Obtaining loads for next leg or backhaul

• US 10,074,065 B2
• Filed: 10/29/2015
• Issued: 09/11/2018
• Est. Priority Date: 02/18/2015
• Status: Active Grant

• Current Assignee: Cargo Chief, Inc.
• Original Assignee: Cargo Chief, Inc.
• Inventors: Jones, Russell, Sarma, Srinivasa, Hamidi, Abtin, Patel, Soob, Harkness, Steve
• Primary Examiner(s): Rosen, Nicholas D
• Application Number:
  US14/926,893
• Publication Number:
  US 20160104111A1
• Time in Patent Office: 1,048 Days
• Field of Search: None
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06Q 10/083 Shipping
• CPC Class Codes: G06Q 10/0833 Tracking
• CPC Class Codes: G06Q 10/0834 Choice of carriers
• CPC Class Codes: G06Q 10/08345 Pricing
• CPC Class Codes: G06Q 10/0835 Relationships between shipper or supplier and carriers
• CPC Class Codes: G06Q 10/08355 Routing methods
• CPC Class Codes: G06Q 10/0837 Return transactions
• CPC Class Codes: G06Q 10/0838 Historical data
• CPC Class Codes: G06Q 30/0611 Request for offers or quotes
• CPC Class Codes: G06Q 30/08 Auctions

Abstract

Transportation services are matched with a specified demand for transportation services. Categories of transportation services met by capabilities of the transportation service provider are determined, along with preference data concerning the transportation service provider, in which non-user-defined preference data is at least partially obtained by machine learning. Candidate service interest is used to match the transportation service provider to the specified demand for transportation services. Competitive bids are obtained to meet a specified demand for transportation services, and the transportation service provider is offered inclusion in the competitive bids based on the candidate service interest. If the transportation service provider wins the bid, provision of the transportation service are arranged, and if the transportation service provider loses the bid, repeating the sequence of using the candidate service interest to match the transportation service provider to the specified demand for transportation services.

Claims

1. A method for matching transportation services with a specified demand for transportation services in accordance with capabilities and business practices of a transportation service provider providing the transportation services, the method comprising:

receiving information indicating availability of the transportation service provider to provide transportation services;

using one or more computing devices to automatically determine one or more categories of transportation services met by capabilities of the transportation service provider;

using one or more computing devices to automatically retrieve preference data concerning the transportation service provider and to apply at least one category of the retrieved preference data to the transportation service provider for use in combination with the one or more categories of transportation services met by the capabilities of the transportation service provider as a candidate service interest;

using the candidate service interest to match the transportation service provider to the specified demand for transportation services;

using one or more computing devices to automatically obtain competitive bids to meet the specified demand for transportation services, and offer the transportation service provider inclusion in the competitive bids based on the candidate service interest;

in the case of the transportation service provider with the candidate service interest winning the bid, arranging provision of the transportation services;

in the case of the transportation service provider with the candidate service interest losing the bid, repeating the sequence of using the candidate service interest to match the transportation service provider to the specified demand for transportation services, and

using machine learning to automatically determine non-user-defined reference criteria and add to the retrieved preference data for filtering and ranking as inferred positive and negative preferences, the non-user-defined preference criteria selected from the group consisting of;

driver or transportation service provider positive preferences derived from common values in transportation services selected for bid, and

driver or transportation service provider negative preferences derived from declined transportation services.

2. The method of claim 1, further comprising selecting criteria for selecting potential loads as transportation services based on driver or transportation service provider load history, the criteria selected from the group consisting of:

lanes,

pricing,

reliability,

win/loss ratio,

cancellations, and

on-time delivery.

3. The method of claim 1, further comprising:

providing a “

learning”

mode comprising presenting fictional or simulated loads for driver evaluation, whereby the response of the driver provides a ranking to the fictional or simulated loads; and

receiving a review of final selections for refinement of preferences from the driver, thereby providing a predictive assessment of driver preferences.

4. The method of claim 1, further comprising using current activity of a driver in combination with capabilities of the transportation service provider and the retrieved preference data to modify the candidate service interest, thereby affording the benefits of automated evaluation of preferences while avoiding restricting the driver to an automated selection process.

5. The method of claim 1, further comprising:

using current activity of a driver to infer a front-haul, backhaul or next leg;

in the case of interest by the driver in a backhaul, determining if a multi-stop return has a predetermined high probability of meeting the driver'"'"'s preferences; and

in the case of availability of a multi-stop return, offering the driver a bid selection from multiple-load options thereby allowing the driver to take into consideration supply and demand, dead-head miles to a “

hot”

market origin and routing through “

hot”

markets for multiple stops for higher payments.

6. The method of claim 1, further comprising:

using location tracking to adjust the availability of the transportation service provider to provide transportation services; and

alerting the transportation service provider of availability of the specified demand for transportation services while in-transit based on new current location tracking.

7. The method of claim 1, further comprising:

automatically calculating a bid for providing the transportation services; and

providing the transportation service provider with an “

accept/reject”

user interface, by which the transportation service provider uses a response or gesture to select or reject the calculated bid.

8. The method of claim 1, further comprising:

automatically calculating a bid for providing the transportation services;

providing the transportation service provider with a sequence of options enabling audio responses from the transportation service provider; and

providing the transportation service provider with an “

accept/reject”

user interface, by which the transportation service provider uses a response or gesture to select or reject the calculated bid.

9. The method of claim 1, wherein:

the machine learning further comprises showing specific loads for the purpose of determining driver or transportation service provider preferences in response to contrasting loads.

10. A method for matching transportation services with a specified demand for transportation services in accordance with capabilities and business practices of a transportation service provider providing the transportation services, the method comprising:

using one or more computing devices to automatically determine one or more categories of transportation services met by capabilities of the transportation service provider and determining preferences of the transportation service provider using profile information stored in a data store;

receiving information indicating availability of the transportation service provider to provide transportation services;

using one or more computing devices to automatically match the transportation service provider to the specified demand for transportation services;

in the case of at least one category of transportation services met by the transportation service provider, and in the case of the preferences of the transportation service provider meeting a predetermined threshold, using one or more computing devices to automatically provide push notification to the transportation service provider of the transportation services using a pre-selected form of communication, and to provide details of information as determined by the form of communication;

using at least one notification criterion of the transportation service provider to adjust the predetermined threshold so as to control the amount, frequency or relevance of notifications provided to the transportation service provider;

using one or more computing devices to automatically obtain competitive bids to meet the specified demand for transportation services;

in the case of the transportation service provider with the candidate service interest winning the bid, arranging provision of the transportation services,

in the case of the transportation service provider with the candidate service interest losing the bid, repeating the sequence of using the candidate service interest to match the transportation service provider to the specified demand for transportation services; and

using machine learning to automatically determine non-user-defined preference criteria and add to the retrieved preference data for filtering and ranking as inferred positive and negative preferences, the non-user-defined preference criteria selected from the group consisting of;

driver or transportation service provider positive preferences derived from common values in transportation services selected for bid, and

driver or transportation service provider negative preferences derived from declined transportation services.

11. The method of claim 10, further comprising using, as the pre-selected form of communication, a communication selected from the group consisting of a smartphone app, email, SMS, phone call or a communication format providing mobile access.

12. The method of claim 10, further comprising:

automatically calculating a bid for providing the transportation services; and

providing the transportation service provider with an “

accept/reject”

user interface, by which the transportation service provider uses a response or gesture to select or reject the calculated bid.

13. The method of claim 10, further comprising:

automatically calculating a bid for providing the transportation services;

providing the transportation service provider with a sequence of options enabling audio responses from the transportation service provider; and

providing the transportation service provider with an “

accept/reject”

user interface, by which the transportation service provider uses a response or gesture to select or reject the calculated bid.

14. The method of claim 10, further comprising:

using the preferences of the transportation service provider using profile information stored in a data store to automatically determine a chance of winning by the transportation service provider; and

adjusting the predetermined threshold so as to limit invitations to drivers that have a predetermined chance of winning.

15. The method of claim 14, further comprising:

showing invited transportation service providers an estimated potential for winning a load based on history of similar loads; and

providing bidding status information on competitors to encourage bidding, in accordance with notification preferences of the transportation service provider and a preferred contact method.

16. The method of claim 10, further comprising, in the case of participation in bidding falling below a predetermined threshold, expanding bidding based on participation and time constraints to a next tier of invited transportation service providers.

17. The method of claim 10, further comprising:

inviting bids from transportation service providers outside of the system'"'"'s database by mining public sources and driver pools; and

using a driver recommendation engine for best potential matches based on shipping lane, equipment, invitation history and pricing.

18. The method of claim 10, further comprising providing feedback to transportation service providers following the bidding period:

where they ranked, bid history, suggestions for winning.

19. The method of claim 10, wherein:

the machine learning further comprises showing specific loads for the purpose of determining driver or transportation service provider preferences in response to contrasting loads.

20. A method for matching transportation services with a specified demand for transportation services in accordance with capabilities and business practices of a transportation service provider providing the transportation services, the method comprising:

a step of receiving information indicating availability of the transportation service provider to provide transportation services;

a step of using one or more computing devices to automatically determine one or more categories of transportation services met by capabilities of the transportation service provider;

a step of using one or more computing devices to automatically retrieve preference data concerning the transportation service provider and to apply at least one category of the retrieved preference data to the transportation service provider for use in combination with the one or more categories of transportation services met by the capabilities of the transportation service provider as a candidate service interest;

using the candidate service interest to match the transportation service provider to the specified demand for transportation services;

a step of using one or more computing devices to automatically obtain competitive bids to meet the specified demand for transportation services, and offer the transportation service provider inclusion in the competitive bids based on the candidate service interest;

a step of, in the case of the transportation service provider with the candidate service interest winning the bid, arranging provision of the transportation services

a step of, in the case of the transportation service provider with the candidate service interest losing the bid, repeating the sequence of using the candidate service interest to match the transportation service provider to the specified demand for transportation services; and

using machine learning to automatically determine non-user-defined preference criteria and add to the retrieved preference data for filtering and ranking as inferred positive and negative preferences, the non-user-defined preference criteria selected from the group consisting of;

driver or transportation service provider positive preferences derived from common values in transportation services selected for bid, and

driver or transportation service provider negative preferences derived from declined transportation services.

21. The method of claim 20, wherein:

the machine learning further comprises showing specific loads for the purpose of determining driver or transportation service provider preferences in response to contrasting loads.