US 12,260,365 B2
Systems and methods for determining estimated alimentary element transfer time
Kenneth Neumann, Lakewood, CO (US)
Filed by KPN INNOVATIONS, LLC., Lakewood, CO (US)
Filed on Aug. 10, 2022, as Appl. No. 17/884,880.
Application 17/884,880 is a continuation of application No. 17/088,167, filed on Nov. 3, 2020, granted, now 11,481,717.
Prior Publication US 2022/0383253 A1, Dec. 1, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 10/06 (2023.01); G06F 16/2457 (2019.01); G06F 16/2458 (2019.01); G06N 20/00 (2019.01); G06Q 10/047 (2023.01); G06Q 10/0833 (2023.01); H04W 4/029 (2018.01)
CPC G06Q 10/0833 (2013.01) [G06F 16/24573 (2019.01); G06F 16/2462 (2019.01); G06N 20/00 (2019.01); G06Q 10/047 (2013.01); H04W 4/029 (2018.02)] 18 Claims
OG exemplary drawing
 
1. A system for determining estimated alimentary element transfer time, the system comprising:
a computing device, the computing device configured to:
receive a plurality of alimentary elements and a plurality of destinations associated with the alimentary elements;
determine an estimated transfer time for at least one alimentary element of the plurality of alimentary elements;
generate an accuracy measure of the estimated transfer time based on the estimated transfer time, wherein generating the accuracy measure further comprises:
computing a set of limitations of each alimentary element in the plurality of alimentary elements, wherein the set of limitations comprises:
at least an ancillary limitation comprising at least a dietary restriction based on a user's biological data;
numerical data relating to a plurality of transfer apparatuses traversing a plurality of transfer paths, including at least inclement weather; and
utilizes an accuracy machine learning process comprising a machine-learning model which further comprises:
receiving a training data set, wherein the training data set comprises outputs correlated to inputs, wherein the inputs comprise at least a plurality of transfer time variations correlated to outputs comprising accuracy measures;
generating an accuracy measure of the estimated transfer time using the trained machine-learning model;
successively determining the accuracy measure of the estimated transfer time for each alimentary element of the plurality of alimentary elements;
successively update the training data set with the input to the machine-learning model and the output of the machine-learning model associated with each successive determination of the accuracy measure; and
successively retrain the machine-learning model;
receive a new alimentary element request for an alimentary element originator from an alimentary element originator device;
determine an updated estimated transfer time and an updated accuracy measure, using the retrained machine-learning model for the new alimentary element request;
generate an accuracy message, based on the updated accuracy measure and the updated estimated transfer time; and
generate a representation, via a graphical user interface, the estimated transfer time and the estimated transfer time accuracy message to the alimentary element originator device.