Alphabet has patented machine-learning architectures for broadcast and multicast communications over wireless systems. The method involves determining user equipment capabilities, configuring deep neural networks based on processing power, and directing communications to targeted groups efficiently. GlobalData’s report on Alphabet gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on Alphabet, was a key innovation area identified from patents. Alphabet's grant share as of January 2024 was 61%. Grant share is based on the ratio of number of grants to total number of patents.

Machine-learning architecture for broadcast and multicast communications in wireless systems

Source: United States Patent and Trademark Office (USPTO). Credit: Alphabet Inc

A recently granted patent (Publication Number: US11886991B2) outlines a method performed by a network entity associated with a wireless communication system. The method involves receiving user equipment (UE) capabilities of multiple UEs in a targeted group, determining the processing power of each UE, and configuring a deep neural network (DNN) for processing broadcast or multicast communications based on these capabilities. Specifically, a gradient version of the DNN is created for UEs with lower processing power, ensuring efficient communication tailored to individual device capabilities. The network entity then forms a network-entity DNN based on these configurations and processes the communications to direct them to the targeted group of UEs within the wireless communication system.

Furthermore, the patent details the network entity apparatus and a non-transitory machine-readable medium with instructions for implementing the method. These components enable the reception of UE capabilities, determination of processing power discrepancies, and the creation of customized DNN configurations for efficient broadcast or multicast communication delivery. The apparatus includes a processor to update the network-entity DNN based on feedback from UEs, ensuring adaptability and optimization in real-time. Additionally, the method considers various characteristics of the targeted group of UEs, such as estimated location, to further enhance communication efficiency. Overall, the patent highlights a sophisticated approach to managing broadcast or multicast communications in wireless systems, catering to the diverse processing capabilities of individual devices within a network.

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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.