Vivek K Singh, 4028 Taylor Rd, Princeton, NJ 08540

2013034, Dec 26, 2013

Jun 10, 2013

13/913685

Cheng-Hao Kuo - Plainsboro NJ, US
Vinay Damodar Shet - Princeton NJ, US
Sameh Khamis - College Park MD, US
Larry Davis - Potomac MD, US
Vivek Kumar Singh - Monmouth Junction NJ, US

SIEMENS CORPORATION - Iselin NJ

G06K 9/62

382159

Automated person re-identification may be assisted by consideration of attributes of the person in a joint classification with matching of the person. By both solving for similarities in a plurality of attributes and identities, discriminative interactions may be captured. Automated person re-identification may be assisted by consideration of a semantic color name. Rather than a color histogram, probability distributions are mapped to color terms of the semantic color name. Using other descriptors as well, similarity measures for the various descriptors are weighted and combined into a score. Either or both considerations may be used.

2022038, Dec 1, 2022

Oct 22, 2020

17/755477

- Tarrytown NY, US
Benjamin S. Pollack - Jersey City NJ, US
Yao-Jen Chang - Princeton NJ, US
Venkatesh NarasimhaMurthy - Hillsborough NJ, US
Vivek Singh - Princeton NJ, US
Ankur Kapoor - Plainsboro NJ, US

Siemens Healthcare Diagnostics Inc. - Tarrytown NY

G06V 10/764
G06V 10/774
G06V 10/82
G06V 20/60
G06V 10/56

A method of training a model of a diagnostic apparatus includes providing one or more first tube assemblies of a first type and one or more second tube assemblies of a second type in a diagnostic apparatus; capturing one or more first images of at least a portion of each of the one or more first tube assemblies and the second tube assemblies using the imaging device. Training the model includes identifying tube assemblies of the first type and tube assemblies of the second type based on the one or more first images and the one or more second images. Tubes assemblies of the first type are grouped into a first group and tube assemblies of the second type are grouped into a second group. Other methods and apparatus are disclosed.

2022034, Nov 3, 2022

Mar 30, 2022

17/657181

- Erlangen, DE
Vivek Singh - Princeton NJ, US
Ankur Kapoor - Plainsboro NJ, US
Andreas Prokein - Bubenreuth, DE
Dorin Comaniciu - Princeton NJ, US

A61B 6/00
A61B 6/03

CT scan parameters for performing a CT scan of an anatomical target region of a patient are determined and/or adjusted. An initial set of the CT scan parameters for starting to perform the CT scan is determined based on an initial set of attenuation curves associated with the anatomical target region of the patient. The initial set of attenuation curves are determined based on optical imaging data depicting the patient.

2022033, Oct 20, 2022

Mar 21, 2022

17/699619

- Erlangen, DE
Boris Mailhe - Plainsboro NJ, US
Vivek Singh - Princeton NJ, US
Ankur Kapoor - Plainsboro NJ, US
Mariappan S. Nadar - Plainsboro NJ, US

G01R 33/3875
G01R 33/56
G01R 33/44
G06N 20/00

Object specific in-homogeneities in an MRI system are corrected. Prescan information available at the MR imaging system is determined. The prescan information includes at least object specific information of an object located in the MR imaging system from which an MR image is to be generated. The prescan information does not include a B1 map of the MRI system with the object being present in the MR imaging system. The prescan information is applied to a trained machine learning module provided at the MRI system. The trained machine learning module determines and generates shimming information as output. The shimming information is applied to a shimming module of the MR imaging system, wherein the shimming module uses the shimming information to generate a corrected magnetic field B0.

2022028, Sep 15, 2022

Mar 9, 2021

17/249651

- Erlangen, DE
Diana Ioana Stoian - Jersey City NJ, US
Puneet Sharma - Princeton Junction NJ, US
Max Schöbinger - North Brunswick NJ, US
Vivek Singh - Princeton NJ, US

A61B 6/00
G06K 9/46
G06T 7/11
G06K 9/62
G06T 11/00
G06T 7/00
G06N 20/00
G16H 30/20

Systems and methods for automated assessment of a vessel are provided. One or more input medical images of a vessel of a patient are received. A plurality of vessel assessment tasks for assessing the vessel is performed using a machine learning based model trained using multi-task learning. The plurality of vessel assessment tasks are performed by the machine learning based model based on shared features extracted from the one or more input medical images. Results of the plurality of vessel assessment tasks or a combination of the results of the plurality of vessel assessment tasks are output.

2023011, Apr 13, 2023

Dec 12, 2022

18/064366

- Erlangen, DE
Shun Miao - Bethesda MD, US
Pierre de Tournemire - Nancy, FR
Julian Krebs - Moers, DE
Li Zhang - Princeton NJ, US
Bogdan Georgescu - Princeton NJ, US
Sasa Grbic - Plainsboro NJ, US
Vivek Kumar Singh - Princeton NJ, US
Daguang Xu - Princeton NJ, US
Tommaso Mansi - Plainsboro NJ, US
Ali Kamen - Skillman NJ, US
Dorin Comaniciu - Princeton NJ, US

G06T 7/00
G06T 7/30
A61B 5/00

Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

2023010, Mar 30, 2023

Sep 29, 2021

17/449263

- Erlangen, DE
Eli Gibson - Plainsboro NJ, US
Thomas Re - Monroe NJ, US
Dorin Comaniciu - Princeton NJ, US
Vivek Singh - Princeton NJ, US

G06T 7/00
G06T 7/143

Systems and methods for generating a probabilistic tree of vessels are provided. An input medical image of vessels of a patient is received. Anatomical landmarks are identified in the input medical image. A centerline of the vessels in the input medical image is determined based on the anatomical landmarks. A probabilistic tree of the vessels is generated based on a probability of fit of the anatomical landmarks and the centerline of the vessels. The probabilistic tree of the vessels is output.

2023009, Mar 23, 2023

Feb 25, 2021

17/905101

- Tarrytown NY, US
Vivek Singh - Princeton NJ, US
Bogdan Georgescu - Princeton NJ, US
Ankur Kapoor - Plainsboro NJ, US

Siemens Healthcare Diagnostics Inc. - Tarrytown NY

G06F 11/07
G06N 3/08
G06N 3/0455

The disclosure provides a computer-implemented method for detecting a failure of a device, wherein the device is connected to a sensor, the method comprising: receiving, by a machine learning model, a trace signal from the sensor indicating a status of the device; encoding, by the machine learning model, the trace signal into a plurality of vector representations; and determining, by the machine learning model, whether the trace signal is valid or invalid based on the plurality of vector representations.