Xing Zhou, 441902 Adams Ct, Mountain View, CA 94040

2023003, Feb 9, 2023

Jul 21, 2022

17/814099

- Cupertino CA, US
Xing Zhou - San Jose CA, US
Alireza Safaee - Cupertino CA, US
Zaid A AbuKhalaf - Sunnyvale CA, US

H02J 50/10
H02J 50/80
H04L 27/04
H04L 27/14

A wireless power transmitter can receive the results of a characterizing signal transmitted by the wireless power receiver, compute at least two parameters of a model characterizing an in-band communications channel based on the received results of the characterizing signal transmitted by the wireless power receiver, compute a plurality of equalizing filter taps from the at least two parameters, and apply the computed equalizing filter to subsequent signals received by the wireless power transmitter via the in-band communications channel. A first parameter can correspond to a time constant of the channel, and a second parameter can correspond to a damping value of the communications channel. The wireless power transmitter can transmit to a wireless power receiver a request to transmit a characterizing signal through the in-band communication channel, wherein the characterizing signal transmitted by the wireless power receiver is sent in response to the transmitted request.

2017030, Oct 26, 2017

Apr 21, 2016

15/135405

- Mountain View CA, US
Huan Hoang - San Jose CA, US
Yan Liu - Sunnyvale CA, US
Qiang Zhu - Sunnyvale CA, US
Wenjing Zhang - Menlo Park CA, US
Clay Blanchard - Danville CA, US
Christopher Fuller - Oakland CA, US
Xing Zhou - Mountain View CA, US

G06N 99/00
G06N 7/00
G06F 17/30
G06F 17/30
G06Q 50/00
G06Q 10/10

Techniques are provided for determining a propensity or likelihood that a person in a social network will perform a particular action. A statistical model that is trained based on multiple features of each member in a first plurality of members of a social network is stored. The multiple features include a subset pertaining to profile information provided by the first plurality of members and stored in a plurality of profiles of the first plurality of members. For each member of a second plurality of members of the social network, multiple feature values that correspond to the plurality of features are identified, the statistical model is used to generate a score for the member. The score indicates a likelihood that the member will perform the particular action.

2017030, Oct 26, 2017

Apr 20, 2016

15/134250

- Mountain View CA, US
Juan Wang - Los Altos CA, US
Zhaoying Han - Mountain View CA, US
Chung-Ting John Chao - Foster City CA, US
Wei Di - Cupertino CA, US
Qiang Zhu - Sunnyvale CA, US
Sui Yan - Sunnyvale CA, US
Xing Zhou - Mountain View CA, US

LinkedIn Corporation - Mountain View CA

G06Q 10/06
G06Q 10/06

The disclosed embodiments provide a system for processing data. During operation, the system obtains a first set of rules for assigning a first set of sales professionals to a first set of accounts, wherein the first set of rules comprises a representative load rule, a matching rule, and a balancing rule. Next, the system applies an optimization technique to the first set of rules and a first set of parameters associated with the first set of sales professionals and the first set of accounts to produce a first set of assignments of the first set of sales professionals to the first set of accounts. The system then outputs the first set of assignments for using in managing sales activity of the first set of sales professionals.

2017026, Sep 14, 2017

Mar 8, 2016

15/063814

- Mountain View CA, US
Wei Di - Cupertino CA, US
Michael A. Davis - Santa Clara CA, US
Xing Zhou - Mountain View CA, US
Yan Liu - Sunnyvale CA, US

G06N 99/00
G06N 5/04
G06F 17/12

Techniques and a system are provided for a variable analysis system that learns from input variables. The variable analysis system may be used to determine how variables relate to other variables, to develop a tool used to predict outcomes for an entity based on other entities. The variable analysis system may employ grouping, so that multiple variables are considered as one input in a given model.

2017006, Mar 2, 2017

Aug 31, 2015

14/841531

- Mountain View CA, US
Juan Wang - Los Altos CA, US
Song Lin - Santa Clara CA, US
Xing Zhou - Mountain View CA, US
Qiang Zhu - Sunnyvale CA, US
SangHyun Park - Fremont CA, US
Yurong Shi - San Jose CA, US
Luke Thomas Whelan - Dublin, IE

LinkedIn Corporation - Mountain View CA

G06Q 10/06
G06Q 30/02

The disclosed embodiments provide a system for processing data. During operation, the system inputs a set of features for a customer of a product into a first statistical model, wherein the set of features comprises a company segment of the customer. Next, the system uses the first statistical model to predict a churn risk of the customer. When the churn risk exceeds a first threshold for the company segment, the system outputs a notification of a high churn risk level for the customer.

2017006, Mar 2, 2017

Aug 31, 2015

14/841547

- Mountain View CA, US
Juan Wang - Los Altos CA, US
Song Lin - Santa Clara CA, US
Xing Zhou - Mountain View CA, US
Qiang Zhu - Sunnyvale CA, US
SangHyun Park - Fremont CA, US
Yurong Shi - San Jose CA, US
Luke Thomas Whelan - Dublin, IE

LinkedIn Corporation - Mountain View CA

G06Q 10/06
G06F 3/0481
G06F 3/0484
G06T 11/20

The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of data for a set of customers of a product, wherein the set of data comprises a set of churn risk levels for the customer. Next, the system uses the set of data to display a graphical user interface (GUI) comprising a chart of renewal opportunities with the set of customers, for the product, over an upcoming time interval. The system then displays, in the GUI, a representation of a churn risk level for each customer in the set of customers with a renewal opportunity in the chart.