-
1.
公开(公告)号:US20240321011A1
公开(公告)日:2024-09-26
申请号:US18631778
申请日:2024-04-10
发明人: Ryo ISHII , Ryuichiro Higashinaka , Taichi Katayama , Junji Tomita , Nozomi Kobayashi , Kyosuke Nishida
CPC分类号: G06V40/28 , G06N20/00 , G06V40/10 , G10L15/22 , G10L2015/225
摘要: A nonverbal information generation apparatus includes a nonverbal information generation unit that generates time-information-stamped nonverbal information that corresponds to time-information-stamped text feature quantities on the basis of the time-information-stamped text feature quantities and a learned nonverbal information generation model. The time-information-stamped text feature quantities are configured to include feature quantities that have been extracted from text and time information representing times assigned to predetermined units of the text. The nonverbal information is information for controlling an expression unit that expresses behavior that corresponds to the text.
-
2.
公开(公告)号:US11651166B2
公开(公告)日:2023-05-16
申请号:US16977422
申请日:2019-02-22
发明人: Itsumi Saito , Kyosuke Nishida , Hisako Asano , Junji Tomita
摘要: A learning device of a phrase generation model includes a memory; and a processor configured to execute learning the phrase generation model including an encoder and a decoder, by using, as training data, a 3-tuple. The 3-tuple includes a combination of phrases and at least one of a conjunctive expression representing a relationship between the phrases, and a relational label indicating the relationship represented by the conjunctive expression. The encoder is configured to convert a phrase into a vector from a 2-tuple. The 2-tuple includes a phrase and at least one of the conjunctive expression and the relational label. The decoder is configured to generate, from the converted vector and the conjunctive expression or the relational label, a phrase having the relationship represented by the conjunctive expression or the relational label with respect to the phrase.
-
公开(公告)号:US11954435B2
公开(公告)日:2024-04-09
申请号:US17764186
申请日:2020-03-03
发明人: Itsumi Saito , Kyosuke Nishida , Kosuke Nishida , Hisako Asano , Junji Tomita , Atsushi Otsuka
IPC分类号: G06F17/00 , G06F40/279
CPC分类号: G06F40/279
摘要: A text generation apparatus includes a memory and a processor configured to execute acquiring a reference text based on an input text and information different from the input text; and generating a text based on the input text and the reference text, wherein the acquiring and the generating are implemented as neural networks based on learned parameters.
-
公开(公告)号:US11893353B2
公开(公告)日:2024-02-06
申请号:US16977045
申请日:2019-03-04
发明人: Kosuke Nishida , Kyosuke Nishida , Hisako Asano , Junji Tomita
IPC分类号: G06F40/30 , G06F40/253 , G06F40/242 , G06N3/08 , G06N3/045
CPC分类号: G06F40/30 , G06F40/242 , G06F40/253 , G06N3/045 , G06N3/08
摘要: To make it possible to accurately generate a word vector even if vocabulary of a word vector data set is not limited.
In a vector generating device 10 that generates vectors representing an input sentence P, when generating a series of the vectors representing the input sentence P based on vectors corresponding to words included in the input sentence P, a definition-sentence-considered-context encode unit 280 generates, based on a dictionary DB 230 storing sets of headwords y and definition sentences Dy, which are sentences defining the headwords y, concerning a word, which is the headword stored in the dictionary DB, among the words included in the input sentence P, the series of the vectors representing the input sentence P using the definition sentence Dy of the headwords y.-
公开(公告)号:US11989976B2
公开(公告)日:2024-05-21
申请号:US16969765
申请日:2019-02-15
发明人: Ryo Ishii , Ryuichiro Higashinaka , Taichi Katayama , Junji Tomita , Nozomi Kobayashi , Kyosuke Nishida
CPC分类号: G06V40/28 , G06N20/00 , G06V40/10 , G10L15/22 , G10L2015/225
摘要: A nonverbal information generation apparatus includes a nonverbal information generation unit that generates time-information-stamped nonverbal information that corresponds to time-information-stamped text feature quantities on the basis of the time-information-stamped text feature quantities and a learned nonverbal information generation model. The time-information-stamped text feature quantities are configured to include feature quantities that have been extracted from text and time information representing times assigned to predetermined units of the text. The nonverbal information is information for controlling an expression unit that expresses behavior that corresponds to the text.
-
公开(公告)号:US11593436B2
公开(公告)日:2023-02-28
申请号:US16968924
申请日:2019-02-13
IPC分类号: G06F16/9035 , G06F16/9038 , G06F16/9032 , G06F40/35 , G06N5/043 , H04L51/10
摘要: To enable provision of appropriate information for a user query even in a case there are multiple information provision modules which are different in answer generation processing. A query sending unit 212 sends a user query to each one of a plurality of information provision module units 220 that are different in the answer generation processing and that each generate an answer candidate for the user query. An output control unit 214 performs control such that the answer candidate acquired from each one of the plurality of information provision module units 220 is displayed on a display unit 300 on a per-agent basis with information on an agent associated with that information provision module unit 220.
-
公开(公告)号:US11182435B2
公开(公告)日:2021-11-23
申请号:US16461201
申请日:2017-11-20
IPC分类号: G06F16/30 , G06F16/903 , G06F16/9032 , G06N3/04 , G06F16/00
摘要: Taking as input a group of text pairs for learning in which each pair is constituted with a first text for learning and a second text for learning that serves as an answer when a question is made with the first text for learning, a query expansion model is learned so as to generate a text serving as an expanded query for a text serving as a query.
-
公开(公告)号:US12056168B2
公开(公告)日:2024-08-06
申请号:US17795868
申请日:2020-01-29
发明人: Taku Hasegawa , Kyosuke Nishida , Junji Tomita , Hisako Asano
CPC分类号: G06F16/3331 , G06F16/31 , G06N3/02
摘要: A learning apparatus according to an embodiment has a feature generation means configured to take a search query, a first document related to the search query, and a second document that is not related to the search query as input, and generate a feature of the search query, a feature of the first document, and a feature of the second document, by using model parameters of a neural network, and an update means configured to take the feature of the search query, the feature of the first document, and the feature of the second document as input, and update the model parameters by using an error function including a cost function that is a differentiable approximation function of an L0 norm.
-
公开(公告)号:US12026472B2
公开(公告)日:2024-07-02
申请号:US17613417
申请日:2019-05-28
发明人: Yasuhito Osugi , Itsumi Saito , Kyosuke Nishida , Hisako Asano , Junji Tomita
摘要: A generation unit that takes a question Qi that is a word sequence representing a current question in a dialogue, a document P used to generate an answer Ai to the question Qi, a question history {Qi-1, . . . , Qi-k} that is a set of word sequences representing k past questions, and an answer history {Ai-1, . . . , Ai-k} that is a set of word sequences representing answers to the k questions as inputs, and generates the answer Ai by machine reading comprehension in an extractive mode or a generative mode using pre-trained model parameters is provided.
-
公开(公告)号:US11972365B2
公开(公告)日:2024-04-30
申请号:US16972187
申请日:2019-04-25
发明人: Atsushi Otsuka , Kyosuke Nishida , Itsumi Saito , Kosuke Nishida , Hisako Asano , Junji Tomita
IPC分类号: G06F16/2453 , G06F16/903 , G06N5/04 , G06N20/00
CPC分类号: G06N5/04 , G06F16/24534 , G06F16/90335 , G06N20/00
摘要: A question generation device includes: generating means which uses a query and a relevant document including an answer to the query as input and, using a machine learning model having been learned in advance, generates a revised query in which a potentially defective portion of the query is supplemented with a word included in a prescribed lexical set.
-
-
-
-
-
-
-
-
-