Min Zhang, 62Hamilton, OH

2010019, Aug 5, 2010

Jun 15, 2009

12/484997

Min Zhang - San Jose CA, US

A63B 31/00
A63B 69/00

482 55, 434254

An apparatus related to a swim training vest with which one or more water resistance induction ducts may be associated; consisting of a front panel and back panel secured together by an adjustable strap; generating a variable amount of resistance in the water by adjustment of a flap at the exhaust end of the water resistance induction ducts.

2021027, Sep 9, 2021

May 21, 2021

17/327239

- Wauwatosa WI, US
Min Zhang - San Ramon CA, US
Zili Ma - San Ramon CA, US
Gopal B. Avinash - San Ramon CA, US

G06T 7/00
G06N 3/08
G16H 30/40

Techniques are provided for deep neural network (DNN) identification of realistic synthetic images generated using a generative adversarial network (GAN). According to an embodiment, a system is described that can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise, a first extraction component that extracts a subset of synthetic images classified as non-real like as opposed to real-like, wherein the subset of synthetic images were generated using a GAN model. The computer executable components can further comprise a training component that employs the subset of synthetic images and real images to train a DNN network model to classify synthetic images generated using the GAN model as either real-like or non-real like

2020034, Nov 5, 2020

Jul 14, 2020

16/928462

- Schenectady NY, US
Min Zhang - San Ramon CA, US
Gopal Avinash - San Ramon CA, US

G06K 9/62

Systems and techniques for classification and localization based on annotation information are presented. In one example, a system trains a convolutional neural network based on training data and a plurality of images. The training data is associated with a plurality of patients from at least one imaging device. The plurality of images is associated with a plurality of masks from a plurality of objects. The convolutional neural network comprises a decoder consisting of at least one up-sampling layer and at least one convolutional layer. The system also generates a loss function based on the plurality of masks, where the loss function is iteratively back propagated to tune parameters of the convolutional neural network. The system also predicts a classification label for an input image based on the convolutional neural network.

2020033, Oct 29, 2020

Nov 27, 2019

16/697736

- Wauwatosa WI, US
Gopal Avinash - San Ramon CA, US
Min Zhang - San Ramon CA, US
Ravi Soni - San Ramon CA, US
Jiahui Guan - San Ramon CA, US
Dibyajyoti Pati - San Ramon CA, US
Zili Ma - San Ramon CA, US

A61B 5/00
G16H 50/30
G16H 40/67
G06N 3/08

Systems, apparatus, instructions, and methods for medical machine time-series event data processing are disclosed. An example time series event data processing apparatus includes memory storing instructions and one-dimensional time series healthcare-related data; and at least one processor. The example at least one processor is to: execute artificial intelligence model(s) trained on aggregated time series data to at least one of a) predict a future medical machine event, b) detect a medical machine event, or c) classify the medical machine event using the one-dimensional time series healthcare-related data; when the artificial intelligence model(s) are executed to predict the future medical machine event, output an alert related to the predicted future medical machine event to trigger a next action; and when the artificial intelligence model(s) are executed to detect and/or classify the medical machine event, label the medical machine event and output the labeled event to trigger the next action.

2020034, Oct 29, 2020

Nov 20, 2019

16/689798

- Wauwatosa WI, US
Min Zhang - San Ramon CA, US
Gopal B. Avinash - San Ramon CA, US
Venkata Ratnam Saripalli - San Ramon CA, US
Jiahui Guan - San Ramon CA, US
Dibyajyoti Pati - San Ramon CA, US
Zili Ma - San Ramon CA, US

G06N 20/20
G16H 10/60
G06N 3/04

Systems, apparatus, instructions, and methods for medical machine time-series event data generation are disclosed. An example synthetic time series data generation apparatus is to generate a synthetic data set including multi-channel time-series data and associated annotation using a first artificial intelligence network model. The example apparatus is to analyze the synthetic data set with respect to a real data set using a second artificial intelligence network model. When the second artificial intelligence network model classifies the synthetic data set as a first classification, the example apparatus is to adjust the first artificial intelligence network model using feedback from the second artificial intelligence network model. When the second artificial intelligence network model classifies the synthetic data set as a second classification, the example apparatus is to output the synthetic data set.

2020034, Oct 29, 2020

Oct 17, 2019

16/656034

- Wauwatosa WI, US
Qian Zhao - San Ramon CA, US
Zili Ma - San Ramon CA, US
Dibyajyoti Pati - San Ramon CA, US
Venkata Ratnam Saripalli - San Ramon CA, US
Ravi Soni - San Ramon CA, US
Jiahui Guan - San Ramon CA, US
Min Zhang - San Ramon CA, US

G16H 15/00
G16H 40/67
G06F 9/451
G06N 20/00
G16H 10/60

Systems, apparatus, instructions, and methods for medical machine time-series event data processing are disclosed. An example apparatus includes a data processor to process one-dimensional data captured over time with respect to patient(s). The example apparatus includes a visualization processor to transform the processed data into graphical representations and to cluster the graphical representations including the first graphical representation into at least first and second blocks arranged with respect to an indicator of a criterion to provide a visual comparison of the first block and the second block with respect to the criterion. The example apparatus includes an interaction processor to facilitate interaction, via the graphical user interface, with the first and second blocks of graphical representations to extract a data set for processing from at least a subset of the first and second blocks.

2020031, Oct 1, 2020

Mar 28, 2019

16/368101

- Schenectady NY, US
Gopal B. Avinash - San Ramon CA, US
Min Zhang - San Ramon CA, US

G06K 9/62
G06N 20/00

Systems and techniques for providing concurrent image and corresponding multi-channel auxiliary data generation for a generative model are presented. In one example, a system generates synthetic multi-channel data associated with a synthetic version of imaging data. The system also predicts multi-channel imaging data and the synthetic multi-channel data with a first predicted class set or a second predicted class set. Furthermore, the system employs the first predicted class set or the second predicted class set for the synthetic multi-channel data to train a generative adversarial network model.

2020031, Oct 1, 2020

Mar 27, 2019

16/366455

- Schenectady NY, US
Zili Ma - San Ramon CA, US
Min Zhang - San Ramon CA, US
Gopal B. Avinash - San Ramon CA, US

G06N 3/08
G06N 3/04
G06N 20/00
G06F 17/18
G06K 9/62

Techniques are provided for evaluating and defining the scope of data-driven deep learning models. In one embodiment, a machine-readable storage medium is provided comprising executable instructions that, when executed by a processor, facilitate performance of operations comprising employing a machine learning model to extract first training data features included in a training data set and first target data features included in a target data set. The operations further comprise determining whether the target data set is within a defined data scope of the training data set based on analysis of correspondences between the first training data features and the first target data feature, and determining whether application of the target data set to a target neural network model developed using the training data set will generate results with an acceptable level of accuracy based on whether the target data set is within the defined data scope.