US 12,230,148 B2
Artificial intelligence system for estimating excess non-sapient payload capacity on mixed-payload aeronautic excursions
Milind Tavshikar, Lexington, MA (US)
Assigned to QuantumID Technologies Inc., Cambridge, MA (US)
Filed by QuantumiD Technologies Inc., Cambridge, MA (US)
Filed on Aug. 5, 2022, as Appl. No. 17/882,301.
Application 17/882,301 is a continuation in part of application No. 16/850,910, filed on Apr. 16, 2020, granted, now 11,410,058.
Application 16/850,910 is a continuation in part of application No. 16/369,892, filed on Mar. 29, 2019, granted, now 10,661,902, issued on May 26, 2020.
Claims priority of provisional application 62/835,048, filed on Apr. 17, 2019.
Prior Publication US 2022/0383757 A1, Dec. 1, 2022
Int. Cl. G08G 5/00 (2006.01)
CPC G08G 5/0034 (2013.01) [G08G 5/0047 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A system for aeronautic path optimization, the system comprising:
at least a server, wherein the at least a server is configured to:
receive at least an order, wherein the at least an order comprises data regarding a physical transport of a non-sapient payload from an initial location to a terminal location;
identify an aeronautic path as a function of the at least an order using a path selection module, wherein the aeronauticoath comprises travel between the initial location and the terminal location;
generate a plurality of optimal routes for the at least an order as a function of the aeronautic path, wherein generating the plurality of optimal routes comprises:
creating optimization training data correlating a plurality of aeronautic path data elements to a plurality of optimal route data elements by:
linking data from a plurality of feeds using hierarchies of data structures to assemble the optimization training data; and
storing the optimization training data in a database;
receiving the optimization training data from the database;
training an optimization machine learning model as a function of the optimization training data using a machine learning process; and
generating the plurality of optimal routes as a function of the aeronautic path using the trained optimization machine learning model; and
produce an assigned route as a function the plurality of optimal routes.