Method and System of Building Actual Travel Fares

• US 20080270254A1
• Filed: 07/21/2006
• Published: 10/30/2008
• Est. Priority Date: 07/29/2005
• Status: Abandoned Application

• Current Assignee: Amadeus S.A.S. (Amadeus Global Travel Distribution S.A.)
• Original Assignee: Amadeus S.A.S. (Amadeus Global Travel Distribution S.A.)
• Inventors: Blaszka, Thierry, Dourthe, Cedric, Dufresne, Thierry, Patoureaux, Marc, Chaumont, Gilles
• Application Number:
  US11/997,164
• Publication Number:
  US 20080270254A1
• Time in Patent Office: Days
• US Class Current: 705/26
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 10/047 Optimisation of routes or paths, e.g. travelling salesman problem

Abstract

A method of building actual travel fares in a computer, from fare databases, is disclosed. A graph of nodes representing travel destinations is built which comprises edges connecting pairs of nodes. Each edge references a lowest travel fare. Also, a tree of fares is built for each graph edge. Trees comprise at least a root node holding the lowest travel fare of the corresponding graph edge. They possibly include more nodes comprising a context key and an associated travel fare. Trees are organized to have children nodes holding a travel fare equal to or larger than travel fare of a parent node. Thus, less expensive fare paths can efficiently be extracted since graph edges, included in the fare paths, reference the associated trees of fares and are gone through in ascending order of their lowest fare values. A learning entity is used to build and update the trees of fares.

Claims

1. A method of building actual travel fares in a computer (160) from at least one fare database (218), said method including the steps of:

building a graph of nodes (220), said nodes (221) representing travel destinations from other said nodes, said graph comprising edges (226) connecting pairs (221, 225) of said nodes, each said edge referencing (240) a lowest travel fare (231) for the said pair of nodes;

building a tree of fares (230) for each said graph edge, said each tree comprising at least a root node (231), said root holding said lowest travel fare for said graph edge, said tree possibly including more nodes (232) comprising a context key (2321) and an associated travel fare (2322), said tree organized to have children nodes (234) holding a said travel fare equal to or larger than said travel fare in a parent node (232);

extracting (200) fare paths from said graph of nodes, said graph edges (222, 224) included in said fare paths referencing associated said trees of fares to built said fare paths.

2. The method according to claim 1 including a learning entity (210), wherein said learning entity is aimed at building and updating said trees of fares (230).

3. The method according to claim 2 wherein said learning entity is gathering data used for building and updating said trees of fares from end-user (140) requests to a plurality of processes (105) aimed at building travel solutions for said end-users.

4. The method according to claim 1 wherein said fare databases (218) are provided and updated by airline carriers.

5. The method according to claim 1 wherein the step of extracting said fare paths from an origin node to a destination node selects said edges of said origin node (505, 545) and said edges of said destination node (510, 530) in ascending order (360) of their respective said lowest travel fare (350).

6. The method according to claim 1 wherein the step of extracting fare paths builds a temporary heap of fares (480) kept organized as a binary tree, wherein parent nodes (482) hold fare paths larger than those of two children nodes (484) and wherein root node (486) holds a most expensive fare path.

7. The method according to claim 6 wherein said heap is initialized with a fare path of length one (500) and further populated (515) with fare paths of lengths two (458) and three (462).

8. The method according to claim 6 wherein said heap is bounded to contain a specified number of k fare paths (520, 540 and 550) and wherein, if said specified number is exceeded (521) then, said most expensive fare path is removed (525) from said heap.

9. The method according to claim 1 wherein the step of extracting said fare paths ends (560) when said heap contains a said specified number of k fare paths and no cheaper fare path can possibly be built (552).

10. A system, in particular a fare learning component (110), comprising means adapted for carrying out each step of the method according to claim 1.

11. A computer program product stored on a computer readable storage medium, comprising computer readable code means for causing at least one computer to operate the method of building actual travel fares according to claim 1.

15. The method according to claim 2 wherein said fare databases (218) are provided and updated by airline carriers.

16. The method according to claim 7 wherein said heap is bounded to contain a specified number of k fare paths (520, 540 and 550) and wherein, if said specified number is exceeded (521) then, said most expensive fare path is removed (525) from said heap.

12. A system, in particular a travel planning system (105), including said fare learning component (110), wherein said travel planning system is made capable of proposing thematic travel options to said end-users (600).

13. The system of claim 12 wherein said travel planning system can return travel options for multiple destinations (650).

14. The system of claim 12 wherein said travel planning system returns multiple dates and fares for a selected destination.