System, RFID chip, server and method for capturing vehicle data
First Claim
Patent Images
1. A system comprising:
- a plurality of radio-frequency identification (RFID) chips, wherein at least a first RFID chip of the plurality of RFID chips is associated with a vehicle;
a data collection engine (DCE) communicating with the first RFID chip, wherein the DCE comprises;
a power transmission subsystem including a power source and arranged to transmit power from the power source to the first RFID chip;
a transceiver configured to receive first data including identification information from the first RFID chip;
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 and location information to be sent by the transceiver to a server device via a network connection,wherein the first RFID chip includes control logic for generating the identification information;
wherein the server device comprises;
a transceiver configured to, via the network connection, receive the one or more messages from the DCE, and a pick-up request from a client device;
a plurality of trained models for generating output values corresponding to a present event associated with the pick-up request based upon at least the identification information;
a controller operatively coupled to the transceiver;
one or more memory sources operatively coupled to the controller, the one or more memory sources configuring the controller to;
perform pre-processing on the location and identification information as a plurality of input attributes to generate an input data set; and
generate the output value from the trained model based upon the input data set, wherein the output value is one or more of;
a travel path, a pick-up location for the pick-up request and a price for the pick-up request;
wherein the trained models include;
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; and
a trained neural network model (NNM) including an input layer, output layer, and a plurality of hidden layers with a plurality of hidden neurons, wherein 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−
x);
(2) the hyperbolic tangent function f(x)=(e2x−
1)/(e2x+1); and
(3) a linear function f(x)=x,wherein x is a summation of input neurons biased by synoptic weights.
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Abstract
A system includes a plurality of tracking devices, such as RFID tags, affixed to items, such as vehicles, 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 systems send attributes of new events to the backend devices. The backend devices can track the items and predict particular outcomes of new events based upon the attributes of the new events utilizing the trained machine learning models.
40 Citations
20 Claims
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1. A system comprising:
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a plurality of radio-frequency identification (RFID) chips, wherein at least a first RFID chip of the plurality of RFID chips is associated with a vehicle; a data collection engine (DCE) communicating with the first RFID chip, wherein the DCE comprises; a power transmission subsystem including a power source and arranged to transmit power from the power source to the first RFID chip; a transceiver configured to receive first data including identification information from the first RFID chip; 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 and location information to be sent by the transceiver to a server device via a network connection, wherein the first RFID chip includes control logic for generating the identification information; wherein the server device comprises; a transceiver configured to, via the network connection, receive the one or more messages from the DCE, and a pick-up request from a client device; a plurality of trained models for generating output values corresponding to a present event associated with the pick-up request based upon at least the identification information; a controller operatively coupled to the transceiver; one or more memory sources operatively coupled to the controller, the one or more memory sources configuring the controller to; perform pre-processing on the location and identification information as a plurality of input attributes to generate an input data set; and generate the output value from the trained model based upon the input data set, wherein the output value is one or more of;
a travel path, a pick-up location for the pick-up request and a price for the pick-up request;wherein the trained models include; 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; and a trained neural network model (NNM) including an input layer, output layer, and a plurality of hidden layers with a plurality of hidden neurons, wherein 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−
x);(2) the hyperbolic tangent function f(x)=(e2x−
1)/(e2x+1); and(3) a linear function f(x)=x, wherein x is a summation of input neurons biased by synoptic weights.
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2. A system comprising:
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a mobile device associated with a vehicle, wherein the mobile device comprises; a transceiver; 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 identification information and location 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 mobile device and a pick-up request from a client device via the network connection; 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; store the location information associated with the vehicle; determine if the location information associated with the vehicle is within a predetermined distance of the pick-up request; determine a travel path associated with the pick-up request and send a message indicative of the travel path to the mobile device; and determine an output value associated with the pick-request from a trained model. - View Dependent Claims (3, 4, 5, 6)
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7. A server device comprising:
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a transceiver configured to receive one or more messages from a location tracking device, the one or more messages from the location tracking device including location information and identification information, the transceiver further configured to receive one or more messages from a client device, the one or messages from the client device including a pick-up request, the pick-up request including a pick-up location and a drop-off location; 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; store data indicative of the identification information and location information of the location tracking device within the one or more memory sources; send the pick-up request to the location tracking device if determined that the location tracking device is within a predetermined distance from the pick-up location; and determining an output value associated with the pick-up request based upon a trained model. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification