Kevin Ka-Ho Leung

US Patent:

20140070626, Mar 13, 2014

Filed:

Sep 11, 2012

Appl. No.:

13/610051

Inventors:

Yoko Parker - Pleasanton CA, US
Kevin Leung - Dublin CA, US
Antonios Zografos - Oakland CA, US

International Classification:

H03K 3/53

US Classification:

307106

Abstract:

The geometry of blumleins and how a switch is placed within the blumlein structure are considered. The thicker the switch, the higher the voltage it charged to without breaking down. A thicker switch can also provide a larger surface to illuminate. In one set of exemplary embodiments, the switch modules is displaced to the sides of the of the blumlein structures in a “necking” arrangement, where the switch region curves out a distance to the side. A stack of blumleins can then alternate sides, allowing for the switch region to each have a greater thickness. Another set of exemplary embodiments uses a tab structure: the top, middle and bottom conductors are all straight, but the top and middle conductors each include tabs, between which the switch is placed.

US Patent:

20200402665, Dec 24, 2020

Filed:

Jun 19, 2020

Appl. No.:

16/906796

Inventors:

- Milwaukee WI, US
Gopal Avinash - San Ramon CA, US
Yrjö Häme - Helsinki, FI
Ali Faisal - Helsinki, FI
Kevin Leung - San Ramon CA, US
Jeff Hersh - Waukesha WI, US

International Classification:

G16H 50/20
G16H 10/60
G16H 50/30
G16H 40/20
G06Q 10/04
G16H 50/70
G16H 70/20
A61B 5/00
G06T 11/20

Abstract:

Techniques are described for predicting readmissions of patients to an inpatient healthcare facility. In an embodiment, a method comprises applying, by a system comprising a processor, applying, by a system operatively coupled to a processor, a readmission risk forecasting model to medical history data for a patient, wherein the readmission risk forecasting model comprises an attention-based graph neural network (A-GNN). The method further comprises, based on the applying, generating, by the system, a readmission risk score for the patient that reflects a probability of readmission of the patient following discharge from an inpatient healthcare facility. The method further comprises facilitating providing, by the system, the readmission risk score to at least one of the patient or a clinician involved in care of the patient.

US Patent:

20200395195, Dec 17, 2020

Filed:

Sep 10, 2018

Appl. No.:

16/960818

Inventors:

- Fremont CA, US
Grayson FORD - San Jose CA, US
Darrell EHRLICH - San Jose CA, US
Aravind ALWAN - San Jose CA, US
Kevin LEUNG - Dublin CA, US
Anthony CONTRERAS - Stockton CA, US
Zhumin HAN - Fremont CA, US
Raphael CASAES - Alameda CA, US
Joanna WU - Redwood City CA, US

International Classification:

H01J 37/32
H01L 21/687
H01L 21/67

Abstract:

A first edge ring for a substrate support is provided. The first edge ring includes an annular-shaped body and one or more lift pin receiving elements. The annular-shaped body is sized and shaped to surround an upper portion of the substrate support. The annular-shaped body defines an upper surface, a lower surface, a radially inner surface, and a radially outer surface. The one or more lift pin receiving elements are disposed along the lower surface of the annular-shaped body and sized and shaped to receive and provide kinematic coupling with top ends respectively of three or more lift pins.

US Patent:

20200349434, Nov 5, 2020

Filed:

Jul 21, 2020

Appl. No.:

16/934650

Inventors:

- Milwaukee WI, US
Gopal B. Avinash - San Ramon CA, US
Zili Ma - San Ramon CA, US
Kevin H. Leung - San Ramon CA, US
Wen Jin - Fremont CA, US

International Classification:

G06N 3/08
G06F 16/28
G06N 3/04

Abstract:

Techniques are provided for determining confident data samples for machine learning (ML) models on unseen data. In one embodiment, a method is provided that comprises extracting, by a system comprising a processor, a feature vector for a data sample based on projection of the data sample onto a standard feature space. The method further comprises processing, by the system, the feature vector using an outlier detection model to determine whether the data sample is within a scope of a training dataset used to train a machine learning model, wherein the outlier detection model was trained using features extracted from the training dataset based on projection of data samples included in the training dataset onto the standard feature space.