Security, luggage tracking, and machine learning models
View Patent ↗Various methods and systems for training and utilizing machine learning models for tracking a luggage item of a passenger are disclosed. Representative systems may include custom computer architecture for operating a machine learning model utilizing a plurality of reference indicator data sets obtained from a first B-Type message used to train the machine learning model to determine a routine route and a plurality of reference indicator data sets from a second B-Type message used to train the machine learning model to determine a non-routine route relative to the routine route. The model may match first travel information of a passenger name and an International Air Transport Association (IATA) license plate number for a luggage item of a passenger in a manifest with second travel information from a created B-Type message comprising a reference indicator representative of a non-routine routed luggage item and generate delivery instruction of the luggage item.
1 . A method comprising:
training, deploying, and updating a machine learning model, by at least one processor, with representative information,
wherein the representative information comprises: a plurality of reference indicator data sets obtained from a first message used to train the machine learning model to determine a routine route and a plurality of reference indicator data sets from a second message used to train the machine learning model to determine a non-routine route relative to the routine route; and
deploying, by the at least one processor, the machine learning model to perform:
matching first travel information comprising a passenger name and an International Air Transport Association (IATA) license plate number for a luggage item of a passenger in a manifest with second travel information from a created message comprising a reference indicator representative of a non-routine routed luggage item,
retrieving third travel information of the luggage item generated by a baggage handling system correlated to a time frame prior to or after the created message to locate the luggage item, and
generating delivery instructions, based on a baggage journey travel record associated with a master travel manifest for a current travel journey of the luggage item,
wherein the delivery instructions include information to reroute the luggage item from its found location to a location of a reservation of a registered passenger.
2 . The method of claim 1 , wherein the first message, second message, and created message are B-Type messages, and wherein the created message is representative of one of:
a baggage not seen message (BNS);
a baggage processing message (BPM);
a baggage transfer message (BTM); and
a baggage source message (BSM).
3 . The method of claim 2 , wherein the machine learned model further performs:
receiving an originating BSM, as the first message; and
determining the routine route for the luggage item based on the originating BSM.
4 . The method of claim 3 , wherein the machine learning model further performs:
determining whether the reference indicator represents a deviation in time or distance between the routine route of the luggage item and a current route of the luggage item is greater than a threshold.
5 . The method of claim 1 , wherein the machine learning model further performs:
determining whether the reference indicator is representative of information indicating that the luggage item is not seen.
6 . The method of claim 1 , wherein the machine learning model further preforms:
generating delivery instructions for the luggage of the passenger; and
creating new delivery instructions to re-route the luggage of the passenger.
7 . The method of claim 1 , wherein the machine learning model further performs:
receiving information associated with a plurality of scanner devices of baggage handling systems of airports, and
wherein scanners of the baggage handling system of an originating airport of a flight routes a luggage item through an airport infrastructure.
8 . The method of claim 1 , wherein the machine learning model further performs:
determining whether the reference indicator represents a non-routine route of the luggage item that includes a deviation in an airport in the second message relative to the first message.
9 . The method of claim 1 , wherein the machine learning model further performs:
accessing location data generated by a tracking device on the luggage item to track a current location of the luggage item; and
generating the delivery instructions using the current location of the luggage item from the tracking device.
10 . The method of claim 1 , wherein the machine learning model further performs:
determining whether the reference indicator represent a non-routine route of the luggage item based on one or more of departure times, connection times, and final arrival times, to determine a probability that the luggage item may be delivered with sufficient time to account for the luggage item making it to a final destination.
11 . A method for utilizing a machine learning model for tracking and rerouting passenger luggage, comprising:
storing, by a cloud-based computing system having at least on processor and memory, a machine learning model;
interacting, by a local terminal, with the cloud-based computing system and deploying the machine learning model;
wherein the machine learning model is trained from representative information comprising a plurality of reference indicator data sets from a first message used to train the machine learning model to determine a routine route and a plurality of reference indicator data sets from a second message used to train the machine learning model to determine a non-routine route relative to the routine route;
deploying, by the local terminal, the machine learning model to perform:
matching first travel information comprising a passenger name and an International Air Transport Association (IATA) license plate number for a luggage item of a passenger in a manifest with second travel information from a created message comprising a reference indicator representative of a non-routine routed luggage item;
retrieving third travel information of the luggage item generated by a baggage handling system correlated to a time frame prior to or after the created message to locate the luggage item; and
generating delivery instructions, based on a baggage journey travel record associated with a master travel manifest for a current travel journey of the luggage item, wherein the delivery instructions include information to reroute the luggage item from its found location to a location of a reservation of a registered passenger.
12 . The method of claim 11 , wherein the first message, second message, and created message are B-Type messages, and wherein the created message is representative of one of:
a baggage not seen message (BNS);
a baggage processing message (BPM);
a baggage transfer message (BTM); and
a baggage source message (BSM).
13 . The method of claim 11 , wherein the machine learning model further performs:
acquiring data from one or more baggage handling systems handling the luggage item; and
detecting that the luggage item is the non-routine routed luggage item based on a difference between a current route and the routine route being greater than a predetermined threshold.
14 . The method of claim 11 , wherein the machine learning model further performs:
receiving an originating BSM; and
determining a routine route for the luggage item.
15 . The method of claim 11 , wherein the machine learning model further performs:
determining that the reference indicator represents a deviation in time or distance between the routine route of the luggage item and a current route of the luggage item is greater than a threshold.
16 . The method of claim 11 , wherein the machine learning model further performs:
determining that the reference indicator is representative of information indicating that the luggage item is not seen.
17 . The method of claim 11 , wherein the machine learning model further performs:
receiving information associated with a plurality of scanner devices of baggage handling systems of airports, and
wherein scanners of the baggage handling system of an originating airport of a flight routes a luggage item through an airport infrastructure.
18 . The method of claim 11 , wherein the machine learning model further performs:
determining that the reference indicator represent a non-routine route of the luggage item that includes a deviation in an airport in the second message relative to the first message.
19 . The method of claim 11 , wherein the machine learning model further performs:
accessing location data generated by a tracking device on the luggage item to track a current location of the luggage item; and
generating the delivery instructions using the current location of the luggage item from the tracking device.
20 . The method of claim 11 , wherein the machine learning model further performs:
determining whether the reference indicator represent a non-routine route of the luggage item based on one or more of departure times, connection times, and final arrival times, to determine a probability that the luggage item may be delivered with sufficient time to account for the luggage item making it to a final destination.
21 . The method of claim 11 , wherein the machine learning model further performs:
generating delivery instructions for the luggage of the passenger; and
creating new delivery instructions to re-route the luggage of the passenger.
22 . A method comprising:
training, deploying, and updating, by at least one processor, a machine learning model with representative information,
wherein the representative information comprises: a plurality of reference indicator data sets obtained from a first message used to train the machine learning model to determine a routine route and a plurality of reference indicator data sets from a second message used to train the machine learning model to determine a non-routine route relative to the routine route;
deploying, by the at least one processor, the machine learning model to perform:
matching first travel information including a passenger name and an International Air Transport Association (IATA) license plate number for a luggage item of a passenger in a flight manifest with second travel information from a created message that includes a reference indicator representative of a non-routine routed luggage item;
generating for the non-routine routed luggage item associated with the flight manifest;
retrieving third travel information of the non-routine routed luggage item generated by a baggage handling system prior to or after the created message; and
locating the non-routine routed the luggage item.
23 . The method of claim 22 , wherein the machine learning model further performs:
generating delivery instructions to reroute the located luggage item based on passenger reservation information in a manifest for a next mode of travel to rendezvous with the passenger.
24 . The method of claim 22 , wherein the first message, second message, and created message are B-Type messages, and wherein the message is representative of one of:
a baggage not seen message (BNS);
a baggage processing message (BPM);
a baggage transfer message (BTM); and
a baggage source message (BSM) from an airline.
25 . The method of claim 22 , wherein the training, by the at least one processor, the machine learning model comprises training the model with:
the reference indicators of the non-routine routed luggage item associated with one or more messages;
data route information representations to create information for a routine route;
one or more current messages related to transport of the luggage item with the IATA license plate number; and
data baggage handling information from each baggage handling system handling the luggage item.
26 . The method of claim 25 , wherein the machine learning model further performs: utilizing machine learning algorithms to detect that the luggage item is the non-routine routed luggage item with a difference from a current route and the routine route being greater than a threshold.
27 . The method of claim 25 , wherein the machine learning model further performs, prior to matching:
receiving an originating BSM to determine a routine route for the luggage item, based on originating reference indicators.
28 . The method of claim 27 , wherein the machine learning model further performs:
determining that the reference indicator represents a deviation in time or distance between the routine route of the luggage item and a current route of the luggage item greater than a threshold.
29 . The method of claim 22 , wherein the machine learning model further performs:
determining that the reference indicator is representative that the luggage item is not seen;
notifying the passenger of the not seen luggage item.
30 . The method of claim 22 , wherein the machine learning model further performs:
receiving the flight manifest on a current day of travel with first travel information of those registered passengers traveling on the current day; and
receiving a terminating baggage source message (BSM) or a transfer BSM with the second travel information.
31 . The method of claim 22 , wherein the machine learning model further performs:
determining that the reference indicator represents the non-routine route of the luggage item;
accessing location data generated by a tracking device on the luggage item to track a current location of the luggage item; and
generating delivery instructions to reroute the recovered luggage item includes using the current location of the luggage item from the tracking device.
32 . The method of claim 31 , wherein the tracking device is configured to perform:
communicating, using one of a WIFI communication protocol, a BLUETOOTH communication protocol, a cellular communication protocol, a long-range radio frequency communication protocol, a short-range communication protocol, a near-field communication protocol, and Global System for Mobile Communications; and
wherein the machine learning model further performs:
communicating the current location information to a computing system for tracking the tracking device.