Juniper Networks had 25 patents in artificial intelligence during Q1 2024. Juniper Networks Inc filed patents in Q1 2024 for techniques to identify root cause failures in a network through hierarchical attribution graphs and machine learning models, a device for processing time series data to generate future predictions, testing and updating machine learning models, monitoring resources with machine learning models for forecasting and taking actions, and identifying remedial actions based on analysis of system log files. GlobalData’s report on Juniper Networks gives a 360-degree 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 44% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Identifying root cause of failures through detection of network scope failures (Patent ID: US20240097969A1)
The patent filed by Juniper Networks Inc. describes a network management system (NMS) configured to identify root cause failures by detecting network scope failures. The NMS generates a hierarchical attribution graph with attributes representing different network scopes, receives network event data indicating operational behavior, and applies a machine learning model to detect failures in specific network scopes. The system can determine the root cause of network failure by analyzing hierarchical levels and severity scores, attributing failures to the highest level with issues.
The NMS includes processors and memory to manage network devices, generate hierarchical graphs with containment and cross-link relationships, and optimize event data by filtering and aggregating information. By applying machine learning models to specific network scopes, the system can detect failures based on thresholds of failure events and severity scores. The method also involves identifying root causes of network failures, assigning priorities based on affected client devices, and determining the highest hierarchical level with issues. The patent also covers a computer-readable storage medium with instructions to generate hierarchical graphs, receive network event data, and apply machine learning models to detect failures in specific network scopes.
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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.
