NETWORK RESOURCE ALLOCATION BASED UPON NETWORK SERVICE PROFILE TRAJECTORIES

• US 20180212837A1
• Filed: 01/25/2017
• Published: 07/26/2018
• Est. Priority Date: 01/25/2017
• Status: Active Application

• Current Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Original Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Inventors: Kalluri, Sudhakar, Hercules, Learie
• Application Number:
  US15/415,516
• Publication Number:
  US 20180212837A1
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: H04L 41/0213 Standardised network management protocols, e.g. simple network management protocol [SNMP]
• CPC Class Codes: H04L 41/0886 Fully automatic configuration
• CPC Class Codes: H04L 41/145 involving simulating, designing, planning or modelling of a network
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 47/808 User-type aware
• CPC Class Codes: H04L 47/83 based on usage prediction

Abstract

A method may include a processor of a telecommunication service provider network receiving a training data set including service profile trajectories for subscribers of the network, each including a network service profile of a subscriber over a plurality of time periods, where for a given time period, each network service profile includes indications of whether a subscriber is subscribed to a plurality of network services. The processor may further create a predictive model based upon the training data set to predict whether a subject subscriber will be subscribed to a given network service at a designated future time period, receive a service profile trajectory for the subject subscriber, apply the service profile trajectory to the predictive model to generate a prediction of whether the subject subscriber will be subscribed to the given network service at the designated future time period, and allocate a network resource based upon the prediction.

Claims

1. A device, comprising:

a processor of a telecommunication service provider network; and

a computer-readable storage medium storing instructions which, when executed by the processor, cause the processor to perform operations, the operations comprising;

receiving a training data set comprising network service profile trajectories for a plurality of subscribers of the telecommunication service provider network, wherein each of the network service profile trajectories includes a network service profile of one of the plurality of subscribers over a plurality of time periods, wherein for a given time period of the plurality of time periods, each of the network service profiles includes indications of whether a subscriber is subscribed to a plurality of network services of the telecommunication service provider network during the given time period;

creating a predictive model based upon the training data set to predict whether a subject subscriber will be subscribed to a given network service of the plurality of network services at a designated future time period;

receiving a network service profile trajectory for the subject subscriber;

applying the network service profile trajectory for the subject subscriber to the predictive model to generate a prediction of whether the subject subscriber will be subscribed to the given network service at the designated future time period; and

allocating a network resource of the telecommunication service provider network based upon the prediction of whether the subject subscriber will be subscribed to the given network service at the designated future time period.

2. The device of claim 1, wherein the network resource comprises a virtual machine deployed on network function virtualization infrastructure of the telecommunication service provider network.

3. The device of claim 1, wherein the network resource comprises a remote radio head or a baseband unit.

4. The device of claim 1, wherein the network resource comprises a marketing automation platform.

5. The device of claim 4, wherein the marketing automation platform is to present an offer to the subject subscriber to maintain a subscription to the given network service, when the prediction is a prediction that the subject subscriber will not be subscribed to the given network service at the designated future time period and the subject subscriber was previously subscribed to the given network service.

6. The device of claim 5, wherein the offer is presented to the subject subscriber prior to the designated future time period.

7. The device of claim 4, wherein the marketing automation platform is to present an offer to the subject subscriber to subscribe to the given network service, when the prediction is a prediction that the subject subscriber will be subscribed to the given network service at the designated future time period and the subject subscriber was not previously subscribed to the given network service.

8. The device of claim 7, wherein the offer is presented to the subject subscriber prior to the designated future time period.

9. The device of claim 1, wherein the plurality of time periods includes time periods following a release of the given network service as a new service.

10. The device of claim 1, wherein the plurality of network services comprises at least two of:

a wireless phone service;

a wireless data service;

a home phone service;

a home broadband data service;

a home television service;

a satellite television service;

a satellite data service;

a data storage service;

or

a home security monitoring service.

11. The device of claim 1, wherein the training data set further comprises customer demographic data over the plurality of time periods for the plurality of subscribers, wherein the predictive model is further based upon the customer demographic data over the plurality of time periods.

12. The device of claim 11, wherein the customer demographic data comprises at least one of:

a home address;

a zip code;

a region;

a number of household members;

a composition of a household;

an income tier;

a number of network-connected devices utilized by a customer;

or

an occupation.

13. The device of claim 1, wherein the training data set further comprises service usage data over the plurality of time periods for the plurality of subscribers, wherein the predictive model is further based upon the service usage data over the plurality of time periods.

14. The device of claim 13, wherein the service usage data comprises at least one of:

information regarding trouble tickets generated with respect to telecommunication services for a subscriber;

a number of phone calls;

a number of long distance calls;

an average call duration;

a number of voice call minutes utilized;

a number of text messages;

a data volume utilized;

a record of excess network resource utilization;

a television channel viewed;

a program viewed;

a media content accessed;

or

an application downloaded.

15. The device of claim 1, wherein the predictive model comprises a binary classifier or a decision tree algorithm.

16. The device of claim 1, wherein the predictive model comprises a distance-based classifier.

17. The device of claim 1, wherein the network service profile trajectory for the subject subscriber comprises a network service profile of the subject subscriber over a set of time periods commensurate in an overall duration with the plurality of time periods.

18. The device of claim 1, wherein the operations further comprise:

applying additional network service profile trajectories for a plurality of additional subscribers to the predictive model to generate a plurality of predictions of whether the plurality of additional subscribers will be subscribed to the given network service at the designated future time period, wherein the allocating the network resource of the telecommunication service provider network is further based upon the plurality of predictions of whether the plurality of additional subscribers will be subscribed to the given network service at the designated future time period.

19. A method, comprising:

receiving, by a processor of a telecommunication service provider network, a training data set comprising network service profile trajectories for a plurality of subscribers of the telecommunication service provider network, wherein each of the network service profile trajectories includes a network service profile of one of the plurality of subscribers over a plurality of time periods, wherein for a given time period of the plurality of time periods, the network service profiles includes indications of whether a subscriber is subscribed to a plurality of network services of the telecommunication service provider network during the given time period;

creating, by the processor, a predictive model based upon the training data set to predict whether a subject subscriber will be subscribed to a given network service of the plurality of network services at a designated future time period;

receiving, by the processor, a network service profile trajectory for the subject subscriber;

applying, by the processor, the network service profile trajectory for the subject subscriber to the predictive model to generate a prediction of whether the subject subscriber will be subscribed to the given network service at the designated future time period; and

allocating, by the processor, a network resource of the telecommunication service provider network based upon the prediction of whether the subject subscriber will be subscribed to the given network service at the designated future time period.

20. A non-transitory computer-readable storage medium storing instructions which, when executed by a processor of a telecommunication service provider network, cause the processor to perform operations, the operations comprising:

receiving a training data set comprising network service profile trajectories for a plurality of subscribers of the telecommunication service provider network, wherein each of the network service profile trajectories includes a network service profile of one of the plurality of subscribers over a plurality of time periods, wherein for a given time period of the plurality of time periods, the network service profiles includes indications of whether a subscriber is subscribed to a plurality of network services of the telecommunication service provider network during the given time period;

creating a predictive model based upon the training data set to predict whether a subject subscriber will be subscribed to a given network service of the plurality of network services at a designated future time period;

receiving a network service profile trajectory for the subject subscriber;

applying the network service profile trajectory for the subject subscriber to the predictive model to generate a prediction of whether the subject subscriber will be subscribed to the given network service at the designated future time period; and

allocating a network resource of the telecommunication service provider network based upon the prediction of whether the subject subscriber will be subscribed to the given network service at the designated future time period.