Shan Li, 38Sammamish, WA

2022020, Jun 23, 2022

Mar 21, 2019

17/601639

- Mountain View CA, US
Aki Kuusela - Palo Alto CA, US
Joseph Young - Mountain View CA, US
Shan Li - Fremont CA, US
Dake He - Sunnyvale CA, US

H04N 19/147
H04N 19/176
H04N 19/96
G06T 9/00

An apparatus for encoding an image block includes a processor that presents, to a machine-learning model, the image block, obtains the partition decision for encoding the image block from the model, and encodes the image block using the partition decision. The model is trained to output a partition decision for encoding the image block by using training data for a plurality of training blocks as input, the training data including for a training block, partition decisions for encoding the training block, and, for each partition decision, a rate-distortion value resulting from encoding the training block using the partition decision. The model is trained using a loss function combining a partition loss function based upon a relationship between the partition decisions and respective predicted partitions, and a rate-distortion cost loss function based upon a relationship between the rate-distortion values and respective predicted rate-distortion values.

2022018, Jun 16, 2022

Jun 17, 2019

17/253589

- Emeryville CA, US
Xiaomei GE - Foster City CA, US
Zhiyuan YANG - Albany CA, US
Lianxing LIU - San Francisco CA, US
Pengbo ZHANG - Fremont CA, US
Yixiang XU - Pearland TX, US
Shan LI - Emeryville CA, US
Lucas HORAN - Emeryville CA, US

C07K 16/30
C07K 14/725
C07K 14/705
G01N 33/574
C07K 16/28
A61P 35/00

The present application provides constructs comprising an antibody moiety that specifically binds to PSMA (e.g., PSMA expressed on the surface of a cell). Also provided are methods of making and using these constructs.

2021032, Oct 21, 2021

Apr 20, 2020

16/853442

- Redmond WA, US
Shan Li - Santa Clara CA, US
Jaewon Yang - Sunnyvale CA, US
Mustafa Emre Kazdagli - Palo Alto CA, US
Feng Guo - Los Gatos CA, US
Fei Chen - Sunnyvale CA, US
Qi He - San Jose CA, US

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

In an example embodiment, a screening question-based online screening mechanism is provided to assess job applicants automatically. More specifically, job-specific questions are automatically generated and asked to applicants to assess the applicants using the answers they provide. Answers to these questions are more recent than facts contained in a user profile and thus are more reliable measures of an appropriateness of an applicant's skills for a particular job.

2021005, Feb 18, 2021

Nov 2, 2020

17/086591

- Mountain View CA, US
Aki Kuusela - Palo Alto CA, US
Shan Li - Fremont CA, US
Dake He - Sunnyvale CA, US

H04N 19/119
H04N 19/19
H04N 19/147
H04N 19/176

An apparatus for encoding a block of a picture includes a convolutional neural network (CNN) for determining a block partitioning of the block, the block having an N×N size and a smallest partition determined by the CNN being of size S×S. The CNN includes feature extraction layers; a concatenation layer that receives, from the feature extraction layers, first feature maps of the block, where each first feature map of the first feature maps is of the smallest possible partition size S×S of the block; and at least one classifier that is configured to infer partition decisions for sub-blocks of size (αS)×(αS) of the block, where α is a power of 2.

2020027, Aug 27, 2020

May 7, 2020

16/868729

- Mountain View CA, US
Dake He - Sunnyvale CA, US
Aki Kuusela - Palo Alto CA, US
Shan Li - Fremont CA, US

H04N 19/124
H04N 19/164
H04N 19/176
H04N 19/96

Encoding an image block using a quantization parameter includes presenting, to an encoder that includes a machine-learning model, the image block and a value derived from the quantization parameter, where the value is a result of a non-linear function using the quantization parameter as input, where the non-linear function relates to a second function used to calculate, using the quantization parameter, a Lagrange multiplier that is used in a rate-distortion calculation, and where the machine-learning model is trained to output mode decision parameters for encoding the image block; obtaining the mode decision parameters from the encoder; and encoding, in a compressed bitstream, the image block using the mode decision parameters.

2020009, Mar 19, 2020

Sep 18, 2018

16/134165

- Mountain View CA, US
Aki Kuusela - Palo Alto CA, US
Shan Li - Fremont CA, US
Dake He - Cupertino CA, US

H04N 19/119
H04N 19/176
H04N 19/147
H04N 19/19

A convolutional neural network (CNN) for determining a partitioning of a block is disclosed. The block is of size N×N and a smallest partition is of size S×S. The CNN includes feature extraction layers; a concatenation layer that receives, from the feature extraction layers, first feature maps of the block, where each first feature map is of size S×S; and classifiers. Each classifier includes classification layers, each classification layer receives second feature maps having a respective feature dimension. Each classifier is configured to infer partition decisions for sub-blocks of size (αS)×(αS) of the block, wherein α is a power of 2 and α=2, . . . , N/S, by: applying, at some of successive classification layers of the classification layers, a kernel of size 1×1 to reduce the respective feature dimension in half; and outputting by a last layer of the classification layers an output corresponding to a N/(αS)×N/(αS)×1 output map.

2020009, Mar 19, 2020

Sep 18, 2018

16/134134

- Mountain View CA, US
Dake He - Cupertino CA, US
Aki Kuusela - Palo Alto CA, US
Shan Li - Fremont CA, US

H04N 19/124
H04N 19/176
H04N 19/96
H04N 19/164

A method for encoding an image block includes presenting, to a machine-learning model, the image block and a first value corresponding to a first quantization parameter; obtaining first mode decision parameters from the machine-learning model; and encoding the image block using the first mode decision parameters. The first value results from a non-linear function using the first quantization parameter as input. The machine-learning model is trained to output mode decision parameters by using training data. Each training datum includes a training block that is encoded by a second encoder, second mode decision parameters used by the second encoder for encoding the training block, and a second value corresponding to a second quantization parameter. The second encoder used the second quantization parameter for encoding the training block and the second value results from the non-linear function using the second quantization parameter as input.

2014013, May 15, 2014

Nov 15, 2012

13/677728

- Redmond WA, US
Siddhartha Roy - Kirkland WA, US
Euan Grant - Redmond WA, US
Shan Li - Bellevue WA, US

MICROSOFT CORPORATION - Redmond WA

G06Q 30/02

705 1466

Systems and methods for providing effective and targeted advertisements to consumers through the generations and use of personas and family identities. A persona relates to a user and a contextual environment associated with the user. The personas allow advertisers to provide advertisements related to the contextual environment of a persona. A family identity relates to a set of users associated with at least one device. The family identities allow advertisers to provide advertisements applicable to a set of users.