fat milk, yogurt, or calcium supplements”, [0516]).
Regarding claims 4 and 14, Hujsak as modified teaches the system and the method system of claim 1, wherein the at least a server is further configured to generate a machine-learning algorithm, wherein the machine-learning algorithm is configured to generate the alimentary instruction set as a function of a classification of the at least an alimentary process label (Hujsak [0049], [0099], Tran [0398] “A classifier is generated by the learning module”; [0508] “Generic model-fitting or classification algorithms e.g., neural networks (e.g., back propagation, feed-forward networks, etc.), meta-learning techniques such as boost, etc., may be applied for predictive data mining”, APPELBAUM [0110], [0130]).
Regarding claims 5 and 15, Hujsak as modified teaches the system and the method, wherein the at least a server is further configured to associate the at least a dietary request with a category (Hujsak [0047], F11K see Diet type “Ketogenic”, [0148], Tran [0253], [0449] as in recommending “low-calorie diet”, [0483], [0487], [0503] “include dietary characteristics”; “recommendations may then be tailored to the particular pre-detectable characteristics exhibited by the particular member”, [0506]-[0507] “classifying a member into a readiness to change category, interventions may be further tailored for the individual member”, [0552]).
Regarding claims 6 and 16, Hujsak as modified teaches the system and the method, wherein the category identifies an impactful condition (Hujsak [0040], [0084], [0087], APPELBAUM [0108]).
Regarding claims 8 and 18, Hujsak as modified teaches the system and the method, wherein the alimentary instruction set further comprises an element of narrative language related to the alimentary instruction set (Hujsak [0150], [0152], [0154] “motivational messages, educational/training) to define the level of motivation to align with recommendations”, also see “Bonus Tip”, “Helpful Tip” in F11, D:1108, F, I, Tran [0452] “provide guidance on diet”, [0278], APPELBAUM [0117], [0142]).
Regarding claims 9 and 19, Hujsak as modified teaches the system and the method, wherein the element of narrative language further comprises a text describing a current alimentary instruction set status of a user (Hujsak [0053] “determine the emotional state of users and provide recommendations for food that satisfies emotional eating with healthier choices or that may intentionally alter emotional state”, [0118], [0137], APPELBAUM [0142], [0152]).
Regarding claims 10 and 20, Hujsak as modified teaches the system and the method, wherein the at least a server is further configured to generate a physical performance instruction set, wherein the physical performance instruction set comprises a pickup location for the at least a physical performance executor and a delivery address for the at least a meal (Hujsak F12F see Locations, [0145], Mirabile [0027], [0032], [0045]-[0046], [0032], [0045], [0125], [0131], [0137], [0139]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Hujsak to generate a physical performance instruction set, wherein the physical performance instruction set comprises a pickup location for the at least a physical performance executor and a delivery address for the at least a meal as disclosed by Mirabile. Doing so would automatically provide a person with satisfactory food options for a specific time and place (Mirabile [0007]).
Response to Arguments
Applicant's arguments, filed 12/11/2025, 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 December 16, 2025