公开(公告)号：US20220138526A1

公开(公告)日：2022-05-05

申请号：US17514350

申请日：2021-10-29

Applicant: SEIKO EPSON CORPORATION

Inventor： Ryoki WATANABE ,  Hikaru KURASAWA

IPC: G06N3/04

Abstract: A method of making a single processor or a plurality of processors perform classification processing of classification target data using a machine learning model includes the steps of (a) preparing N machine learning models in a memory assuming N as an integer no smaller than 2, and (b) performing the classification processing of the classification target data using the N machine learning models. Each of the N machine learning models is configured so as to classify input data into any of a plurality of classes with learning using training data, and is configured so as to have at least one class different from a class of another of the N machine learning models.

公开(公告)号：US20200242808A1

公开(公告)日：2020-07-30

申请号：US16774101

申请日：2020-01-28

Applicant: Seiko Epson Corporation

Inventor： Ryoki WATANABE ,  Hikaru KURASAWA ,  Naoki KUWATA ,  Masashi KANAI

IPC: G06T7/90 ,  G01J3/28 ,  G06Q30/00

Abstract: An identification method for identifying a target to be measured includes accepting, from an input section, an information representing a condition for acquiring spectral information specific to a target to be measured, capturing, by a spectrometry camera, an image of the target, acquiring the spectral information specific to the target based on the captured image, and identifying the target based on (i) the spectral information and (ii) a database, stored in a memory, containing a plurality of pieces of spectral information corresponding to a plurality of objects. Acquiring the spectral information includes preferentially acquiring the spectral information specific to the target in a specific wavelength region where the target is identifiable.

公开(公告)号：US20150063058A1

公开(公告)日：2015-03-05

申请号：US14474796

申请日：2014-09-02

Applicant: SEIKO EPSON CORPORATION

Inventor： Ryoki WATANABE ,  Masaki HAYASHI ,  Kazuyuki KANO ,  Kenji MURAKAMI

IPC: G01S7/52

CPC classification number: G01S7/52017 ,  A61B8/4494 ,  G01S7/52047 ,  G01S7/52063 ,  G01S15/8915

Abstract: Provided are an ultrasonic measurement apparatus, an ultrasonic imaging apparatus and an ultrasonic measurement method that achieve an increase in processing speed together with an increase in resolution and are user friendly. An image is generated by adding together, with a weight having a fixed value, reception signals obtained by ultrasonic echoes being received by an ultrasonic element array, and an area of interest is set within the area in which the generated image is to be displayed. When an area of interest is acquired, the reception signals received by the ultrasonic element array are added together with weights that depend on the reception signals, with respect to data forming the basis of the image to be displayed in the area of interest, and image generation is performed.

Abstract translation: 提供了一种超声波测量装置，超声波成像装置和超声波测量方法，其与分辨率的增加一起实现处理速度的增加并且是用户友好的。 通过将由超声波元件阵列接收的超声波回波获得的接收信号与具有固定值的加权相加在一起生成图像，并且将感兴趣区域设置在生成的图像要显示的区域内。 当获取感兴趣区域时，相对于形成要在感兴趣区域中显示的图像的基础的数据，由超声波元件阵列接收的接收信号与依赖于接收信号的权重相加，并且图像 一代被执行。

公开(公告)号：US20140276060A1

公开(公告)日：2014-09-18

申请号：US14208022

申请日：2014-03-13

Applicant: SEIKO EPSON CORPORATION

Inventor： Masaki HAYASHI ,  Ryoki WATANABE

IPC: A61B8/14

CPC classification number: A61B8/14 ,  A61B8/42 ,  A61B8/4405 ,  A61B8/5207

Abstract: An ultrasonic measuring device can acquire inclination information regarding an ultrasonic probe and notify the user if the ultrasonic probe is inclined. The ultrasonic measuring device includes an emission unit that performs ultrasound emission processing, a reception unit that performs ultrasonic echo reception processing, and a processing unit that performs ultrasonic measurement control processing. The processing unit acquires inclination information regarding the ultrasonic probe based on a reception signal resulting from ultrasonic echoes from an interface between the test subject and an ultrasonic measurement sheet, or resulting from ultrasonic echoes from the ultrasonic measurement sheet.

Abstract translation: 超声波测量装置可获取关于超声波探头的倾斜信息，并且通知用户超声波探头是否倾斜。 超声波测量装置包括执行超声波发射处理的发射单元，执行超声波回波接收处理的接收单元和执行超声波测量控制处理的处理单元。 处理单元根据来自超声波测量片的界面的超声波回波得到的接收信号，或从超声波测量片的超声回波获得关于超声波探头的倾斜信息。

公开(公告)号：US20140200451A1

公开(公告)日：2014-07-17

申请号：US14150978

申请日：2014-01-09

Applicant: Seiko Epson Corporation

Inventor： Masaki HAYASHI ,  Ryoki WATANABE

IPC: A61B8/00

CPC classification number: A61B8/54 ,  A61B8/44 ,  A61B8/4405 ,  A61B8/4427 ,  A61B8/4444 ,  A61B8/585

Abstract: An appropriate parameter can be automatically set according to the subject in an ultrasonic measuring device. The ultrasonic measuring device includes an emission unit that performs ultrasound emission processing, a reception unit that performs ultrasonic echo reception processing, and a processing unit that performs ultrasonic measurement control processing. The emission unit performs processing for emitting ultrasound toward a subject via an ultrasonic measurement sheet, and the reception unit performs processing for receiving ultrasonic echoes from the ultrasonic measurement sheet, and outputs a reception signal to the processing unit. The processing unit performs processing for analyzing ultrasonic measurement code information recorded in the ultrasonic measurement sheet based on the reception signal from the reception unit.

Abstract translation: 可以根据超声波测量装置中的被摄体自动设置适当的参数。 超声波测量装置包括执行超声波发射处理的发射单元，执行超声波回波接收处理的接收单元和执行超声波测量控制处理的处理单元。 发射单元经由超声波测量片进行向对象发射超声波的处理，并且接收单元执行从超声波测量片接收超声回波的处理，并将接收信号输出到处理单元。 处理单元基于来自接收单元的接收信号执行用于分析记录在超声波测量薄片中的超声测量代码信息的处理。

公开(公告)号：US20240273357A1

公开(公告)日：2024-08-15

申请号：US18438534

申请日：2024-02-12

Applicant: SEIKO EPSON CORPORATION

Inventor： Yuki URUSHIBATA ,  Ryoki WATANABE ,  Hikaru KURASAWA

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: An evaluation method for evaluating target data includes: inputting a plurality of training sets to a vector neural network machine learning model having a plurality of vector neuron layers to train the machine learning model, the training sets including of general-purpose training data having a type different from the target data and a label corresponding to the general-purpose training data; acquiring a reference feature spectrum; acquiring a target feature spectrum; calculating a spectral similarity that is a similarity between the reference feature spectrum and the target feature spectrum; and evaluating the target data using the spectral similarity.

公开(公告)号：US20230186085A1

公开(公告)日：2023-06-15

申请号：US18063855

申请日：2022-12-09

Applicant: SEIKO EPSON CORPORATION

Inventor： Shin NISHIMURA ,  Ryoki WATANABE ,  Hikaru KURASAWA

IPC: G06N3/08 ,  G06F18/2431

CPC classification number: G06N3/08 ,  G06F18/2431

Abstract: Provided is a learning method including (a) preparing a plurality of pieces of data for learning; (b) dividing the plurality of pieces of data for learning into one or more groups to generate one or more input learning data groups; and (c) training M number of machine learning models, wherein (b) includes (b1) dividing the plurality of pieces of data for input into one or more regions to generate, as one of the input learning data groups, a collection of first type divided input data after division belonging to the same region, or (b2) dividing the plurality of pieces of data for learning belonging to one class into one or more groups to generate, as one of the input learning data groups, a collection of second type divided input data after division.

公开(公告)号：US20220277198A1

公开(公告)日：2022-09-01

申请号：US17680928

申请日：2022-02-25

Applicant: SEIKO EPSON CORPORATION

Inventor： Ryoki WATANABE ,  Hikaru KURASAWA ,  Shin NISHIMURA ,  Kana KANAZAWA

IPC: G06N3/08 ,  G06N3/04

Abstract: A class discrimination method includes: (a) a step of preparing, for each class, a known feature spectrum group obtained based on an output of a specific layer among a plurality of vector neuron layers when a plurality of pieces of training data are input to a machine learning model; and (b) a step of executing a class discrimination processing of the data to be discriminated using the machine learning model and the known feature spectrum group. The step (b) includes: (b1) a step of calculating a feature spectrum based on an output of the specific layer according to the data to be discriminated to the machine model; (b2) a step for each of the one or more classes; (b3) a step of creating an explanatory text of a class discrimination result for the data to be discriminated according to the similarity; and (b4) a step of outputting the explanatory text.

公开(公告)号：US20220245450A1

公开(公告)日：2022-08-04

申请号：US17590930

申请日：2022-02-02

Applicant: SEIKO EPSON CORPORATION

Inventor： Shin NISHIMURA ,  Ryoki WATANABE ,  Hikaru KURASAWA

IPC: G06N3/08

Abstract: A class determination method includes: step (a): preparing, for each of a plurality of classes, a known feature spectrum group obtained when a plurality of pieces of training data are input to a vector neural network type machine learning model; and step (b): executing, by using the machine learning model and the known feature spectrum group, a class determination processing on data to be determined. The step (b) includes step (b1), calculating a feature spectrum according to an input of the data to be determined to the machine learning model, step (b2), calculating a class similarity between the feature spectrum and the known feature spectrum group related to each of the plurality of classes, and step (b3), determining a class of the data to be determined according to the class similarity.

公开(公告)号：US20210381955A1

公开(公告)日：2021-12-09

申请号：US17332057

申请日：2021-05-27

Applicant: SEIKO EPSON CORPORATION

Inventor： Ryoki WATANABE ,  Hikaru KURASAWA ,  Yoshihiro OSHITA ,  Masashi KANAI

IPC: G01N21/25 ,  G01J3/45 ,  G01J3/28 ,  G01J3/12

Abstract: A determination method includes: obtaining measurement data; selecting 0 or more second wavelengths from a plurality of first wavelengths including at least one of a plurality of measurement wavelengths to generate a plurality of individuals, by using a genetic algorithm; inputting, to a first model learned to reproduce a correct answer label of a target object, the measurement data of the target object belonging to a remaining group and a second spectroscopic spectrum determined by the second wavelength to discriminate a label of the target object belonging to the remaining group, for each of the plurality of individuals; and determining whether or not to use the second wavelength as the wavelength of the spectroscopic spectrum for discrimination based on a rate at which the label is correctly discriminated.