公开(公告)号：US20250097738A1

公开(公告)日：2025-03-20

申请号：US18370757

申请日：2023-09-20

Applicant: DISH Wireless L.L.C.

Inventor： Sumugam Balachandran

IPC: H04W24/04 ,  H04L41/0631 ,  H04W24/08

Abstract: A method for automatically identifying a fault condition in a wireless network can include receiving, at a trained machine-learning model from multiple subsystems of the wireless network, information associated with multiple alerts triggered across the multiple subsystems, each of the multiple alerts being indicative of a corresponding potential fault condition in one of the multiple subsystems, identifying, by the machine-learning model, a subset of alerts of the multiple alerts, where the subset of alerts represents a set of one or more root fault conditions associated with the multiple alerts triggered across the multiple subsystems, and identifying, by the machine-learning model, one or more remediating actions configured to address the one or more root fault conditions in at least one corresponding subsystem of the multiple subsystems. The machine-learning model is trained using training data that identifies correlation between alerts generated in the multiple subsystems.

公开(公告)号：US20250097094A1

公开(公告)日：2025-03-20

申请号：US18370753

申请日：2023-09-20

Applicant: DISH Wireless L.L.C.

Inventor： Sumugam Balachandran

IPC: H04L41/06 ,  G06N5/022 ,  H04L41/16 ,  H04W24/08

Abstract: A method for predicting a fault condition in a wireless network includes: receiving, at a trained machine-learning model from multiple subsystems of the wireless network, information associated with multiple alerts triggered across the multiple subsystems, each of the multiple alerts being indicative of a corresponding potential fault condition in one of the multiple subsystems, receiving, at the machine-learning model from a plurality of external devices, observation information regarding the wireless network, receiving, at the machine-learning model from a network platform, information regarding resources of the wireless network; training the machine-learning model using the multiple alerts, the observation information, and the information, identifying, by the machine-learning model, a subset of alerts of the multiple alerts, the subset of alerts representing a set of one or more predicted fault conditions associated with the multiple alerts triggered across the multiple subsystems, and training the machine-learning model using the subset of alerts.