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公开(公告)号：US12039967B2
公开(公告)日：2024-07-16
申请号：US17520799
申请日：2021-11-08
Inventor： Yanyan Li , Dejing Dou
CPC classification number: G10L15/01 , G10L15/02 , G10L15/063 , G10L15/22 , G10L2015/225
Abstract: A method for evaluating satisfaction with voice interaction, a device, and a storage medium are provided, which are related to a technical field of artificial intelligence, in particular, to fields of natural language processing, knowledge graph and deep learning, and can be applied to user intention understanding. The specific implementation includes: acquiring sample interaction data of a plurality of rounds of sample voice interaction behaviors; performing feature extractions on respective sample interaction data, to obtain a sample interaction feature sequence; acquiring satisfaction marks corresponding to the respective sample interaction data, to obtain a satisfaction mark sequence; and training an initial model by using a plurality of sets of sample interaction feature sequences and of satisfaction mark sequences, to obtain the model for evaluating satisfaction.
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352.发明公开公开(公告)号：US20240231840A1
公开(公告)日：2024-07-11
申请号：US17914893
申请日：2021-10-14
Inventor： Chao LIU
IPC: G06F9/445
CPC classification number: G06F9/44505
Abstract: Provided are a method and apparatus for microservice configuration, an electronic device, a system and a storage medium in the present disclosure, relates to the field of computer technologies, and in particular to the field of cloud computing, Internet of things, etc. A specific implementation solution is as follows. Configuration data is received from a main cluster; a service data acquisition request is sent to the main cluster in response to determining that service data in the main cluster has been changed, and the changed service data is received from the main cluster; and at least one service governance task is executed according to the configuration data and the changed service data.
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公开(公告)号：US12034820B2
公开(公告)日：2024-07-09
申请号：US17676056
申请日：2022-02-18
IPC: H04L41/042 , G06F11/07 , H04L41/16 , H04L43/12 , H04L67/51
CPC classification number: H04L67/51 , G06F11/076 , H04L43/12
Abstract: Provided are a fusing and degradation method and apparatus for a micro-service, a device, a medium, and a program product, which relate to the field of the Internet, in particular to the field of fusing and degradation. The specific implementation is as follows: instantiating, by a monitoring probe woven into a micro-service traffic process, a fuse component according to an obtained fusing and degradation rule, and weaving, by the monitoring probe, the fuse component into the traffic process, where the fuse component is used for performing fusing and degradation on a traffic request processed in the traffic process. The fusing and degradation technology during the running of a micro-service is achieved through a probe so that the micro-service developer can fuse and degrade a micro-service application that is running without hard coding, thereby improving the efficiency and stability of the micro-service fusing and degradation.
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354.发明公开公开(公告)号：US20240221346A1
公开(公告)日：2024-07-04
申请号：US17800880
申请日：2022-01-29
Inventor： Zhigang WANG , Jian WANG , Hao SUN , Errui DING
IPC: G06V10/44 , G06T9/00 , G06V10/74 , G06V10/762 , G06V10/80
CPC classification number: G06V10/44 , G06T9/00 , G06V10/761 , G06V10/762 , G06V10/806
Abstract: The present disclosure provides a model training method and apparatus, a pedestrian re-identification method and apparatus, and an electronic device, and relates to the field of artificial intelligence, and specifically to computer vision and deep learning technologies, which can be applied to smart city scenarios. A specific implementation solution is: performing, by using a first encoder, feature extraction on a first pedestrian image and a second pedestrian image in a sample dataset, to obtain an image feature of the first pedestrian image and an image feature of the second pedestrian image; fusing the image feature of the first pedestrian image and the image feature of the second pedestrian image, to obtain a fused feature; performing, by using a first decoder, feature decoding on the fused feature, to obtain a third pedestrian image; and determining the third pedestrian image as a negative sample image of the first pedestrian image, and using the first pedestrian image and the negative sample image to train a first preset model to convergence, to obtain a pedestrian re-identification model. The embodiments of the present disclosure can improve the effect of the model in distinguishing between pedestrians with similar appearances but different identities.
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355.发明公开公开(公告)号：US20240193923A1
公开(公告)日：2024-06-13
申请号：US17908070
申请日：2022-01-29
Inventor： Xiaodi WANG , Shumin HAN , Yuan FENG , Ying XIN , Yi GU , Bin ZHANG , Chao LI , Xiang LONG , Honghui ZHENG , Yan PENG , Zhuang JIA , Yunhao WANG
IPC: G06V10/778
CPC classification number: G06V10/778 , G06V2201/07
Abstract: A method of training a target object detection model includes: extracting a plurality of feature maps of a sample image according to a training parameter, fusing the plurality of feature maps to obtain at least one fused feature map, and obtaining an information of a target object based on the at least one fused feature map, by using the target object detection model; determining a loss of the target object detection model based on the information of the target object and a tag information of the sample image, and adjusting the training parameter according to the loss of the target object detection model. A method of detecting a target object and an apparatus are also provided.
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Patent Agency Ranking
公开(公告)号：US12007965B2
公开(公告)日：2024-06-11
申请号：US17663044
申请日：2022-05-12
Inventor： Yifei Wang , Yang Wang , Yu Wang
IPC: G06F16/215 , G06F16/901
CPC classification number: G06F16/215 , G06F16/9014 , G06F16/9024
Abstract: The disclosure provides a method for deduplicating entity nodes in a graph database. The method includes: obtaining a set of entity nodes to be deduplicated from the knowledge graph, in which the set includes a plurality of entity nodes; selecting an untraversed entity node from the set as a target entity node; determining a range located by a node identifier corresponding to the target entity node; determining the target entity node that has appeared in traversed entity nodes according to a deduplicating mode corresponding to the range; and deleting the target entity node from the set.
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357.发明公开公开(公告)号：US20240177469A1
公开(公告)日：2024-05-30
申请号：US17793999
申请日：2021-11-17
Inventor： Miao FAN , Jizhou HUANG , Haifeng WANG
IPC: G06V10/82 , G06V10/771 , G06V10/80
CPC classification number: G06V10/82 , G06V10/771 , G06V10/80
Abstract: A method and apparatus for encoding a geographic location region as well as a method and apparatus for establishing an encoding model, which relate to big data and deep learning technologies in the field of artificial intelligence technologies are disclosed. An implementation includes: determining a to-be-encoded geographic location region; acquiring at least one kind of geographic function information and at least one kind of surface-feature distribution information of the geographic location region; and inputting the acquired geographic function information and the acquired surface-feature distribution information into an encoding model, the encoding model performing embedding on the geographic function information and the surface-feature distribution information, and fusing vector representations obtained by the embedding to obtain an encoding result of the geographic location region.
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公开(公告)号：US11995154B2
公开(公告)日：2024-05-28
申请号：US17901403
申请日：2022-09-01
Inventor： Yongqing Wang
CPC classification number: G06F18/23 , G06V10/757
Abstract: A method of determining a state of a target object, an electronic device, and a storage medium, relate to fields of a computer technology, cloud computing and Internet of things, and apply to smart cities. The method includes: receiving a transmitted first moving point sequence for the target object, the first moving point sequence including a plurality of target moving point elements, and each target moving point element containing a timestamp information and a displacement information that indicate a stay state of the target object; determining, from the first moving point sequence, a target stay point of the target object, according to the timestamp information and the displacement information; and determining that the state of the target object at the target stay point is an abnormal stay state, in response to a distance between the target stay point and a first preset position being less than a first preset threshold.
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359.发明授权公开(公告)号：US11989516B2
公开(公告)日：2024-05-21
申请号：US17572068
申请日：2022-01-10
Inventor： Lijie Wang , Shuai Zhang , Xinyan Xiao , Yue Chang , Tingting Li
IPC: G06F40/289 , G06N20/00
CPC classification number: G06F40/289 , G06N20/00
Abstract: The present disclosure provides a method and apparatus for acquiring a pre-trained model, an electronic device and a storage medium, and relates to the field of artificial intelligence, such as the natural language processing field, the deep learning field, or the like. The method may include: adding, in a process of training a pre-trained model using training sentences, a learning objective corresponding to syntactic information for a self-attention module in the pre-trained model; and training the pre-trained model according to the defined learning objective. The solution of the present disclosure may improve a performance of the pre-trained model, and reduce consumption of computing resources, or the like.
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360.发明授权公开(公告)号：US11929871B2
公开(公告)日：2024-03-12
申请号：US17718149
申请日：2022-04-11
Inventor： Cheng Cui , Tingquan Gao , Shengyu Wei , Yuning Du , Ruoyu Guo , Bin Lu , Ying Zhou , Xueying Lyu , Qiwen Liu , Xiaoguang Hu , Dianhai Yu , Yanjun Ma
IPC: G06K9/62 , G06F18/214 , H04L41/0806 , H04L41/084 , H04L41/0894
CPC classification number: H04L41/0806 , G06F18/214 , H04L41/0846 , H04L41/0894
Abstract: The present disclosure provides a method for generating a backbone network, an apparatus for generating a backbone network, a device, and a storage medium. The method includes: acquiring a set of a training image, a set of an inference image, and a set of an initial backbone network; training and inferring, for each initial backbone network in the set of the initial backbone network, the initial backbone network by using the set of the training image and the set of the inference image, to obtain an inference time and an inference accuracy of a trained backbone network in an inference process; determining a basic backbone network based on the inference time and the inference accuracy of the trained backbone network in the inference process; and obtaining a target backbone network based on the basic backbone network and a preset target network.
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