Proofpoint had 12 patents in artificial intelligence during Q1 2024. Proofpoint Inc’s patents filed in Q1 2024 focus on cybersecurity training and analysis. The first patent involves generating simulated attack messages for user annotation to identify training areas, creating customized training modules, and displaying them on user devices. The second patent involves dynamic message analysis using machine learning to detect bi-directional messaging traffic between enterprise and external domains, ranking external domains for conversation detection, and executing enhanced protection actions. The third patent involves anomaly detection in cybersecurity training modules by capturing screenshots, inputting them into an auto-encoder to identify outlier permutations, and generating a user interface to display the outlier permutation information on user devices. GlobalData’s report on Proofpoint gives a 360-degree view of the company including its patenting strategy. Buy the report here.

Proofpoint grant share with artificial intelligence as a theme is 66% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Prompting users to annotate simulated phishing emails in cybersecurity training (Patent ID: US20240078924A1)

Proofpoint Inc. has developed a system for generating simulated attack messages for cybersecurity training purposes. The system involves a computing platform that sends these messages to enterprise user devices, allowing users to interactively annotate selected elements of the message. Based on these annotations, the system identifies training areas for the user, generates customized training modules specific to these areas, and sends them to the user's device for display. The system utilizes machine learning to adapt training modules and calculate user performance scores, providing interactive training interfaces for users to complete the training.

The system also includes features such as identifying training areas based on user selections, calculating user performance scores, receiving user selections through various interactions with the simulated attack message, providing annotation tools for user selections, aggregating user selections for analysis, computing the frequency of correctly selected elements, updating machine learning models based on user selections, and adjusting score weightings for elements based on user interactions. Overall, the system aims to enhance cybersecurity training by providing personalized and interactive training modules based on user annotations of simulated attack messages.

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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.