Ling Liu

US Patent:

20020105543, Aug 8, 2002

Filed:

Feb 7, 2001

Appl. No.:

09/779046

Inventors:

Sheng Dong - Fremont CA, US
Qiaoyun Li - Cupertino CA, US
Ling Liu - Milpitas CA, US

International Classification:

G06F013/00

US Classification:

345/744000, 345/740000

Abstract:

In one embodiment, the present invention determines an identification corresponding to a device and a user interface corresponding to the identification of the device is remotely loaded from a remote source. In another embodiment, a user interface corresponding to the identification of the device is locally loaded from a local source. In yet another embodiment, a basic user interface corresponding to the identification of the device is loaded if a user interface corresponding to the identification of the device is not found by searching remotely or locally.

US Patent:

20110093459, Apr 21, 2011

Filed:

Oct 15, 2009

Appl. No.:

12/579855

Inventors:

Anlei Dong - Fremont CA, US
Yi Chang - Santa Clara CA, US
Ruiqiang Zhang - Cupertino CA, US
Zhaohui Zheng - Mountain View CA, US
Gilad Avraham Mishne - Oakland CA, US
Jing Bai - San Jose CA, US
Karolina Barbara Buchner - San Jose CA, US
Ciya Liao - Fremont CA, US
Shihao Ji - Santa Clara CA, US
Gilbert Leung - Mountain View CA, US
Georges-Eric Albert Marie Robert Dupret - Mountain View CA, US
Ling Liu - Mountain View CA, US

Assignee:

YAHOO! INC. - Sunnyvale CA

International Classification:

G06F 17/30

US Classification:

707725, 707E17014, 707780

Abstract:

In one embodiment, access a set of recency ranking data comprising one or more recency search queries and one or more recency search results, each of the recency search queries being recency-sensitive with respect to a particular time period and being associated with a query timestamp representing the time at which the recency search query is received at a search engine, each of the recency search results being generated by the search engine for one of the recency search queries and comprising one or more recency network resources. Construct a plurality of recency features from the set of recency ranking data. Train a first ranking model via machine learning using at least the recency features.