SYSTEMS AND METHODS FOR AI-BASED DETECTION OF DELAYS IN A SHIPPING NETWORK

1 . A computer-implemented method of assessing in-progress shipments, the method comprising:

accessing, by at least one processor, a set of records representing a plurality of shipments, each shipment in the plurality of shipments having a training set of multiple in-flight snapshots, wherein each in-flight snapshot in the training set of multiple in-flight snapshots represents a record of that shipment at a respective snapshot time while that shipment was in-flight and before that shipment was delivered;

for each training set of multiple in-flight snapshots respectively corresponding to the plurality of shipments:

extracting, by the at least one processor, a set of features from that training set of multiple in-flight snapshots, and

generating, by the at least one processor, a feature vector based on the set of features that was extracted;

training, by the at least one processor, a machine learning model using the feature vectors that were respectively generated for the training sets of multiple in-flight snapshots;

accessing, by the at least one processor, shipment information for an in-progress shipment, the shipment information comprising a status and an estimated delivery date for the in-progress shipment; and

analyzing, by the at least one processor using the machine learning model, the shipment information for the in-progress shipment to assess whether delivery of the in-progress shipment will be on-time or delayed.

2 . The computer-implemented method of claim 1 , wherein accessing the set of records comprises:

accessing, by the at least one processor, the set of records representing a plurality of historical shipments.

3 . The computer-implemented method of claim 1 , wherein analyzing the shipment information for the in-progress shipment comprises:

analyzing, by the at least one processor using the machine learning model, the shipment information for the in-progress shipment to determine a probability of whether the in-progress shipment will be delayed.

4 . The computer-implemented method of claim 1 , further comprising:

accessing, by the at least one processor, additional shipment information for the in-progress shipment; and

analyzing, by the at least one processor using the machine learning model, the additional shipment information for the in-progress shipment to further assess whether delivery of the in-progress shipment will be on-time or delayed.

5 . The computer-implemented method of claim 1 , further comprising:

extracting, by the at least one processor from the shipment information for the in-progress shipment, a set of additional features for the in-progress shipment; and

generating, by the at least one processor, an additional feature vector based on the set of additional features for the in-progress shipment.

6 . The computer-implemented method of claim 5 , wherein analyzing the shipment information for the in-progress shipment comprises:

analyzing, by the at least one processor using the machine learning model, the additional feature vector to assess whether delivery of the in-progress shipment will be on-time or delayed.

7 . The computer-implemented method of claim 1 , further comprising:

storing, in a database indexed for an application, information indicating whether the delivery of the in-progress shipment will be on-time or delayed.

8 . A system for assessing in-progress shipments, comprising:

a memory storing a set of computer-executable instructions; and

at least one processor interfaced with the memory and configured to execute the set of computer-executable instructions to cause the at least one processor to:

access a set of records representing a plurality of shipments, each shipment in the plurality of shipments having a training set of multiple in-flight snapshots, wherein each in-flight snapshot in the training set of multiple in-flight snapshots represents a record of that shipment at a respective snapshot time while that shipment was in-flight and before that shipment was delivered,

for each training set of multiple in-flight snapshots respectively corresponding to the plurality of shipments:

extract a set of features from that training set of multiple in-flight snapshots, and

generate a feature vector based on the set of features that was extracted,

train a machine learning model using the feature vectors that were respectively generated for the training sets of multiple in-flight snapshots,

access shipment information for an in-progress shipment, the shipment information comprising a status and an estimated delivery date for the in-progress shipment, and

analyze, using the machine learning model, the shipment information for the in-progress shipment to assess whether delivery of the in-progress shipment will be on-time or delayed.

9 . The system of claim 8 , wherein the set of records represents a plurality of historical shipments.

10 . The system of claim 8 , wherein to analyze the shipment information for the in-progress shipment, the at least one processor is configured to:

analyze, using the machine learning model, the shipment information for the in-progress shipment to determine a probability of whether the in-progress shipment will be delayed.

11 . The system of claim 8 , wherein the at least one processor is configured to execute the set of computer-executable instructions to further cause the at least one processor to:

access additional shipment information for the in-progress shipment, and

analyze, using the machine learning model, the additional shipment information for the in-progress shipment to further assess whether delivery of the in-progress shipment will be on-time or delayed.

12 . The system of claim 8 , wherein the at least one processor is configured to execute the set of computer-executable instructions to further cause the at least one processor to:

extract, from the shipment information for the in-progress shipment, a set of additional features for the in-progress shipment, and

generate an additional feature vector based on the set of additional features for the in-progress shipment.

13 . The system of claim 12 , wherein to analyze the shipment information for the in-progress shipment, the at least one processor is configured to:

analyze, using the machine learning model, the additional feature vector to assess whether delivery of the in-progress shipment will be on-time or delayed.

14 . The system of claim 8 , further comprising:

a database indexed for an application;

wherein the at least one processor is configured to execute the set of computer-executable instructions to further cause the at least one processor to:

store, in the database indexed for the application, information indicating whether the delivery of the in-progress shipment will be on-time or delayed.

15 . A non-transitory computer-readable storage medium configured to store instructions executable by one or more processors, the instructions comprising:

instructions for accessing a set of records representing a plurality of shipments, each shipment in the plurality of shipments having a training set of multiple in-flight snapshots, wherein each in-flight snapshot in the training set of multiple in-flight snapshots represents a record of that shipment at a respective snapshot time while that shipment was in-flight and before that shipment was delivered;

for each training set of multiple in-flight snapshots respectively corresponding to the plurality of shipments:

instructions for extracting a set of features from that training set of multiple in-flight snapshots, and

instructions for generating a feature vector based on the set of features that was extracted;

instructions for training a machine learning model using the feature vectors that were respectively generated for the training sets of multiple in-flight snapshots;

instructions for accessing shipment information for an in-progress shipment, the shipment information comprising a status and an estimated delivery date for the in-progress shipment; and

instructions for analyzing, using the machine learning model, the shipment information for the in-progress shipment to assess whether delivery of the in-progress shipment will be on-time or delayed.

16 . The non-transitory computer-readable storage medium of claim 15 , wherein the instructions for analyzing the shipment information for the in-progress shipment comprise:

instructions for analyzing, using the machine learning model, the shipment information for the in-progress shipment to determine a probability of whether the in-progress shipment will be delayed.

17 . The non-transitory computer-readable storage medium of claim 15 , wherein the instructions further comprise:

instructions for accessing additional shipment information for the in-progress shipment; and

instructions for analyzing, using the machine learning model, the additional shipment information for the in-progress shipment to further assess whether delivery of the in-progress shipment will be on-time or delayed.

18 . The non-transitory computer-readable storage medium of claim 15 , wherein the instructions further comprise:

instructions for extracting, from the shipment information for the in-progress shipment, a set of additional features for the in-progress shipment; and

instructions for generating an additional feature vector based on the set of additional features for the in-progress shipment.

19 . The non-transitory computer-readable storage medium of claim 18 , wherein the instructions for analyzing the shipment information for the in-progress shipment comprise:

instructions for analyzing, using the machine learning model, the additional feature vector to assess whether delivery of the in-progress shipment will be on-time or delayed.

20 . The non-transitory computer-readable storage medium of claim 15 , wherein the instructions further comprise:

instructions for storing, in a database indexed for an application, information indicating whether the delivery of the in-progress shipment will be on-time or delayed.