Patent Document ID: 10014076
Application ID: 15891114

Base Claim:
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.

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Claim 2:
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.