Cheng Lun Li, 44New York, NY

2021033, Nov 4, 2021

Apr 29, 2020

16/861687

- NEWARK NJ, US
CHENG LI - KEARNY NJ, US
SAGAR ROY - NEWARK NJ, US
KABIR MITRA - FREMONT CA, US

B01D 61/36
B01D 63/10
B01D 63/02

A membrane distillation (MD) system includes a sweep gas MD (SGMD) module and a knockout chamber. The MD module includes a feed inlet, a feed outlet, a condensing media inlet, and a condensing media outlet. The condensing media is sweep gas. The knockout chamber is positioned after the feed outlet. The knockout chamber includes a liquid inlet, a liquid outlet, and a vapor outlet. Direct gas phase stripping within the SGMD module leads to additional water evaporation at the knockout chamber and contributes to enhanced water or VOCs removal of the MD system.

2021011, Apr 15, 2021

Dec 22, 2020

17/130495

- San Francisco CA, US
Parag Agrawal - San Francisco CA, US
Cheng Li - Ann Arbor MI, US
Sandeep Pandey - San Francisco CA, US
Michael Jahr - San Francisco CA, US
Jeremy Ginsberg - San Francisco CA, US

G06Q 30/02
G06N 20/00

In some examples, a computing device includes at least one processor and at least one module, operable by the at least one processor to receive, from a client device of a user, a request for one or more advertisements to display at the client device with a set of messages. The set of messages is associated with the user in a social network messaging service. The at least one module may be further operable to determine a probability that the user will select a candidate advertisement using a machine learning model based on point-wise learning and pair-wise learning. The at least one module may be further operable to determine, based on the probability that the user will select the candidate advertisement, a candidate score for the candidate advertisement, determine that the candidate score satisfies a threshold, and send, for display at the client device, the candidate advertisement.

2020015, May 21, 2020

Jan 24, 2020

16/751704

- Dulles VA, US
Ruiheng WANG - Jersey City NJ, US
Cheng LI - Jersey City NJ, US
Bhagyasri CANUMALLA - Jersey City NJ, US
Hsiao-Ching LIN - Astoria NY, US
Yanbo LI - New York NY, US
Davood SHAMSI - New York NY, US

G06F 11/34
G06F 3/16

Systems and methods are disclosed for determining an engagement level of a user interacting with an electronic program, comprising receiving or determining a decision node tree, each node in the decision node tree corresponding to a user decision point in the electronic program, determining a number of levels in the decision node tree, determining a user traversed count comprising a number of levels the user has traversed in the decision node tree, and determining the engagement level of the user with the electronic program based upon the user traversed count and the number of levels in the decision node tree.

2018005, Feb 22, 2018

Aug 21, 2017

15/681810

- Dulles VA, US
Ruiheng Wang - Jersey City NJ, US
Cheng Li - Jersey City NJ, US
Bhagyasri Canumalla - Jersey City NJ, US
Hsiao-Ching Lin - Astoria NY, US
Yanbo Li - New York NY, US
Davood Shamsi - New York NY, US

G06F 11/34
G06F 3/16

Systems and methods are disclosed for determining an engagement level of a user interacting with an electronic program, comprising receiving or determining a decision node tree, each node in the decision node tree corresponding to a user decision point in the electronic program, determining a number of levels in the decision node tree, determining a user traversed count comprising a number of levels the user has traversed in the decision node tree, and determining the engagement level of the user with the electronic program based upon the user traversed count and the number of levels in the decision node tree.

2017029, Oct 12, 2017

Apr 11, 2016

15/095517

- Mountain View CA, US
Vijay Garg - Sunnyvale CA, US
Sujith Ravi - Santa Clara CA, US
Cheng Li - Ann Arbor MI, US

G06F 17/30
G06N 99/00
G06Q 10/10

A computing device may generate, a graph that includes a plurality of nodes, wherein the plurality of nodes includes a plurality of entity nodes representing a plurality of entities and a plurality of feature nodes representing a plurality of features, and wherein each of the plurality of entity nodes is connected in the graph to one or more of the plurality of feature nodes. The computing device may perform label propagation to associate a distribution of labels with each of the plurality of nodes. The computing device may be configured to receive an indication of at least one of a feature of interest or an entity of interest. The computing device may further be configured to output an indication of one or more related entities that are related to the feature of interest or the entity of interest.