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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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Patent Agency Ranking
公开(公告)号：US20250086389A1
公开(公告)日：2025-03-13
申请号：US18367310
申请日：2023-09-12
Inventor： Gayathri SARANATHAN , Nway Nway AUNG , Ariel BECK , Chandra Suwandi WIJAYA , Jianyu CHEN , Debdeep PAUL , Sahim YAMAURA , Koji MIURA
IPC: G06F40/279 , G06N20/00
Abstract: According to an embodiment, a method for generating textual features corresponding to text documents from a raw dataset is disclosed. The method includes preprocessing the text documents and determining topic probability scores (TPS) and confidence scores (CS) using unsupervised and supervised machine learning models, respectively. The combination of TPS and CS is used to generate a compound distribution score (CDS), which forms a comprehensive representation of the output of the machine learning models. The determined TPS, CS, and CDS are then used to generate a set of textual features, which serve as independent variables for a forecasting model.
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公开(公告)号：US20210110162A1
公开(公告)日：2021-04-15
申请号：US17128839
申请日：2020-12-21
Inventor： Koji MIURA , Hidehiko SHIN
Abstract: The operation sequence identification device identifies an operation sequence including a series of individual operations. The operation sequence identification device includes an communication circuit that acquires first sensing information indicating the position of a moving object in an operation area in chronological order and plural pieces of second sensing information indicating surrounding environment states at different positions in the operation area in chronological order, and a control circuit that specifies the order of the series of individual operations based on the first sensing information and that identifies the operation content of each of the series of individual operations based on the second sensing information.
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公开(公告)号：US20250068982A1
公开(公告)日：2025-02-27
申请号：US18236754
申请日：2023-08-22
Inventor： Yizhou HUANG , Chandra Suwandi WIJAYA , Debdeep PAUL , Koji MIURA
IPC: G06N20/20
Abstract: According to an embodiment, a method for determining feature importance in an ensemble model including a plurality of Machine Learning (ML) models is disclosed. The method includes receiving a dataset comprising input features and a forecast result. The method also includes generating a ranking-based feature list based on the input features. Further, the method includes generating a feature importance output based on the ranking-based features lists. Furthermore, the method includes determining a weightage value corresponding to each of the plurality of ML models based on an accuracy value associated with the corresponding machine learning model. The method also includes determining a weightage-based feature importance value corresponding to each input feature corresponding to the feature importance output based on the determined weightage value corresponding to each ML model responsible for the corresponding input feature in the feature importance output.
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公开(公告)号：US20240346426A1
公开(公告)日：2024-10-17
申请号：US18751674
申请日：2024-06-24
Inventor： Koji MIURA , Hidehiko Shin
IPC: G06Q10/0639 , G06Q20/20 , G06V10/764 , G06V10/82 , G06V20/00 , G06V20/52 , H04W4/029 , H04W4/33 , H04W4/38
CPC classification number: G06Q10/06398 , G06Q20/203 , G06V10/764 , G06V10/82 , G06V20/36 , G06V20/52 , H04W4/029 , H04W4/33 , H04W4/38
Abstract: The operation sequence identification device identifies an operation sequence including a series of individual operations. The operation sequence identification device includes an communication circuit that acquires first sensing information indicating the position of a moving object in an operation area in chronological order and plural pieces of second sensing information indicating surrounding environment states at different positions in the operation area in chronological order, and a control circuit that specifies the order of the series of individual operations based on the first sensing information and that identifies the operation content of each of the series of individual operations based on the second sensing information.
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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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