NVIDIA had 58 patents in metaverse during Q2 2024. NVIDIA Corp’s patents in Q2 2024 focus on improving efficiency and accuracy in various applications. This includes using neural network models for object association in multi-sensor systems, enhancing audio quality through machine learning-based processing, generating digital representations of environments with multiple objects, optimizing authorization request processing in cloud-based services using location tracking data, and executing multiple processes in real-time streaming applications efficiently through parallel execution and aggregation functions. GlobalData’s report on NVIDIA gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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NVIDIA had no grants in metaverse as a theme in Q2 2024.
Recent Patents
Application: Determining object associations using machine learning in autonomous systems and applications (Patent ID: US20240211748A1)
The patent filed by NVIDIA Corp. describes systems and methods for determining associations between objects in sensor data and predicted states of those objects in multi-sensor systems, particularly in autonomous or semi-autonomous vehicle perception systems. The disclosed systems employ neural network models, such as multi-layer perceptron (MLP) models, to learn association costs between sensor measurements and predicted object states. During training, the systems generate data to update parameters of the neural network models, enabling them to provide association costs between sensor data and predicted states during deployment.
The patent includes claims for a processor that determines predicted states of detected objects, generates association scores using neural network models based on sensor data and input parameters, and updates data based on association thresholds. The processor utilizes MLP models, input parameters like predicted states or sensor identifications, and can trigger system changes based on updated data. Additionally, the patent covers methods for determining predicted object states, generating association scores using neural network models processing multi-sensor data, and updating object tracks accordingly. The methods involve MLP models, input parameters, and the generation of neural network models based on modeling data.
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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.
