Statistics for continuous location tracking

• US 8,880,093 B2
• Filed: 01/20/2012
• Issued: 11/04/2014
• Est. Priority Date: 01/20/2012
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Papakipos, Matthew Nicholas, Garcia, David Harry, Bayer, Ross David, Felton, Nicholas, Shaffer, Justin Alexander
• Primary Examiner(s): Khan, Suhail
• Application Number:
  US13/354,804
• Publication Number:
  US 20130190004A1
• Time in Patent Office: 1,019 Days
• Field of Search: 455/456.1, 455/404.2, 455/412.1, 455/412.2, 4554141-4143, 455/456.3, 455/457, 455/459, 455/461, 455/456.2, 455/456.6
• US Class Current: 455/456.1
• CPC Class Codes: H04L 12/1813 for computer conferences, e.g. chat rooms instant messaging H04L51/04; protocols for multimedia communication H04L65/1101; arrangements for multi-party communication H04L65/403; telephonic conference arrangements H04M3/56; television conference systems H04N7/15
• 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: H04W 24/04 Arrangements for maintaining operational condition
• CPC Class Codes: H04W 24/08 Testing, supervising or monitoring using real traffic
• 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 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

In one embodiment, one or more computing devices receive, from one or more mobile devices respectively associated with one or more users, one or more sets of data, wherein each set of data comprises: a user identifier indicating to which user the set of data corresponds; a location where the corresponding user was at; and a time when the corresponding user was at the location. The computing devices store the one or more sets of data; and extract one or more statistics from the one or more sets of data that represent behavioral pattern of at least one of the one or more users.

Claims

1. A method comprising:

by one or more computing devices, receiving, from each of a plurality of mobile computing devices associated with a plurality of users, a plurality of data sets that each comprise data identifying;

the user that the data set corresponds to;

one or more locations where the user was or is at; and

for each of the locations, a time when the user was or is at the location;

by one or more computing devices, generating one or more statistics corresponding to a particular user based at least in part on one or more of the data sets, wherein the statistics repersent behavioral patterns of the particular user;

by one or more computing devices, generating for the particular user a recommmendation for one or more sleeping activities of the particular user based at least in part on one or more of the statistics; and

by one or more computing devices, sending the recommendation to the particular user.

2. The method of claim 1, wherein one or more of the statistics are generated based at least on part on one or more amounts of time the particular user has spent at one or more of the locations.

3. The method of claim 1, wherein one or more of the statistics are generated based at least in part on one or more speeds or accelerations of the user at one or more of the locations.

4. The method of claim 1, wherein each data set further comprises, for each of the times, one or more of:

an acceleration associated with the user;

or

a speed associated with the user.

5. The method of claim 1, wherein one or more of the statistics represent how much time the particular user has spent sleeping either on average or in total during one or more periods of time.

6. The method of claim 1, wherein:

one or more of the computing devices are associated with a social-networking system; and

one or more of the users are members of the social-networking system.

13. The method of claim 1, wherein the recommendation comprises a suggestion that the user engage in more or less of one or more of the sleeping activities.

7. A system comprising:

one or more processors; and

a memory coupled to the processors comprising;

instructions executable by the processors, the processors being operable when executing the instructions to;

receive, from each of a plurality of mobile devices associated with a plurality of users, a plurality of data sets that each comprise data identifying;

the user that the set corresponds to;

one or more locations where the user was or is at; and

for each of the locations, a time when the user was or is at the location;

generate one or more statistics corresponding to a particular user based at least in part on one or more of the data sets, wherein the statistics represent behavioral patterns of the particular user;

generated for the particular user a recommenedation for one or more sleeping activities of the particular user based at least in part on one or more of the statistics; and

send the recommendation to the particular user.

8. The system of claim 7, wherein one or more of the statistics are generated based at least in part on one or more amounts of time the particular users has spent at one or more of the locations.

9. The system of claim 7, wherein one or more of the statistics are generated based at least in part on one or more speeds or accelerations of the user at one or more of the locations.

10. The system of claim 7, wherein each data set further comprises, for each of the times, one or more of:

an acceleration associated with the user;

or

a speed associated with the user.

11. The system of claim 7, wherein one or more of the statistics represent how much time the particular user has spent sleeping either on average or in total during one or more periods of time.

14. The system of claim 7, wherein the recommendation comprises a suggestion that the user engage in more or less one or more of the sleeping activities.

12. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

receive, from each of a plurality of mobile devices associated with a plurality of users, a plurality of data sets that each comprise data identifying;

the user that the data set corresponds to;

one or more locations where the user was or is at; and

for each of the locations, a time when the user was at the location;

generate one or more statistics corresponding to a particular user based at least in part on one or more of the data sets, wherein the statistics represent behavioral patterns of the particular user;

generate for the particular user a recommendation for one or more sleeping activities of the particular user based at least in part on one or more of the statistics; and

send the recommendation to the particular user.