Rubrik had five patents in big data during Q1 2024. Rubrik Inc filed patents related to implementing a scalable automated training framework for anomaly and ransomware detection, real-time detection of ransomware and system threats using file system audit events, and consolidating snapshots efficiently by identifying overlaps and hard linking data parts. These techniques aim to improve data management, security, and efficiency in storage systems. GlobalData’s report on Rubrik gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Rubrik grant share with big data as a theme is 20% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Scalable automated training framework (Patent ID: US20240089282A1)
The patent filed by Rubrik Inc. describes a method and apparatus for implementing a scalable automated training framework for anomaly and ransomware detection. The technique involves simulating user actions and ransomware on virtual machines to generate training data for malware detection. This training data is then used to train a machine learning model for malware detection, with the ability to detect different types of malware based on subsets of training data. The method includes obtaining snapshots of virtual machines to track changes in file systems, comparing these snapshots to generate training data, and simulating various user actions such as interface manipulation, resource navigation, and web browsing to simulate malware presence.
The apparatus described in the patent includes one or more processors executing code to simulate user actions on virtual machines in the presence of simulated malware, generate training data based on these actions, and train a machine learning model for malware detection. The processors are further capable of obtaining snapshots of virtual machines, comparing changes in file systems, and simulating different user actions to enhance the detection capabilities of the model. Additionally, the patent covers a non-transitory computer-readable medium storing code that enables the execution of the described method for training a machine learning model to detect malware, including specific types of malware based on subsets of training data.
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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.
