NVIDIA had 100 patents in robotics during Q1 2024. NVIDIA Corp’s patents filed in Q1 2024 focus on various techniques for in-cabin monitoring systems, generating topological data for computer-generated environments, compressed grid-based graph representations for efficient graph-mapped computing applications, parallel edge decimation on high-resolution meshes, and machine learning models for controlling robots. These patents showcase NVIDIA‘s innovation in the fields of computer graphics, computational geometry, and robotics. GlobalData’s report on NVIDIA gives a 360-degree view of the company including its patenting strategy. Buy the report here.

NVIDIA grant share with robotics as a theme is 34% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Sensor calibration using fiducial markers for in-cabin monitoring systems and applications (Patent ID: US20240104941A1)

The patent filed by NVIDIA Corp. describes sensor parameter calibration techniques for in-cabin monitoring systems, specifically focusing on an occupant monitoring system (OMS) used in vehicles or machines. The system involves determining calibration parameters for interior image sensors to reference 2D captured images to an in-cabin 3D coordinate system. This calibration process involves detecting fiducial points in images, determining 2D and 3D coordinates for these points, computing a rotation-translation transform, and configuring operations based on the calibration parameter. The system can be used for various applications, including autonomous machines, simulation operations, digital twin operations, light transport simulation, content creation, deep learning, real-time streaming, virtual, augmented, or mixed reality content generation, conversational AI, synthetic data generation, and cloud computing resources.

The patent also includes claims detailing the system's components and functionalities, such as processing units determining 2D and 3D coordinates for fiducial points, computing transforms, detecting fiducial points using computer vision algorithms, determining calibration accuracy metrics, and configuring operations based on calibration parameters. The system can be integrated into control systems for autonomous or semi-autonomous machines, perception systems, simulation operations, digital twin operations, collaborative content creation, deep learning operations, real-time streaming, edge devices, robots, conversational AI systems, virtual machines, data centers, and cloud computing resources. Additionally, a method is outlined for determining occupant information in a machine based on sensor-generated images calibrated using fiducial markers' 2D and 3D coordinates in the interior space.

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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.