Telefonaktiebolaget LM Ericsson had 41 patents in big data during Q1 2024. The patents filed by Telefonaktiebolaget LM Ericsson in Q1 2024 focus on methods for predicting user behavior in communication networks using artificial neural networks, building machine learning models with dominant filters, detecting anomalous access behaviors in network nodes, retrieving and executing machine learning models based on specified input and output features, and managing traffic processed by network functions based on established priorities. These innovations aim to improve network performance, security, and efficiency. GlobalData’s report on Telefonaktiebolaget LM Ericsson gives a 360-degree view of the company including its patenting strategy. Buy the report here.

Telefonaktiebolaget LM Ericsson grant share with big data as a theme is 46% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Node, and method performed thereby, for predicting a behavior of users of a communications network (Patent ID: US20240104365A1)

The patent filed by Telefonaktiebolaget LM Ericsson describes a method for predicting user behavior in a communications network using an artificial neural network. The method involves merging pre-existing predictive models of user behavior in different user groups, establishing connections between these models, learning connection weights based on data from a third user group, and obtaining a new predictive model for this third group. The size of data from the third group can be smaller, and the merging process can involve multiple predictive models from different user groups.

Furthermore, the method includes training and testing the new predictive model by learning weights in different layers of the neural network, utilizing pre-existing connections, and adjusting learning rates based on node dropout probabilities. The patent also covers a computer program implementing this method and a node configured to manage the artificial neural network for behavior prediction in user groups. The node can merge multiple predictive models, select models based on criteria, and train the new predictive model effectively.

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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.