System and method for aggregating and associating mobile device location data

• US 8,958,820 B2
• Filed: 08/02/2013
• Issued: 02/17/2015
• Est. Priority Date: 11/01/2010
• Status: Active Grant

• Current Assignee: Smith Micro Software Incorporated
• Original Assignee: Wavemarket Incorporated (Gen Digital Inc.)
• Inventors: Weiss, Andrew
• Primary Examiner(s): LE, DANH C
• Application Number:
  US13/957,900
• Publication Number:
  US 20130316734A1
• Time in Patent Office: 564 Days
• Field of Search: 455/456.1, 455/456.5, 455/418
• US Class Current: 455/456.1
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/0267 Wireless devices
• 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 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

A computer-implemented method for processing mobile device location data is provided. Location data is filtered based on age and accuracy or precision of location coordinates. Location data is partitioned based on the location coordinates into a plurality of clusters including a plurality of location data instances. The plurality of clusters are filtered based on a number of data instances in a particular cluster and a period of time over which data instances of the particular data cluster are generated. A distance is determined from a location coordinate corresponding to the data instances of one or more of the plurality of clusters to a particular predetermined location, and the one or more of the plurality of clusters are correlated with the particular predetermined location in response to the distance being less than a predetermined distance. The invention further provides a location data processing system.

Claims

1. A computer-implemented method for processing mobile device location data, the method comprising:

partitioning a collection of tuples including time stamps and location coordinates, generated via a particular mobile device associated with a user, into a plurality of clusters wherein distances between location coordinates of the tuples in each of the plurality of clusters are less than a predetermined distance;

deriving from the plurality of clusters size-based clusters, wherein the number of tuples in each size-based cluster is greater than a predetermined number;

deriving from the plurality of clusters time-based clusters, wherein a time period from a substantially earliest tuple to a substantially latest tuple in each of the time-based clusters, as indicated by respective time stamps, is greater than a predetermined length of time; and

designating at least one base location cluster from at least one of the size-based clusters and the time-base clusters wherein at least a predetermined percentage of the tuples correspond to at least one particular time window.

2. The method of claim 1, further comprising designating the at least one base location cluster as the largest size-based cluster wherein at least a predetermined percentage of the tuples correspond to the at least one particular time window.

3. The method of claim 1, further comprising for each particular one of the size-based clusters and the time-based clusters different from the at least one base location cluster, determining a centroid of the particular cluster and designating the particular cluster as a venue cluster if the centroid of the particular cluster is within a predetermined distance from a location coordinate of a known venue.

4. The method of claim 3, further comprising transmitting location-related data corresponding to the known venue to the user.

5. The method of claim 1, further comprising deriving the time-based clusters from those of the plurality of clusters not derived as size-based clusters.

6. The method of claim 1, further comprising partitioning the collection of tuples wherein the distances between location coordinates of each of the tuples in each of the plurality of clusters are less than a predetermined distance.

7. The method of claim 1, further comprising partitioning the collection of tuples wherein the distances between location coordinates of a majority of the tuples in each of the plurality of clusters are less than a predetermined distance.