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公开(公告)号：US12260492B2
公开(公告)日：2025-03-25
申请号：US18099602
申请日：2023-01-20
Inventor： Di Wang , Ruizhi Chen , Chen Zhao , Jingtuo Liu , Errui Ding , Tian Wu , Haifeng Wang
Abstract: A method for training a three-dimensional face reconstruction model includes inputting an acquired sample face image into a three-dimensional face reconstruction model to obtain a coordinate transformation parameter and a face parameter of the sample face image; determining the three-dimensional stylized face image of the sample face image according to the face parameter of the sample face image and the acquired stylized face map of the sample face image; transforming the three-dimensional stylized face image of the sample face image into a camera coordinate system based on the coordinate transformation parameter, and rendering the transformed three-dimensional stylized face image to obtain a rendered map; and training the three-dimensional face reconstruction model according to the rendered map and the stylized face map of the sample face image.
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公开(公告)号：US12223271B2
公开(公告)日：2025-02-11
申请号：US17874394
申请日：2022-07-27
Inventor： Zeyu Chen , Haifeng Wang , Tian Wu , Dianhai Yu , Yanjun Ma , Xiaoguang Hu
IPC: G06F40/10 , G06F40/284 , G06F40/47
Abstract: Provided are a text processing method, a device and a storage medium, relating to a field of computer technology, and especially to a field of artificial intelligence, such as natural language processing and deep learning. The specific implementation scheme includes: performing text processing on first text, by using a text processing acceleration operator; and processing, in parallel and faster, content after the text processing, by using the text processing acceleration operator. Text processing and parallel acceleration are carried out by the text processing acceleration operator, which can improve the speed of text processing.
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公开(公告)号：US12196572B2
公开(公告)日：2025-01-14
申请号：US17961930
申请日：2022-10-07
Inventor： Deguo Xia , Jizhou Huang , Haifeng Wang
IPC: G01C21/00
Abstract: The present disclosure provides a method and apparatus for automatically producing map data. The method includes: performing track rectification on crowdsourcing tracks based on corresponding standard tracks, and locating each map element included, based on depth information of track point images included in the rectified crowdsourcing tracks; comparing a latest map element obtained based on the rectified crowdsourcing tracks locating and an old map element at a corresponding locating position using a pre-built entity semantic map; determining, in response to a change in the latest map element compared to the old map element, a target processing method according to a processing standard of a changed map element pre-abstracted from a map element update specification; and processing the latest map element according to the target processing method to obtain a processed latest map.
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14.发明授权公开(公告)号：US12118319B2
公开(公告)日：2024-10-15
申请号：US17655772
申请日：2022-03-21
Inventor： Jun Xu , Zeming Liu , Zeyang Lei , Zhengyu Niu , Hua Wu , Haifeng Wang
Abstract: The present disclosure provides a dialog method and system, an electronic device and a storage medium, and relates to the field of artificial intelligence (AI) technologies such as deep learning and natural language processing. A specific implementation scheme involves: rewriting a corresponding dialog state based on received dialog information of a user; determining to-be-used dialog action information based on the dialog information of the user and the dialog state; and generating a reply statement based on the dialog information of the user and the dialog action information. According to the present disclosure, the to-be-used dialog action information can be determined based on the dialog information of the user and the dialog state; and then the reply statement is generated based on the dialog action information, thereby providing an efficient dialog scheme.
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公开(公告)号：US12086555B2
公开(公告)日：2024-09-10
申请号：US17643053
申请日：2021-12-07
Inventor： Jianglu Hu , Hehan Li , Huifeng Sun , Shuqi Sun , Yue Chang , Tingting Li , Hua Wu , Haifeng Wang
IPC: G06F40/35 , G06F16/332
CPC classification number: G06F40/35 , G06F16/3329
Abstract: The disclosure provides a method for generating a dialogue. The method includes: obtaining an input sentence; determining a type of a task-based response sentence that is to be generated, by updating a current dialogue state based on the input sentence; generating the task-based response sentence by inputting the input sentence into a task-based dialogue response generator; and determining the task-based response sentence as a target response sentence in response to the type of the task-based response sentence being a designated type.
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Patent Agency Ranking
公开(公告)号：US20230029687A1
公开(公告)日：2023-02-02
申请号：US17655772
申请日：2022-03-21
Inventor： Jun Xu , Zeming Liu , Zeyang Lei , Zhengyu Niu , Hua Wu , Haifeng Wang
Abstract: The present disclosure provides a dialog method and system, an electronic device and a storage medium, and relates to the field of artificial intelligence (AI) technologies such as deep learning and natural language processing. A specific implementation scheme involves: rewriting a corresponding dialog state based on received dialog information of a user; determining to-be-used dialog action information based on the dialog information of the user and the dialog state; and generating a reply statement based on the dialog information of the user and the dialog action information. According to the present disclosure, the to-be-used dialog action information can be determined based on the dialog information of the user and the dialog state; and then the reply statement is generated based on the dialog action information, thereby providing an efficient dialog scheme.
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公开(公告)号：US11531529B2
公开(公告)日：2022-12-20
申请号：US17500779
申请日：2021-10-13
Inventor： Liujie Zhang , Xiang Lan , Huihuang Zheng , Hongyu Liu , Wei Zhou , Yanjun Ma , Dianhai Yu , Haifeng Wang
Abstract: The present disclosure discloses a method, an apparatus and an electronic device for deploying an operator in a deep learning framework and relates to the field of artificial intelligence technology such as deep learning. And the solution is: acquiring a source file of the operator; compiling the source file of the operator to form a dynamic link library of the operator; generating an interface file transferred from the dynamic link library of the operator; generating an installable library file according to the dynamic link library and the interface file; installing the installable library file to a target programming language library.
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公开(公告)号：US12131728B2
公开(公告)日：2024-10-29
申请号：US17828773
申请日：2022-05-31
Inventor： Siyu Ding , Chao Pang , Shuohuan Wang , Yanbin Zhao , Junyuan Shang , Yu Sun , Shikun Feng , Hao Tian , Hua Wu , Haifeng Wang
CPC classification number: G10L15/063 , G10L15/02 , G10L15/18
Abstract: The present application provides a method of training a natural language processing model, which relates to a field of artificial intelligence, and in particular to a field of natural language processing. A specific implementation scheme includes: performing a semantic learning for multi-tasks on an input text, so as to obtain a semantic feature for the multi-tasks, wherein the multi-tasks include a plurality of branch tasks; performing a feature learning for each branch task based on the semantic feature, so as to obtain a first output result for each branch task; calculating a loss for each branch task according to the first output result for the branch task; and adjusting a parameter of the natural language processing model according to the loss for each branch task. The present application further provides a method of processing a natural language, an electronic device, and a storage medium.
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公开(公告)号：US12019990B2
公开(公告)日：2024-06-25
申请号：US17124030
申请日：2020-12-16
Inventor： Haifeng Wang , Wenbin Jiang , Yajuan Lv , Yong Zhu , Hua Wu
IPC: G06N20/00 , G06F18/214 , G06F18/2413 , G06F40/279 , G06F40/30 , G06N5/022
CPC classification number: G06F40/30 , G06F18/214 , G06F18/24147 , G06F40/279 , G06N5/022
Abstract: The present application discloses a text processing method and device based on natural language processing and a knowledge graph, and relates to the in-depth field of artificial intelligence technology. A specific implementation is: an electronic device uses a joint learning model to obtain a semantic representation, which is obtained by the joint learning model by combining knowledge graph representation learning and natural language representation learning, it combines a knowledge graph representation learning and a natural language representation learning, compared to using only the knowledge graph representation learning or the natural language representation learning to learn semantic representation of a prediction object, factors considered by the joint learning model are more in quantity and comprehensiveness, so accuracy of semantic representation can be improved, and thus accuracy of text processing can be improved.
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公开(公告)号：US20230419592A1
公开(公告)日：2023-12-28
申请号：US18099602
申请日：2023-01-20
Inventor： Di WANG , Ruizhi Chen , Chen Zhao , Jingtuo Liu , Errui Ding , Tian Wu , Haifeng Wang
CPC classification number: G06T15/20 , G06V40/168 , G06T15/04 , G06T15/40 , G06T19/20 , G06V10/82 , G06V10/774 , G06T2219/2004 , G06T2219/2012 , G06T2219/2016
Abstract: A method for training a three-dimensional face reconstruction model includes inputting an acquired sample face image into a three-dimensional face reconstruction model to obtain a coordinate transformation parameter and a face parameter of the sample face image; determining the three-dimensional stylized face image of the sample face image according to the face parameter of the sample face image and the acquired stylized face map of the sample face image; transforming the three-dimensional stylized face image of the sample face image into a camera coordinate system based on the coordinate transformation parameter, and rendering the transformed three-dimensional stylized face image to obtain a rendered map; and training the three-dimensional face reconstruction model according to the rendered map and the stylized face map of the sample face image.
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