Palo Alto Networks had eight patents in artificial intelligence during Q2 2024. Palo Alto Networks Inc filed patents in Q2 2024 related to improving the accuracy of classification models using machine learning techniques like random forest, training neural networks to learn credibility vectors for partially labeled data samples, and detecting abnormal endpoint activity by generating adaptive normal profiles and identifying abnormal chains of processes. These innovations aim to reduce false positives and improve threat detection capabilities. GlobalData’s report on Palo Alto Networks gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Palo Alto Networks had no grants in artificial intelligence as a theme in Q2 2024.
Recent Patents
Application: Data blaming (Patent ID: US20240202554A1)
The patent filed by Palo Alto Networks Inc. describes a system and method for identifying misclassified items by a machine learning model, such as a random forest, and updating the training data set to improve classification accuracy. The system includes a processor that receives the misclassified item, identifies a subset of training data associated with the item, updates the training data set, and generates an updated classification model. The method involves similar steps of identifying the misclassified item, updating the training data set, and generating an updated classification model. The system and method aim to improve the accuracy of classification models by addressing misclassifications caused by problematic training samples.
The system and method are particularly useful for items like Uniform Resource Locators (URLs) that are misclassified by the model. By identifying problematic training samples associated with misclassifications, updating the training data set, and generating an updated classification model, the system and method help in refining the classification process. Additionally, the system includes features like creating a prioritized list of training set examples and updating the training set data in response to problematic training items. Overall, the patent focuses on enhancing the performance of machine learning models by addressing misclassifications and improving the training data set.
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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.
