Patents by Inventor Takeshi OSOEKAWA
Takeshi OSOEKAWA 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: 11620530Abstract: A learning method executed by a computer, the learning method includes: learning parameters of a machine learning model having intermediate feature values by inputting a plurality of augmented training data, which is generated by augmenting original training data, to the machine learning model so that specific intermediate feature values, which are calculated from specific augmented training data augmented from a same original training data, become similar to each other.Type: GrantFiled: January 14, 2020Date of Patent: April 4, 2023Assignee: FUJITSU LIMITEDInventors: Takashi Katoh, Kento Uemura, Suguru Yasutomi, Takeshi Osoekawa
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Patent number: 11486866Abstract: When chromatogram data for a target sample have been acquired, a peak position estimator determines an estimated result of the position of the starting and/or ending point of a peak as well as the confidence value representing the reliability of the estimation, using a trained model stored in the trained model storage section. Normally, a plurality of estimated results of the starting point and/or ending point of the peak are acquired for one peak. A peak information correction processor identifies a candidate having the highest confidence as a prime candidate, and superposes a plurality of candidates including the prime candidate, with their respective confidence values, on a displayed chromatogram. An operator referring to the confidence values selects a peak which needs close checking or correction, and corrects the starting point and/or ending point of the selected peak, for example, by selecting and indicating a candidate other than the prime candidate.Type: GrantFiled: November 9, 2017Date of Patent: November 1, 2022Assignee: SHIMADZU CORPORATIONInventors: Takeshi Osoekawa, Yusuke Hida, Yuzi Kanazawa, Shinji Kanazawa, Yohei Yamada, Hiroyuki Yasuda, Akihiro Kunisawa
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Patent number: 11302039Abstract: A model constructed by a training process using the technique of deep learning using the training data including images created from a large number of chromatograms and correct peak information is previously stored in a trained model storage section. When chromatogram data for a target sample acquired with an LC measurement unit are inputted, an image creator converts the chromatogram into an image and creates an input image in which one of the two areas divided by the chromatogram curve as the boundary in the image is filled. A peak position estimator inputs the pixel values of the input image into a trained model using a neural network, and obtains the position information of the starting point and/or ending point of the peak and a peak detection confidence as the output. A peak determiner determines the starting point and/or ending point of each peak based on the peak detection confidence.Type: GrantFiled: November 9, 2017Date of Patent: April 12, 2022Assignee: SHIMADZU CORPORATIONInventors: Takeshi Osoekawa, Yusuke Hida, Yuzi Kanazawa, Shinji Kanazawa, Yohei Yamada, Hiroyuki Yasuda, Akihiro Kunisawa, Hidetoshi Terada
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Publication number: 20200292509Abstract: When chromatogram data for a target sample have been acquired, a peak position estimator determines an estimated result of the position of the starting and/or ending point of a peak as well as the confidence value representing the reliability of the estimation, using a trained model stored in the trained model storage section. Normally, a plurality of estimated results of the starting point and/or ending point of the peak are acquired for one peak. A peak information correction processor identifies a candidate having the highest confidence as a prime candidate, and superposes a plurality of candidates including the prime candidate, with their respective confidence values, on a displayed chromatogram. An operator referring to the confidence values selects a peak which needs close checking or correction, and corrects the starting point and/or ending point of the selected peak, for example, by selecting and indicating a candidate other than the prime candidate.Type: ApplicationFiled: November 9, 2017Publication date: September 17, 2020Applicant: SHIMADZU CORPORATIONInventors: Takeshi OSOEKAWA, Yusuke HIDA, Yuzi KANAZAWA, Shinji KANAZAWA, Yohei YAMADA, Hiroyuki YASUDA, Akihiro KUNISAWA
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Publication number: 20200279408Abstract: A model constructed by a training process using the technique of deep learning using the training data including images created from a large number of chromatograms and correct peak information is previously stored in a trained model storage section. When chromatogram data for a target sample acquired with an LC measurement unit are inputted, an image creator converts the chromatogram into an image and creates an input image in which one of the two areas divided by the chromatogram curve as the boundary in the image is filled. A peak position estimator inputs the pixel values of the input image into a trained model using a neural network, and obtains the position information of the starting point and/or ending point of the peak and a peak detection confidence as the output. A peak determiner determines the starting point and/or ending point of each peak based on the peak detection confidence.Type: ApplicationFiled: November 9, 2017Publication date: September 3, 2020Applicant: SHIMADZU CORPORATIONInventors: Takeshi OSOEKAWA, Yusuke HIDA, Yuzi KANAZAWA, Shinji KANAZAWA, Yohei YAMADA, Hiroyuki YASUDA, Akihiro KUNISAWA, Hidetoshi TERADA
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Publication number: 20200234140Abstract: A learning method executed by a computer, the learning method includes: learning parameters of a machine learning model having intermediate feature values by inputting a plurality of augmented training data, which is generated by augmenting original training data, to the machine learning model so that specific intermediate feature values, which are calculated from specific augmented training data augmented from a same original training data, become similar to each other.Type: ApplicationFiled: January 14, 2020Publication date: July 23, 2020Applicant: FUJITSU LIMITEDInventors: TAKASHI KATOH, Kento UEMURA, Suguru YASUTOMI, Takeshi OSOEKAWA
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Publication number: 20200193329Abstract: A computer-implemented learning method includes inputting a plurality of pieces of input data and labels representing the plurality of pieces of input data into an encoder configured to output context variables associated with each of the plurality of pieces of input data, inputting the plurality of pieces of input data and the context variables output by the encoder into a decoder configured to output decision labels associated with the plurality of pieces of input data respectively, and learning parameters of the encoder and the decoder so that each of the decision labels matches with a corresponding label of the labels representing the plurality of the plurality of pieces of input data.Type: ApplicationFiled: December 13, 2019Publication date: June 18, 2020Applicant: FUJITSU LIMITEDInventors: Takeshi OSOEKAWA, TAKASHI KATOH, Yusuke Hida, Yuzi KANAZAWA
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Patent number: 9646265Abstract: A model updating method is provided. The model updating method that is executed by a computer includes calculating a score that indicates a degree of normality or abnormality of each of a plurality of pieces of data by using as a judgment model each of the pieces of data, predicting as a predicted condition whether each of the pieces of data is normal or abnormal according to score, judging whether or not the predicted condition is correct for each of the plurality of pieces of data, calculating the accuracy rate for the predicted conditions of a top specified number of pieces of data in order of decreasing abnormality as indicated by the score when the plurality of pieces of data are arranged in a specified order of score, and judging whether or not it is necessary to update the judgment model according to the accuracy rate.Type: GrantFiled: September 10, 2015Date of Patent: May 9, 2017Assignee: FUJITSU LIMITEDInventors: Takahisa Ando, Takeshi Osoekawa, Seishi Okamoto
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Publication number: 20170032331Abstract: A clip detects that the physical link with an object, such as a document, is generated and/or the physical link with an object is eliminated. When the clip detects that the link with an object is generated, the clip transmits an identifier of the clip. Alternatively, when the clip detects that the link with an object is eliminated, the clip transmits an identifier of the clip.Type: ApplicationFiled: June 23, 2016Publication date: February 2, 2017Inventors: Kazuki Takano, Motoshi Sumioka, Takeshi Osoekawa, Takuya Sakamoto, Daichi Shimada, Ayuri Morimoto, Tazuru Tanamori
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INFORMATION PROCESSING METHOD, INFORMATION PROCESSING DEVICE, AND COMPUTER-READABLE RECORDING MEDIUM
Publication number: 20160307222Abstract: An information processing method includes: dividing processing target data, which indicates an action detected for each of a plurality of persons in a certain period, by a predetermined period length with reference to information related to a time contained in the data and separately performing a principal component analysis in each of the divided period length, by a processor; specifying corresponding axes in temporally adjacent analysis periods based on an axis calculated as a result of each principal component analysis, by the processor; and considering axes associated in the temporally adjacent periods as the same axis throughout all of the processing target data, and grouping the plurality of persons into a plurality of groups, by the processor.Type: ApplicationFiled: June 20, 2016Publication date: October 20, 2016Applicant: FUJITSU LIMITEDInventors: Takeshi OSOEKAWA, Junichi Hirose, Takahisa Ando, Seishi OKAMOTO -
Publication number: 20160063186Abstract: A server device displays a graph indicating time-series variation in load calculated according to a category of an event acquired from schedule information in which events of at least one user have been registered, and displays information indicating a payment object or a body region or symptom related to the payment object extracted from registered payment data of the one user in a manner temporally associated with the graph.Type: ApplicationFiled: August 13, 2015Publication date: March 3, 2016Inventors: Shiho Miyatake, Takeshi Osoekawa, Junya Fujimoto
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Publication number: 20150379432Abstract: A model updating method is provided. The model updating method that is executed by a computer includes calculating a score that indicates a degree of normality or abnormality of each of a plurality of pieces of data by using as a judgment model each of the pieces of data, predicting as a predicted condition whether each of the pieces of data is normal or abnormal according to score, judging whether or not the predicted condition is correct for each of the plurality of pieces of data, calculating the accuracy rate for the predicted conditions of a top specified number of pieces of data in order of decreasing abnormality as indicated by the score when the plurality of pieces of data are arranged in a specified order of score, and judging whether or not it is necessary to update the judgment model according to the accuracy rate.Type: ApplicationFiled: September 10, 2015Publication date: December 31, 2015Inventors: Takahisa Ando, Takeshi OSOEKAWA, Seishi OKAMOTO
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Publication number: 20140297690Abstract: A disclosed method includes: obtaining, for each node of plural nodes in a graph, which are associated each other, a display position at which the node is displayed; calculating, for each node, a movement vector according to a total sum of forces in conformity with a mechanics model in which an inertial force does not work, wherein the total sum of the forces is obtained by adding, with respect to all of nodes other than the node, a force that works in association with a distance concerning the display position with another node; moving, for each node, the display position by the calculated movement vector; and while repeating or before the obtaining, the calculating and the moving, accepting an instruction corresponding to a user's operation for causing a display position of a certain node to be changed, and changing the display position of the certain node according to the instruction.Type: ApplicationFiled: February 7, 2014Publication date: October 2, 2014Applicant: FUJITSU LIMITEDInventors: Takeshi OSOEKAWA, Takahisa ANDO, Seishi OKAMOTO