Granted Patent B2

US 12,646,619 · App. 16/781,572 · Granted Jun 2, 2026

Inventor: Kenneth Neumann (Lakewood, CO)

Assignee: KPN INNOVATIONS, LLC

G16H50/00G06N20/00G06N5/022G06N20/10G06N20/20G16H50/20G16H50/30G16H50/50

Abstract

Claims (61)

1 . A method for delivery based on an alimentary instruction, comprising:

receiving, by a diagnostic engine operating on a computing device, information related to a biological extraction of a user;

generating, by the diagnostic engine operating on the computing device, a diagnostic output based upon the information related to the biological extraction of the user, wherein the generating comprises:

identifying, by a machine learning module operating on the computing device, a condition of the user as a function of the information related to the biological extraction and physiological state of the user and a first training set, said first training set including a plurality of data entries, each first data entry of the plurality of data entries including an element of physiological state data and a correlated first prognostic label;

modifying, by the machine learning module operating on the computing device, the first training set by removing physiological state data that have been erroneously recorded;

identifying, by the machine learning module operating on the computing device, an alimentary element related to the identified condition of the user as a function of the identified condition of the user and a second training set, said second training set including a plurality of second data entries, each second data entry including a second prognostic label and a correlated ameliorative process label, wherein the correlated ameliorative process label is stored in an expert knowledge database;

generating, by an alimentary instruction label learner operating on the computing device, at least an alimentary label using the second prognostic label as an input;

generating, by an alimentary instruction set generator operating on the computing device, an alimentary instruction set identifying the alimentary element to be delivered to the user; and

generating, by a delivery instruction set generator operating on the computing device, a delivery instruction set, said delivery instruction set indicating a delivery performance for the alimentary element, wherein the generating the delivery instruction set comprises:

generating a delivery machine-learning model as a function of a delivery machine-learning process using a delivery learner operating on the computing device, the delivery machine-learning model configured to relate the delivery instruction set to at least one of user ameliorative process and user entries containing an alimentary delivery action;

receiving delivery training data, the delivery training data comprising the modified first training set and the second training set, wherein the modified first training set comprises updated physiological state data correlated to prognostic labels, wherein the second training set comprises prognostic labels correlated to ameliorative process labels;

training the delivery machine-learning model as a function of the delivery training data;

generating a subsequent delivery instruction set as a function of delivery machine-learning model;

generating, by a plan generator module operating on the computing device, a comprehensive instruction set associated with the user as a function of the diagnostic output, wherein generating the comprehensive instruction set associated with the user comprises generating a current prognostic descriptor using a language processing module;

transmitting, by the computing device, the comprehensive instruction set associated with the user to at least a user client device; and

identifying, by the alimentary instruction set generator, a non-alimentary instruction within the comprehensive instruction set, wherein an alimentary analog to the non-alimentary instruction is determined, wherein the alimentary analog is introduced into the alimentary instruction set; and

updating, by the alimentary instruction set generator, the delivery instruction set based on the alimentary analog.

2 . The method of claim 1 , further comprising:

transmitting, by the computing device, the delivery instruction set to a client device associated with a delivery performer.

3 . The method of claim 2 , wherein the delivery instruction set identifies the alimentary element and a delivery preference of the user.

4 . The method of claim 1 , wherein the alimentary instruction set includes a suggestion for a second alimentary element and a third alimentary element.

5 . The method of claim 4 , further comprising:

selecting, by the delivery instruction set generator, an alimentary element from the suggested second and third alimentary elements based upon a geolocation of the user.

6 . The method of claim 4 , further comprising:

selecting, by the delivery instruction set generator, an alimentary element from the suggested second and third alimentary elements based upon a preference of the user.

7 . The method of claim 1 , wherein the biological extraction comprises a physical extraction from the user.

8 . The method of claim 1 , further comprising:

modifying, by the delivery instruction set generator, the delivery instruction set as a function of a behavior of the user.

9 . The method of claim 1 , wherein the delivery instruction set is generated as a function of a machine learning model.

10 . A system for delivery based on an alimentary instruction, comprising:

a computing device;

a diagnostic engine operating on the computing device and configured to:

receive information related to a biological extraction of a user;

generate a diagnostic output based upon the information related to the biological extraction of the user, wherein the generating comprises:

identifying, by a machine learning module operating on the computing device, a condition of the user as a function of the information related to the biological extraction and physiological state of the user and a first training set, said first training set including a plurality of data entries, each first data entry of the plurality of data entries including an element of physiological state data and a correlated first prognostic label;

modifying, by the machine learning module operating on the computing device, the first training set by removing physiological state data that have been erroneously recorded;

identifying, by the machine learning module operating on the computing device, an alimentary element related to the identified condition of the user as a function of the identified condition of the user and a second training set, said second training set including a plurality of second data entries, each second data entry including a second prognostic label and a correlated ameliorative process label, wherein the correlated ameliorative process label is stored in an expert knowledge database;

generating, by an alimentary instruction label learner operating on the computing device, at least an alimentary label using the second prognostic label as an input;

an alimentary instruction set generator operating on the computing device and configured to:

generate an alimentary instruction set identifying the alimentary element to be delivered to the user; and

a delivery instruction set generator operating on the computing device and configured to:

generate a delivery instruction set, said delivery instruction set indicating a delivery performance for the alimentary element,

wherein the delivery instruction set generator comprises a delivery learner configured to:

generate a delivery machine-learning model as a function of a delivery machine-learning process using a delivery learner operating on the computing device, the delivery machine-learning model configured to relate the delivery instruction set to at least one of user ameliorative process and user entries containing an alimentary delivery action;

receive delivery training data, the delivery training data comprising the modified first training set and the second training set, wherein the modified first training set comprises updated physiological state data correlated to prognostic labels, wherein the second training set comprises prognostic labels correlated to ameliorative process labels;

train the delivery machine-learning model as a function of the delivery training data;

generate a subsequent delivery instruction set as a function of delivery machine-learning model;

generate a comprehensive instruction set associated with the user as a function of the diagnostic output, wherein generating the comprehensive instruction set associated with the user comprises generating a current prognostic descriptor using a language processing module;

transmit the comprehensive instruction set associated with the user to at least a user client device; and

identify, by the alimentary instruction set generator, a non-alimentary instruction within the comprehensive instruction set, wherein an alimentary analog to the non-alimentary instruction is determined, wherein the alimentary analog is introduced into the alimentary instruction set; and

updating, by the alimentary instruction set generator, the delivery instruction set based on the alimentary analog.

11 . The system of claim 10 wherein the computing device is configured to transmit the delivery instruction set to a client device associated with a delivery performer.

12 . The system of claim 11 , wherein the delivery instruction set identifies the alimentary element and a delivery preference of the user.

13 . The system of claim 10 , wherein the alimentary instruction set includes a suggestion for a second alimentary element and a third alimentary element.

14 . The system of claim 13 , wherein the delivery instruction set generator is further configured to select an alimentary element from the suggested second and third alimentary elements based upon a geolocation of the user.

15 . The system of claim 13 , wherein the delivery instruction set generator is further configured to select, an alimentary element from the suggest second and third alimentary elements based upon a preference of the user.

16 . The system of claim 10 , wherein the biological extraction comprises a physical extraction from the user.

17 . The system of claim 10 , wherein the delivery instruction set generator is further configured to modify the delivery instruction set as a function of a behavior of the user.

18 . The system of claim 10 , wherein the delivery instruction set is generated as a function of a machine learning model.

19 . The method of claim 1 , wherein the method further comprises generating, by an advisory module operating on the computing device, at least an advisory output as a function of the comprehensive instruction set.

20 . The system of claim 10 , wherein the diagnostic engine operating on the computing device is further configured to generate at least an advisory output as a function of the comprehensive instruction set.

Assignments (1)

Continuity (3)

References Cited (44)