POINTS OF INTEREST (POI) RANKING BASED ON MOBILE USER RELATED DATA

• US 20130262479A1
• Filed: 06/01/2013
• Published: 10/03/2013
• Est. Priority Date: 10/08/2011
• Status: Abandoned Application

• Current Assignee: Alohar Mobile Inc. (Alibaba Group Holding Ltd.)
• Original Assignee: Alohar Mobile Inc. (Alibaba Group Holding Ltd.)
• Inventors: Liang, Sam Song, Yang, Jun, Wang, Chenyu, Fu, Yun
• Application Number:
  US13/907,865
• Publication Number:
  US 20130262479A1
• Time in Patent Office: Days
• US Class Current: 707/748
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences

Abstract

Methods, systems and apparatus for ranking potential points of interest (POIs) of a user stay are disclosed. One system includes an upstream server connected through a network to a mobile device. At least one of the upstream server and a controller of the mobile device is operative to estimate a location of a user stay of the mobile device, access a database of POIs, and parameters of the POIs, and generate a ranking score for a plurality of POIs based on a weighted comparison of each of the parameters of the POIs with corresponding parameters of the user stay.

Claims

1. A system for ranking potential points of interest (POIs) of a user stay, comprising:

an upstream server connected through a network to a mobile device, wherein at least one of the upstream server and a controller of the mobile device is operative to;

estimate a location of a user stay of the mobile device;

access a database of POIs, and parameters of the POIs; and

generate a ranking score for a plurality of POIs based on a weighted comparison of each of the parameters of the POIs with corresponding parameters of the user stay.

2. The system of claim 1, wherein the at least one of the upstream server and the controller of the mobile device is further operative to match the user stay with a POI based on the rankings of the potential POIs with an estimated confidence level of the matching.

3. The system of claim 1, wherein the parameters of the POI comprises at least a physical distance between the POI and the user stay.

4. The system of claim 3, wherein the physical distance between the POI and the user stay has a greatest weighting.

5. The system of claim 1, wherein the parameters of the POI comprises at least an edit distance between an address of the POI and an address of the user stay.

6. The system of claim 1, wherein the ranking score is additionally influenced by whether a wireless signature of the user stay matches a wireless signature associated with the POI.

7. The system of claim 1, wherein the ranking score is additionally influenced by whether an SSID of the user stay matches a name of the POI.

8. The system of claim 1, wherein the ranking score is additionally influenced by whether the user stay is within a boundary of the POI.

9. The system of claim 1, wherein the ranking score is additionally influenced by whether a motion pattern of the user stay matches a motion pattern of the POI based on a category of the POI.

10. The system of claim 2, wherein the parameters for each POI further includes at least one of an edit distance between an address of the POI and an address of the user stay, a timing of the POI in comparison to timing of the user stay, a motion pattern associated with the POI in comparison to motion of the user stay, or a popularity of the POI.

11. The system of claim 10, wherein the timing of the POI comprises general business hours based on the POI'"'"'s category or hours of operation of the POI.

12. The system of claim 10, wherein the popularity of the POI includes at least one of a number of reviews and ratings of the POI, a number of check-ins associated with the POI.

13. The system of claim 1, wherein the ranking scoring is additionally influenced by personal places of a user of a mobile device associated with the user stay.

14. The system of claim 13, wherein the personal places includes at least one of home/work of the user, prior user corrected POI, number of previous visits by the user, context information of the user, such as, internet or location searches by the user.

15. The system of claim 1, wherein the ranking scoring is additionally adjusted by the size of the POI based on big or small POI heuristic of importance.

16. A method of ranking potential points of interest (POIs) of a user stay, comprising:

estimating a location of a user stay;

accessing a database of POIs, and parameters of the POIs; and

generating a ranking score for a plurality of POIs based on a weighted comparison of each of the parameters of the POIs with corresponding parameters of the user stay.

17. The method of claim 16, wherein the parameters for each POI include at least one of a physical distance between the POI and the user stay, an edit distance between an address of the POI and an address of the user stay, a timing of the POI in comparison to timing of the user stay, a motion pattern associated with the POI in comparison to motion of the user stay, and/or a popularity of the POI.

18. The method of claim 17, wherein the physical distance between the POI and the user stay has the greatest weighting.

19. The method of claim 17, wherein the timing of the POI comprises general business hours based on the POI'"'"'s category or hours of operation of the POI.

20. The method of claim 17, wherein the popularity of the POI includes at least one of a number of reviews and ratings of the POI, a number of check-ins associated with the POI.

21. The method of claim 16, wherein the ranking scoring is additionally influenced by personal places of a user of a mobile device associated with the user stay.

22. The method of claim 21, wherein the personal places includes at least one of home/work of the user, prior user corrected POI, number of previous visits by the user, context information of the user, such as, internet or location searches by the user.

23. The method of claim 16, wherein the ranking scoring is additionally adjusted by the size of the POI based on big or small POI heuristic of importance.

24. The method of claim 16, further comprising matching the user stay with a POI based on the rankings of the potential POIs with estimated confidence level of the matching.