Patents by Inventor Andre S. Yoon
Andre S. Yoon 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).
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Patent number: 11948292Abstract: Disclosed is a non-transitory computer readable medium storing a computer program, in which when the computer program is executed by one or more processors of a computing device, the computer program performs operations to provide methods for detecting flaws, and the operations may include: extracting a flaw patch from a flaw image including a flaw; preprocessing at least one of the flaw image or non-flaw image not including a flaw; extracting a non-flaw patch from at least one of the preprocessed flaw image or non-flaw image; and training a neural network model for classifying patches to flaw or non-flaw with a training data set including the flaw patch and the non-flaw patch.Type: GrantFiled: July 1, 2020Date of Patent: April 2, 2024Assignee: MakinaRocks Co., Ltd.Inventors: Andre S. Yoon, Sangwoo Shim, Yongsub Lim, Ki Hyun Kim, Byungchan Kim, JeongWoo Choi, Jongsun Shinn
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Patent number: 11816578Abstract: The disclosed technology generally relates to novelty detection and more particularly to novelty detection methods using a deep learning neural network and apparatuses and non-transitory computer-readable media configured for performing the methods. In one aspect, a method for detecting novelty using a deep learning neural network model comprises providing a deep learning neural network model. The deep learning neural network model comprises an encoder comprising a plurality of encoder layers and a decoder comprising a plurality of decoder layers. The method additionally comprises feeding a first input into the encoder and successively processing the first input through the plurality of encoder layers to generate a first encoded input, wherein successively processing the first input comprises generating a first intermediate encoded input from one of the encoder layers prior to generating the first encoded input.Type: GrantFiled: March 3, 2022Date of Patent: November 14, 2023Assignee: MakinaRocks Co., Ltd.Inventors: Andre S. Yoon, Sangwoo Shim, Yongsub Lim, Ki Hyun Kim, Byungchan Kim, JeongWoo Choi, Jongseob Jeon
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Publication number: 20230127656Abstract: Disclosed is a non-transitory computer readable medium storing a computer program. When the computer program is executed by one or more processors of a computing device, the computer program performs the following operations for processing data, and the operations may include: determining an uncertainty level with respect to labeling criteria for each of one or more data included in a dataset; determining a similarity level for one or more data included in a data subset; and selecting at least some of data included in the dataset based on the uncertainty level and the similarity level, and additionally labeling the selected data.Type: ApplicationFiled: December 21, 2022Publication date: April 27, 2023Inventors: Andre S. Yoon, Sangwoo Shim, Yongsub Lim, Ki Hyun Kim, Byungchan Kim, JeongWoo Choi
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Patent number: 11562167Abstract: Disclosed is a non-transitory computer readable medium storing a computer program. When the computer program is executed by one or more processors of a computing device, the computer program performs the following operations for processing data, and the operations may include: determining an uncertainty level with respect to labeling criteria for each of one or more data included in a dataset; determining a similarity level for one or more data included in a data subset; and selecting at least some of data included in the dataset based on the uncertainty level and the similarity level, and additionally labeling the selected data.Type: GrantFiled: March 27, 2020Date of Patent: January 24, 2023Assignee: MakinaRocks Co., Ltd.Inventors: Andre S. Yoon, Sangwoo Shim, Yongsub Lim, Ki Hyun Kim, Byungchan Kim, JeongWoo Choi
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Patent number: 11537900Abstract: According to an exemplary embodiment of the present disclosure, a computer program stored in a computer readable storage medium is disclosed. The computer program performs operations for processing input data when the computer program is executed by one or more processors of a computer device, the operations including: obtaining input data based on sensor data obtained during manufacturing of an article by using one or more manufacturing recipes in one or more manufacturing equipment; inputting the input data to a neural network model loaded to the computer device; generating an output by processing the input data by using the neural network model; and detecting an anomaly for the input data based on the output of the neural network model.Type: GrantFiled: December 23, 2019Date of Patent: December 27, 2022Assignee: MakinaRocks Co., Ltd.Inventors: Andre S. Yoon, Sangwoo Shim, Yongsub Lim, Ki Hyun Kim, Byungchan Kim
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Publication number: 20220309354Abstract: According to an exemplary embodiment of the present disclosure, a computer program stored in a computer readable storage medium is disclosed. The computer program performs operations for processing input data when the computer program is executed by one or more processors of a computer device, the operations including: obtaining input data based on sensor data obtained during manufacturing of an article by using one or more manufacturing recipes in one or more manufacturing equipment; inputting the input data to a neural network model loaded to the computer device; generating an output by processing the input data by using the neural network model; and detecting an anomaly for the input data based on the output of the neural network model.Type: ApplicationFiled: June 15, 2022Publication date: September 29, 2022Inventors: Andre S. Yoon, Sangwoo Shim, Yongsub Lim, Ki Hyun Kim, Byungchan Kim
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Publication number: 20220198280Abstract: The disclosed technology generally relates to novelty detection and more particularly to novelty detection methods using a deep learning neural network and apparatuses and non-transitory computer-readable media configured for performing the methods. In one aspect, a method for detecting novelty using a deep learning neural network model comprises providing a deep learning neural network model. The deep learning neural network model comprises an encoder comprising a plurality of encoder layers and a decoder comprising a plurality of decoder layers. The method additionally comprises feeding a first input into the encoder and successively processing the first input through the plurality of encoder layers to generate a first encoded input, wherein successively processing the first input comprises generating a first intermediate encoded input from one of the encoder layers prior to generating the first encoded input.Type: ApplicationFiled: March 3, 2022Publication date: June 23, 2022Inventors: Andre S. Yoon, Sangwoo Shim, Yongsub Lim, Ki Hyun Kim, Byungchan Kim, JeongWoo Choi, Jongseob Jeon
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Patent number: 11301756Abstract: A method for detecting novelty using an encoder and a decoder comprises: feeding a first input into the encoder and processing the first input through a plurality of encoder layers to generate a first encoded input, wherein processing the first input comprises generating a first intermediate encoded input prior to generating the first encoded input, feeding the first encoded input from the encoder into the decoder and processing the first encoded input through a plurality of decoder layers to generate a first reconstructed output, feeding the first reconstructed output from the decoder as a second or subsequent input into the encoder and processing the first reconstructed output through the plurality of encoder layers, wherein processing the first reconstructed output comprises generating a second intermediate encoded input from the one of the encoder layers, and detecting a novelty based on the first intermediate encoded input and the second intermediate encoded input.Type: GrantFiled: March 5, 2020Date of Patent: April 12, 2022Assignee: MakinaRocks Co., Ltd.Inventors: Andre S. Yoon, Sangwoo Shim, Yongsub Lim, Ki Hyun Kim, Byungchan Kim, JeongWoo Choi, Jongseob Jeon
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Patent number: 11120336Abstract: According to an exemplary embodiment of the present disclosure, disclosed is a computer program stored in a computer readable storage medium. When the computer program is executed in one or more processors, the computer program performs the following method for anomaly detection of data using a network function, and the method includes: generating an anomaly detection model including a plurality of anomaly detection sub models including a trained network function using a plurality of training data sub sets included in the training data set; calculating input data using at least one of the plurality of generated anomaly detection sub models; and determining whether there is an anomaly in the input data based on output data for input data of at least one of the plurality of generated anomaly detection sub models and the input data.Type: GrantFiled: September 10, 2020Date of Patent: September 14, 2021Assignee: MAKINAROCKS CO., LTD.Inventors: Andre S. Yoon, Yongsub Lim, Sangwoo Shim
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Publication number: 20210004946Abstract: Disclosed is a non-transitory computer readable medium storing a computer program, in which when the computer program is executed by one or more processors of a computing device, the computer program performs operations to provide methods for detecting flaws, and the operations may include: extracting a flaw patch from a flaw image including a flaw; preprocessing at least one of the flaw image or non-flaw image not including a flaw; extracting a non-flaw patch from at least one of the preprocessed flaw image or non-flaw image; and training a neural network model for classifying patches to flaw or non-flaw with a training data set including the flaw patch and the non-flaw patch.Type: ApplicationFiled: July 1, 2020Publication date: January 7, 2021Inventors: Andre S. Yoon, Sangwoo Shim, Yongsub LIM, Ki Hyun KIM, Byungchan KIM, JeongWoo CHOI, Jongsun SHINN
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Publication number: 20200410350Abstract: According to an exemplary embodiment of the present disclosure, disclosed is a computer program stored in a computer readable storage medium. When the computer program is executed in one or more processors, the computer program performs the following method for anomaly detection of data using a network function, and the method includes: generating an anomaly detection model including a plurality of anomaly detection sub models including a trained network function using a plurality of training data sub sets included in the training data set; calculating input data using at least one of the plurality of generated anomaly detection sub models; and determining whether there is an anomaly in the input data based on output data for input data of at least one of the plurality of generated anomaly detection sub models and the input data.Type: ApplicationFiled: September 10, 2020Publication date: December 31, 2020Inventors: Andre S. Yoon, Yongsub LIM, Sangwoo SHIM
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Patent number: 10803384Abstract: According to an exemplary embodiment of the present disclosure, disclosed is a computer program stored in a computer readable storage medium. When the computer program is executed in one or more processors, the computer program performs the following method for anomaly detection of data using a network function, and the method includes: generating an anomaly detection model including a plurality of anomaly detection sub models including a trained network function using a plurality of training data sub sets included in the training data set; calculating input data using at least one of the plurality of generated anomaly detection sub models; and determining whether there is an anomaly in the input data based on output data for input data of at least one of the plurality of generated anomaly detection sub models and the input data.Type: GrantFiled: September 17, 2018Date of Patent: October 13, 2020Assignee: MAKINAROCKS CO., LTD.Inventors: Andre S. Yoon, Yongsub Lim, Sangwoo Shim
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Publication number: 20200320337Abstract: Disclosed is a non-transitory computer readable medium storing a computer program. When the computer program is executed by one or more processors of a computing device, the computer program performs the following operations for processing data, and the operations may include: determining an uncertainty level with respect to labeling criteria for each of one or more data included in a dataset; determining a similarity level for one or more data included in a data subset; and selecting at least some of data included in the dataset based on the uncertainty level and the similarity level, and additionally labeling the selected data.Type: ApplicationFiled: March 27, 2020Publication date: October 8, 2020Inventors: Andre S. Yoon, Sangwoo Shim, Yongsub Lim, Ki Hyun Kim, Byungchan Kim, JeongWoo Choi
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Publication number: 20200320402Abstract: The disclosed technology generally relates to novelty detection and more particularly to novelty detection methods using a deep learning neural network and apparatuses and non-transitory computer-readable media configured for performing the methods. In one aspect, a method for detecting novelty using a deep learning neural network model comprises providing a deep learning neural network model. The deep learning neural network model comprises an encoder comprising a plurality of encoder layers and a decoder comprising a plurality of decoder layers. The method additionally comprises feeding a first input into the encoder and successively processing the first input through the plurality of encoder layers to generate a first encoded input, wherein successively processing the first input comprises generating a first intermediate encoded input from one of the encoder layers prior to generating the first encoded input.Type: ApplicationFiled: March 5, 2020Publication date: October 8, 2020Inventors: Andre S. Yoon, Sangwoo Shim, Yongsub Lim, Ki Hyun Kim, Byungchan Kim, JeongWoo Choi, Jongseob Jeon
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Publication number: 20200234143Abstract: According to an exemplary embodiment of the present disclosure, a computer program stored in a computer readable storage medium is disclosed. The computer program performs operations for processing input data when the computer program is executed by one or more processors of a computer device, the operations including: obtaining input data based on sensor data obtained during manufacturing of an article by using one or more manufacturing recipes in one or more manufacturing equipment; inputting the input data to a neural network model loaded to the computer device; generating an output by processing the input data by using the neural network model; and detecting an anomaly for the input data based on the output of the neural network model.Type: ApplicationFiled: December 23, 2019Publication date: July 23, 2020Inventors: Andre S. Yoon, Sangwoo Shim, Yongsub Lim, Ki Hyun Kim, Byungchan Kim
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Publication number: 20200019852Abstract: According to an exemplary embodiment of the present disclosure, disclosed is a computer program stored in a computer readable storage medium. When the computer program is executed in one or more processors, the computer program performs the following method for anomaly detection of data using a network function, and the method includes: generating an anomaly detection model including a plurality of anomaly detection sub models including a trained network function using a plurality of training data sub sets included in the training data set; calculating input data using at least one of the plurality of generated anomaly detection sub models; and determining whether there is an anomaly in the input data based on output data for input data of at least one of the plurality of generated anomaly detection sub models and the input data.Type: ApplicationFiled: September 17, 2018Publication date: January 16, 2020Inventors: Andre S. Yoon, Yongsub LIM, Sangwoo SHIM