Patents by Inventor Chi-Chou Huang
Chi-Chou Huang 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: 11983475Abstract: A semiconductor device includes: M*1st conductors in a first layer of metallization (M*1st layer) and being aligned correspondingly along different corresponding ones of alpha tracks and representing corresponding inputs of a cell region in the semiconductor device; and M*2nd conductors in a second layer of metallization (M*2nd layer) aligned correspondingly along beta tracks, and the M*2nd conductors including at least one power grid (PG) segment and one or more of an output pin or a routing segment; and each of first and second ones of the input pins having a length sufficient to accommodate at most two access points; each of the access points of the first and second input pins being aligned to a corresponding different one of first to fourth beta tracks; and the PG segment being aligned with one of the first to fourth beta tracks.Type: GrantFiled: February 7, 2023Date of Patent: May 14, 2024Assignee: TAIWAN SEMICONDUCTOR MANUFACTURING COMPANY, LTD.Inventors: Pin-Dai Sue, Po-Hsiang Huang, Fong-Yuan Chang, Chi-Yu Lu, Sheng-Hsiung Chen, Chin-Chou Liu, Lee-Chung Lu, Yen-Hung Lin, Li-Chun Tien, Yi-Kan Cheng
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Patent number: 10909948Abstract: A ubiquitous auto calibration device is provided, which includes microcontroller unit, flex bus, image receiver image processing module, and an image output unit. The microcontroller unit is provided for receiving the electronic signal and performing a self-adjusting process to the electronic signal. The flex bus is connected with the microcontroller unit, and is provided for transmitting the electronic signal to the image processing module after performing the self-adjusting process. The image receiver is provided for receiving the image signal from the image receiving interface. The image processing module is provided for performing an image calibration process to the image signal, so that the image signal can obey the color temperature standard, Gamma value, uniformity and color gamut standards when the panel outputs the image signal.Type: GrantFiled: August 30, 2019Date of Patent: February 2, 2021Assignee: Diva Laboratories, Ltd.Inventors: Chih-An Chen, Wei-Peng Wang, Ching-Min Huang, Tzu-Hui Lee, Chuan-Ling Peng, Chi-Chou Huang, Huei-Jiun Li, Mei-Chuan Ku
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Patent number: 10891523Abstract: Four computerized machine learning methods for deep semantic segmentation are fast machine learning method, active machine learning method, optimal machine learning method, and optimal transfer learning method. The fast machine learning method performs a fast deep semantic segmentation learning on training images to generate a deep model. The active machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an active deep semantic segmentation learning to generate a second deep model. The optimal machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an optimal deep semantic segmentation learning to generate a second deep model. The optimal transfer learning method applies a pre-trained first deep model on transfer training images and then an optimal deep semantic segmentation transfer learning to generate a second deep model.Type: GrantFiled: April 17, 2020Date of Patent: January 12, 2021Assignee: DRVISION TECHNOLOGIES LLCInventors: Hideki Sasaki, Chi-Chou Huang, Shih-Jong James Lee
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Publication number: 20200372617Abstract: A computerized robust deep image transformation method performs a deep image transformation learning on multi-variation training images and corresponding desired outcome images to generate a deep image transformation model, which is applied to transform an input image to an image of higher quality mimicking a desired outcome image. A computerized robust training method for deep image prediction performs a deep image prediction learning on universal modality training images and corresponding desired modality prediction images to generate a deep image prediction model, which is applied to transform universal modality images into a high quality image mimicking a desired modality prediction image.Type: ApplicationFiled: August 11, 2020Publication date: November 26, 2020Inventors: Hideki Sasaki, Chi-Chou Huang, Luciano Andre Guerreiro Lucas, Shih-Jong James Lee
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Publication number: 20200372616Abstract: A computerized robust deep image transformation method performs a deep image transformation learning on multi-variation training images and corresponding desired outcome images to generate a deep image transformation model, which is applied to transform an input image to an image of higher quality mimicking a desired outcome image. A computerized robust training method for deep image integration performs a deep image integration learning on multi-modality training images and corresponding desired integrated images to generate a deep image integration model, which is applied to transform multi-modality images into a high quality integrated image mimicking a desired integrated image.Type: ApplicationFiled: August 11, 2020Publication date: November 26, 2020Inventors: Hideki Sasaki, Chi-Chou Huang, Luciano Andre Guerreiro Lucas, Shih-Jong James Lee
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Publication number: 20200365113Abstract: A ubiquitous auto calibration device is provided, which includes microcontroller unit, flex bus, image receiver image processing module, and an image output unit. The microcontroller unit is provided for receiving the electronic signal and performing a self-adjusting process to the electronic signal. The flex bus is connected with the microcontroller unit, and is provided for transmitting the electronic signal to the image processing module after performing the self-adjusting process. The image receiver is provided for receiving the image signal from the image receiving interface. The image processing module is provided for performing an image calibration process to the image signal, so that the image signal can obey the color temperature standard, Gamma value, uniformity and color gamut standards when the panel outputs the image signal.Type: ApplicationFiled: August 30, 2019Publication date: November 19, 2020Inventors: Chih-An CHEN, Wei-Peng WANG, Ching-Min HUANG, Tzu-Hui LEE, Chuan-Ling PENG, Chi-Chou HUANG, Huei-Jiun LI, Mei-Chuan KU
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Publication number: 20200242414Abstract: Four computerized machine learning methods for deep semantic segmentation are fast machine learning method, active machine learning method, optimal machine learning method, and optimal transfer learning method. The fast machine learning method performs a fast deep semantic segmentation learning on training images to generate a deep model. The active machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an active deep semantic segmentation learning to generate a second deep model. The optimal machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an optimal deep semantic segmentation learning to generate a second deep model. The optimal transfer learning method applies a pre-trained first deep model on transfer training images and then an optimal deep semantic segmentation transfer learning to generate a second deep model.Type: ApplicationFiled: April 17, 2020Publication date: July 30, 2020Inventors: Hideki Sasaki, Chi-Chou Huang, Shih-Jong James Lee
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Patent number: 10691978Abstract: Four computerized machine learning methods for deep semantic segmentation are fast machine learning method, active machine learning method, optimal machine learning method, and optimal transfer learning method. The fast machine learning method performs a fast deep semantic segmentation learning on training images to generate a deep model. The active machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an active deep semantic segmentation learning to generate a second deep model. The optimal machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an optimal deep semantic segmentation learning to generate a second deep model. The optimal transfer learning method applies a pre-trained first deep model on transfer training images and then an optimal deep semantic segmentation transfer learning to generate a second deep model.Type: GrantFiled: June 18, 2018Date of Patent: June 23, 2020Assignee: DRVISION TECHNOLOGIES LLCInventors: Hideki Sasaki, Chi-Chou Huang, Shih-Jong James Lee
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Publication number: 20190385282Abstract: A computerized robust deep image transformation method performs a deep image transformation learning on multi-variation training images and corresponding desired outcome images to generate a deep image transformation model, which is applied to transform an input image to an image of higher quality mimicking a desired outcome image. A computerized robust training method for deep image integration performs a deep image integration learning on multi-modality training images and corresponding desired integrated images to generate a deep image integration model, which is applied to transform multi-modality images into a high quality integrated image mimicking a desired integrated image.Type: ApplicationFiled: June 18, 2018Publication date: December 19, 2019Inventors: Hideki Sasaki, Chi-Chou Huang, Luciano Andre Guerreiro Lucas, Shih-Jong James Lee
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Publication number: 20190385021Abstract: Four computerized machine learning methods for deep semantic segmentation are fast machine learning method, active machine learning method, optimal machine learning method, and optimal transfer learning method. The fast machine learning method performs a fast deep semantic segmentation learning on training images to generate a deep model. The active machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an active deep semantic segmentation learning to generate a second deep model. The optimal machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an optimal deep semantic segmentation learning to generate a second deep model. The optimal transfer learning method applies a pre-trained first deep model on transfer training images and then an optimal deep semantic segmentation transfer learning to generate a second deep model.Type: ApplicationFiled: June 18, 2018Publication date: December 19, 2019Inventors: Hideki Sasaki, Chi-Chou Huang, Shih-Jong James Lee
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Patent number: 9152884Abstract: A computerized teachable pattern scoring method receives a teaching image and region pattern labels. A region segmentation is performed using the teaching image to generate regions of interest output. A feature measurement is performed using the teaching image and the regions of interest to generate region features output. A pattern score learning is performed using the region features and the region pattern labels to generate pattern score recipe output. A computerized region classification method using the region features and the pattern score recipe to generate pattern scores output. A region classification is performed using the pattern scores and region features to generate region class output.Type: GrantFiled: June 5, 2012Date of Patent: October 6, 2015Assignee: DRVision Technologies LLCInventors: Shih-Jong J. Lee, Chi-Chou Huang
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Publication number: 20130322741Abstract: A computerized teachable pattern scoring method receives a teaching image and region pattern labels. A region segmentation is performed using the teaching image to generate regions of interest output. A feature measurement is performed using the teaching image and the regions of interest to generate region features output. A pattern score learning is performed using the region features and the region pattern labels to generate pattern score recipe output. A computerized region classification method using the region features and the pattern score recipe to generate pattern scores output. A region classification is performed using the pattern scores and region features to generate region class output.Type: ApplicationFiled: June 5, 2012Publication date: December 5, 2013Inventors: Shih-Jong J. Lee, Chi-Chou Huang
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Publication number: 20130247210Abstract: A method for protecting a software authorization utilized in a software installed in a hardware device, which includes a timer to utilize a system time, and including a plurality of functions is disclosed. The method includes setting an authorized time, an accumulated authorization time, an accumulated running time and a last recorded time when the software is initially installed in the hardware device, operating a time-out check for determining whether the software authorization is expired or not according to the system time, the authorized time, the accumulated authorization time, the accumulated running time and the last recorded time when the software is initiated or the plurality of functions are initiated or terminated, and stopping the software from running in the hardware device when the software authorization is expired.Type: ApplicationFiled: June 1, 2012Publication date: September 19, 2013Inventor: Chi-Chou Huang
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Patent number: 7849024Abstract: A Recognition Frame presents multi-level application elements to the user simultaneously through a computer graphical user interface. The interface consists of an image display panel for displaying image channels; a data display panel for displaying object measurements and summary statistics; a configuration display panel for displaying recipe content; a master tab for selecting the panels. It also consists of a processing toolbar for context dependent processing tool display. The Recognition Frame further comprises a second side frame for data object display and charting. The second side frame has a tabular arrangement consisting of properties tab, controls tab, and charts tab. The Recognition Frame links application elements through a complex data model wherein interface display is automatically updated when one element is changed.Type: GrantFiled: August 16, 2006Date of Patent: December 7, 2010Assignee: DRVision Technologies LLCInventors: Shih-Jong J. Lee, Samuel V. Alworth, Tuan Phan, Chi Chou Huang, Christopher Birnbaum
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Publication number: 20080044084Abstract: A Recognition Frame presents multi-level application elements to the user simultaneously through a computer graphical user interface. The interface consists of an image display panel for displaying image channels; a data display panel for displaying object measurements and summary statistics; a configuration display panel for displaying recipe content; a master tab for selecting the panels. It also consists of a processing toolbar for context dependent processing tool display. The Recognition Frame further comprises a second side frame for data object display and charting. The second side frame has a tabular arrangement consisting of properties tab, controls tab, and charts tab. The Recognition Frame links application elements through a complex data model wherein interface display is automatically updated when one element is changed.Type: ApplicationFiled: August 16, 2006Publication date: February 21, 2008Inventors: Shih-Jong J. Lee, Samuel V. Alworth, Tuan Phan, Chi Chou Huang, Christopher Birnbaum
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Patent number: 7116825Abstract: A multilevel Chain-And-Tree model provides a framework for an image based decision system. The decision system enables separation of effects of defects within one component from other components within a common subject. The framework provides for linking of structure constraints of components of a common subject and for checking and resolving their consistency. The framework allows discrimination between subtle image changes and natural variations of the subject. The framework for standard data representation facilitates production process control.Type: GrantFiled: March 22, 2002Date of Patent: October 3, 2006Inventors: Shih-Jong J. Lee, Chi-Chou Huang
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Patent number: 7031529Abstract: A systematic way of linking structure constraints of components of a common object and checking and resolving their inconsistency is used to improve detection results in image-based decision systems. A multilevel Chain-And-Tree (CAT) model is used to direct processing using both forward and backward scans through the related components. Since components occur as parts of an object, the context (relational structure) in which the component appears can be used to reduce noise and variation affects. In the method, object knowledge is translated into constraints between components. The constraints are used to enhance feature detection, defect detection, and measurement accuracy and consistency.Type: GrantFiled: June 24, 2002Date of Patent: April 18, 2006Inventors: Shih-Jong J. Lee, Chi-Chou Huang, Seho Oh
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Publication number: 20030235324Abstract: A systematic way of linking structure constraints of components of a common object and checking and resolving their inconsistency is used to improve detection results in image-based decision systems. A multilevel Chain-And-Tree (CAT) model is used to direct processing using both forward and backward scans through the related components. Since components occur as parts of an object, the context (relational structure) in which the component appears can be used to reduce noise and variation affects. In the method, object knowledge is translated into constraints between components. The constraints are used to enhance feature detection, defect detection, and measurement accuracy and consistency.Type: ApplicationFiled: June 24, 2002Publication date: December 25, 2003Inventors: Shih-Jong J. Lee, Chi-Chou Huang, Seho Oh
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Publication number: 20030179936Abstract: A multilevel Chain-And-Tree model provides a framework for an image based decision system. The decision system enables separation of effects of defects within one component from other components within a common subject. The framework provides for linking of structure constraints of components of a common subject and for checking and resolving their consistency. The framework allows discrimination between subtle image changes and natural variations of the subject. The framework for standard data representation facilitates production process control.Type: ApplicationFiled: March 22, 2002Publication date: September 25, 2003Inventors: Shih-Jong J. Lee, Chi-Chou Huang
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Patent number: 6507675Abstract: A structure-guided automatic learning system for image feature enhancement uses a learning image together with an application domain structure and detection target specification to produce a feature enhancement image processing recipe. An enhancement goodness measure is used to select between alternatives in the learning process. The feature enhancement recipe is used in an application module to process input images and produce a feature enhanced image output. Calipers are used for application domain structure and detection target specification. To unify the processing steps for all caliper specifications, a non-directional box caliper defined region such as a circle caliper or an arc caliper or other connected structures can be converted into a directional box caliper defined region so that a directional box caliper based feature enhancement method can be applied. The process can be inverted to convert a converted directional box caliper region back to the original format.Type: GrantFiled: March 23, 2001Date of Patent: January 14, 2003Inventors: Shih-Jong J. Lee, Seho Oh, Chi-Chou Huang