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公开(公告)号：US12147448B2
公开(公告)日：2024-11-19
申请号：US18088712
申请日：2022-12-26
Inventor： Zhengli Yi
Abstract: The present disclosure provides a data reading method, including: in response to receiving a read response request generated by a replication group for an application and sent by a storage terminal, setting, in a dedicated mapping table corresponding to the application, a commit index corresponding to the replication group as a commit index carried by the read response request; searching for a target replication group corresponding to a first read request generated by a target application based on the first read request; determining, in a dedicated mapping table corresponding to the target application, a commit index corresponding to the target replication group as a target commit index; sending a second read request carrying the target commit index to the storage terminal; and obtaining the data read by the storage terminal.
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公开(公告)号：US20240378077A1
公开(公告)日：2024-11-14
申请号：US18782617
申请日：2024-07-24
Inventor： Guoxia WANG , Jinle ZENG , Xiyuan XIAO , Jiabin YANG , Dianhai YU , Haifeng WANG
Abstract: A method of executing a task for a large language model, a device, and a storage medium are provided, which relate to a field of artificial intelligence technology, and in particular to fields of deep learning, large language model, natural language processing and computer vision technologies. The method includes: determining, by using a determination unit, a target attention task from a plurality of attention tasks to be processed, based on a sparse representation corresponding to a feature to be processed, where the target attention task is a task corresponding to a non-fully masked region of the feature, the sparse representation represents a mask position of the feature, and the mask position represents mask endpoint positions in at least two non-intersecting intervals in a mask matrix corresponding to the feature; and executing the target attention task by using a computing unit, so as to obtain an attention feature.
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公开(公告)号：US12140441B2
公开(公告)日：2024-11-12
申请号：US17531529
申请日：2021-11-19
Inventor： Weijia Zhang , Hao Liu , Dejing Dou , Hui Xiong
IPC: G01C21/34 , G06N20/00 , G06Q30/0601 , H04L67/12
Abstract: A method for recommending a station for a vehicle, a device, and a storage medium are provided. The method comprises: receiving, by a server, an access request from a vehicle; obtaining, based on the access request, a plurality of observation values from a plurality of stations associated with the vehicle, respectively, each observation value is based on a corresponding pre-trained recommendation model, each observation value includes factors associated with access of the vehicle to the station corresponding to the observation value; determining, an action value for the station based on the observation value and the pre-trained recommendation model for the station, the action value for the station indicates a matching degree between the access request and the station; determining a recommended station among the plurality of stations based on the action values of the plurality of stations; and sending to the vehicle an instruction of driving to the recommended station.
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334.发明授权公开(公告)号：US12125131B2
公开(公告)日：2024-10-22
申请号：US18075346
申请日：2022-12-05
Inventor： Zhe Peng , Yuqiang Liu , Fanyu Geng
CPC classification number: G06T13/40 , G06T7/20 , G10L15/02 , G10L15/063 , G10L15/16 , G10L25/57 , G06T2207/20081 , G06T2207/20084 , G06T2207/30201
Abstract: A method of generating a 3D video, a method of training a neural network model, an electronic device, and a storage medium, which relate to a field of image processing, and in particular to technical fields of computer vision, augmented/virtual reality and deep learning. The method includes: determining, based on an input speech feature, a principal component analysis (PCA) coefficient by using a first network, wherein the PCA coefficient is used to generate the 3D video; correcting the PCA coefficient by using a second network; generating a lip movement information based on the corrected PCA coefficient and a PCA parameter for a neural network model, wherein the neural network model includes the first network and the second network; and applying the lip movement information to a pre-constructed 3D basic avatar model to obtain a 3D video with a lip movement effect.
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335.发明授权公开(公告)号：US12118319B2
公开(公告)日：2024-10-15
申请号：US17655772
申请日：2022-03-21
Inventor： Jun Xu , Zeming Liu , Zeyang Lei , Zhengyu Niu , Hua Wu , Haifeng Wang
Abstract: The present disclosure provides a dialog method and system, an electronic device and a storage medium, and relates to the field of artificial intelligence (AI) technologies such as deep learning and natural language processing. A specific implementation scheme involves: rewriting a corresponding dialog state based on received dialog information of a user; determining to-be-used dialog action information based on the dialog information of the user and the dialog state; and generating a reply statement based on the dialog information of the user and the dialog action information. According to the present disclosure, the to-be-used dialog action information can be determined based on the dialog information of the user and the dialog state; and then the reply statement is generated based on the dialog action information, thereby providing an efficient dialog scheme.
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Patent Agency Ranking
336.发明公开公开(公告)号：US20240338564A1
公开(公告)日：2024-10-10
申请号：US18744501
申请日：2024-06-14
Inventor： Zhifan FENG , Hua WU , Qiaoqiao SHE , Tian WU
IPC: G06N3/08
CPC classification number: G06N3/08
Abstract: A large model optimization training method in the artificial intelligence fields, such as large models, deep learning, natural language processing, may include: taking, as candidate queries, queries collected from a predetermined data source and capable of serving as input to a large model in response to determining that an optimization triggering condition is met; screening out target queries from the candidate queries, the target queries being queries which cannot be correctly processed by the large model; and constructing respectively corresponding training samples according to the target queries, the training samples being used for carrying out optimization training on the large model.
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337.发明授权公开(公告)号：US12112746B2
公开(公告)日：2024-10-08
申请号：US17476333
申请日：2021-09-15
Inventor： Jinfeng Bai , Zhijian Wang , Cong Gao
IPC: G10L15/22
CPC classification number: G10L15/22 , G10L2015/223
Abstract: The present disclosure provides a method and a device for processing voice interaction, an electronic device and a storage medium. The method includes: determining a first integrity of a voice instruction from a user by using a pre-trained integrity detection model in response to detecting that the voice instruction from the user is not a high-frequency instruction; determining a waiting duration for the voice instruction based on the first integrity and a preset integrity threshold, wherein the waiting duration for the voice instruction indicates a length of period between a time when a voice interaction device determines that receiving the voice instruction is completed and a time when the voice interaction device performs an operation in response to the voice instruction of the user; and controlling the voice interaction device to respond to the voice instruction of the user based on the waiting duration.
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338.发明授权公开(公告)号：US12100388B2
公开(公告)日：2024-09-24
申请号：US17747732
申请日：2022-05-18
Inventor： Qingen Zhao
CPC classification number: G10L15/063 , G10L15/02 , G10L15/16 , G10L15/22
Abstract: A method and apparatus for training a speech recognition model, an electronic device and a storage medium are provided. An implementation of the method may include: determining a plurality of feature vectors based on audio feature data corresponding to a first target frame in a sample speech, wherein the sample speech comprises a conversation among a plurality of objects and the sample speech has a corresponding sample text; generating a predicted text element corresponding to the first target frame based on an adjacent text element preceding to a text element corresponding to the first target frame in the sample text, wherein the text element and the adjacent text element are targeting at a target object in the plurality of objects; obtaining a first target text element based on the predicted text element and a first feature vector in the plurality of feature vectors; and adjusting the speech recognition model based on the first target text element and the sample text, to obtain a trained speech recognition model.
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公开(公告)号：US12086555B2
公开(公告)日：2024-09-10
申请号：US17643053
申请日：2021-12-07
Inventor： Jianglu Hu , Hehan Li , Huifeng Sun , Shuqi Sun , Yue Chang , Tingting Li , Hua Wu , Haifeng Wang
IPC: G06F40/35 , G06F16/332
CPC classification number: G06F40/35 , G06F16/3329
Abstract: The disclosure provides a method for generating a dialogue. The method includes: obtaining an input sentence; determining a type of a task-based response sentence that is to be generated, by updating a current dialogue state based on the input sentence; generating the task-based response sentence by inputting the input sentence into a task-based dialogue response generator; and determining the task-based response sentence as a target response sentence in response to the type of the task-based response sentence being a designated type.
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公开(公告)号：US20240281609A1
公开(公告)日：2024-08-22
申请号：US18041207
申请日：2022-05-16
Inventor： Pengyuan LV , Jingquan LI , Chengquan ZHANG , Kun YAO , Jingtuo LIU , Junyu HAN
Abstract: The present application provides a method of training a text recognition model. The method includes: inputting a first sample image into the visual feature extraction sub-model to obtain a first visual feature and a first predicted text, the first sample image contains a text and a tag indicating a first actual text; obtaining, by using the semantic feature extraction sub-model, a first semantic feature based on the first predicted text; obtaining, by using the sequence sub-model, a second predicted text based on the first visual feature and the first semantic feature; and training the text recognition model based on the first predicted text, the second predicted text and the first actual text. The present disclosure further provides a method of recognizing a text, an electronic device, and a storage medium.
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