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11.发明申请公开(公告)号：US20250094739A1
公开(公告)日：2025-03-20
申请号：US18968830
申请日：2024-12-04
Inventor： Zhongjun He , Hua Wu , Haifeng Wang
Abstract: An information processing method. The method includes obtaining a first bilingual sentence pair, in which the first bilingual sentence pair comprises a source language sentence and a target language sentence; and obtaining a distilled second bilingual sentence pair by distilling a first language sentence in the first bilingual sentence pair based on a large language model (LLM), in which the first language sentence is the source language sentence or the target language sentence.
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12.发明授权公开(公告)号：US12210982B2
公开(公告)日：2025-01-28
申请号：US17872318
申请日：2022-07-25
Inventor： Wenbin Jiang , Yajuan Lyu , Yong Zhu , Hua Wu , Haifeng Wang
Abstract: The present disclosure provides a method for processing intelligent question-answering, an intelligent question-answering system, an electronic device and a storage medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, natural language processing technologies, or the like. An implementation includes: acquiring an input question and input data information; and based on the question, the data information and a plurality of knowledge bases, deciding an answer to the question by multilayer appreciation using a plurality of understanding module layers.
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公开(公告)号：US12210956B2
公开(公告)日：2025-01-28
申请号：US18074853
申请日：2022-12-05
Inventor： Ruiqing Zhang , Hui Liu , Zhongjun He , Zhi Li , Hua Wu
Abstract: The present disclosure provides a translation method and apparatus, an electronic device, and a non-transitory storage medium. An implementation includes: determining an encoded feature of a sentence to be translated by an encoding module; determining, by a graph network module, a knowledge fusion feature of the sentence to be translated based on a preset graph network, wherein the preset graph network is constructed based on a polysemous word in a source language corresponding to the sentence to be translated and a plurality of translated words corresponding to the polysemous word in a target language; determining, by a decoding network, a translated sentence corresponding to the sentence to be translated based on the encoded feature and the knowledge fusion feature.
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公开(公告)号：US12197882B2
公开(公告)日：2025-01-14
申请号：US17885152
申请日：2022-08-10
Inventor： Ruiqing Zhang , Xiyang Wang , Zhongjun He , Zhi Li , Hua Wu
IPC: G06F40/58
Abstract: A translation method, an electronic device and a storage medium, which relate to the field of artificial intelligence technologies, such as machine learning technologies, information processing technologies, are disclosed. An implementation includes: acquiring an intermediate translation result generated by each of multiple pre-trained translation models for a to-be-translated specified sentence in a same iteration of a translation process, so as to obtain multiple intermediate translation results; acquiring a co-occurrence word based on the multiple intermediate translation results; and acquiring a target translation result of the specified sentence based on the co-occurrence word.
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15.发明授权公开(公告)号：US12293300B2
公开(公告)日：2025-05-06
申请号：US17930221
申请日：2022-09-07
Inventor： Yingqi Qu , Yuchen Ding , Jing Liu , Hua Wu , Haifeng Wang
IPC: G06N5/01 , G06F16/2457 , G06F40/30
Abstract: The disclosure provides a method for training a semantic retrieval network, an electronic device and a storage medium. The method includes: obtaining a training sample including a search term and n candidate files corresponding to the search term, where n is an integer greater than 1; inputting the training sample into the ranking model, to obtain n first correlation degrees output by the ranking model, in which each first correlation degree represents a correlation between a candidate document and the search term; inputting the training sample into the semantic retrieval model, to obtain n second correlation degrees output by the semantic retrieval model, wherein each second correlation degree represents a correlation between a candidate document and the search term; and training the semantic retrieval model and the ranking model jointly based on the n first correlation degrees and the n second correlation degrees.
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Patent Agency Ranking
公开(公告)号：US12277401B2
公开(公告)日：2025-04-15
申请号：US17502108
申请日：2021-10-15
Inventor： Guocheng Niu , Wei Li , Can Gao , Xinyan Xiao , Hua Wu
IPC: G06F18/25 , G06F40/205 , G06F40/47 , G06F40/58 , G06N3/02
Abstract: The present disclosure discloses a method and apparatus for acquiring a pre-trained model, and relates to natural language processing and deep learning technologies in the field of artificial intelligence technologies. An implementation includes: acquiring training data, the training data including a single-modal language material and a multi-modal language material, and the multi-modal language material including a language material pair formed by a first-modal language material and a second-modal language material; and performing a multi-task training operation on a pre-trained model using the training data, the multi-task including at least one cross-modal contrastive learning task and at least one single-modal learning task; the pre-trained language model obtained in the present disclosure may learn from different forms of language materials, i.e., the single-modal language material and the multi-modal language material, such that the pre-trained language model may effectively process information in various modals.
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公开(公告)号：US12265842B2
公开(公告)日：2025-04-01
申请号：US18817035
申请日：2024-08-27
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：US12236203B2
公开(公告)日：2025-02-25
申请号：US17951216
申请日：2022-09-23
Inventor： Ruiqing Zhang , Xiyang Wang , Hui Liu , Zhongjun He , Zhi Li , Hua Wu
Abstract: A translation method, a model training method, apparatuses, electronic devices and storage mediums, which relate to the field of artificial intelligence technologies, such as machine learning technologies, information processing technologies, are disclosed. In an implementation, a weight for each translation model in at least two pre-trained translation models translating a to-be-translated specified sentence is acquired based on the specified sentence and a pre-trained weighting model; and the specified sentence is translating using the at least two translation models based on the weight for each translation model translating the specified sentence.
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公开(公告)号：US20250054494A1
公开(公告)日：2025-02-13
申请号：US18930081
申请日：2024-10-29
Inventor： Pengzhi Gao , Ruiqing Zhang , Zhongjun He , Hua Wu
Abstract: A method for training a speech translation model includes: obtaining a trained first text translation model and a speech recognition model, and constructing a candidate speech translation model to be trained based on the first text translation model and the speech recognition model; obtaining at least one of a first sample source language speech or a first sample source language text to obtain a training sample of the candidate speech translation model; and training the candidate speech translation model based on the training sample until the training is completed, and obtaining a trained target speech translation model.
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20.发明申请公开(公告)号：US20230004819A1
公开(公告)日：2023-01-05
申请号：US17930221
申请日：2022-09-07
Inventor： Yingqi Qu , Yuchen Ding , Jing Liu , Hua Wu , Haifeng Wang
IPC: G06N5/00 , G06F40/30 , G06F16/2457
Abstract: The disclosure provides a method for training a semantic retrieval network, an electronic device and a storage medium. The method includes: obtaining a training sample including a search term and n candidate files corresponding to the search term, where n is an integer greater than 1; inputting the training sample into the ranking model, to obtain n first correlation degrees output by the ranking model, in which each first correlation degree represents a correlation between a candidate document and the search term; inputting the training sample into the semantic retrieval model, to obtain n second correlation degrees output by the semantic retrieval model, wherein each second correlation degree represents a correlation between a candidate document and the search term; and training the semantic retrieval model and the ranking model jointly based on the n first correlation degrees and the n second correlation degrees.
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