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 .
Priority
Independent claims 1 and 11 of this application add disclosures not presented in the prior application, e.g. “classifying the plurality of data sets into the one or more user groups as a function of the plurality of data elements.”
The filing date of this non-provisional application, November 6, 2023, is the effective priority date for instant claims 1-20.
Response to Amendment
All pending claims 1-20 filed December 29, 2025 are examined in this non-final office action necessitated by new grounds of rejection under 35 USC 101.
Response to Arguments
35 USC 102
Applicant's arguments filed remarks filed December 29, 2025 have been fully considered but they are not persuasive. The office asserts that Murdoch discloses "identifying a plurality of data elements comprising an appetite size."
In Murdoch see at least:
[Murdoch: 0080] … The method of operating a meal plan optimization algorithm further involves adjusting the portion size of the food items for the individual meals in the meal day plan through operation of the kcal target evaluator configured by the kcal target as specified by the user, and generating a 1-n-day food menu, displayable through a user interface associated with the at least one user profile, from meal day plans typically grouped together as menus through operation of a constructor. Please note: This is one excerpt from Murdoch pertaining to appetite size as defined by Applicant’s instant specification
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-7, 10-17 and 20 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without adding significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to either a practical application of the abstract idea or significantly more than the abstract idea itself. Groupings of abstract ideas include: Mathematical Concepts, Mental Processes and Certain Methods of Organizing Human Activity.
Certain Methods of Organizing Human Activity include:
Fundamental economic principles or practices,
Commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), and
Managing personal behavior or relationships or interaction between people (including social activities, teaching and following rules or instructions).
Mathematical Concepts
Mathematical relationships
Mathematical formulas
Mathematical calculations
Mental Processes
Concepts performed in the human mind (including an observation, evaluation, judgement, opinion)
Step 1
In the instant case, claim 11 is directed to a process. Analysis of claim 11 applies to analysis of claims 1-7, 10, 12-17 and 20.
Step 2A Revised (First Prong)
Determine whether claim 11 is directed to a judicial exception. Elements of an abstract idea are underlined. See Analysis.
Step 2A Revised (Second Prong)
Determine whether claim 11 has additional elements (in italics) integrated into a practical application:
a) requires an additional element or a combination of elements in the claim to apply, rely on, or use the judicial exception in a manger that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception; and
b) uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application.
See Analysis.
Step 2B (Revised)
In Step 2B, evaluate whether claim 11 recites additional elements that amount to an inventive concept that adds significantly more than the recited judicial exception. See Analysis.
Analysis
In Claim 11:
(Currently Amended) A method for generating food preference menu, wherein the method comprises:
receiving, using a computing device, a plurality of data sets from one or more data sources;
classifying, using the computing device, the plurality of data sets into one or more user groups, wherein classifying the plurality of data sets comprises:
identifying a plurality of data elements comprising an appetite size from the plurality of data sets; and
classifying the plurality of data sets into the one or more user groups as a function of the plurality of data elements;
identifying, using the computing device, a food pattern of the plurality of data sets in the one or more user groups;
generating, using the computing device, a food preference menu as a function of the food pattern, wherein the food preference menu comprises a nourishment strategy; and
updating, using the computing device, the food preference menu as a function of feedback data.
Claim 11 executes methods that are directed to abstract ideas comprising processes that can be executed by a human while following a procedure that organizes human activity related to commercial interactions using conventional computing elements
No evidence of an improvement to the functioning of a computer, or to any other technology or technical field.
No evidence exists in the instant specification or claims of a particular machine.
No evidence exists of a transformation or reduction of a particular article to a different state or thing.
The instant specification teaches that the machine learning model can be known, generic third-party models:
[0084] With continued reference to FIG. 1, other exemplary embodiments of generative machine learning models may include, without limitation, long short-term memory networks (LSTMs), (generative pre-trained) transformer (GPT) models, mixture density networks (MDN), and/or the like. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various generative machine learning models may be used to generate descriptions for food preference menu.
[0087] With continued reference to FIG. 1, LLM, in some embodiments, may include Generative Pretrained Transformer (GPT), GPT-2, GPT-3, GPT-4, and the like. GPT, GPT-2, GPT-3, and GPT-4 are products of Open AI Inc., of San Francisco, CA.
The claim does not go beyond generally linking the use of the judicial exception to a particular technological environment, e.g. processor, device. Independent claims 1 and 11 do not recite any technical details regarding its use of model training or training data to determine a food pattern using machine learning. These aspects would need to be recited in the claims in order to integrate the abstract idea into a practical application.
Claim 11 does not recite additional elements that amount to inventive concepts that are “significantly more” than the recited judicial exception. Courts have routinely found conventional computer processing functions (e.g. sending/receiving data, formatting data, storing data, retrieving data, manipulating data, calculating, searching data, displaying data, organizing data) insignificant to transform an abstract idea into a patent-eligible invention. See Alice, 134 S. Ct. at 2360. As such, the claims amount to nothing significantly more than an instruction to implement the abstract idea across a generic computer network which is not enough to transform an abstract idea into a patent-eligible invention.
The elements of the instant process, when taken in combination, together do not offer substantially more than the sum of the functions of the steps when each is taken alone. That is, the steps involved in the recited process undertake their roles in performance of their activities according to their generic functionalities which are well-understood, routine and conventional. The elements together execute in routinely and conventionally accepted coordinated manners and interact with their partner elements to achieve an overall outcome which, similarly, is merely the combined and coordinated execution of generic computer functionalities which are well-understood, routine and conventional activities previously known to the industry.
Conclusion
Accordingly, the examiner concludes there are no meaningful limitations in claims 1-20 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action.
Claims 1-5, 10-15 and 20 are rejected under 35 USC 102(a)(1) as being anticipated by Murdoch et al., US 2020/0098466 “Murdoch.”
Murdoch teaches all the limitations of claims 1-5, 10-15 and 20. In Murdoch see at least (underlined text is for emphasis):
Regarding claim 11: A method for generating food preference menu, wherein the method comprises:
receiving, using a computing device, a plurality of data sets from one or more data sources;
[Murdoch: 0069] Food Likes…. As the user enters food into the menus a dataset is created that can be mined for behavior. As the pattern develops, a neural network can start to accurately predict and inject foods into the menu post generation.
classifying, using the computing device, the plurality of data sets into one or more user groups, wherein classifying the plurality of data sets comprises:
identifying a plurality of data elements comprising an appetite size from the plurality of data sets; and
[Murdoch: 0076] In some configurations, a user profile may include user preferences such as food preferences (e.g., likes/dislikes, which may be further broken down into preferred tastes and/or textures, smells, etc.), restrictions (e.g., allergies or disease), health objectives (e.g., lose weight), financial budget, grocer or food distributor (e.g., grocer supplier or direct-to-consumer provider in certain food distribution scenarios), preferred brands or private labels, preferred recipes, and preferred restaurants. Please note: Underlined items are data elements consistent with instant specification examples.
[Murdoch: 0080] … The method of operating a meal plan optimization algorithm further involves adjusting the portion size of the food items for the individual meals in the meal day plan through operation of the kcal target evaluator configured by the kcal target as specified by the user, and generating a 1-n-day food menu, displayable through a user interface associated with the at least one user profile, from meal day plans typically grouped together as menus through operation of a constructor. Please note: This is one excerpt from Murdoch pertaining to appetite size as defined by Applicant’s instant specification.
[Murdoch: 0212] Referencing FIG. 16, a system 1600 illustrates a behavior learning process in which aggregated information (different users) 1606, user data, may be collected from similar users 1622 with similar behaviors 1624 and runs an unsupervised learning process 1602 and a supervised learning process 1608. The unsupervised learning process 1602 incorporates behavior 1604, user profiles 1614, user preferences 1616, and user history 1620 to generate suggestions 1610. The suggestions 1610 may be provided with feedback 1612, which serves as validation 1618 for the unsupervised learning process 1602. The supervised learning process 1608 may incorporate the user preferences 1616, user profiles 1614, and user history 1620 to generate suggestions 1610. The suggestions 1610 may be provided with feedback 1612, which helps improve the supervised learning process 1608. The user profiles 1614 and the user preferences 1616 may also include taste profiles 1626 utilized by either the unsupervised learning process 1602 or the supervised learning process 1608 for generating suggestions 1610.
classifying the plurality of data sets into the one or more user groups as a function of the plurality of data elements;
[Murdoch: 0232] Referencing FIG. 34, a system 3400 illustrates a configuration where a group menu 3416 may be generated for a set of users based on their proximity to each other within a geolocation 3408. For example, based on proximity, a geofenced equivalence recommendations algorithm (AI cloud server) 2616 may determine that a first user 3402, an nth user 3404, and nth user 3406, are in the same geolocation 3408. The geofenced equivalence recommendations algorithm (AI cloud server) 2616 may then identify a user group classification 3410 to determine if an existing relationship exists between the users and if so determine if there is a group event classification 3412. Based on the user group classification 3410, and/or the group event classification 3412, the user preferences from a user preference database 3414, the food source selector 2612 may select relevant proximal food databases to all the users to generate a group menu 3416.
identifying, using the computing device, a food pattern of the plurality of data sets in the one or more user groups;
[Murdoch: 0069] Food Likes—This functions much like the above. As the user enters food into the menus a dataset is created that can be mined for behavior. As the pattern develops, a neural network can start to accurately predict and inject foods into the menu post generation.
[Murdoch: 0071] Price sensitivity—People may not purchase various food items based on the price of the food. So, as the data is gathered the neural network can start to see what people are adding to their menus. As this dataset grows we can start to determine that people will or will not purchase a food based on its price.
generating, using the computing device, a food preference menu as a function of the food pattern, wherein the food preference menu comprises a nourishment strategy; and
[Murdoch: 0077] A method of operating a meal plan optimization algorithm may involve retrieving at least one user profile from a profile database. A menu generation algorithm may be configured with user preferences from the at least one user profile. A selector may be configured to retrieve food items from at least one proximal food database for the menu generation algorithm from the user preferences. The menu generation algorithm configured by the user preferences may be operated to set preferred food items for meal components of individual meals from the food items in the at least one proximal food database through operation of a food preference discriminator, set a non-user specified food item for a meal component of the individual meals without a preferred food item and additional food items through operation of the food preference discriminator, and update a meal total for the individual meals with the non-user specified food item. The menu generation algorithm may operate a meal target evaluator recursively, through operation of an iterator, to adjust and substitute meal components. A meal day plan may be generated from the individual meals. A kcal target evaluator may be operated to adjust the portion size for the individual meals in the meal day plan configured by the user preferences. And a food menu for at least one day displayable through a user interface associated with the at least one user profile, from meal day plans may be generated through operation of a constructor. Please note: Murdoch is describing a nourishment strategy- meal plan, kcal target evaluator.
updating, using the computing device, the food preference menu as a function of feedback data.
[Murdoch: 0077] … The menu generation algorithm may operate a meal target evaluator recursively, through operation of an iterator, to adjust and substitute meal components. A meal day plan may be generated from the individual meals.
[Murdoch: 0098] The system may allow the user to go through their profile and set the preferences for the generated menu. A user may identify/update foods that they like for an already created menu. The system may then take those updated values and reconstruct the menu based on the user's updated preferences.
[Murdoch: 0166] … The user selects a day/meal range identifying the days/times that the user will be in the particular area, and then the meal plan generation algorithm generates the needed items and updates the menu.
[Murdoch: 0212] … The supervised learning process 1608 may incorporate the user preferences 1616, user profiles 1614, and user history 1620 to generate suggestions 1610. The suggestions 1610 may be provided with feedback 1612, which helps improve the supervised learning process 1608.
Regarding claim 12: Rejection is based upon the disclosures applied to claim 11 and further upon Murdoch:
[Murdoch: 0076] In some configurations, a user profile may include user preferences such as food preferences (e.g., likes/dislikes, which may be further broken down into preferred tastes and/or textures, smells, etc.), restrictions (e.g., allergies or disease), health objectives (e.g., lose weight), financial budget, grocer or food distributor (e.g., grocer supplier or direct-to-consumer provider in certain food distribution scenarios), preferred brands or private labels, preferred recipes, and preferred restaurants.
Regarding claim 13: Rejection is based upon the disclosures applied to claim 11 and further upon Murdoch:
[Murdoch: 0076] In some configurations, a user profile may include user preferences such as food preferences (e.g., likes/dislikes, which may be further broken down into preferred tastes and/or textures, smells, etc.), restrictions (e.g., allergies or disease), Please note: Underlined items are genetically related food preferences.
Regarding claim 14: Rejection is based upon the disclosures applied to claim 11 and further upon Murdoch:
[Murdoch: 0076] In some configurations, a user profile may include user preferences such as food preferences (e.g., likes/dislikes, which may be further broken down into preferred tastes and/or textures, smells, etc.), restrictions (e.g., allergies or disease), health objectives (e.g., lose weight), financial budget, …
Regarding claim 15: Rejection is based upon the disclosures applied to claim 11 and further upon Murdoch:
[Murdoch: 0077] … A meal day plan may be generated from the individual meals. A kcal target evaluator may be operated to adjust the portion size for the individual meals in the meal day plan configured by the user preferences.
Regarding claim 20: Rejection is based upon the disclosures applied to claim 11 and further upon Murdoch:
[Murdoch: 0099] The food menu may also work in combination with a user food log or food tracker allowing a user to provide feedback on whether they have been eating the foods suggested by the food menu and also provide the food menu with the ability to adjust the meal options or portion size for the day in order to meet the user's nutrient targets. Users could enter data in the food log in multiple ways, including through standard keyboard entry, scanning the product code (e.g., UPC or QR), taking a picture of the food item/meal (with subsequent AI processing identifying the meal), voice, or other means.
Regarding claim 1: Rejection is based upon the disclosures applied to claim 11 and further upon Murdoch regarding system computing elements.
Regarding claims 2-5 and 10: Rejections are based upon the disclosures applied to claims 1 and 11, and dependents of claim 11 reciting similar subject matter.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 6-9 and 16-19 are rejected under 35 USC 103 as being unpatentable over Murdoch, US 2020/0098466, in view of Neumann, US 2021/0035661.
Regarding claim 16: Rejection is based in part upon the teachings applied to claim 11 by Murdoch and further upon the combination of Murdoch-Neumann.
Claim 16. The method of claim 11, wherein classifying the plurality of data sets further comprises:
generating, using the computing device, group training data, wherein the group training data comprises correlations between exemplary data sets and exemplary user groups;
In Murdoch see at least:
[Murdoch: 0213] Referencing FIG. 17, a system 1700 involves a history cloud 1712 that bidirectionally communicates with system logs 1716, location 1718, and preferences 1714 storage. A server 1708 may receive user interactions via a user interface 1706 by way of a mobile device 1704 or a computing device 1710. The server 1708 may communicate the user information to an artificial intelligence (AI) cloud 1102, which may correlate the user interactions received from the server to information stored on the history cloud 1712.
[Murdoch: 0218] Referencing FIG. 22, a process 2200 involves coalescing/correlating individual user preferences/requirements for their dietary needs (block 2202). In block 2204, the process 2200 optimizes the food preferences for an individual user. The process 2200 includes an additional branch running parallel with the branch starting with block 2202. This additional branch begins with block 2210, where the process 2200 coalesces/correlates similar users' food preferences/requirements. The branch then continues to block 2212, where the process 2200 optimize food preferences for the group of similar users. Both branches meet at block 2206, where the process 2200 generates food item suggestions. In block 2208, the process 2200 reincorporates feedback to improve food item suggestions.
Although Murdoch correlates user interactions and identifies a user group classification to determine if an existing relationship exists between the users, Murdoch does not expressly mention training a group classifier using the group training data. Neumann on the other hand would have taught Murdoch such techniques.
In Neumann see at least:
[Neumann: 0022] With continued reference to FIG. 1, a first probing element, as used herein, includes at least a dataset correlated to at least a user input datum and/or at least an antidote. Dataset, as used herein, includes any data and/or cohort of data that is related to at least a user input datum, where related indicates a relationship to at least a user input datum. Commonality label, as used herein, includes any suggested data and/or cluster of data that may be utilized as training data to create at least a supervised machine-learning model. Commonality label may identify certain datasets and/or cluster of datasets generated by clustering unsupervised machine-learning model that may be used as input and output pairs or labeled training data that contain associations of inputs containing input datums correlated to outputs containing antidotes that may be useful in generating supervised machine-learning algorithms.
[Neumann: 0024] … As another non-limiting example, an unsupervised process may be performed on data concerning a particular cohort of persons; cohort may include, without limitation, a demographic group such as a group of people having a shared age range, ethnic background, nationality, sex, and/or gender. Cohort may include, without limitation, a group of people having a shared value for an element and/or category of user input datum, a group of people having a shared value for an element and/or category of antidote; as illustrative examples, cohort could include all people having a certain level or range of levels of blood triglycerides, all people diagnosed with a genetic single nucleotide polymorphism, all people experiencing the same symptom or cluster of symptoms, all people with a SRD5A2 gene mutation, or the like.
[Neumann: 0046] … Antidote, as used herein includes any treatment, medication, supplement, nourishment, nutrition instruction, supplement instruction, remedy, dietary advice, recommended food, recommended meal plan, or the like that may remedy at least a user input datum.
[Neumann: 0058] With continued reference to FIG. 2, unsupervised learning module 116 may include a data clustering model 208. Data clustering model 208 may group and/or segment datasets with shared attributes to extrapolate algorithmic relationships. Data clustering model 208 may group data to create clusters that may be categorized by certain classifications and/or commonality labels.
[Neumann: 0073] … Training sets may include at least a first element of classified data and at least a correlated second element of classified data. Classified data may include any data that has been classified such as by unsupervised learning module 116 by clustering to generate classifications. Classifications generated by unsupervised learning module 116 such as by data clustering, or hierarchical clustering may be utilized to classify data to select training sets utilized by unsupervised learning module 116. Selecting at least a training set may also be done by extracting at least a keyword from a user input datum 108 such as by parsing module 132 as described above. Please note: Neumann’s system uses a classifier.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Neumann, which create training data for a group classification using a classifier, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Neumann to the teachings of Murdoch would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
training, using the computing device, a group classifier using the group training data,
Rejection is based upon the teachings and rationale applied to claim 11 by Murdoch-Neumann and further upon the combination of Murdoch-Neumann:
[Neumann: 0057] … Unsupervised database 128 may include data describing different users and populations categorized into categories having shared characteristics as described below in more detail in reference to FIG. 1. Probing element output 200 and/or commonality label 204 may be utilized by at least a server to select at least a training set to be utilized by supervised learning module 160. Training sets may be stored and contained within training set database 152. Probing element output 200 and/or commonality label 204 generated by unsupervised learning module 116 may be utilized to select at least a first training set from training set database 152.
[Neumann: 0073] … Classifications generated by unsupervised learning module 116 such as by data clustering, or hierarchical clustering may be utilized to classify data to select training sets utilized by unsupervised learning module 116. Selecting at least a training set may also be done by extracting at least a keyword from a user input datum 108 such as by parsing module 132 as described above.
wherein the group training data is iteratively updated through a feedback loop; and
Rejection is based upon the teachings and rationale applied to claim 11 by Murdoch-Neumann and further upon the combination of Murdoch-Neumann:
[Murdoch: 0067] … Feedback Loops—There are a number of other feedback loops that will be implemented as the menu generation platform database grows. Each user interaction point is an opportunity to engage machine learning and assist the user in creating a food & exercise regimen that is tailored for them based on their feedback.
[Neumann: 0023] … In an embodiment, as additional data is added to system 100, at least a server 104 and/or unsupervised machine-learning module may continuously or iteratively perform unsupervised machine-learning processes to detect relationships between different elements of the added and/or overall data; in an embodiment, this may enable system 100 to use detected relationships to discover new correlations between biomarkers, body dimensions, tissue data, medical test data, sensor data, training set components and/or compatible substance label 120 and one or more elements of data in large bodies of data, such as genomic, proteomic, and/or microbiome-related data, enabling future supervised learning and/or lazy learning processes as described in further detail below to identify relationships between, e.g., particular clusters of genetic alleles and particular antidotes and/or suitable antidotes. Use of unsupervised learning may greatly enhance the accuracy and detail with which system 100 may generate antidotes using supervised machine-learning models …
classifying, using the computing device, the plurality of data sets into the one or more user groups using the trained group classifier.
Rejection is based upon the teachings and rationale applied to claim 11 by Murdoch-Neumann and further upon the combination of Murdoch-Neumann: see at least [Neumann: 0057 & 0073] previously recited.
Regarding claim 6 reciting similar subject matter: Rejection is based upon the teachings and rationale applied to claim 16 by Murdoch-Neumann.
Regarding claim 17: Rejection is based upon teachings and rationale applied to the combination of Murdoch-Neumann above and further upon the combination of Murdoch-Neumann:
The method of claim 11, further comprising:
identifying, using the computing device, at least a keyword of the plurality of data sets using a language processing module.
[Neumann: 0023] With continued reference to FIG. 1, at least a server 104 and/or unsupervised machine-learning module may detect further significant categories of user input datums, relationships of such categories to first probing elements, categories of commonality labels and/or categories of first probing elements using machine-learning processes, including without limitation unsupervised machine-learning processes as described above; such newly identified categories, as well as categories entered by experts in free-form fields, may be added to pre-populated lists of categories, lists used to identify language elements for language processing module, and/or lists used to identify and/or score categories detected in documents, as described in more detail below.
[Neumann: 0026] With continued reference to FIG. 1, system 100 may include at least a parsing module 132 operating on the at least a server. Parsing module 132 may parse the at least a user input for at least a keyword and select at least a dataset as function of the at least a keyword. Parsing module 132 may select at least a dataset by extracting one or more keywords containing words, phrases, test results, numerical scores, and the like from the at least a user input datum 108 and analyze the one or more keywords utilizing for example, language processing module as described in more detail below.
[Neumann: 0046] … Antidote, as used herein includes any treatment, medication, supplement, nourishment, nutrition instruction, supplement instruction, remedy, dietary advice, recommended food, recommended meal plan, or the like that may remedy at least a user input datum.
Regarding claim 7 reciting similar subject matter: Rejection is based upon the teachings and rationale applied to claim 17 by Murdoch-Neumann.
Regarding claim 18: Rejection is based upon teachings and rationale applied to the combination of Murdoch-Neumann above and further upon the combination of Murdoch-Neumann:
The method of claim 11, further comprising:
generating, using the computing device, pattern training data, wherein the pattern training data comprises correlations between exemplary data sets and exemplary food patterns;
See at least [Murdoch: 0067] previously recited regarding trained cloud, food patterns, and correlating user food preferences. See Neumann regarding correlating data sets
training, using the computing device, a pattern machine-learning model using the pattern training data, wherein the pattern training data is iteratively updated through a feedback loop; and determining, using the computing device, the food pattern using the trained pattern machine- learning model.
See at least [Murdoch: 0067] and [Neumann: 0023] previously recited regarding feedback loops.
Regarding claim 8 reciting similar subject matter: Rejection is based upon the teachings and rationale applied to claim 18 by Murdoch-Neumann.
Regarding claim 19: Rejection is based upon teachings and rationale applied to the combination of Murdoch-Neumann above and further upon the combination of Murdoch-Neumann:
The method of claim 11, wherein generating a food preference menu comprises:
generating, using the computing device, menu training data, wherein the menu training data comprises correlations between exemplary data patterns and exemplary food preference menus;
See at least [Murdoch: 0067] previously recited regarding trained cloud, food patterns, and correlating user food preferences. See Neumann regarding correlating data sets
training, using the computing device, a menu machine-learning model using the menu training data, wherein the menu training data is iteratively updated through a feedback loop; and generating, using the computing device, the food preference menu using the trained menu machine-learning model.
See at least [Murdoch: 0067] and [Neumann: 0023] previously recited regarding iterative feedback loops.
Regarding claim 9 reciting similar subject matter: Rejection is based upon the teachings and rationale applied to claim 19 by Murdoch-Neumann.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2019/0228856 (Leifer et al.) “Method and System for Preference-Driven Food Personalization,” discloses: [Abstract] A method for improving food-related personalized for a user including determining food-related preferences associated with a plurality of users to generate a user food preferences database; collecting dietary inputs from a subject matter expert (SME) at an SME interface associated with the user food preferences database; determining personalized food parameters for the user based on the user food-related preferences and the dietary inputs; receiving feedback associated with the personalized food parameters from the user; and updating the user food preferences database based on the feedback.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT M POND whose telephone number is (571)272-6760. The examiner can normally be reached M-F, 8:30 AM-6:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Smith can be reached at 571-272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT M POND/Primary Examiner, Art Unit 3688 March 26, 2026