LOGISTICAL SERVICE FOR PROCESSING MODULAR DELIVERY REQUESTS

• US 20200134557A1
• Filed: 10/31/2019
• Published: 04/30/2020
• Est. Priority Date: 10/31/2018
• Status: Abandoned Application

• Current Assignee: Quisto Company Limited
• Original Assignee: Quisto Company Limited
• Inventors: Pevzner, Boris, Lubashevskiy, Felix, Konshin, Evgeniy, Kolodkin, Evgeniy, Dovgal, Dmitry, Turinge, Denis, Kazeev, Alexander, Tcygantcev, Alexander
• Application Number:
  US16/670,454
• Publication Number:
  US 20200134557A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/0834 Choice of carriers
• CPC Class Codes: G06Q 10/08345 Pricing
• CPC Class Codes: G06Q 10/1097 Task assignment

Abstract

A system and method (referred to as a system) schedules vehicles transporting freight on a mesh network using a machine learning process. The system receives transportation requests over a network from a plurality of customer portals that include loading times, loading locations, destination locations, delivery times, and freight requirements. The system mines large data sets from remote sites that reflect distances between the loading locations and the destination locations and corresponding freight rates associated with distances through the machine learning process. The system predicts shipping schedules that include predicted departure time and a predicted arrival time associated with the plurality of shipping schedules. The system matches the transportation requests with the plurality of shipping schedules in real time based on a plurality of shipping preferences, carrier availabilities, and projected probabilities that a plurality of carriers will accept loads.

Claims

1. A method of scheduling a vehicle transporting freight on a mesh network using a machine learning process comprising:

receiving transportation requests over a network from a plurality of customer portals that include loading times, loading locations, destination locations, delivery times, and freight requirements;

mining a plurality of large data sets from remote sites that reflect distances between the loading locations and the destination locations and corresponding freight rates associated with distances through the machine learning process;

predicting a plurality of shipping schedules that include predicted departure time and a predicted arrival time associated with the plurality of shipping schedules; and

matching the transportation requests with the plurality of shipping schedules in real time based on a plurality of shipping preferences, carrier availabilities, and projected probabilities that a plurality of carriers will accept loads.

2. The method of claim 1 further including generating a recommendation sequence that identified the plurality of carriers and an order and a duration in which a potential hauling assignment is proffered.

3. The method of claim 1 further comprising posting the plurality of shipping schedules through a price-finder user interface.

4. The method of claim 3 further comprising recommending one of the plurality of shipping schedules and corresponding prices.

5. The method of claim 3 where the recommending of the one of plurality shipping schedules is based on a plurality of parameters that include a vehicle transport selection, one of the loading locations, and one of the destination locations.

6. The method of claim 5 where the recommending is devoid of any other parameters.

7. The method of claim 5 where the shipping schedules include an intermodal transport and corresponding prices that are based on a repositioning of a plurality of shipping containers.

8. A non-transitory machine-readable medium encoded with machine-executable instructions for scheduling a vehicle transporting freight on a mesh network using a machine learning process, where execution of the machine-executable instructions is for:

receiving transportation requests over a network from a plurality of customer portals that include loading times, loading locations, destination locations, delivery times, and freight requirements;

mining a plurality of large data sets from remote sites that reflect distances between the loading locations and the destination locations and corresponding freight rates associated with distances through the machine learning process;

predicting a plurality of shipping schedules that include predicted departure time and a predicted arrival time associated with the plurality of shipping schedules; and

matching the transportation requests with the plurality of shipping schedules in real time based on a plurality of shipping preferences, carrier availabilities, and projected probabilities that a plurality of carriers will accept loads.

9. The non-transitory machine-readable medium of claim 8 further including generating a recommendation sequence that identified the plurality of carriers and an order and a duration in which a potential hauling assignment is proffered.

10. The non-transitory machine-readable medium of claim 8 further comprising posting the plurality of shipping schedules through a price-finder user interface.

11. The non-transitory machine-readable medium of claim 10 further comprising recommending one of the plurality of shipping schedules and corresponding prices.

12. The non-transitory machine-readable medium of claim 10 where the recommending of the one of plurality of shipping schedules is based on a plurality of parameters that include a vehicle transport selection, one of the loading locations, and one of the destination locations.

13. The non-transitory machine-readable medium of claim 12 where the recommending is devoid of any other parameters.

14. The non-transitory machine-readable medium of claim 12 where the shipping schedules include an intermodal transport.

15. The non-transitory machine-readable medium of claim 14 further including corresponding prices that are based on a repositioning of a plurality of shipping containers.

16. The non-transitory machine-readable medium of claim 12 where the shipping schedules include a multistop or consolidation transport.

17. A system that schedules a vehicle transporting freight comprising:

a customer portal for receiving transportation requests over a network from a plurality of customer portals that include loading times, loading locations, destination locations, delivery times, and freight requirements;

a mileage miner for mining a plurality of large data sets from remote sites that reflect distances between the loading locations and the destination locations through a machine learning process;

a mileage miner for mining a second plurality of large data and corresponding freight rates associated with distances through the machine learning process;

a routing module programmed to predict a plurality of shipping schedules that include predicted departure time and a predicted arrival time associated with the plurality of shipping schedules; and

a matching engine that matches the transportation requests with the plurality of shipping schedules in real time based on a plurality of shipping preferences, carrier availabilities, and projected probabilities that a plurality of carriers will accept loads.

18. The system of claim 17 where the matching engine generates a recommendation sequence that identified the plurality of carriers and an order and a duration in which a potential hauling assignment is proffered.

19. The system of claim 17 where the routing module posting the plurality of shipping schedules through a price-finder user interface.

20. The system of claim 19 where the routing module recommends one of the plurality of shipping schedules and corresponding prices.