Juniper Networks. has filed a patent for a monitoring device that uses machine learning models to generate multi-step forecast values for resources based on time-series data streams. The device can determine correlations between resources and trigger actions based on forecast values. GlobalData’s report on Juniper Networks gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on Juniper Networks, VPN tunneling was a key innovation area identified from patents. Juniper Networks's grant share as of January 2024 was 76%. Grant share is based on the ratio of number of grants to total number of patents.
Machine learning based monitoring device for multi-step forecast values
The patent application (Publication Number: US20240037419A1) describes a device and method for generating multi-step forecast values associated with a plurality of resources using machine learning models. The device includes one or more memories and processors that obtain time-series data streams for multiple resources, generate forecast values using different machine learning models, determine particular forecast values for the resources, and trigger actions based on these values. The machine learning models are trained on non-overlapping sets of training data to ensure accuracy in forecasting.
Furthermore, the device can identify correlations between different resources based on the forecast values generated, allowing for the adjustment of operation or access parameters associated with these resources. The device can also provide forecast values to another device for display, enabling users to make informed decisions based on the forecasted data. Additionally, the method involves obtaining data streams for resources, generating multi-step forecast values using machine learning models, determining specific forecast values, and executing actions based on these values. In cases where correlations are identified between resources, additional forecast values are generated and actions are performed accordingly, showcasing the device's adaptability and predictive capabilities.
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