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公开(公告)号：US20220237388A1
公开(公告)日：2022-07-28
申请号：US17714891
申请日：2022-04-06
Inventor： Xinjiang LU , Yanyan LI , Jingbo ZHOU , Dejing DOU
IPC: G06F40/40 , G06F40/30 , G06F40/177 , G06F40/20
Abstract: A method and apparatus for generating a table description text, a device, and a storage medium are provided. An implementation of the method includes: acquiring a to-be-described table, and analyzing the to-be-described table to obtain a set of metalanguage of the to-be-described table, and finally generating a description text of the to-be-described table based on the metalanguage in the set of metalanguage.
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2.发明申请公开(公告)号：US20220130495A1
公开(公告)日：2022-04-28
申请号：US17570505
申请日：2022-01-07
Inventor： Shuangli LI , Jingbo ZHOU , Liang HUANG , Haoyi XIONG , Fan WANG , Tong XU , Hui XIONG , Dejing DOU
Abstract: A method for determining correlation between a drug and a target, and an electronic device are provided. The method includes: establishing a spatial molecular graph of a candidate drug and the target, the spatial molecular graph including an atomic node set and an edge set, the atomic node set including atoms in the candidate drug and atoms in the target, the edge set including at least one atom connection edge; inputting a first atom feature of the atomic node set and the spatial molecular graph into a first GAT for prediction, to obtain a second atom feature of the atomic node set; and determining a parameter value of the correlation between the candidate drug and the target in accordance with the second atom feature of the atomic node set.
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公开(公告)号：US20240371259A1
公开(公告)日：2024-11-07
申请号：US18393376
申请日：2023-12-21
Inventor： Yixiong XIAO , Hao YUAN , Jingbo ZHOU
IPC: G08G1/01
Abstract: Provided is a source tracing method for traffic congestion, an electronic device and a storage medium, relating to the field of smart transportation, traffic management, traffic information processing and other technologies. The method includes: determining an undetermined road section and at least two reference road sections related to the undetermined road section from a target road network; obtaining a congestion infection distance between the undetermined road section and the reference road section within a target period; calculating a congestion time difference between a first congestion moment of the undetermined road section within the target period and a second congestion moment of the reference road section within the target period; and determining the undetermined road section as a congestion source of the target road network within the target period when determining that a correlation between the congestion infection distance and the congestion time difference meets a preset correlation requirement.
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公开(公告)号：US20240370719A1
公开(公告)日：2024-11-07
申请号：US18512766
申请日：2023-11-17
Inventor： Congxi XIAO , Jizhou HUANG , Jingbo ZHOU
Abstract: This disclosure provides a data generation method, model training method, electronic device, and medium. The data generation method includes: obtaining urban graph data, the urban graph data including a node set, an edge set and a feature set, wherein the node set includes a central node corresponding to a predetermined urban entity, the edge set includes a neighborhood corresponding to the central node, the neighborhood includes other nodes in the node set connected to the central node via an edge, and the feature set includes features of nodes in the node set; partitioning a target region into at least two sub-regions to obtain a region partition set; obtaining a regional feature of each sub-region by aggregating features corresponding to all nodes in the sub-region; and updating a feature of the central node based on the regional features of the sub-regions in the region partition set to obtain target feature data.
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公开(公告)号：US20240273297A1
公开(公告)日：2024-08-15
申请号：US18642593
申请日：2024-04-22
Inventor： Yu LI , Jiawei ZHENG , Xinjiang LU , Hongwei XIE , Xuejiao LIN , Jingbo ZHOU
IPC: G06F40/295
CPC classification number: G06F40/295
Abstract: An entity recognition method, a model training method, an electronic device, and a medium, which relate to fields of artificial intelligence, information acquiring technologies. The entity recognition method includes: extracting specified entities from a text in a source file of a webpage to be recognized, and acquiring a text encoding result for each specified entity; determining a text block formed by each specified entity in the webpage, and encoding a relative layout information between each two text blocks, to obtain a position encoding result; constructing a triple by the position encoding result for each two text blocks and the text encoding results for respective specified entities of the two text blocks; and performing a graph convolution on each triple to obtain a relation recognition result for the webpage to be recognized, where the relation recognition result indicates whether an association exists between each two text blocks in the webpage.
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Patent Agency Ranking
公开(公告)号：US20240330328A1
公开(公告)日：2024-10-03
申请号：US18741744
申请日：2024-06-12
Inventor： Siyuan HAO , Le ZHANG , Le DAI , Jingbo ZHOU , Shengming ZHANG , Chuan QIN , Hui XIONG
IPC: G06F16/28
CPC classification number: G06F16/288
Abstract: A method is provided. The method includes: obtaining an object relationship diagram; for a target object of a plurality of first objects, obtaining at least one meta-path corresponding to the target object in the object relationship diagram; for each meta-path, performing the following operations: determining a plurality of first attention weights of the target object based on inherent attribute data of the target object and inherent attribute data of each of a plurality of second objects on the meta-path; obtaining a second representation vector of the target object based on a first representation vector of the target object and the plurality of first attention weights; and obtaining a target indicator prediction result of the target object based at least on at least one second representation vector of the target object corresponding to the at least one meta-path.
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公开(公告)号：US20240273113A1
公开(公告)日：2024-08-15
申请号：US18642571
申请日：2024-04-22
Inventor： Ming ZHANG , Yanyan LI , Lili LI , Jingbo ZHOU
IPC: G06F16/25
CPC classification number: G06F16/258
Abstract: The present application relates to a field of big data technology, in particular to a field of data storage technology. More specifically, the present disclosure relates to a method of importing data to a database, an electronic device, and a storage medium. A specific implementation solution is: acquiring incoming data from a data source according to a database config file; the incoming data is original data directly acquired from the data source; calculating and processing the incoming data according to the database config file to obtain computational data; the computational data is obtained by integrating and calculating the incoming data; writing the incoming data and the computational data into a database.
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公开(公告)号：US20240169462A1
公开(公告)日：2024-05-23
申请号：US17758687
申请日：2021-11-17
Inventor： Hao ZHANG , Jingbo ZHOU , Jizhou HUANG
IPC: G06Q50/47 , G06Q30/0283 , G06Q30/0601
CPC classification number: G06Q50/47 , G06Q30/0284 , G06Q30/0635
Abstract: An online ride-hailing information processing method and apparatus, a device, and a computer storage medium, relating to big data computing and deep learning technologies in the field of AI technologies, are disclosed. A specific solution involves: acquiring an online ride-hailing query condition including information of an origin and a destination sent by a client; determining a query time range according to the query condition; calculating cost information of arrival at the destination departing at a plurality of times in the query time range respectively; determining, according to the cost information of arrival at the destination departing at the plurality of times, a time meeting the query condition as a recommended departure time; determining a recommended order-sending time according to the recommended departure time; and returning a query result to the client, the query result including the recommended order-sending time, or further including cost information corresponding to the recommended order-sending time. According to the present disclosure, users can select a low-cost order-sending time, which improves user experience, saves network resources, and reduces the influence on system performance.
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公开(公告)号：US20240071222A1
公开(公告)日：2024-02-29
申请号：US18503538
申请日：2023-11-07
Inventor： Qian SUN , Le ZHANG , Jingbo ZHOU , Hui XIONG , Weijia ZHANG , Huan YU , Yu MEI , Weicen LING
IPC: G08G1/0967 , G06N20/00 , G08G1/081
CPC classification number: G08G1/096725 , G06N20/00 , G08G1/081 , G08G1/096766 , B60W60/001
Abstract: A method for controlling a traffic light, a method and apparatus for navigating an unmanned vehicle and a method and apparatus for training a model are provided. An implementation comprises: generating a reinforced traffic light state parameter according to vehicle state representation information of an unmanned vehicle currently contained in a preset area of a target traffic light and a current traffic light state parameter of the target traffic light; and generating a traffic light control action according to the reinforced traffic light state parameter; where the reinforced traffic light state parameter is used to cause an unmanned vehicle navigation end to generate a reinforced vehicle state parameter according to a reinforced traffic light state and a current vehicle state parameter of a target unmanned vehicle, and generate an unmanned vehicle navigation action according to the reinforced vehicle state parameter.
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10.发明申请公开(公告)号：US20220374776A1
公开(公告)日：2022-11-24
申请号：US17868113
申请日：2022-07-19
Inventor： Ji LIU , Beichen MA , Chendi ZHOU , Jingbo ZHOU , Ruipu ZHOU , Dejing DOU
IPC: G06N20/00
Abstract: The present disclosure provides a method and apparatus for federated learning, which relate to the technical fields such as big data and deep learning. A specific implementation is: generating, for each task in a plurality of different tasks trained simultaneously, a global model for each task; receiving resource information of each available terminal in a current available terminal set; selecting a target terminal corresponding to each task from the current available terminal set, based on the resource information and the global model; and training the global model using the target terminal until a trained global model for each task meets a preset condition.
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