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公开(公告)号：US12186913B2
公开(公告)日：2025-01-07
申请号：US17564919
申请日：2021-12-29
Inventor： Ziyan Wu , Srikrishna Karanam , Meng Zheng , Abhishek Sharma
IPC: G06V20/52 , A61B5/055 , A61B6/03 , A61B6/10 , A61B34/30 , B25J9/16 , B25J13/08 , G05D1/00 , G06T7/215 , G06T7/254 , G06T19/00 , G06V10/25 , G06V10/70 , G06V20/64
Abstract: An apparatus for automated collision avoidance includes a sensor configured to detect an object of interest, predicting a representation of the object of interest at a future point in time, calculating an indication of a possibility of a collision with the object of interest based on the representation of the object of interest at the future point in time, and executing a collision avoidance action based on the indication.
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公开(公告)号：US20240341903A1
公开(公告)日：2024-10-17
申请号：US18134234
申请日：2023-04-13
Inventor： Abhishek Sharma , Arun Innanje , Terrence Chen
CPC classification number: A61B90/36 , A61B34/10 , A61B2034/105 , A61B2090/365
Abstract: An object or person in a medical environment may be identified based on images of the medical environment. The identification may include determining an identifier associated with the object or the person, a position of the object or the person in the medical environment, and a three-dimensional (3D) shape/pose of the object or the person. Representation information that indicates at least the determined identifier, position in the medical environment, and 3D shape/pose of the object or the person may be generated and then used (e.g., by a visualization device) together with one or more predetermined 3D models to determine a 3D model for the object or the person identified in the medical environment and generate a visual depiction of at least the object or the person in the medical environment based on the determined 3D model and the position of the object or the person in the medical environment.
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公开(公告)号：US20240331446A1
公开(公告)日：2024-10-03
申请号：US18126853
申请日：2023-03-27
Inventor： Zhongpai Gao , Abhishek Sharma , Meng Zheng , Benjamin Planche , Ziyan Wu , Terrence Chen
CPC classification number: G06V40/20 , G06V10/26 , G06V10/40 , G06V10/82 , G06V40/11 , G06V10/774 , G06V2201/03
Abstract: Automatic hand gesture determination may be a challenging task considering the complex anatomy and high dimensionality of the human hand. Disclosed herein are systems, methods, and instrumentalities associated with recognizing a hand gesture in spite of the challenges. An apparatus in accordance with embodiments of the present disclosure may use machine learning based techniques to identify the area of an image that may contain a hand and to determine an orientation of the hand relative to a pre-defined direction. The apparatus may then adjust the area of the image containing the hand to align the orientation of the hand with the pre-defined direction and/or to scale the image area to a pre-defined size. Based on the adjusted image area, the apparatus may detect a plurality of hand landmarks and predict a gesture indicated by the hand based on the plurality of detected landmarks.
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公开(公告)号：US12051204B2
公开(公告)日：2024-07-30
申请号：US17538232
申请日：2021-11-30
Inventor： Ziyan Wu , Srikrishna Karanam , Meng Zheng , Abhishek Sharma
CPC classification number: G06T7/0014 , G06N3/08 , G06T7/50 , G06T7/74 , G16H30/40 , G16H50/50 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004
Abstract: The shape and/or location of an organ may change in accordance with changes in the body shape and/or pose of a patient. Described herein are systems, methods, and instrumentalities for automatically determining, using an artificial neural network (ANN), the shape and/or location of the organ based on human models that reflect the body shape and/or pose the patient. The ANN may be trained to learn the spatial relationship between the organ and the body shape or pose of the patient. Then, at an inference time, the ANN may be used to determine the relationship based on a first patient model and a first representation (e.g., a point cloud) of the organ so that given a second patient model thereafter, the ANN may automatically determine the shape and/or location of the organ corresponding to the body shape or pose of the patient indicated by the second patient model.
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公开(公告)号：US12045695B2
公开(公告)日：2024-07-23
申请号：US16804907
申请日：2020-02-28
Inventor： Srikrishna Karanam , Ziyan Wu , Abhishek Sharma , Arun Innanje , Terrence Chen
Abstract: Data samples are transmitted from a central server to at least one local server apparatus. The central server receives a set of predictions from the at least one local server apparatus that are based on the transmitted set of data samples. The central server trains a central model based on the received set of predictions. The central model, or a portion of the central model corresponding to a task of interest, can then be sent to the at least one local server apparatus. Neither local data from local sites nor trained models from the local sites are transmitted to the central server. This ensures protection and security of data at the local sites.
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Patent Agency Ranking
公开(公告)号：US11937967B2
公开(公告)日：2024-03-26
申请号：US18149111
申请日：2023-01-01
Inventor： Ziyan Wu , Srikrishna Karanam , Meng Zheng , Abhishek Sharma , Ren Li
Abstract: Systems, methods and instrumentalities are described herein for automating a medical environment. The automation may be realized using one or more sensing devices and at least one processing device. The sensing devices may be configured to capture images of the medical environment and provide the images to the processing device. The processing device may determine characteristics of the medical environment based on the images and automate one or more aspects of the operations in the medical environment. These characteristics may include, e.g., people and/or objects present in the images and respective locations of the people and/or objects in the medical environment. The operations that may be automated may include, e.g., maneuvering and/or positioning a medical device based on the location of a patient, determining and/or adjusting the parameters of a medical device, managing a workflow, providing instructions and/or alerts to a patient or a physician, etc.
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27.发明公开公开(公告)号：US20240077562A1
公开(公告)日：2024-03-07
申请号：US17939251
申请日：2022-09-07
Inventor： Abhishek Sharma , Arun Innanje , Ziyan Wu , Terrence Chen
CPC classification number: G01R33/5608 , A61B5/055 , A61N5/1049 , A61N2005/1055
Abstract: A power management apparatus for a workflow to enable low power MR patient positioning on edge devices is disclosed. The power management apparatus changes an operational mode of an edge device from a first power mode to a second power mode after a defined time-interval. The power management apparatus further controls the edge device to capture a first image of a first scene. The power management apparatus further determines a trigger point based on a detection of a plurality of objects in the captured first image. The power management apparatus further changes the operational mode of the edge device from the second power mode to a third power mode to control a consumption of electric power while a set of operations is executed at the edge device. The operational mode of the edge device may be changed at the determined trigger point.
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公开(公告)号：US20240070905A1
公开(公告)日：2024-02-29
申请号：US17897465
申请日：2022-08-29
Inventor： Benjamin Planche , Ziyan Wu , Meng Zheng , Abhishek Sharma
IPC: G06T7/73
CPC classification number: G06T7/74 , G06T2207/30196
Abstract: The 3D pose of a person may be estimated by triangulating 2D representations of body keypoints (e.g., joint locations) of the person. The triangulation may leverage various metrics such as confidence scores associated with the 2D representations of a keypoint and/or temporal consistency between multiple 3D representations of the keypoint. The 2D representations may be arranged into groups, a candidate 3D representation may be determined for each group, taking into account of the confidence score of each 2D representation in the group, and the candidate 3D representation that has the smallest error may be used to represent the keypoint. Other 3D representation(s) of the keypoint determined from images taken at different times may be used to refine the 3D representation of the keypoint.
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公开(公告)号：US11756240B2
公开(公告)日：2023-09-12
申请号：US16804985
申请日：2020-02-28
Inventor： Arun Innanje , Shanhui Sun , Abhishek Sharma , Zhang Chen , Ziyan Wu
CPC classification number: G06T11/005 , G06T1/20 , G06T19/20 , G06T2207/10081 , G06T2207/10088 , G06T2207/10104 , G06T2207/30004
Abstract: A standalone image reconstruction device is configured to reconstruct the raw signals received from a radiology scanner device into a reconstructed output signal. The image reconstruction device is a vendor neutral interface between the radiology scanner device and the post processing imaging device. The reconstructed output signal is a user readable domain that can be used to generate a medical image or a three-dimensional (3D) volume. The apparatus is configured to reconstruct signals from different types of radiology scanner devices using any suitable image reconstruction protocol.
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公开(公告)号：US11604984B2
公开(公告)日：2023-03-14
申请号：US16686539
申请日：2019-11-18
Inventor： Abhishek Sharma , Arun Innanje , Ziyan Wu , Shanhui Sun , Terrence Chen
Abstract: A system comprising a first computing apparatus in communication with multiple second computing apparatuses. The first computing apparatus may obtain a plurality of first trained machine learning models for a task from the multiple second computing apparatuses. At least a portion of parameter values of the plurality of first trained machine learning models may be different from each other. The first computing apparatus may also obtain a plurality of training samples. The first computing apparatus may further determine, based on the plurality of training samples, a second trained machine learning model by learning from the plurality of first trained machine learning models.
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