A SYSTEM FOR DETERMINING WHETHER MOBILE SUBSCRIBERS ARE LINE HOLDERS OR NOT

• US 20190320072A1
• Filed: 12/11/2017
• Published: 10/17/2019
• Est. Priority Date: 12/12/2016
• Status: Active Grant

• Current Assignee: Turkcell Teknoloji Arastirma ve Gelistirme Anonim Sirketi (Turkcell Iletisim Hizmetleri A.S.)
• Original Assignee: Turkcell Teknoloji Arastirma ve Gelistirme Anonim Sirketi (Turkcell Iletisim Hizmetleri A.S.)
• Inventors: CANAK, Caner, ONES, Onur
• Granted Patent: US 11,140,272 B2
• Time in Patent Office: Days
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04M 15/58 based on statistics of usage or network monitoring
• CPC Class Codes: H04M 15/61 based on the service used
• CPC Class Codes: H04M 15/725 by the operator
• CPC Class Codes: H04M 15/755 Account identification
• CPC Class Codes: H04M 15/7652 shared by users
• 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

Abstract

The present invention relates to a system for determining whether subscribers of mobile network operators are line holders for the line being used by them or not. The data collection unit, the data interpretation unit, the learning unit, the estimation unit and the database are included within the inventive system.

Claims

1. A system (1) for determining whether mobile subscribers are line holders or not;

comprising

at least data collection unit (2) which collects data about subscribers'"'"' usages of mobile network, fixed network and value-added services from different data sources (V);

at least one data interpretation unit (3);

at least one data learning unit (4);

at least one estimation unit (5) which realizes an estimation about whether the subscribers are line holders or not by running the model generated by the data learning unit (4) for the subscribers and can transfer the estimation results to various terminal systems (U);

at least one database (51) which keeps the data about the estimations managed by the estimation unit (5) and realized by the estimation unit (5);

and characterized by

at least one data interpretation unit (3) whereto the data collected by the data collection unit (2) are transferred and which enriches the data by carrying out summarization and interpretation transactions on these data and generates meaningful variables for each subscriber;

at least one data learning unit (4) which carries out learning transaction by means of machine learning methods on the data of the type determined by the data interpretation unit (3) and generates an estimation method;

2. A system (1) according to claim 1;

characterized by the data collection unit (2) which collects history of network performance indicators;

location history of subscribers that emerge with their signaling and call detail information occurring by their mobile network usages;

data about their mobile data usages;

data of TV and entertainment usage and purchase that is received from fixed and mobile network system;

history of music usage;

data about data usages performed over fixed networks;

information of the mobile device that is used by the subscriber;

data about club, classification, membership information of the subscribers on the mobile network operator from data sources (V).

3. A system (1) according to claim 1;

characterized by the data interpretation unit (3) which determines POI (Point-of-Interest) information (such as university, hospital, plaza, AVM, concert, stadium) over the location history collected by the data collection unit (2) and enriches these location information.

4. A system (1) according to claim 1;

characterized by the data interpretation unit (3) which analyses the data about the mobile data usage collected by the data collection unit (2) and the data about the data usages realized over the fixed networks and determines over which applications or sites these data usages are realized by exhibiting fields of these applications and sites (such as game, social media, health, music).

5. A system (1) according to claim 1;

characterized by the data learning unit (4) which carries out learning transaction by means of regression method that is one of machine learning methods.

6. A system (1) according to claim 1;

characterized by the data learning unit (4) which periodically realizes learning and model development applications.

7. A system (1) according to claim 1;

characterized by the estimation unit (5) which has the quality to run the estimation model for all subscribers or a certain group of subscribers.

8. A system (1) according to claim 1;

characterized by the estimation unit (5) which has the quality to realize the estimation transaction again for all subscribers or one or several subscriber group in the event that the estimation model changes/is updated as a result of the transactions realized by the data learning unit (4).

9. A system (1) according to claim 1;

characterized by the estimation unit (5) which has the quality to test the model created by the data learning unit (4) by using a control data and to determine whether the model has exceeded the correct estimation threshold value or not.

10. A system (1) according to claim 1;

characterized by the data learning unit (4) which creates an estimation model wherein environmental factors are also taken into account together with information of calling/being called and information of social network analysis.