Hong Wu

20020192256, Dec 19, 2002

Dec 21, 2001

10/026361

Chang-An Wu - Burlingame CA, US
Hong Wu - Burlingame CA, US
Lin Lei - Meishan, CN

A01N043/40
A01N025/00
A01N037/02

424/405000, 514/546000, 514/343000

Compositions and methods are provided for controlling pests by using cocktails of plant alkaloids. In particular, the pesticidal composition is formulated with water-miscible solvent and comprises two or more plant alkaloids selected from the group consisting of toosendanin, azadirachtin, tomatine, stemonine, nicotine, anabasine, matrine, oxymatrine, sophocarpine, N-oxysophocarpine, cytisine, and aloperine. The water-miscible pesticide can be used to protect crop plants, wood structures and animals from damages by harmful pests, overcome resistance of pests to current commercial pesticides, and reduce contamination to the environment.

20220383042, Dec 1, 2022

May 25, 2021

17/329528

- Mountain View CA, US
Hong Wu - Los Altos CA, US
Bodi Yuan - Sunnyvale CA, US
Zihao Li - San Jose CA, US

G06K 9/62

Implementations are described herein for automatically labeling synthetic plant parts in synthetic training images, where the synthetic training images and corresponding labels can be used as training data for training machine learning models to detect, segment, and/or classify various parts of plants in digital images. In various implementations, a digital image may be obtained that captures an area. The synthetic training image may be generated to depict one or more three-dimensional synthetic plants in the area. In many implementations, a plant mask, identifying individual plants as a whole in the synthetic training image, as well as a part mask, uniquely identifying one or more parts of the synthetic plant models, can be overlaid on the synthetic training image to label the one or more parts of the synthetic plant models.

20220358265, Nov 10, 2022

May 4, 2021

17/307849

- Mountain View CA, US
Bodi Yuan - Sunnyvale CA, US
Zhiqiang Yuan - San Jose CA, US
Hong Wu - Los Altos CA, US
Daniel Ribeiro Silva - San Jose CA, US
Zihao Li - San Jose CA, US

G06F 30/27
G06T 17/00
G06N 3/04

Implementations are described herein for realistic plant growth modeling and various applications thereof. In various implementations, a plurality of two-dimensional (2D) digital images that capture, over time, one or more of a particular type of plant based on one or more machine learning models to generate output, may be processed. The output may be analyzed to extract temporal features that capture change over time to one or more structural features of the particular type of plant. Based on the captured temporal features, a first parameter subspace of whole plant parameters may be learned, wherein the whole plant parameters are usable to generate a three-dimensional (3D) growth model that realistically simulates growth of the particular type of plant over time. Based on the first parameter subspace, one or more 3D growth models that simulate growth of the particular type of plant may be non-deterministically generated and used for various purposes.

20210117447, Apr 22, 2021

Oct 16, 2019

16/655111

- George Town, KY
Hong WU - San Mateo CA, US
Tieying ZHANG - San Mateo CA, US
Ruiping LI - San Mateo CA, US
Fang ZHENG - San Mateo CA, US
Xiaowei ZHU - San Mateo CA, US
Congnan LUO - San Mateo CA, US
Cheng ZHU - San Mateo CA, US

G06F 16/28
G06F 16/2455
G06F 16/22
G06K 9/62
G06N 3/08
G06N 20/10

The present disclosure provides a method for generating a classification model in a database system. The method comprises generating a plurality of database tables by performing data clustering on a database, acquiring a plurality of queries associated with information from the database, generating training data by executing each of the plurality of queries on the plurality of database tables, and training a classification model using the training data, wherein the classification model is used to select a database table for a query based on corresponding performance information associated with the query on the database during executing of the query. The present disclosure also provides a method for executing a query on a database using a classification model. The method comprises obtaining the classification model, obtaining a database query, generating a target table by inputting the database query into the classification model, and executing the database query on the target table. In addition, the present disclosure provides database systems and non-transitory computer-readable media for generating a classification model or executing a query on a database using a classification model.

20190066732, Feb 28, 2019

Oct 25, 2018

16/171116

- Wilmington DE, US
Zheng Yuan - San Jose CA, US
Yu Huang - Sunnyvale CA, US
Dapeng Oliver Wu - Gainesville FL, US
Hong Heather Wu - West Windsor NJ, US

G11B 27/031
G11B 27/034
G11B 27/28
G06K 9/00

In an embodiment, an apparatus and method of creating a skimming preview of a video includes electronically receiving a plurality of video shots, analyzing each frame in a video shot from the plurality of video shots, where analyzing includes determining a saliency of each frame of the video shot. The method also includes determining a key frame of the video shot based on the saliency of each frame the video shot, extracting visual features from the key frame, performing shot clustering of the plurality of video shots to determine concept patterns based on the visual features, and generating a reconstruction reference tree based on the shot clustering. The reconstruction reference tree includes video shots categorized according to each concept pattern.

20190025297, Jan 24, 2019

Sep 14, 2014

14/485791

- San Antonio TX, US
Hong Ye Wu - San Francisco CA, US
Catherine Luk - San Francisco CA, US

G01N 33/543
B01L 3/00
G05D 7/06
G06F 19/12
G06F 17/18
G06F 19/24
G06F 19/28
C12M 1/36
G01N 35/10

A “breadboard” approach by which a biochemical pathway under study is separated or segmented into smaller portions, at least one of which can to a degree of approximation be accurately emulated with a replica microscale and/or nanoscale fluidic implementation whose constituent species, inhibitor(s), catalyst(s) and other reaction agent(s) can be closely controlled and at least one aspect of whose behavior can be measured. Control and measurement information interfaces with a computer that executes algorithms comprising one or more of a control process, control event-script, experiment, data recording, and mathematical model. A model can be used to simulate the actions, behavior, or other aspects of another portion of the biochemical signaling process, pathway, or network. Replica constituents can include enzymes, other proteins, lipids, ions, peptides, and other materials provided under controlled conditions and timing, as well as varying degrees of competitive species, drugs, environmental influences, and substitute or representative molecular crowding.

20140143336, May 22, 2014

Oct 11, 2013

14/052662

- Mountain View CA, US
Balamurugan Krishnan - Daly City CA, US
Hong Wu - San Jose CA, US
Leslie Andrew Prock - Edgewood WA, US
Gregory Charles Lindley - Sunnyvale CA, US
Sivasubramanian Muthukumarasamy - Santa Clara CA, US
Christopher Steven Lock - Scotts Valley CA, US
Avinash Shetty - San Jose CA, US

Peel Technologies, Inc. - Mountain View CA

H04L 29/06

709204

A Device-Based Control System (DBCS) enables a device-agnostic and source-agnostic entertainment experience through use of an internet-enabled device (IED). The IED includes a media management application for navigating through media or entertainment content, controlling media devices according to a type of media content selected by the user, and sharing media experiences via social networks. The IED includes smartphones, tablet computers, and other internet-enabled processor-based devices. The DBCS leverages the internet access of the IED to enable search and discovery of all available media content. A content recommendation system (CRS) coupled to the media management application learns media preferences from user behavior, generates from numerous disparate media sources media choices corresponding to the media preferences, and presents the media choices on the IED.