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公开(公告)号：US20250021610A1
公开(公告)日：2025-01-16
申请号：US18901831
申请日：2024-09-30
Inventor： Zeyang LEI , Siqi BAO , Hua WU , Haifeng WANG
IPC: G06F16/9535
Abstract: A human-machine interaction solution which relates to the field of artificial intelligence technologies, such as natural language processing technologies, large language models, deep learning technologies, or the like, is proposed. The solution may include: acquiring a question input by a user during a conversation with a large language model; retrieving memory information in a memory bank, the memory information being historical memory information about the user; and in response to retrieved memory information required for generating answer information corresponding to the question, taking the retrieved memory information as matched memory information, and generating the answer information by the large language model in conjunction with the matched memory information.
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公开(公告)号：US20240419484A1
公开(公告)日：2024-12-19
申请号：US18817035
申请日：2024-08-27
IPC: G06F9/48
Abstract: A method for processing information is provided. The method includes obtaining input information to be processed. The method further includes determining execution information associated with processing of the input information. The execution information includes at least one of memory information to be retrieved or tool information to be invoked. The method further includes obtaining, by using the execution information, at least one piece of processing result information corresponding to the processing of the input information. The method further includes the at least one piece of processing result information to generate output information for feedback.
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公开(公告)号：US20230252354A1
公开(公告)日：2023-08-10
申请号：US18179627
申请日：2023-03-07
Inventor： Junyuan SHANG , Shuohuan WANG , Siyu DING , Yanbin ZHAO , Chao PANG , Yu SUN , Hao TIAN , Hua WU , Haifeng WANG
IPC: G06N20/00 , G06F40/40 , G06F40/279
CPC classification number: G06N20/00 , G06F40/40 , G06F40/279
Abstract: A method for pre-training a language model includes: constructing a pre-training language data set, in which the pre-training language data set comprises unsupervised language data and supervised language data; generating a hierarchical multi-template and multi-task language data set based on the pre-training language data set; and pre-training the language model based on the hierarchical multi-template and multi-task language data set.
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公开(公告)号：US20230214423A1
公开(公告)日：2023-07-06
申请号：US18174481
申请日：2023-02-24
Inventor： Haifeng WANG , Hao TIAN , Xinyan XIAO , Xing LI , Tian WU
IPC: G06F16/783 , G06F16/73 , G06N3/0895 , G06F40/30 , G06F40/40 , G06F40/295 , G10L15/22 , G10L15/18 , G10L15/16 , G10L25/57
CPC classification number: G06F16/7844 , G06F16/73 , G06F40/30 , G06F40/40 , G06F40/295 , G06N3/0895 , G10L15/16 , G10L15/22 , G10L15/1815 , G10L25/57
Abstract: A video generation method is provided. The video generation method includes: obtaining global semantic information and local semantic information of a text, where the local semantic information corresponds to a text fragment in the text, searching, based on the global semantic information, a database to obtain at least one first data corresponding to the global semantic information; searching, based on the local semantic information, the database to obtain at least one second data corresponding to the local semantic information; obtaining, based on the at least one first data and the at least one second data, a candidate data set; matching, based on a relevancy between each of at least one text fragment and corresponding candidate data in the candidate data set, target data for the at least one text fragment; and generating, based on the target data matched with each of the at least one text fragment, a video.
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公开(公告)号：US20230147798A1
公开(公告)日：2023-05-11
申请号：US18052143
申请日：2022-11-02
Inventor： Haifeng WANG , Hao TIAN , Jing LIU , Hua WU , Tian WU , Yu SUN , Qiaoqiao SHE
CPC classification number: G06F16/3347 , G06F40/30
Abstract: A method is provided. The method includes converting a search request of a user into a first request semantic vector. The method further includes searching a search resource database for at least one first data semantic vector matched with the first request semantic vector, wherein the search resource database is constructed as a semantic vector space in which different types of data are converted into corresponding data semantic vectors, and the different types of data include at least texts, pictures and videos. The method further includes generating, based on the at least one first data semantic vector, a search result.
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Patent Agency Ranking
公开(公告)号：US20230130006A1
公开(公告)日：2023-04-27
申请号：US18145724
申请日：2022-12-22
Inventor： Dongliang HE , Errui DING , Haifeng WANG
IPC: G06V20/40 , G06V10/774 , G06V10/86 , G06F16/73 , G06F16/783
Abstract: The present application provides a method of processing a video, a method of querying a video, and a method of training a video processing model. A specific implementation solution of the method of processing the video includes: extracting, for a video to be processed, a plurality of video features under a plurality of receptive fields; extracting a local feature of the video to be processed according to a video feature under a target receptive field in the plurality of receptive fields; obtaining a global feature of the video to be processed according to a video feature under a largest receptive field in the plurality of receptive fields; and merging the local feature and the global feature to obtain a target feature of the video to be processed.
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27.发明申请公开(公告)号：US20230120253A1
公开(公告)日：2023-04-20
申请号：US18082997
申请日：2022-12-16
Inventor： Jie Li , Haojie LIU , Yan ZHANG , Xuecen SHEN , Ruizhi CHEN , Chen ZHAO , Yuqiao TENG , Errui DING , Tian WU , Haifeng WANG
Abstract: A method and apparatus for generating a virtual character, an electronic device and a computer readable storage medium are provided. The method includes: performing mesh simplification on an initial model of a virtual character to obtain a mesh-simplified model; obtaining a first target model by performing white model mapping rendering on an area of each material type on the mesh-simplified model, and obtaining a second target model by performing hyper-realistic rendering on the area of each material type on the mesh-simplified model; and establishing a bidirectional mapping between the first target model and the second target model, and obtaining a target virtual character through iterative updating of the bidirectional mapping.
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28.发明申请公开(公告)号：US20230061398A1
公开(公告)日：2023-03-02
申请号：US17984034
申请日：2022-11-09
Inventor： Shangwen LYU , Hongyu LI , Jing LIU , Hua WU , Haifeng WANG
IPC: G06V30/19 , G06V30/412 , G06V30/194 , G06F40/205
Abstract: A method for training a document reading comprehension model includes: acquiring a question sample and a rich-text document sample, in which the rich-text document sample includes a real answer of the question sample; acquiring text information and layout information of the rich-text document sample by performing OCR processing on image information of the rich-text document sample; acquiring a predicted answer of the question sample by inputting the text information, the layout information and the image information of the rich-text document sample into a preset reading comprehension model; and training the reading comprehension model based on the real answer and the predicted answer. The method may enhance comprehension ability of the reading comprehension model to the long rich-text document, and save labor cost.
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公开(公告)号：US20220129768A1
公开(公告)日：2022-04-28
申请号：US17646851
申请日：2022-01-03
Inventor： Dongling XIAO , Yukun LI , Han ZHANG , Yu SUN , Hao TIAN , Hua WU , Haifeng WANG
IPC: G06N5/02
Abstract: The present disclosure provides a method and apparatus for training a model. The method can include: acquiring at least one paragraph text, each paragraph text comprising a plurality of fine-grained samples; processing a fine-grained sample in the each paragraph text to obtain a coarse-grained sample; annotating the coarse-grained sample in the each paragraph text and obscuring one coarse-grained sample using a mask of one fine-grained sample to obtain a training sample set, wherein the training sample set comprises a plurality of annotated texts, and each annotated text comprises at least one of a fine-grained sample or an annotated coarse-grained sample; and training a fine-grained model using the training sample set to obtain a trained fine-grained model, the fine-grained model being used to learn content of a previous fine grain size and predict content of an adjacent coarse grain size.
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30.发明申请公开(公告)号：US20220004930A1
公开(公告)日：2022-01-06
申请号：US17480292
申请日：2021-09-21
Inventor： Qingqing DANG , Kaipeng DENG , Lielin JIANG , Sheng GUO , Xiaoguang HU , Chunyu ZHANG , Yanjun MA , Tian WU , Haifeng WANG
Abstract: Embodiments of the present disclosure provide a method and apparatus of training a model, an electronic device, a storage medium and a development system, which relate to a field of deep learning. The method may include calling a training preparation component to set at least a loss function and an optimization function for training the model, in response to determining that a training preparation instruction is received. The method further includes calling a training component to set a first data reading component, in response to determining that a training instruction is received. The first data reading component is configured to load a training data set for training the model. In addition, the method may further include training the model based on the training data set from the first data reading component, by using the loss function and the optimization function through the training component.
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