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公开(公告)号:US20230032011A1
公开(公告)日:2023-02-02
申请号:US17389042
申请日:2021-07-29
Inventor: Koji MIURA , Yukinori SASAKI , Akira MINEGISHI , Yizhou HUANG , Debdeep PAUL , Yongning YIN , Khai JUN KEK
Abstract: A system for generating a forecast including a classifier module for receiving from a user, at least one feature and classifying the at least one feature into a plurality of priority groups based on a user preference. The system further includes an artificial intelligence (AI) forecast module in communication with the classifier module for processing the plurality of priority groups with at least one feature. The AI forecast module derive a learning from classification of the at least one feature into the plurality of priority groups; and generate the forecast based on the learning.
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公开(公告)号:US20240184284A1
公开(公告)日:2024-06-06
申请号:US18549811
申请日:2022-03-15
Inventor: Kazuhiko NAKAGAWA , Yukinori SASAKI , Koji MIURA , Hironori OHIGASHI
IPC: G05B23/02
CPC classification number: G05B23/0283
Abstract: An information processing device includes: a quality evaluator that evaluates the quality of a plurality of instances of first data to generate a first evaluation result and evaluates the quality of a plurality of instances of second data to generate a second evaluation result; a learner that performs machine learning, using the plurality of instances of first data, to generate a machine learning model for detecting an anomaly; a detector that compares the first evaluation result and the second evaluation result and detects a concept drift, based on a comparison result; and an anomaly estimator that applies the machine learning model to the plurality of instances of second data to estimate whether an anomaly is present in the plurality of instances of second data.
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公开(公告)号:US20230384759A1
公开(公告)日:2023-11-30
申请号:US18249467
申请日:2021-10-29
Inventor: Hironori OHIGASHI , Akira MINEGISHI , Yuta SHIMAZAKI , Yukinori SASAKI
IPC: G05B19/4065
CPC classification number: G05B19/4065 , G05B2219/40335
Abstract: An information processing method includes: calculating a probability density distribution of a work time that is at least part of a setup time, based on production performance data read from storage, the setup time being time taken for a setup work that is performed between lots; determining whether the work time is anormal, based on the probability density distribution; and outputting a result of the determining.
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