Siemens Healthineers GAN Generates Medical Robot Configurations
Summary
The USPTO granted patent US12605218B2 to Siemens Healthineers AG on April 21, 2026. The patent covers a generative adversarial network (GAN) or other generative modeling technique trained to translate performance, operation, safety, or task-specific specifications into configurations of robot modules forming a robotic system. A second machine-learning system converts the estimated configurations back to performance, enabling anatomy-based modeling relative to medical imaging.
What changed
USPTO granted patent US12605218B2 to Siemens Healthineers AG for a machine-learned system using a generative adversarial network to generate medical robot configurations from specifications. The network translates anatomical, performance, and safety specifications into robot module layouts, with a second neural network converting configurations back to estimated performance for iterative optimization. The system enables anatomy-based modeling relative to medical imaging. Medical device manufacturers developing AI-driven robotic surgical or diagnostic systems should conduct freedom-to-operate analyses before commercializing similar configuration-generation capabilities, as the underlying generative modeling method may be covered by this patent family.
Archived snapshot
Apr 21, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Machine-learned network for medical robot generation from configurable modules
Grant US12605218B2 Kind: B2 Apr 21, 2026
Assignee
Siemens Healthineers AG
Inventors
Ankur Kapoor, Tommaso Mansi, Erin Girard
Abstract
A generative adversarial network (GAN) (21, 24), or any other generative modeling technique, is used to learn (12) how to generate (68) an optimal robotic system given performance, operation, safety, or any other specifications. For instance, the specifications may be modeled (65) relative to anatomy to confirm satisfaction of anatomy-based or another task specific constraint. A machine-learning system, for instance neural network, is trained (12) to translate given specifications to a robotic configuration. The network may convert task-specific specifications into one or more configurations of robot modules into a robotic system. The user may enter (67) changes to performance in order for the network to estimate (62) appropriate configurations. The configurations may be converted (64) to estimated performance by another machine-learning system, for instance neural network, allowing modeling (65) of operation relative to the anatomy, such as anatomy based on medical imaging. The configuration satisfying the constraints from the modeling (65) may be assembled (69) and used.
CPC Classifications
A61B 34/30 A61B 34/10 A61B 2034/105 A61B 2560/0443 B25J 9/163 B25J 9/1653
Filing Date
2020-01-24
Application No.
17754491
Claims
12
Parties
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