US 11,882,005 B2
Predictive scoring based on key performance indicators in telecommunications system
Charles W. Boyle, Upton, MA (US); Surya Kumar Kovvali, Plano, TX (US); and Nizar K Purayil, Bangalore (IN)
Assigned to RIBBON COMMUNICATIONS OPERATING COMPANY, INC., Westford, MA (US)
Filed by Ribbon Communications Operating Company, Inc., Westford, MA (US)
Filed on Mar. 1, 2022, as Appl. No. 17/652,967.
Application 17/652,967 is a division of application No. 16/962,802, previously published as PCT/US2019/041537, filed on Jul. 12, 2019.
Claims priority of provisional application 62/763,969, filed on Jul. 12, 2018.
Prior Publication US 2022/0188732 A1, Jun. 16, 2022
Int. Cl. H04L 41/16 (2022.01); G06F 16/907 (2019.01); G06N 3/08 (2023.01); G06Q 10/0639 (2023.01); G06N 5/04 (2023.01); H04L 41/5009 (2022.01); H04L 43/0817 (2022.01); H04L 43/0823 (2022.01); H04W 24/02 (2009.01); H04W 24/04 (2009.01); H04W 24/08 (2009.01); H04W 24/10 (2009.01); H04L 65/1073 (2022.01); H04M 3/51 (2006.01); H04L 41/0631 (2022.01); H04L 65/65 (2022.01); H04L 65/1104 (2022.01); G06F 18/23 (2023.01); G06F 18/214 (2023.01); G06F 18/2415 (2023.01)
CPC H04L 41/16 (2013.01) [G06F 16/907 (2019.01); G06F 18/2148 (2023.01); G06F 18/23 (2023.01); G06F 18/24155 (2023.01); G06N 3/08 (2013.01); G06N 5/04 (2013.01); G06Q 10/06393 (2013.01); H04L 41/0631 (2013.01); H04L 41/5009 (2013.01); H04L 43/0817 (2013.01); H04L 43/0823 (2013.01); H04L 65/1073 (2013.01); H04L 65/1104 (2022.05); H04L 65/65 (2022.05); H04M 3/5175 (2013.01); H04W 24/02 (2013.01); H04W 24/04 (2013.01); H04W 24/08 (2013.01); H04W 24/10 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A non-transitory machine readable medium having stored thereon instructions for performing a method comprising machine executable code which when executed by at least one machine, causes the machine to:
receive protocol event data from a plurality of probes within a telecommunication system;
determine a multi-protocol Key Performance Indicator (KPI) of a call event from the protocol event data;
apply the multi-protocol KPI to a trained machine learning algorithm that includes the multi-protocol KPI as the trained machine learning algorithm's input and a telecommunication system score as the trained machine learning algorithm's output; and
in response to an output score from the trained machine learning algorithm, perform a corrective action for a plurality of network users that are expected to be affected by the multi- protocol KPI, the corrective action including determining a most probable cause for the multi- protocol KPI, using the most probable cause as a key to search through call records to identify the plurality of network users, and sending a short message service (SMS) message to the plurality of network users, the SMS message indicating the most probable cause or an action to take.