Patent · US Active

Prediction of network events via rule set representations of machine learning models

US11669751B2 · kind B2 · utility

0Cited by

5References

20Claims

0Family size

Assignees

AT&T Intellectual Property I, L.P. · US
President and Fellows of Harvard College · US
University of Southern California · US

Inventors

Yaron Kanza · Fair Lawn, US
Balachander Krishnamurthy · New York, US
Sivaramakrishnan Ramanathan · Los Angeles, US
Minian Yu · Belmont, US
Jelena Mirkovic · Aachen, DE

Key dates

Filing date	Nov 27, 2020
Grant date	Jun 6, 2023
Priority date	—
Expiry date	Jul 14, 2041

Classification

Technology area (CPC H)Electricity
CPC primaryH04L41/0876
WIPO fieldDigital communication
WIPO sectorElectrical engineering

Abstract

A processing system including at least one processor may obtain a time series of measurement values from a communication network and train a prediction model in accordance with the time series of measurement values to predict future instances of an event of interest, where the time series of measurement values is labeled with one or more indicators of instances of the event of interest. The processing system may then generate a deterministic finite automaton based upon the prediction model, convert the deterministic finite automaton into a rule set, and deploy the rule set to at least one network component of the communication network.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.