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公开(公告)号：US11822581B2
公开(公告)日：2023-11-21
申请号：US17706706
申请日：2022-03-29
Inventor： Xinjiang Lu , Dejing Dou
CPC classification number: G06F16/285
Abstract: The present disclosure provides a region information processing method and apparatus, and relates to the field of artificial intelligence in computer technologies. The specific implementation is: acquiring a first distance between a first region and a second region, a first object set included in the first region, and a second object set included in the second region; determining spatial dependency information between the first region and the second region according to the first distance; determining object dependency information between the first region and the second region according to the first object set and the second object set; and determining a symbiosis between the first region and the second region according to the spatial dependency information and the object dependency information.
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公开(公告)号：US20220156988A1
公开(公告)日：2022-05-19
申请号：US17650214
申请日：2022-02-07
Inventor： Yanyan Li , Dejing Dou
Abstract: Embodiments of the present disclosure provide a method, an apparatus, a device, a medium and a product for configuring a color, relates to the field of computer technology, and particularly to the data visualization technology. A specific implementation comprises: acquiring a set of chart entities in a chart; determining target color information corresponding to the chart entities in the set of the chart entities based on a preset target function and a constraint condition; and configuring colors corresponding to the chart entities in the set of the chart entities based on the target color information.
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3.发明公开公开(公告)号：US20230206024A1
公开(公告)日：2023-06-29
申请号：US17891617
申请日：2022-08-19
Inventor： Ji Liu , Zhihua Wu , Danlei Feng , Chendi Zhou , Minxu Zhang , Xinxuan Wu , Xuefeng Yao , Dejing Dou , Dianhai Yu , Yanjun Ma
CPC classification number: G06N3/04 , G06F11/3409
Abstract: A resource allocation method, including: determining a neural network model to be allocated resources, and determining a set of devices capable of providing resources for the neural network model; determining, based on the set of devices and the neural network model, first set of evaluation points including first number of evaluation points, each of which corresponds to one resource allocation scheme and resource use cost corresponding to the resource allocation scheme; updating and iterating first set of evaluation points to obtain second set of evaluation points including second number of evaluation points, each of which corresponds to one resource allocation scheme and resource use cost corresponding to the resource allocation scheme, and second number being greater than first number; and selecting a resource allocation scheme with minimum resource use cost from the second set of evaluation points as a resource allocation scheme for allocating resources to the neural network model.
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公开(公告)号：US11657550B2
公开(公告)日：2023-05-23
申请号：US17450650
申请日：2021-10-12
Inventor： Yanyan Li , Airong Jiang , Dejing Dou
CPC classification number: G06T11/206 , G06F16/2246
Abstract: A method for generating an electronic report, an electronic device and a storage medium, related to the field of large data and the field of artificial intelligence, are disclosed. The method for generating an electronic report includes: establishing a template tree comprising a plurality of branches, wherein the branches comprise at least one intermediate node and bottom layer nodes comprising identification information; and calling, for respective branches, data groups corresponding to the identification information of the bottom layer nodes from a database, respectively, and displaying the called data groups at positions corresponding to the bottom layer nodes in an electronic report. Labor consumption may be reduced, and advantages of low cost, high efficiency, automation and routinization may be achieved.
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公开(公告)号：US20230127699A1
公开(公告)日：2023-04-27
申请号：US18088872
申请日：2022-12-27
Inventor： Ji Liu , Sunjie Yu , Weijia Zhang , Hao Liu , Hengshu Zhu , Dejing Dou , Hui Xiong
Abstract: A method of training a model, a method of determining an asset valuation, a device, a storage medium, and a program product, which relate to a field of artificial intelligence, in particular to fields of deep learning and natural language understanding. A specific implementation can include: determining an event-level representation according to a first set of feature data; performing a multi-task learning for a first model according to the event-level representation, to obtain first price distribution data, and transmitting the first price distribution data to a central server; determining a first intra-region representation according to a second set of feature data; adding a noise signal to the first intra-region representation, and transmitting the noised intra-region representation to a client; and adjusting a parameter of the first model according to a noised parameter gradient in response to the noised parameter gradient being received from the central server.
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Patent Agency Ranking
公开(公告)号：US20220391672A1
公开(公告)日：2022-12-08
申请号：US17820972
申请日：2022-08-19
Inventor： Kafeng Wang , Haoyi Xiong , Chengzhong Xu , Dejing Dou
Abstract: The disclosure provides a multi-task deployment method, and an electronic device. The method includes: obtaining N first tasks and K network models, in which N and K are positive integers greater than or equal to 1; allocating the N first tasks to the K network models differently for operation, to obtain at least one candidate combination of tasks and network models, in which each candidate combination includes a mapping relation between the N first tasks and the K network models; selecting a target combination with a maximum combination operation accuracy from the at least one candidate combination; and deploying a target mapping relation comprised in the target combination and the K network models on a prediction machine.
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公开(公告)号：US20220237376A1
公开(公告)日：2022-07-28
申请号：US17718285
申请日：2022-04-11
Inventor： Yaqing Wang , Dejing Dou
IPC: G06F40/279 , G06F40/253 , G06F40/117 , G06N3/04
Abstract: A computer-implemented method for text classification is provided. The method for text classification includes obtaining an entity category set and a part-of-speech tag set associated with a text. The method further includes constructing a first isomorphic graph for the entity category set and a second isomorphic graph for the part-of-speech tag set. A node of the first isomorphic graph corresponds to an entity category in the entity category set, and a node of the second isomorphic graph corresponds to a part-of-speech tag in the part-of-speech tag set. The method further includes obtaining, based on the first isomorphic graph and the second isomorphic graph, a first text feature and a second text feature of the text through a graph neural network. The method further includes classifying the text based on a fused feature of the first text feature and the second text feature.
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公开(公告)号：US20210312288A1
公开(公告)日：2021-10-07
申请号：US17349280
申请日：2021-06-16
Inventor： Yaqing Wang , Dejing Dou
Abstract: The present application discloses a method for training a classification model, a classification method, an apparatus and a device. A specific implementation is: acquiring behavior information of multiple users and personal basic information of the multiple users; where categories of at least part of users of the multiple users are known; inputting the personal basic information of the multiple users into a classification model to be trained to obtain feature information of the multiple users and predicted categories of users with known categories; and training the classification model to be trained according to the behavior information of the multiple users, the feature information of the multiple users, the predicted categories of the users with the known categories, and real categories of the users with the known categories, to obtain a trained classification model. The user categories determined by using the classification model are more accurate.
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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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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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