US 11,743,132 B2
Most probable cause determination for telecommunication events
Charles W. Boyle, Upton, MA (US); Sreenivas NVR Kaki, Nashua, NH (US); Nizar K. Purayil, Bangalore (IN); and Vsevolod V. Ostapenko, Boxborough, MA (US)
Assigned to RIBBON COMMUNICATIONS OPERATING COMPANY, INC., Westford, MA (US)
Appl. No. 16/962,814
Filed by Ribbon Communications Operating Company, Inc., Westford, MA (US)
PCT Filed Jul. 12, 2019, PCT No. PCT/US2019/041543
§ 371(c)(1), (2) Date Jul. 16, 2020,
PCT Pub. No. WO2020/014574, PCT Pub. Date Jan. 16, 2020.
Claims priority of provisional application 62/763,969, filed on Jul. 12, 2018.
Prior Publication US 2021/0056487 A1, Feb. 25, 2021
Int. Cl. H04L 41/16 (2022.01); G06F 16/907 (2019.01); 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 24/08 (2009.01); H04L 41/0631 (2022.01); H04L 65/65 (2022.01); G06N 3/08 (2023.01); H04W 24/02 (2009.01); H04W 24/04 (2009.01); H04W 24/10 (2009.01); H04L 65/1073 (2022.01); H04M 3/51 (2006.01); 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)] 20 Claims
OG exemplary drawing
 
1. A method performed by a computing system, the method comprising:
collecting information on transactions in a telecommunication system, including digests of data packets from a core network and a radio access network of the telecommunications system and information from user devices interacting with the telecommunication system;
using the information on transactions to create a plurality of event objects by correlating the information on transactions between a user plane tunnel and a control plane, each of the event objects associated with a telecommunication failure event, and the event objects including a set of parameters based on the information on transactions;
associating parameters of the set of parameters of each of the event objects with at least one Key Performance Indicator (KPI);
applying the event objects to a plurality of machine-learning inference functions, each of the inference functions using the set of parameters as inputs and at least one KPI of the event objects as outputs;
analyzing metadata from each of the plurality of machine-learning inference functions to determine a first parameter of the set of parameters was used to predict an outcome associated with the at least one KPI, including scoring input field occurrences of the parameters of the set of parameters across the inference functions, the scoring indicating a respective relationship between each one of the input field occurrences and the at least one KPI, and determining that the first parameter of the set of parameters is a most probable cause of the at least one KPI based on the scoring;
applying additional event objects to the plurality of machine-learning inference functions in response to determining that a confidence score associated with the most probable cause is below a threshold; and
addressing the most probable cause, including mitigating a network failure associated with the most probable cause.