CROWD FORMATION FOR MOBILE DEVICE USERS

• US 20100198917A1
• Filed: 12/23/2009
• Published: 08/05/2010
• Est. Priority Date: 02/02/2009
• Status: Abandoned Application

• Current Assignee: IP3 2017, Series 200 of Allied Security Trust I (Allied Security Trust)
• Original Assignee: Kota Enterprises, LLC (Concert Technology Corporation)
• Inventors: Curtis, Scott, Petersen, Steven L., Jennings, Kenneth
• Application Number:
  US12/645,546
• Publication Number:
  US 20100198917A1
• Time in Patent Office: Days
• US Class Current: 709/204
• CPC Class Codes: G06F 21/6227 where protection concerns the structure of data, e.g. records, types, queries
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 41/0893 Assignment of logical groups to network elements
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 67/54 Presence management, e.g. monitoring or registration for receipt of user log-on information, or the connection status of the users
• 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 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

A system and method are provided for forming crowds of users and providing access to corresponding crowd data. In one embodiment, a central system, which includes one or more servers, operates to obtain current locations for users of mobile devices. The central system receives a crowd request from a requesting device. In response, the central system establishes a bounding region for the request and identifies one or more crowds, each including number of users, that are relevant to the bounding region for the request. The central system then generates crowd data for the one or more crowds and returns the crowd data for the one or more crowds to the requesting device.

Claims

1. A method comprising:

obtaining current locations of a plurality of users of a plurality of mobile devices, each of the plurality of users being a user of a corresponding one of the plurality of mobile devices;

forming a plurality of crowds based on the current locations of the plurality of users using a spatial crowd formation process that is based on user density within a bounding region for the spatial crowd formation process, wherein each of the plurality of crowds includes a number of the plurality of users;

receiving a request from a requesting device;

establishing a bounding region for the request;

identifying one or more crowds from the plurality of crowds that are relevant to the bounding region for the request;

for each crowd of the one or more crowds, generating crowd data for the crowd; and

returning the crowd data for the one or more crowds to the requesting device.

2. The method of claim 1 wherein the one or more crowds relevant to the bounding region for the request are one or more crowds from the plurality of crowds that are within the bounding region for the request.

3. The method of claim 1 wherein the one or more crowds relevant to the bounding region for the request are one or more crowds from the plurality of crowds that are within the bounding region for the request and one or more crowds from the plurality of crowds that intersect the bounding region for the request.

4. The method of claim 1 wherein forming the plurality of crowds based on the current locations of the plurality of users using the spatial crowd formation process comprises performing the spatial crowd formation process in response to a triggering event.

5. The method of claim 4 wherein the triggering event is reception of a location update for one of the plurality of users.

6. The method of claim 1 wherein forming the plurality of crowds based on the current locations of the plurality of users using the spatial crowd formation process comprises forming the plurality of crowds based on an optimal inclusion distance that is a function of user density within the bounding region for the spatial crowd formation process.

7. The method of claim 1 wherein forming the plurality of crowds based on the current locations of the plurality of users using the spatial crowd formation process comprises:

establishing the bounding region for the spatial crowd formation process;

identifying individual users within the bounding region that are not already included in a crowd;

identifying crowds relevant to the bounding region;

creating a crowd for each of the individual users within the bounding region;

assigning an optimal inclusion distance to each of the crowds created for the individual users within the bounding region, wherein the optimal inclusion distance is a function of user density within the bounding region; and

iteratively repeating the following steps until an ending event has occurred;

a identifying two closest crowds for the bounding region;

determining a distance between the two closest crowds for the bounding region;

determining whether the distance between the two closest crowds for the bounding region is less than an optimal inclusion distance for at least one of the two closest crowds; and

if the distance between the two closest crowds is less than the optimal inclusion distance for the at least one of the two closest crowds;

combining the two closest crowds to provide a resulting crowd; and

computing a new optimal inclusion distance for the resulting crowd as a function of the user density within the bounding region.

8. The method of claim 7 wherein forming the plurality of crowds based on the current locations of the plurality of users using the spatial crowd formation process further comprises discarding crowds with less than three users.

9. The method of claim 7 further comprising, prior to identifying the two closest crowds for the bounding region:

removing users that violate optimal inclusion distances of crowds in which the users are included from the crowds in which the users are included; and

creating a crowd for each user removed from the crowd in which the user was included.

10. The method of claim 7 further comprising:

receiving a location update for one of the plurality of users;

wherein receiving the location update for the one of the plurality of users triggers the spatial crowd formation process, and the bounding region for the spatial crowd formation process is a geographic region of a predefined shape and size centered at the current location of the one of the plurality of users as indicated by the location update.

11. The method of claim 7 further comprising:

receiving a location update for one of the plurality of users;

wherein receiving the location update for the one of the plurality of users triggers the spatial crowd formation process and establishing the bounding region for the spatial crowd formation process comprises;

determining a new bounding region that is a geographic region of a predefined shape and size centered at the current location of the one of the plurality of users as indicated by the location update;

determining an old bounding region that is a geographic region of a predefined shape and size centered at an old location of the one of the plurality of users prior to the location update;

determining whether the new bounding region and the old bounding region overlap; and

if the new bounding region and the old bounding region overlap, establishing the bounding region for the spatial crowd formation process as a geographic region that encompasses both the new bounding region and the old bounding region.

12. The method of claim 7 further comprising:

receiving a location update for one of the plurality of users;

wherein receiving the location update for the one of the plurality of users triggers the spatial crowd formation process and establishing the bounding region for the spatial crowd formation process comprises;

determining a new bounding region that is a geographic region of a predefined shape and size centered at the current location of the one of the plurality of users as indicated by the location update;

determining an old bounding region that is a geographic region of a predefined shape and size centered at an old location of the one of the plurality of users prior to the location update;

determining whether the new bounding region and the old bounding region overlap; and

if the new bounding region and the old bounding region do not overlap, establishing the bounding region for the spatial crowd formation process as a first one of the new and old bounding regions.

13. The method of claim 12 wherein, if the new bounding region and the old bounding region do not overlap, forming the plurality of crowds based on the current locations of the plurality of users using the spatial crowd formation process further comprises establishing the bounding region for the spatial crowd formation process as a second one of the new and old bounding regions and repeating the steps of:

identifying individual users within the bounding region that are not already included in a crowd;

identifying crowds relevant to the bounding region;

creating a crowd for each of the individual users within the bounding region;

assigning an optimal inclusion distance to each of the crowds created for the individual users within the bounding region, wherein the optimal inclusion distance is a function of a user density within the bounding region; and

iteratively repeating the following steps until an ending event has occurred;

identifying two closest crowds for the bounding region;

determining a distance between the two closest crowds for the bounding region;

determining whether the distance between the two closest crowds for the bounding region is less than an optimal inclusion distance for at least one of the two closest crowds; and

if the distance between the two closest crowds is less than the optimal inclusion distance for at least one of the two closest crowds;

combining the two closest crowds to provide a resulting crowd; and

computing an optimal inclusion distance for the resulting crowd as a function of the user density within the crowd.

14. The method of claim 7 wherein computing the optimal inclusion distance for the resulting crowd comprises computing the optimal inclusion distance for the resulting crowd as a function of the user density within the bounding region and distances between users in the crowd and a center of the crowd.

15. The method of claim 7 further comprising:

receiving a location update for one of the plurality of users;

wherein receiving the location update for the one of the plurality of users triggers the spatial crowd formation process and establishing the bounding region for the spatial crowd formation process comprises establishing a geographic region centered at the current location of the one of the plurality of users as indicated by the location update as the bounding region for the spatial crowd formation process, wherein the geographic region established as the bounding region for the spatial crowd formation process has a size that is a function of a location accuracy of the location of the one of the plurality of users as indicated by the location update.

16. The method of claim 7 wherein assigning the optimal inclusion distance to each of the crowds created for the individual users within the bounding region comprises:

computing an initial optimal inclusion distance based on the user density within the bounding region for the spatial crowd formation process;

determining whether a distance defining a location accuracy for the current location of the individual user in the crowd is greater than the initial optimal inclusion distance;

setting the optimal inclusion distance for the crowd of the individual user to the initial optimal inclusion distance if the distance defining the location accuracy for the current location of the individual user in the crowd is less than the initial optimal inclusion distance; and

setting the optimal inclusion distance for the crowd of the individual user to the distance defining the location accuracy for the current location of the individual user in the crowd if the distance defining the location accuracy for the current location of the individual user in the crowd is greater than the initial optimal inclusion distance.

17. The method of claim 7 wherein computing the new optimal inclusion distance for the resulting crowd as a function of the user density within the bounding region comprises:

generating a computed value for the new optimal inclusion distance based on the user density within the bounding region for the spatial crowd formation process and an average distance between users in the resulting crowd and a crowd center of the resulting crowd;

computing an average location accuracy distance as an average of distances defining location accuracies for the current locations of the users in the resulting crowd;

setting the new optimal inclusion distance for the resulting crowd to the computed value if the average distance is less than the computed value; and

setting the new optimal inclusion distance for the resulting crowd to the average distance if the average distance is greater than the computed value.

18. The method of claim 1 wherein the request from the requesting device is a request for a Point of Interest (POI) and establishing the bounding region for the request comprises establishing a geographic region centered at the POI as the bounding region for the request.

19. The method of claim 1 wherein the request from the requesting device is a request for an Area of Interest (AOI) and establishing the bounding region for the request comprises establishing a geographic region corresponding to the AOI as the bounding region for the request.

20. A server comprising:

a communication interface communicatively coupling the server to a plurality of mobile devices of a plurality of users via a network, each of the plurality of users being a user of a corresponding one of the plurality of mobile devices; and

a control system associated with the communication interface and adapted to;

obtain current locations of the plurality of users of the plurality of mobile devices;

form a plurality of crowds based on the current locations of the plurality of users using a spatial crowd formation process that is based on user density within a bounding region for the spatial crowd formation process, wherein each of the plurality of crowds includes a number of the plurality of users;

receive a request from a requesting device;

establish a bounding region for the request;

identify one or more crowds from the plurality of crowds that are relevant to the bounding region for the request;

for each crowd of the one or more crowds, generate crowd data for the crowd; and

return the crowd data for the one or more crowds to the requesting device.