Juniper Networks had 18 patents in big data during Q1 2024. Juniper Networks Inc filed patents in Q1 2024 related to machine learning model testing and updating, resilient high-availability router architecture, redundant multicast flow management, and virtual network controllers for routing traffic in multi-tenant data centers. The patents focus on improving network performance, reliability, and automation in various network operations. 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 big data as a theme is 38% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Testing and baselining a machine learning model and test data (Patent ID: US20240078289A1)
The patent filed by Juniper Networks Inc. describes a method and device for evaluating and updating machine learning models. The device receives a machine learning model, training data, and test data, and performs unit tests, regression tests, scale and longevity tests to assess the model's performance. The device then updates the model based on the test results, model scores, graphs, inference delays, missing points, and resource utilization. Additionally, the method includes processing training data to identify errors and outliers, correcting data based on test scores, and updating the model accordingly.
The device further calculates test data scores, identifies miscoding errors and outliers, and updates training data based on score thresholds. The method involves static analysis models like lint models, and parameters such as data quantity, stagnancy, spikes, and statistical tests. The device also corrects test data that fails to meet score thresholds and updates training data accordingly. The patent also covers the creation of graphs to compare predicted and actual values, calculation of errors like mean absolute percentage error, and training and testing of updated machine learning models. Overall, the patent outlines a comprehensive approach to evaluating, correcting, and updating machine learning models to ensure optimal performance and accuracy.
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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.
