Yu M Lee

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.

20200097340, Mar 26, 2020

Oct 24, 2018

16/169731

- Redmond WA, US
Anav Silverman - Sammamish WA, US
Alyssa Rachel Mayo - Seattle WA, US
Shikha Devesh Desai - Bellevue WA, US
Gwenyth Alanna Vabalis Hardiman - Seattle WA, US
Penelope Ann Collisson - Edmonds WA, US
Yu Been Lee - Bellevue WA, US
Susan Michele Hendrich - Redmond WA, US

G06F 9/54
G06F 17/30
G06F 15/18

The present disclosure relates to processing operations configured to tailor notifications of productivity feature suggestions based on predictive relevance to a context associate with user access to an electronic document. Machine learning modeling executes a contextual evaluation of user access to predictively determine relevance of a suggestion that relates to: 1) a confidence in the quality of the suggestion; and 2) a timing prediction as to the urgency for surfacing the suggestion to the user so that the suggestion is most applicable. Example notifications are proactive interruptions that aim to aid processing efficiency in task execution as well as an improve user interface experience when users work with an application/service and/or an application platform that comprises a suite of applications/services. A manner in which the notification is presented may vary based on the confidence in the relevance of the suggestion and timing relevance for interrupting a user's workflow.