Patents by Inventor Masayoshi Ishikawa
Masayoshi Ishikawa 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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Publication number: 20240362892Abstract: Provided are an image classification device and method that are capable of extracting and mapping an important feature in an image. The image classification device includes: a feature extraction unit 101 that generates a first image group generated by applying different noises to the same image among images included in an image group and a second image group including different images, is trained such that features obtained from the first image group are approximate, is trained such that features obtained from the second image group are more different, and extracts features; a feature mapping unit 102 that maps the extracted plurality of features two-dimensionally or three-dimensionally using manifold learning; and a display unit 103 that displays a mapping result and constructs a training information application task screen.Type: ApplicationFiled: July 30, 2021Publication date: October 31, 2024Inventors: Sota KOMATSU, Masayoshi ISHIKAWA, Fumihiro BEKKU
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Patent number: 12125176Abstract: An inspection apparatus includes an image distortion estimation unit that estimates a distortion amount between a reference image and an inspection image, an image distortion correction unit that corrects the inspection image and/or the reference image using an estimated distortion amount, and an inspection unit that performs inspection using a corrected inspection image and the reference image or the inspection image and a corrected reference image. The image distortion estimation unit estimates a distortion amount in which only distortion occurring in an entire image can be corrected by adjustment of a correction condition.Type: GrantFiled: May 20, 2022Date of Patent: October 22, 2024Assignee: HITACHI HIGH-TECH CORPORATIONInventors: Kosuke Fukuda, Masayoshi Ishikawa, Yasuhiro Yoshida, Hiroyuki Shindo
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Patent number: 12103570Abstract: A traveling vehicle system includes a track and a ceiling traveling vehicle. The track includes a first track, a second track, and a connection track. The ceiling traveling vehicle includes a direction changer that turns a coupler, which couples a traveling wheel and a main body to each other and passes through a gap between the first track or the second track and the connection track. A guider is provided in the coupler, moves along a first guide surface in a first state in which the traveling wheel rolls on the first track, and moves along a second guide surface in a second state in which the traveling wheel rolls on the second track.Type: GrantFiled: October 23, 2019Date of Patent: October 1, 2024Assignee: MURATA MACHINERY, LTD.Inventors: Haruki Ogo, Kazuhiro Ishikawa, Yasuhisa Ito, Masayoshi Torazawa
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Publication number: 20240177333Abstract: In order to facilitate generation of teacher data and to detect position coordinates with high reliability, there is provided a localization apparatus including: a deep learning model trained by using training image data in which position coordinates desired to be detected are specified and teacher image data in which a pixel group representing a shape independent of a subject of the training image data is arranged at a position relative to the position coordinates desired to be detected; a position coordinate calculation unit calculating position coordinates by using inference image data output from the deep learning model, and a reliability calculation unit calculating reliability by using global information of the pixel group of the inference image data output from the deep learning model.Type: ApplicationFiled: March 25, 2021Publication date: May 30, 2024Inventors: Mitsuji IKEDA, Yasutaka TOYODA, Yuichi ABE, Makoto SATO, Haruhiko HIGUCHI, Masayoshi ISHIKAWA
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Patent number: 11978345Abstract: A moving object behavior prediction device improves prediction accuracy and includes: an input map generation unit that generates a single input map in which a region capable of containing a plurality of the moving objects is divided into a plurality of cells, in which each of the cells stores related information of a static object and related information of the moving object; a movement amount estimation unit that estimates a movement amount as a feature amount of each cell from the input map, using a trained convolutional neural network; a movement amount acquisition unit that acquires a movement amount at a current position of the moving object from a movement amount stored in a peripheral cell the moving object; and a future position prediction unit that predicts a future position of the moving object based on a feature amount at a current position of the moving object.Type: GrantFiled: October 11, 2019Date of Patent: May 7, 2024Assignee: HITACHI ASTEMO, LTD.Inventors: Masayoshi Ishikawa, Hideaki Suzuki, Hidehiro Toyoda
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Patent number: 11899437Abstract: The present disclosure proposes a diagnostic system capable of properly identifying the cause of even an error for which multiple factors or multiple compound factors may be accountable. The diagnostic system according to the present disclosure is provided with a learning device for learning at least one of a recipe defining operations of an inspection device, log data describing states of the device, or specimen data describing characteristics of a specimen in association with error types of the device, and estimates the cause of the error by using the learning device (refer to FIG. 4).Type: GrantFiled: March 30, 2020Date of Patent: February 13, 2024Assignee: Hitachi High-Tech CorporationInventors: Fumihiro Sasajima, Masami Takano, Kazuhiro Ueda, Masayoshi Ishikawa, Yasuhiro Yoshida
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Publication number: 20230402249Abstract: A defect inspection apparatus includes: a feature value calculation unit calculating a feature value based on a captured image of a sample; an image information reduction unit generating a latent variable by reducing an information quantity of the feature value; a statistic value estimation unit estimating an image statistic value that can be taken by a normal image based on the latent variable; and a defect detection unit detecting a defect in an inspection image based on the image statistic value and the inspection image of the sample.Type: ApplicationFiled: May 16, 2023Publication date: December 14, 2023Applicant: Hitachi High-Tech CorporationInventors: Yasuhiro YOSHIDA, Masayoshi Ishikawa, Toshinori Yamauchi, Kosuke Fukuda, Hiroyuki Shindo
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Patent number: 11836906Abstract: An object of the present invention is to achieve both suppression of data amount of an image processing system that learns a collation image to be used for image identification using a discriminator and improvement of identification performance of the discriminator. In order to achieve the above object, there is proposed an image processing system including a discriminator that identifies an image using a collation image, the image processing system further including a machine learning engine that performs machine learning of collation image data required for image identification. The machine learning engine searches for a successfully identified image using an image for which identification has been failed, and adds information, obtained based on a partial image of the image for which identification has been failed and which has been selected by an input device to the successfully identified image obtained by the search to generate corrected collation image data.Type: GrantFiled: October 18, 2021Date of Patent: December 5, 2023Assignee: HITACHI HIGH-TECH CORPORATIONInventors: Shinichi Shinoda, Yasutaka Toyoda, Shigetoshi Sakimura, Masayoshi Ishikawa, Hiroyuki Shindo, Hitoshi Sugahara
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Publication number: 20230325413Abstract: An error cause estimation device comprising: a data pre-processing unit that uses data to be processed and generates training data that has an appropriate format for input to a machine learning model; and a model tree generation unit that generates error detection models that are training models for detecting errors and uses the training data as inputs therefor, and generates a model tree that expresses the relationship between error detection models by using a tree structure that has the error detection models as node therefor. Thus, it is possible to generate a training model that detects errors for each of a plurality of types of errors that occur, even when there has been no prior annotation of error causes.Type: ApplicationFiled: September 17, 2020Publication date: October 12, 2023Inventors: Yasuhiro YOSHIDA, Masayoshi ISHIKAWA, Fumihiro SASAJIMA, Masami TAKANO, Koichi HAYAKAWA
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Patent number: 11769355Abstract: A system for guiding a driver to an ideal driving pattern in order to eliminate dependency on the driver's driving pattern in a fault diagnosis of an automobile part based on automobile running data, even if the driver's driving pattern is far from the ideal driving pattern. The system comprises a fault diagnosis support device equipped with: a diagnosis model selector for outputting a diagnosis model in which, for a feature value used for an examination of an automobile part, an available range available for making a diagnosis and a reference point are stipulated; a driver model generator generating, as a representative point of the feature value that corresponds to a driver's driving pattern; and a recommendation model generator generating, if the representative point is outside the available range, a recommendation model in which a boundary of the available range is set as a recommendation point.Type: GrantFiled: November 21, 2018Date of Patent: September 26, 2023Assignee: HITACHI, LTD.Inventors: Takehisa Nishida, Mariko Okude, Masayoshi Ishikawa, Kazuo Muto, Zixian Zhang
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Publication number: 20230298137Abstract: An image quality improvement system includes: an image quality improvement unit that improves the image quality of a low quality image; a deformation prediction unit that predicts a deformation amount that has occurred between a first low quality image and a different second low quality image, included in a series of input low quality images; and a deformation correction unit that corrects, based on the deformation amount predicted by the deformation prediction unit, one of a first prediction image obtained by applying processing by the image quality improvement unit to the first low quality image, the second low quality image, or a second prediction image obtained by applying processing by the image quality improvement unit to the second low quality image. The image quality improvement system learns to reduce the evaluation of a loss function between the first prediction and the second low quality image or the second prediction image.Type: ApplicationFiled: September 29, 2020Publication date: September 21, 2023Inventors: Masayoshi ISHIKAWA, Sota KOMATSU, Yasutaka TOYODA, Shinichi SHINODA
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Publication number: 20230229965Abstract: A machine learning system and a machine learning method capable of selecting a pretrained model to be used in transfer learning in a short time without actually executing the transfer learning includes a pretrained model acquisition unit which acquires a pretrained model from a pretrained model storage unit storing a plurality of pretrained models obtained by learning a transfer source task under respective conditions; a transfer learning dataset storage unit configured to store dataset related to a transfer target task; a pretrained model adaptability evaluation unit configured to evaluate adaptability of each pretrained model acquired by the pretrained model acquisition unit to the dataset related to the transfer target task; and a transfer learning unit configured to execute, based on an evaluation result of the pretrained model adaptability evaluation unit, transfer learning using a selected pretrained model and the dataset, and outputs a learning result as a trained model.Type: ApplicationFiled: December 20, 2022Publication date: July 20, 2023Applicant: Hitachi High-Tech CorporationInventors: Masayoshi ISHIKAWA, Daisuke ASAI, Yuichi ABE, Yohei MINEKAWA, Mitsuji IKEDA
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Publication number: 20230222764Abstract: An image processing method whereby data pertaining to an estimated captured image obtained from reference data of a sample is acquired using an input acceptance unit, an estimation unit, and an output unit. The data is used when comparing the estimated image and an actual image of the sample, wherein the method includes: an input acceptance unit accepting input of the reference data, process information pertaining to the sample, and trained model data; the estimation unit using the reference data, the process information, and the model data to calculate captured image statistics representing a probabilistic distribution of values attained by the data of the captured image; and the output unit outputting the captured image statistics, and generating the estimated captured image from the captured image statistics. This permits reducing the time required for estimation and to perform comparison in real time.Type: ApplicationFiled: June 16, 2020Publication date: July 13, 2023Applicant: Hitachi High-Tech CorporationInventors: Masanori OUCHI, Masayoshi ISHIKAWA, Yasutaka TOYODA, Hiroyuki SHINDO
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Patent number: 11645916Abstract: The present invention improves the accuracy of predicting rarely occurring behavior of moving bodies, without reducing the accuracy of predicting commonly occurring behavior of moving bodies. A vehicle 101 is provided with a moving body behavior prediction device 10. The moving body behavior prediction device 10 is provided with a first behavior prediction unit 203 and a second behavior prediction unit 207. The first behavior prediction unit 203 learns first predicted behavior 204 so as to minimize the error between behavior prediction results for moving bodies and behavior recognition results for the moving bodies after a prediction time has elapsed. The second behavior prediction unit 207 learns future second predicted behavior 208 of the moving bodies around the vehicle 101 so that the vehicle 101 does not drive in an unsafe manner.Type: GrantFiled: November 28, 2018Date of Patent: May 9, 2023Assignee: Hitachi Astemo, Ltd.Inventors: Masayoshi Ishikawa, Hiroaki Ito
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Publication number: 20230122653Abstract: An error cause estimation device comprises a feature value generation unit for using data transmitted from the outside to generate feature values suitable for a machine learning model; a model database having a plurality of error prediction models, for determining whether an error has occurred using the feature values as input data; a model evaluation unit for evaluating the performance of an error prediction model by comparing a prediction result of the error prediction model and an actually measured error; a model selection unit for selecting from the model database an error prediction model for which an evaluation value calculated by the model evaluation unit is greater than or equal to a preset defined value; and an error prediction model generation unit for generating a new error prediction model with respect to the measured error when no corresponding error prediction model has been selected by the model selection unit.Type: ApplicationFiled: March 31, 2020Publication date: April 20, 2023Applicant: Hitachi High-Tech CorporationInventors: Yasuhiro YOSHIDA, Masayoshi ISHIKAWA, Kouichi HAYAKAWA, Masami TAKANO, Fumihiro SASAJIMA
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Publication number: 20230095532Abstract: The present disclosure proposes a diagnostic system capable of properly identifying the cause of even an error for which multiple factors or multiple compound factors may be accountable. The diagnostic system according to the present disclosure is provided with a learning device for learning at least one of a recipe defining operations of an inspection device, log data describing states of the device, or specimen data describing characteristics of a specimen in association with error types of the device, and estimates the cause of the error by using the learning device (refer to FIG. 4).Type: ApplicationFiled: March 30, 2020Publication date: March 30, 2023Inventors: Fumihiro SASAJIMA, Masami TAKANO, Kazuhiro UEDA, Masayoshi ISHIKAWA, Yasuhiro YOSHIDA
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Publication number: 20230077332Abstract: A defect inspection system includes: a defect detection unit that detects defect positions in an inspection image by comparing an inspection image with a reference image that is an image having no defect; a filter model that classifies detected defect positions into false defect or a designated type of defect; a filter condition holding unit that holds a filter condition; a defect region extraction unit that collects the defect positions detected by the defect detection unit for each predetermined distance; a defect filter unit that determines whether or not each defect region satisfies the filter condition and extracts only the defect region that satisfies the filter condition; and a normalization unit that normalizes the inspection image based on a processing step at the time of inspection and a normalization condition set for each processing step or each imaging condition.Type: ApplicationFiled: August 25, 2022Publication date: March 16, 2023Inventors: Yuko SANO, Masayoshi ISHIKAWA, Hiroyuki SHINDO
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Publication number: 20230058441Abstract: The objective of the present invention is to provide an image classification device and a method therefor with which suitable teaching data can be created. An image classification device that carries out image classification using images which are in a class to be classified and include teaching information, and images which are in a class not to be classified and to which teaching information has not been assigned, said image classification device being characterized by being provided with: an image group input unit for receiving inputs of an image group belonging to a class to be classified and an image group belonging to a class not to be classified; and a subclassification unit for extracting a feature amount for each image in an image group, clustering the feature amounts of the images in the image group belonging to a class not to be classified, and thereby dividing the images into sub-classes.Type: ApplicationFiled: November 6, 2020Publication date: February 23, 2023Inventors: Sota KOMATSU, Masayoshi ISHIKAWA, Fumihiro BEKKU, Takefumi KAKINUMA
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Publication number: 20230004811Abstract: A learning processing device and method achieves learning of a lightweight model that is completed in a short amount of time. The learning processing device obtains a new, second learning model from an existing first learning model. An input unit acquires a first learning model generated in advance by learning a first learning data set, and an unpruned neural network (hereinafter, NN). An important parameter identification unit uses the first learning model and the NN to initialize a NN to be learned, and uses a second learning data set and the initialized NN to identify a degree of importance of parameters in a recognition process of the initialized NN. A new model generation unit carries out a pruning process for deleting parameters which are not important from the initialized NN, thereby generating a second NN; and a learning unit uses the second learning data set to learn the second NN.Type: ApplicationFiled: February 7, 2020Publication date: January 5, 2023Inventors: Masayoshi ISHIKAWA, Masanori OUCHI, Hiroyuki SHINDO, Yasutaka TOYODA, Shinichi SHINODA
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Publication number: 20220415024Abstract: This disclosure relates to a system for performing efficient learning of a specific portion. To achieve this purpose, there is proposed a system configured to generate a converted image on the basis of input of an input image, the system comprising a learning model in which parameters are adjusted so as to suppress an error between the input image and a second image converted upon input of the input image, the learning model being subjected to different learning at least between a first area in the image and a second area different from the first area.Type: ApplicationFiled: January 9, 2020Publication date: December 29, 2022Inventors: Yasutaka TOYODA, Masayoshi ISHIKAWA, Masanori OUCHI