Shuai Zheng, 397020 N Blue Ridge Rd, Edmond, OK 73034

2021006, Mar 4, 2021

Aug 27, 2019

16/552846

- Palo Alto CA, US
Ning Zhang - Mountain View CA, US
Shuai Zheng - Campbell CA, US
Jordan Hill Hurwitz - Burlingame CA, US
Ziyu Zhang - Mountain View CA, US
Mohammadhadi Kiapour - San Francisco CA, US

G16H 40/67
G08B 21/02
G06N 3/08
G06N 3/04
A61B 5/11
A61B 5/00

Privacy-preserving activity monitoring systems and methods are described. In one embodiment, a plurality of sensors is configured for contact-free monitoring of at least one user state. A signal processing module communicatively coupled to the sensors is configured to receive data from the sensors. A first sensor is configured to generate a first set of quantitative data associated with a first user state. A second sensor is configured to generate a second set of quantitative data associated with a second user state. A third sensor is configured to generate a third set of quantitative data associated with a third user state. The signal processing module is configured to process the three sets of quantitative data using a machine learning module, and identify a user activity and detect a condition associated with the user, where no user-identifying information is communicated more than 100 meters to or from the signal processing module.

2020038, Dec 3, 2020

May 31, 2019

16/428016

- Tokyo, JP
Shuai ZHENG - San Jose CA, US
Ahmed FARAHAT - Santa Clara CA, US
Susumu SERITA - San Jose CA, US
Takashi SAEKI - Sunnyvale CA, US
Chetan GUPTA - San Mateo CA, US

G06N 5/04
G06N 20/00

Example implementations described herein are directed to constructing prediction models and conducting predictive maintenance for systems that provide sparse sensor data. Even if only sparse measurements of sensor data are available, example implementations utilize the inference of statistics with functional deep networks to model prediction for the systems, which provides better accuracy and failure prediction even if only sparse measurements are available.

2020021, Jul 9, 2020

Jan 3, 2020

16/733766

- San Jose CA, US
Shuai Zheng - Berkeley CA, US
Robinson Piramuthu - Oakland CA, US
Omid Poursaeed - New York NY, US

G06K 9/66
G06Q 30/06
G06K 9/00
G06K 9/32
G06K 9/62
G06F 16/9535
G06F 16/583
G06F 16/532

Various embodiments described herein utilize multiple levels of generative adversarial networks (GANs) to facilitate generation of digital images based on user-provided images. Some embodiments comprise a first generative adversarial network (GAN) and a second GAN coupled to the first GAN, where the first GAN includes an image generator and at least two discriminators, and the second GAN includes an image generator and at least one discriminator. According to some embodiments, the (first) image generator of the first GAN is trained by processing a user-provided image using the first GAN. For some embodiments, the user-provided image and the first generated image, generated by processing the user-provided image using the first GAN, are combined to produce a combined image. For some embodiments, the (second) image generator of the second GAN is trained by processing the combined image using the second GAN.

2019028, Sep 19, 2019

Mar 16, 2018

15/923347

- San Jose CA, US
Shuai Zheng - Berkeley CA, US
Robinson Piramuthu - Oakland CA, US
Omid Poursaeed - New York NY, US

G06K 9/66
G06K 9/62
G06K 9/32
G06K 9/00
G06F 17/30
G06Q 30/06

Various embodiments described herein utilize multiple levels of generative adversarial networks (GANs) to facilitate generation of digital images based on user-provided images. Some embodiments comprise a first generative adversarial network (GAN) and a second GAN coupled to the first GAN, where the first GAN includes an image generator and at least two discriminators, and the second GAN includes an image generator and at least one discriminator. According to some embodiments, the (first) image generator of the first GAN is trained by processing a user-provided image using the first GAN. For some embodiments, the user-provided image and the first generated image, generated by processing the user-provided image using the first GAN, are combined to produce a combined image. For some embodiments, the (second) image generator of the second GAN is trained by processing the combined image using the second GAN.

2019024, Aug 8, 2019

Apr 18, 2019

16/388473

- San Jose CA, US
Timothy Samuel Keefer - San Jose CA, US
Kenneth Clark Crookston - Freiberg, DE
Ashmeet Singh Rekhi - Campbell CA, US
Niaz Ahamed Khaja Nazimudeen - Fremont CA, US
Padmapriya Gudipati - San Jose CA, US
Shane Lin - Mountain View CA, US
John F. Weigel - San Jose CA, US
Fujun Zhong - Saratoga CA, US
Suchitra Ramesh - San Jose CA, US
Mohammadhadi Kiapour - San Francisco CA, US
Shuai Zheng - Berkeley CA, US
Alberto Ordonez Pereira - Santa Clara CA, US
Ravindra Surya Lanka - San Jose CA, US
Md Atiq ul Islam - San Jose CA, US
Nicholas Anthony Whyte - San Jose CA, US
Giridharan Iyengar - San Jose CA, US
Bryan Allen Plummer - Urbana IL, US

eBay Inc. - San Jose CA

G06Q 30/06
G06K 9/62

Computer vision and image characteristic search is described. The described system leverages visual search techniques by determining visual characteristics of objects depicted in images and comparing the determined characteristics to visual characteristics of other images, e.g., to identify similar visual characteristics in the other images. In some aspects, the described system performs searches that leverage a digital image as part of a search query to locate digital content of interest. In some aspects, the described system surfaces multiple user interface instrumentalities that include images of patterns, textures, or materials and that are selectable to initiate a visual search of digital content having a similar pattern, texture, or material. The described aspects also include pattern-based authentication in which the system determines authenticity of an item in an image based on a similarity of its visual characteristics to visual characteristics of known authentic items.

2019020, Jul 4, 2019

Dec 28, 2018

16/235290

- San Jose CA, US
Timothy Samuel Keefer - San Jose CA, US
Ashmeet Singh Rekhi - Campbell CA, US
Padmapriya Gudipati - San Jose CA, US
Mohammadhadi Kiapour - San Francisco CA, US
Shuai Zheng - Berkeley CA, US
Alberto Ordonez Pereira - Santa Clara CA, US
Ravindra Surya Lanka - San Jose CA, US
Md Atiq ul Islam - San Jose CA, US
Nicholas Anthony Whyte - San Jose CA, US
Giridharan Iyengar - San Jose CA, US
Bryan Allen Plummer - Urbana IL, US

eBay Inc. - San Jose CA

G06F 16/532
G06K 9/66
G06F 16/538
G06F 16/9535

Computer vision for unsuccessful queries and iterative search is described. The described system leverages visual search techniques by determining visual characteristics of objects depicted in images and describing them, e.g., using feature vectors. In some aspects, these visual characteristics are determined for search queries that are identified as not being successful. Aggregated information describing visual characteristics of images of unsuccessful search queries is used to determine common visual characteristics and objects depicted in those images. This information can be used to inform other users about unmet needs of searching users. In some aspects, these visual characteristics are used in connection with iterative image searches where users select an initial query image and then the search results are iteratively refined. Unlike conventional techniques, the described system iteratively refines the returned search results using an embedding space learned from binary attribute labels describing images.

2019020, Jul 4, 2019

Dec 28, 2018

16/235007

- San Jose CA, US
Timothy Samuel Keefer - San Jose CA, US
Ashmeet Singh Rekhi - Campbell CA, US
Padmapriya Gudipati - San Jose CA, US
Mohammadhadi Kiapour - San Francisco CA, US
Shuai Zheng - Berkeley CA, US
Md Atiq ul Islam - San Jose CA, US
Nicholas Anthony Whyte - San Jose CA, US
Giridharan Iyengar - San Jose CA, US

eBay Inc. - San Jose CA

G06K 9/00
G06N 20/00
G06F 3/01
G06F 16/583

Techniques and systems are described that leverage computer vision as part of search to expand functionality of a computing device available to a user and increase operational computational efficiency as well as efficiency in user interaction. In a first example, user interaction with items of digital content is monitored. Computer vision techniques are used to identify digital images in the digital content, objects within the digital images, and characteristics of those objects. This information is used to assign a user to a user segment of a user population which is then used to control output of subsequent digital content to the user, e.g., recommendations, digital marketing content, and so forth.

2019020, Jul 4, 2019

Dec 28, 2018

16/235140

- San Jose CA, US
Timothy Samuel Keefer - San Jose CA, US
Kenneth Clark Crookston - Freiberg, DE
Ashmeet Singh Rekhi - Campbell CA, US
Niaz Ahamed Khaja Nazimudeen - Fremont CA, US
Padmapriya Gudipati - San Jose CA, US
Shane Lin - Mountain View CA, US
John F. Weigel - San Jose CA, US
Fujun Zhong - Saratoga CA, US
Suchitra Ramesh - San Jose CA, US
Mohammadhadi Kiapour - San Francisco CA, US
Shuai Zheng - Berkeley CA, US
Alberto Ordonez Pereira - Santa Clara CA, US
Ravindra Surya Lanka - San Jose CA, US
Md Atiq ul Islam - San Jose CA, US
Nicholas Anthony Whyte - San Jose CA, US
Giridharan Iyengar - San Jose CA, US
Bryan Allen Plummer - Urbana IL, US

eBay Inc. - San Jose CA

G06Q 30/06
G06K 9/62

Computer vision and image characteristic search is described. The described system leverages visual search techniques by determining visual characteristics of objects depicted in images and comparing the determined characteristics to visual characteristics of other images, e.g., to identify similar visual characteristics in the other images. In some aspects, the described system performs searches that leverage a digital image as part of a search query to locate digital content of interest. In some aspects, the described system surfaces multiple user interface instrumentalities that include images of patterns, textures, or materials and that are selectable to initiate a visual search of digital content having a similar pattern, texture, or material. The described aspects also include pattern-based authentication in which the system determines authenticity of an item in an image based on a similarity of its visual characteristics to visual characteristics of known authentic items.