SYSTEM AND METHOD FOR DETERMINING A DURATION FOR USER ACTIVITIES BASED ON SOCIAL-NETWORK EVENTS

• US 20140122483A1
• Filed: 10/26/2012
• Published: 05/01/2014
• Est. Priority Date: 10/26/2012
• Status: Abandoned Application

• Current Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Original Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Inventors: Zhang, Rui, Price, Robert R., Brdiczka, Oliver
• Application Number:
  US13/662,273
• Publication Number:
  US 20140122483A1
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

An activity-modeling system computes an amount of time that a user is expected to spend when performing activities of a certain type. During operation, the system can obtain a plurality of location events associated with the user, such that a respective location event indicates a time at which a user logged his location while performed an activity related to the activity type. The system selects, from the plurality of location events, a set of location events associated with the activity type. The system determines an activity start-time and an activity end-time for the activity type from the set of location events, and computes an activity-duration time for the activity type based on the determined activity start-time and the activity end-time.

Claims

1. A computer-implemented method, comprising:

selecting, from a plurality of location events, a set of location events associated with an activity type, wherein a respective location event indicates a time at which a user performed an activity related to the activity type;

determining, from the set of location events, an activity start-time and an activity end-time for the activity type; and

computing an activity duration for the activity type based on the determined activity start-time and the activity end-time.

2. The method of claim 1, wherein the location event also indicates at least one of:

a user identifier;

a geographic location;

a venue identifier;

a venue type;

an activity identifier;

an activity type; and

a text description of the activity.

3. The method of claim 1, wherein the activity duration indicates one or more of:

a participation time of an activity;

a commute time between venues for a first activity and a second activity;

a time interval between a first activity and a second activity;

a wait period before performing an activity; and

a preparation time for an activity.

4. The method of claim 1, wherein selecting the set of location events associated with the activity type involves determining whether the user'"'"'s location events correspond to a trace of periodic location events or a trace of manual location events.

5. The method of claim 4, further comprising:

responsive to determining that the location events correspond to a trace of periodic location events;

detecting, from the plurality of location events, one or more location-event sequences, wherein a respective location-event sequence includes location events that correspond to a common activity; and

selecting the set of location events to include a sequence of location events for one or more activities of the activity type.

6. The method of claim 5, wherein determining the activity start-time and activity end-time involves:

selecting the activity start-time for a respective activity based on an earliest location event for the respective activity'"'"'s location-event sequence; and

selecting the activity end-time for the respective activity based on a latest location event for the respective activity'"'"'s location-event sequence.

7. The method of claim 5, wherein computing the activity duration for the activity type involves:

computing an activity duration for a respective activity based on the determined activity start-time and activity end-time for the respective activity; and

computing an average duration for the activity type based on activity durations for one or more activities of the activity type.

8. The method of claim 4, further comprising:

responsive to determining that the location events correspond to a trace of manual location events;

identifying one or more users with a similar behavior pattern;

selecting the set of location events to include events for the activity type from the one or more users;

determining, from the set of location events, a first cluster of location events associated with an activity start-time; and

determining, from the set of location events, a second cluster of location events associated with an activity end-time.

9. The method of claim 8, wherein determining the activity start-time for the activity type involves computing an average time value for the first cluster;

and

wherein determining the activity end-time for the activity type involves computing an average time value for the second cluster.

10. The method of claim 8, wherein identifying the one or more users with a similar behavior pattern involves determining that the one or more users have similar parameters for one or more of:

a work schedule;

a commute schedule;

a sleep schedule;

a geographic location;

a job title;

a career type; and

an activity type.

11. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method, the method comprising:

selecting, from a plurality of location events, a set of location events associated with an activity type, wherein a respective location event indicates a time at which a user performed an activity related to the activity type;

determining, from the set of location events, an activity start-time and an activity end-time for the activity type; and

computing an activity duration for the activity type based on the determined activity start-time and the activity end-time.

12. The storage medium of claim 11, wherein the location event also indicates at least one of:

a user identifier;

a geographic location;

a text description of a location;

a venue identifier;

a venue type;

a text description of a venue;

an activity identifier;

an activity type; and

a text description of the activity.

13. The storage medium of claim 11, wherein selecting the set of location events associated with the activity type involves determining whether the user'"'"'s location events correspond to a trace of periodic location events or a trace of manual location events.

14. The storage medium of claim 13, wherein the method further comprises:

responsive to determining that the location events correspond to a trace of periodic location events;

detecting, from the plurality of location events, one or more location-event sequences, wherein a respective location-event sequence includes location events that correspond to a common activity; and

selecting the set of location events to include a sequence of location events for one or more activities of the activity type.

15. The storage medium of claim 14, wherein determining the activity start-time and activity end-time involves:

selecting the activity start-time for a respective activity based on an earliest location event for the respective activity'"'"'s location-event sequence; and

selecting the activity end-time for the respective activity based on a latest location event for the respective activity'"'"'s location-event sequence.

16. The storage medium of claim 14, wherein computing the activity duration for the activity type involves:

computing an activity duration for a respective activity based on the determined activity start-time and activity end-time for the respective activity; and

computing an average duration for the activity type based on activity durations for one or more activities of the activity type.

17. The storage medium of claim 13, wherein the method further comprises:

responsive to determining that the location events correspond to a trace of manual location events;

identifying one or more users with a similar behavior pattern;

selecting the set of location events to include events for the activity type from the one or more users;

determining, from the set of location events, a first cluster of location events associated with an activity start-time; and

determining, from the set of location events, a second cluster of location events associated with an activity end-time.

18. The storage medium of claim 17, wherein determining the activity start-time for the activity type involves computing an average time value for the first cluster;

and

wherein determining the activity end-time for the activity type involves computing an average time value for the second cluster.

19. An apparatus comprising:

an event-selecting module to select, from a plurality of location events, a set of location events associated with an activity type, wherein a respective location event indicates a time at which a user performed an activity related to the activity type;

an activity-analyzing module to determine, from the set of location events, an activity start-time and an activity end-time for the activity type; and

a duration-computing module to compute an activity duration for the activity type based on the determined activity start-time and the activity end-time.

20. The apparatus of claim 19, wherein while selecting the set of location events associated with the activity type, the event-selecting module is further configured to determine whether the user'"'"'s location events correspond to a trace of periodic location events or a trace of manual location events.

21. The apparatus of claim 20, wherein responsive to the event-selecting module determining that the location events correspond to a trace of periodic location events, the event-selecting module is further configured to:

detect, from the plurality of location events, one or more location-event sequences, wherein a respective location-event sequence includes location events that correspond to a common activity; and

select the set of location events to include a sequence of location events for one or more activities of the activity type.

22. The apparatus of claim 21, wherein while determining the activity start-time and activity end-time, the activity-analyzing module is further configured to:

select the activity start-time for a respective activity based on an earliest location event for the respective activity'"'"'s location-event sequence; and

select the activity end-time for the respective activity based on a latest location event for the respective activity'"'"'s location-event sequence.

23. The apparatus of claim 21, wherein while computing the activity duration for the activity type, the duration-computing module is further configured to:

compute an activity duration for a respective activity based on the determined activity start-time and activity end-time for the respective activity; and

compute an average duration for the activity type based on activity durations for one or more activities of the activity type.

24. The apparatus of claim 20, wherein responsive to the event-selecting module determining that the location events correspond to a trace of manual location events, the event-selecting module is further configured to:

identify one or more users with a similar behavior pattern; and

select the set of location events to include events for the activity type from the one or more users;

determining, from the set of location events, a first cluster of location events associated with an activity start-time; and

determining, from the set of location events, a second cluster of location events associated with an activity end-time.

25. The apparatus of claim 24, wherein while determining the activity start-time for the activity type, the activity-analyzing module is further configured to compute an average time value for the first cluster;

and

wherein while determining the activity end-time for the activity type, the activity-analyzing module is further configured to compute an average time value for the second cluster.