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公开(公告)号：US11729273B2
公开(公告)日：2023-08-15
申请号：US17647094
申请日：2022-01-05
发明人： Jin Wang , Lei Gao , A Peng Zhang , Kai Li , Jun Wang , Yan Liu , Jia Xing Tang
IPC分类号： H04L67/143
CPC分类号： H04L67/143
摘要： Systems and techniques for determining an idle timeout for a cloud computing session are described. An example technique includes determining a first one or more attributes associated with a user of the cloud computing session and determining a second one or more attributes associated with an operation of the cloud computing session. An idle timeout for the cloud computing session is determined, based at least in part on the first one or more attributes and the second one or more attributes. User activity is monitored during the cloud computing session. Upon determining, based on the monitoring, an absence of the activity of the user within a duration of the idle timeout, the cloud computing session is terminated.
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公开(公告)号：US20220326982A1
公开(公告)日：2022-10-13
申请号：US17225427
申请日：2021-04-08
发明人： A Peng Zhang , Lei Gao , Jin Wang , Jing James Xu , Jun Wang , Dong Hai Yu
摘要： Mechanisms are provided for intelligently identifying an execution environment to execute a computing job. An execution time of the computing job in each execution environment of a plurality of execution environments is predicted by applying a set of existing machine learning models matching execution context information and key parameters of the computing job and execution environment information of the execution environment. The predicted execution time of the machine learning models is aggregated. The aggregated predicted execution times of the computing job are summarized for the plurality of execution environments. Responsive to a selection of an execution environment from the plurality of execution environments based on the summary of the aggregated predicted execution times of the computing job, the computing job is executed in the selected execution environment. Related data during the execution of the computing job in the selected execution environment is collected.
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公开(公告)号：US20220147720A1
公开(公告)日：2022-05-12
申请号：US17093879
申请日：2020-11-10
摘要： A machine translation system, a ChatOps system, a method for a context-aware language machine identification, and computer program product. One embodiment of the machine translation system may include a density calculator. The density calculator may be adapted to calculate a part of speech (POS) density for a plurality of word tokens in an input text, calculate a knowledge density for the plurality of word tokens, and calculate an information density for the plurality of word tokens using the POS density and the knowledge density. In some embodiments, the machine translation system may further comprise a sememe attacher and a context translator.
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公开(公告)号：US11170018B2
公开(公告)日：2021-11-09
申请号：US16551313
申请日：2019-08-26
发明人： Lei Gao , Xiao Ming Ma , Wu Yan , Xu Qin Zhao , Shuang Li
IPC分类号： G06F16/248 , G06F16/2457 , G06F16/2458
摘要： A computer-implemented method, system and computer program product for identifying an appropriate contact across collaborative applications. Contact information is collected from monitored messages, communication lists and contact lists in each collaborative application. Contact records are generated based on the collected contact information, where such records are inserted into a contact list. After receiving a query from a user containing a keyword(s) that include a nickname (or portion thereof) of a second user whom the user desires to interact via the current collaborative application, a search is performed in the contact list for any record containing a nickname that is similar to the provided keyword(s). Record(s) in the contact list containing a nickname that exceeds a threshold degree of similarity as the keyword(s) are identified. Such identified records may contain a user identifier which is used to identify the appropriate nickname of the second user associated with the current collaborative application.
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公开(公告)号：US20210133092A1
公开(公告)日：2021-05-06
申请号：US16675759
申请日：2019-11-06
发明人： Lei Gao , Jin Wang , Kai Li , Dong Hai Yu , Rui Wang
IPC分类号： G06F11/36
摘要： Facilitating localization of code defect of an application includes receiving a set of element-value pairs generated by running the application with a test case. Further differences are identified between the set of element-value pairs and a baseline data result for the test case. Tree maps associated with respective elements are displayed in the set of element-value pairs, each of the tree maps representing relationship of code entities of the application related to its associated element, wherein one or more of the tree maps are marked out to show the differences thereby identifying potential defective codes of the application that have caused the differences.
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公开(公告)号：US12099628B2
公开(公告)日：2024-09-24
申请号：US17661780
申请日：2022-05-03
发明人： Jin Wang , Lei Gao , A Peng Zhang , Kai Li , Jun Wang , Xiao Ming Ma , Xin Feng Zhu , Geng Wu Yang
CPC分类号： G06F21/6245 , G06F16/35 , G06F18/23
摘要： The present disclosure relates to privacy protection in a search process. According to a method, a target emotion vector is extracted from a search interaction, the target emotion vector representing emotional information in the search interaction. Respective emotion distances between the target emotion vector and respective emotion vectors associated with a plurality of text clusters are determined. The plurality of text clusters is clustered from a dictionary of text elements. A first number of text clusters are selected from the plurality of text clusters based on the determined respective emotion distances. The first number of text clusters have emotion distances larger than at least one unselected text cluster among the plurality of text clusters. A plurality of confused search interactions are constructed for the search interaction based on the first number of text clusters, and the plurality of confused search interactions are performed.
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7.发明授权公开(公告)号：US11741130B2
公开(公告)日：2023-08-29
申请号：US17403306
申请日：2021-08-16
发明人： Jing James Xu , Ji Hui Yang , Jing Xu , Lei Gao , Si Er Han , Xue Ying Zhang
CPC分类号： G06F16/285 , G06F16/2272 , G06Q30/04
摘要： An embodiment includes parsing conversation data to extract a message dataset and a user dataset. The embodiment classifies the message dataset into a category using machine learning processing and identifies the category as a top category based at least in part on an amount of the conversation data associated with the category. The embodiment generates impact data associated with the user dataset based on actions in the conversation data by the user. The embodiment generates role data associated with the user by applying a rule to the conversation data for the user. The embodiment generates key index data associated with the message dataset by identifying interactions with a message represented by the message dataset. The embodiment generates output data arranged according to a specified data format that is compatible with a user interface.
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公开(公告)号：US20230155916A1
公开(公告)日：2023-05-18
申请号：US17985066
申请日：2022-11-10
发明人： Bo Shen , Yao Dong Liu , Jing James Xu , Lei Gao , Yan Liu
IPC分类号： H04L43/50 , G06F17/18 , H04L43/0817 , H04L43/045 , H04L43/067 , H04L43/0864
CPC分类号： H04L43/50 , G06F17/18 , H04L43/0817 , H04L43/045 , H04L43/067 , H04L43/0864
摘要： A computer-implemented method, system and computer program product for accurately identifying an execution time of a performance test. Network latency data is grouped into clustered groups of network latency data. Furthermore, the performance test execution times for the same group of performance tests run in the local and remote cluster environments are obtained. The test execution times impacted by network latency (compensation times) are then determined based on such obtained performance test execution times in the local and remote cluster environments. Such compensation times are then grouped into clustered groups of compensation times. A regression model is built to predict a performance test execution time impacted by network latency (compensation time) using the clustered groups of network latency data and compensation times. The execution time of a performance test run in the remote cluster environment is then generated that takes into consideration the compensation time predicted by the regression model.
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公开(公告)号：US20230119654A1
公开(公告)日：2023-04-20
申请号：US17451495
申请日：2021-10-20
发明人： Jin Wang , Lei Gao , Kai Li , A Peng Zhang , Yan Liu , Jia Xing Tang , Xin Feng Zhu
IPC分类号： G06N20/00
摘要： Identifying node importance in a machine learning pipeline is provided. Changes in accuracy of the machine learning pipeline are recorded for each respective node setting change in a randomly generated group of node settings inputted into each corresponding node included in the machine learning pipeline. A regression model is generated to determine a relationship between each respective node setting change in the randomly generated group of node settings inputted into each corresponding node and the changes in the accuracy of the machine learning pipeline. A node of importance is identified in the machine learning pipeline using the regression model based on the relationship between each respective node setting change in the randomly generated group of node settings inputted into each corresponding node and the changes in the accuracy of the machine learning pipeline.
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公开(公告)号：US20230119568A1
公开(公告)日：2023-04-20
申请号：US17504662
申请日：2021-10-19
发明人： Jin Wang , Lei Gao , A PENG ZHANG , Kai Li , Yan Liu
摘要： A computer-implemented method includes: obtaining, by a computing device, data from sensors that collect the data in a system during a time, wherein the data is multi-dimensional time series data; creating, by the computing device, matrices based on the data; determining, by the computing device using a first computer-based numerical modeling method, patterns based on the matrices; creating, by the computing device using a second computer-based numerical modeling method, a single time series model based on the patterns; and predicting, by the computing device, a future condition of the system using the time series model with current data of the system.
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