Juniper Networks had seven patents in edge computing during Q4 2023. Juniper Networks Inc filed patents in Q4 2023 for systems and methods related to network management. The patents describe techniques for analyzing network data from access points to detect network events, determine root causes, and take management actions. Additionally, the patents detail a method for generating upgrade plans for access points based on received signal strength indicator values, clustering the access points, and sending the upgrade plan to an orchestrator for execution. Another method involves subscribing to telemetry flow data from network devices, aggregating the data, and sending it to a network analyzer device when certain thresholds are met. GlobalData’s report on Juniper Networks gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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Juniper Networks grant share with edge computing as a theme is 28% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Network management actions based on access point classification (Patent ID: US20230396485A1)
The patent filed by Juniper Networks Inc. describes a system that includes access point (AP) devices providing a wireless network and a network management system storing network data received from the AP devices. The system uses SLE metrics to determine network events, identify root causes, classify AP devices as edge or non-edge devices, and take network management actions based on the classification and event. The classification is determined based on factors such as the AP device's location in a network graph, bandwidth utilization, join and leave counts, and more. The network management actions include bypassing or executing mitigation actions depending on the classification and event, all determined through machine learning models.
The claims associated with the patent detail the components and processes involved in the system, including the determination of root causes, classification of AP devices, and network management actions. The system distinguishes between edge and non-edge devices based on various criteria like network performance degradation, join and leave counts, and bandwidth utilization. By analyzing network data and utilizing machine learning models, the system efficiently manages AP devices in a wireless network by taking appropriate actions tailored to the specific classification and event. This patent showcases an innovative approach to network management, enhancing the overall performance and reliability of wireless networks through intelligent decision-making processes.
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