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

• US 10,186,329 B1
• Filed: 05/10/2018
• Issued: 01/22/2019
• 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
• Application Number:
  US15/976,832
• Time in Patent Office: 257 Days
• Field of Search: 340 101
• CPC Class Codes: A61B 2503/20 Workers
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 2562/08 Sensors provided with means for identification, e.g. barcodes or memory chips
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/1113 Local tracking of patients, e.g. in a hospital or private home
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• 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 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• 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 conveyor device including a conveyor belt for transporting baggage items and a RAIN reader device configured to communicate with a radio-frequency identification (RFID) tag associated with one of the baggage items, wherein the RAIN reader device comprises;

at least one external 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 first RFID tag includes an antenna for wirelessly receiving the power from the transceiver of the reader device and control logic for generating the identification information;

wherein the server device comprises;

a transceiver configured to receive the one or more messages from the 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 a message indicative of the identification associated with the one of the baggage items.

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 whether each of the baggage item identifications is authorized for being loaded, the instructions further configure the controller of the server device to generate a stop message to be sent by the transceiver to the reader device when the controller determines that the attribute associated with the identification information associated with the RFID tag indicates that the one of the baggage items is not authorized for being loaded.

3. The baggage system of claim 2, wherein the controller of the reader device is further configured to stop motion of the conveyor belt in response to the stop message received from the server device.

4. 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 a destination route of each of the baggage item identifications, the instructions further configure the controller of the server device to generate a stop message to be sent by the transceiver to the reader device when the controller determines that the attribute associated with the identification information associated with the RFID tag indicates that the one of the baggage items has been misrouted from the destination route.

5. 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.

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

receive a plurality of input attributes of the present event;

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

generating 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.

7. The baggage system of claim 1, where the server 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.

8. The baggage system of claim 7, wherein:

the NNM includes an input layer, output layer, and a plurality of hidden layers with a plurality of hidden neurons; and

each of the plurality of hidden neurons includes an activation function, the activation function is one of;

the sigmoid function ƒ

(x)=1/(1+e<sup>−

x</sup>);

 

 

(1)

the hyperbolic tangent function ƒ

(x)=(e^2x−

1)/(e^2x+1); and

 

 

(2)

a linear function ƒ

(x)=x, 

 

(3)

wherein x is a summation of input neurons biased by the synoptic weights.

9. The baggage system of claim 1, wherein:

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; 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.

10. The baggage system of claim 9, 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

training 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.

11. The baggage system of claim 10, 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.

12. A RAIN RFID reader device for a baggage system, the reader device comprising:

at least one external RAIN RFID 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;

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.

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

14. The RAIN RFID reader device of claim 12, wherein the transceiver is configured to operate in a 902 MHz-928 MHz, 920 MHz-925 MHz and 860 MHz-868 MHz frequency ranges.