SYSTEM AND METHOD FOR DYNAMICALLY MONITORING, ANALYZING, MANAGING, AND ALERTING PACKET DATA TRAFFIC AND APPLICATIONS

• US 20150295808A1
• Filed: 10/31/2013
• Published: 10/15/2015
• Est. Priority Date: 10/31/2012
• Status: Abandoned Application

• Current Assignee: O'Malley, Matt
• Original Assignee: O'Malley, Matt
• Inventors: O'Malley, Matt, Ware, Lance
• Application Number:
  US14/439,949
• Publication Number:
  US 20150295808A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 43/10 Active monitoring, e.g. heartbeat, ping or trace-route
• CPC Class Codes: H04L 47/20 Traffic policing
• CPC Class Codes: H04L 47/22 Traffic shaping
• CPC Class Codes: H04L 47/2441 relying on flow classification, e.g. using integrated services [IntServ]

Abstract

A computer-implemented system and method is describe, having a usage and performance analyzer system (UP AS) for receiving a data packet or information from a computer or mobile device via a usage and performance analyzer module (UP AM) for evaluating and implementing policies via a dynamic offender polices and enforcement (DOPE) module. Further, wherein the DOPE module comprises a past actions, status, and timers (PAST) module for classifying specific users. A targeted offers notifications and enforcement (TONE) module is for interacting with specific users and devices.

Claims

1. A computer-implemented method, comprising:

receiving a data packet from a second node at a first node, wherein the first node comprises a usage and performance analyzer module (UPAM);

determining a data attribute from the data packet, wherein the data attribute identifies the second node;

classifying the data attribute according to a predetermined stored policy, the policy with an association with a predetermined behavior of the second node or of an application of the second node;

determining, if the classification meets a predefined criteria; and

generating and transmitting a message to the second node in accordance with the predetermined stored policy.

2. The computer-implemented method of claim 1, wherein the second node comprises a mobile device.

3. The computer-implemented method of claim 2, wherein the mobile device includes an on-device applet for monitoring usage and performance.

4. The computer-implemented method of claim 3, wherein the usage and performance analyzer module (UPAM) implements the predetermined stored policy, and the policy comprises a dynamic offender policies and enforcement (DOPE) module, the dynamic offender policies and enforcement module comprising a dynamic offender policies and enforcement criteria and parameters.

5. The computer-implemented method of claim 4, wherein the dynamic offender policies and enforcement criteria and parameters comprise:

a past actions status and timers (PAST) module comprising past actions status and timers criteria and parameters;

a targeted offers notifications and enforcement (TONE) module comprising targeted offers notifications and enforcement criteria and parameters;

a success of offers remedies and termination (SORT) module comprising success of offers notifications and enforcement criteria and parameters; and

a potential offender profile score (POPS) module comprising potential offender profile score criteria and parameters.

6. The computer-implemented method of claim 5, wherein the predetermined stored policy is persistently monitored according to the dynamic offender policies and enforcement module.

7. The computer-implemented method of claim 6, wherein the dynamic offender policies and enforcement module performs an exchange and update of information with a mobile network operator and vice versa.

8. The computer-implemented method of claim 7, wherein the classification comprises a past offense count representing a number of offenses of the predetermined stored policy.

9. The computer-implemented method of claim 8, wherein the classification is tracked by the past actions status and timers module.

10. The computer-implemented method of claim 9, wherein the classifying the data attribute according to a predetermined stored policy includes classifying the data attribute into one of a plurality of classifications, the plurality of classifications comprising:

a perceived non-offender classification, a first time offender classification, a repeat offender classification, and a habitual offender classification.

11. The computer-implemented method of claim 10, wherein the classifications are associated with a specific computer.

12. The computer-implemented method of claim 11, wherein the specific computer comprises a unique identifier, wherein the unique identifier preferably comprises at least one of:

a mobile directory number, electronic serial number, locally-assigned-number, port number, destination address, media access control address, phone number, directory number, forwarding number, call-back number, mobile phone number, fax number, VoIP number, extension phone number, phone number area code, phone number prefix, and country code phone number.

13. The computer-implemented method of claim 12, wherein the second node is associated with a specific user, wherein preferably the specific user is associated with a current plan and a specific mobile network operator.

14. The computer-implemented method of claim 13, wherein the current plan includes a limit, wherein preferably the limit comprises an in-networking calling limit, calling limit, and roaming limit.

15. An arrangement, comprising:

a processing circuitry configured to;

receive a data packet from a second node at a first node, wherein the first node comprises a usage and performance analyzer module (UPAM);

determine a data attribute from the data packet, wherein the data attribute identifies the second node;

classify the data attribute according to a predetermined stored policy, the policy describing a predetermined behavior of the second node or of an application of the second node;

determine, if the classification meets a predefined criteria; and

generate and transmitting a message to the second node in accordance with predetermined stored policy.

16. A computer-implemented method, comprising:

classifying a first usage pattern from a plurality of inputs from an at least one node;

wherein the first usage pattern is stored as a first stored usage pattern;

monitoring the plurality of inputs of the at least one node;

determining, if a second usage pattern of the at least one node meets a stored relationship policy in relationship with the first stored usage pattern; and

generating and transmitting an event in accordance with the determination of the stored relationship policy.

17. The computer-implemented method of claim 16, wherein the at least one node comprises a mobile device.

18. The computer-implemented method of claim 17, wherein the mobile device includes an on-device applet for persistently monitoring usage and performance.

19. The computer-implemented method of claim 18, wherein the on-device applet for monitoring usage and performance includes a usage and performance analyzer module (UPAM).

20. The computer-implemented method of claim 19, wherein the usage and performance analyzer module (UPAM) implements the stored relationship policy based in part on a web browsing input and/or an application input.

21. The computer-implemented method of claim 20, wherein the stored relationship policy in relationship with the first stored usage pattern is affected by an input from the group comprising the plurality of inputs from the at least one node, a plurality of inputs from a plurality of nodes, a plurality of inputs from a predefined group of nodes, a plurality of inputs from a predefined segmentation of nodes, some combinations of these inputs, or some permutation of these inputs.

22. The computer-implemented method of claim 21, wherein the generated and transmission of the event in accordance with the stored relationship policy and determination includes an offer to the at least one node.

23. The computer-implemented method of claim 22, wherein the classification comprises a past usage pattern representing a number of inputs according to the stored relationship policy in relationship with the first stored usage pattern.

24. The computer-implemented method of claim 23, wherein the classification is tracked by the past actions status and timers module.

25. The computer-implemented method of claim 24, wherein the classifying the first usage pattern from a plurality of inputs from an at least one node includes classifying the data attribute into one of a plurality of classifications, the plurality of classifications comprising:

a perceived non-opportunity classification, a first time opportunity classification, a repeat opportunity classification, and a repeat-loyalty opportunity classification.

26-150. -150. (canceled)