Li Ui Yang

US Patent:

8030354, Oct 4, 2011

Filed:

Oct 3, 2008

Appl. No.:

12/287036

Inventors:

Sean P. Brown - San Francisco CA, US
Qiong Cao - Daly City CA, US
Paul John Dransfield - San Francisco CA, US
Xiaohui Du - Belmont CA, US
Zice Fu - Foster City CA, US
Jonathan Houze - San Mateo CA, US
Xian Yun Jiao - Belmont CA, US
Yong-Jae Kim - Foster City CA, US
Todd J. Kohn - San Mateo CA, US
SuJen Lai - Burlingame CA, US
Daniel Lin - Redwood City CA, US
Jiwen Liu - Foster City CA, US
Jian Luo - Albany CA, US
Julio C. Medina - San Carlos CA, US
Jeffrey D. Reagan - Woodside CA, US
Vatee Pattaropong - Burlingame CA, US
Margrit Schwarz - San Carlos CA, US
Wang Shen - San Mateo CA, US
Yongli Su - Foster City CA, US
Gayathri Swaminath - Fremont CA, US
Marc Vimolratana - San Mateo CA, US
Yingcai Wang - Millbrae CA, US
Yumei Xiong - San Bruno CA, US
Li Yang - San Francisco CA, US
Ming Yu - Foster City CA, US
Jie Zhang - Sunnyvale CA, US
Liusheng Zhu - Foster City CA, US

Assignee:

Amgen Inc. - Thousand Oaks CA

International Classification:

A61K 31/192
C07C 53/134

US Classification:

514570, 562496, 562512, 546342, 514357

Abstract:

The present invention provides compounds useful, for example, for treating metabolic disorders in a subject. Such compounds have the general formula I:.

US Patent:

20100262499, Oct 14, 2010

Filed:

Apr 10, 2009

Appl. No.:

12/385554

Inventors:

Niklas Karlsson - Mountain VIew CA, US
Li Yang - Mountain View CA, US

International Classification:

G06Q 30/00
G06Q 99/00

US Classification:

705 1472, 705400

Abstract:

A system is provided for controlling initialization of an online advertising campaign. The system includes a sensor configured to determine current performance of the online advertising campaign and a memory for storing performance information obtained by the sensor, a set of campaign initialization controller instructions, a campaign volume model, and a network average time-of-day pattern. The system also includes an estimator configured to predict future campaign performance based on the performance information obtained by the sensor, the campaign volume model, and the network average time-of-day pattern. The system also includes an initialization controller configured to generate a bid allocation control signal and generate a bid price control signal based on the future campaign performance predicted by the estimator, according to the instructions stored in the memory. A method for controlling initialization of an online advertising campaign is also provided.

US Patent:

20130197994, Aug 1, 2013

Filed:

Nov 5, 2012

Appl. No.:

13/668828

Inventors:

AOL Advertising Inc. - New York NY, US
Jianlong ZHANG - Sunnyvale CA, US
Robert Alden LUENBERGER - Los Altos CA, US
Scott Robert STRICKLAND - Redwood City CA, US
Yih-Shin Cindy HUANG - Cupertino CA, US
Seyed Mohammad ZIAEMOHSENI - East Palo Alto CA, US
Li YANG - Sunnyvale CA, US
Hans W. UHLIG - Hayward CA, US

Assignee:

AOL ADVERTISING INC. - New York NY

International Classification:

G06Q 30/02

US Classification:

705 1445, 705 1441

Abstract:

System and methods are provided for analyzing and displaying digital content and advertisements over electronic networks. In accordance with one implementation, a method is provided that calculates, using at least one processor, an engagement metric (including, for example, click probability) for a specific combination of time, promotion slot, and user segment, for an individual promotion of a set of promotions, and calculates a value of a landing page associated with the promotion. The method then can multiply the engagement metric and the value to determine the valuation of the promotion. The method can operate recursively by using previous calculations in later runs of the same method.

US Patent:

20210304457, Sep 30, 2021

Filed:

Feb 19, 2021

Appl. No.:

17/180182

Inventors:

- Oakland CA, US
- Otawara-shi, JP
Zhaoheng XIE - Oakland CA, US
Wenyuan QI - Vernon Hills IL, US
Li YANG - Vernon Hills IL, US
Chung CHAN - Vernon Hills IL, US
Evren ASMA - Vernon Hills IL, US

Assignee:

The Regents of the University of California - Oakland CA
CANON MEDICAL SYSTEMS CORPORATION - Otawara-shi

International Classification:

G06T 11/00
G06T 7/246

Abstract:

To reduce the effect(s) caused by patient breathing and movement during PET data acquisition, an unsupervised non-rigid image registration framework using deep learning is used to produce motion vectors for motion correction. In one embodiment, a differentiable spatial transformer layer is used to warp the moving image to the fixed image and use a stacked structure for deformation field refinement. Estimated deformation fields can be incorporated into an iterative image reconstruction process to perform motion compensated PET image reconstruction. The described method and system, using simulation and clinical data, provide reduced error compared to at least one iterative image registration process.

US Patent:

20200403958, Dec 24, 2020

Filed:

Oct 31, 2018

Appl. No.:

16/177263

Inventors:

- Menlo Park CA, US
Li Yang - Palo Alto CA, US
Henri Romeo Liriani - San Francisco CA, US
Tao Jiang - Newark CA, US
Xu He - Fremont CA, US

International Classification:

H04L 12/58

Abstract:

Systems, methods, and non-transitory computer readable media can determine information associated with a web page being accessed by a user operating a computing device. The information associated with the web page can be verified. A messaging interface for exchanging messages with an entity associated with the web page can be provided. The messaging interface can be provided within the web page being accessed.

US Patent:

20200314201, Oct 1, 2020

Filed:

Mar 29, 2019

Appl. No.:

16/370690

Inventors:

- Redmond WA, US
Li Yang - Palo Alto CA, US
Yongzheng Zhang - San Jose CA, US
Shen Huang - San Jose CA, US
Clayton Sanford - San Francisco CA, US

International Classification:

H04L 29/08
G06N 20/00
H04L 12/911
G09B 5/00

Abstract:

An embodiment of the disclosed technologies includes extracting, from an online connection network, digital data comprising target profile data and current profile data; where the target profile data is associated with an online submission process that has a plurality of possible outcomes and is executable via the online connection network; where the current profile data is associated with a member node of the online connection network; using an active learning process, in response to the current profile data, identifying attribute data that is in the target profile data but is not in the current profile data and is predicted to have a relationship with a positive outcome of the online submission process; outputting the attribute data for use by a downstream process or an automated digital assistant to determine a digital resource to associate with the member node through the online connection network or through an online learning system.

US Patent:

20200101388, Apr 2, 2020

Filed:

Dec 3, 2019

Appl. No.:

16/702253

Inventors:

- Menlo Park CA, US
Richard Kenneth Zadorozny - Menlo Park CA, US
Jeremy Harrison Goldberg - San Francisco CA, US
Peter Henry Martinazzi - San Francisco CA, US
Lei Guang - Bellevue WA, US
Li Yang - Palo Alto CA, US
Jennifer A. Burge - Oakland CA, US

International Classification:

A63F 13/87
G06Q 50/00
A63F 13/79
A63F 13/2145
A63F 13/48
A63F 13/92
A63F 13/35
G06F 21/10
A63F 13/795
A63F 13/49

Abstract:

This disclosure is directed to enabling access to a game instance through a messenger application. A client-side player component facilitates communication between a game instance on the client device and a backend server of a social networking system. The communication information can be stored in or retrieved from a social graph of the social networking system. The game instance can be initialized from a social platform of the social networking system, and the game can be played without leaving the messenger application.

US Patent:

20200106804, Apr 2, 2020

Filed:

Sep 27, 2018

Appl. No.:

16/145032

Inventors:

- Redmond WA, US
Kaiyu Yang - Sunnyvale CA, US
Li Yang - Palo Alto CA, US
Bing Wang - Mountain View CA, US

International Classification:

H04L 29/06
G06N 99/00
H04L 12/58

Abstract:

A spammer profile detector uses multi-stage machine learning approach, where a content-based machine learning model, a connection graph machine learning model, and a behavior-based machine learning model are used sequentially, each model generating a score indicating the likelihood that a profile is a spammer profile. The content-based machine learning model examines and evaluates information stored in a member profile. The connection graph machine learning model examines and evaluates a member's connections. The behavior-based machine learning model examines and evaluates activities of a member represented by a member profile. The score produced by the spammer profile detector can be used to determine whether the profile should be flagged as a spammer profile, whether the profile should be omitted when determining a count of the total number of active member profiles within the system, whether the profile should be restricted or removed from the system, etc.