Patents by Inventor Charles Jin Young
Charles Jin Young 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: 10943168Abstract: A system may include a decentralized communication network and multiple processing devices on the network. Each processing device may have an artificial intelligence (AI) chip, the device may be configured to generate an AI model, determine the performance value of the AI model on the AI chip, receive a chain from the network where the chain contains a performance measure. If the performance value of the AI model is better than the performance measure, then the processing device may broadcast the AI model to the network for verification. If the AI model is verified by the network, the device may update the chain with the performance value so that the chain can be shared by the multiple processing devices on the network. Any processing device on the network may also verify an AI model broadcasted by any other device. Methods for generating the AI model are also provided.Type: GrantFiled: April 10, 2018Date of Patent: March 9, 2021Assignee: Gyrfalcon Technology Inc.Inventors: Lin Yang, Charles Jin Young, Jason Zeng Dong, Patrick Zeng Dong, Baohua Sun, Yequn Zhang
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Patent number: 10902313Abstract: A system may include a decentralized communication network and multiple processing devices on the network. Each processing device may have an artificial intelligence (AI) chip, the device may be configured to generate an AI model, determine the performance value of the AI model on the AI chip, receive a chain from the network where the chain contains a performance measure. If the performance value of the AI model is better than the performance measure, then the processing device may broadcast the AI model to the network for verification. If the AI model is verified by the network, the device may update the chain with the performance value so that the chain can be shared by the multiple processing devices on the network. Any processing device on the network may also verify an AI model broadcasted by any other device. Methods for generating the AI model are also provided.Type: GrantFiled: April 10, 2018Date of Patent: January 26, 2021Assignee: Gyrfalcon Technology Inc.Inventors: Lin Yang, Charles Jin Young, Jason Zeng Dong, Patrick Zeng Dong, Baohua Sun, Yequn Zhang
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Patent number: 10482374Abstract: An ensemble learning based image classification system contains multiple cellular neural networks (CNN) based integrated circuits (ICs) operatively coupling together as a set of base learners of an ensemble for an image classification task. Each CNN based IC is configured with at least one distinct deep learning model in form of filter coefficients. The ensemble learning based image classification system further contains a controller configured as a meta learner of the ensemble and a memory based data buffer for holding various data used in the ensemble by the controller and the CNN based ICs. Various data may include input imagery data to be classified. Various data may also include extracted feature vectors or image classification outputs out of the set of base learners. The extracted feature vectors or image classification outputs are then used by the meta learner to further perform the image classification task.Type: GrantFiled: July 3, 2019Date of Patent: November 19, 2019Assignee: Gyrfalcon Technology Inc.Inventors: Lin Yang, Patrick Z. Dong, Charles Jin Young, Jason Z. Dong, Michael Lin, Baohua Sun
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Publication number: 20190318226Abstract: A deep learning image processing system contains at least first and second groups of cellular neural networks (CNN) based integrated circuits (ICs). The first group and the second group are operatively connected in parallel via a network bus. CNN based ICs within each of the first and second groups are operatively connected in series via the network bus. The first group is configured for performing convolutional operations in respective portions of a deep learning model for extracting features out of a first subsection of input data. The second group is configured for performing convolutional operations in respective portions of the deep learning model for extracting features out of a second subsection of the input data. The deep learning model is divided into a plurality of consecutive portions being handled by the respective CNN based ICs. The input data is partitioned into at least first and second subsections.Type: ApplicationFiled: April 12, 2018Publication date: October 17, 2019Inventors: Lin Yang, Baohua Sun, Jason Z. Dong, Charles Jin Young
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Publication number: 20190311247Abstract: A system may include a decentralized communication network and multiple processing devices on the network. Each processing device may have an artificial intelligence (AI) chip, the device may be configured to generate an AI model, determine the performance value of the AI model on the AI chip, receive a chain from the network where the chain contains a performance measure. If the performance value of the AI model is better than the performance measure, then the processing device may broadcast the AI model to the network for verification. If the AI model is verified by the network, the device may update the chain with the performance value so that the chain can be shared by the multiple processing devices on the network. Any processing device on the network may also verify an AI model broadcasted by any other device. Methods for generating the AI model are also provided.Type: ApplicationFiled: April 10, 2018Publication date: October 10, 2019Applicant: GYRFALCON TECHNOLOGY INC.Inventors: Lin Yang, Charles Jin Young, Jason Zeng Dong, Patrick Zeng Dong, Baohua Sun, Yequn Zhang
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Publication number: 20190311246Abstract: A system may include a decentralized communication network and multiple processing devices on the network. Each processing device may have an artificial intelligence (AI) chip, the device may be configured to generate an AI model, determine the performance value of the AI model on the AI chip, receive a chain from the network where the chain contains a performance measure. If the performance value of the AI model is better than the performance measure, then the processing device may broadcast the AI model to the network for verification. If the AI model is verified by the network, the device may update the chain with the performance value so that the chain can be shared by the multiple processing devices on the network. Any processing device on the network may also verify an AI model broadcasted by any other device. Methods for generating the AI model are also provided.Type: ApplicationFiled: April 10, 2018Publication date: October 10, 2019Applicant: GYRFALCON TECHNOLOGY INC.Inventors: Lin Yang, Charles Jin Young, Jason Zeng Dong, Patrick Zeng Dong, Baohua Sun, Yequn Zhang
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Patent number: 10402628Abstract: Image classification system contains a CNN based IC configured for extracting features out of input data by performing convolution operations using filter coefficients of ordered convolutional layers and a classifier IC configured for classifying the input data using reduced set of the extracted features based on a light-weight classifier. Light-weight classifier is derived by: training filter coefficients of the ordered convolutional layers using a dataset containing N labeled data, the trained filter coefficients are for the CNN based IC; outputting respective extracted features of the N labeled data after performing convolution operations of ordered convolutional layers using the trained filter coefficients, each labeled data contains X features; creating the reduced set of the extracted features by eliminating those of the X features that contain zeros in at least M of the N labeled data; and adjusting M until the light-weight classifier achieves satisfactory results using the reduced set.Type: GrantFiled: April 26, 2018Date of Patent: September 3, 2019Assignee: Gyrfalcon Technology Inc.Inventors: Lin Yang, Patrick Z. Dong, Charles Jin Young, Jason Dong, Wenhan Zhang, Baohua Sun
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Patent number: 10387772Abstract: An ensemble learning based image classification system contains multiple cellular neural networks (CNN) based integrated circuits (ICs) operatively coupling together as a set of base learners of an ensemble for an image classification task. Each CNN based IC is configured with at least one distinct deep learning model in form of filter coefficients. The ensemble learning based image classification system further contains a controller configured as a meta learner of the ensemble and a memory based data buffer for holding various data used in the ensemble by the controller and the CNN based ICs. Various data may include input imagery data to be classified. Various data may also include extracted feature vectors or image classification outputs out of the set of base learners. The extracted feature vectors or image classification outputs are then used by the meta learner to further perform the image classification task.Type: GrantFiled: October 22, 2018Date of Patent: August 20, 2019Assignee: Gyrfalcon Technology Inc.Inventors: Lin Yang, Patrick Z. Dong, Charles Jin Young, Jason Z. Dong, Michael Lin, Baohua Sun
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Patent number: 10387740Abstract: A deep learning object detection and recognition system contains a number of cellular neural networks (CNN) based integrated circuits (ICs) operatively coupling together via the network bus. The system is configured for detecting and then recognizing one or more objects out of a two-dimensional (2-D) imagery data. The 2-D imagery data is divided into N set of distinct sub-regions in accordance with respective N partition schemes. CNN based ICs are dynamically allocated for extracting features out of each sub-region for detecting and then recognizing an object potentially contained therein. Any two of the N sets of sub-regions overlap each other. N is a positive integer. Object detection is achieved with a two-category classification using a deep learning model based on approximated fully-connected layers, while object recognition is performed using a local database storing feature vectors of known objects.Type: GrantFiled: May 19, 2018Date of Patent: August 20, 2019Assignee: Gyrfalcon Technology Inc.Inventors: Lin Yang, Patrick Z. Dong, Charles Jin Young, Jason Z. Dong, Wenhan Zhang, Baohua Sun
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Patent number: 10339445Abstract: Operations of a combination of first and second original convolutional layers followed by a short path are replaced by operations of a set of three particular convolutional layers. The first contains 2N×N filter kernels formed by placing said N×N filter kernels of the first original convolutional layer in left side and N×N filter kernels of an identity-value convolutional layer in right side. The second contains 2N×2N filter kernels formed by placing the N×N filter kernels of the second original convolutional layer in upper left corner, N×N filter kernels of an identity-value convolutional layer in lower right corner, and N×N filter kernels of two zero-value convolutional layers in either off-diagonal corner. The third contains N×2N of kernels formed by placing N×N filter kernels of a first identity-value convolutional layer and N×N filter kernels of a second identity-value convolutional layer in a vertical stack. Each filter kernel contains 3×3 filter coefficients.Type: GrantFiled: February 14, 2018Date of Patent: July 2, 2019Assignee: Gyrfalcon Technology Inc.Inventors: Lin Yang, Patrick Z. Dong, Charles Jin Young, Baohua Sun
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Patent number: 10331983Abstract: An artificial intelligence inference computing device contains a printed circuit board (PCB) and a number of electronic components mounted thereon. Electronic components include a wireless communication module, a controller module, a memory module, a storage module and at least one cellular neural networks (CNN) based integrated circuit (IC) configured for performing convolutional operations in a deep learning model for extracting features out of input data. Each CNN based IC includes a number of CNN processing engines operatively coupled to at least one input/output data bus. CNN processing engines are connected in a loop with a clock-skew circuit. Wireless communication module is configured for transmitting pre-trained filter coefficients of the deep learning model, input data and classification results.Type: GrantFiled: September 11, 2018Date of Patent: June 25, 2019Assignee: Gyrfalcon Technology Inc.Inventors: Lin Yang, Patrick Z. Dong, Charles Jin Young, Jason Z. Dong, Dan Bin Liu, Baohua Sun
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Patent number: 10311149Abstract: Natural language translation device contains a bus, an input interface connecting to the bus for receiving a source sentence in a first natural language to be translated to a target sentence in second natural language one word at a time in sequential order. A two-dimensional (2-D) symbol containing a super-character characterizing the i-th word of the target sentence based on the received source sentence is formed in accordance with a set of 2-D symbol creation rules. The i-th word of the target sentence is obtained by classifying the 2-D symbol via a deep learning model that contains multiple ordered convolution layers in a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based integrated circuit.Type: GrantFiled: August 8, 2018Date of Patent: June 4, 2019Assignee: Gyrfalcon Technology Inc.Inventors: Lin Yang, Patrick Z. Dong, Catherine Chi, Charles Jin Young, Jason Z Dong, Baohua Sun
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Patent number: 10296817Abstract: Apparatus for recognition of handwritten Chinese characters contains a bus, an input means connecting to the bus for receiving input imagery data created from a handwritten Chinese character, a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based integrated circuit operatively connecting to the bus for extracting features out of the input imagery data using pre-trained filter coefficients of a plurality of order convolutional layers stored therein, a memory connecting the bus, the memory being configured for storing weight coefficients of fully-connected (FC) layers, a processing unit connecting to the bus for performing computations of FC layers to classify the extracted features from the CNN based integrated circuit to a particular Chinese character in a predefined Chinese character set, and a display unit connecting to the bus for displaying the particular Chinese character.Type: GrantFiled: March 30, 2018Date of Patent: May 21, 2019Assignee: Gyrfalcon Technology Inc.Inventors: Lin Yang, Patrick Z. Dong, Charles Jin Young, Jason Dong, Wenhan Zhang, Baohua Sun
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Patent number: 10192148Abstract: A string of Latin-alphabet based language texts is received and formed a multi-layer 2-D symbol in a computing system. The received string contains at least one word with each word containing at least one letter of the Latin-alphabet based language. 2-D symbol comprises a matrix of N×N pixels of data representing a super-character. The matrix is divided into M×M sub-matrices. Each sub-matrix represents one ideogram formed from the at least one letter contained in a corresponding word in the received string. Ideogram has a square format with a dimension EL letters by EL letters (i.e., row and column). EL is determined from the total number of letters (LL) contained in the corresponding word. EL, LL, N and M are positive integers. Super-character represents a meaning formed from a specific combination of at least one ideogram. Meaning of the super-character is learned with image classification of the 2-D symbol.Type: GrantFiled: September 18, 2018Date of Patent: January 29, 2019Assignee: Gyrfalcon Technology Inc.Inventors: Lin Yang, Patrick Z. Dong, Charles Jin Young, Jason Z. Dong, Baohua Sun
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Publication number: 20180268234Abstract: A deep learning object detection and recognition system contains a number of cellular neural networks (CNN) based integrated circuits (ICs) operatively coupling together via the network bus. The system is configured for detecting and then recognizing one or more objects out of a two-dimensional (2-D) imagery data. The 2-D imagery data is divided into N set of distinct sub-regions in accordance with respective N partition schemes. CNN based ICs are dynamically allocated for extracting features out of each sub-region for detecting and then recognizing an object potentially contained therein. Any two of the N sets of sub-regions overlap each other. N is a positive integer. Object detection is achieved with a two-category classification using a deep learning model based on approximated fully-connected layers, while object recognition is performed using a local database storing feature vectors of known objects.Type: ApplicationFiled: May 19, 2018Publication date: September 20, 2018Inventors: Lin Yang, Patrick Z. Dong, Charles Jin Young, Jason Z. Dong, Wenhan Zhang, Baohua Sun
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Publication number: 20180247113Abstract: Image classification system contains a CNN based IC configured for extracting features out of input data by performing convolution operations using filter coefficients of ordered convolutional layers and a classifier IC configured for classifying the input data using reduced set of the extracted features based on a light-weight classifier. Light-weight classifier is derived by: training filter coefficients of the ordered convolutional layers using a dataset containing N labeled data, the trained filter coefficients are for the CNN based IC; outputting respective extracted features of the N labeled data after performing convolution operations of ordered convolutional layers using the trained filter coefficients, each labeled data contains X features; creating the reduced set of the extracted features by eliminating those of the X features that contain zeros in at least M of the N labeled data; and adjusting M until the light-weight classifier achieves satisfactory results using the reduced set.Type: ApplicationFiled: April 26, 2018Publication date: August 30, 2018Inventors: Lin Yang, Patrick Z. Dong, Charles Jin Young, Jason Dong, Wenhan Zhang, Baohua Sun
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Publication number: 20180174031Abstract: Operations of a combination of first and second original convolutional layers followed by a short path are replaced by operations of a set of three particular convolutional layers. The first contains 2N×N filter kernels formed by placing said N×N filter kernels of the first original convolutional layer in left side and N×N filter kernels of an identity-value convolutional layer in right side. The second contains 2N×2N filter kernels formed by placing the N×N filter kernels of the second original convolutional layer in upper left corner, N×N filter kernels of an identity-value convolutional layer in lower right corner, and N×N filter kernels of two zero-value convolutional layers in either off-diagonal corner. The third contains N×2N of kernels formed by placing N×N filter kernels of a first identity-value convolutional layer and N×N filter kernels of a second identity-value convolutional layer in a vertical stack. Each filter kernel contains 3×3 filter coefficients.Type: ApplicationFiled: February 14, 2018Publication date: June 21, 2018Inventors: Lin Yang, Patrick Z. Dong, Charles Jin Young, Baohua Sun