US 12,334,974 B1
Optic power monitoring system
Raja Balasubramanian, Skillman, NJ (US); Rene Wilfredo Villatoro Escalante, Irving, TX (US); Chike Gideon Okechukwu, Denton, TX (US); Terry John Jenkins, Irving, TX (US); and Scott Taylor, Keller, TX (US)
Assigned to Citibank, N.A., New York, NY (US)
Filed by Citibank, N.A., New York, NY (US)
Filed on Dec. 11, 2024, as Appl. No. 18/977,029.
Application 18/977,029 is a continuation in part of application No. 18/615,364, filed on Mar. 25, 2024.
Application 18/615,364 is a continuation of application No. 18/376,051, filed on Oct. 3, 2023, granted, now 11,943,096, issued on Mar. 26, 2024.
Int. Cl. H04B 10/03 (2013.01); G06F 11/07 (2006.01); H04B 10/079 (2013.01)
CPC H04B 10/03 (2013.01) [G06F 11/0709 (2013.01); G06F 11/079 (2013.01); H04B 10/079 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for monitoring fault events at a fiber optical network, the system comprising:
one or more processors; and
one or more non-transitory computer-readable media comprising instructions that, when executed by the one or more processors, cause operations comprising:
receiving, for each interconnected component of an optical network, corresponding optical data comprising optical measurements obtained at each interconnected component, wherein the optical network is represented by a topology comprising a plurality of interconnected components;
extracting, from each corresponding optical data comprising corresponding optical measurements obtained at each interconnected component, a set of component metrics for light transmission signals transmitted or received, via fiber optic transmission lines, at a corresponding interconnected component;
modifying each corresponding optical data according to a standardized schema using the set of component metrics to obtain a modified component data structure;
generating a network event dataset by appending, for each interconnected component, (1) a corresponding modified component data structure and (2) event data comprising an indication as to whether an event has occurred and an event type if the event has occurred;
training a machine learning model using the network event dataset and the topology to identify occurrence of new events at components of the optical network;
responsive to determining a difference between a predicted network event and an actual network event, generating an adjusted standardized schema by modifying the set of component metrics; and
retraining the machine learning model using component data structures updated according to the adjusted standardized schema.