System for characterizing geographical locations based on multi sensors anonymous data sources

• US 9,609,483 B2
• Filed: 07/11/2014
• Issued: 03/28/2017
• Est. Priority Date: 07/15/2013
• Status: Active Grant

• Current Assignee: Deutsche Telekom AG
• Original Assignee: Deutsche Telekom AG
• Inventors: Elovici, Yuval, Chizi, Bark, Binun, Alexander, Bixenspaner, Moshe, Ackerman, Pavel, Mimran, Dudu
• Primary Examiner(s): MITCHELL, NATHAN A
• Application Number:
  US14/328,781
• Publication Number:
  US 20150018020A1
• Time in Patent Office: 991 Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 67/306 User profiles
• 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 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/08 User group management

Abstract

System and method for characterizing a geographical location by finding the behavioral patterns of groups of users of mobile devices being in the vicinity of the geographical location. Accordingly, after an examined Point of Interest is determined, signaling data related to the users is aggregated over a limited period of time, from the cellular network they use. The location, including change in the location of the users, is continuously calculated and cross-checked with anonymous multi-sensors with data records from external data sources. Then the mobility patterns of users are derived and the users are classified to groups according to the cross-checking results. Finally, the geographical location is characterized according to the mobility patterns and the classification.

Claims

1. A privacy complying computerized method for characterizing a geographical location by finding the behavioral patterns of groups of users of mobile devices being in the vicinity of said geographical location, comprising:

a) selecting a Point of Interest (POI) to be examined;

b) by a processor enabled computerized device comprising an electronic privacy watcher device;

i) receiving at a first time, user-specific signaling data transmitted from a transceiver of one or more base stations in the vicinity of said POI which is related to all communication events involving a mobile device of each of said users within a corresponding cellular network and in the vicinity of said POI, wherein said received signaling data includes an identifier of each of said mobile devices and is stored in a secured address of a memory device of said privacy watcher device which can be accessed only by said processor;

ii) continuously extracting from said received signaling data a location, including changes in the location, of each of said users as indicated by the identifier of a corresponding mobile device, to produce location data;

iii) associating the produced location data with a map to identify one or more known points of said map in the vicinity of said POI which have been frequented by each of said users within a second period of time prior to said first time;

iv) deriving a mobility pattern of users by generating a path coinciding with each of said known points that have been frequented thereby within said second period of time;

v) aggregating a plurality of said mobility patterns into groups; and

vi) characterizing said POI according to said mobility patterns; and

c) by said privacy watcher device, permanently erasing the identifier of each of said mobile devices from said memory device by a secure deletion at a predetermined third time following said first time.

2. The method according to claim 1, wherein the aggregated mobility patterns are related to a group of users who have frequented a common path in a same direction within said second period of time.

3. The method according to claim 1, wherein the multi sensors data sources are selected from the group consisting of:

Call Data Records (CDRs);

Infrastructure data including geographical properties;

Location Data Records (LDRs);

CRM data;

Static Geographical Data Sets; and

Dynamical datasets that are of a geographical nature.

4. The method according to claim 1, wherein the mobility patterns of users are summed during aggregation by using statistical models, while periodically re-summing said mobility patterns.

5. The method according to claim 4, wherein the statistical models are selected from the group consisting of:

Voronoi spaces; and

server estimation method.

6. The method according to claim 1, wherein the aggregation is selected from the group consisting of:

aggregations of movements; and

aggregations of analyzed properties.

7. The method according to claim 1, wherein the point of interest is selected from the group consisting of:

a street;

a particular address; and

a shop.

8. The method according to claim 1, further comprising the step of cross-checking the location data with anonymous multi-sensors having data records from external data sources in order to increase accuracy in producing the location data.

9. A privacy complying computerized system for characterizing a geographical locations by finding the behavioral patterns of groups of users of mobile devices being in the vicinity of said geographical location, comprising:

a) a plurality of mobile devices located in the vicinity of a point of interest (POI) to be examined;

b) one or more base stations in the vicinity of said POI which are configured to generate user-specific signaling data related to all communication events involving each of said plurality of mobile devices within a corresponding cellular network and in the vicinity of said POI, wherein said signaling data includes an identifier of each of said mobile devices;

c) a base station radio-frequency (RF) transceiver for selectively transmitting said generated signaling data;

d) a computerized device for receiving and processing said signaling data, comprising;

i) an RF receiver configured to receive said transmitted signaling data;

ii) a secured database in which is storable said received user specific signaling data at a first time;

iii) a processor having sole authorization for accessing said database;

iv) a storage device coupled to said processor which is configured to store an aggregation and fusion engine that, when executed by said processor, causes said processor to, (1) continuously extract from said received signaling data a location, including changes in the location, of each of said users as indicated by the identifier of a corresponding mobile device, (2) produce location data, (3) associate the produced location data with a map to identify one or more known points of said map in the vicinity of said POI which have been frequented by each of said users within a second period of time prior to said first time, (4) determine a mobility pattern of each of said users by generating a path coinciding with each of said known points that have been frequented thereby within said second period of time, (5) aggregate a plurality of said mobility patterns into groups, and (6) characterize said POI according to said mobility patterns; and

v) an electronic privacy watcher device comprising an electronic timer, for permanently erasing the identifier of each of said mobile devices from said database by a secure deletion at a predetermined third time following said first time.

10. The system according to claim 9, further comprising a plurality of anonymous multi-sensors having data records from external data sources, wherein the computerized device is in data communication with said plurality of multi-sensors, wherein the processor is also configured to extract data from one or more of said records for use in increasing accuracy while producing the location data.

11. The system according to claim 10, wherein the aggregation and fusion engine, when executed by the processor, also causes the processor to connect different data types of the external data sources which were aggregated into a unified data set.

12. The system according to claim 11, wherein the processor comprises an actionable insight provider for receiving and processing said unified data set and for providing insights regarding the determined mobility patterns or the aggregated groups of mobility patterns.

13. The system according to claim 12, wherein the actionable insight provider includes:

a) a periodical reports module for issuing periodical reports that provide an answer to predefined business questions;

b) a Report Generator module, for allowing enable the user to create individual reports and to formulates individual business questions;

c) an Algorithm Engine module for supporting insight for predicting, verifying and discovering behavioral patterns;

d) an Alerts Mechanism module, for allowing the user to define an event and to attached an alert to it; and

e) a Visual Exploration Interface module, for discovery and exploration by allowing the user to browse all outputs from the insight provider in order to gain more knowledge.

14. The system according to claim 10, wherein the external data sources are selected from the group consisting of:

Call Data Records (CDRs);

Infrastructure data including geographical properties;

Location Data Records (LDRs);

CRM data;

Static Geographical Data Sets; and

Dynamical datasets that are of a geographical nature.