Toyota Motor had two patents in edge computing during Q2 2024. Toyota Motor Corp’s patents in Q2 2024 focus on improving communication-aware federated learning systems for edge nodes and servers, as well as enhancing the use of sensor data in a mobile context by indexing the data with derived content. The first patent describes a system where edge nodes train machine learning models using local data, compress the models based on available network bandwidth, and transmit them to a server for aggregation. The server then decompresses and aggregates the models before transmitting them back to the edge nodes. The second patent discusses a method for acquiring sensor data from a vehicle, generating an index of the data based on how it was acquired, deriving additional content using a model, updating the index with the additional content, and providing the index as a report to a remote device. GlobalData’s report on Toyota Motor gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Toyota Motor had no grants in edge computing as a theme in Q2 2024.
Recent Patents
Application: Systems and methods for communication-aware federated learning (Patent ID: US20240174254A1)
The patent filed by Toyota Motor Corp. describes a system for communication-aware federated learning that involves a server and edge nodes. Each edge node trains a machine learning model using local data obtained by sensors and then transmits the compressed model to the server based on the available network bandwidth. The server aggregates the models and sends them back to the edge nodes for further processing. The system allows for efficient communication and collaboration between the server and edge nodes in a federated learning environment.
The patent also includes claims related to a vehicle equipped with a controller programmed to train and compress machine learning models based on network bandwidth, transmit data to the server, receive aggregated models, and control autonomous driving functions. The controller can adjust compression levels based on changes in network bandwidth, utilize various compression techniques, and ensure timely transmission of data to the server. Additionally, the system can be applied to multiple vehicles in a network, allowing for collaborative learning and autonomous driving capabilities based on aggregated machine learning models.
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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.
