NVIDIA had 94 patents in edge computing during Q2 2024. NVIDIA Corp’s patents in Q2 2024 focus on rendering complex surfaces with neural signed distance functions, determining associations between objects in sensor data using neural network models, improving efficiency of cloud-based access servers with location tracking data, enhancing audio through machine learning-based super-resolution processing, and automatically generating digital representations of environments with accurate object models. These patents showcase advancements in graphics rendering, sensor data processing, cloud computing, audio processing, and environmental modeling. 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 edge computing as a theme in Q2 2024.
Recent Patents
Application: Real-time rendering with implicit shapes (Patent ID: US20240212261A1)
The patent filed by NVIDIA Corp. describes systems and methods for rendering complex surfaces or geometry using neural signed distance functions (SDFs) to efficiently capture multiple levels of detail (LODs) and reconstruct multi-dimensional geometry with high image quality. The architecture can represent complex shapes in a compressed format with high visual fidelity and generalize across different geometries from a single learned example. The use of extremely small multi-layer perceptrons (MHLPs) with an octree-based feature representation for the learned neural SDFs is highlighted in the patent.
The claims in the patent outline a computer-implemented method and system for rendering surfaces based on complexity thresholds or criteria, dividing surfaces into patches, determining surface patch representations using neural networks, and rendering the surface accordingly. The method includes determining complexity based on size, shape, or variation, adapting to changing complexity over time, and specifying preset complexity levels. The system can be used for simulation operations, testing autonomous machine applications, rendering graphical output, deep learning operations, and can be implemented in various environments such as edge devices, data centers, or cloud computing resources. Additionally, a processor is described that uses neural networks to generate surface patch representations based on complexity criteria and can render surfaces using these representations efficiently.
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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.
