Yuxi Zhang, 30New York, NY

2023011, Apr 13, 2023

Oct 13, 2021

17/500762

- Grand Cayman, KY
Jiacheng Yang - Culver City CA, US
Bo Hu - Culver City CA, US
Shouhan Gao - Culver City CA, US
Vishnuvardhan Tanguturi - Culver City CA, US
Yunjiu Li - Los Angeles CA, US
Yaxi Gao - Los Angeles CA, US
Zhili Chen - Los Angeles CA, US
Yiheng Zhu - Los Angeles CA, US
Yuxi Zhang - Los Angeles CA, US
Chaoran Huang - Los Angeles CA, US

G06F 3/0484
G06K 9/00
H04L 29/08

Systems and methods for managing a presentation of a dynamic profile photo are provided. In particular, a server may receive contextual information associated with one or more users. The server may determine that the contextual information is consistent with a predetermined contextual action. In some examples, the server may identify a dynamic profile photo associated with the predetermined contextual information; and present, in response to the determination that the contextual information is consistent with the predetermined contextual action, the dynamic profile photo to the one or more users.

2023009, Mar 30, 2023

Sep 30, 2021

17/491008

- Grand Cayman, KY
Peilin Li - Los Angeles CA, US
Qingyu Chen - Los Angeles CA, US
Yuxi Zhang - Los Angeles CA, US
Xiaojie Li - Los Angeles CA, US

G06K 9/00
G06K 9/62
G06F 3/0484
G06K 9/46
G06N 3/08
G06F 16/14

The present disclosure describes techniques for social networking based on collecting asset items. Features may be extracted from an image comprising an object. The object may be associated with a location. One or more pre-stored files may be determined based on the location. The one or more pre-stored files may each correspond to one or more objects proximate to the location. Each of the one or more pre-stored files may comprise data indicative of a corresponding object. The object may be recognized based on comparing the features extracted from the image with data comprised in the one or more pre-stored files. An asset item may be received in response to recognizing the object.

2023009, Mar 30, 2023

Apr 6, 2022

17/714920

- Grand Cayman, KY
Xiaojie Li - Los Angeles CA, US
Yuxi Zhang - Los Angeles CA, US
Qingyu Chen - Los Angeles CA, US
Peilin Li - Los Angeles CA, US

G06T 11/00
H04L 65/60
G06F 3/0482
H04N 5/265

The present disclosure describes techniques for social networking based on collecting asset items. Features may be extracted from an image comprising an object and captured by a camera. The object may be associated with a location. The location may be determined based on information indicating a position where the camera is located. The object may be recognized based at least in part on the features extracted from the image. An asset item may be displayed in response to recognizing the object.

2023010, Mar 30, 2023

Sep 30, 2021

17/491098

- Grand Cayman, KY
Xiaojie Li - Los Angeles CA, US
Yuxi Zhang - Los Angeles CA, US
Qingyu Chen - Los Angeles CA, US
Peilin Li - Los Angeles CA, US

G06T 11/00
H04N 5/265
G06F 3/0482
H04L 29/06

The present disclosure describes techniques for social networking based on trading asset items. The techniques comprise sending a request by a first client computing device for connecting with a second client computing device, displaying a first video comprising a first user and a second video comprising a second user on an interface, determining a first body part of the first user based on a selection of a representative of a first asset item associated with the second user, determining a position of rendering an image of the first asset item on the interface, rendering the image of the first asset item and combining the image into the first video for display of an effect of the first asset item being tried on the first body part of the first user, and receiving the first asset item from the second user based on the effect and user input.

2023000, Jan 5, 2023

Jul 2, 2021

17/366249

- San Francisco CA, US
Yuxi Zhang - San Francisco CA, US
Kexin Xie - San Mateo CA, US

G06N 20/00
G06N 7/00

Methods, computer readable media, and devices for determining a hyperparameter for influencing non-local samples in machine learning are disclosed. One method may include identifying a set of local samples associated with a first entity, identifying a set of non-local samples comprising samples associated with a plurality of entities other than the first entity, assigning a local sample weight to one or more samples of the set of local samples, determining a range of non-local sample weights, determining a range of hyperparameters based on the range of non-local sample weights, determining an optimized hyperparameter based on the range of hyperparameters, assigning an optimized non-local sample weight to one or more samples of the set of non-local samples, and generating a prediction using machine learning.

2021030, Sep 30, 2021

Mar 31, 2020

16/835451

- San Francisco CA, US
Michael Alan Huff - Emeryville CA, US
Flavius Popescu - San Francisco CA, US
Chad Lawrence Bettencourt - San Francisco CA, US
Tejeswara Reddy Pogula - San Francisco CA, US
Yuxi Zhang - San Mateo CA, US
Jiajian Zhou - San Francisco CA, US
Christophe Philippona - San Francisco CA, US
Tal Benisty - San Francisco CA, US
Myung Jin Kim - San Mateo CA, US

GM Cruise Holdings LLC - San Francisco CA

G08G 1/16
B60W 60/00
B60W 50/14
G08G 1/01
G08G 1/09

To visualize planned behavior of an autonomous vehicle (AV) traveling along a roadway, a user interface engine receives data describing a planned pathway of the AV along the roadway and object data describing an object having a predicted pathway crossing the planned pathway of the AV at a cross point. The user interface engine classifies the object either an asserting object or a yielding object based on a prediction of whether the object reaches the cross point before the AV or after the AV. The user interface engine generates an image that includes the planned pathway of the AV and the object in the environment of the AV. The image of the object indicates whether the object is classified as an asserting object or a yielding object.