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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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13.发明公开公开(公告)号：US20240303430A1
公开(公告)日：2024-09-12
申请号：US18667504
申请日：2024-05-17
Inventor： Meng TIAN , Lin YANG , Xinwei FENG , Zhifan FENG , Xiaopeng CUI , Qiaoqiao SHE , Hua WU
IPC: G06F40/20
CPC classification number: G06F40/20
Abstract: A technical solution for processing a model generation result, which relates to the field of artificial intelligence technologies is disclosed. An implementation includes: disassembling a text generation result of a generative large model to obtain a plurality of result logic units; wherein each result logic unit includes a segment in the text generation result; each segment is capable of independently identifying one premise or conclusion in a logical inference relationship of the text generation result; and the text generation result is a response result generated by the generative large model based on text input information; generating a logical inference graph capable of characterizing a logical inference relationship among the plurality of result logic units based on the plurality of result logic units; and determining whether logical inference of generation of the text generation result by the generative large model is correct or not based on the logical inference graph.
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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：US20230196026A1
公开(公告)日：2023-06-22
申请号：US18109813
申请日：2023-02-14
Inventor： Xiyang WANG , Ruiqing ZHANG , Zhongjun HE , Zhi LI , Hua WU
IPC: G06F40/30 , G06F40/289
CPC classification number: G06F40/30 , G06F40/289
Abstract: A method for evaluating a text content, which may include: after splitting a to-be-evaluated text into a plurality of clauses arranged in sequence according to punctuation information of the to-be-evaluated text, determining a first clause of the plurality of clauses as an actual tune name; then, determining actual prosodic information based on a Chinese phonetic alphabet text of a third clause to a last clause in response to that a number of clauses, whose numbers of Chinese characters satisfy character count requirements of clauses corresponding to the actual tune name, from the third clause to the last clause exceeds a number threshold; and finally, in response to the actual prosodic information being consistent with a standard prosodic information of the actual tune name, evaluating the to-be-evaluated text as a Ci-poetry text.
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Patent Agency Ranking
公开(公告)号：US20230153548A1
公开(公告)日：2023-05-18
申请号：US17885152
申请日：2022-08-10
Inventor： Ruiqing ZHANG , Xiyang WANG , Zhongjun HE , Zhi LI , Hua WU
IPC: G06F40/58
CPC classification number: 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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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：US20230076471A1
公开(公告)日：2023-03-09
申请号：US17982965
申请日：2022-11-08
Inventor： Xiyang WANG , Ruiqing ZHANG , Zhongjun HE , Zhi LI , Hua WU
Abstract: A training method, a text translation method, an electronic device, and a storage medium, which relate to a field of artificial intelligence, in particular to fields of natural language processing and deep learning technologies. A specific implementation solution includes: performing a feature extraction on source sample text data to obtain a sample feature vector sequence; obtaining a target sample feature vector according to the sample feature vector sequence; performing an autoregressive decoding and a non-autoregressive decoding on the sample feature vector sequence, respectively; performing a length prediction on the target sample feature vector; training a predetermined model by using translation sample data, the autoregressive text translation result, the non-autoregressive text translation result, a true length value of the source sample text, the first predicted length value, a true length value of the translation sample text, and the second predicted length value to obtain the text translation model.
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19.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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