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公开(公告)号：US11151182B2
公开(公告)日：2021-10-19
申请号：US16596938
申请日：2019-10-09
Applicant: Huawei Technologies Co., Ltd.
Inventor： Yasheng Wang , Yang Zhang , Shuzhan Bi , Youliang Yan
Abstract: A classification model training method includes obtaining a positive training set and a first negative training set, where the positive training set includes samples of a positive sample set in a corpus, where the first negative training set includes samples of an unlabeled sample set in the corpus, training, using the positive training set and the first negative training set, to obtain a first classification model, determining, using the first classification model, a pseudo negative sample in the first negative training set, removing the pseudo negative sample from the first negative training set, updating the first negative training set to a second negative training set, and training, using the positive training set and the second negative training set, to obtain a target classification model.
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公开(公告)号：US11210292B2
公开(公告)日：2021-12-28
申请号：US16396381
申请日：2019-04-26
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Yasheng Wang , Yang Zhang , Hongbo Zhang
IPC: G06F16/00 , G06F16/2455 , G16H10/60
Abstract: Embodiments of the present invention relate to the field of computer technologies, and provide a search method and apparatus to resolve a problem that a reference text, of a text in a professional field, that is determined by using the prior art has relatively low accuracy. The method includes: obtaining n named entities in a current to-be-analyzed target case (S300); determining a first characteristic and a second characteristic (S301); generating, based on the first characteristic and the second characteristic and according to a preset vector generation rule, a target characteristic vector corresponding to the target case (S302); obtaining each historical case in a database and a characteristic vector corresponding to each historical case (S303); and separately calculating a similarity between the target characteristic vector and the characteristic vector corresponding to each historical case, and selecting a historical case whose similarity result meets a preset condition as a reference case (S304).
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公开(公告)号：US20240192736A1
公开(公告)日：2024-06-13
申请号：US18583820
申请日：2024-02-21
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Yaqin Hong , Biaoke Zhong , Shengli Hu , Yangming Lin , Shuqin Xie , Yake Zou , Yang Zhang , Kenji Nagai , Yuji HAZAMA , Shiquan Yang , Zhongliang Zhang
CPC classification number: G06F1/1681 , F16C11/04 , G06F1/1624
Abstract: A support assembly, includes a rotating shaft mechanism and a support portion. The rotating shaft mechanism includes a rotating assembly. The rotating assembly includes a first fastening piece, a second fastening piece, a connecting piece, and a connection rod assembly. The first fastening piece and the second fastening piece are located on a same side of the connecting piece. A first sliding slot is disposed on an end face that is of the first fastening piece and that faces the second fastening piece, and a second sliding slot is disposed on an end face that is of the second fastening piece and that faces the first fastening piece. The support portion includes a first support board, a second support board, and a third support board. The second support board is rotatively connected to the first support board and the third support board.
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Patent Agency Ranking
公开(公告)号：US11900959B2
公开(公告)日：2024-02-13
申请号：US17451061
申请日：2021-10-15
Applicant: Huawei Technologies Co., Ltd.
Inventor： Yang Zhang , Oxana Verkholyak , Alexey Karpov , Li Qian
IPC: G10L25/63 , G10L25/30 , G10L19/038 , G10L15/04 , G10L15/16
CPC classification number: G10L25/63 , G10L15/16 , G10L15/04 , G10L19/038 , G10L25/30
Abstract: A plurality of pieces of emotional state information corresponding to a plurality of speech frames in a current utterance are obtained based on a first neural network model; statistical operation is performed on the plurality of pieces of emotional state information, to obtain a statistical result, and then the emotional state information corresponding to the current utterance is obtained based on a second neural network device, the statistical result corresponding to the current utterance, and statistical results corresponding to a plurality of utterances before the current utterance.
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公开(公告)号：US11630957B2
公开(公告)日：2023-04-18
申请号：US16807997
申请日：2020-03-03
Applicant: Huawei Technologies Co., Ltd.
Inventor： Yasheng Wang , Jiansheng Wei , Yang Zhang
IPC: G06F40/30 , G06F40/242 , G06N20/00
Abstract: A natural language processing method includes obtaining a to-be-processed phrase, where the to-be-processed phrase includes M words, determining polarity characteristic information of m to-be-processed words in the M words, where polarity characteristic information of an ith word in the m to-be-processed words includes n polarity characteristic values, and each polarity characteristic value corresponds to one sentiment polarity, determining a polarity characteristic vector of the to-be-processed phrase based on the polarity characteristic information of the m to-be-processed words, where the polarity characteristic vector includes n groups of components in a one-to-one correspondence with n sentiment polarities, and determining a sentiment polarity of the to-be-processed phrase based on the polarity characteristic vector of the to-be-processed phrase using a preset classifier, and outputting the sentiment polarity.
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公开(公告)号：US20220036916A1
公开(公告)日：2022-02-03
申请号：US17451061
申请日：2021-10-15
Applicant: Huawei Technologies Co., Ltd.
Inventor： Yang Zhang , Oxana Verkholyak , Alexey Karpov , Li Qian
Abstract: A plurality of pieces of emotional state information corresponding to a plurality of speech frames in a current utterance are obtained based on a first neural network model; statistical operation is performed on the plurality of pieces of emotional state information, to obtain a statistical result, and then the emotional state information corresponding to the current utterance is obtained based on a second neural network device, the statistical result corresponding to the current utterance, and statistical results corresponding to a plurality of utterances before the current utterance.
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公开(公告)号：US20200042829A1
公开(公告)日：2020-02-06
申请号：US16596938
申请日：2019-10-09
Applicant: Huawei Technologies Co., Ltd.
Inventor： Yasheng Wang , Yang Zhang , Shuzhan Bi , Youliang Yan
IPC: G06K9/62
Abstract: A classification model training method includes obtaining a positive training set and a first negative training set, where the positive training set includes samples of a positive sample set in a corpus, where the first negative training set includes samples of an unlabeled sample set in the corpus, training, using the positive training set and the first negative training set, to obtain a first classification model, determining, using the first classification model, a pseudo negative sample in the first negative training set, removing the pseudo negative sample from the first negative training set, updating the first negative training set to a second negative training set, and training, using the positive training set and the second negative training set, to obtain a target classification model.
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公开(公告)号：USD828819S1
公开(公告)日：2018-09-18
申请号：US29574612
申请日：2016-08-17
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Designer： Shuang Li , Liang Pan , Yang Zhang
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