System, RFID chip, server and method for capturing vehicle data

• US 10,482,377 B1
• Filed: 12/24/2018
• Issued: 11/19/2019
• 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:
  US16/231,832
• Time in Patent Office: 330 Days
• Field of Search: 340 101
• CPC Class Codes: G01C 21/3476 using point of interest [POI] information, e.g. a route passing visible POIs
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G06F 17/11 for solving equations , e.g. nonlinear equations, general mathematical optimization problems optimization specially adapted for a specific administrative, business or logistic context G06Q10/04
• 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 3/0409 Adaptive resonance theory [ART] networks
• 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 5/048 Fuzzy inferencing
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G16H 10/65 stored on portable record carriers, e.g. on smartcards, RFID tags or CD
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: H04W 4/02 : Services making use of location information

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.

Claims

1. A server device for determining a location relating to a transport service, the server device comprising:

a transceiver configured to, via a network connection, receive a plurality of messages associated with a plurality of vehicles, each of the plurality of messages indicative of location information, and a pick-up request indicative of location information associated with a user;

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 an output value from a trained model based upon the location information in the pick-up request and the location information in the plurality of messages associated with the plurality of vehicles.

2. The server device of claim 1, wherein:

the controller is further configuring to;

perform pre-processing on the location 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 trained model includes one of a trained Self-Organizing Map (SOM) and a trained neural network model (NNM),

wherein the trained SOM includes 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

wherein the trained NNM includes 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 ƒ

(x)=1/(1+e<sup>−

x</sup>);

(2) the hyperbolic tangent function ƒ

(x)=(e^2x−

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

(3) a linear function ƒ

(x)=x,

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

3. The server device of claim 1, wherein the controller is further configured to generate the output value from the trained model based upon one or more historical locations related to the user.

4. The server device of claim 3, wherein the one or more historical locations related to the user includes one or more locations recently visited by the user.

5. The server device of claim 3, wherein the one or more historical locations related to the user includes one or more locations commonly visited by the user.

6. The server device of claim 1, wherein:

the transceiver further receives a drop-off request along with the pick-up request associated with the user, the drop-off request also indicative of another location information; and

the output value is associated with one or more locations of interests within at least a predetermined distance from the location associated with the pick-up request or the another location associated with the drop-off request.

7. The server device of claim 6,

wherein the controller is further configured to generate the output value from the trained model based upon one or more historical locations related to the user,

wherein the one or more historical locations related to the user includes one or more locations previously visited by the user that is closest to the location associated with the pick-up request or the another location associated with the drop-off request.

8. The server device of claim 1, wherein the controller generates the output further based upon referencing a business directory included in a database.

9. The server device of claim 1, wherein the output value is associated with one or more candidate vehicles of the plurality of vehicles to assign the pick-up request.

10. The server device of claim 1, wherein:

the transceiver receives a plurality of present events, each including a plurality of input attributes, wherein the input attributes of each of the plurality of present events are the location information in the pick-up request and the location information in the plurality of messages associated with the plurality of vehicle;

the server is further configured to generate 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.

11. A method for determining a location relating to a transport service on a computing device, the method being performed by one or more processors and comprising:

receiving a transport request from a user, the transport request specifying at least one of a pick-up region or a drop-off region;

determining one or more locations of interests within the at least one of the pick-up region or the drop-off region;

inputting the determined one or more locations of interest into a trained model to generate an output value associated with difficulty a person or an autonomous vehicle encounters in reaching each of the one or more locations; and

determining a likely location based on the output value.

12. The method of claim 11, wherein determining one or more locations of interests includes referencing a business directory.

13. The method of claim 11, wherein the inputting of the determined one or more locations of interest into the trained model to generate the output value further includes inputting one or more historical locations related to the user.

14. The method of claim 13, wherein the one or more historical locations related to the user includes one or more locations commonly visited by the user.

15. The method of claim 13, wherein the one or more historical locations related to the user includes one or more locations previously visited by the user that is closest to the at least one of the pick-up region or the drop-off region.

16. The method of claim 13, wherein the one or more historical locations related to the user includes one or more locations recently visited by the user.

17. The method of claim 11, further comprising:

in response to determining the likely location, providing a prompt on a user interface feature that asks the user whether the likely location is a location the user would like to be picked up at or dropped off at.

18. The method of claim 17, further comprising:

receiving a user confirmation that the likely location is the location the user would like to be picked up at or dropped off at; and

arranging the transport service for the user based on the likely location.

19. The method of claim 17, further comprising:

receiving a user input that specifies that the likely location is not the location the user would like to be picked up at or dropped off at; and

determining a second likely location based on the determined one or more locations of interest.

20. A method for determining a location relating to a transport service on a computing device, the method being performed by one or more processors and comprising:

receiving a transport request from a user, the transport request specifying at least one of a pick-up region or a drop-off region;

determining one or more locations of interests within the at least one of the pick-up region or the drop-off region;

inputting the determined one or more locations of interest into a trained model to generate an output value associated with logical constraints a person or an autonomous vehicle encounters in reaching each of the one or more locations; and

determining a likely location based on the output value.