System for providing extensible location-based services

• US 9,436,967 B2
• Filed: 06/05/2015
• Issued: 09/06/2016
• Est. Priority Date: 03/14/2012
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Dempski, Kelly L.
• Primary Examiner(s): Eng, George
• Assistant Examiner(s): Gao, Jing
• Application Number:
  US14/732,410
• Publication Number:
  US 20150271636A1
• Time in Patent Office: 459 Days
• Field of Search: 455/456.3
• US Class Current: 1/1
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 30/0635 Processing of requisition or of purchase orders
• CPC Class Codes: G06Q 30/0641 Shopping interfaces
• CPC Class Codes: G06Q 50/12 Hotels or restaurants
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04M 1/72457 according to geographic location
• CPC Class Codes: H04W 4/02 Services making use of location information
• 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 4/024 Guidance services
• CPC Class Codes: H04W 4/026 using orientation information, e.g. compass

Abstract

A system for providing location-based services to a mobile device regularly determines the location of the mobile device. Using the location of the device, the system queries a database of virtual geographic regions to determine if the location of the mobile device falls within one or more of the virtual geographic regions. The virtual geographic regions correspond to service entities that have purchased, rented, leased, or otherwise acquired the virtual geographic regions. The virtual geographic regions cover a certain area of a map. If the mobile device is within a virtual geographic region, the system notifies the mobile device. A location-based services application installed on the mobile device launches an applet for the entity that acquired the virtual geographic region within which the mobile device is located. When the mobile device is located within multiple overlapping virtual geographic regions, the system generates a prioritization scheme that defines which of the corresponding service entities should be displayed on the mobile device, and in what order the service entities should be displayed.

Claims

1. A computer-implemented method comprising:

receiving, at a computer system, location information for a mobile computing device that is associated with a user;

identifying, by the computer system and based, at least in part, on the location information, a plurality of candidate restaurants to be used for requests from the mobile computing device;

determining, by the computer system, distances between a current location of the mobile computing device to locations for the plurality of candidate restaurants;

selecting, by the computer system, historical behavior data for the user with regard to the plurality of candidate restaurants, wherein the historical behavior data identifies the user'"'"'s previous interactions with each of the plurality of candidate restaurants;

detecting, by the computer system, a current time of day;

identifying, by the computer system and from the historical behavior data, temporal information that indicates times of the day at which the user previously interacted with each of the plurality of candidate restaurants;

identifying, by the computer system and from the temporal information, time distributions for the user'"'"'s interactions with each of the plurality of candidate restaurants;

determining, by the computer system, probabilities that the user is most likely of visiting each of the plurality of restaurants based on comparisons of the current time of day with the time distributions;

determining, by the computer system, a plurality of scores for the plurality of candidate restaurants based, at least in part, on (i) the distances between the mobile computing device and the plurality of candidate restaurants (ii) the historical behavior data for the user with regard to the plurality of candidate restaurants, and (iii) the determined probabilities, wherein the plurality of scores indicate likelihoods that the user is currently interested in using each of the plurality of candidate restaurants;

selecting, by the computer system, a particular restaurant from the plurality of candidate restaurants based, at least in part, on the plurality of scores; and

causing, by the computer system, the particular restaurant to be used for requests from the mobile computing device.

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

identifying, by the computer system, quantities of interactions with each of the plurality of candidate restaurants;

determining, by the computer system, weighted probabilities that the user is most likely of visiting each of the plurality of restaurants based on products of the probabilities with the quantities of interactions with each of the plurality of candidate restaurants,

wherein the plurality of scores being determined further based on the determined probabilities comprises the plurality of scores being determined further based on the weighted probabilities.

3. The computer-implemented method of claim 1, wherein the previous interactions comprise previous orders placed by the user with the plurality of candidate restaurants.

4. The computer-implemented method of claim 1, wherein the previous interactions comprise previous instances when the user was physically located at or near the plurality of candidate restaurants.

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

identifying, by the computer system and from the historical behavior data, dwell times that indicate lengths of time that the user was physical located at or near the plurality of candidate restaurants;

wherein the plurality of scores are determined further based on the dwell times.

6. The computer-implemented method of claim 5, wherein the plurality of scores are determined based on combinations of the dwell times and the distances for each of the plurality of candidate restaurants.

7. The computer-implemented method of claim 6, wherein the combinations comprise, for each of the plurality of candidate restaurants, an aggregate value of dwell times for a particular candidate restaurant divided by a distance between the particular candidate restaurant and the mobile computing device.

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

determining, by the computer system and based on the location information, a direction that the mobile computing device is travelling,

wherein the plurality of scores are determined further based on the direction that the mobile computing device is travelling.

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

determining, by the computer system, dot products for each of the plurality of candidate restaurants based on (i) the direction that the mobile computing device is travelling and (ii) the distances between the mobile computing device and the plurality of candidate restaurants,

wherein the plurality of scores being determined further based on the direction that the mobile computing device is travelling comprises the plurality of scores being determined further based on the dot products for each of the plurality of candidate restaurants.

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

accessing, by the computer system, restaurant preferences previously identified by the user;

wherein the plurality of scores are determined further based on the restaurant preferences for the user.

11. The computer-implemented method of claim 1, wherein the restaurant preferences comprise types of restaurants for which the user previously identified a preference.

12. The computer-implemented method of claim 1, wherein identifying the plurality of candidate restaurants comprises:

accessing, by the computer system, information that identifies geographic regions that are associated with restaurants;

comparing, by the computer system, the current location of the mobile computing device with the geographic regions; and

selecting, by the computer system, the plurality of candidate restaurants from among the restaurants based on the current location of the mobile computing device being located within geographic regions associated with the plurality of candidate restaurants.

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

receiving, at the computer system and from a computing device associated with one of the restaurants, a request to associated the one of the restaurants with a particular geographic region;

determining, by the computer system, a price for the one of the restaurants to virtually rent the particular geographic region with the computer system;

providing, to the computing device, the determined price to virtually rent the particular geographic region.

14. The computer-implemented method of claim 13, wherein the price is determined based, at least in part, on a size of the particular geographic region.

15. The computer-implemented method of claim 13, wherein the price is determined based, at least in part, on a density of people within the particular geographic region.

16. A computer system comprising:

one or more processors;

memory storing instructions that, when executed, cause the one or more processors to;

receive location information for a mobile computing device that is associated with a user;

identify, based, at least in part, on the location information, a plurality of candidate restaurants to be used for requests from the mobile computing device;

determine distances between a current location of the mobile computing device to locations for the plurality of candidate restaurants;

select historical behavior data for the user with regard to the plurality of candidate restaurants, wherein the historical behavior data identifies the user'"'"'s previous interactions with each of the plurality of candidate restaurants;

detect a current time of day;

identify, from the historical behavior data, temporal information that indicates times of the day at which the user previously interacted with each of the plurality of candidate restaurants;

identify, from the temporal information, time distributions for the user'"'"'s interactions with each of the plurality of candidate restaurants;

determine probabilities that the user is most likely of visiting each of the plurality of restaurants based on comparisons of the current time of day with the time distributions;

determine a plurality of scores for the plurality of candidate restaurants based, at least in part, on (i) the distances between the mobile computing device and the plurality of candidate restaurants (ii) the historical behavior data for the user with regard to the plurality of candidate restaurants, and (iii) the determined probabilities, wherein the plurality of scores indicate likelihoods that the user is currently interested in using each of the plurality of candidate restaurants;

select a particular restaurant from the plurality of candidate restaurants based, at least in part, on the plurality of scores; and

cause the particular restaurant to be used for requests from the mobile computing device.

17. The computer system of claim 16, wherein the instructions, when executed, further cause the one or more processors to:

identify quantities of interactions with each of the plurality of candidate restaurants;

determine weighted probabilities that the user is most likely of visiting each of the plurality of restaurants based on products of the probabilities with the quantities of interactions with each of the plurality of candidate restaurants, wherein the plurality of scores being determined further based on the determined probabilities comprises the plurality of scores being determined further based on the weighted probabilities.

18. The computer system of claim 16, wherein the previous interactions comprise previous orders placed by the user with the plurality of candidate restaurants.

19. The computer system of claim 16, wherein the previous interactions comprise previous instances when the user was physically located at or near the plurality of candidate restaurants.

20. The computer system of claim 19, wherein the instructions, when executed, further cause the one or more processors to:

identify, from the historical behavior data, dwell times that indicate lengths of time that the user was physical located at or near the plurality of candidate restaurants;

wherein the plurality of scores are determined further based on the dwell times.