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公开(公告)号：US20250005067A1
公开(公告)日：2025-01-02
申请号：US18745859
申请日：2024-06-17
Inventor： Yushen Chen , Guangyao Han , Lei Su , Hongda Yue , Yi Wang , Yunjing An , Lin Chang
IPC: G06F16/383 , G06F16/33 , G06F40/30
Abstract: The disclosure provides a sentence generation method based on a large language model including: obtaining a query sentence, in which the query sentence has at least one candidate description object; performing semantic recognition on the query sentence to obtain first semantic information and second semantic information corresponding to the query sentence, in which categories of the first semantic information and the second semantic information are different; inputting the at least one candidate description object and the first semantic information into the large model, to identify a target description object from the at least one candidate description object based on the large model; selecting target service data from at least one piece of service data corresponding to the target description object based on the second semantic information; and generating a reply sentence corresponding to the query sentence according to the target service data.
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公开(公告)号：US20250005053A1
公开(公告)日：2025-01-02
申请号：US18749305
申请日：2024-06-20
Inventor： Peiguo DONG , Fangfang SHI , Meiyuan DING , Huibin ZHAO
IPC: G06F16/332
Abstract: A method and an apparatus for answer feedback, which are applied to a large language model, are provided. The method includes receiving a question input by a user; generating a candidate answer set of the question by using a pre-trained large language model, and selecting an answer from the candidate answer set as a target answer, and displaying the target answer to the user; in response to receiving a feedback request for the target answer sent by the user: generating a feedback page and displaying the feedback page to the user, where content of the feedback page includes the candidate answer set; determining, in response to receiving an update request sent by the user based on the feedback page, an answer indicated by the update request from the candidate answer set as a new target answer, and displaying the new target answer to the user.
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公开(公告)号：US20250004771A1
公开(公告)日：2025-01-02
申请号：US18755148
申请日：2024-06-26
Inventor： Haifeng WANG , Hua WU , Dai DAI , Jing LIU , Hongyu LI , Gangqiang HU
Abstract: A method, apparatus, device, and medium for generating instruction data is provided. The method includes: obtaining a natural language-based reference instruction to direct a large model to generate response data meeting multiple first requirements; obtaining a structured disassembly result of the reference instruction to derive several reference slots and slot values corresponding to these requirements; determining multiple sample slots and sample slot values based on the reference slots, slot values, and a predetermined rule; and generating a natural language-based sample instruction from these sample slots and values, which directs the large model to generate response data that fulfills multiple second requirements.
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公开(公告)号：US12183323B2
公开(公告)日：2024-12-31
申请号：US17644749
申请日：2021-12-16
Inventor： Xiaoyin Fu , Mingxin Liang , Zhijie Chen , Qiguang Zang , Zhengxiang Jiang , Liao Zhang , Qi Zhang , Lei Jia
IPC: G10L15/02 , G10L15/16 , G10L19/032
Abstract: The present disclosure provides a method of recognizing speech offline, electronic device, and a storage medium, relating to a field of artificial intelligence such as speech recognition, natural language processing, and deep learning. The method may include: decoding speech data to be recognized into a syllable recognition result; transforming the syllable recognition result into a corresponding text as a speech recognition result of the speech data.
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公开(公告)号：US20240430434A1
公开(公告)日：2024-12-26
申请号：US18749540
申请日：2024-06-20
Inventor： Jian ZOU
IPC: H04N19/137 , H04N19/119 , H04N19/154
Abstract: A method and apparatus for predicting motion vector for inter-frame encoding are provided. An implementation scheme of the method includes: acquiring first and second sets of motion vectors; dividing, in response to the number of valid adjacent PUs being greater than or equal to a preset number, the second set of motion vectors into at least one motion vector subset; calculating a correlation between the first set of motion vectors and each motion vector subset respectively, to obtain a priority of each adjacent PU; calculating, sequentially according to the priority in descending order, a rate distortion based on a motion vector of each adjacent PU, and stop calculating until a rate distortion smaller than a predetermined threshold is obtained; and determining a motion vector of an adjacent PU used when the rate distortion smaller than the predetermined threshold is obtained as the motion vector of the current PU.
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Patent Agency Ranking
公开(公告)号：US20240427606A1
公开(公告)日：2024-12-26
申请号：US18026649
申请日：2022-12-29
Inventor： QIANG WANG , ZHANBEI WANG , GUANGJUN LAN
IPC: G06F9/4401 , G06F11/14 , G06F11/30 , H05K7/20
Abstract: In one embodiment, a system monitors a temperature of a computing system for an autonomous driving system (ADS) of the autonomous driving vehicle (ADV). The system determines the monitored temperature is below a predetermined temperature threshold. The system controls a heating module to distribute heat to the computing system. The system determines the temperature of the computing system is above a predetermined temperature threshold. The system initiates a boot up sequence to boot up the computing system, where the temperature of components of the computing system is above the predetermined threshold when the boot up sequence is initiated.
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公开(公告)号：US20240411979A1
公开(公告)日：2024-12-12
申请号：US18749479
申请日：2024-06-20
Inventor： Minlong PENG , Mingming SUN , Yabing SHI
Abstract: A method, apparatus, device, and medium for determining the similarity of text processing tasks is provided. The method includes: determining a first task, a second task, and a neural network, the neural network includes a plurality of network modules and a plurality of importance coefficients corresponding to the plurality of network modules, and the importance coefficients are used to scale output values of a corresponding network module; respectively performing a target operation using the first task and the second task as a target task to obtain an embedding feature of the first task and an embedding feature of the second task; and determining the task similarity between the first task and the second task based on the embedding features. The target operation includes: training using text samples and obtaining a plurality of trained importance coefficients; and determining an embedding feature of the target task based on trained importance coefficients.
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公开(公告)号：US20240411790A1
公开(公告)日：2024-12-12
申请号：US18747415
申请日：2024-06-18
Inventor： Zhuangzhuang Cui , Bo Fu
IPC: G06F16/332 , G06F16/33 , G06F16/35 , G06F40/186 , G06F40/30
Abstract: The present disclosure provides an answer information generation method and apparatus based on a large language model, and a device, and relates to the technical field of artificial intelligence, and in particular, to the fields of document retrieval, natural language processing, and large language models. An implementation solution includes: obtaining, in response to receiving a question text from a user, a semantic vector of the question text and event information related to a specific field; obtaining a plurality of candidate documents from a document library of the specific field based on at least two of the semantic vector of the question text, the at least one piece of argument information and the event category; determining quality evaluation information for a candidate document in the plurality of candidate documents based on the event category; and determining at least one target document from the plurality of candidate documents based on the quality evaluation information of the candidate document and a correlation between the candidate document and the question text, to obtain, based on the at least one target document, answer information used to answer the question text.
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公开(公告)号：US20240403344A1
公开(公告)日：2024-12-05
申请号：US18803085
申请日：2024-08-13
Inventor： Wanpeng NIU , Junwei XING , Sai GAO , Haonan FANG , Hui LI , Bingfei ZHANG
Abstract: There is provided a code retrieval method and apparatus based on a large language model, an electronic device and a readable storage medium, which relates to the field of artificial intelligence technologies, such as large language model technologies, big data technologies, cloud service technologies, or the like. The method for code retrieval based on a large language model includes: acquiring a code retrieval query to obtain a retrieval vector of the code retrieval query; acquiring a first index of a target code library, the first index including a plurality of code blocks and a plurality of code block vectors; acquiring a target code block according to the retrieval vector and the first index; acquiring a second index of the target code library, the second index being a code architecture knowledge graph; acquiring a target code file corresponding to the target code block according to a source code file corresponding to the target code block and the second index; and acquiring a retrieval result according to the target code block and the target code file.
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公开(公告)号：US12158801B2
公开(公告)日：2024-12-03
申请号：US18157429
申请日：2023-01-20
Inventor： Zhigang Zeng , Zhenyuan Sun , Bingqing Shao , Pengfei Yan , Shiyong Li , Yanpeng Wang
IPC: G06F11/07
Abstract: A method of responding to an operation, an electronic device and a storage medium are provided, which relate to a field of cloud computing, and in particular to a field of cluster technology. The specific implementation solution includes: performing, in response to determining that a target operation performed by a target client on a shared resource has timed out, a fault detection on the target client to obtain a fault detection result; and implementing, in response to determining that the fault detection result represents that the target client has a fault, an update operation to obtain a target authority identifier, so that the target client is prevent from continuing to perform the target operation by using the target authority identifier.
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