Takuya Kudo

US Patent:

20220328332, Oct 13, 2022

Filed:

Apr 13, 2021

Appl. No.:

17/229293

Inventors:

- Dublin, IE
Shin Moriga - Tokyo, JP
Atsushi Suyama - Edogawa-ku, JP
Motoaki Hayashi - Shibuya-ku, JP
Takuya Kudo - Kirkland WA, US

International Classification:

H01L 21/67
G05B 19/418

Abstract:

The present disclosure describes a computer-implemented method for detecting anomalies during lot production, wherein the products within a production lot are processed according to a sequence of steps that include manufacturing steps and one or more quality control steps interspersed among the manufacturing steps, the method comprising: obtaining process quality inspection data from each of the one or more quality control steps for a first production lot; obtaining product characteristics data for the products in the first production lot after the final step in the sequence; training a Gaussian process regression model using the process quality inspection data and the product characteristics data from the first production lot; generating a predictive distribution of the product characteristics data using the Gaussian process regression model that uses a bathtub kernel function; obtaining process quality inspection data from each of the quality control steps for a second production lot; identifying anomalies in the second production lot using the predictive distribution of the product characteristics data and the process quality inspection data from the second production lot; if no anomalies are detected in the second production lot, updating the Gaussian process regression model using the process quality inspection data from the second production lot; setting target values for one or more values in the process quality inspection data based on the predictive distribution of the product characteristic; and adjusting settings of one or more manufacturing steps based on the target values.

US Patent:

20210073972, Mar 11, 2021

Filed:

Sep 6, 2019

Appl. No.:

16/562642

Inventors:

- Dublin, IE
Sean Michael O`Connor - Kenmore WA, US
Takuya Kudo - Kirkland WA, US

International Classification:

G06T 7/00
G06N 20/00
G06N 3/08
G06T 3/00
G06T 7/174

Abstract:

Implementations include receiving image data representative of images of items within a physical environment and depicting defects in at least one item, providing one or more of a set of augmented images using image augmentation based on the image data and a set of synthetic images using ML-based image synthesis based on the image data, processing one of the set of augmented images and the set of synthetic images using an ML model to provide a set of defect characteristics representative of defects in the at least one item, providing one or more root causes of each of the one or more defects by processing the set of defect characteristics and ancillary data, the ancillary data representative of the physical environment, and generating one or more alerts based on the one or more root causes for remediation of at least one root cause of the one or more defects.

US Patent:

20210056430, Feb 25, 2021

Filed:

Aug 23, 2019

Appl. No.:

16/548926

Inventors:

- Dublin, IE
Sean Michael O'Connor - Kenmore WA, US
Takuya Kudo - Kirkland WA, US

International Classification:

G06N 3/08

Abstract:

Implementations include receiving two or more time-series data sequences representative of a target process executed within a physical environment, executing automated time-series process segmentation to provide a plurality of subsequence segments for each of the two or more time-series data sequences, each subsequence segment corresponding to a phase of the target process, processing the two or more subsequence segments using at least one time-series transformation to provide a feature data set for each subsequence segment, applying each feature data set to provide time-series models for anomaly detection and forecasting, respectively, each time-series model being provided as one of a recurrent neural network (RNN), a convolution neural network (CNN), and a generative adversarial network (GAN), determining anomaly scores based on the time-series models, and selectively providing an alert to one or more users, each alert indicating at least one anomaly and a respective probability.

US Patent:

20190012721, Jan 10, 2019

Filed:

Jul 5, 2017

Appl. No.:

15/641935

Inventors:

- Dublin 4, IE
Takahiro SASAKI - Tokyo, JP
Takuya KUDO - Kirkland WA, US
Kana CORNETT - Saitama, JP
Akira YOTSUYANAGI - Tokyo, JP
Takaaki HARAGUCHI - Chiba, JP

Assignee:

ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin 4

International Classification:

G06Q 30/06
G06Q 10/08
G06K 7/10

Abstract:

A system for automatic ordering of products comprises a receiver to receive stock parameters including a number of products sold till a point in time, an expiration duration for usage of products, or a number of products available in an inventory at the time. The system also includes a stock analyzer to determine a stock-out probability factor based on the number of products sold and estimated short term sales, determine a disposal probability factor based on the number of products sold, the expiration duration and estimated long term sales, ascertain a risk balance factor indicative of a ratio of the disposal probability factor to the stock-out probability factor, determine a number of products to replenish the inventory, based on the stock-out probability factor, the disposal probability factor, and the risk balance factor, and place a purchase order to a vendor for acquiring the number of products for replenishing the inventory.

US Patent:

20180293488, Oct 11, 2018

Filed:

Apr 5, 2017

Appl. No.:

15/480011

Inventors:

- Dublin, IE
Takuya Kudo - Kirkland WA, US
Takafumi Mizuno - Tokyo, JP
Makoto Yomosa - Tokyo, JP
Sayaka Tanaka - Tokyo, JP
Miku Yoshio - Tokyo, JP

International Classification:

G06N 3/08
G06N 5/04
G06N 3/04

Abstract:

A rating prediction engine builds and applies models to predict ratings based on an analysis of textual reviews and comments. The engine can build multiple models simultaneously through distributed parallel model building that employs deep convolutional neural networks (CNNs). The engine can also incorporate user moment feature data, including user status and context information, to provide better performance and more accurate predictions. The engine can also employ heuristic unsupervised pre-training and/or adaptive over-fitting reduction for model building. In some instances, the techniques described herein can be used in a service to predict personalized ratings for reviews or other published items, in instances where the original author of the item did not include a rating and/or in instances where the publication channel does not provide a mechanism to enter ratings.