TRAVEL SERVICES SEARCH

• US 20130041902A1
• Filed: 05/21/2012
• Published: 02/14/2013
• Est. Priority Date: 05/20/2011
• Status: Active Grant

• Current Assignee: Deem, Incorporated (Jack Taylor Family Voting Trust U A D 4 14 99)
• Original Assignee: Rearden Commerce Inc
• Inventors: Shroff, Rajat, Pritchett, Daniel L., Swann, Garner, Sundaram, Parthasarathy, Todd, Jeremy Paul, Kaiser, Alan Todd, Briggs, Nicholas, Falk, David
• Granted Patent: US 9,449,288 B2
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 30/0625 Directed, with specific intent or strategy
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 50/14 Travel agencies

Abstract

A system and method for searching travel services. A server computer receives a travel request from a client device operated by a user. The server computer identifies travel options according to the travel request. The server computer classifies the travel options into predefined groups, the classifying based on at least one of past transactions, input from domain experts, input from semantic analysts, analytics data, user preferences, and company policies. The server computer presents the options via presentation of the predefined groups.

Claims

1. A method comprising:

receiving, by a server computer from a client device operated by a user, a travel request;

identifying, by the server computer, travel options according to the travel request;

classifying, by the server computer, the travel options into predefined groups, the classifying based on at least one of past transactions, input from domain experts, input from semantic analysts, analytics data, user preferences, and company policies; and

presenting, by the server computer, the options via presentation of the predefined groups.

2. The method of claim 1, wherein the presenting of the options is personalized for the user.

3. The method of claim 1, further comprising defining, by the server computer, the groups.

4. The method of claim 3, wherein the defining of the groups is personalized for the user.

5. The method of claim 1, wherein the classifying of the travel options into the predefined groups further comprises defining, by the user, a function to classify an option.

6. The method of claim 5, wherein the defining, by the user, of the function further comprises defining the function via one or more votes.

7. The method of claim 1, further comprising classifying a first group, the classifying based on a model function of the first group.

8. The method of claim 7, wherein the classifying of the first group is further based on attributes associated with the travel options.

9. The method of claim 1, further comprising determining, by the server computer, a plurality of buckets specifically for the user.

10. The method of claim 9, further comprising providing, by the server computer to the client device, the plurality of buckets as recommendations for the user based on the travel request.

11. The method of claim 9, wherein the determining of the plurality of buckets further comprises determining buckets that are one or more of personalized to a user'"'"'s search behavior and personalized based on social connections.

12. A system comprising:

at least one processor; and

memory storing instructions configured to instruct the at least one processor to;

receive, from a client device operated by a user, a travel request;

identify travel options according to the travel request;

classify the travel options into predefined groups, the classifying based on at least one of past transactions, input from domain experts, input from semantic analysts, analytics data, user preferences, and company policies; and

present the options via presentation of the predefined groups.

13. The system of claim 12, wherein the instructions to present the options further comprises instructions to present that are personalized for the user.

14. The system of claim 12, further comprising instructions to define the groups.

15. The system of claim 14, wherein the instructions to define the groups further comprise instructions that are personalized for the user.

16. The system of claim 12, wherein the instructions to classify the travel options into the predefined groups further comprises instructions to enable the user to define a function to classify an option.

17. The system of claim 16, wherein the instructions to enable the user to define the function further comprises instructions to enable the user to define the function via one or more votes.

18. The system of claim 12, further comprising instructions to classify a first group, the instructions based on a model function of the first group.

19. The system of claim 18, wherein the instructions to classify the first group is further based on attributes associated with the travel options.

20. The system of claim 12, further comprising instructions to determine a plurality of buckets specifically for the user.

21. The system of claim 20, further comprising instructions to provide, to the client device, the plurality of buckets as recommendations for the user based on the travel request.

22. The system of claim 20, wherein the instructions to determine the plurality of buckets further comprises instructions to determine buckets that are one or more of personalized to a user'"'"'s search behavior and personalized based on social connections.

23. A non-transitory computer readable storage medium storing computer program instructions configured to instruct a computer processor to at least:

receive, from a client device operated by a user, a travel request;

identify travel options according to the travel request;

classify the travel options into predefined groups, the classifying based on at least one of past transactions, input from domain experts, input from semantic analysts, analytics data, user preferences, and company policies; and

present the options via presentation of the predefined groups.