Baggage system, RFID chip, server and method for capturing baggage data

• US 10,460,837 B1
• Filed: 01/21/2019
• Issued: 10/29/2019
• Est. Priority Date: 02/06/2015
• Status: Active Grant

• Current Assignee: Brain Trust Innovations I, LLC
• Original Assignee: Brain Trust Innovations I, LLC
• Inventors: LaBorde, David
• Primary Examiner(s): Blouin, Mark S
• Application Number:
  US16/252,969
• Time in Patent Office: 281 Days
• Field of Search: 340 101
• CPC Class Codes: G06K 19/0723 the record carrier comprising an arrangement for non-contact communication, e.g. wireless communication circuits on transponder cards, non-contact smart cards or RFIDs
• CPC Class Codes: G06K 19/07749 the record carrier being capable of non-contact communication, e.g. constructional details of the antenna of a non-contact smart card
• CPC Class Codes: G06K 7/10366 the interrogation device being adapted for miscellaneous applications
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 3/006 based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/047 Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/048 Activation functions
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 3/086 using evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/041 Abduction
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G16H 10/65 stored on portable record carriers, e.g. on smartcards, RFID tags or CD

Abstract

A baggage system includes a plurality of RFID tags affixed to baggage items, a data collection engine, client devices and backend devices. The backend devices include trained machine learning models, business logic, and attributes of a plurality of events. A plurality of data collection engines and baggage terminal systems send attributes of new events to the backend devices. The backend devices can track the baggage items and predict particular outcomes of new events based upon the attributes of the new events utilizing the trained machine learning models.

Claims

1. A baggage system comprising:

a transfer reader device configured to communicate with a radio-frequency identification (RFID) tag associated with a baggage item, wherein the transfer reader device comprises;

an RFID antenna;

a power transmission subsystem including a power source and an antenna arranged to wirelessly transmit power from the power source to the RFID tag;

a transceiver configured to receive first data from the RFID tag, the first data including identification information;

a controller operatively coupled to the transceiver; and

one or more memory sources operatively coupled to the controller, the one or more memory sources including instructions for configuring the controller to generate one or more messages indicative of the identification information to be sent by the transceiver to a server device via a network connection,

wherein the server device comprises;

a transceiver configured to receive the one or more messages from the transfer reader device;

a controller operatively coupled to the transceiver; and

one or more memory sources operatively coupled to the controller, the one or more memory sources including instructions for configuring the controller to generate another message indicative of the identification information associated with the baggage item.

2. The baggage system of claim 1, wherein in the server device the one or more memory sources store a database including baggage item identifications and attributes of each of the baggage item identifications, the attributes indicative of destination information of each of the baggage item identifications, the instructions further configure the controller of the server device to determine whether the baggage item has arrived at a final arrival airport based upon the one or more messages from the transfer reader device.

3. The baggage system of claim 1, wherein in the server device the one or more memory sources further store a trained model for generating an output value corresponding to a present event based upon at least the identification information.

4. The baggage system of claim 3, where the server device is further configured to:

receive a plurality of input attributes of the present event;

perform pre-processing on the plurality of input attributes to generate an input data set;

generate the output value from the trained model based upon the input data set; and

predict an outcome associated with the present event based upon the output value.

5. The baggage system of claim 1, where the server device is further configured to:

store a plurality of past events, each of the plurality of past events including a plurality of input attributes and a quantifiable outcome; and

train an NNM to generate a trained model, wherein the training of the NNM includes;

performing pre-processing on the plurality of input attributes for each of the plurality of past events to generate a plurality of input data sets;

dividing the plurality of past events into a first set of training data and a second set of validation data;

iteratively performing a machine learning algorithm (MLA) to update synaptic weights of the NNM based upon the training data; and

validating the NNM based upon the second set of validation data.

6. The baggage system of claim 1, wherein:

the one or more memory sources of the server device further store a trained model for generating an output value corresponding to a present event based upon at least the identification information; and

the trained model is a trained Self-Organizing Map (SOM) including a plurality of network nodes arranged in a grid or lattice and in fixed topological positions, an input layer with a plurality of input nodes representing input attributes of past events, wherein each of the plurality of input nodes is connected to all of the plurality of network nodes by a plurality of synaptic weights.

7. The baggage system of claim 6, wherein the controller of the server device is further configured to:

store a plurality of past events, each of the plurality of past events including a plurality of input attributes;

perform pre-processing on the plurality of attributes for each of the plurality of past events to generate a plurality of input data sets; and

train a SOM to generate the trained model, wherein the training of the SOM includes;

initializing values of the plurality of synaptic weights to random values,

randomly selecting one past event and determining which of the plurality of network nodes is a best matching unit (BMU) according to a discriminant function, wherein the discriminant function is a Euclidean Distance; and

iteratively calculating a neighborhood radius associated with the BMU to determine neighboring network nodes for updating, and updating values of synoptic weights for neighboring network nodes within the calculated neighborhood radius for a fixed number of iterations to generate the trained model.

8. The baggage system of claim 7, wherein the controller of the server device is further configured to generate another SOM including the plurality of input attributes to reduce dimensionality and thereby normalize the plurality of input attributes.

9. The baggage system of claim 1, wherein in the server device the one or more memory sources store a database including baggage item identifications and attributes of each of the baggage item identifications, wherein the another message generated by the controller of the server device includes the attributes associated with the identification of the baggage item.

10. The baggage system of claim 1,

wherein the transceiver of the transfer reader device receives the another message from the server device;

wherein the transfer reader device is further configured to determine whether the baggage item has been misrouted based upon the attributes in the another message received from the server device.

11. A pier and claim reader device for a baggage system, comprising:

an antenna;

a power transmission subsystem including a power source and an antenna arranged to wirelessly transmit power from the power source to an RFID tag associated with a baggage item;

a transceiver configured to receive first data from the RFID tag, the first data including identification information;

a motion sensor;

a controller operatively coupled to the transceiver; and

one or more memory sources operatively coupled to the controller, the one or more memory sources including instructions for configuring the controller to generate one or more messages indicative of the identification information to be sent by the transceiver to a server device via a network connection.

12. The pier and claim reader device of claim 11, wherein the transceiver is configured to communicate with RAIN ISO 18000-6C, EPC Class 1 Gen2 compliant RFID tags.

13. The pier and claim reader device of claim 11, wherein the transceiver is configured to operate in 902 MHz-928 MHz, 920 MHz-925 MHz and 860 MHz-868 MHz frequency ranges.

14. The pier and claim reader device of claim 11, wherein the antenna includes first and second RAIN RFID antennas.

15. The pier and claim reader device of claim 11, wherein the controller is configured with Expandable Data Exchange Communication System (EDECS) RFID integration software.

16. The pier and claim reader device of claim 11, wherein the transceiver is configured to send and receive data over a WiFi or 3G network.