Ye Y Yuan, 3836410 Ruschin Dr, Newark, CA 94560

2023008, Mar 16, 2023

Nov 17, 2022

18/056700

- San Francisco CA, US
Ye Yuan - San Francisco CA, US
Neil Stegall - Pittsburg CA, US
Brent Justin Goldman - San Francisco CA, US
Kane Sweeney - San Francisco CA, US
Rei Chiang - San Francisco CA, US

G05D 1/02
G05D 1/00

Systems and methods for evaluating and deploying fleets of autonomous in operational domains are described. A computing system may obtain data indicative of one or more capabilities of at least one autonomous vehicle, data indicative of vehicle service dynamics in an operational domain over a period of time, and determining a plurality of resource performance parameters respectively for a plurality of autonomous vehicle fleets associated with potential deployment in the operational domain. Each autonomous vehicle fleet can be associated with a different number of autonomous vehicles The resource performance parameter for each autonomous vehicle fleet can be based at least in part on the one or more capabilities of the at least one autonomous vehicle and the vehicle service dynamics in the operational domain. The computing system can initiate an action associated with the operational domain based at least in part on the plurality of resource performance parameters.

2021031, Oct 7, 2021

Sep 11, 2019

17/275633

- Fremont CA, US
Ye Yuan - Fremont CA, US
Kenneth Hwang - San Francisco CA, US
Peter M. Baumgart - Pleasanton CA, US
Stephen Christopher Kekoa Hager - Hayward CA, US

G03B 21/60
G02B 5/124
G02B 5/04
G02B 5/02

The disclosed technology generally relates to displays, and more particularly to projection screens configured to display images with increased brightness and improved contrast and uniformity by using optical layers to control the direction and shape of the return light profiles. The disclosed technology comprises an optical layer configured such that incident light from a light source is directed towards specific positions for all locations on the reflective or transmissive display medium, and a light profile shaping optical layer configured to shape an intensity distribution of light reflected or transmitted from projection screen, prior to displaying the image to a viewer. The direction controlling optical layer and the light profile shaping optical layer may be combined into a single optical medium.

2020037, Nov 26, 2020

May 22, 2019

16/419755

- Redmond WA, US
Ye Yuan - Mountain View CA, US
Varun Mithal - Sunnyvale CA, US
Igor Vladimir Yagolnitser - Palo Alto CA, US

G06F 16/9536
G06F 16/9535
G06Q 50/20

Techniques for using online user activity in determining relevance of attributes to improve computer functionality in generating recommendations of online content are disclosed herein. In some embodiments, a computer system calculates a corresponding relevance score for each attribute of a user based on a total number of online postings for which the user has performed at least one of a plurality of online actions within a particular sliding window of time defining a most recent time period, an attribute activity number representing a number of online postings in the plurality of online postings that have the attribute, and an inverse of a frequency value representing how many of a total number of online postings published within the particular sliding window of time have the attribute. In some embodiments, the computer system causes at least one recommendation associated with the user to be displayed based on the calculated relevance scores.

2020031, Oct 1, 2020

Mar 28, 2019

16/367760

Ye Yuan - Mountain View CA, US
Huichao Xue - Santa Clara CA, US
Varun Mithal - Sunnyvale CA, US
Ada Cheuk Ying Yu - Santa Clara CA, US
Junrui Xu - Fremont CA, US

G06F 16/9535
G06N 5/04
G06F 16/9538
G06F 17/18
G06F 16/901
G06F 16/903

In some embodiments, a computer system determines that online postings belong to a cohort based on the postings having an attribute of the cohort, identifies skills from the postings, determines that a user belongs to the cohort based on a determination that a profile of the user includes the attribute(s) of the cohort, determines that one or more of the skills is stored in association with the profile, determines a user confidence score that indicates a relevance level of the skill to the user for each one of the one or more of the skills, determines a cohort confidence score for each one of the one or more of the skills based on how many of the postings include the skill, and displays a recommendation associated based on a combination of the user confidence score and the cohort confidence score for at least a portion of the skills.

2020031, Oct 1, 2020

Mar 26, 2019

16/365050

- Redmond WA, US
Ye Yuan - Mountain View CA, US
Varun Mithal - Sunnyvale CA, US
Junrui Xu - Fremont CA, US
Ada Cheuk Ying Yu - Santa Clara CA, US

G06N 5/04
G06F 16/9535
G06F 16/9536
G06N 20/00
G06F 16/901

In some embodiments, a computer system selects a first subset of candidate content items based on their filter scores that are generated based on a partial generalized linear mixed model comprising a baseline model and a user-based model, with the baseline model being a generalized linear model, and the user-based model being a random effects model based on user actions by the target user directed towards reference content items related to the candidate content items. In some embodiments, the computer system then selects a second subset from the first subset based on recommendation scores that are generated based on a full generalized linear mixed model comprising the baseline model, the user-based model, and an item-based model, with the item-based model being a random effects model based on user actions directed towards the candidate online content item by reference users related to the target user.

2020020, Jun 25, 2020

Jun 11, 2019

16/437227

- San Francisco CA, US
Rei Chiang - San Francisco CA, US
Brent Justin Goldman - San Francisco CA, US
Arvind Srinivasan - Menlo Park CA, US
Neil Stegall - Pittsburgh PA, US
Wei Wang - Fremont CA, US
Ye Yuan - Belmont CA, US

G05D 1/00
G05B 13/04
G05D 1/02
G01C 21/36
G06Q 10/06

The present disclosure is directed to configuring and instructing autonomous vehicles. In particular, one or more computing devices can receive, from a plurality of different autonomous vehicles, data describing travel completed by the plurality of different autonomous vehicles within a geographic area. The computing device(s) can also receive data indicating one or more model parameters for the geographic area. Based at least in part on the data describing the travel and the data indicating the model parameter(s), the computing device(s) can generate one or more models indicating one or more effects of the plurality of different autonomous vehicles on a transportation market for the geographic area. Based at least in part on the model(s), the computing device(s) can generate data indicating instructions for at least one autonomous vehicle of the plurality of different autonomous vehicles and can communicate such data to the at least one autonomous vehicle.

2020011, Apr 16, 2020

Oct 14, 2019

16/601013

- San Francisco CA, US
Ye Yuan - Belmont CA, US
Andrew Raymond Sturges - San Francisco CA, US
Neil Stegall - Pittsburgh PA, US
Brent Justin Goldman - San Francisco CA, US
Rei Chiang - San Francisco CA, US
Arvind Srinivasan - Menlo Park CA, US
Yifan Liu - Dublin CA, US

G01C 21/34
G01C 21/36
G05D 1/00

Systems and methods for determining autonomous vehicle operational domains improve vehicle data usage are provided. In one example embodiment, a computing system can obtain data indicative of a capability of an autonomous vehicle. The computing system can identify one or more a plurality of operational domains. The computing system can determine, for each of the operational domain(s), a respective level of addressability associated with the respective operational domain based at least in part on the capability of the autonomous vehicle. The computing system can provide an output based at least in part on the levels of addressability associated with the operational domain(s).

2019028, Sep 19, 2019

May 31, 2019

16/428947

- Mountain View CA, US
Nitsan Shai - Palo Alto CA, US
Timothy Raymond Zwiebel - Mountain View CA, US
Ye Yuan - Mountain View CA, US

H04L 29/06
H04W 12/00
H04W 4/80
H04W 4/70
H04L 29/08
H04W 12/06
H04W 12/08

Requesting, by a requesting device, from an Internet of Things (IoT) device, an IoT device identifier over a communication link between the devcies. Requesting, by the requesting device from an authorization device over a communication network including at least one TCP/IP link, authorization to command the IoT device to perform an action. Determining, by the authorization device, an authorization of the requesting device to command the identified IoT device to perform the requested action based on the IoT device identifier, the requesting device identifier, and the command. For a requesting device determined authorized, transmitting an encrypted authorization to the requesting device over the communication network. Relaying, by the requesting device to the IoT device via the first communication link, the authorization. Decrypting, by the IoT device, the authorization and performing the action specified therein.