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公开(公告)号：US20230013508A1
公开(公告)日：2023-01-19
申请号：US17378495
申请日：2021-07-16
Inventor： Abhishek Sharma , Arun Innanje , Ziyan Wu
Abstract: Image-based key points detection using a convolutional neural network (CNN) may be impacted if the key points are occluded in the image. Images obtained from additional imaging modalities such as depth and/or thermal images may be used in conjunction with RGB images to reduce or minimize the impact of the occlusion. The additional images may be used to determine adjustment values that are then applied to the weights of the CNN so that the convolution operations may be performed in a modality aware manner to increase the robustness, accuracy, and efficiency of key point detection.
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公开(公告)号：US11386537B2
公开(公告)日：2022-07-12
申请号：US16802989
申请日：2020-02-27
Inventor： Abhishek Sharma , Meng Zheng , Srikrishna Karanam , Ziyan Wu , Arun Innanje , Terrence Chen
Abstract: Abnormality detection within a defined area includes obtaining a plurality of images of the defined area from image-capture devices. An extent of deviation of one or more types of products from an inference of each of the plurality of images is determined using a trained neural network. A localized dimensional representation is generated in a portion of an input image associated with a first location of the plurality of locations, based on gradients computed from the determined extent of deviation. The generated localized dimensional representation provides a visual indication of an abnormality located in the first location within the defined area. An action associated with the first location is executed based on the generated dimensional representation for proactive control or prevention of occurrence of undesired event in the defined area.
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公开(公告)号：US20210272258A1
公开(公告)日：2021-09-02
申请号：US16802989
申请日：2020-02-27
Inventor： Abhishek Sharma , Meng Zheng , Srikrishna Karanam , Ziyan Wu , Arun Innanje , Terrence Chen
Abstract: Abnormality detection within a defined area includes obtaining a plurality of images of the defined area from image-capture devices. An extent of deviation of one or more types of products from an inference of each of the plurality of images is determined using a trained neural network. A localized dimensional representation is generated in a portion of an input image associated with a first location of the plurality of locations, based on gradients computed from the determined extent of deviation. The generated localized dimensional representation provides a visual indication of an abnormality located in the first location within the defined area. An action associated with the first location is executed based on the generated dimensional representation for proactive control or prevention of occurrence of undesired event in the defined area.
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公开(公告)号：US20210272014A1
公开(公告)日：2021-09-02
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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公开(公告)号：US20210090736A1
公开(公告)日：2021-03-25
申请号：US16580053
申请日：2019-09-24
Inventor： Arun Innanje , Ziyan Wu , Abhishek Sharma , Srikrishna Karanam
Abstract: The present disclosure relates to systems and methods for anomaly detection for a medical procedure. The method may include obtaining image data collected by one or more visual sensors via monitoring a medical procedure and a trained machine learning model for anomaly detection. The method may include determining a detection result for the medical procedure based on the image data using the trained machine learning model. The detection result may include whether an anomaly regarding the medical procedure exists. In response to the detection result that the anomaly exists, the method may further include providing feedback relating to the anomaly.
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Patent Agency Ranking
公开(公告)号：US20240378731A1
公开(公告)日：2024-11-14
申请号：US18195009
申请日：2023-05-09
Inventor： Zhongpai Gao , Abhishek Sharma , Meng Zheng , Benjamin Planche , Ziyan Wu , Terrence Chen
Abstract: Detecting motions associated with a body part of a patient may include using an image sensor installed inside a medical scanner to capture first and second images of the patient inside the medical scanner, wherein the first image may depict the patient in a first state and the second image may depict the patient in a second state. A first area, in the first image, that corresponds to the body part of the patient may be identified and a second area, in the second image, that corresponds to the body part may also be identified so that a first plurality of features may be extracted from the first area of the first image and a second plurality of features may be extracted from the second area of the second image. A motion associated with the body part of the patient may be determined based on the first and second pluralities of features.
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公开(公告)号：US20240061951A1
公开(公告)日：2024-02-22
申请号：US17891307
申请日：2022-08-19
Inventor： Arun Innanje , Abhishek Sharma , Benjamin Planche , Meng Zheng , Shanhui Sun , Ziyan Wu , Terrence Chen
CPC classification number: G06F21/6245 , H04L9/50 , H04L9/32 , G16H10/60 , G16H50/20 , G06F2221/2141
Abstract: A method and a system for managing healthcare records of a user are provided. The method includes storing an electronic medical record related to the user in form of a non-fungible token (NFT) written to a blockchain, associating a smart contract to the NFT in the blockchain, authorizing a request to access the electronic medical record related to the user based on the defined ownership of the electronic medical record stored in the blockchain, identifying one or more NFTs from the blockchain comprising one or more electronic medical records related to the user based on processing of the identifier information in associated one or more smart contracts therewith, in response to the request, and sending the one or more electronic medical records corresponding to the identified one or more NFTs to a requestor associated with the request.
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公开(公告)号：US11896408B2
公开(公告)日：2024-02-13
申请号：US17525488
申请日：2021-11-12
Inventor： Meng Zheng , Abhishek Sharma , Srikrishna Karanam , Ziyan Wu
CPC classification number: A61B6/0407 , G06F18/24 , G06N20/20 , G06T7/70 , G06T2207/20084
Abstract: Automated patient positioning and modelling includes a hardware processor to obtain image data from an imaging sensor, classify the image data, using a first machine learning model, as a patient pose based on one or more pre-defined protocols for patient positioning, provide a confidence score based on the classification of the image data and if the confidence score is less than a pre-determined value, re-classify the image data using a second machine learning model; or if the confidence score is greater than a pre-determined value, identify the image data as corresponding to a patient pose based on one or more pre-defined protocols for patient positioning during a scan procedure.
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公开(公告)号：US20230414132A1
公开(公告)日：2023-12-28
申请号：US17849109
申请日：2022-06-24
Inventor： Abhishek Sharma , Arun Innanje , Benjamin Planche , Meng Zheng , Shanhui Sun , Ziyan Wu , Terrence Chen
CPC classification number: A61B5/1124 , A61B5/1113 , A61B5/749 , A61B5/7267 , A61B5/4848 , G09B19/003 , A61B2505/09 , G06F3/011
Abstract: A system for providing rehabilitation in a virtual environment includes an extended reality (XR) headset to present a first rehabilitation therapy to a patient in a virtual environment. A sensing device is configured to track physical movements of the patient and a processor is configured to receive the sensing data to determine pose information. The processor is configured to determine a performance metric associated with the physical movements and compare the performance metric with a reference metric to determine whether the patient has successfully performed the defined physical movements. The processor is configured to change the first rehabilitation therapy to a second rehabilitation therapy based on a difference between the performance metric and the reference metric upon determining that the patient has unsuccessfully performed the defined physical movements. The system aids the patient by changing the rehabilitation therapies according to the performance of the patient.
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公开(公告)号：US20230206496A1
公开(公告)日：2023-06-29
申请号：US17564792
申请日：2021-12-29
Inventor： Ziyan Wu , Srikrishna Karanam , Meng Zheng , Abhishek Sharma
IPC: G06T7/80
CPC classification number: G06T7/80
Abstract: Automatically validating the calibration of an visual sensor network includes acquiring image data from visual sensors that have partially overlapping fields of view, extracting a representation of an environment in which the visual sensors are disposed, calculating one or more geometric relationships between the visual sensors, comparing the calculated one or more geometric relationships with previously obtained calibration information of the visual sensors, and verifying a current calibration of the visual sensors based on the comparison.
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