Hong Ren Yao3180 Apperson Ridge Dr, San Jose, CA 95148

2011023, Sep 22, 2011

Jul 23, 2010

12/842934

Wu Wang - Los Altos CA, US
Mei Marker - Santa Clara CA, US
Kun Lu - San Jose CA, US
Haijun Cao - Belmont CA, US
Hong Yao - Newark CA, US
Wei Wang - Milpitas CA, US

KINDSIGHT, INC. - Sunnyvale CA

G06Q 30/00

705 1453

A method for generating a behavioral model for a targeted advertisement category (TAC), including: obtaining click stream data including ad-clicks and events preceding the ad-clicks and performed on web pages; assigning features having categories and keywords associated with the web pages to the events; identifying an ad-click of the ad-clicks and a subset of the events preceding the ad-click that result in the ad-click, where the subset of the events is associated with at least one feature; generating an aggregated event sequence by aggregating the ad-click and the subset of the events; selecting, in response to the at least one feature being associated with the TAC, a training data set including at least the aggregated event sequence; generating the behavioral model for the TAC by applying a learning algorithm to a portion of the training data set; and evaluating performance of built models and select model based on performance result.

2011027, Nov 10, 2011

Jul 23, 2010

12/842921

Hong Yao - Newark CA, US
Wu Wang - Los Altos CA, US
Mei Marker - Santa Clara CA, US
Kelvin Edmison - Ottawa, CA
Wei Wang - Milpitas CA, US

KINDSIGHT, INC. - Sunnyvale CA

G06F 17/30

707749, 707748, 707750, 707E17014

A system and method for generating feature vectors of documents in different languages are provided. The feature vectors provide scores associated with keywords defined in a base language for use by a profiler for generating or updating a user profile. The system and method use a plurality of keyword sets comprising: a base language keyword set comprising a plurality of base language keywords each associated with a respective identifier (ID); and a second language keyword set comprising a plurality of second language keywords each corresponding in meaning to a respective one of the base language keywords and associated with the ID of the corresponding base language keyword. One of a plurality of tokenizers is selected to parse a document based on the language of the document and to generate the feature vector using the keyword set of the corresponding language.

2020040, Dec 24, 2020

Jun 20, 2019

16/447433

- Redmond WA, US
Hong Yao - Fremont CA, US
Jefferson Lai - San Jose CA, US

G06F 3/0482
G06N 20/00
G06N 5/04

Techniques for providing a user interface configured to provide recommendations of content reviewers using machine learning are disclosed herein. In some embodiments, a computer system receives an indication of a first user associated with a creation of an electronic message, identifies a first set of one or more other users for the first user using a recommendation model, and causes a corresponding indication of each one of the identified first set of one or more other users to be displayed as a recommended recipient of the electronic message within a user interface of a computing device. In some example embodiments, a user selection of the corresponding indication of one of the identified first set of other users is received, and an address field of the electronic message is populated with an electronic address of the selected one of the identified first set of other users based on the user selection.

2019013, May 2, 2019

Oct 31, 2017

15/799490

- Redmond WA, US
Yi Zhang - Sunnyvale CA, US
Hong Yao - Fremont CA, US
Onkar A. Dalal - Santa Clara CA, US
Yu Liu - Sunnyvale CA, US

Microsoft Technology Licensing, LLC - Redmond WA

G06Q 10/10
G06Q 50/00
G06Q 10/06

During operation, a system obtains member features associated with a member of a network, wherein the set of member features include a job-seeking status of the member. Next, the system analyzes the member features to predict an interest of the member in career services associated. The system then uses the predicted interest to output a recommendation of the career services to the member.

2017003, Feb 2, 2017

Jul 31, 2015

14/815334

- Sunnyvale CA, US
Srinath Ravindran - Chennai, IN
Mohammad Saberian - Sunnyvale CA, US
Sandeep Soni - Ahmednagar Maharashtra, IN
Kuang-chih Lee - Union City CA, US
Hong Yao - Fremont CA, US
Jian Yang - Palo Alto CA, US
Pradhan Pattanayak - San Jose CA, US

Yahoo! Inc. - Sunnyvale CA

G06Q 30/02

The technologies described herein serve contextually relevant advertisements under a guaranteed advertisement campaign. A publisher retrieves a guaranteed advertisement campaign related to a webpage available for serving an advertisement, and identifies a set of advertisements relating to the guaranteed advertisement campaign. Advertisement selecting circuitry of the publisher determines whether an advertisement that is contextually relevant to content published at the webpage is present in the set of advertisements. If there is no contextually relevant advertisement in the set of advertisements, the advertisement selecting circuitry selects an alternative advertisement from the set of advertisements that minimizes an under-delivery risk related to the guaranteed advertisement campaign. If there is a contextually relevant advertisement in the set of advertisements, the advertisement selecting circuitry selects the contextually relevant advertisement. Then, the publisher provides the selected advertisement to a client device.