Xiaoyi Zhang, 3218302 NW Montreux Dr, Issaquah, WA 98027

2021039, Dec 23, 2021

Aug 24, 2020

17/001674

- Suwon-si, KR
Wentao WU - San Jose CA, US
Glenn YU - San Jose CA, US
Wei ZHAO - San Jose CA, US
Fnu VIKRAM SINGH - San Jose CA, US
Xiaoyi ZHANG - San Jose CA, US
Yong YANG - San Jose CA, US

G06F 9/38
G06F 9/30
G06F 11/30

A method may include determining, with a queue availability module, that an entry is available in a queue, asserting a bit in a register based on determining that an entry is available in the queue, determining, with a processor, that the bit is asserted, and processing, with the processor, the entry in the queue based on determining that the bit is asserted. The method may further include storing the register in a tightly coupled memory associated with the processor. The method may further include storing the queue in the tightly coupled memory. The method may further include determining, with the queue availability module, that an entry is available in a second queue, and asserting a second bit in the register based on determining that an entry is available in the second queue. The method may further include finding the first bit in the register using a find first instruction.

2021034, Nov 11, 2021

Oct 9, 2020

17/067601

- Cupertino CA, US
Colin S. LEA - Pittsburgh PA, US
Jason WU - Pittsburgh PA, US
Xiaoyi ZHANG - Shoreline WA, US

G06F 3/0484
G06F 3/0488

Representative embodiments set forth techniques for optimizing user interfaces on a client device. A method may include receiving a spatial difficulty map associated with the user interface. The method also includes identifying one or more user interface elements using an element detection model and generating a user interface layout based on at least the spatial difficulty map. The method also includes generating an updated user interface by editing the one or more user interface elements using the user interface layout and rendering, on a display of the client device, the updated user interface.

2021006, Mar 11, 2021

Sep 8, 2020

17/014938

- Washington DC, US
- Seattle WA, US
Jasmine ZIA - Seattle WA, US
Rafal KOCIELNIK - Seattle WA, US
Ravi KARKAR - Seattle WA, US
Sean MUNSON - Seattle WA, US
Xiaoyi ZHANG - Seattle WA, US

A61B 5/16
A61B 5/00

Methods, systems, and apparatuses are described causing light to be emitted, causing a frequency at which the light is emitted to vary, receiving, based on the frequency variation, a user input, determining a critical flicker frequency (CFF) corresponding to the user input, and determining, based on the CFF, a disease state.

2020040, 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.

2017028, Oct 5, 2017

Mar 30, 2016

15/085841

- Menlo Park CA, US
Ben Chen - San Jose CA, US
Jeremy Samuel Friedland - San Francisco CA, US
Alan Thomas Clark - San Francisco CA, US
Ryan Michael Houx - Menlo Park CA, US
Xiao Yang Xie - Foster City CA, US
Xiaoyi Zhang - Redwood City CA, US
Rahul Rajagopalan - Mountain View CA, US
Adam Ankenmann - Mountain View CA, US
Mitchell Sadao Fukumoto - Santa Clara CA, US
Noah Bradley Blecher - San Francisco CA, US
Adam Shih - San Francisco CA, US
Jing Chen - Mountain View CA, US
David Martin Kaye - Redwood City CA, US
Crystal Rhan-Tsor Chen - Mountain View CA, US

G06F 17/30

In one embodiment, a method includes accessing, from a data store of the communication network, user information associated with a first user of the communication network, identifying one or more entities associated with the communication network that are relevant to the first user based on the user information, and retrieving, for each identified entity, one or more content frames associated with the entity. The method includes ranking the one or more content frames based on the user information. The method also includes sending, to a client device of the first user, one or more of the content frames for display to the first user in ranked order, wherein each content frame is selectable by the first user to display the selected content frame in association with a particular content item for a specified period of time.

2015030, Oct 29, 2015

Apr 23, 2015

14/694131

- Mountain View CA, US
Xiaoyi Zhang - Los Angeles CA, US

G06F 3/01
G06F 3/0482
G06F 3/0484
G06T 7/00
G06K 9/00

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying a direction in which a user is looking. In one aspect, a method includes receiving an image of a sequence of images. The image can depict a face of a user. A template image for each particular facial feature point can be compared to one or more image portions of the image. The template image for the particular facial feature point can include a portion of a previous image of the sequence of images that depicted the facial feature point. Based on the comparison, a matching image portion of the image that matches the template image for the particular facial feature point is identified. A location of the matching image portion is identified in the image. A direction in which the user is looking is determined based on the identified location for each template image.