Auris Health, Inc.

Auris Surgical Robotics, Inc.

Alife Health Inc.

Mark A Lown

David S Mintz

Frederic H Moll

Enrique Romo

Yanan Huang

Allen Jiang

Jason Tomas Wilson

Michael Shyh-Yen Ho

Edward Joseph Menard

Siddharth Oli

Nicholas J Eyre

Alexander Tarek Hassan

Paxton H Maeder-York

Ying Mao

Ryan J Murphy

Jason Thomas Wilson

Kevin Loewke

Arielle Sarah Rothman

Jordan Tang

Michael Fanton

Melissa Teran

Veronica Isabella Nutting

Pouya Sabetian

Felix Malinkevich

Janet Helene Goldenstein

Siddarth Oli

Certain aspects relate to admittance control modes for a robotic surgery system. The admittance control modes can be based on detecting and/or measuring forces (rotational and/or nonrotational) on a robotic arm and moving the robotic arm in response to such interactions. The forces can include direct manual interaction with the robotic arm by a clinician. The movement of the robotic arm can be within a nullspace that maintains the positions of a medical instrument.

Passive and active arm control schemes with sensor integration to support tele-operation and direct manual interaction

Robotic systems can be capable of collision detection and avoidance. A robotic medical system can include a robotic arm, an input device configured to receive one or more user inputs for controlling the robotic arm, and a display configured to provide information related to the robotic medical system. The display can include a first icon that is representative of the robotic arm and includes at least a first state and a second state. The robotic medical system can be configured to control movement of the robotic arm based on the user inputs received at the input device in real time, determine a distance between the robotic arm and a component, and provide information to the user about potential, near, and/or actual collisions between the arm and the component.

Robotic medical system with collision proximity indicators

Systems and methods for implementing machine-learning models for ovarian stimulation is described herein. In some variations, a computer-implemented method may include optimizing an ovarian stimulation process may include receiving patient-specific data associated with a patient, and predicting an egg outcome for the patient for each of a plurality of treatment options for an ovarian stimulation process based on at least one predictive model and the patient-specific data, where the at least one predictive model is trained using prior patient-specific data associated with a plurality of prior patients.

Machine learning for optimizing ovarian stimulation

The systems and methods disclosed herein are directed to robotically controlling a medical device to utilize manual skills and techniques developed by surgeons. The system can include an emulator representing a medical device. The system can include at least one detector configured to track the emulator. The system can also include an imaging device configured to track the medical device. The system may be configured to move the medical device to reduce an alignment offset between the location of the emulator and the location of the medical device, to move the imaging device based on the translational movement of the emulator, and/or to move the medical device based on data indicative of an orientation of the emulator.

System with emulator movement tracking for controlling medical devices

Systems and methods for moving or manipulating robotic arms are provided. A group of robotic arms are configured to form a virtual rail or line between the end effectors of the robotic arms. The robotic arms are responsive to outside force such as from a user. When a user moves a single one of the robotic arms, the other robotic arms will automatically move to maintain the virtual rail alignments. The virtual rail of the robotic arm end effectors may be translated in one or more of three dimensions. The virtual rail may be rotated about a point on the virtual rail line. The robotic arms can detect the nature of the contact from the user and move accordingly. Holding, shaking, tapping, pushing, pulling, and rotating different parts of the robotic arm elicits different movement responses from different parts of the robotic arm.

Configurable robotic surgical system with virtual rail and flexible endoscope

The systems and methods disclosed herein are directed to robotically controlling a medical device to utilize manual skills and techniques developed by surgeons. The system may comprise an emulator representing a medical device. The system may comprise at least one detector configured to track the emulator. The system may further comprise an imaging device configured to track the medical device. The system may be configured to move the medical device to reduce an alignment offset between the location of the emulator and the location of the medical device, to move the imaging device based on the translational movement of the emulator, and/or to move the medical device based on data indicative of an orientation of the emulator.

Growing community of inventors

Union City, CA, United States of America

Mark A Lown