Yu Sun Lee

20070047224, Mar 1, 2007

Aug 29, 2005

11/213077

Yu Lee - La Palma CA, US

F21V 33/00

362132000, 362133000, 362253000

The invention provides light without the need for electrical wiring. Battery power is applied to the lamp using a magnetically operated switch. The power switch and battery holders are built into the light fixture. The light fixture can be easily mounted in locations such as filing cabinets, desk drawers, bedroom furniture, kitchen drawers, kitchen cabinets, and similar locations. When the device is within close proximity of the magnet, power from the battery is not applied to the lamp. When the light is a specific distance away from the magnet, power from the battery is applied and light is emitted from the lamp.

20090025287, Jan 29, 2009

Jul 25, 2007

11/782854

Yu Mei Lee - Woodside NY, US

A01G 9/24
A01G 9/00

47 17

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.

20210329035, Oct 21, 2021

Jun 28, 2021

17/361106

- 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

H04L 29/06
G06N 20/00

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.

20210297444, Sep 23, 2021

Jun 7, 2021

17/341200

- 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

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

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.

20200396258, Dec 17, 2020

Jul 13, 2020

16/927335

- 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

H04L 29/06
G06N 20/00

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.

20200389486, Dec 10, 2020

Jul 13, 2020

16/927478

- 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

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

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.

20200344251, Oct 29, 2020

Jul 13, 2020

16/927427

- 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

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

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.

20200204572, Jun 25, 2020

Nov 4, 2019

16/672854

- 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

H04L 29/06

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.