Systems and methods for determining a relevance rank for a point of interest

• US 20060287810A1
• Filed: 06/16/2005
• Published: 12/21/2006
• Est. Priority Date: 06/16/2005
• Status: Active Grant

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Sadri, Pasha, Gossain, Vineet
• Granted Patent: US 7,826,965 B2
• Time in Patent Office: Days
• US Class Current: 701/200
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G01C 21/3679 Retrieval, searching and output of POI information, e.g. hotels, restaurants, shops, filling stations, parking facilities G01C21/3611 takes precedence
• CPC Class Codes: G09B 29/006 Representation of non-cartographic information on maps, e.g. population distribution, wind direction, radiation levels, air and sea routes

Abstract

The present invention provides systems and methods for computing a relevance rank or score of a point of interest based on its proximity to clusters of other points of interest (POI). The methods of the present invention are based upon novel approaches that use location information and weighting functions in order to filter and sort searches of hotels, destinations, and other properties or other points of interest. In one approach, a relevance rank of a target POI at a location is determined by obtaining a plurality of component POIs that are each within the same geographical region as the target POI. Component POI scores for each of the component POIs are calculated as a function of a distance between the locations of the component POI and the location of the target POI. The relevance rank of the target POI is a combination of the plurality of component POI scores.

Claims

2. The method of claim 1, wherein a component POI score in said plurality of component POI scores is determined by the distance between (i) the component POI corresponding to the component POI score and (ii) the target POI.

22. A computer system for computing a relevance rank of a target point of interest (POI) at a location, the computer system comprising:

a central processing unit;

a memory, coupled to the central processing unit, the memory storing;

instructions for accessing a property data structure, wherein said property data structure comprises information about said target POI;

instructions for accessing a POI data structure; and

instructions for accessing a proximity processing module, wherein the proximity processing module comprises;

instructions for obtaining a plurality of component points of interest from said POI data structure, wherein each component POI in said plurality of component POIs is within the same geographical region as the target POI location; and

instructions for calculating a plurality of component POI scores, each component POI score in the plurality of component POI scores corresponding to a component POI in said plurality of component POI, and wherein;

each respective component POI score in said plurality of component POI scores is determined by a function of a distance between (i) the component POI corresponding to the respective component POI score and (ii) the target POI; and

said relevance rank of said target POI is a combination of said plurality of component POI scores.

24. The computer system of claim 23, wherein said property data structure is resident in said memory.

25. The computer system of claim 23, wherein said property data structure is located in a computer that is addressable by said computer system across a network.

26. The computer system of claim 23, wherein said POI data structure is resident in said memory.

27. The computer system of claim 23, wherein said POI data structure is located in a computer that is addressable by said computer system across a network.

28. The computer system of claim 23, wherein a component POI score in said plurality of component POI scores is determined by the distance between (i) the component POI corresponding to the component POI score and (ii) the target POI.

29. The computer system of claim 23, wherein a component POI score in said plurality of component POI scores is determined by an inverse of the distance between (i) the component POI corresponding to the component POI score and (ii) the target POI.

30. The computer system of claim 23, wherein said target POI is a hotel, a house, an apartment, a real estate parcel, a restaurant, a bar, or a school.

31. The computer system of claim 23, wherein a component POI in said plurality of component POI is a restaurant, a hotel, an attraction, an activity, a park, a store, a shopping mall, a school, a highway on-ramp, an earthquake fault, a train track, or a location of a sex offender.

32. The computer system of claim 23, wherein a component POI score in said plurality of component POI scores is further determined by a weight assigned to the component POI represented by the component POI score.

33. The computer system of claim 32, wherein said weight is determined by a category parameter, a type parameter, a rating parameter, a quality parameter, a price parameter, or a user preference parameter.

34. The computer system of claim 23, wherein a component POI in said plurality of component POI and the target POI are in the same vicinity when they are in the same city.

35. The computer system of claim 23, wherein a component POI is assigned a component POI score of zero when the distance between the component POI and the target POI is greater than a threshold value.

1. A method of computing a relevance rank of a target point of interest (POI) at a location, the method comprising:

obtaining a plurality of component points of interest, wherein each component POI in said plurality of component POIs is within the same geographical region as the target POI location; and

calculating a plurality of component POI scores, each component POI score in the plurality of component POI scores corresponding to a component POI in said plurality of component POI, and wherein

each respective component POI score in said plurality of component POI scores is determined by a function of a distance between (i) the component POI corresponding to the respective component POI score and (ii) the target POI; and

said relevance rank of said target POI is a combination of said plurality of component POI scores.

3. The method of claim 1, wherein a component POI score in said plurality of component POI scores is determined by an inverse of the distance between (i) the component POI corresponding to the component POI score and (ii) the target POI.

4. The method of claim 1, wherein said target POI is a hotel, a house, an apartment, a real estate parcel, a restaurant, a bar, or a school.

5. The method of claim 1, wherein a component POI in said plurality of component POI is a restaurant, a hotel, an attraction, an activity, a park, a store, a shopping mall, a school, a highway on-ramp, an earthquake fault, a train track, or a location of a sex offender.

6. The method of claim 1, wherein a component POI score in said plurality of component POI scores is further determined by a weight assigned to the component POI represented by the component POI score.

7. The method of claim 5, wherein said weight is determined by a category parameter, a type parameter, a rating parameter, a quality parameter, a price parameter, or a user preference parameter.

8. The method of claim 1, wherein a component POI in said plurality of component POI and the target POI are in the same vicinity when they are in the same state.

9. The method of claim 1, wherein a component POI in said plurality of component POI and the target POI are in the same vicinity when they are in the same county.

10. The method of claim 1, wherein a component POI in said plurality of component POI and the target POI are in the same vicinity when they are in the same city.

11. The method of claim 1, wherein a component POI is assigned a component POI score of zero when the distance between the component POI and the target POI is greater than a threshold value.

12. The method of claim 11, wherein said threshold value is 500 yards or less.

13. The method of claim 12, wherein said threshold value is 1000 yards or less.

14. The method of claim 12, wherein said threshold value is one mile or less.

15. The method of claim 12, wherein said threshold value is between 10 feet and 1 mile.

16. The method of claim 12, wherein the distance between the component POI and the target POI is computed as a direct distance between the component POI and the target POI.

17. The method of claim 12, wherein the distance between the component POI and the target POI is computed as a driving or walking distance between the component POI and the target POI.

18. The method of claim 12, wherein the distance between the component POI and the target POI is computed as the driving time between the component POI and the target POI.

19. The method of claim 1, wherein the first plurality of component POIs consists of between 5 and 1000 POIs.

20. The method of claim 1, wherein the first plurality of component POIs comprises more than five POIs.

21. The method of claim 1, wherein the first plurality of component POIs consists of less than 10,000 POIs.

36. The computer system of claim 35, wherein the distance between the component POI and the target POI is computed as a direct distance between the component POI and the target POI.

36-1. The computer system of claim 35, wherein the distance between the component POI and the target POI is computed as the driving time between the component POI and the target POI.

37. The computer system of claim 35, wherein the distance between the component POI and the target POI is computed as a driving or walking distance between the component POI and the target POI.

37-2. A computer program product for computing a relevance rank of a target point of interest (POI) at a location, the computer program product comprising a computer readable storage medium and a computer program mechanism embedded therein, the computer program mechanism comprising:

instructions for accessing a property data structure, wherein said property data structure comprises information about said target POI;

instructions for accessing a POI data structure; and

instructions for accessing a proximity processing module, wherein the proximity processing module comprises;

instructions for obtaining a plurality of component points of interest from said POI data structure, wherein each component POI in said plurality of component POIs is within the same geographical region as the target POI location; and

instructions for calculating a plurality of component POI scores, each component POI score in the plurality of component POI scores corresponding to a component POI in said plurality of component POI, and wherein;

each respective component POI score in said plurality of component POI scores is determined by a function of a distance between (i) the component POI corresponding to the respective component POI score and (ii) the target POI; and

said relevance rank of said target POI is a combination of said plurality of component POI scores.

38. A method of determining a target location, the method comprising:

obtaining, over the Internet, a location of a first person associated with the target location;

obtaining, over the Internet, a location of a second person associated with the target location; and

computing, for each respective candidate location in a plurality of candidate locations, a score that is a function both (i) a distance between the location of the first person and the respective candidate location and (ii) a distance between the location of the second person and the candidate location, wherein the candidate location in the plurality of candidate locations that receives the best score is deemed to be the target location.

39. The method of claim 38, wherein the distance between the location of the first person and the respective candidate location is computed as a direct distance.

40. The method of claim 38, wherein the distance between the location of the first person and the respective candidate location is computed as a driving or walking distance.

41. The method of claim 38, wherein the distance between the location of the first person and the respective candidate location is computed as the driving time.

42. The method of claim 38, wherein the target location is a meeting place for the first person and the second person.

43. The method of claim 38, wherein the target location is associated with additional people and the location of these additional people is used to identify the target location from among the plurality of candidate locations based on the respective distances of each of the additional people and the geographic positions of each of the candidate locations in the plurality of candidate locations.

44. The method of claim 38, wherein

the target location is a place for the first person and second person to rent or buy;

the location of the first person is the place where the first person works or goes to school; and

the location of the second person is the place where the second person works or goes to school.

45. A computer system for determining a target location, the computer system comprising:

a central processing unit;

a memory, coupled to the central processing unit, the memory storing;

instructions for obtaining, over the Internet, a location of a first person associated with the target location;

instructions for obtaining, over the Internet, a location of a second person associated with the target location; and

instructions for computing, for each respective candidate location in a plurality of candidate locations, a score that is a function both (i) a distance between the location of the first person and the respective candidate location and (ii) a distance between the location of the second person and the candidate location, wherein the candidate location in the plurality of candidate locations that receives the best score is deemed to be the target location.

46. A computer program product for determining a target location, the computer program product comprising a computer readable storage medium and a computer program mechanism embedded therein, the computer program mechanism comprising:

instructions for obtaining, over the Internet, a location of a first person associated with the target location;

instructions for obtaining, over the Internet, a location of a second person associated with the target location; and

instructions for computing, for each respective candidate location in a plurality of candidate locations, a score that is a function both (i) a distance between the location of the first person and the respective candidate location and (ii) a distance between the location of the second person and the candidate location, wherein the candidate location in the plurality of candidate locations that receives the best score is deemed to be the target location.