Siemens had 81 patents in internet of things during Q2 2024. Siemens AG filed patents in Q2 2024 for methods and systems related to autonomous bin picking, multi-port charging systems for electric vehicles, shape-memory alloy actuators for electrical switches, managing sensor data in IoT environments, and providing procedural signals based on physiological patient parameters during medical procedures. GlobalData’s report on Siemens gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Siemens had no grants in internet of things as a theme in Q2 2024.
Recent Patents
Application: High-level sensor fusion and multi-criteria decision making for autonomous bin picking (Patent ID: US20240198530A1)
Siemens AG has developed a method for autonomous bin picking that involves perceiving a physical environment with objects in a bin using sensors. Artificial intelligence modules analyze the data from the sensors to compute grasping alternatives and detect objects of interest. A high-level sensor fusion module combines the outputs of the AI modules to compute attributes for the grasping alternatives, which are then ranked by a multi-criteria decision-making module to select the best option for execution. The system operates a controllable device to grasp an object from the bin based on the selected grasping alternative, optimizing application utility while meeting specified constraints.
The method includes capturing images of the environment, utilizing object detection and grasp detection modules to identify objects and generate grasping alternatives, aligning outputs to a common coordinate system, and computing attributes such as quality of grasp and object affiliation. Multiple neural networks are employed for object and grasp detection, with the HLSF module combining their outputs. The MCDM module ranks grasping alternatives based on various criteria mapped to attributes, with weights adjusted based on feedback from real-world executions. The system can be implemented in an autonomous system with sensors, processors, and memory to execute the method, providing a comprehensive solution for efficient and effective autonomous bin picking.
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