US 12,248,973 B2
Method and system for geographically tracking nourishment selection
Kenneth Neumann, Lakewood, CO (US)
Assigned to KPN INNOVATIONS LLC, Lakewood, CO (US)
Filed by KPN INNOVATIONS, LLC., Lakewood, CO (US)
Filed on Jul. 11, 2022, as Appl. No. 17/861,911.
Application 17/861,911 is a continuation of application No. 16/886,673, filed on May 28, 2020, granted, now 11,386,477.
Prior Publication US 2022/0351272 A1, Nov. 3, 2022
Int. Cl. G06Q 30/00 (2023.01); G01C 21/36 (2006.01); G06N 20/00 (2019.01); G06Q 30/0601 (2023.01); G16H 20/60 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01); H04L 67/306 (2022.01); H04W 4/02 (2018.01); H04W 4/029 (2018.01)
CPC G06Q 30/0631 (2013.01) [G01C 21/3679 (2013.01); G06N 20/00 (2019.01); G16H 20/60 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01); H04L 67/306 (2013.01); H04W 4/023 (2013.01); H04W 4/029 (2018.02)] 20 Claims
OG exemplary drawing
 
1. An apparatus for geographically tracking nourishment selection, the apparatus comprising:
at least a computing device; and
a memory communicatively connected to the at least a computing device, the memory containing instructions configuring the at least a computing device to:
receive a location associated with a user's commute from a user device;
determine a nourishment provider within the location, wherein the nourishment provider is associated with a plurality of nourishment possibilities;
locate a performance character associated with the user, wherein the performance character contains a nourishment score and at least a behavior relating to the plurality of nourishment possibilities, wherein the nourishment score is calculated using a nourishment machine-learning process as a function of a nourishment behavioral target and a logged nourishment entry, wherein training the nourishment machine-learning process comprises:
using a first training set correlating at least a logged nourishment entry as an input and at least a nourishment score as an output;
generate a user profile wherein the user profile comprises the location, the nourishment provider, and the performance character;
generate a selector machine-learning model, wherein the selector machine-learning model is trained using a second training set configured to correlate at least a user profile input to a nourishment possibility index output, wherein the nourishment possibility index comprises an indication identifying an impact of a nourishment possibility on the calculated nourishment score;
grade, using the selector machine-learning model, the plurality of nourishment possibilities associated with the nourishment provider using a plurality of corresponding nourishment possibility indexes;
receiving a user selection comprising at least a selection of one or more nourishment possibilities of the plurality of nourishment possibilities;
logging a user entry comprising a user preference for the user selection;
retraining the selector machine-learning model with subsequent training data comprising the user selection correlator to the user preference, wherein the user preference comprises data indicating a tolerability of the user selection;
generating the nourishment possibility index output; and
a graphical user interface configured to display the plurality of graded nourishment possibilities.