Juniper Networks had 36 patents in artificial intelligence during Q4 2023. The patents filed by Juniper Networks Inc in Q4 2023 focus on techniques for predicting future behavior of network links, managing wireless networks, improving energy efficiency in data centers, implementing policies across multiple cloud domains, and generating predictions for multiple sites based on site-specific models. These patents aim to enhance network performance, optimize energy usage, and streamline policy implementation in complex network environments. 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 artificial intelligence as a theme is 27% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Link behavior prediction for use in path selection (Patent ID: US20230412488A1)
The patent filed by Juniper Networks Inc. describes techniques for predicting future link behavior in a network and generating dynamic thresholds for link metrics to aid in path selection. By utilizing historical values of link metrics and a machine learning system, the computing system can predict future values of link metrics for each link and determine if these values are anomalous. Based on these predictions, the system computes a path that excludes links with anomalous metrics, enabling network devices to forward traffic along the optimal path. The method involves processing historical data, applying machine learning algorithms, and dynamically adjusting path selection based on predicted future values and anomaly thresholds.
The patent claims detail the specific steps involved in the method, such as receiving link metric values, applying deep learning systems like LSTM, computing paths based on anomaly predictions, and provisioning these paths for network traffic forwarding. The claims also cover scenarios like network changes, the inclusion of multiple metrics in the analysis, and the use of time-series databases for storing historical data. Additionally, the patent outlines the role of machine learning in path computation, the exclusion of anomalous links, and the periodic updating of computed paths. Overall, the patent focuses on leveraging historical data and machine learning to optimize path selection in networks based on predicted future link behavior and anomaly detection.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
