PREDICTIVE CONNECTIVITY SERVICE LAYERS

• US 20160330746A1
• Filed: 05/09/2016
• Published: 11/10/2016
• Est. Priority Date: 05/08/2015
• Status: Active Grant

• Current Assignee: Federated Wireless, Inc.
• Original Assignee: Federated Wireless, Inc.
• Inventors: TARAZI, Iyad, MEHRABANZAD, Sepehr, DAS, Deepak
• Granted Patent: US 10,764,933 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: H04B 7/18523 Satellite systems for providing broadcast service to terrestrial stations, i.e. broadcast satellite service arrangements specially adapted for satellite broadcast receiving H04H40/90; picture transmission via satellite H04N1/00103; television transmission via satellite H04N7/20
• CPC Class Codes: H04L 12/14 Charging , metering or billing arrangements for data wireline or wireless communications
• CPC Class Codes: H04L 12/66 Arrangements for connecting between networks having differing types of switching systems, e.g. gateways
• CPC Class Codes: H04L 41/026 using e-messaging for transporting management information, e.g. email, instant messaging or chat
• CPC Class Codes: H04L 41/12 Discovery or management of network topologies
• CPC Class Codes: H04L 41/40 using virtualisation of network functions or resources, e.g. SDN or NFV entities
• CPC Class Codes: H04L 41/5051 Service on demand, e.g. definition and deployment of services in real time
• CPC Class Codes: H04L 41/5087 wherein the managed service relates to voice services management of VoIP services H04M7/0081
• CPC Class Codes: H04L 41/5096 wherein the managed service relates to distributed or central networked applications
• CPC Class Codes: H04L 49/70 Virtual switches
• CPC Class Codes: H04L 5/0048 Allocation of pilot signals, i.e. of signals known to the receiver
• CPC Class Codes: H04L 61/4505 using standardised directories; using standardised directory access protocols
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/142 Managing session states for stateless protocols; Signalling session states; State transitions; Keeping-state mechanisms
• 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: H04M 15/55 for hybrid networks
• CPC Class Codes: H04M 15/66 Policy and charging system
• CPC Class Codes: H04W 12/06 Authentication
• CPC Class Codes: H04W 12/08 Access security
• CPC Class Codes: H04W 12/086 : using security domains
• CPC Class Codes: H04W 12/088 : using filters or firewalls
• CPC Class Codes: H04W 4/24 : Accounting or billing
• CPC Class Codes: H04W 4/60 : Subscription-based services using application servers or record carriers, e.g. SIM application toolkits
• CPC Class Codes: H04W 48/06 : based on traffic conditions
• CPC Class Codes: H04W 48/16 : Discovering, processing access restriction or access information
• CPC Class Codes: H04W 56/0015 : one node acting as a reference for the others
• CPC Class Codes: H04W 72/0453 : Resources in frequency domain, e.g. a carrier in FDMA
• CPC Class Codes: H04W 72/52 : based on load
• CPC Class Codes: H04W 72/542 : using measured or perceived quality
• CPC Class Codes: H04W 76/10 : Connection setup
• CPC Class Codes: H04W 76/27 : Transitions between radio resource control [RRC] states
• CPC Class Codes: H04W 84/20 : Master-slave selection or change arrangements
• CPC Class Codes: H04W 88/08 : Access point devices

Abstract

A system and method is provided for a predictive connectivity layer. In the disclosed embodiments, resources, such as bandwidth, processing, and memory, at a network node are dynamically allocated based on one or more predicted user behaviors. A predicted user behavior may be determined based on one or more previous actions of a user or a group of users at the network node. For example, if a user accesses the network node to download a particular web site at the same time every morning, the predictive technique may determine that the user will attempt to download the same web site the next morning, and therefore cache a copy of the web site before the user'"'"'s next attempt to access the network through the network node. Similarly, the network node may predict an amount of bandwidth or other resources to allocate based on previous behavior of one or more users.

Claims

1. A method for predictive learning in a network node of a network, the method comprising:

receiving information relating to one or more previous actions of a user when the user is accessing the network through the network node;

determining a user behavior based on the one or more previous actions of the user when the user is accessing the network through the network node; and

pre-populating user access information in the network node based on the determined user behavior before the user'"'"'s next attempt to access the network through the network node.

2. The method of claim 1, wherein the user access information includes at least one of an updated authentication rule and updated policy information.

3. The method of claim 2, wherein the updated policy information includes a Quality of Service (QoS) level for one or more users.

4. The method of claim 2, wherein the updated authentication rule includes one or more users permitted to access the network node.

5. The method of claim 1, further comprising altering at least one QoS parameter at one or more network nodes based on the determined user behavior and including the at least one altered QoS parameter in the user access information.

6. The method of claim 1, further comprising altering at least one user-capacity parameter at one or more network nodes based on the determined user behavior and including the at least one altered capacity parameter in the user access information.

7. The method of claim 1, further comprising altering at least one bandwidth allocation at one or more network nodes based on the determined user behavior and including the at least one altered bandwidth in the user access information.

8. The method of claim 1, further comprising altering at least one RF parameter at one or more network nodes based on the determined user behavior and including the at least one altered RF parameter in the user access information.

9. The method of claim 1, further comprising altering at least one mobility parameter at one or more network nodes based on the determined user behavior and including the at least one altered mobility parameter in the user access information.

10. The method of claim 1, further comprising switching on or off one or more network nodes based on the determined user behavior.

11. The method of claim 1, further comprising restricting network access for one or more users at one or more network nodes based on the determined user behavior.

12. The method of claim 2, wherein the authentication rules and policy information comprise at least one of temporal, geographic, network traffic-based, or individual user-based parameters.

13. The method of claim 1, wherein the network is a cloud-based network.

14. A network node for predictive learning in a network, the network node comprising:

a processor;

a memory configured to store computer-readable instructions for execution by the processor, the instructions for performing the steps of;

receiving information relating to one or more previous actions of a user when the user is accessing the network through the network node;

determining a user behavior based on the one or more previous actions of a user when the user is accessing the network through the network node; and

pre-populating user access information in the network node based on the determined user behavior before the user'"'"'s next attempt to access the network through the network node.

15. The network node of claim 14, wherein the user access information includes at least one of an updated authentication rule and updated policy information.

16. The network node of claim 15, wherein the updated policy information includes a Quality of Service (QoS) level for one or more users.

17. The network node of claim 15, wherein the updated authentication rule includes one or more users permitted to access the network node.

18. The network node of claim 14, wherein the memory further comprises instructions executable by the processor for altering at least one QoS parameter at one or more network nodes based on the determined user behavior and including the at least one altered QoS parameter in the user access information.

19. The network node of claim 14, wherein the memory further comprises instructions executable by the processor for altering at least one user-capacity parameter at one or more network nodes based on the determined user behavior and including the at least one altered capacity parameter in the user access information.

20. The network node of claim 14, wherein the memory further comprises instructions executable by the processor for altering at least one bandwidth allocation at one or more network nodes based on the determined user behavior and including the at least one altered bandwidth in the user access information.

21. The network node of claim 14, wherein the memory further comprises instructions executable by the processor for altering at least one RF parameter at one or more network nodes based on the determined user behavior and including the at least one altered RF parameter in the user access information.

22. The network node of claim 14, wherein the memory further comprises instructions executable by the processor for altering at least one mobility parameter at one or more network nodes based on the determined user behavior and including the at least one altered mobility parameter in the user access information.

23. The network node of claim 14, wherein the memory further comprises instructions executable by the processor for switching on or off one or more network nodes based on the determined user behavior.

24. The network node of claim 14, wherein the memory further comprises instructions executable by the processor for restricting network access for one or more users at one or more network nodes based on the determined user behavior.

25. The network node of claim 15, wherein the authentication rules and policy information comprise at least one of temporal, geographic, network traffic-based, or individual user-based parameters.

26. A computer-readable medium comprising computer-readable instructions for execution by a processor that perform a method for predictive learning, the method comprising:

receiving information relating to one or more previous actions of a user when the user is accessing the network through the network node;

determining a user behavior based on the one or more previous actions of a user when the user is accessing the network through the network node; and

pre-populating user access information in the network node based on the determined user behavior before the user'"'"'s next attempt to access the network through the network node.

27. A method for predictive learning on a network, the method comprising:

receiving information from one or more network nodes of the network, the information relating to a user of the network, wherein the user accesses the network via one or more access points using one or more user equipment;

identifying data to be requested by the user in the future based on the received information; and

caching the identified data such that the cached data is accessible to one or more access points used by the user.

28. The method of claim 27, wherein the one or more network nodes include an enterprise productivity server.

29. The method of claim 28, wherein the enterprise productivity server provides data for at least one of an email and a calendar of the user.

30. The method of claim 27, wherein the caching of the identified data includes storing the identified data using a service executing on a cloud platform.

31. The method of claim 27, wherein the caching of the identified data includes storing the identified data on the one or more access points used by the user.

32. A method for predictive learning on a network, the method comprising:

receiving information from one or more network nodes of the network, the information relating to at least one user of the network, wherein the at least one user accesses the network via one or more access points using one or more user equipments;

determining aggregate behavior of the at least one user based on the information; and

configuring at least one network node of the network based on the aggregate behavior.

33. The method of claim 32, wherein the configuring includes changing QoS-related parameters on the at least one network node of the network.

34. The method of claim 32, wherein the configuring includes powering off the at least one network node of the network.