Youjun Liu

20130145043, Jun 6, 2013

Jan 28, 2013

13/752216

Microsoft Corporation - Redmond CA, US
Jiahe Helen Wang - Issaquah WA, US
Qing Yu - Beijing, CN
Yongguang Zhang - Beijing, CN
Youjun Liu - Mountain View CA, US

MICROSOFT CORPORATION - Redmond WA

H04L 29/12

709245

A network address mapping system is described. The network address mapping system can identify a set of Web pages, collects information from the Web pages indicating geographical locations (“geolocations”), and correlate the geolocations with the network addresses from which the identified Web pages are served. The collected information can be weighted based on various factors, such as its relative position in a Web page. The collected information can then be used to identify a geolocation. The network mapping system can deduce geolocations for portions of ranges of network addresses based on the score, and can infer geolocations for other portions based on the deduced geolocations. This mapping can then be stored in a database and provided as a geomapping service. The network address mapping system is able to map network addresses to geographical locations. Thereafter, when a user's client computing device accesses a Web server, the Web server can easily and accurately determine a geographical location by querying the database storing the mapping or a geomapping service.

20230066050, Mar 2, 2023

Oct 18, 2022

18/047324

- Redmond WA, US
Youjun LIU - Palo Alto CA, US
Amit SRIVASTAVA - San Jose CA, US

G06F 21/62
G06N 20/00
G06K 9/62

The present disclosure relates to processing operations that execute image classification training for domain-specific traffic, where training operations are entirely compliant with data privacy regulations and policies. Image classification model training, as described herein, is configured to classify meaningful image categories in domain-specific scenarios where there is unknown data traffic and strict data compliance requirements that result in privacy-limited image data sets. Iterative image classification training satisfies data compliance requirements through a combination of online image classification training and offline image classification training. This results in tuned image recognition classifiers that have improved accuracy and efficiency over general image recognition classifiers when working with domain-specific data traffic. One or more image recognition classifiers are independently trained and tuned to detect an image class for image classification. Training of independent image recognition classifiers is also utilized for training and tuning of deeper learning models for image classification.

20210064690, Mar 4, 2021

Aug 27, 2019

16/552210

- Redmond WA, US
Xiaozhi Yu - San Jose CA, US
Gregory Alexander DePaul - San Jose CA, US
Youjun Liu - San Jose CA, US
Amit Srivastava - San Jose CA, US

G06F 17/21
G06N 20/00

A textual user input is received and a plurality of different text-to-content models are run on the textual user input. A selection system attempts to identify a suggested content item, based upon the outputs of the text-to-content models. The selection system first attempts to generate a completed suggestion based on outputs from a single text-to-content model. It then attempts to mix the outputs of the text-to-content models to obtain a completed content suggestion.

20200265153, Aug 20, 2020

Feb 15, 2019

16/276908

- Redmond WA, US
Youjun Liu - Palo Alto CA, US
Amit Srivastava - San Jose CA, US

G06F 21/62
G06K 9/62
G06N 20/00

The present disclosure relates to processing operations that execute image classification training for domain-specific traffic, where training operations are entirely compliant with data privacy regulations and policies. Image classification model training, as described herein, is configured to classify meaningful image categories in domain-specific scenarios where there is unknown data traffic and strict data compliance requirements that result in privacy-limited image data sets. Iterative image classification training satisfies data compliance requirements through a combination of online image classification training and offline image classification training. This results in tuned image recognition classifiers that have improved accuracy and efficiency over general image recognition classifiers when working with domain-specific data traffic. One or more image recognition classifiers are independently trained and tuned to detect an image class for image classification. Training of independent image recognition classifiers is also utilized for training and tuning of deeper learning models for image classification.