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公开(公告)号：US20240232534A9
公开(公告)日：2024-07-11
申请号：US18279584
申请日：2021-03-01
发明人： Shota ORIHASHI , Masato SAWADA
IPC分类号： G06F40/295 , G06F40/157
CPC分类号： G06F40/295 , G06F40/157
摘要： A word selection support device according to the present disclosure includes processing circuitry configured to derive, for each extracted unknown word that is a term that is extracted from a target corpus and is not registered in dictionary data, statistical information regarding the extracted unknown word in a plurality of corpuses including the target corpus, and calculate appropriateness as a registered unknown word possibility that is a possibility of an unknown word to be registered in the dictionary data for each of the extracted unknown word on the basis of the statistical information.
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公开(公告)号：US20240135249A1
公开(公告)日：2024-04-25
申请号：US18279595
申请日：2021-03-01
发明人： Shota ORIHASHI , Masato SAWADA
IPC分类号： G06N20/00
CPC分类号： G06N20/00
摘要： A learning device (10) according to the present disclosure includes a data set division unit (11) as a training data processing unit and a divided data set learning unit (12) as a model learning unit. The data set division unit (11) divides a new training data set into a plurality of divided data sets on the basis of attribute information. After performing model learning processing using an existing model as a learning target model, the divided data set learning unit (12) creates a new model by repeating the model learning processing until all the divided data sets are learned using a learned model created by the model learning processing as a new learning target model.
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公开(公告)号：US20240232706A9
公开(公告)日：2024-07-11
申请号：US18279583
申请日：2021-03-01
发明人： Shota ORIHASHI , Masato SAWADA
IPC分类号： G06N20/00
CPC分类号： G06N20/00
摘要： A support device according to the present disclosure includes a label inference unit that infers inference labels that are labels corresponding to elements included in training data in which elements and correct labels corresponding to the elements are associated with each other using a model that is learned using the training data and infers labels corresponding to the elements, and an evaluation unit that generates training data confirmation screens including elements included in the training data, correct labels of the elements, and inference labels of the elements.
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公开(公告)号：US20240135104A1
公开(公告)日：2024-04-25
申请号：US18279584
申请日：2021-03-01
发明人： Shota ORIHASHI , Masato SAWADA
IPC分类号： G06F40/295 , G06F40/157
CPC分类号： G06F40/295 , G06F40/157
摘要： A word selection support device according to the present disclosure includes processing circuitry configured to derive, for each extracted unknown word that is a term that is extracted from a target corpus and is not registered in dictionary data, statistical information regarding the extracted unknown word in a plurality of corpuses including the target corpus, and calculate appropriateness as a registered unknown word possibility that is a possibility of an unknown word to be registered in the dictionary data for each of the extracted unknown word on the basis of the statistical information.
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公开(公告)号：US20240303265A1
公开(公告)日：2024-09-12
申请号：US18279592
申请日：2021-03-01
发明人： Shota ORIHASHI , Masato SAWADA
CPC分类号： G06F16/353 , G10L15/26 , G10L17/02
摘要： A label assignment support device according to the present disclosure includes a preliminary label estimation unit that assigns preliminary labels for each of a plurality of elements, a label assignment work screen output unit that generates a label assignment work screen for each of the plurality of elements and an update operation for labels assigned to the plurality of elements by a user, the label assignment work screen indicating each of the plurality of elements and labels assigned to each of the plurality of elements in association with each other, and a label update unit that, when a label assigned to one of the elements is updated by the update operation via the label assignment work screen, assigns the label after update to the one of the elements.
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公开(公告)号：US20240232707A9
公开(公告)日：2024-07-11
申请号：US18279595
申请日：2021-03-01
发明人： Shota ORIHASHI , Masato SAWADA
IPC分类号： G06N20/00
CPC分类号： G06N20/00
摘要： A learning device (10) according to the present disclosure includes a data set division unit (11) as a training data processing unit and a divided data set learning unit (12) as a model learning unit. The data set division unit (11) divides a new training data set into a plurality of divided data sets on the basis of attribute information. After performing model learning processing using an existing model as a learning target model, the divided data set learning unit (12) creates a new model by repeating the model learning processing until all the divided data sets are learned using a learned model created by the model learning processing as a new learning target model.
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公开(公告)号：US20240144057A1
公开(公告)日：2024-05-02
申请号：US18279590
申请日：2021-03-01
发明人： Shota ORIHASHI , Masato SAWADA
IPC分类号： G06N5/046
CPC分类号： G06N5/046
摘要： A training data confirmation support device according to the present disclosure includes a label inference unit that infers inference labels that are labels corresponding to elements included in training data in which elements and correct labels corresponding to the elements are associated with each other using a model that is learned using the training data and infers labels corresponding to the elements, and an evaluation unit that generates evaluation results of the training data creators on the basis of comparison between correct labels corresponding to elements included in the training data and inference labels of the elements.
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公开(公告)号：US20240135248A1
公开(公告)日：2024-04-25
申请号：US18279583
申请日：2021-03-01
发明人： Shota ORIHASHI , Masato SAWADA
IPC分类号： G06N20/00
CPC分类号： G06N20/00
摘要： A support device according to the present disclosure includes a label inference unit that infers inference labels that are labels corresponding to elements included in training data in which elements and correct labels corresponding to the elements are associated with each other using a model that is learned using the training data and infers labels corresponding to the elements, and an evaluation unit that generates training data confirmation screens including elements included in the training data, correct labels of the elements, and inference labels of the elements.
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公开(公告)号：US20210307699A1
公开(公告)日：2021-10-07
申请号：US17352499
申请日：2021-06-21
发明人： Keitaro HORIKAWA , Yoshitaka NAKAMURA , Masato SAWADA , Akihiro YAMANAKA , Shingo TSUKADA , Toshiya YAMADA
摘要： A wearable device attached to a subject includes an accelerometer that measures acceleration information, and a biological sensor that measures biological signal information of the subject. From the measured acceleration information and biological signal information, first feature data corresponding to a first predetermined period and second feature data corresponding to a second predetermined period are extracted. By machine learning based on the first feature data, a dynamic/static activity identification model, a dynamic-activity identification model, and a static-activity identification model, for the subject, are generated. By combination of results of determination based on each of the identification models, a posture and an activity of the subject are identified and correspondence information, which associates the identified posture and activity with the biological signal information of the subject, is generated.
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10.发明申请公开(公告)号：US20180064397A1
公开(公告)日：2018-03-08
申请号：US15551331
申请日：2016-02-16
发明人： Keitaro HORIKAWA , Yoshitaka NAKAMURA , Masato SAWADA , Akihiro YAMANAKA , Shingo TSUKADA , Toshiya YAMADA
CPC分类号： A61B5/721 , A61B5/021 , A61B5/024 , A61B5/02405 , A61B5/0456 , A61B5/1116 , A61B5/1118 , A61B5/1123 , A61B5/16 , A61B5/4035 , A61B5/6804 , A61B5/7267 , A61B5/7282 , A61B2503/10 , A61B2503/12 , A61B2505/09 , G16H20/30 , G16H50/30
摘要： A wearable device attached to a subject includes an accelerometer that measures acceleration information, and a biological sensor that measures biological signal information of the subject. From the measured acceleration information and biological signal information, first feature data corresponding to a first predetermined period and second feature data corresponding to a second predetermined period are extracted. By machine learning based on the first feature data, a dynamic/static activity identification model, a dynamic-activity identification model, and a static-activity identification model, for the subject, are generated. By combination of results of determination based on each of the identification models, a posture and an activity of the subject are identified, and correspondence information, which associates the identified posture and activity with the biological signal information of the subject, is generated.
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