Shen E Huang, Age 652012 Burnside Dr, Allen, TX 75013

2021009, Apr 1, 2021

Sep 26, 2019

16/584623

- Redmond WA, US
Yongzheng Zhang - San Jose CA, US
Shen Huang - San Jose CA, US
Burcu Baran - Sunnyvale CA, US
Chi-Yi Kuan - Fremont CA, US

G06N 20/00
G06F 17/27

The disclosed embodiments provide a system for processing data. During operation, the system determines output of a machine learning model, which includes a score generated by the model based on features inputted into the model and feature importance metrics representing effects of the features on the score. Next, the system maps the features to elements in a feature hierarchy that groups the features under a first level of parent features. The system also generates a ranking of the first level of parent features based on the feature importance metrics. The system then combines, based on the ranking, feature values of the mapped features with a set of insight templates to produce a list of narrative insights, wherein each narrative insight includes a natural language description of a factor that contributes to the model's output. Finally, the system outputs the list of narrative insights in a user interface.

2020034, Oct 29, 2020

Apr 29, 2019

16/397686

- Redmond WA, US
Haowen Cao - Sunnyvale CA, US
Shen Huang - San Jose CA, US

G06N 20/00
G06N 5/04
G06Q 10/06

Techniques for using machine learning techniques to distinguish between different types of uses of an online service are provided. In one technique, first training data is used to train a first prediction model and second training data is used to train a second prediction model. The label of training instances in the first training data indicates whether an online action with respect to an online service of one type of action or another type of action. The label of training instances in the second training data indicates whether an entity using the online service initiated a particular action. The first prediction model is used to classify multiple actions performed by an entity relative to the online service. The second prediction model takes the classifications produced by the first prediction model to determine a likelihood that the entity will initiate the particular action.

2020031, Oct 1, 2020

Mar 29, 2019

16/370690

- 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

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

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.

2020000, Jan 2, 2020

Jun 28, 2018

16/022291

- Redmond WA, US
Huan V. Hoang - San Jose CA, US
Shen Huang - San Jose CA, US
Yongzheng Zhang - San Jose CA, US
Chi-Yi Kuan - Fremont CA, US

Microsoft Technology Licensing, LLC - Redmond WA

G06Q 30/02
G06F 17/30

The disclosed embodiments provide a system for performing A/B testing for search engine optimization (SEO). During operation, the system divides a set of web pages into a control group, an inbound treatment group, an outbound treatment group, and a full treatment group. Next, the system generates a first set of links from the outbound treatment group to the inbound treatment group and a second set of links within the full treatment group. The system then applies an A/B test to pairs of groups selected from the control group, the inbound treatment group, the outbound treatment group, and the full treatment group. Finally, the system outputs a result of the A/B test for use in assessing an effect of the first and second sets of links on search engine results associated with the set of web pages.

2020000, Jan 2, 2020

Jun 29, 2018

16/023230

- Redmond WA, US
Shen Huang - San Jose CA, US
Xiaonan Duan - Mountain View CA, US
Suhai Liu - Palo Alto CA, US

H04L 29/08
G06Q 30/02
G06F 17/30
G06F 17/16
G06K 9/62

A historical online user behavior-based approach is used to make a recommendation of a cross-online service vertical entity for a primary online service vertical entity with which a user is currently interacting online. The recommendation is made based on the similarity of historical online user behavior between the vertical entities. To do this, historical online user behavior of each of the vertical entities is represented as a respective vector. Each dimension of a vector represents a historical level of interaction between a separate user or a separate group of related users and the vertical entity represented by the vector. A similarity measure is used to measure the similarity between the vectors for the vertical entities. The recommendation of the cross-online service vertical entity is then made for the primary online service vertical entity based on the extent of the similarity between the vectors according to a similarity measure.

2019030, Oct 3, 2019

Mar 30, 2018

15/941236

- Redmond WA, US
Shen Huang - San Jose CA, US
Yu Wang - Sunnyvale CA, US
Yongzheng Zhang - San Jose CA, US
Paul Ko - San Francisco CA, US
Shady Elasra - San Francisco CA, US
Fanbin Bu - Fremont CA, US

G06N 5/04
G06N 99/00

Techniques are provided for predicting talent flow to and/or from a geographical region. In one technique, multiple entity profiles are stored and analyzed to generate training data that is labeled indicating whether a corresponding entity has moved to or moved from a region. A machine-learned prediction model is generated or trained based on the training data. Using the machine-learned prediction model, a prediction is made whether, for each entity corresponding to another entity profile, that entity will move to or move from a particular geographic region. Based on multiple predictions, a number of entities that are predicted to move to or move from the particular geographic region is determined. Talent flow data that is based on the number of entities is presented on a computer display.

2019030, Oct 3, 2019

Mar 30, 2018

15/941280

- Redmond WA, US
Yue Li - Sunnyvale CA, US
Eric Weber - Sunnyvale CA, US
Yanjin Kuang - Foster City CA, US
Shen Huang - San Jose CA, US

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

In an example embodiment, profile and/or usage data of a social networking service is leveraged to automatically generate potential career paths for users of the social networking service. Additionally, specific recommendations as to actions the users can take to increase their odds of progressing along particular career paths can be determined, and these recommendations can be shared with users. Both recommendations may be performed in a manner that is scalable for personalized service.