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1.发明申请公开(公告)号：US20210224567A1
公开(公告)日：2021-07-22
申请号：US16097291
申请日：2017-08-31
发明人： Jianzong Wang , Jin Ma , Zhangcheng Huang , Tianbo Wu , Jing Xiao
摘要： A deep learning based license plate identification method, device, equipment, and storage medium. The deep learning based license plate identification method comprises: extracting features of an original captured image by using a single shot multi-box detector to obtain a target license plate image; correcting the target license plate image to obtain a corrected license plate image; identifying the corrected license plate image by using a bi-directional long short-term memory model to obtain target license plate information. When the deep learning based license plate identification method performs license plate identification, the identification efficiency is high and the accuracy is higher.
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公开(公告)号：US10977447B2
公开(公告)日：2021-04-13
申请号：US16318818
申请日：2017-09-28
发明人： Jianzong Wang , Zhangcheng Huang , Tianbo Wu , Jing Xiao
IPC分类号： G06F40/279 , G06F40/30 , G06N20/00 , G06F40/166 , G06F16/35 , G06N5/04
摘要： Disclosed is a method for identifying a user interest, including: obtaining training samples and test samples, the training samples being obtained by manually labeling after the corresponding topic models have been trained based on text data; extracting characteristics of the training samples and of the test samples, and computing optimal model parameters of a logistic regression model by an iterative algorithm based on the characteristics of the training samples; evaluating the logistic regression model based on the characteristics of the test samples and an area AUC under an ROC curve to train and obtain a first theme classifier; determining a theme to which the text data belongs using the first theme classifier, computing a score of the theme to which the text data belongs based on the logistic regression model, and computing a confidence score of the user being interested in the theme according to a second preset algorithm. Further disclosed are a device for identifying a user interest and a computer-readable storage medium.
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公开(公告)号：US11544639B2
公开(公告)日：2023-01-03
申请号：US16084565
申请日：2017-06-30
发明人： Jianzong Wang , Zhangcheng Huang , Tianbo Wu , Jing Xiao
摘要： The disclosure relates to a data source-based service customizing device, method and system, and a computer readable storage medium. The data source-based service customizing device includes: a memory, a processor and the data source-based service customizing system stored on the memory and operated on the processor. The data source-based service customizing system is executed by the processor to implement the following steps: acquiring user generated contents in various predetermined data sources; recognizing the user generated contents by using a user group label recognition model generated by pre-training to recognize user group labels corresponding to the various data sources; determining group services corresponding to the various data sources according to a predetermined mapping relation between the user group labels and the group services, and sending the various data sources and the corresponding group services to a predetermined terminal to perform group service customization on the various data sources.
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公开(公告)号：US20190386962A1
公开(公告)日：2019-12-19
申请号：US16463740
申请日：2017-08-31
发明人： Jianzong Wang , Zhangcheng Huang , Tianbo Wu , Jing Xiao
摘要： A method for data distribution includes: receiving a request for data distribution sent from a terminal; searching for raw association network data, wherein the raw association network data includes a set of nodes and a set of edges existing between the nodes; searching for a corresponding user grade according to a user identifier carried in the request for data distribution, and determining a privacy budget parameter corresponding to the user grade; determining the distributing probability distribution corresponding to the raw association network data to be distributed according to a pre-constructed Laplace Model and the determined privacy budget parameter; selecting an arbitrary value within the distributing probability distribution as a distributing probability for each network edge of the set of edges, generating a random number between 0 to 1 for each network edge, comparing the random number to the distributing probability, and distributing the corresponding network edge when the distributing probability is greater than the random number.
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公开(公告)号：US20190278822A1
公开(公告)日：2019-09-12
申请号：US16348966
申请日：2017-09-29
发明人： Jianzong Wang , Zhangcheng Huang , Tianbo Wu , Jing Xiao
IPC分类号： G06F16/9536 , G06Q50/00 , G06F16/9535 , G06F16/951
摘要： A method of matching cross-platform data, comprising: receiving a data matching request sent by a terminal; obtaining a group behavior data corresponding to the first user group in the first social network platform, and learning the group behavior data to obtain a group feature distribution function; obtaining associated users of the designated root node users and corresponding behavior data in the second social network platform; learning the behavior data of the root node users, and generating the group feature distribution function after matching the root node users; performing the behavior learning to the behavior data of the associated users; calculating a maximum entropy value of the group feature distribution function after matching the associated users, and determining the associated users corresponding to the largest maximum entropy value as the matching users of the first user group; and regarding the determined matching users as current root node users, determining a next matching user until the determined matching users meet a set quantity condition, and completing a group matching.
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6.发明授权公开(公告)号：US11164027B2
公开(公告)日：2021-11-02
申请号：US16097291
申请日：2017-08-31
发明人： Jianzong Wang , Jin Ma , Zhangcheng Huang , Tianbo Wu , Jing Xiao
摘要： A deep learning based license plate identification method, device, equipment, and storage medium. The deep learning based license plate identification method comprises: extracting features of an original captured image by using a single shot multi-box detector to obtain a target license plate image; correcting the target license plate image to obtain a corrected license plate image; identifying the corrected license plate image by using a bi-directional long short-term memory model to obtain target license plate information. When the deep learning based license plate identification method performs license plate identification, the identification efficiency is high and the accuracy is higher.
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公开(公告)号：US20210224481A1
公开(公告)日：2021-07-22
申请号：US16090351
申请日：2017-06-28
发明人： Jianzong Wang , Zhangcheng Huang , Tianbo Wu , Jing Xiao
IPC分类号： G06F40/284
摘要： A method for topic early warning includes: acquiring a self-defined keyword; calculating similarity between the self-defined keyword and each word in a corpus, and acquiring extended keywords related to the self-defined keyword from the corpus according to the similarity; selecting a target keyword from the extended keywords according to a type of the extended keywords and similarity between the extended keywords and the self-defined keyword, and adding the target keyword to a target keyword list; performing real-time monitoring according to the target keyword in the target keyword list; and performing topic early warning when it is monitored that the number of topics corresponding to the target keyword reaches a preset threshold.
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8.发明申请公开(公告)号：US20200175397A1
公开(公告)日：2020-06-04
申请号：US16314398
申请日：2017-09-28
发明人： Jianzong Wang , Zhangcheng Huang , Tianbo Wu , Jing Xiao
摘要： Provided is a method for training a topic classifier: obtaining a training sample and a test sample, wherein the training sample is obtained by manually labeling after a corresponding topic model having been trained based on text data; extracting features of the training sample and of the test sample respectively using a preset algorithm, computing optimal model parameters of a logistic regression model by an iterative algorithm based on the features of the training sample, to train and get a logistic regression model containing the optimal model parameters; and drawing a ROC curve based on the features of the test sample and the logistic regression model containing the optimal model parameters, evaluating the logistic regression model containing the optimal model parameters based on the area AUC under the ROC curve, to train and get a first topic classifier. It further discloses a device and computer-readable storage medium thereof.
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公开(公告)号：US11205046B2
公开(公告)日：2021-12-21
申请号：US16090351
申请日：2017-06-28
发明人： Jianzong Wang , Zhangcheng Huang , Tianbo Wu , Jing Xiao
IPC分类号： G06F40/247 , G06F40/284 , G06F40/30
摘要： A method for topic early warning includes: acquiring a self-defined keyword; calculating similarity between the self-defined keyword and each word in a corpus, and acquiring extended keywords related to the self-defined keyword from the corpus according to the similarity; selecting a target keyword from the extended keywords according to a type of the extended keywords and similarity between the extended keywords and the self-defined keyword, and adding the target keyword to a target keyword list; performing real-time monitoring according to the target keyword in the target keyword list; and performing topic early warning when it is monitored that the number of topics corresponding to the target keyword reaches a preset threshold.
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公开(公告)号：US20210326995A1
公开(公告)日：2021-10-21
申请号：US17263899
申请日：2019-11-12
发明人： Jianzong Wang , Zhangcheng Huang
IPC分类号： G06Q40/08 , G06Q20/40 , G16H10/60 , G16H40/20 , G16H50/70 , G16H50/20 , G06F16/901 , G06F16/906
摘要： A claim settlement anti-fraud method, an apparatus, a computer device, and a storage medium are provided. The method includes generating a sub-graph of doctor and patient, a sub-graph of doctor and medical advice, and a fused large graph according to medical data. A patient relationship network with several community close loops is generated by mapping the sub-graph of doctor and patient according to the fuses large graph. A similarity between any two vertexes in the patient relationship network are computed. An average similarity of each community close loop is computed. The insurance fraud actions are confirmed based on the average similarity.
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