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

• US 10,014,076 B1
• Filed: 02/07/2018
• Issued: 07/03/2018
• 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:
  US15/891,114
• Time in Patent Office: 146 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 plurality of radio-frequency identification (RFID) chips, wherein at least a first RFID chip of the plurality of RFID chips is a passive-type RFID chip and associated with a baggage item;

a data collection engine (DCE) device communicating with the plurality of RFID chips, wherein the DCE comprises;

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

a transceiver configured to receive first data from at least one of the first RFID chip and a second RFID chip of the plurality of RFID chips while the first RFID chip is activated by the power received, the first data including identification information of the at least one of the first and second RFID chips;

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 the network connection,

wherein the first RFID chip includes an antenna for wirelessly receiving the power from the transceiver of the DCE 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 DCE;

a controller operatively coupled to the transceiver; and

one or more memory sources operatively coupled to the controller, the one or more memory sources configuring the controller to generate a message indicative of location information associated with the baggage item.

2. 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;

the one or more memory sources further store a plurality of past events, each of the plurality of past events including a plurality of input attributes and a quantifiable outcome; and

the controller further configured to;

train a neural network model (NNM) to generate the 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.

3. The baggage system of claim 2, where the server is further configured to:

receive 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.

4. 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.

5. The baggage system of claim 4, 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;

(1) the sigmoid function f(x)=1/(1+e<sup>−

x</sup>);

(2) the hyperbolic tangent function f(x)=(e^2x−

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

(3) a linear function f(x)=x,

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

6. The baggage system of claim 4, wherein the NNM is one or more of a feed forward structure Neural Network;

ADALINE Neural Network, Adaptive Resonance Theory 1 (ART1), Bidirectional Associative Memory (BAM), Boltzmann Machine, Counterpropagation Neural Network (CPN), Elman Recurrent Neural Network, Hopfield Neural Network, Jordan Recurrent Neural Network, Long Short Term Memory networks (LSTMs), Neuroevolution of Augmenting Topologies (NEAT), Convolutional Neural Network (CNN or ConvNet), Radial Basis Function Network, Modular Neural Network; and

Physical Neural Network.

7. The baggage system of claim 4, wherein the performing of the MLA includes measuring a global error in each training iteration for the NNM by:

calculating a local error, the local error being a difference between the output value of the NNM and the quantifiable outcome;

calculating the global error by summing all of the local errors in accordance with one of;

(1) Mean Square Error (MSE) formula

8. 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.

9. The baggage system of claim 8, 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.

10. The baggage system of claim 9, 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.

11. The baggage system of claim 3, wherein the receiving the plurality of input attributes of the present event further includes receiving one or more messages including a baggage identification, location information associated with a first RFID tag, a baggage handler identification and location information associated with a second RFID tag from a data collection engine (DCE).

12. The baggage system of claim 3, wherein the present event is a baggage item registering with the DCE and the output value is an indication of deviation or no deviation for the present event.

13. The baggage system of claim 3, wherein the input attributes include:

a location and identification of a baggage item; and

a baggage handler identification associated with the baggage item.

14. 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;

the server receives a plurality of present events, each including a plurality of input attributes;

the generating of the output value further includes generating a graphical image including clusters of the input attributes for each of the plurality of present events;

the server receives a graphical display request from a remote client device and transmits the graphical image to the remote client device as a response;

the trained model is a trained SOM; and

the graphical image is a cluster diagram including a plurality of clusters of present events having a similar characteristic.

17. The baggage system of claim 1, wherein:

the one or more messages received by the server device from the DCE include an indication that near field communication (NFC) has been established between the first and second RFID chips.

18. The baggage system of claim 17, wherein:

the one or more messages received by the server device from the DCE include an indication a mobile device registered with a particular user has established NFC with one of the first and second RFID chips.

15. A Throughput Manager Device (TMD) comprising:

a transceiver for receiving input attributes associated with an event from one or more remote entities via a network connection;

the transceiver further for receiving an information request from a remote client access device via the network connection, the information request being a request for calculated quantifiable outcomes for a plurality of events;

a controller operatively coupled to the transceiver; and

one or more memory sources operatively coupled to the controller, the one or more memory sources storing instructions for configuring the controller to;

calculate a quantifiable outcome for each of the events from a trained model based upon at least two or more input attributes of the respective event, wherein the outcome represents a numerical, continuous or categorical outcome; and

generate an information reply including a graphical display indicating the numerical/continuous output value or the category output of each of the events.

16. The TMD of claim 15, wherein the one or more remote entities include:

a client device; and

a server device.