SYSTEM FOR THE ANALYSIS AND MONITORING OF IP COMMUNICATIONS

• US 20070030842A1
• Filed: 07/18/2006
• Published: 02/08/2007
• Est. Priority Date: 07/18/2005
• Status: Abandoned Application

• Current Assignee: Naveen Agnihotri, Walter Borden
• Original Assignee: Naveen Agnihotri, Walter Borden
• Inventors: Borden, Walter, Agnihotri, Naveen
• Application Number:
  US11/458,259
• Publication Number:
  US 20070030842A1
• Time in Patent Office: Days
• US Class Current: 370/352
• CPC Class Codes: G06F 18/213 Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: H04L 41/16 using machine learning or artificial intelligence
• CPC Class Codes: H04L 43/00 Arrangements for monitoring or testing data switching networks
• CPC Class Codes: H04L 63/0227 Filtering policies mail message filtering H04L51/212
• CPC Class Codes: H04L 63/1408 by monitoring network traffic monitoring network traffic per se H04L43/00

Abstract

A system is claimed that deploys a multi-layer neural network for analyzing, categorizing and organizing conversations and video conferencing on Voice over Internet Protocol (VoIP), The parallel architecture of this implementation is an artificial neural network which allows it to process very large amounts of voice data very efficiently, both in dealing with large, continuous streams of information in real time as well as on voice data archives This invention uses its neural network to operate considerably faster than its linear counterparts as found ill current VoIP message and call management software.

Claims

1. A system for the analysis of IP packets comprising

a. a data gathering module, for gathering input data consisting of a plurality of IP packets comprising an IP communications

b. a factoring module for performing non-negative matrix factorization on said input data;

c. a neural network module for analyzing the content of said factorized input data, based upon historical, learned preferences, to identify instances of said preferences in said input data; and

d. a reporting module for providing reports regarding the results of said analysis of said content and for providing alerts when said preferences are identified.