# US 12,217,841 B1 · System, RFID chip, server and method for capturing vehicle data

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- Number: US 12,217,841 B1
- Document ID: US12217841B1
- Status: Granted
- Kind: B1
- Application number: 18/150,749
- Filed: 2023-01-05
- Granted: 2025-02-04
- Earliest claimed: 2015-02-06
- Assignees: Brain Trust Innovations I, LLC
- Applicants: Brain Trust Innovations I, LLC
- Inventors: David LaBorde
- Web page: https://patentgrep.com/patent/US12217841B1

## CPC

- G16H 10/65: stored on portable record carriers, e.g. on smartcards, RFID tags or CD
- G01C 21/3476: {using point of interest [POI] information, e.g. a route passing visible POIs}
- G05D 1/00: Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots (drive control systems specially adapted for autonomous road vehicles B60W60/00)
- 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)}
- G06K 7/10366: {the interrogation device being adapted for miscellaneous applications}
- G06N 3/04: Architecture, e.g. interconnection topology
- G06N 3/048: Activation functions
- G06N 3/0499: Feedforward networks
- G06N 3/08: Learning methods
- G06N 3/088: Non-supervised learning, e.g. competitive learning
- G06N 3/09: Supervised learning
- G16H 40/63: for local operation
- H04W 4/02: Services making use of location information
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06N 3/0409: {Adaptive resonance theory [ART] networks}
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/047: Probabilistic or stochastic networks
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/086: using evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06N 3/126: Evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 5/048: Fuzzy inferencing
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)
- G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06K: GRAPHICAL DATA READING (image or video recognition or understanding G06V); PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## 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

### Claim 1

A server device for 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,
wherein the output value includes an arrival time,
wherein the controller is further configured to determine a route from a specific one of the plurality of vehicles to the location information of the pickup-request.

### Claim 2 (depends on 1)

The server device of claim 1, wherein the location information includes global positioning satellite coordinate information.

### Claim 3 (depends on 1)

The server device of claim 1, wherein:
the controller is further configured to generate an arrival time message including the arrival time; and
the transceiver is further configured to send the arrival time message to the user.

### Claim 4 (depends on 1)

The server device of claim 1, wherein the controller is further configured to generate an alert message to be sent to the user when the controller determines that the user is within a certain proximity to one or more of the plurality of vehicles based upon the plurality of messages and the pick-up request.

### Claim 5 (depends on 1)

The server device of claim 1, wherein the server device is a central assignment system combined with one or more databases.

### Claim 6 (depends on 1)

The server device of claim 1, wherein the server device is further configured to assign the specific one of the plurality of vehicles to the user based upon the output value.

### Claim 7 (depends on 6)

The server device of claim 6, wherein the server device is further configured to generate an assignment message to be sent to the user informing the user of the assigned specific one of the plurality of vehicles and an expected time at which the vehicle will arrive at the location information associated with the pick-up request.

### Claim 8 (depends on 1)

The server device of claim 1, wherein the output value is indicative of one of the plurality of vehicles and wherein the controller is further configured to track the user and the vehicle from an initial time of the pick-up request until the user arrives at a destination indicated in the pick-up request based upon the messages and the pick-up request.

### Claim 9 (depends on 1)

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−x);
(2) the hyperbolic tangent function ƒ(x)=(e2x−1)/(e2x+1); and
(3) a linear function ƒ(x)=x,
wherein x is a summation of input neurons biased by synoptic weights.

### Claim 10 (depends on 1)

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.

### Claim 11 (depends on 1)

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

### Claim 12 (depends on 1)

The server device of claim 1, wherein the trained model includes one of a trained Self-Organizing Map (SOM) and a trained neural network model (NNM).

### Claim 13

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;
receiving a plurality of messages associated with a plurality of vehicles, each of the plurality of messages indicative of location information of the respective one of the plurality of vehicles;
generating one or more output values associated with difficulties of the plurality of vehicles of reaching each of the one or more locations of interest based upon a trained model, the determined one or more locations of interest and location information of the plurality of vehicles;
determining a specific one of the plurality of vehicles based upon the one or more output values; and
determining a route from the specific one of the plurality of vehicles to the one or more locations of interest based upon the one or more output values.

### Claim 14 (depends on 13)

The method of claim 13, further comprising inputting the determined one or more locations of interest and one or more historical locations related to the user into the trained model to generate the one or more output values.

### Claim 15 (depends on 13)

The method of claim 13, wherein the trained model includes one of a trained Self-Organizing Map (SOM) and a trained neural network model (NNM).

### Claim 16

A server device for 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 and a destination request;
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 output values 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,
wherein one of the output values includes an arrival time one of the plurality of vehicles will arrive at the pick-up request,
wherein the controller generates route information to be sent to the user via the transmitter so that a device at the user displays a travel path of the one of the plurality of vehicles to the pick-up request.

### Claim 17 (depends on 16)

The server device of claim 16, wherein the controller is further generated to determine an optimal route from the pick-up request to the destination request based upon the trained model.