Patents by Inventor Janghwan Lee

Janghwan Lee 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).

  • Patent number: 12136205
    Abstract: A system for manufacturing defect classification is presented. The system includes a first neural network receiving a first data as input and generating a first output, a second neural network receiving a second data as input and generating a second output, wherein first neural network and the second neural network are trained independently from each other, and a fusion neural network receiving the first output and the second output and generating a classification. The first data and the second data do not have to be aligned. Hence, the system and method of this disclosure allows various type of data that are collected during manufacturing to be used in defect classification.
    Type: Grant
    Filed: May 19, 2023
    Date of Patent: November 5, 2024
    Assignee: Samsung Display Co., Ltd.
    Inventors: Yan Kang, Janghwan Lee, Shuhui Qu, Jinghua Yao, Sai MarapaReddy
  • Publication number: 20240346806
    Abstract: A system and a method are disclosed for synthetic image generation. In some embodiments, the system includes one or more processors; and a memory storing instructions which, when executed by the one or more processors, cause performance of: receiving weather input data; receiving time input data; receiving pixel coordinates; using a light-source-modeling neural network, computing a light source model based on inputs of the weather input data and time input data; and using an image-generating system, generating an image based on the pixel coordinates and the light source model.
    Type: Application
    Filed: September 14, 2023
    Publication date: October 17, 2024
    Inventors: Shuhui QU, Janghwan LEE
  • Publication number: 20240338419
    Abstract: A method of convolution operation based sparse data using artificial neural network comprises: a step of extracting index information, location information about a valid data where actual data exists in an input data; a step of generating first location information including computable row information where actual operations are performed in a kernel based on a path along which the kernel moves to perform a convolution operation on the input data and the index information; a step of generating second location information including computable column information where an actual operation is performed in the kernel based on the first location information, the index information, and the kernel size; a step of generating an operation rule for each point of the valid data and convolution output data based on the index information, and the first and second location information; and a step of performing the convolution operation based on the operation rule.
    Type: Application
    Filed: June 17, 2024
    Publication date: October 10, 2024
    Inventors: Minjae Lee, Janghwan Lee, Jun Won Choi, Jungwook Choi
  • Patent number: 12106226
    Abstract: A system and method for classifying products. A processor generates first and second instances of a first classifier, and trains the instances based on an input dataset. A second classifier is trained based on the input, where the second classifier is configured to learn a representation of a latent space associated with the input. A first supplemental dataset is generated in the latent space, where the first supplemental dataset is an unlabeled dataset. A first prediction is generated for labeling the first supplemental dataset based on the first instance of the first classifier, and a second prediction is generated for labeling the first supplemental dataset based on the second instance of the first classifier. Labeling annotations are generated for the first supplemental dataset based on the first prediction and the second prediction. A third classifier is trained based on at least the input dataset and the annotated first supplemental dataset.
    Type: Grant
    Filed: June 7, 2023
    Date of Patent: October 1, 2024
    Assignee: Samsung Display Co., Ltd.
    Inventor: Janghwan Lee
  • Publication number: 20240312193
    Abstract: A method may include providing a data set including rows of data. The rows of data may include at least one row of unpaired modality including a first modality, and at least one row of paired modality may include both the first modality and a second modality. The method may further include imputing, by a modality-specific encoder, the at least one row of unpaired modality by interpolating embeddings from the second modality of the paired modality; training, in a latent space, the modality-specific encoder based on the imputation for unimodal prediction and bimodal prediction; and generating a confidence value for the unimodal prediction and the bimodal prediction.
    Type: Application
    Filed: June 21, 2023
    Publication date: September 19, 2024
    Inventors: Qisen Cheng, Shuhui Qu, Kaushik Balakrishnan, Janghwan Lee
  • Publication number: 20240242494
    Abstract: A method and a system are presented for controlling a performance of a fusion model. The method includes obtaining a first set and a second set of candidate models for a first and second neural networks, respectively. Each of the first and second set of candidate models is pre-trained with a first source and a second source, respectively. For each possible pairing of one candidate model from the first neural network and one candidate model from the second neural network, a model distance Dm is determined. A subset of possible pairings of one first candidate model and one second candidate model is selected based on the model distance Dm between them. Using the subset of possible parings, the first neural network and the second neural network are combined to generate two branches for a fusion model neural network.
    Type: Application
    Filed: April 1, 2024
    Publication date: July 18, 2024
    Inventors: Shuhui Qu, Janghwan Lee, Yan Kang, Jinghua Yao, Sai MarapaReddy
  • Publication number: 20240127030
    Abstract: A classification system includes: one or more processors; and memory including instructions that, when executed by the one or more processors, cause the one or more processors to: calculate reference Shapley values for features of a data sample based on a first classification model; and train a second classification model though multi-task distillation to: predict Shapley values for the features of the data sample based on the reference Shapley values and a distillation loss; and predict a class label for the data sample based on the predicted Shapley values and a ground truth class label for the data sample.
    Type: Application
    Filed: February 14, 2023
    Publication date: April 18, 2024
    Inventors: Qisen Cheng, Shuhui Qu, Kaushik Balakrishnan, Janghwan Lee
  • Patent number: 11948347
    Abstract: A method and a system are presented for controlling a performance of a fusion model. The method includes obtaining a first set and a second set of candidate models for a first and second neural networks, respectively. Each of the first and second set of candidate models is pre-trained with a first source and a second source, respectively. For each possible pairing of one candidate model from the first neural network and one candidate model from the second neural network, a model distance Dm is determined. A subset of possible pairings of one first candidate model and one second candidate model is selected based on the model distance Dm between them. Using the subset of possible parings, the first neural network and the second neural network are combined to generate two branches for a fusion model neural network.
    Type: Grant
    Filed: July 24, 2020
    Date of Patent: April 2, 2024
    Assignee: Samsung Display Co., Ltd.
    Inventors: Shuhui Qu, Janghwan Lee, Yan Kang, Jinghua Yao, Sai MarapaReddy
  • Patent number: 11922301
    Abstract: A system and method for classification. In some embodiments, the method includes forming a first training dataset and a second training dataset from a labeled input dataset; training a first classifier with the first training dataset; training a variational auto encoder with the second training dataset, the variational auto encoder comprising an encoder and a decoder; generating a third dataset, by feeding pseudorandom vectors into the decoder; labeling the third dataset, using the first classifier, to form a third training dataset; forming a fourth training dataset based on the third dataset; and training a second classifier with the fourth training dataset.
    Type: Grant
    Filed: June 14, 2019
    Date of Patent: March 5, 2024
    Assignee: Samsung Display Co., Ltd.
    Inventor: Janghwan Lee
  • Publication number: 20240048724
    Abstract: According to some embodiments, a system includes: a memory, an encoder; a decoder, wherein the system is operable to: receive, at the encoder, an input video; divide, by the encoder, the input video into a plurality of video patches; select, by the encoder, codes corresponding to the plurality of video patches of the input video, from a codebook comprising the codes; determine, by the encoder, an assigned code matrix comprising the codes corresponding to the plurality of video patches of the input video; receive, by the decoder, the assigned code matrix from the encoder; and generate, by the decoder, a reconstructed video based on the assigned code matrix.
    Type: Application
    Filed: December 2, 2022
    Publication date: February 8, 2024
    Inventors: Shuhui QU, Qisen CHENG, Yannick BLIESENER, Janghwan LEE
  • Patent number: 11830240
    Abstract: A system includes a memory; and a processor configured to train a first machine learning model based on the first dataset labeling; provide the second dataset to the trained first machine learning model to generate an updated second dataset including an updated second dataset labeling, determine a first difference between the updated second dataset labeling and the second dataset labeling; train a second machine learning model based on the updated second dataset labeling if the first difference is greater than a first threshold value; provide the first dataset to the trained second machine learning model to generate an updated first dataset including an updated first dataset labeling, determine a second difference between the updated first dataset labeling and the first dataset labeling; and train the first machine learning model based on the updated first dataset labeling if the second difference is greater than a second threshold value.
    Type: Grant
    Filed: December 27, 2022
    Date of Patent: November 28, 2023
    Assignee: Samsung Display Co., Ltd.
    Inventor: Janghwan Lee
  • Publication number: 20230333533
    Abstract: A method for detecting a fault includes: receiving a plurality of time-series sensor data obtained in one or more manufacturing processes of an electronic device; arranging the plurality of time-series sensor data in a two-dimensional (2D) data array; providing the 2D data array to a convolutional neural network model; identifying a pattern in the 2D data array that correlates to a fault condition using the convolutional neural network model; providing a fault indicator of the fault condition in the one or more manufacturing processes of the electronic device; and determining that the electronic device includes a fault based on the fault indicator. The 2D data array has a dimension of an input data to the convolutional neural network model.
    Type: Application
    Filed: June 22, 2023
    Publication date: October 19, 2023
    Inventor: Janghwan Lee
  • Publication number: 20230316084
    Abstract: A system and method for classifying products. A processor generates first and second instances of a first classifier, and trains the instances based on an input dataset. A second classifier is trained based on the input, where the second classifier is configured to learn a representation of a latent space associated with the input. A first supplemental dataset is generated in the latent space, where the first supplemental dataset is an unlabeled dataset. A first prediction is generated for labeling the first supplemental dataset based on the first instance of the first classifier, and a second prediction is generated for labeling the first supplemental dataset based on the second instance of the first classifier. Labeling annotations are generated for the first supplemental dataset based on the first prediction and the second prediction. A third classifier is trained based on at least the input dataset and the annotated first supplemental dataset.
    Type: Application
    Filed: June 7, 2023
    Publication date: October 5, 2023
    Inventor: Janghwan Lee
  • Publication number: 20230316493
    Abstract: A system for manufacturing defect classification is presented. The system includes a first neural network receiving a first data as input and generating a first output, a second neural network receiving a second data as input and generating a second output, wherein first neural network and the second neural network are trained independently from each other, and a fusion neural network receiving the first output and the second output and generating a classification. The first data and the second data do not have to be aligned. Hence, the system and method of this disclosure allows various type of data that are collected during manufacturing to be used in defect classification.
    Type: Application
    Filed: May 19, 2023
    Publication date: October 5, 2023
    Inventors: Yan Kang, Janghwan Lee, Shuhui Qu, Jinghua Yao, Sai MarapaReddy
  • Publication number: 20230267600
    Abstract: A system including: a memory, an encoder, a decoder, and a processor, the processor being connected to the memory, the encoder, and the decoder. The system is configured to: receive, at the encoder, an input image, divide, by the encoder, the input image into a plurality of image patches, select, by the encoder, codes corresponding to the plurality of image patches of the input image, from a codebook including the codes. The system is further configured to determine, by the encoder, an assigned code matrix including the codes corresponding to the plurality of image patches of the input image, receive, by the decoder, the assigned code matrix from the encoder. The system is further configured to generate, by the decoder, a reconstructed image based on the assigned code matrix.
    Type: Application
    Filed: April 25, 2022
    Publication date: August 24, 2023
    Inventors: Shuhui Qu, Qisen Cheng, Janghwan Lee
  • Publication number: 20230267599
    Abstract: A system and method for defect detection. In some embodiments, the method includes: identifying, by a first neural network, a suspicious area in a first image; selecting, from among a set of defect-free reference images, by a second neural network, a defect-free reference image corresponding to the first image; identifying, by a third neural network, in the defect-free reference image, a reference region corresponding to the suspicious area; and determining, by a fourth neural network, a measure of similarity between the suspicious area and the reference region.
    Type: Application
    Filed: April 21, 2022
    Publication date: August 24, 2023
    Inventors: Shuhui QU, Qisen CHENG, Janghwan LEE
  • Publication number: 20230259760
    Abstract: A system and method for defect detection. The method may include training, with a first set of images, a first neural network including a first student neural network, and a first teacher neural network. The training of the first neural network may include introducing defects into a first subset of the first set of images, and training the first student neural network with the first set of images. The training of the first student neural network may include using a first cost function, that: for an image of the first set and not of the first subset, rewards similarity between a feature map of the first student neural network and a feature map of the first teacher neural network, and for an image of the first subset, rewards dissimilarity between a feature map of the first student neural network and a feature map of the first teacher neural network.
    Type: Application
    Filed: April 21, 2022
    Publication date: August 17, 2023
    Inventors: Qisen CHENG, Shuhui QU, Janghwan LEE
  • Patent number: 11714397
    Abstract: A method for detecting a fault includes: receiving a plurality of time-series sensor data obtained in one or more manufacturing processes of an electronic device; arranging the plurality of time-series sensor data in a two-dimensional (2D) data array; providing the 2D data array to a convolutional neural network model; identifying a pattern in the 2D data array that correlates to a fault condition using the convolutional neural network model; providing a fault indicator of the fault condition in the one or more manufacturing processes of the electronic device; and determining that the electronic device includes a fault based on the fault indicator. The 2D data array has a dimension of an input data to the convolutional neural network model.
    Type: Grant
    Filed: May 3, 2019
    Date of Patent: August 1, 2023
    Assignee: Samsung Display Co., Ltd.
    Inventor: Janghwan Lee
  • Patent number: 11710045
    Abstract: A system and method for classifying products. A processor generates first and second instances of a first classifier, and trains the instances based on an input dataset. A second classifier is trained based on the input, where the second classifier is configured to learn a representation of a latent space associated with the input. A first supplemental dataset is generated in the latent space, where the first supplemental dataset is an unlabeled dataset. A first prediction is generated for labeling the first supplemental dataset based on the first instance of the first classifier, and a second prediction is generated for labeling the first supplemental dataset based on the second instance of the first classifier. Labeling annotations are generated for the first supplemental dataset based on the first prediction and the second prediction. A third classifier is trained based on at least the input dataset and the annotated first supplemental dataset.
    Type: Grant
    Filed: November 13, 2019
    Date of Patent: July 25, 2023
    Assignee: Samsung Display Co., Ltd.
    Inventor: Janghwan Lee
  • Patent number: 11694319
    Abstract: A system for manufacturing defect classification is presented. The system includes a first neural network receiving a first data as input and generating a first output, a second neural network receiving a second data as input and generating a second output, wherein first neural network and the second neural network are trained independently from each other, and a fusion neural network receiving the first output and the second output and generating a classification. The first data and the second data do not have to be aligned. Hence, the system and method of this disclosure allows various type of data that are collected during manufacturing to be used in defect classification.
    Type: Grant
    Filed: July 24, 2020
    Date of Patent: July 4, 2023
    Assignee: Samsung Display Co., Ltd.
    Inventors: Yan Kang, Janghwan Lee, Shuhui Qu, Jinghua Yao, Sai MarapaReddy