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Patent Agency Ranking
1.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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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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3.发明授权公开(公告)号：US11645316B2
公开(公告)日：2023-05-09
申请号：US17088053
申请日：2020-11-03
IPC: G06F16/33 , G06F16/332 , G06F16/338 , G06N3/08
CPC classification number: G06F16/3346 , G06F16/338 , G06F16/3329 , G06F16/3344 , G06F16/3347 , G06N3/08
Abstract: Provided are a question answering method and language model training method, apparatus, device, and storage media, including: acquiring at least one candidate table matching a question to be queried, each candidate table includes a candidate answer corresponding to the question; processing the at least one candidate table to obtain at least one table text, the table text includes textual content of respective fields in the candidate table; inputting the question and each table text into a preset language model respectively to obtain a degree of matching between the question and each candidate table; and outputting a reply table according to the degree of matching of each candidate table, the reply table is a candidate table out of the at least one candidate table whose degree of matching with the question is greater than a preset value or a candidate table that corresponds to a maximum degree of matching.
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4.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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公开(公告)号：US12300012B2
公开(公告)日：2025-05-13
申请号：US17984034
申请日：2022-11-09
Inventor： Shangwen Lyu , Hongyu Li , Jing Liu , Hua Wu , Haifeng Wang
IPC: G06V30/19 , G06F40/205 , G06V30/194 , G06V30/412
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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