US 12,112,294 B2
Multi-phase consolidation optimization tool
Benjamin Grisz, Armonk, NY (US); Casey Mccord, Armonk, NY (US); Emily Richards, Armonk, NY (US); Carol Crawford, Atlanta, GA (US); Jose Landeros, Atlanta, GA (US); Ayan Das, Atlanta, GA (US); and Christopher Martin Rubio, Alpharetta, GA (US)
Assigned to United Parcel Service of America, Inc., Atlanta, GA (US)
Filed by United Parcel Service Of America, Inc., Atlanta, GA (US)
Filed on Feb. 28, 2023, as Appl. No. 18/115,422.
Application 18/115,422 is a continuation of application No. 16/992,311, filed on Aug. 13, 2020, granted, now 11,593,753.
Claims priority of provisional application 62/886,154, filed on Aug. 13, 2019.
Prior Publication US 2023/0214761 A1, Jul. 6, 2023
Int. Cl. G06Q 10/08 (2024.01); G06F 9/451 (2018.01); G06N 20/00 (2019.01); G06Q 10/083 (2023.01); G06Q 10/0831 (2023.01); G06Q 10/087 (2023.01); G06Q 10/10 (2023.01); G06Q 30/0601 (2023.01); G06Q 50/26 (2024.01)
CPC G06Q 10/0838 (2013.01) [G06F 9/451 (2018.02); G06N 20/00 (2019.01); G06Q 10/0831 (2013.01); G06Q 10/087 (2013.01); G06Q 10/10 (2013.01); G06Q 30/0635 (2013.01); G06Q 50/26 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method comprising:
training a machine learning model by:
extracting a set of historical features from historical data comprising past purchase orders that have been labeled as inspected or not inspected;
converting the set of historical features into one or more representative feature vectors; and
processing the one or more representative feature vectors using the machine learning model to identify a vector space representative of the set of historical features; subsequent to training the machine learning model:
extracting, by at least one computer processor, a set of features from a plurality of purchase orders, wherein each purchase order of the plurality of purchase orders comprises a feature of a good that is to be received for importing;
converting, by the at least one computer processor, the set of features into one or more feature vectors;
processing, by the at least one computer processor, the one or more feature vectors using the machine learning model to generate a prediction for a set of goods associated with the plurality of purchase orders of being inspected by a customs clearance agent, wherein the machine learning model generates the prediction based at least in part on determining a distance, in the vector space, between the one or more feature vectors and the one or more representative feature vectors;
accessing rule data sets associated with constraints controlling how the plurality of purchase orders is to be consolidated to an entry, wherein the entry corresponds to a customs clearance document identifying at least one of the set of goods that is to be received for importing;
based at least in part on the prediction and the rule data sets, automatically generating, by the at least one computer processor, an estimated risk for inspection by the customs clearance agent for the set of goods;
based at least in part on the estimated risk, automatically consolidating, by the at least one computer processor, the plurality of purchase orders to a single entry for importing the set of goods;
in response to automatically consolidating the plurality of purchase orders, storing, by the at least one computer processor, the single entry to a storage device such that the plurality of purchase orders is configured to be accessed from the storage device via a single computer input/output operation; and
based at least in part on consolidating the plurality of purchase orders, causing a user interface to be generated that renders information associated with consolidating the plurality of purchase orders.