US-12620323-B2 - Methods and systems for self-fulfillment of a dietary request

Abstract

A system for self-fulfillment includes at least a server. The at least a server is designed and configured to receive training data, wherein receiving the training data further comprises receiving at least a dietary request and at least a correlated alimentary process label. The at least a server is configured to receive at least a dietary request from a user device. The at least a server generates at least an alimentary instruction set as a function of the at least a dietary request from the user device and the training data. The at least a server generates at least a self-fulfillment instruction set as a function of the at least an alimentary instruction set containing at least a self-fulfillment action. The at least a server receives at least a user entry containing an alimentary self-fulfillment action.

Inventors

• Kenneth Neumann

Assignees

• KPN INNOVATIONS, LLC.

Dates

Publication Date

20260505

Application Date

20211103

Claims (18)

1. 1 . A system for self-fulfillment of a dietary request, the system comprising: at least a server, wherein the at least a server is designed and configured to: receive an alimentary instruction set as a function of a dietary request, wherein receiving the alimentary instruction set comprises: parsing the alimentary instruction set into a plurality of tokens; storing the plurality of tokens as one or more chains; assigning an alimentary label to one or more of the plurality of tokens using a Hidden Markov Model; and generating an alimentary instruction set descriptor by converting one or more alimentary labels into narrative language by retrieving one or more elements of narrative language from a table associating alimentary labels with elements of narrative language; generate at least a self-fulfillment instruction set, the self-fulfillment instruction set comprising a data structure identifying a plurality of self-fulfilling actions a user can take to self-fulfill a suggested nourishment requirement, as a function of at least a self-fulfillment action, wherein generating the at least a self-fulfillment instruction set comprises: receiving training data including a plurality of data entries, each data entry of the plurality of data entries including at least an alimentary instruction and at least a correlated self-fulfillment action datum; training a machine-learning model as a function of the training data; and generating the at least a self-fulfillment instruction set as a function of the alimentary instruction set and the machine-learning model; generating a query as a function of at least an instruction of the self-fulfillment instruction set; querying a web index as a function of the query by classifying, using an index classifier, the user to a physiologically linked web index, and querying the physiologically linked web index, wherein the index classifier is iteratively trained with elements of physiological data sets correlated to user cohort labels, wherein the server is configured to continuously generate, using a feature learning algorithm implementing a clustering analysis, cohort labels, using a feature learning algorithm, when a threshold number of physiological data sets fails to converge within a threshold distance of existing clusters; receiving a search result corresponding to the query; and modifying the at least an instruction as a function of the search result; receive at least an entry containing an alimentary self-fulfillment action; and generate a modified self-fulfillment instruction set as a function of the alimentary self-fulfillment action.
2. 2 . The system of claim 1 , wherein receiving the alimentary instruction set further comprises: receiving at least a dietary request; and generating the alimentary instruction set as a function of the at least a dietary request.
3. 3 . The system of claim 1 , wherein generating the modified self-fulfillment instruction set further comprises: generating a modified alimentary instruction set as a function of the alimentary self-fulfillment action; and generating the at least a self-fulfillment instruction set as a function of the modified alimentary instruction set.
4. 4 . The system of claim 3 , wherein generating the at least a self-fulfillment instruction set as a function of the modified alimentary instruction set further comprises generating the at least a self-fulfillment instruction set as a function of the modified alimentary instruction set and the machine-learning model.
5. 5 . The system of claim 1 , wherein generating the modified self-fulfillment instruction set further comprises generating the modified self-fulfillment instruction set as a function of the alimentary self-fulfillment action and the machine-learning model.
6. 6 . The system of claim 1 , wherein the server is further configured to generate the self-fulfillment instruction set by: generating a loss function of user specific variables; and minimizing the loss function.
7. 7 . The system of claim 6 , wherein a user specific variable further comprises an ingredient standard request.
8. 8 . The system of claim 6 , wherein a user specific variable further comprises an ingredient requirement request.
9. 9 . A method of self-fulfillment of a dietary request, the method comprising: receiving, by at least a server, an alimentary instruction set as a function of a dietary request, wherein receiving the alimentary instruction set comprises: parsing the alimentary instruction set into a plurality of tokens; storing the plurality of tokens as one or more chains; assigning an alimentary label to one or more of the plurality of tokens using a Hidden Markov Model; and generating an alimentary instruction set descriptor by converting one or more alimentary labels into narrative language by retrieving one or more elements of narrative language from a table associating alimentary labels with elements of narrative language; generating, by the at least a server, at least a self-fulfillment instruction set, the self-fulfillment instruction set comprising a data structure identifying a plurality of self-fulfilling actions a user can take to self-fulfill a suggested nourishment requirement, as a function of at least a self-fulfillment action, wherein generating the at least a self-fulfillment instruction set comprises: receiving training data including a plurality of data entries, each data entry of the plurality of data entries including at least an alimentary instruction and at least a correlated self-fulfillment action datum; training a machine-learning model as a function of the training data; and generating the at least a self-fulfillment instruction set as a function of the alimentary instruction set and the machine-learning model; generating a query as a function of at least an instruction of the self-fulfillment instruction set; querying a web index as a function of the query by classifying, using an index classifier, the user to a physiologically linked web index, and querying the physiologically linked web index, wherein the index classifier is iteratively trained with elements of physiological data sets correlated to user cohort labels, wherein the server is configured to continuously generate, using a feature learning algorithm implementing a clustering analysis, cohort labels when a threshold number of physiological data sets fails to converge within a threshold distance of existing clusters; receiving a search result corresponding to the query; and modifying the at least an instruction as a function of the search result; receiving, by the at least a server, at least an entry containing an alimentary self-fulfillment action; generating, by the at least a server a modified self-fulfillment instruction set as a function of the alimentary self-fulfillment action.
10. 10 . The method of claim 9 , wherein receiving the alimentary instruction set further comprises: receiving at least a dietary request; and generating the alimentary instruction set as a function of the at least a dietary request.
11. 11 . The method of claim 9 , wherein generating the modified self-fulfillment instruction set further comprises: generating a modified alimentary instruction set as a function of the alimentary self-fulfillment action; and generating the at least a self-fulfillment instruction set as a function of the modified alimentary instruction set.
12. 12 . The method of claim 11 , wherein generating the at least a self-fulfillment instruction set as a function of the modified alimentary instruction set further comprises generating the at least a self-fulfillment instruction set as a function of the modified alimentary instruction set and the machine-learning model.
13. 13 . The method of claim 9 , wherein generating the modified self-fulfillment instruction set further comprises generating the modified self-fulfillment instruction set as a function of the alimentary self-fulfillment action and the machine-learning model.
14. 14 . The method of claim 9 , wherein the server is further configured to generate the self-fulfillment instruction set by: generating a loss function of user specific variables; and minimizing the loss function.
15. 15 . The method of claim 14 , wherein a user specific variable further comprises an ingredient standard request.
16. 16 . The method of claim 14 , wherein a user specific variable further comprises an ingredient requirement request.
17. 17 . The system of claim 1 , wherein generating the cohort labels using the feature learning algorithm comprises: dividing physiological data from a given user into a plurality of sub-combinations to create a plurality of physiological data sets using a cluster analysis; and evaluating which physiological data sets tend to co-occur with which other physiological data sets as a function of a degree of similarity index value, wherein the cluster analysis is configured to configured to: generate an initial set of user cohort labels from an initial set of user physiological data; and iteratively identify new clusters to generate new user cohort labels.
18. 18 . The method of claim 9 , wherein generating the cohort labels using the feature learning algorithm comprises: dividing physiological data from a given user into a plurality of sub-combinations to create a plurality of physiological data sets using a cluster analysis; and evaluating which physiological data sets tend to co-occur with which other physiological data sets as a function of a degree of similarity index value, wherein the cluster analysis is configured to configured to: generate an initial set of user cohort labels from an initial set of user physiological data; and iteratively identify new clusters to generate new user cohort labels.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation-in-part of Non-provisional application Ser. No. 16/430,401 filed on Jun. 3, 2019 and entitled “METHODS AND SYSTEMS FOR SELF-FULFILLMENT OF A DIETARY REQUEST,” the entirety of which is incorporated herein by reference. FIELD OF THE INVENTION The present invention generally relates to the field of artificial intelligence. In particular, the present invention is directed to methods and systems for self-fulfillment of a dietary request. BACKGROUND Effective and accurate analysis of data to produce practical and useful instruction sets is challenging. Generating accurate instruction sets is complex in part due to the vast amount of data to be analyzed. Current solutions fail to account for the intricate complexities involved in both producing and receiving meaningful instruction sets. SUMMARY OF THE DISCLOSURE These and other aspects and features of non-limiting embodiments of the present invention will become apparent to those skilled in the art upon review of the following description of specific non-limiting embodiments of the invention in conjunction with the accompanying drawings. In an aspect, a system for self-fulfillment of a dietary request includes at least a server, wherein the at least a server is designed and configured to receive an alimentary instruction set as a function of a user dietary request generate at least a self-fulfillment instruction set, the self-fulfillment instruction set including a data structure identifying a plurality of self-fulfilling actions a user can take to self-fulfill a suggested nourishment requirement, as a function of at least a self-fulfillment action, wherein generating the at least a self-fulfillment instruction set includes receiving training data including a plurality of data entries, each data entry of the plurality of data entries including at least an alimentary instruction and at least a correlated self-fulfillment action datum, training a machine-learning model as a function of the training data, and generating the at least a self-fulfillment instruction set as a function of the alimentary instruction set and the machine-learning model, receive at least a user entry containing an alimentary self-fulfillment action, and generate a modified self-fulfillment instruction set as a function of the alimentary self-fulfillment action. In another aspect, a method of self-fulfillment of a dietary request includes receiving, by at least a server, an alimentary instruction set as a function of a user dietary request, generating, by the at least a server, at least a self-fulfillment instruction set, the self-fulfillment instruction set including a data structure identifying a plurality of self-fulfilling actions a user can take to self-fulfill a suggested nourishment requirement, as a function of at least a self-fulfillment action, wherein generating the at least a self-fulfillment instruction set includes receiving training data including a plurality of data entries, each data entry of the plurality of data entries including at least an alimentary instruction and at least a correlated self-fulfillment action datum, training a machine-learning model as a function of the training data, generating the at least a self-fulfillment instruction set as a function of the alimentary instruction set and the machine-learning model, receiving, by the at least a server, at least a user entry containing an alimentary self-fulfillment action, and generating, by the at least a server a modified self-fulfillment instruction set as a function of the alimentary self-fulfillment action. BRIEF DESCRIPTION OF THE DRAWINGS For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein: FIG. 1 is a block diagram illustrating an exemplary embodiment of a system for self-fulfillment of a dietary request; FIG. 2 is a block diagram illustrating embodiments of data storage facilities for use in disclosed systems and methods; FIG. 3 is a block diagram illustrating an exemplary embodiment of a dietary data database; FIG. 4 is a block diagram illustrating an exemplary embodiment of an expert knowledge database; FIG. 5 is a block diagram illustrating an exemplary embodiment of an alimentary process label database; FIG. 6 is a block diagram illustrating an exemplary embodiment of an alimentary instruction set generator module; FIG. 7 is a block diagram illustrating an exemplary embodiment of an alimentary instruction label classification database; FIG. 8 is a block diagram illustrating an exemplary embodiment of a self-fulfillment generator module; FIG. 9 is a block diagram illustrating an exemplary embodiment of a self-fulfillment learner and associated system elements; FIG. 10 is a block diagram illustrating an exemplary