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公开(公告)号：US20240370668A1
公开(公告)日：2024-11-07
申请号：US18031511
申请日：2022-03-08
Inventor： Boran JIANG , Chao JI , Hongxiang SHEN , Zhenzhong ZHANG , Ge OU , Chuqian ZHONG , Shuqi WEI , Pengfei ZHANG
IPC: G06F40/58
Abstract: The present disclosure relates to a method for training a natural language processing model, including: obtaining a sample text of natural language; determining one or more triples in the sample text, wherein each of the triples comprises two entities in the sample text and a relation between the two entities; processing the sample text based on the triples to obtain one or more knowledge fusion vectors; and training a natural language processing model by inputting the knowledge fusion vectors into the natural language processing model to obtain a target model.
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2.发明公开公开(公告)号：US20240362259A1
公开(公告)日：2024-10-31
申请号：US18291902
申请日：2021-09-18
Inventor： Ge OU , Boran JIANG , Chao JI , Shuqi WEI , Hongxiang SHEN
IPC: G06F16/335
CPC classification number: G06F16/335
Abstract: Provided in the present disclosure are a text recommendation method and apparatus, a model training method and apparatus, and a readable storage medium. The text recommendation method includes: acquiring text retrieval information from a user; when it is determined that there is historical text retrieval information for the user, determining text information of each text in a text set retrieved by using the text retrieval information; performing embedded representation on the text information of each text based on a self-attention model, and determining a text embedding vector of each text; inputting the text embedding vector of each text into a trained graph convolutional network model, to obtain the probability of interaction between the user and each text in the text set; and screening out, from the text set, target text which meets a preset interaction probability, and recommending the target text to the user.
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公开(公告)号：US20240320858A1
公开(公告)日：2024-09-26
申请号：US18272360
申请日：2021-10-15
Inventor： Chao JI , Hongxiang SHEN , Ge OU , Boran JIANG , Shuqi WEI
CPC classification number: G06T7/75 , G06T7/11 , G06T9/00 , G06T2207/20021 , G06T2207/20081
Abstract: The present disclosure relates to a meter recognition method, which includes: determining embedded features of pixels in a target image of a meter, and encoding position information of the pixels to obtain encoded position features; inputting superimposed features obtained by superimposing the encoded position features and the embedded features into an encoder of a target model; wherein an input of the target model includes the labels, and an output of the target model includes coordinates of key points in a sample image of the meter. According to the present disclosure, the image of the meter can be processed by the trained target model, the coordinates of key points in the target image of the meter are outputted. It can reduce manual operations and improve efficiency, and on the other hand, it can avoid possible misoperations during manual operations, which is beneficial for improving accuracy.
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公开(公告)号：US20250069326A1
公开(公告)日：2025-02-27
申请号：US18576357
申请日：2022-12-23
Applicant: BOE Technology Group Co., Ltd.
Inventor： Zhe ZHANG , Naichuan CHEN , Danfeng ZHU , Hongxiang SHEN
Abstract: Provided is a method for rendering an image. The method includes: acquiring a plurality of pieces of raw point cloud data by scanning an object with a scanning device, wherein any one piece of raw point cloud data is configured to describe a data acquisition point on the object; determining at least one piece of boundary line data based on the plurality of pieces of raw point cloud data, wherein any one piece of boundary line data is configured to describe a contour boundary on the object; and determining an object image based on the plurality of pieces of raw point cloud data and the at least one piece of boundary line data, and rendering the object image.
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公开(公告)号：US20230419466A1
公开(公告)日：2023-12-28
申请号：US18038197
申请日：2020-11-26
Applicant: BOE Technology Group Co., Ltd.
Inventor： Quanguo ZHOU , Kaiqin XU , Jiahong ZOU , Guolin ZHANG , Xun HUANG , Qing ZHANG , Lijia ZHOU , Zhidong WANG , Hongxiang SHEN , Hao TANG , Jiuyang CHENG
CPC classification number: G06T7/0002 , G06T3/40 , G06T2207/20084 , G06T2207/30168
Abstract: Provided are a method and apparatus for identifying the defect grade of a bad picture, and a storage medium. The method includes: determining the defect size of a defect from a bad picture; according to a product model corresponding to the bad picture, determining the design size of a pattern corresponding to a component that is adjacent to the position of the defect; determining the defect grade of the defect according to the defect type of the defect and a magnitude relationship between the defect size and the design size, wherein the defect grade is the degree to which the defect affects product yield.
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Patent Agency Ranking
公开(公告)号：US20220269901A1
公开(公告)日：2022-08-25
申请号：US17507918
申请日：2021-10-22
Applicant: BOE TECHNOLOGY GROUP CO., LTD.
Inventor： Guolin ZHANG , Kaiqin XU , Jiahong ZOU , Quanguo ZHOU , Xun HUANG , Hongxiang SHEN , Jiuyang CHENG
Abstract: An image processing method includes: receiving a processing instruction for an image; screening out a plurality of target images from a plurality of acquired images; determining a first feature value and a second feature value of the target area of each of the target images; determining a similarity between the first feature values of the target areas in every two target images according to all the determined first feature values, and determining a similarity between the second feature values of the target areas in every two target images according to all the determined second feature values; and, if a first target similarity is within a first preset range, and a second target similarity is within a second preset range, determining that a first image and a second image belong to the same image set.
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公开(公告)号：US20220245782A1
公开(公告)日：2022-08-04
申请号：US17476321
申请日：2021-09-15
Applicant: BOE Technology Group Co., Ltd.
Inventor： Quanguo ZHOU , Kaiqin XU , Jiahong ZOU , Guolin ZHANG , Xun HUANG , Qing ZHANG , Zhidong WANG , Lijia ZHOU , Hongxiang SHEN , Jiuyang CHENG , Hao TANG
Abstract: A method for classifying an image of a displaying base plate includes: acquiring an image to be checked; from a first predetermined-type set, determining a type of the image to be checked. The first predetermined-type set includes: a first image type, a second image type and a third image type. An image of the first image type is a no-defect image, an image of the second image type is a blurred image, and an image of the third image type is a defect image. When the type of the image to be checked is the third image type, by using a first convolutional neural network, determining a defect data of the image to be checked, wherein the defect image refers to an image of a displaying base plate having a defect, and the defect data contains a defect type of the displaying base plate in the image to be checked.
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公开(公告)号：US20250123816A1
公开(公告)日：2025-04-17
申请号：US18688079
申请日：2022-12-27
Applicant: BOE Technology Group Co., Ltd.
Inventor： Sujia JIANG , Zhe ZHANG , Chao WANG , Kun LI , Mingming YANG , Hongxiang SHEN
IPC: G06F8/34
Abstract: The present disclosure discloses an application development platform and method, a device, and a storage medium. The platform includes: a program development module, configured to display a page editing interface; and generate a page program according to a user's operation on the page editing interface, where the operation includes generating a page layout using materials and components, and configuring page information for the page layout; a mid-end configuration module, configured to configure an application mid-end for the page program; and an application module, configured to integrate the page program and the application mid-end to obtain an application program.
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9.发明公开公开(公告)号：US20240356767A1
公开(公告)日：2024-10-24
申请号：US18035488
申请日：2022-04-29
Inventor： Lirong XU , Hongxiang SHEN , Xiao CHU , Xiying ZHANG , Mingyuan MA
CPC classification number: H04L9/50 , H04L9/3218 , H04L9/3247
Abstract: A data storage method, a data storage apparatus, an electronic device, and a non-transitory computer-readable storage medium. The method is applied to a node device deployed with a blockchain node, wherein the blockchain node belongs to a user blockchain network and an object blockchain network, the user blockchain network is used to store evaluation data generated by evaluation behavior of a target user on at least one object, and the object blockchain network is used to store evaluation data generated by at least one user performing evaluation behavior on the target object, the method includes: obtaining target evaluation data on a target object, the target evaluation data being generated by the target user performing target evaluation behavior on the target object; storing the target evaluation data separately to the user blockchain network and the object blockchain network through the blockchain node.
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公开(公告)号：US20240303798A1
公开(公告)日：2024-09-12
申请号：US18263230
申请日：2021-11-30
Inventor： Chao JI , Yaoping WANG , Hongxiang SHEN , Ge OU , Boran JIANG , Shuqi WEI , Chuqian ZHONG , Pengfei ZHANG
IPC: G06T7/00
CPC classification number: G06T7/0004 , G06T2207/20021 , G06T2207/20081 , G06T2207/20084 , G06T2207/30121
Abstract: The present disclosure relates to an image recognition method and system for a display panel, a training method, and an electronic device and a non-volatile computer-readable storage medium. The image recognition method includes: acquiring an image of a display panel, wherein the image includes gate lines extending in a first direction and data lines extending in a second direction, the gate lines and the data lines intersecting to define a plurality of sub-pixel regions, and the image further includes a defect pattern; and recognizing the defect pattern in the image by using an image recognition model to obtain defect information, wherein the defect information includes at least one of a defect type or a defect position of the defect pattern, the image recognition model comprises a first attention model configured to learn a weight proportion of a feature of the defect pattern in the image.
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