Patents by Inventor Takashi Katoh

Takashi Katoh 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).

  • Patent number: 11409988
    Abstract: A learning device learns at last one parameter of a learning model such that each intermediate feature quantity becomes similar to a reference feature quantity, the each intermediate feature quantity being calculated as a result of inputting a plurality of sets of augmentation training data to a first neural network in the learning model, the plurality of augmentation training data being generated by performing data augmentation based on same first original training data. The learning device learns at last one parameter of a second network, in the learning model, using second original training data, which is different than the first original training data, and using the reference feature quantity.
    Type: Grant
    Filed: January 8, 2020
    Date of Patent: August 9, 2022
    Assignee: FUJITSU LIMITED
    Inventors: Takashi Katoh, Kento Uemura, Suguru Yasutomi
  • Publication number: 20220245405
    Abstract: A deterioration suppression device generates a plurality of trained machine learning models having different characteristics on the basis of each training data included in a first training data set and assigned with a label indicating correct answer information. In a case where estimation accuracy of label estimation with respect to input data to be estimated by any trained machine learning model among the plurality of trained machine learning models becomes lower than a predetermined standard, the deterioration suppression device generates a second training data set including a plurality of pieces of training data using an estimation result by a trained machine learning model with the estimation accuracy equal to or higher than the predetermined standard. The deterioration suppression device executes re-learning of the trained machine learning model with the estimation accuracy lower than the predetermined standard using the second training data set.
    Type: Application
    Filed: April 25, 2022
    Publication date: August 4, 2022
    Applicant: FUJITSU LIMITED
    Inventors: TAKASHI KATOH, Kento UEMURA, Suguru YASUTOMI, Tomohiro Hayase, YUHEI UMEDA
  • Publication number: 20220237407
    Abstract: A non-transitory computer-readable storage medium storing an estimation program that causes a computer to execute a process includes specifying representative points of each of training clusters that corresponds to each of labels targeted for estimation; setting boundaries between each of input clusters under a condition that a number of the input clusters and a number of the representative points coincide with each other, the input clusters being generated by clustering in a feature space for input data; acquiring estimation results for the labels with respect to the input data based on a correspondence relationship between the input clusters and the training clusters based on the boundaries; and estimating determination accuracy for the labels by using the machine learning model with respect to the input data based on the estimation results.
    Type: Application
    Filed: April 19, 2022
    Publication date: July 28, 2022
    Applicant: FUJITSU LIMITED
    Inventors: YUHEI UMEDA, TAKASHI KATOH, Yuichi Ike, Mari Kajitani, Masatoshi Takenouchi
  • Patent number: 11371783
    Abstract: A disclosed loop heat pipe includes an evaporator configured to absorb heat from outside by a wall to evaporate a working fluid from a liquid phase to a gas phase; a condenser configured to condense a gas phase working fluid introduced from the evaporator into a liquid phase; an elastic wick configured to contact an inner wall of the evaporator by an elastic force from the elastic wick; and a wick deformation member configured to deform the elastic wick increase a contact pressure of the elastic wick against the inner wall of the evaporator.
    Type: Grant
    Filed: June 18, 2019
    Date of Patent: June 28, 2022
    Assignee: RICOH COMPANY, LTD.
    Inventors: Tomoyasu Hirasawa, Kiyotada Katoh, Takeshi Endoh, Takashi Tanaka, Toshihiko Baba, Keisuke Ikeda, Hiroki Yamasaki
  • Patent number: 11367003
    Abstract: A non-transitory computer-readable storage medium storing a program that causes a computer to execute a process, the process including obtaining a feature quantity of input data by using a feature generator, generating a first output based on the feature quantity by using a supervised learner for labeled data, generating a second output based on the feature quantity by using an unsupervised learning processing for unlabeled data, and changing a contribution ratio between a first error and a second error in a learning by the feature generator, the first error being generated from the labeled data and the first output, the second error being generated from the unlabeled data and the second output.
    Type: Grant
    Filed: April 6, 2018
    Date of Patent: June 21, 2022
    Assignee: Fujitsu Limited
    Inventors: Takashi Katoh, Kento Uemura, Suguru Yasutomi, Toshio Endoh
  • Publication number: 20220147764
    Abstract: A non-transitory computer-readable storage medium storing a data generation program that causes at least one computer to execute a process, the process includes, acquiring a data generation model that is trained by using a first dataset corresponding to a first domain and a second dataset corresponding to a second domain, and that includes an identification loss by an identification model in a parameter; inputting first data corresponding to the first domain to the identification model to acquire a first identification loss, and inputting second data corresponding to the second domain to the identification model to acquire a second identification loss; generating data in which the second identification loss approximates the first identification loss, by using the data generation model; and outputting the data that is generated.
    Type: Application
    Filed: September 13, 2021
    Publication date: May 12, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Takashi KATOH, Kento UEMURA, Suguru YASUTOMI, Tomohiro HAYASE
  • Publication number: 20220101124
    Abstract: A non-transitory computer-readable storage medium storing an information processing program that causes at least one computer to execute a process, the process includes acquiring a first machine learning model trained by using a training data set including first data and a second machine learning model not trained with the specific data; and retraining the first machine learning model so that an output of the first machine learning model and an output of the second machine learning model when second data corresponding to the first data is input get close to each other.
    Type: Application
    Filed: July 7, 2021
    Publication date: March 31, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Suguru YASUTOMI, Tomohiro HAYASE, Takashi KATOH
  • Publication number: 20220076162
    Abstract: A non-transitory computer-readable storage medium storing a data presentation program that causes at least one computer to execute a process, the process includes acquiring certain data from an estimation target data set that uses an estimation model, based on an estimation result for the estimation target data set; and presenting data obtained by changing the certain data in a direction orthogonal to a direction in which loss of the estimation model fluctuates, in a feature space that relates to feature amounts obtained from the estimation target data set.
    Type: Application
    Filed: July 21, 2021
    Publication date: March 10, 2022
    Applicant: FUJITSU LIMITED
    Inventors: TAKASHI KATOH, Kento UEMURA, Suguru YASUTOMI, Tomohiro Hayase
  • Patent number: 11263479
    Abstract: An anomaly detection apparatus generates pieces of image data using a generator and train the generator and a discriminator that discriminates whether an image data, generated by the generator, is real or fake. The anomaly detection apparatus trains the generator such that the generator, in generating the pieces of image data to maximize the discrimination error of the discriminator, generate at least a piece of specified image data to reduce the discrimination error at a fixed rate with respect to the pieces of image data and trains, based on the pieces of image data and the at least a piece of specified image data, the discriminator to minimize the discrimination error.
    Type: Grant
    Filed: January 28, 2020
    Date of Patent: March 1, 2022
    Assignee: FUJITSU LIMITED
    Inventors: Takashi Katoh, Kazuki Iwamoto, Kento Uemura, Suguru Yasutomi
  • Patent number: 11250297
    Abstract: An anomaly detection apparatus performs training for the generator and the discriminator such that the generator maximizes a discrimination error of the discriminator and the discriminator minimizes the discrimination error The anomaly detection apparatus stores, while the training is being performed, a state of the generator that is half-trained and satisfies a pre-set condition, and retrains the discriminator by using an image generated by the half-trained generator that has the stored state.
    Type: Grant
    Filed: January 28, 2020
    Date of Patent: February 15, 2022
    Assignee: FUJITSU LIMITED
    Inventors: Takashi Katoh, Kazuki Iwamoto, Kento Uemura, Suguru Yasutomi
  • Patent number: 11145062
    Abstract: An estimation method implemented by a computer, the estimation method includes: executing learning processing by training an autoencoder with a data group corresponding to a specific task; calculating a degree of compression of each part regarding data included in the data group by using the trained autoencoder; and estimating a common part with another piece of data included in the data group regarding the data corresponding to the specific task based on the calculated degree of compression of each part.
    Type: Grant
    Filed: March 6, 2020
    Date of Patent: October 12, 2021
    Assignee: FUJITSU LIMITED
    Inventors: Kento Uemura, Suguru Yasutomi, Takashi Katoh
  • Publication number: 20210232854
    Abstract: A non-transitory computer-readable recording medium recording a learning program for causing a computer to execute processing includes: generating restored data using a plurality of restorers respectively corresponding to a plurality of features from the plurality of features generated by a machine learning model corresponding to each piece of input data, for each piece of the input data input to the machine learning model; and making the plurality of restorers perform learning so that each of the plurality of pieces of restored data respectively generated by the plurality of restorers approaches the input data.
    Type: Application
    Filed: April 12, 2021
    Publication date: July 29, 2021
    Applicant: FUJITSU LIMITED
    Inventors: Kento UEMURA, Suguru YASUTOMI, TAKASHI KATOH
  • Publication number: 20210012193
    Abstract: A machine learning method includes: calculating, by a computer, a first loss function based on a first distribution and a previously set second distribution, the first distribution being a distribution of a feature amount output from an intermediate layer when first data is input to an input layer of a model that has the input layer, the intermediate layer, and an output layer; calculating a second loss function based on second data and correct data corresponding to the first data, the second data being output from the output layer when the first data is input to the input layer of the model; and training the model based on both the first loss function and the second loss function.
    Type: Application
    Filed: July 7, 2020
    Publication date: January 14, 2021
    Applicant: FUJITSU LIMITED
    Inventors: Suguru YASUTOMI, TAKASHI KATOH, Kento UEMURA
  • Publication number: 20200356872
    Abstract: A rule presentation method by a computer, includes specifying a plurality of rules that specify one of examples according to the number of positive examples and the number of negative examples for one or more combinations of attributes, based on training data; acquiring first data that has a combination of attributes different from the combination of attributes included in the training data and is not associated with a label that designates the positive example or the negative example; selecting a rule related to the combination of attributes from among the plurality of specified rules; generating second data in which a label different from examples specified by the selected rule is associated with the first data; specifying the number of samples of the first data in which the label of the positive example or the negative example specified by the selected rule changes; and determining an order of rules.
    Type: Application
    Filed: April 28, 2020
    Publication date: November 12, 2020
    Applicant: FUJITSU LIMITED
    Inventors: KEN KOBAYASHI, TAKASHI KATOH, Akira URA
  • Patent number: 10809946
    Abstract: An optional array in a memory includes an array having blocks each including an address word and a data word, and a boundary that is a position where a ratio between the numbers of unwritten blocks in M area and written blocks in W area is an integer ratio. The controlling process includes when a second write for writing a special value in a written block in the second area is invoked, executing a shrink process of shifting the boundary to shrink the first area; in a case where the first adjacent block at the boundary is a written block, storing an address of the first adjacent block and of a first link destination block forming a link with the write destination block in address words of the first link destination block and of the first adjacent block respectively to form a link.
    Type: Grant
    Filed: October 31, 2018
    Date of Patent: October 20, 2020
    Assignee: FUJITSU LIMITED
    Inventors: Takashi Katoh, Keisuke Goto
  • Patent number: 10803357
    Abstract: An object detection device extracts feature for input data utilizing an encoder, the input data including labeled data and unlabeled data and detects object in each of the input data, utilizing an object detector. The object detection device generates region data for each of the input data, each of the region data corresponding to the detected object and generates restoration data from the region data and meta-information related to the detected object for each of the input data utilizing a decoder corresponding to the encoder. The object detection device executes learning of the encoder and the object detector based on a result detected by the object detector and a label associated with the input data, when the input data is labeled data. The object detection device executes learning of the encoder, the object detector, and the decoder, based on the input data and the restoration data.
    Type: Grant
    Filed: May 30, 2018
    Date of Patent: October 13, 2020
    Assignee: FUJITSU LIMITED
    Inventors: Suguru Yasutomi, Toshio Endoh, Takashi Katoh, Kento Uemura
  • Publication number: 20200302611
    Abstract: An estimation method implemented by a computer, the estimation method includes: executing learning processing by training an autoencoder with a data group corresponding to a specific task; calculating a degree of compression of each part regarding data included in the data group by using the trained autoencoder; and estimating a common part with another piece of data included in the data group regarding the data corresponding to the specific task based on the calculated degree of compression of each part.
    Type: Application
    Filed: March 6, 2020
    Publication date: September 24, 2020
    Applicant: FUJITSU LIMITED
    Inventors: Kento UEMURA, Suguru YASUTOMI, TAKASHI KATOH
  • Publication number: 20200280525
    Abstract: An allocation method executed by a computer includes dividing each of a plurality of pieces of time-series data into a plurality of segments, allocating a label to each of the pieces of time-series data based on features of each segment in the pieces of time-series data, and allocating a predetermined segment in time-series data, included in the pieces of time-series data, with a label allocated to the time-series data to which the predetermined segment belongs.
    Type: Application
    Filed: February 26, 2020
    Publication date: September 3, 2020
    Inventors: Yasushi SAKURAI, Yasuko MATSUBARA, Yasuaki IRIFUNE, Saeru YAMAMURO, Kouki KAWABATA, Akira URA, TAKASHI KATOH, YUHEI UMEDA
  • Publication number: 20200250544
    Abstract: A learning method executed by a computer, the learning method includes inputting a first data being a data set of transfer source and a second data being one of data sets of transfer destination to an encoder to generate first distributions of feature values of the first data and second distributions of feature values of the second data; selecting one or more feature values from among the feature values so that, for each of the one or more feature values, a first distribution of the feature value of the first data is similar to a second distribution of the feature value of the second data; inputting the one or more feature values to a classifier to calculate prediction labels of the first data; and learning parameters of the encoder and the classifier such that the prediction labels approach correct answer labels of the first data.
    Type: Application
    Filed: February 4, 2020
    Publication date: August 6, 2020
    Applicant: FUJITSU LIMITED
    Inventors: TAKASHI KATOH, Kento UEMURA, Suguru YASUTOMI, Takuya Takagi, KEN KOBAYASHI, Akira URA, Kenichi KOBAYASHI
  • Patent number: 10732333
    Abstract: The present invention relates to an infrared sensor, a near-infrared ray absorption composition, a photosensitive resin composition, a compound, a near-infrared ray absorption filter, and an image pick-up device. Provided is an infrared sensor 100 that detects an object by detecting light in wavelengths of 900 nm to 1,000 nm, including infrared ray transmission filters 113 and near-infrared ray absorption filters 111, in which the near-infrared ray absorption filters 111 contains a near-infrared ray absorption substance having a maximum absorption wavelength in wavelengths of 900 nm to 1,000 nm.
    Type: Grant
    Filed: October 19, 2016
    Date of Patent: August 4, 2020
    Assignee: FUJIFILM Corporation
    Inventors: Yutaro Norizuki, Takashi Katoh, Satoru Murayama, Yoshihiro Jimbo, Daisuke Sasaki, Keisuke Arimura, Takuya Tsuruta