Adaptive advisory engine and methods to predict preferential activities available at a region associated with lodging

• US 9,996,998 B2
• Filed: 12/05/2014
• Issued: 06/12/2018
• Est. Priority Date: 12/05/2014
• Status: Active Grant

• Current Assignee: HomeAway.com, Inc. (Expedia Group, Inc.)
• Original Assignee: HomeAway.com, Inc. (Expedia Group, Inc.)
• Inventors: Turner, Ryan Hedley, Haligas, Daniel Steven, Venugopal, Velayudhan, Devine, Alex Holm
• Primary Examiner(s): Ho, Huy C
• Application Number:
  US14/562,629
• Publication Number:
  US 20160165402A1
• Time in Patent Office: 1,285 Days
• Field of Search: 4554563
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 30/0645 Rental transactions; Leasing transactions
• CPC Class Codes: G07B 15/00 Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points handling coins or valuable papers, e.g. banknotes, G07D; apparatus for vending or hiring articles or services activated by coins, credit cards, paper currency or the like G07F7/00, G07F17/00
• CPC Class Codes: G07C 2209/08 With time considerations, e.g. temporary activation, valid time window or time limitations
• CPC Class Codes: G07C 9/00174 Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys mechanical locks with electric permutation E05B49/00; arrangements for sensing or reading record carriers G06K7/00; electronic switching H03K17/00
• CPC Class Codes: G07C 9/00571 operated by interacting with a central unit
• CPC Class Codes: G07C 9/00896 specially adapted for particular uses
• CPC Class Codes: G07C 9/38 with central registration
• CPC Class Codes: H04L 61/4535 using an address exchange platform which sets up a session between two nodes, e.g. rendezvous servers, session initiation protocols [SIP] registrars or H.323 gatekeepers
• CPC Class Codes: H04M 15/56 for VoIP communications
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/029 Location-based management or tracking services

Abstract

Various embodiments relate generally to electrical and electronic hardware, computer software, wired and wireless network communications, and wearable computing devices for identifying activities and/or destinations of relative importance. More specifically, a system, a device and a method provide to predict activities (e.g., preferential activities) available at a geographic region association with lodging and to adaptively generate advisory data. In one or more embodiments, a method can include accessing traveler profile data associated with a user, identifying data representing a portion of traveler attributes associated with a geographic region including lodging for the user, and receiving data indicative of locality-related characteristics. The method also includes predicting a first activity as a function of a locality-related characteristic and a travel attribute, and generating customized advisory data configured to engage in an activity.

Claims

1. A computer-implemented method comprising:

under control of a computing device configured to execute specific computer-executable instructions,

accessing traveler profile data representing traveler attributes associated with a traveler;

determining a location of the traveler;

identifying, based at least in part on the location of the traveler, a companion of the traveler;

determining, based at least in part on companion attributes associated with the companion of the traveler, a companion type of the companion of the traveler;

determining, based at least in part on the traveler attributes and the companion type of the companion of the traveler, a preference of the traveler for an activity;

determining a plurality of activities available at the location of the traveler;

determining an environmental condition at the location of the traveler;

predicting a first preferential activity based at least in part on the plurality of activities available at the location of the traveler, the environmental condition at the location of the traveler, the companion type of the companion of the traveler, and the preference of the traveler;

generating a first activity recommendation identifying the first preferential activity; and

causing presentation of the first activity recommendation to the traveler.

2. The computer-implemented method of claim 1 further comprising:

predicting a next preferential activity based at least in part on a location coordinate associated with the first preferential activity and at least one of the traveler attributes;

generating a second activity recommendation identifying the next preferential activity; and

causing presentation of the second activity recommendation.

3. The computer-implemented method of claim 2 further comprising:

detecting performance of the first preferential activity; and

determining a likelihood that the next preferential activity is contingent on the performance of the first preferential activity;

wherein predicting the next preferential activity is based at least in part on the location coordinate associated with the first preferential activity, at least one of the traveler attributes, and the likelihood that the next preferential activity is contingent on the performance of the first preferential activity.

4. The computer-implemented method of claim 2 further comprising:

receiving other traveler data indicating participation of other travelers in the next preferential activity; and

aggregating the other traveler data to form aggregated traveler data,

wherein predicting the next preferential activity is based at least in part on the location coordinate associated with the first preferential activity, at least one of the traveler attributes, and the aggregated traveler data.

5. The computer-implemented method of claim 2, wherein identifying the companion of the traveler comprises:

receiving data indicative of a presence of a companion mobile computing device at the location of the traveler; and

identifying the companion, wherein the companion is associated with the companion mobile computing device.

6. The computer-implemented method of claim 1, further comprising:

detecting a change in the location of the traveler to form a modified location of the traveler;

determining a plurality of activities available at the modified location of the traveler;

determining an environmental condition at the modified location of the traveler;

predicting a second preferential activity based at least in part on the plurality of activities available at the modified location of the traveler, the environmental condition at the modified location of the traveler, and the preference of the traveler;

generating a second activity recommendation identifying the second preferential activity; and

causing presentation of the second activity recommendation.

7. The computer-implemented method of claim 1, wherein the location of the traveler is a geographic location, and wherein determining the location of the traveler comprises receiving in-situ traveler data indicative of the geographic location, the in-situ traveler data being generated at a mobile computing device at the geographic location.

8. The computer-implemented method of claim 7 further comprising:

determining that at least one travel attribute associated with the traveler is indicative of current travel; and

activating in-situ travel data acquisition.

9. The computer-implemented method of claim 7, wherein receiving traveler in-situ data indicative of the geographic location comprises receiving from the mobile computing device a request to generate a recommendation for an activity at the geographic location.

10. The computer-implemented method of claim 7, wherein receiving in-situ traveler data indicative of the geographic location comprises receiving data representing a location coordinate of the mobile computing device.

11. The computer-implemented method of claim 10, further comprising:

determining a relationship between the location coordinate of the mobile computing device and the preference of the traveler;

identifying a third preferential activity based on the relationship between the location coordinate of the mobile computing device and the preference of the traveler;

generating a third activity recommendation identifying the third preferential activity; and

presenting the third activity recommendation to the traveler.

12. The computer-implemented method of claim 1 further comprising:

obtaining a travel timeline;

receiving in-situ traveler data including a first location coordinate of a mobile computing device; and

predicting, based at least in part upon the travel timeline and the first location coordinate, that the mobile computing device will be at a second location coordinate at an estimated arrival time;

wherein determining the location of the traveler comprises identifying the second location coordinate as the location of the traveler.

13. The computer-implemented method of claim 1, further comprising:

extracting symbolic data from a description of the first preferential activity;

correlating at least one subset of the symbolic data to determine an item tag associated with the first preferential activity; and

storing the item tag associated with the first preferential activity as traveler profile data.

14. The computer-implemented method of claim 13, wherein the at least one subset of the symbolic data comprises data representing a string of text.

15. The computer-implemented method of claim 13, wherein the at least one subset of the symbolic data comprises data representing a location coordinate associated with the first preferential activity and a time stamp corresponding to the traveler arriving at the location coordinate associated with the first preferential activity.

16. The computer-implemented method of claim 13, further comprising:

detecting performance of the first preferential activity;

analyzing the item tag associated with the first preferential activity to determine a category tag; and

generating data associating the category tag to the traveler.

17. The computer-implemented method of claim 1, wherein the environmental condition comprises at least one of a current weather at the location of the traveler or a current temperature at the location of the traveler.

18. A device comprising:

a data store configured to store computer-executable instructions;

a processor in communication with the data store, the processor configured to execute the computer-executable instructions to at least;

access traveler profile data representing a traveler attribute associated with a traveler;

determine a location of the traveler;

identify, based at least in part on the location of the traveler, a companion of the traveler;

determine, based at least in part on a companion attribute associated with the companion of the traveler, a companion type of the companion of the traveler;

determine a plurality of activities available at the location of the traveler;

determine an environmental condition at the location of the traveler;

predict a first preferential activity based at least in part on the plurality of activities available at the location of the traveler, the environmental condition at the location of the traveler, the companion type of the companion of the traveler, and the travel attribute associated with the traveler; and

generate a first activity recommendation including the first preferential activity.

19. The device of claim 18 wherein the processor is further configured to generate for display a user interface, and wherein the user interface is configured to present the first activity recommendation.