Future Location Determination Using Social Networks

• US 20090054043A1
• Filed: 08/21/2007
• Published: 02/26/2009
• Est. Priority Date: 08/21/2007
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: O'Connell, Brian M., Katz, Neil A., Hamilton, Rick A., Walker, Keith R.
• Granted Patent: US 8,031,595 B2
• Time in Patent Office: Days
• US Class Current: 455/414.200
• CPC Class Codes: G01S 5/0284 Relative positioning
• CPC Class Codes: G01S 5/0294 Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
• CPC Class Codes: H04M 3/42357 where the information is provided to a monitoring entity such as a potential calling party or a call processing server
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/21 for social networking applications
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

A computer-implemented method of predicting a future location of a mobile node can include determining a current location of a first mobile node, determining a location of at least a second mobile node, wherein the second mobile node is associated with the first mobile node via a social network, and generating a list including at least one candidate destination determined, at least in part, according to the location of the first mobile node and the location of the second mobile node. For each candidate destination on the list, a probability that the first mobile node is in route to that candidate destination location can be calculated. A candidate destination can be selected, according to the probabilities, from the list as a predicted future location of the first mobile node. The predicted future location of the first mobile node can be output.

Claims

1. A computer-implemented method of predicting a future location of a first mobile node comprising:

determining a location of at least a second mobile node, wherein the second mobile node is associated with the first mobile node via a social network;

generating a list comprising at least one candidate destination determined, at least in part, according to the location of the first mobile node and the location of the second mobile node;

for each candidate destination on the list, calculating a probability that the first mobile node is in route to the candidate destination;

selecting a candidate destination, according to the probabilities, from the list as a predicted future location of the first mobile node; and

outputting the predicted future location of the first mobile node.

2. The computer-implemented method of claim 1, further comprising determining that the second mobile node is located within a predetermined distance of the first mobile node and that each candidate destination on the list is located within a predetermined distance of the second mobile node.

3. The computer-implemented method of claim 1, further comprising selecting the location of the second mobile node to be the at least one candidate destination of the list.

4. The computer-implemented method of claim 1, further comprising calculating a heading of the first mobile node, wherein calculating a probability further comprises increasing the probability for each candidate destination on the list located within a predetermined distance from a point on a path along the heading.

5. The computer-implemented method of claim 1, further comprising determining that the first mobile node is currently traveling along a route previously traveled by the first mobile node, wherein calculating a probability further comprises increasing the probability of each candidate destination on the list located within a predetermined distance of a point on the route currently traveled by the first mobile node.

6. The computer-implemented method of claim 1, further comprising identifying at least one candidate destination on the list as a destination previously visited by the first mobile node, wherein calculating a probability further comprises increasing the probability of each candidate destination on the list previously visited by the first mobile node.

7. The computer-implemented method of claim 1, wherein calculating a probability further comprises increasing the probability of each candidate destination according to a current time stamp and a schedule associated with the first mobile node.

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

determining a measure of time that a third mobile device is proximate to the first mobile device; and

selectively adding the third mobile device to the social network of the first mobile device according to the measure of time that the third mobile device is proximate to the first mobile device.

9. The computer-implemented method of claim 1, wherein a location of each of a plurality of second mobile nodes is determined, wherein calculating a probability further comprises:

identifying each candidate destination on the list associated with two or more of the plurality of second mobile nodes; and

for each identified candidate destination, increasing the probability of the candidate destination according to a number of the plurality of second mobile nodes associated with the candidate destination.

10. A computer-implemented method of predicting a future location of a mobile node comprising:

determining a location for each of a plurality of mobile nodes associated with a base mobile node through a social network;

for each of the plurality of mobile nodes, identifying at least one candidate destination within a predetermined distance of the mobile node;

for each candidate destination, calculating a probability that the base mobile node is in route to the candidate destination, at least in part, according to a measure of connectivity between the base mobile node and the mobile node associated with each respective candidate destination;

selecting a candidate destination, according to the probabilities, as a predicted future location of the base mobile node; and

outputting the predicted future location of the base mobile node.

11. The computer-implemented method of claim 10, wherein identifying at least one candidate destination comprises, for at least one mobile node, identifying the location of the mobile node as the candidate destination associated with the mobile node.

12. The computer-implemented method of claim 10, further comprising identifying at least one candidate destination as a destination previously visited by the base mobile node, wherein calculating a probability further comprises increasing the probability of each candidate destination identified as a destination previously visited by the base mobile node.

13. The computer-implemented method of claim 10, further comprising determining that the base mobile node is traveling along a route previously traveled by the base mobile node, wherein calculating a probability further comprises increasing the probability of each candidate destination located within a predetermined distance of a point on the route currently traveled by the base mobile node.

14. The computer-implemented method of claim 10, wherein calculating a probability further comprises increasing the probability of each candidate destination according to a current time and a schedule associated with the based mobile node.

15. A computer program product comprising:

a computer-usable medium having computer-usable program code that predicts a future location of a first mobile node, the computer-usable medium comprising;

computer-usable program code that determines a location of at least a second mobile node, wherein the second mobile node is associated with the first mobile node via a social network;

computer-usable program code that generates a list comprising at least one candidate destination determined, at least in part, according to the location of the second mobile node;

computer-usable program code that, for each candidate destination on the list, calculates a probability that the first mobile node is in route to that candidate destination;

computer-usable program code that selects a candidate destination, according to the probabilities, from the list as a predicted future location of the first mobile node; and

computer-usable program code that outputs the predicted future location of the first mobile node.

16. The computer program product of claim 15, wherein the computer-usable medium further comprises computer-usable program code that determines that the second mobile node is located within a predetermined distance of the first mobile node and that each candidate destination on the list is located within a predetermined distance of the second mobile node.

17. The computer program product of claim 15, wherein the computer-usable medium further comprises computer-usable program code that selects the location of the second mobile node as the at least one candidate location of the list.

18. The computer program product of claim 15, wherein the computer-usable medium further comprises computer-usable program code that calculates a heading of the first mobile node, wherein the computer-usable program code that calculates a probability further comprises computer-usable program code that increases the probability for each candidate destination on the list located within a predetermined distance from a point on a path along the heading.

19. The computer program product of claim 15, wherein the computer-usable medium further comprises computer-usable program code that determines that the first mobile node is currently traveling along a route previously traveled by the first mobile node, wherein the computer-usable program code that calculates a probability further comprises computer-usable program code that increases the probability of each candidate destination on the list located within a predetermined distance of a point on the route currently traveled by the first mobile node.

20. The computer program product of claim 15, wherein the computer-usable medium further comprises computer-usable program code that identifies at least one candidate destination on the list as a destination previously visited by the first mobile node, wherein the computer-usable program code that calculates a probability further comprises computer-usable program code that increases the probability of each candidate destination on the list previously visited by the first mobile node.