Apparatus, systems, and methods for analyzing movements of target entities

• US 10,579,600 B2
• Filed: 03/13/2019
• Issued: 03/03/2020
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Foursquare Labs, Inc.
• Original Assignee: Factual Incorporated (Foursquare Labs, Inc.)
• Inventors: Bell, Tyler, Michels, Bill, Tipping, Spencer, White, Tom, Shimanovsky, Boris
• Primary Examiner(s): Zewdu, Meless N
• Application Number:
  US16/352,664
• Publication Number:
  US 20190213173A1
• Time in Patent Office: 356 Days
• CPC Class Codes: G05B 13/0265 the criterion being a learning criterion
• CPC Class Codes: G06F 16/21 Design, administration or maintenance of databases
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06F 16/235 Update request formulation
• CPC Class Codes: G06F 16/2379 Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/2386 Bulk updating operations data conversion details G06F16/258
• CPC Class Codes: G06F 16/24564 Applying rules; Deductive queries
• CPC Class Codes: G06F 16/2477 Temporal data queries
• CPC Class Codes: G06F 16/282 Hierarchical databases, e.g. IMS, LDAP data stores or Lotus Notes
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06Q 10/101 Collaborative creation, e.g. joint development of products or services
• CPC Class Codes: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 50/01 : Social networking
• CPC Class Codes: H04L 41/14 : Network analysis or design
• CPC Class Codes: H04W 16/00 : Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
• CPC Class Codes: H04W 16/24 : Cell structures
• CPC Class Codes: H04W 16/30 : Special cell shapes, e.g. doughnuts or ring cells
• CPC Class Codes: H04W 16/32 : Hierarchical cell structures
• 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 4/025 : using location based information parameters
• CPC Class Codes: H04W 4/029 : Location-based management or tracking services
• CPC Class Codes: H04W 4/50 : Service provisioning or reconfiguring
• CPC Class Codes: H04W 64/00 : Locating users or terminals or network equipment for network management purposes, e.g. mobility management
• CPC Class Codes: H04W 64/003 : locating network equipment
• CPC Class Codes: H04W 76/38 : triggered by timers
• CPC Class Codes: H04W 8/08 : Mobility data transfer
• CPC Class Codes: H04W 8/16 : selectively restricting mobility data tracking
• CPC Class Codes: H04W 8/18 : Processing of user or subscriber data, e.g. subscribed services, user preferences or user profiles; Transfer of user or subscriber data
• CPC Class Codes: H04W 88/00 : Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
• CPC Class Codes: H04W 88/02 : Terminal devices

Abstract

The present disclosure relates to apparatus, systems, and methods for providing a location information analytics mechanism. The location information analytics mechanism is configured to analyze location information to extract contextual information (e.g., profile) about a mobile device or a user of a mobile device, collectively referred to as a target entity. The location information analytics mechanism can include analyzing location data points associated with a target entity to determine features associated with the target entity, and using the features to predict attributes associated with the target entity. The set of predicted attributes can form a profile of the target entity.

Claims

1. An apparatus comprising:

a processor configured to acquire computer readable instructions stored in one or more memory devices and execute the instructions to;

process a time-series of location data points for a target entity, wherein the time-series of location data points are received from a computing device associated with the target entity;

determine one or more sessions from the time-series of location data points by grouping one or more of the time-series of location data points that are bounded in space and/or time;

determine one or more attributes associated with the target entity based on one or more of, the time-series of location data points, and the one or more sessions; and

generate at least one prediction of a future estimated location of the target entity based on the one or more attributes associated with the target entity.

2. The apparatus of claim 1, wherein the processor is further configured to execute instructions to determine an accuracy of the time-series of the location data points, and discard, based on the determined accuracy, one or more of the location data points in the time-series of the location data points.

3. The apparatus of claim 2, wherein the processor is further configured to execute instructions to determine the accuracy of the time-series of the location data points based on a time-series of the location data points associated with other target entities.

4. The apparatus of claim 3, wherein the processor is further configured to execute instructions to determine the accuracy of the time-series of the location data points at a particular time instance based on location information associated with the other target entities at the particular time instance.

5. The apparatus of claim 1, wherein the processor is further configured to execute instructions to:

determine one or more clusters of sessions based on the one or more sessions and based on a physical proximity between the sessions; and

determine the one or more attributes associated with the target entity based on the one or more sessions and the one or more clusters of sessions.

6. The apparatus of claim 5, wherein the processor is further configured to execute instructions to associate at least one of the location data points, the sessions, or the clusters of sessions with annotation information associated with a geographical location of the location data points, sessions, or clusters of sessions, and use the annotation information to determine the one or more attributes associated with the target entity.

7. The apparatus of claim 6, wherein the processor is further configured to execute instructions to determine the one or more attributes associated with the target entity based on movements of the target entity between two or more clusters of sessions.

8. The apparatus of claim 7, wherein the processor is further configured to execute instructions to determine a home location attribute based on, at least in part, statistical measures on the movements of the target entity and the annotation information associated with the target entity.

9. The apparatus of claim 5, wherein the processor is further configured to execute instructions to determine a home location attribute based on, at least in part, a likelihood that a particular location is associated with a residence.

10. The apparatus of claim 5, wherein the processor is further configured to execute instructions to determine a home location attribute based on, at least in part, timestamps of the location data points associated with the target entity.

11. The apparatus of claim 1, wherein the processor is further configured to execute instructions to determine a predictive model based on the one or more attributes, wherein the predictive model is configured to predict a behavior of the target entity in a future.

12. The apparatus of claim 1, wherein the processor is further configured to execute instructions to rank patterns of movement of the target entity, and determine the one or more attributes associated with the target entity based on the ranking of the patterns of movement of the target entity.

13. A method comprising:

processing, by a first computing device, a time-series of location data points for a target entity received from a second computing device associated with the target entity;

determining, by the first computing device, one or more sessions from the time-series of the location data points by grouping one or more of the time-series of the location data points that are bounded in space and/or time;

determining, by the first computing device, one or more attributes associated with the target entity based on one or more of, the time-series of the location data points, and the one or more sessions; and

generating, by the first computing device, at least one prediction of a future estimated location of the target entity based on the one or more attributes associated with the target entity.

14. The method of claim 13, further comprising determining an accuracy of the time-series of the location data points, and discarding, based on the determined accuracy, one or more of the location data points in the time-series of the location data points.

15. The method of claim 13, further comprising:

determining, by the first computing device, one or more clusters of sessions based on the one or more sessions and based on a physical proximity between sessions; and

determining, by the first computing device, the one or more attributes associated with the target entity based on the one or more sessions and the one or more clusters of sessions.

16. The method of claim 15, further comprising annotating the one or more clusters of sessions with annotation information associated with a geographical location of the one or more clusters of sessions, and determining the one or more attributes associated with the target entity based on the annotation information.

17. The method of claim 13, further comprising ranking, by the first computing device, patterns of movement of the target entity, and generating, by the first computing device, the at least one prediction of a future estimated location of the target entity based on a ranking of the patterns of movement of the target entity.

18. A non-transitory computer readable medium having executable instructions executable to cause a data processing apparatus to:

process a time-series of location data points for a target entity, wherein the time-series of location data points are received from a computing device associated with the target entity;

determine one or more sessions from the time-series of location data points by grouping one or more of the time-series of the location data points that are bounded in space and/or time;

determine one or more attributes associated with the target entity based on one or more of, the time-series of the location data points, and the one or more sessions; and

generate at least one prediction of a future estimated location of the target entity based on the one or more attributes associated with the target entity.

19. The non-transitory computer readable medium of claim 18, wherein the executable instructions are further executable to cause the data processing apparatus to determine an accuracy of the time-series of the location data points, and discard, based on the determined accuracy, one or more of the location data points in the time-series of the location data points.

20. The non-transitory computer readable medium of claim 18, wherein the executable instructions are further executable to cause the data processing apparatus to:

determine one or more clusters of sessions based on the one or more sessions and based on a physical proximity between the sessions; and

determine the one or more attributes associated with the target entity based on the one or more sessions and the one or more clusters of sessions.

21. The non-transitory computer readable medium of claim 18, wherein the executable instructions are further executable to cause the data processing apparatus to annotate one or more clusters of sessions with annotation information associated with a geographical location of the one or more clusters of sessions, and determine the one or more attributes associated with the target entity based on the annotation information.

22. The non-transitory computer readable medium of claim 18, wherein the executable instructions are further executable to cause the data processing apparatus to rank patterns of movement of the target entity, and determine the one or more attributes associated with the target entity based on the ranking of the patterns of movement of the target entity.