Dan D Liu

8423405, Apr 16, 2013

Nov 1, 2010

12/917106

Karthik Gopalratnam - San Francisco CA, US
Myles Sussman - San Francisco CA, US
Marc Berndl - San Francisco CA, US
Dan Liu - Sunnyvale CA, US
Sridhar Ramaswamy - Cupertino CA, US
Nicholas C. Fox - San Francisco CA, US
Jonathan G. Alferness - San Francisco CA, US
Adam I. Juda - New York NY, US

Google Inc. - Mountain View CA

G06Q 30/00

705 144

In general, in one aspect, a first request to provide one or more advertisements on a first web page is received, the first web page comprising one or more items of web content related to a query. A first quality score is calculated for a first advertisement included in a set of one or more candidate advertisements. The first advertisement is presented on the first web page based at least in part on the first quality score. A second request is received to provide one or more advertisements on a second web page, the second web page comprising one or more different items of web content related to the query. A second quality score for the first advertisement is calculated based at least in part on the previous presentation of the first advertisement. It is determined whether to present the first advertisement on the second web page based at least in part on the second quality score.

20060088863, Apr 27, 2006

Oct 4, 2005

11/243621

Melvin Yamamoto - Fremont CA, US
Clifford Oostman - Quilcene WA, US
John Sze - Saratoga CA, US
Keith Pearson - Campbell CA, US
Philip Trenholme - Santa Cruz CA, US
Dan Liu - Sunnyvale CA, US
Chi Yu - Saratoga CA, US

Affymetrix, INC. - Santa Clara CA

C12Q 1/68
H01L 29/15
C12M 1/34
H01L 31/0256

435006000, 435287200, 257076000

The present invention provides methods to process multiple sensors by providing a sensor plate and HT plates. In a preferred embodiment of the invention, methods for assembling microarray pegs and microarray plates are described for high throughput microarray processing.

20060141612, Jun 29, 2006

Feb 3, 2006

11/347654

Melvin Yamamoto - Fremont CA, US
Clifford Oostman - Quilcene WA, US
John Sze - Saratoga CA, US
Keith Pearson - Campbell CA, US
Philip Trenholme - Santa Cruz CA, US
Dan Liu - Sunnyvale CA, US
Chi Yu - Saratoga CA, US

Affymetrix, INC. - Santa Clara CA

C12M 1/34
H01L 21/00

435287200, 438001000

The present invention provides methods to process multiple sensors by providing a sensor plate and HT plates. In a preferred embodiment of the invention, methods for assembling microarray pegs and microarray plates are described for high throughput microarray processing.

20200401594, Dec 24, 2020

Jun 21, 2019

16/449122

- Redmond WA, US
Dan Liu - Santa Clara CA, US
Qi Guo - Sunnyvale CA, US

G06F 16/2457
G06F 17/18
G06F 17/15
G06N 3/04

In an example embodiment, a platform is provided that utilizes information available to a computer system to feed a neural network. The neural network is trained to determine both the probability that a searcher would select a given potential search result if it was presented to him or her and the probability that a subject of the potential search result would respond to a communication from the searcher. These probabilities are combined to produce a single score that can be used to determine whether to present the searcher with the potential search result and, if so, how high to rank the potential search result among other search results. During the training process, a rescaling transformation for each input feature is learned and applied to the values for the input features.

20200401644, Dec 24, 2020

Jun 21, 2019

16/449149

- Redmond WA, US
Dan Liu - Santa Clara CA, US
Qi Guo - Sunnyvale CA, US
Wenxiang Chen - Sunnyvale CA, US
Xiaoyi Zhang - Sunnyvale CA, US
Lester Gilbert Cottle - Sunnyvale CA, US
Xuebin Yan - Sunnyvale CA, US
Yu Gong - Santa Clara CA, US
Haitong Tian - San Jose CA, US
Siyao Sun - Mountain View CA, US
Pei-Lun Liao - Sunnyvale CA, US

G06F 16/9538
G06N 20/00
G06F 17/27
G06N 3/04

In an example embodiment, position bias and other types of bias may be compensated for by using two-phase training of a machine-learned model. In a first phase, the machine-learned model is trained using non-randomized training data. Since certain types of machine-learned models, such as those involving deep learning (e.g., neural networks) require a lot of training data, this allows the bulk of the training to be devoted to training using non-randomized training data. However, since this non-randomized training data may be biased, a second training phase is then used to revise the machine-learned model based on randomized training data to remove the bias from the machine-learned model. Since this randomized training data may be less plentiful, this allows the deep learning machine-learned model to be trained to operate in an unbiased manner without the need to generate additional randomized training data.