Method and apparatus for subscription adaptation

• US 10,284,729 B2
• Filed: 04/14/2014
• Issued: 05/07/2019
• Est. Priority Date: 01/15/2014
• Status: Active Grant

• Current Assignee: Telefonaktiebolaget LM Ericsson
• Original Assignee: Telefonaktiebolaget LM Ericsson
• Inventors: Da Silva, Icaro L. J., Bertze, Åsa, Fu, Jing, Wang, Yu
• Primary Examiner(s): Heiber, Shantell L
• Application Number:
  US15/110,675
• Publication Number:
  US 20160337525A1
• Time in Patent Office: 1,849 Days
• CPC Class Codes: H04L 12/1485 Tariff-related aspects
• CPC Class Codes: H04M 15/58 based on statistics of usage or network monitoring
• CPC Class Codes: H04M 15/66 Policy and charging system
• CPC Class Codes: H04M 15/80 Rating or billing plans; Tariff determination aspects
• CPC Class Codes: H04M 15/8027 based on network load situation
• CPC Class Codes: H04M 15/8033 location-dependent, e.g. business or home
• CPC Class Codes: H04M 15/8083 involving reduced rates or discounts, e.g. time-of-day reductions or volume discounts
• CPC Class Codes: H04M 15/81 Dynamic pricing, e.g. change of tariff during call
• CPC Class Codes: H04W 4/24 Accounting or billing
• 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

A subscription adaptation engine computes a network utilization metric for a plurality of resource bins, each resource bin representing the network utilization at a network location within a time slot. A subscriber utilization metric is computed for activities by a subscriber within the resource bins and a subscriber predictability metric is computed for the subscriber based on the subscriber utilization metric and network utilization metric. Subscriber profiling is performed for the subscriber based on the subscriber utilization metrics and subscriber predictability metric and a subscription profile for the subscriber is adapted, thereby enabling service charging based on the adapted subscription profile. The amount of real-time or near real-time signaling needed to show a different price for each location and/or time of a user may thereby be reduced.

Claims

1. A method for performing subscription adaptation based on network utilization in a network at a Subscription Adaptation Engine, SAE, the method comprising:

computing a network utilization metric for a plurality of resource bins of the network, wherein;

each resource bin corresponds to a respective network location and a respective time slot; and

the network utilization metric comprises network utilization data corresponding to at least one of the plurality of resource bins;

computing a subscriber utilization metric for a subscriber of the network, wherein the subscriber utilization metric comprises subscriber utilization data based on activities by the subscriber within the at least one of the plurality of resource bins;

computing a subscriber predictability metric for the subscriber based on the subscriber utilization metric and the network utilization metric;

performing subscriber profiling for the subscriber based on the subscriber utilization metric and the subscriber predictability metric; and

adapting a subscription profile for the subscriber based on the subscriber profiling, thereby enabling service charging based on the adapted subscription profile.

2. The method according to claim 1, wherein the time slots are determined as a pre-defined time window divided into a specified number of time slots.

3. The method according to claim 1, wherein the network utilization metric comprises at least one of a Composite Available Capacity in LTE, a Pair Resource Block (PRB) utilization in LTE, a code and power utilization in UMTS, a hardware/backhaul/license utilization in LTE and/or UMTS, and a plurality of active users comprising respective profiles in LTE and/or UMTS.

4. The method according to claim 1, further comprising:

temporarily modifying the network utilization metric for at least one of the plurality of resource bins to reflect an operator charging policy.

5. The method according to claim 1, further comprising:

calculating the network utilization metric for at least one carrier and/or at least one Radio Access Technology, RAT.

6. The method according to claim 1, further comprising:

obtaining the network utilization metric by integrating information from at least one carrier and/or at least one Radio Access Technology, RAT.

7. The method according to claim 1, wherein the network utilization metric is based on infrastructure energy efficiency in the network.

8. The method according to claim 1, wherein computing the subscriber utilization metric for the subscriber of the network comprises calculating a sum of a number of resource bins of the plurality of resource bins wherein the subscriber has utilized mobile broadband.

9. The method according to claim 1, wherein computing the subscriber utilization metric for the subscriber of the network comprises calculating the subscriber utilization metric for the subscriber of the network based on a volume of data utilized for mobile broadband activity in each resource bin of the plurality of resource bins by the subscriber.

10. The method according to claim 1, wherein performing subscriber profiling for the subscriber based on the subscriber utilization metric and the subscriber predictability metric comprises classifying the subscriber into a subscriber segment based on the subscriber utilization metric and the subscriber predictability metric.

11. The method according to claim 10, wherein the subscriber predictability metric comprises a subscriber predictability group classification corresponding to the subscriber based on an average network utilization corresponding to the subscriber and the at least one of the plurality of resource bins and a standard deviation of network utilization corresponding to the subscriber and the at least one of the plurality of resource bins.

12. A method according to claim 10 wherein the step of performing subscriber profiling includes segmenting into:

predictable high-network utilization subscribers when average network utilization per bin exceeds a first threshold value and standard deviation of network utilization per bin undercuts a second threshold value; and

segmenting into predictable low network utilization subscribers when average network utilization per bin undercuts the first threshold value and standard deviation of network utilization per bin undercuts the second threshold value.

13. A Subscription Adaptation Engine, SAE, for subscription adaptation based on network utilization in a network, comprising:

a processor; and

a memory comprising program code executed by the processor to;

compute a network utilization metric for a plurality of resource bins of the network, wherein;

each resource bin corresponds to a respective network location and a respective time slot; and

the network utilization metric comprises network utilization data corresponding to at least one of the plurality of resource bins;

compute a subscriber utilization metric for a subscriber of the network, wherein the subscriber utilization metric comprises subscriber utilization data based on activities by the subscriber within the at least one of the plurality of resource bins;

compute a subscriber predictability metric for the subscriber based on the subscriber utilization metric and the network utilization metric;

perform subscriber profiling for the subscriber based on the subscriber utilization metric and the subscriber predictability metric; and

adapt a subscription profile for the subscriber based on the subscriber profiling, thereby enabling service charging based on the adapted subscription profile.

14. The SAE according to claim 13, wherein the time slots are determined as a pre-defined time window divided into a specified number of time slots.

15. The SAE according to claim 13, wherein the network utilization metric comprises at least one of a Composite Available Capacity in LTE, a Pair Resource Block (PRB) utilization in LTE, a code and power utilization in UMTS, a hardware/backhaul/license utilization in LTE and/or UMTS, and a plurality of active users comprising respective profiles in LTE and/or UMTS.

16. The SAE according to claim 13, wherein the SAE is further configured to temporarily modify the network utilization metric for at least one of the plurality of resource bins to reflect an operator charging policy.

17. The SAE according to claim 13, wherein the SAE is further configured to calculate the network utilization metric for at least one carrier and/or at least one Radio Access Technology, RAT.

18. The SAE according to claim 13, wherein the SAE is further configured to obtain the network utilization metric by integrating information from at least one carrier and/or at least one Radio Access Technology, RAT.

19. The SAE according to claim 13, wherein the network utilization metric is based on infrastructure energy efficiency in the network.

20. The SAE according to claim 13, wherein the SAE is further configured to calculate the subscriber utilization metric as a sum of a number of resource bins of the plurality of resource bins wherein the subscriber has utilized mobile broadband.

21. The SAE according to claim 13, wherein the SAE is further configured to calculate the subscriber utilization metric for the subscriber of the network based on a volume of data utilized for mobile broadband activity in each resource bin of the plurality of resource bins by the subscriber.

22. The SAE according to claim 13, wherein subscriber profiling includes classifying the subscriber into a subscriber segment based on the subscriber utilization metric and the subscriber predictability metric.

23. The SAE according to claim 22, wherein the subscriber predictability metric comprises a subscriber predictability group classification corresponding to the subscriber based on an average network utilization corresponding to the subscriber and the at least one of the plurality of resource bins and a standard deviation of network utilization corresponding to the subscriber and the at least one of the plurality of resource bins.

24. The SAE according to claim 22 wherein performing subscriber profiling includes segmenting into:

predictable high-network utilization subscribers when average network utilization per bin exceeds a first threshold value and standard deviation of network utilization per bin undercuts a second threshold value; and

segmenting into predictable low network utilization subscribers when average network utilization per bin undercuts the first threshold value and standard deviation of network utilization per bin undercuts the second threshold value.

25. An Online Charging System, OCS, including the SAE according to claim 13, wherein the OCS is further configured to perform service charging based on the adapted subscription profile.

26. The OCS according to claim 25, wherein the service charging includes rating the service based on the adapted subscription profile.

27. A Subscription Adaptation Engine, SAE, for subscription adaptation, the SAE comprising:

a processor circuitry;

a memory containing instructions that, when executed by the processor circuitry, cause the Subscription Adaptation Engine to;

compute a network utilization metric for a plurality of resource bins, wherein;

each resource bin corresponds to a respective network location and a respective time slot; and

the network utilization metric comprises network utilization data corresponding to at least one of the plurality of resource bins;

compute a subscriber utilization metric for a subscriber, wherein the subscriber utilization metric comprises subscriber utilization data based on activities by the subscriber within the at least one of the plurality of resource bins;

compute a subscriber predictability metric for the subscriber based on the subscriber utilization metric and the network utilization metric;

perform subscriber profiling for the subscriber based on the subscriber utilization metric and the subscriber predictability metric; and

adapt a subscription profile for the subscriber based on the subscriber profiling, thereby enabling service charging based on the adapted subscription profile.

28. A computer program product comprising a non-transitory computer readable storage medium storing program code, which when run in a computer being configured as a Subscription Adaption Engine, the program code causes the computer to perform the following steps:

computing a network utilization metric for a plurality of resource bins, wherein;

each resource bin corresponds to a respective network location and a respective time slot; and

the network utilization metric comprises network utilization data corresponding to at least one of the plurality of resource bins;

computing a subscriber utilization metric for a subscriber, wherein the subscriber utilization metric comprises subscriber utilization data based on activities by the subscriber within the at least one of the plurality of resource bins;

computing a subscriber predictability metric for the subscriber based on the subscriber utilization metric and the network utilization metric;

performing subscriber profiling for the subscriber based on the subscriber utilization metric and the subscriber predictability metric; and

adapting a subscription profile for the subscriber based on the subscriber profiling, thereby enabling service charging based on the adapted subscription profile.