Yu Huei Lee from Somerset, NJ, age 51

Fluorinated Pyridine N-Oxide Thrombin Modulators And Process For N-Oxidation Of Nitrogen Containing Heteroaryls

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US Patent:

7262210, Aug 28, 2007

Filed:

Mar 21, 2006

Appl. No.:

11/385056

Inventors:

Kevin Kreutter - Plainsboro NJ, US
Tianbao Lu - Churchville PA, US
Yu Kai Lee - Exton PA, US
Christopher Teleha - Fort Washington PA, US
Mark Player - Phoenixville PA, US
Xizhen Zhu - Cranbury NJ, US

Assignee:

Janssen Pharmaceutica N.V. - Beerse

International Classification:

A61K 31/44
C07D 213/00

US Classification:

514332, 546265

Abstract:

The present invention describes compounds of Formula I.

Plant Growing System

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US Patent:

20090025287, Jan 29, 2009

Filed:

Jul 25, 2007

Appl. No.:

11/782854

Inventors:

Yu Mei Lee - Woodside NY, US

International Classification:

A01G 9/24
A01G 9/00

US Classification:

47 17

Abstract:

A plant growing system for growing plants. The system includes a control module, an atmospheric condition sensor module, an atmospheric condition response module, a nutrient concentration sensor probe module, and a nutrient pump module. The atmospheric condition sensor module may include: a photo sensor, a humidity sensor, and an air temperature sensor. The atmospheric condition response module may include: a lighting module, a humidifying module, a dehumidifying module, a heating module, and a cooling module. A nutrient level sensor module and a communication module are configured to communicate a detected level of water. The communication module comprises an audio communication module and a graphical user interface module. The nutrient pump module comprises: a first nutrient reservoir, a nutrient pump, a first nutrient dispersion member, a second nutrient reservoir, and a second nutrient dispersion member. There is a power module comprising a solar panel which is configured to provide energy.

Retrospective Learning Of Communication Patterns By Machine Learning Models For Discovering Abnormal Behavior

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US Patent:

20210329035, Oct 21, 2021

Filed:

Jun 28, 2021

Appl. No.:

17/361106

Inventors:

- San Francisco CA, US
Jeshua Alexis Bratman - Brooklyn NY, US
Dmitry Chechik - San Carlos CA, US
Abhijit Bagri - Oakland CA, US
Evan James Reiser - San Francisco CA, US
Sanny Xiao Yang Liao - San Francisco CA, US
Yu Zhou Lee - San Francisco CA, US
Carlos Daniel Gasperi - New York NY, US
Kevin Lau - Long Island NY, US
Kai Jing Jiang - San Francisco CA, US
Su Li Debbie Tan - San Mateo CA, US
Jeremy Kao - Corona CA, US
Cheng-Lin Yeh - Menlo Park CA, US

International Classification:

H04L 29/06
G06N 20/00

Abstract:

Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Programmatic Discovery, Retrieval, And Analysis Of Communications To Identify Abnormal Communication Activity

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US Patent:

20210297444, Sep 23, 2021

Filed:

Jun 7, 2021

Appl. No.:

17/341200

Inventors:

- San Francisco CA, US
Jeshua Alexis Bratman - Brooklyn NY, US
Dmitry Chechik - San Carlos CA, US
Abhijit Bagri - Oakland CA, US
Evan James Reiser - San Francisco CA, US
Sanny Xiao Yang Liao - San Francisco CA, US
Yu Zhou Lee - San Francisco CA, US
Carlos Daniel Gasperi - New York NY, US
Kevin Lau - Long Island NY, US
Kai Jing Jiang - San Francisco CA, US
Su Li Debbie Tan - San Mateo CA, US
Jeremy Kao - Corona CA, US
Cheng-Lin Yeh - Menlo Park CA, US

International Classification:

H04L 29/06
G06Q 10/10
G06F 16/901
H04L 12/24
H04L 12/58

Abstract:

Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Retrospective Learning Of Communication Patterns By Machine Learning Models For Discovering Abnormal Behavior

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US Patent:

20200396258, Dec 17, 2020

Filed:

Jul 13, 2020

Appl. No.:

16/927335

Inventors:

- San Francisco CA, US
Jeshua Alexis Bratman - Brooklyn NY, US
Dmitry Chechik - San Carlos CA, US
Abhijit Bagri - Oakland CA, US
Evan James Reiser - San Francisco CA, US
Sanny Xiao Yang Liao - San Francisco CA, US
Yu Zhou Lee - San Francisco CA, US
Carlos Daniel Gasperi - New York NY, US
Kevin Lau - Long Island NY, US
Kai Jing Jiang - San Francisco CA, US
Su Li Debbie Tan - San Mateo CA, US
Jeremy Kao - Corona CA, US
Cheng-Lin Yeh - Menlo Park CA, US

International Classification:

H04L 29/06
G06N 20/00

Abstract:

Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Programmatic Discovery, Retrieval, And Analysis Of Communications To Identify Abnormal Communication Activity

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US Patent:

20200389486, Dec 10, 2020

Filed:

Jul 13, 2020

Appl. No.:

16/927478

Inventors:

- San Francisco CA, US
Jeshua Alexis Bratman - Brooklyn NY, US
Dmitry Chechik - San Carlos CA, US
Abhijit Bagri - Oakland CA, US
Evan James Reiser - San Francisco CA, US
Sanny Xiao Yang Liao - San Francisco CA, US
Yu Zhou Lee - San Francisco CA, US
Carlos Daniel Gasperi - New York NY, US
Kevin Lau - Long Island NY, US
Kai Jing Jiang - San Francisco CA, US
Su Li Debbie Tan - San Mateo CA, US
Jeremy Kao - Corona CA, US
Cheng-Lin Yeh - Menlo Park CA, US

International Classification:

H04L 29/06
G06Q 10/10
H04L 12/58
H04L 12/24
G06F 16/901

Abstract:

Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Multistage Analysis Of Emails To Identify Security Threats

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US Patent:

20200344251, Oct 29, 2020

Filed:

Jul 13, 2020

Appl. No.:

16/927427

Inventors:

- San Francisco CA, US
Jeshua Alexis Bratman - Brooklyn NY, US
Dmitry Chechik - San Carlos CA, US
Abhijit Bagri - Oakland CA, US
Evan James Reiser - San Francisco CA, US
Sanny Xiao Yang Liao - San Francisco CA, US
Yu Zhou Lee - San Francisco CA, US
Carlos Daniel Gasperi - New York NY, US
Kevin Lau - Long Island NY, US
Kai Jing Jiang - San Francisco CA, US
Su Li Debbie Tan - San Mateo CA, US
Jeremy Kao - Corona CA, US
Cheng-Lin Yeh - Menlo Park CA, US

International Classification:

H04L 29/06
G06F 16/958
G06F 16/955
G06F 16/951
G06Q 10/10
G06N 20/00

Abstract:

Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Threat Detection Platforms For Detecting, Characterizing, And Remediating Email-Based Threats In Real Time

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US Patent:

20200204572, Jun 25, 2020

Filed:

Nov 4, 2019

Appl. No.:

16/672854

Inventors:

- San Francisco CA, US
Jeshua Alexis Bratman - Brooklyn NY, US
Dmitry Chechik - San Carlos CA, US
Abhijit Bagri - Oakland CA, US
Evan James Reiser - San Francisco CA, US
Sanny Xiao Yang Liao - San Francisco CA, US
Yu Zhou Lee - San Francisco CA, US
Carlos Daniel Gasperi - New York NY, US
Kevin Lau - Long Island NY, US
Kai Jing Jiang - San Francisco CA, US
Su Li Debbie Tan - San Mateo CA, US
Jeremy Kao - Corona CA, US
Cheng-Lin Yeh - Menlo Park CA, US

International Classification:

H04L 29/06

Abstract:

Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.