Determining a size of a significant user location

• US 9,843,894 B2
• Filed: 09/30/2014
• Issued: 12/12/2017
• Est. Priority Date: 05/30/2014
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Marti, Lukas M., Dal Santo, Michael P., Huang, Ronald K., Cao, Lili
• Primary Examiner(s): Ngo, Chuong A
• Application Number:
  US14/502,464
• Publication Number:
  US 20150350828A1
• Time in Patent Office: 1,169 Days
• CPC Class Codes: G06F 9/54 Interprogram communication
• CPC Class Codes: G06Q 10/1095 Meeting or appointment
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal
• CPC Class Codes: H04L 67/55 Push-based network services
• CPC Class Codes: H04M 1/72451 according to schedules, e.g. using calendar applications
• CPC Class Codes: H04M 1/72457 according to geographic location
• 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 4/024 Guidance services
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/30 Services specially adapted for particular environments, situations or purposes

Abstract

Systems, methods, and program products for providing services to a user by a mobile device based on the user'"'"'s daily routine of movement. The mobile device determines whether a location cluster indicates a significant location for the user based on one or more hints that indicate an interest of the user in locations in the cluster. The mobile device can perform adaptive clustering to determine a size of area of the significant location based on how multiple locations converge in the location cluster. The mobile device can provide location-based services for calendar items, including predicting a time of arrival at an estimated location of a calendar item. The mobile device can provide various services related to a location of the mobile device or a significant location of the user through an application programming interface (API).

Claims

1. A method comprising:

determining, by one or more processors, that locations of a mobile device converge into a location cluster that indicates a location of interest of a user of the mobile device;

determining a convergence rate of the locations in the location cluster based on a statistical deviation among the locations in the location cluster and a period of time during which the locations converge into the location cluster;

determining a size of the location cluster based on the convergence rate; and

storing the size in association with the location cluster.

2. The method of claim 1, wherein the locations of the mobile device are recorded during a plurality of time periods, each time period being disconnected with another time period, a total amount of time of the time periods satisfying a pre-specified convergence threshold amount of time.

3. The method of claim 2, wherein determining the convergence rate comprises determining the convergence rate using locations recorded in a present time period and each prior time period.

4. The method of claim 1, wherein determining that the locations converge into a location cluster comprises:

determining an initial location indicating an entry into the location cluster;

receiving a series of a plurality of subsequent locations;

determining whether each respective subsequent location is included in the location cluster using a statistical filter configured to filter out outliers; and

determining that the locations converge into the location cluster upon determining at least a portion of the subsequent locations are included in the location cluster.

5. The method of claim 1, comprising, after determining the size of the location cluster, adjusting the size according to additional locations of the mobile device, wherein an increased convergence among the additional locations reduces the size of the location cluster.

6. The method of claim 1, comprising designating the location of interest as a state in a state model for estimating a place that the user is likely to move to at a future time and for providing predictive user assistance according to the estimated place, wherein the location of interest is associated with the size of the location cluster and representable in a virtual map by a marker having a display size corresponding to the size of the location cluster.

7. The method of claim 6, wherein determining the convergence rate, determining the size of the location cluster, and designating the location of interest as the state in the state model occur in real time while the mobile device determines and records locations of the mobile device.

8. The method of claim 1, comprising:

identifying a venue from map data, the venue having a location that matches the location of interest and having a size that matches the size of the location cluster; and

snapping the location of interest to the venue, including designating a shape of the venue as a shape of the location of interest.

9. A non-transitory computer-readable medium storing instructions that, upon execution by one or more computer processors, cause the one or more computer processors to perform operations comprising:

determining, by one or more processors, that locations of a mobile device converge into a location cluster that indicates location of interest of a user of the mobile device;

determining a convergence rate of the location cluster based on a statistical deviation among the locations in the location cluster and a period of time during which the locations converge into the location cluster;

determining a size of the location cluster based on the convergence rate; and

storing the size in association with the location cluster.

10. The non-transitory computer-readable medium of claim 9, wherein the locations of the mobile device are recorded during a plurality of time periods, each time period being disconnected with another time period, a total amount of time of the time periods satisfying a pre-specified convergence threshold amount of time.

11. The non-transitory computer-readable medium of claim 10, wherein determining the convergence rate comprises determining the convergence rate using locations recorded in a present time period and each prior time period.

12. The non-transitory computer-readable medium of claim 9, wherein determining that the locations converge into a location cluster comprises:

determining an initial location indicating an entry into the location cluster;

receiving a series of a plurality of subsequent locations;

determining whether each respective subsequent location is included in the location cluster using a statistical filter configured to filter out outliers; and

determining that the locations converge into the location cluster upon determining at least a portion of the subsequent locations are included in the location cluster.

13. The non-transitory computer-readable medium of claim 9, the operations comprising, after determining the size of the location cluster, adjusting the size according to additional locations of the mobile device, wherein an increased convergence among the additional locations reduces the size of the location cluster.

14. The non-transitory computer-readable medium of claim 9, the operations comprising designating the location of interest as a state in a state model for estimating a place that the user is likely to move to at a future time and for providing predictive user assistance according to the estimated place, wherein the location of interest is associated with the size of the location cluster and representable in a virtual map by a marker having a display size corresponding to the size of the location cluster.

15. The non-transitory computer-readable medium of claim 14, wherein determining the convergence rate, determining the size of the location cluster, and designating the location of interest as the state in the state model occur in real time while the mobile device determines and records locations of the mobile device.

16. The non-transitory computer-readable medium of claim 9, the operations comprising:

identifying a venue from map data, the venue having a location that matches the location of interest and having a size that matches the size of the location cluster; and

snapping the location of interest to the venue, including designating a shape of the venue as a shape of the location of interest.

17. A system, comprising:

one or more processors; and

a non-transitory computer-readable medium storing instructions that, upon execution by one or more computer processors, cause the one or more computer processors to perform operations comprising;

determining, by the one or more computer processors, that locations of a mobile device converge into a location cluster that indicates a location of interest of a user of the mobile device;

determining a convergence rate of the location cluster based on a statistical deviation among the locations in the location cluster and a period of time during which the locations converge into the location cluster;

determining a size of the location cluster based on the convergence rate; and

storing the size in association with the location cluster.

18. The system of claim 17, wherein the locations of the mobile device are recorded during a plurality of time periods, each time period being disconnected with another time period, a total amount of time of the time periods satisfying a pre-specified convergence threshold amount of time.

19. The system of claim 18, wherein determining the convergence rate comprises determining the convergence rate using locations recorded in a present time period and each prior time period.

20. The system of claim 17, wherein determining that the locations converge into a location cluster comprises:

determining an initial location indicating an entry into the location cluster;

receiving a series of a plurality of subsequent locations;

determining whether each respective subsequent location is included in the location cluster using a statistical filter configured to filter out outliers; and

determining that the locations converge into the location cluster upon determining at least a portion of the subsequent locations are included in the location cluster.

21. The system of claim 17, the operations comprising, after determining the size of the location cluster, adjusting the size according to additional locations of the mobile device, wherein an increased convergence among the additional locations reduces the size of the location cluster.

22. The system of claim 17, the operations comprising designating the location of interest as a state in a state model for estimating a place that the user is likely to move to at a future time and for providing predictive user assistance according to the estimated place, wherein the location of interest is associated with the size of the location cluster and representable in a virtual map by a marker having a display size corresponding to the size of the location cluster.

23. The system of claim 22, wherein determining the convergence rate, determining the size of the location cluster, and designating the location of interest as the state in the state model occur in real time while the mobile device determines and records locations of the mobile device.

24. The system of claim 17, the operations comprising:

identifying a venue from map data, the venue having a location that matches the location of interest and having a size that matches the size of the location cluster; and

snapping the location of interest to the venue, including designating a shape of the venue as a shape of the location of interest.