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公开(公告)号：US20250094713A1
公开(公告)日：2025-03-20
申请号：US18967529
申请日：2024-12-03
Inventor： Shuohuan WANG , Yekun CHAI , Siyu DING , Junyuan SHANG , Zhenyu ZHANG , Yu SUN , Hao TIAN , Hua WU , Haifeng WANG
IPC: G06F40/284 , G06F16/3329
Abstract: A multimodal data generation method is provided. The method includes: inputting a query data sequence into a multimodal model, to obtain a plurality of tokens in a response data sequence, where a current token is generated through the following operations: inputting the query data sequence and a current response data sequence into the multimodal model, so that the multimodal model generates the current token based on the query data sequence and the current response data sequence, in response to determining that the current token belongs to a first data modality; or inputting the query data sequence and a current response data sequence into the multimodal model, so that the multimodal model denoises an initial token sequence based on the query data sequence and the current response data sequence, to generate a result token sequence, in response to determining that the current token belongs to a second data modality.
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公开(公告)号：US20220300697A1
公开(公告)日：2022-09-22
申请号：US17835717
申请日：2022-06-08
Inventor： Yukun LI , Han ZHANG , Weichong YIN , Dongling XIAO , Yu SUN , Hao TIAN
Abstract: A method for generating a target object is provided. A first discrete encoded sequence corresponding to an original object is generated by performing discrete encoding on the original object. The original object is of an image type, a text type, or a text-image-combined type. A second discrete encode sequence is obtained by inputting the first discrete encoded sequence into a generative model. A target object is generated based on the second discrete encoded sequence. The target object is of an image type or a text type. When the original object is of the image type, the target object is of the text type. When the original object is of the text type, the target object is of the image type.
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3.发明公开公开(公告)号：US20240086717A1
公开(公告)日：2024-03-14
申请号：US18098514
申请日：2023-01-18
Inventor： Ji LIU , Hao TIAN , Ruipu ZHOU , Dejing DOU
IPC: G06N3/098
CPC classification number: G06N3/098
Abstract: Disclosed is a model training control method based on asynchronous federated learning, an electronic device and a storage medium, relating to data processing technical field, and especially to technical fields such as edge computing and machine learning. The method includes: sending a first parameter of a first global model to a plurality of edge devices; receiving a second parameter of a second global model returned by a first edge device of plurality of edge devices, the second global model being a global model obtained after the first edge device trains the first global model according to a local data set; and sending a third parameter of a third global model to a second edge device of the plurality of edge devices in a case of the third global model is obtained based on aggregation of at least one second global model.
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公开(公告)号：US20240028909A1
公开(公告)日：2024-01-25
申请号：US18478833
申请日：2023-09-29
IPC: G06N3/096
CPC classification number: G06N3/096
Abstract: A data generation method based on a deep learning model and a training method is provided. The data generation method includes: determining an initial input of the deep learning model based on input data; obtaining a first output of the model, where in response to the model determining that generating a reply based on the initial input requires calling a first functional component different from the deep learning model, the first output includes a first token for calling the first functional component and a first intermediate inquiry determined based on the initial input and recognizable by the first functional component; obtaining a first intermediate result determined by the first functional component based on the first intermediate inquiry; determining a second input for the model based on the initial input and the first intermediate result; and obtaining a second output of the model for generating a reply to the initial input.
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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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Patent Agency Ranking
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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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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10.发明申请公开(公告)号：US20250094460A1
公开(公告)日：2025-03-20
申请号：US18969597
申请日：2024-12-05
Inventor： Haifeng WANG , Hua WU , Hao TIAN , Jing LIU , Ruiqing ZHANG , Yan CHEN , Yu RAN
IPC: G06F16/3329 , G06F16/3332 , G06F16/334
Abstract: A query answering method, an electronic device, a storage medium, and an intelligent agent are provided, which relate to a field of artificial intelligence technology, and in particular to fields of large model, intelligent search and information processing technology. The method includes: inputting, in response to a retrieval content set retrieved based on a query, the query, the retrieval content set and prompt information for answer generation into the large model, so that the large model performs operations of: processing, based on a current task in the prompt information and the query, a current text corresponding to the retrieval content set to obtain a processed text, where the current task is determined based on a task execution order in the prompt information; and obtaining, in a case of determining that the processed text meets a preset condition, an answer to the query based on the processed text.
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