Telefonaktiebolaget LM Ericsson had 204 patents in artificial intelligence during Q1 2024. The patents filed by Telefonaktiebolaget LM Ericsson in Q1 2024 focus on wireless communication networks utilizing Machine Learning based air-interfaces. The patents include methods for predicting user behavior, determining bias in machine learning models, distributed machine learning, and prediction of Radio Access Network performance. These methods involve training neural networks, merging predictive models, analyzing biasing data parameters, and training prediction models based on network environment properties. GlobalData’s report on Telefonaktiebolaget LM Ericsson gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Telefonaktiebolaget LM Ericsson grant share with artificial intelligence as a theme is 18% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Machine learning non-standalone air-interface (Patent ID: US20240107429A1)
The patent filed by Telefonaktiebolaget LM Ericsson relates to wireless communication networks with Machine Learning-based air-interfaces. The method described involves managing network interfaces in a communication network, specifically focusing on capabilities in supporting communication between wireless devices and network entities. The method includes transmitting capabilities, receiving control information, and transmitting wireless signals between entities. The patent emphasizes the use of Machine Learning-based air-interfaces to handle and improve data transmission, with control information enabling training and controlling wireless signaling.
Furthermore, the method involves receiving capabilities from wireless devices, transmitting control information, and updating based on feedback information. The patent highlights the involvement of Artificial Intelligence reinforced network entities and Machine Learning-based air-interfaces in enhancing data transmission. The control information facilitates training the ML-based air-interface and controlling wireless signaling, with feedback information providing insights into data packet errors, neural network outputs, and triggering ML model updates. Overall, the patent focuses on optimizing wireless communication networks through advanced technologies like Machine Learning and Artificial Intelligence.
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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.
