Patents by Inventor Jiahui YU

Jiahui YU has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20220405579
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting a neural network to perform a particular machine learning task while satisfying a set of constraints.
    Type: Application
    Filed: March 3, 2021
    Publication date: December 22, 2022
    Inventors: Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Mintzer Bender, Pieter-Jan Kindermans, Mingxing Tan, Xiaodan Song, Ruoming Pang, Quoc V. Le
  • Patent number: 11526447
    Abstract: A data service layer running on a storage director node generates a request to destage host data from a plurality of cache slots in a single back-end track. The destage request includes pointers to addresses of the cache slots and indicates an order in which the host application data in the cache slots is to be included in the back-end track. A back-end redundant array of independent drives (RAID) subsystem running on a drive adapter is responsive to the request to calculate parity information using the host application data in the cache slots. The back-end RAID subsystem assembles the single back-end track comprising the host application data from the plurality of cache slots of the request, and destages the single back-end track to a non-volatile drive in a single back-end input-output (IO) operation.
    Type: Grant
    Filed: June 30, 2021
    Date of Patent: December 13, 2022
    Assignee: EMC IP HOLDING COMPANY LLC
    Inventors: Peng Wu, Rong Yu, Jiahui Wang, Lixin Pang
  • Publication number: 20220317500
    Abstract: The present disclosure discloses a display panel, a method for manufacturing the same, and a display device, which belong to the field of display technologies. The display panel includes: a first substrate and a second substrate which are oppositely disposed. The first substrate may include one or more light-emitting unit, and the second substrate may include two or more reflective electrodes. All first reflective electrodes in the display panel are capable of reflecting light emitted by the light-emitting unit to a first view zone, and all second reflective electrodes are capable of emitting the light emitted by the light-emitting unit to a second view zone. Therefore, a complete picture is viewed in the first view zone, and another complete picture can be viewed in the second view zone.
    Type: Application
    Filed: October 20, 2020
    Publication date: October 6, 2022
    Inventors: Yanliu SUN, Pengxia LIANG, Shiyu ZHANG, Ge SHI, Zheng FANG, Yuyao WANG, Meina YU, Jiahui HAN
  • Patent number: 11450991
    Abstract: A connector housing includes an accommodation space defined by four walls and formed with an insertion port. The four walls include a first wall extending in a first plane and a second wall extending in a second plane perpendicular to the first plane, the first wall is connected with the second wall at a corner of the connector housing. A first positioning groove is disposed in an edge of the first wall proximate to the insertion port and a first positioning tooth is disposed on an edge of the second wall. The first positioning tooth extends in the first plane by vertically bending and engaging within the first positioning groove. The first positioning tooth and the first positioning groove have a first locking feature preventing the first positioning tooth from being disengaged from the first positioning groove in a direction perpendicular to the second plane.
    Type: Grant
    Filed: December 2, 2020
    Date of Patent: September 20, 2022
    Assignees: Tyco Electronics (Shanghai) Co. Ltd., Tyco Electronics (Zhuhai) Ltd.
    Inventors: Jikang Wei, Huiliang Luo, Shufeng Jia, Qiang Yu, Hongwen Yang, Hongqiang Han, Jiahui Chen
  • Patent number: 11436775
    Abstract: Predicting patch displacement maps using a neural network is described. Initially, a digital image on which an image editing operation is to be performed is provided as input to a patch matcher having an offset prediction neural network. From this image and based on the image editing operation for which this network is trained, the offset prediction neural network generates an offset prediction formed as a displacement map, which has offset vectors that represent a displacement of pixels of the digital image to different locations for performing the image editing operation. Pixel values of the digital image are copied to the image pixels affected by the operation.
    Type: Grant
    Filed: March 2, 2020
    Date of Patent: September 6, 2022
    Assignee: Adobe Inc.
    Inventors: Zhe Lin, Xin Lu, Xiaohui Shen, Jimei Yang, Jiahui Yu
  • Publication number: 20220236601
    Abstract: An array substrate is provided. One of a first electrode layer and a second electrode layer in the array substrate includes at least one slit electrode. The slit electrode is disposed between two adjacent data leads in the array substrate, and includes an electrode connecting portion and a plurality of first strip-shaped sub-electrodes. The electrode connecting portion includes a first connecting section parallel to and adjacent to the data lead, and a distance between two adjacent first strip-shaped sub-electrodes in a direction parallel to an extending direction of the first connecting section gradually increases along a direction going away from the first connecting section.
    Type: Application
    Filed: January 25, 2022
    Publication date: July 28, 2022
    Inventors: Zheng FANG, Pengxia LIANG, Meina YU, Ge SHI, Song YANG, Yujie LIU, Jiahui HAN, Yanliu SUN, Hyunsic CHOI, Hongpeng LI
  • Publication number: 20220216809
    Abstract: An electronic sensing apparatus and a method of producing the electronic sensing apparatus includes a triboelectric generator encapsulated between a bottom substrate and a top encapsulation layer, wherein the triboelectric generator is arranged to generate a triboelectric sensing signal in response to a deformation of the bottom substrate and/or the top encapsulation layer.
    Type: Application
    Filed: December 20, 2021
    Publication date: July 7, 2022
    Inventors: Xinge YU, Jiahui HE
  • Publication number: 20220207321
    Abstract: Systems and methods can utilize a conformer model to process a data set for various data processing tasks, including, but not limited to, speech recognition, sound separation, protein synthesis determination, video or other image set analysis, and natural language processing. The conformer model can use feed-forward blocks, a self-attention block, and a convolution block to process data to learn global interactions and relative-offset-based local correlations of the input data.
    Type: Application
    Filed: December 31, 2020
    Publication date: June 30, 2022
    Inventors: Anmol Gulati, Ruoming Pang, Niki Parmar, Jiahui Yu, Wei Han, Chung-Cheng Chiu, Yu Zhang, Yonghui Wu, Shibo Wang, Weikeng Qin, Zhengdong Zhang
  • Patent number: 11372562
    Abstract: A storage system that supports multiple RAID levels presents storage objects with front-end tracks corresponding to back-end tracks on non-volatile drives and accesses the drives using a single type of back-end allocation unit that is larger than a back-end track. When the number of members of a protection group of a RAID level does not align with the back-end allocation unit, multiple back-end tracks are grouped and accessed using a single IO. The number of back-end tracks in a group is selected to align with the back-end allocation unit size. If the front-end tracks are variable size, then front-end tracks may be destaged into a smaller number of grouped back-end tracks in a single IO.
    Type: Grant
    Filed: April 8, 2021
    Date of Patent: June 28, 2022
    Assignee: Dell Products L.P.
    Inventors: Peng Wu, Rong Yu, Jiahui Wang, Lixin Pang
  • Patent number: 11334971
    Abstract: Digital image completion by learning generation and patch matching jointly is described. Initially, a digital image having at least one hole is received. This holey digital image is provided as input to an image completer formed with a dual-stage framework that combines a coarse image neural network and an image refinement network. The coarse image neural network generates a coarse prediction of imagery for filling the holes of the holey digital image. The image refinement network receives the coarse prediction as input, refines the coarse prediction, and outputs a filled digital image having refined imagery that fills these holes. The image refinement network generates refined imagery using a patch matching technique, which includes leveraging information corresponding to patches of known pixels for filtering patches generated based on the coarse prediction. Based on this, the image completer outputs the filled digital image with the refined imagery.
    Type: Grant
    Filed: July 14, 2020
    Date of Patent: May 17, 2022
    Assignee: Adobe Inc.
    Inventors: Zhe Lin, Xin Lu, Xiaohui Shen, Jimei Yang, Jiahui Yu
  • Publication number: 20220122586
    Abstract: A computer-implemented method of training a streaming speech recognition model that includes receiving, as input to the streaming speech recognition model, a sequence of acoustic frames. The streaming speech recognition model is configured to learn an alignment probability between the sequence of acoustic frames and an output sequence of vocabulary tokens. The vocabulary tokens include a plurality of label tokens and a blank token. At each output step, the method includes determining a first probability of emitting one of the label tokens and determining a second probability of emitting the blank token. The method also includes generating the alignment probability at a sequence level based on the first probability and the second probability. The method also includes applying a tuning parameter to the alignment probability at the sequence level to maximize the first probability of emitting one of the label tokens.
    Type: Application
    Filed: September 9, 2021
    Publication date: April 21, 2022
    Applicant: Google LLC
    Inventors: Jiahui Yu, Chung-cheng Chiu, Bo Li, Shuo-yiin Chang, Tara Sainath, Wei Han, Anmol Gulati, Yanzhang He, Arun Narayanan, Yonghui Wu, Ruoming Pang
  • Publication number: 20220122622
    Abstract: An automated speech recognition (ASR) model includes a first encoder, a second encoder, and a decoder. The first encoder receives, as input, a sequence of acoustic frames, and generates, at each of a plurality of output steps, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The second encoder receives, as input, the first higher order feature representation generated by the first encoder at each of the plurality of output steps, and generates, at each of the plurality of output steps, a second higher order feature representation for a corresponding first higher order feature frame. The decoder receives, as input, the second higher order feature representation generated by the second encoder at each of the plurality of output steps, and generates, at each of the plurality of time steps, a first probability distribution over possible speech recognition hypotheses.
    Type: Application
    Filed: April 21, 2021
    Publication date: April 21, 2022
    Applicant: Google LLC
    Inventors: Arun Narayanan, Tara Sainath, Chung-Cheng Chiu, Ruoming Pang, Rohit Prabhavalkar, Jiahui Yu, Ehsan Variani, Trevor Strohman
  • Patent number: 11250548
    Abstract: Digital image completion using deep learning is described. Initially, a digital image having at least one hole is received. This holey digital image is provided as input to an image completer formed with a framework that combines generative and discriminative neural networks based on learning architecture of the generative adversarial networks. From the holey digital image, the generative neural network generates a filled digital image having hole-filling content in place of holes. The discriminative neural networks detect whether the filled digital image and the hole-filling digital content correspond to or include computer-generated content or are photo-realistic. The generating and detecting are iteratively continued until the discriminative neural networks fail to detect computer-generated content for the filled digital image and hole-filling content or until detection surpasses a threshold difficulty.
    Type: Grant
    Filed: February 14, 2020
    Date of Patent: February 15, 2022
    Assignee: Adobe Inc.
    Inventors: Zhe Lin, Xin Lu, Xiaohui Shen, Jimei Yang, Jiahui Yu
  • Patent number: 10839575
    Abstract: Certain embodiments involve using an image completion neural network to perform user-guided image completion. For example, an image editing application accesses an input image having a completion region to be replaced with new image content. The image editing application also receives a guidance input that is applied to a portion of a completion region. The image editing application provides the input image and the guidance input to an image completion neural network that is trained to perform image-completion operations using guidance input. The image editing application produces a modified image by replacing the completion region of the input image with the new image content generated with the image completion network. The image editing application outputs the modified image having the new image content.
    Type: Grant
    Filed: March 15, 2018
    Date of Patent: November 17, 2020
    Assignee: ADOBE INC.
    Inventors: Zhe Lin, Xin Lu, Xiaohui Shen, Jimei Yang, Jiahui Yu
  • Publication number: 20200342576
    Abstract: Digital image completion by learning generation and patch matching jointly is described. Initially, a digital image having at least one hole is received. This holey digital image is provided as input to an image completer formed with a dual-stage framework that combines a coarse image neural network and an image refinement network. The coarse image neural network generates a coarse prediction of imagery for filling the holes of the holey digital image. The image refinement network receives the coarse prediction as input, refines the coarse prediction, and outputs a filled digital image having refined imagery that fills these holes. The image refinement network generates refined imagery using a patch matching technique, which includes leveraging information corresponding to patches of known pixels for filtering patches generated based on the coarse prediction. Based on this, the image completer outputs the filled digital image with the refined imagery.
    Type: Application
    Filed: July 14, 2020
    Publication date: October 29, 2020
    Applicant: Adobe Inc.
    Inventors: Zhe Lin, Xin Lu, Xiaohui Shen, Jimei Yang, Jiahui Yu
  • Patent number: 10769493
    Abstract: The embodiments of the present invention provide training and construction methods and apparatus of a neural network for object detection, an object detection method and apparatus based on a neural network and a neural network. The training method of the neural network for object detection, comprises: inputting a training image including a training object to the neural network to obtain a predicted bounding box of the training object; acquiring a first loss function according to a ratio of the intersection area to the union area of the predicted bounding box and a true bounding box, the true bounding box being a bounding box of the training object marked in advance in the training image; and adjusting parameters of the neural network by utilizing at least the first loss function to train the neural network.
    Type: Grant
    Filed: July 26, 2017
    Date of Patent: September 8, 2020
    Assignees: BEIJING KUANGSHI TECHNOLOGY CO., LTD., MEGVII (BEIJING) TECHNOLOGY CO., LTD.
    Inventors: Jiahui Yu, Qi Yin
  • Patent number: 10755391
    Abstract: Digital image completion by learning generation and patch matching jointly is described. Initially, a digital image having at least one hole is received. This holey digital image is provided as input to an image completer formed with a dual-stage framework that combines a coarse image neural network and an image refinement network. The coarse image neural network generates a coarse prediction of imagery for filling the holes of the holey digital image. The image refinement network receives the coarse prediction as input, refines the coarse prediction, and outputs a filled digital image having refined imagery that fills these holes. The image refinement network generates refined imagery using a patch matching technique, which includes leveraging information corresponding to patches of known pixels for filtering patches generated based on the coarse prediction. Based on this, the image completer outputs the filled digital image with the refined imagery.
    Type: Grant
    Filed: May 15, 2018
    Date of Patent: August 25, 2020
    Assignee: Adobe Inc.
    Inventors: Zhe Lin, Xin Lu, Xiaohui Shen, Jimei Yang, Jiahui Yu
  • Publication number: 20200202601
    Abstract: Predicting patch displacement maps using a neural network is described. Initially, a digital image on which an image editing operation is to be performed is provided as input to a patch matcher having an offset prediction neural network. From this image and based on the image editing operation for which this network is trained, the offset prediction neural network generates an offset prediction formed as a displacement map, which has offset vectors that represent a displacement of pixels of the digital image to different locations for performing the image editing operation. Pixel values of the digital image are copied to the image pixels affected by the operation.
    Type: Application
    Filed: March 2, 2020
    Publication date: June 25, 2020
    Applicant: Adobe Inc.
    Inventors: Zhe Lin, Xin Lu, Xiaohui Shen, Jimei Yang, Jiahui Yu
  • Publication number: 20200184610
    Abstract: Digital image completion using deep learning is described. Initially, a digital image having at least one hole is received. This holey digital image is provided as input to an image completer formed with a framework that combines generative and discriminative neural networks based on learning architecture of the generative adversarial networks. From the holey digital image, the generative neural network generates a filled digital image having hole-filling content in place of holes. The discriminative neural networks detect whether the filled digital image and the hole-filling digital content correspond to or include computer-generated content or are photo-realistic. The generating and detecting are iteratively continued until the discriminative neural networks fail to detect computer-generated content for the filled digital image and hole-filling content or until detection surpasses a threshold difficulty.
    Type: Application
    Filed: February 14, 2020
    Publication date: June 11, 2020
    Applicant: Adobe Inc.
    Inventors: Zhe Lin, Xin Lu, Xiaohui Shen, Jimei Yang, Jiahui Yu
  • Patent number: 10672164
    Abstract: Predicting patch displacement maps using a neural network is described. Initially, a digital image on which an image editing operation is to be performed is provided as input to a patch matcher having an offset prediction neural network. From this image and based on the image editing operation for which this network is trained, the offset prediction neural network generates an offset prediction formed as a displacement map, which has offset vectors that represent a displacement of pixels of the digital image to different locations for performing the image editing operation. Pixel values of the digital image are copied to the image pixels affected by the operation by: determining the vectors pixels that correspond to the image pixels affected by the image editing operation and mapping the pixel values of the image pixels represented by the determined offset vectors to the affected pixels.
    Type: Grant
    Filed: October 16, 2017
    Date of Patent: June 2, 2020
    Assignee: Adobe Inc.
    Inventors: Zhe Lin, Xin Lu, Xiaohui Shen, Jimei Yang, Jiahui Yu