US 12,437,256 B2
Methods and systems for transport of an alimentary component based on dietary required eliminations
Kenneth Neumann, Lakewood, CO (US)
Assigned to KPN INNOVATIONS LLC, Lakewood, CO (US)
Filed by KPN INNOVATIONS, LLC., Lakewood, CO (US)
Filed on Oct. 4, 2021, as Appl. No. 17/492,993.
Application 17/492,993 is a continuation of application No. 16/430,394, filed on Jun. 3, 2019, granted, now 11,182,729.
Prior Publication US 2022/0036297 A1, Feb. 3, 2022
Int. Cl. G06F 16/9535 (2019.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06Q 10/0832 (2023.01); H04L 67/306 (2022.01); G06F 16/23 (2019.01)
CPC G06Q 10/0832 (2013.01) [G06N 5/04 (2013.01); G06N 20/00 (2019.01); H04L 67/306 (2013.01); G06F 16/2365 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A system for arranging transport of an alimentary component, the system comprising: a computing device comprising a processor and a memory, wherein the computing device is configured to:
identify at least a significant category as a function of a dietary request for a user, wherein the dietary request for the user comprises at least an elimination of a food group as a function of user preference, and wherein elimination of a food group comprises at least excluding at least one ingredient;
generate an alimentary instruction set as a function of the at least a significant category using an alimentary instruction set generator module, wherein the alimentary instruction set generator module includes an alimentary instruction label learner, and wherein the alimentary instruction set generator module is designed and configured to:
generate, using a first machine-learning model, an alimentary instruction set as an output, wherein the first machine-learning model relates the dietary request to an alimentary process label, wherein generating the alimentary instruction set further comprises:
identifying a compatible food group;
training the first machine-learning model, iteratively, using training data correlating a plurality of dietary requests and a plurality of alimentary process labels;
generating a machine-learning process after each iteration of the training, wherein the machine-learning process is configured to output the alimentary instruction set;
identifying the alimentary process label from a plurality of mutually exclusive alimentary process labels, as generated by the first machine-learning model, by ranking each mutually exclusive alimentary process label based on a relative probability of correctness determined by analyzing additional user data; and
generating the alimentary instruction set as a function of the at least a significant category and the compatible food group; generate a transport request as a function of the alimentary instruction set and the dietary request using a transport request generator module, wherein the transport request generator module comprises a transport request learner, and wherein the transport request learner is designed and configured to:
generate a second machine-learning model as an output and using at least an alimentary instruction label and the training data as a function of the first machine-learning model as inputs, wherein the second machine-learning model relates the transport request to the alimentary process label; and enact the transport request as a function of a fulfillment network, wherein the fulfillment network further comprises a plurality of physical performance entity networks, and wherein the transport request represents a plurality of performances so that each of the plurality of performances is accounted for by one of the plurality of physical performance entity networks.