System and Method for Vehicle Delivery Tracking Service

• US 20150278759A1
• Filed: 03/26/2015
• Published: 10/01/2015
• Est. Priority Date: 03/26/2014
• Status: Abandoned Application

• Current Assignee: Go Taxi Truck LLC (Zootly LLC)
• Original Assignee: Go Taxi Truck LLC (Zootly LLC)
• Inventors: HARRIS, William, CALLEGARI, Rudy, CALLEGARI, Greg, BACSO, Stephen Robert, FOISY, Nick Eugene
• Application Number:
  US14/669,825
• Publication Number:
  US 20150278759A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/08355 Routing methods
• CPC Class Codes: G06Q 10/0838 Historical data
• CPC Class Codes: H04W 4/024 Guidance services
• CPC Class Codes: H04W 4/44 for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Abstract

Systems and methods including receiving, via a software application, delivery task attributes over a network for delivering a physical load from a source to a destination, the delivery task attributes including at least source specifications, destination specifications and load attributes, automatically generating routing information for the physical load based on at least one of the delivery task attributes, automatically assigning a transportation resource to the delivery task based on at least one of the delivery task attributes and transportation resource attributes, transmitting a dispatch over the network to the assigned transportation resource, the dispatch including the routing information and at least one of the delivery task attributes, receiving delivery monitoring information during an execution of the delivery task, and transmitting a notification over the network to at least one of the assigned transportation resource and a user of the software application of the delivery monitoring information.

Claims

1. A method, comprising:

receiving, via a software application, delivery task attributes over a network for delivering a physical load from a source to a destination, the delivery task attributes including at least source specifications, destination specifications and load attributes;

automatically generating routing information for the physical load based on at least one of the delivery task attributes;

automatically assigning a transportation resource to the delivery task based on at least one of the delivery task attributes and transportation resource attributes;

transmitting a dispatch over the network to the assigned transportation resource, the dispatch including the routing information and at least one of the delivery task attributes;

receiving delivery monitoring information during an execution of the delivery task; and

transmitting a notification over the network to at least one of the assigned transportation resource and a user of the software application of the delivery monitoring information.

2. The method according to claim 1, further comprising:

adjusting at least one of the routing information and the assigned transportation resource based on the delivery monitoring information, wherein the delivery monitoring information include real-time route information based on at least one of audio-based monitoring, video-based monitoring, location-based monitoring, environmental condition monitoring and load attribute monitoring; and

transmitting an updated notification over the network to at least one of the assigned transportation resource and a user of the software application of the delivery monitoring information.

3. The method according to claim 1, further comprising:

storing the routing information, delivery task attributes and transportation resource attributes into a historical database; and

generating a predictive model for optimizing future delivery tasks, wherein the optimizing includes automatically generating further routing information and automatically assigning a further transportation resource during a time period including at least one of prior to the execution of the delivery task, during the execution of the delivery task, and at the completion of the execution of the deliver task.

4. The method according to claim 3, wherein the predictive model is generated using supervised learning techniques wherein a feature set used within the predictive model is augmented throughout the delivery task process as additional features become available.

5. The method according to claim 1, wherein the software application uses machine-to-machine interactions for at least one of the receiving delivery task attributes, transmitting the dispatch, receiving delivery monitoring information, and transmitting the notification.

6. The method according to claim 1, wherein the transportation resources attributes includes at least one of an availability of the transportation resource, a location of the transportation resource, a scheduled use of the transportation resource, and a current capacity of a resource provider.

7. The method according to claim 1, wherein the assigned transportation resource includes the use of a third-party resource, and the generated routing information include real-time scheduling of the third-party resource.

8. A server, comprising:

a memory storing a plurality of rules; and

a processor coupled to the memory and configured to perform actions that include;

receiving, via a software application, delivery task attributes over a network for delivering a physical load from a source to a destination, the delivery task attributes including at least source specifications, destination specifications and load attributes;

generating routing information for the physical load based on at least one of the delivery task attributes;

assigning a transportation resource to the delivery task based on at least one of the delivery task attributes and transportation resource attributes;

transmitting a dispatch over the network to the assigned transportation resource, the dispatch including the routing information and at least one of the delivery task attributes;

receiving delivery monitoring information during an execution of the delivery task; and

transmitting a notification over the network to at least one of the assigned transportation resource and a user of the software application of the delivery monitoring information.

9. The server according to claim 8, wherein the actions performed by the processor further comprise:

adjusting at least one of the routing information and the assigned transportation resource based on the delivery monitoring information, wherein the delivery monitoring information include real-time route information at least one of audio-based monitoring, video-based monitoring, location-based monitoring, environmental condition monitoring and load attribute monitoring; and

transmitting an updated notification over the network to at least one of the assigned transportation resource and a user of the software application of the delivery monitoring information.

10. The server according to claim 8, wherein the actions performed by the processor further comprise:

storing the routing information, delivery task attributes and transportation resource attributes into a historical database; and

generating a predictive model for optimizing future delivery tasks, wherein the optimizing includes automatically generating further routing information and automatically assigning a further transportation resource during a time period including at least one of prior to the execution of the delivery task, during the execution of the delivery task, and at the completion of the execution of the deliver task.

11. The server according to claim 10, wherein the predictive model is generated using supervised learning techniques wherein a feature set used within the predictive model is augmented throughout the delivery task process as additional features become available.

12. The server according to claim 8, wherein the software application uses machine-to-machine interactions for at least one of the receiving delivery task attributes, transmitting the dispatch, receiving delivery monitoring information, and transmitting the notification.

13. The server according to claim 8, wherein the transportation resources attributes includes at least one of an availability of the transportation resource, a location of the transportation resource, a scheduled use of the transportation resource, and a current capacity of a resource provider.

14. The server according to claim 8, wherein the assigned transportation resource includes the use of a third-party resource, and the generated routing information include real-time scheduling of the third-party resource.

15. A non-transitory computer readable storage medium with an executable program stored thereon, wherein the program instructs a processor to perform actions that include:

receiving delivery task attributes over a network for delivering a physical load from a source to a destination, the delivery task attributes including at least source specifications, destination specifications and load attributes;

automatically generating routing information for the physical load based on at least one of the delivery task attributes;

automatically assigning a transportation resource to the delivery task based on at least one of the delivery task attributes and transportation resource attributes;

transmitting a dispatch over the network to the assigned transportation resource, the dispatch including the routing information and at least one of the delivery task attributes;

receiving delivery monitoring information during an execution of the delivery task; and

transmitting a notification over the network to at least one of the assigned transportation resource and a user of the software application of the delivery monitoring information.

16. The non-transitory computer readable storage medium according to claim 15, wherein the program further instructs the processor to:

adjust at least one of the routing information and the assigned transportation resource based on the delivery monitoring information, wherein the delivery monitoring information include real-time route information at least one of audio-based monitoring, video-based monitoring, location-based monitoring, environmental condition monitoring and load attribute monitoring; and

transmit an updated notification over the network to at least one of the assigned transportation resource and a user of the software application of the delivery monitoring information.

17. The non-transitory computer readable storage medium according to claim 15, wherein the program further instructs the processor to:

store the routing information, delivery task attributes and transportation resource attributes into a historical database; and

generate a predictive model using supervised learning techniques wherein a feature set used within the predictive model is augmented throughout the delivery task process as additional features become available for optimizing future delivery tasks, wherein the optimizing includes automatically generating further routing information and automatically assigning a further transportation resource during a time period including at least one of prior to the execution of the delivery task, during the execution of the delivery task, and at the completion of the execution of the deliver task.

18. The non-transitory computer readable storage medium according to claim 15, wherein the program uses machine-to-machine interactions for at least one of the receiving delivery task attributes, transmitting the dispatch, receiving delivery monitoring information, and transmitting the notification.

19. The non-transitory computer readable storage medium according to claim 15, wherein the transportation resources attributes includes at least one of an availability of the transportation resource, a location of the transportation resource, a scheduled use of the transportation resource, and a current capacity of a resource provider.

20. The non-transitory computer readable storage medium according to claim 15, wherein the assigned transportation resource includes the use of a third-party resource, and the generated routing information include real-time scheduling of the third-party resource.