Modifying a user'S contribution to an aggregate profile based on time between location updates and external events

• US 8,825,074 B2
• Filed: 12/23/2009
• Issued: 09/02/2014
• Est. Priority Date: 02/02/2009
• Status: Active Grant

• Current Assignee: Stripe, Inc.
• Original Assignee: Waldeck Technology, LLC (Concert Technology Corporation)
• Inventors: Petersen, Steven L., Kandekar, Kunal
• Primary Examiner(s): MILLER, BRANDON J
• Application Number:
  US12/645,544
• Publication Number:
  US 20100197319A1
• Time in Patent Office: 1,714 Days
• Field of Search: 4554561-457, 455/459, 455/463, 455/414.1, 455418-420, 455517-519
• US Class Current: 455/456.1
• 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 that maintain current locations for users of a number of mobile devices and compute confidence levels for the current locations of the users are provided. In one embodiment, the confidence levels of the users are computed as a function of amounts of time since location updates were received for the users. In addition, detection of location confidence events may be used to modify the confidence levels for the current locations of the users. The location confidence events may include positive location confidence events that result in an increase in the confidence levels of the current locations of corresponding users, negative location confidence events that result in a decrease in the confidence levels of the current locations of corresponding users, or both. Once computed, the confidence levels of the current locations of the users may then be utilized in a desired manner.

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 crowd including at least a subset of the plurality of users based on the current locations of the at least the subset of the plurality of users;

generating crowd data regarding the crowd;

computing a confidence level of the current location of at least one user of the at least the subset of the plurality of users based on an amount of time since a location update for the current location of the at least one user was received; and

utilizing the confidence level of the current location of the at least one user to perform an action associated with the crowd data.

2. The method of claim 1 wherein computing the confidence level of the current location of the at least one user comprises computing the confidence level of the current location of the at least one user based on an algorithm wherein the confidence level decreases as a function of the amount of time since the location update for the current location of the at least one user was received.

3. The method of claim 2 wherein an original confidence level of the current location of the at least one user is set to an original value upon receipt of the location update, and computing the confidence level of the current location of the at least one user based on the algorithm wherein the confidence level decreases as a function of the amount of time since the location update for the current location of the at least one user was received comprises:

at a first time after the location update for the current location of the at least one user was received, computing a first confidence level of the current location of the at least one user as a function of the original confidence level and an amount of time between the first time and a time at which the location update for the current location of the at least one user was received; and

at a second time after the location update for the current location of the at least one user is received, computing a second confidence level of the current location of the at least one user as a function of the first confidence level and an amount of time between the first time and the second time.

4. The method of claim 2 wherein the confidence level linearly decreases as a function of the amount of time since the location update for the current location of the at least one user was received.

5. The method of claim 4 wherein the confidence level linearly decreases as a function of the amount of time since the location update for the current location of the at least one user was received and a predefined decrease rate selected such that the confidence level reaches a minimum value after a desired amount of time has expired since the location update for the current location of the at least one user was received.

6. The method of claim 1 wherein computing the confidence level of the current location of the at least one user comprises computing the confidence level of the current location of the at least one user of the plurality of users based on the amount of time since the location update for the current location of the at least one user was received and detection of one or more location confidence events.

7. The method of claim 6 wherein computing the confidence level of the current location of the at least one user of the plurality of users based on the amount of time since the location update for the current location of the at least one user was received and detection of the one or more location confidence events comprises decreasing the confidence level of the current location of the at least one user by a predefined amount in response to detection of a negative location confidence event.

8. The method of claim 6 wherein computing the confidence level of the current location of the at least one user of the plurality of users based on the amount of time since the location update for the current location of the at least one user was received and detection of the one or more location confidence events comprises increasing the confidence level of the current location of the at least one user by a predefined amount in response to detection of a positive location confidence event.

9. The method of claim 6 wherein the confidence level of the current location of the at least one user linearly decreases as a function of the amount of time since the location update for the current location of the at least one user was received and a decrease rate, and computing the confidence level of the current location of the at least one user of the plurality of users based on the amount of time since the location update for the current location of the at least one user was received and detection of the one or more location confidence events comprises:

detecting a negative location confidence event;

increasing the decrease rate to provide a new decrease rate in response to detecting the negative location confidence event; and

computing the confidence level of the current location of the at least one user as a function of the amount of time since the location update for the current location of the at least one user was received and the new decrease rate.

10. The method of claim 6 wherein the confidence level of the current location of the at least one user linearly decreases as a function of the amount of time since the location update for the current location of the at least one user was received and a decrease rate, and computing the confidence level of the current location of the at least one user of the plurality of users based on the amount of time since the location update for the current location of the at least one user was received and detection of the one or more location confidence events comprises:

detecting a positive location confidence event;

decreasing the decrease rate to provide a new decrease rate in response to detecting the positive location confidence event; and

computing the confidence level of the current location of the at least one user as a function of the amount of time since the location update for the current location of the at least one user was received and the new decrease rate.

11. The method of claim 6 wherein computing the confidence level of the current location of the at least one user of the plurality of users based on the amount of time since the location update for the current location of the at least one user was received and detection of the one or more location confidence events comprises:

detecting a positive location confidence event; and

in response to detecting the positive location confidence event, computing the confidence level of the current location of the at least one user as a function of the amount of time since the location update for the current location of the at least one user was received and an increase rate.

12. The method of claim 1 further comprising resetting the confidence level of the current location of the at least one user in response to receiving a new location update for the current location of the at least one user.

13. The method of claim 12 wherein resetting the confidence level comprises resetting the confidence level to a value of 1.

14. The method of claim 1 wherein:

forming the crowd comprises including three or more users from the plurality of users based on the current locations of at least the three or more users; and

wherein;

computing the confidence level of the current location of the at least one user comprises computing confidence levels for the current locations of the three or more users in the crowd based on amounts of time since location updates for the current locations of the three or more users in the crowd were received; and

utilizing the confidence level of the current location of the at least one user comprises determining a quality level for the crowd data for the crowd based on the confidence levels of the three or more users in the crowd.

15. The method of claim 14 wherein the crowd data is an aggregate profile for the crowd.

16. The method of claim 14 wherein determining the quality level for the crowd data for the crowd comprises combining the confidence levels of the three or more users in the crowd to provide the quality level for the crowd data for the crowd.

17. The method of claim 1 further comprising:

forming a crowd including three or more users from the plurality of users based on the current locations of at least the three or more users; and

generating an aggregate profile for the crowd that includes one or more keywords;

wherein;

computing the confidence level of the current location of the at least one user comprises computing confidence levels for the current locations of the three or more users in the crowd based on amounts of time since location updates for the current locations of the three or more users in the crowd were received; and

utilizing the confidence level of the current location of the at least one user comprises, for each keyword of the one or more keywords in the aggregate profile, determining a confidence level for the keyword based on the confidence levels of current locations of one or more users in the crowd having user profiles including the keyword.

18. The method of claim 17 wherein, for each keyword of the one or more keywords in the aggregate profile, determining the confidence level for the keyword comprises combining the confidence levels of the current locations of the one or more users in the crowd having user profiles including the keyword to provide the confidence level for the keyword.

19. 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 crowd including at least a subset of the plurality of users based on the current locations of the at least the subset of the plurality of users;

generate crowd data regarding the crowd;

compute a confidence level of the current location of at least one user of the at least the subset of the plurality of users based on an amount of time since a location update for the current location of the at least one user was received; and

utilize the confidence level of the current location of the at least one user to perform an action associated with the crowd data.

20. A non-transitory computer readable medium storing software for instructing a controller of a server to:

obtain 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;

form a crowd including at least a subset of the plurality of users based on the current locations of the at least the subset of the plurality of users;

generate crowd data regarding the crowd;

compute a confidence level of the current location of at least one user of the at least the subset of the plurality of users based on an amount of time since a location update for the current location of the at least one user was received; and

utilize the confidence level of the current location of the at least one user to perform an action associated with the crowd data.