利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

1685 条记录 (耗时 0.019 秒)

    Hybrid Wireless Processing Chains that Include Deep Neural Networks and Static Algorithm Modules
    1.
    发明公开
    Hybrid Wireless Processing Chains that Include Deep Neural Networks and Static Algorithm Modules 审中-公开

    公开(公告)号：US20240365137A1

    公开(公告)日：2024-10-31

    申请号：US18687226

    申请日：2022-09-12

    申请人： Google LLC

    发明人： Jibing Wang ,  Erik Richard Stauffer

    IPC分类号： H04W24/02 ,  H04L41/16

    CPC分类号： H04W24/02 ,  H04L41/16

    摘要： Techniques and apparatuses are described for hybrid wireless communications processing chains that include deep neural networks (DNNs) and static algorithm modules. In aspects, a first wireless communication device communicates with a second wireless device using a hybrid transmitter processing chain. The first wireless communication device selects a machine-learning configuration (ML configuration) that forms a modulation deep neural network (DNN) that generates a modulated signal using encoded bits as an input. The first wireless communication device forms, based on the modulation ML configuration, the modulation DNN as part of a hybrid transmitter processing chain that includes the modulation DNN and at least one static algorithm module. In response to forming the modulation DNN, the first wireless communication devices processes wireless communications associated with the second wireless communication device using the hybrid transmitter processing chain.

    METHOD AND SYSTEM FOR CLASSIFYING ENCRYPTED NETWORK TRAFFIC USING ARTIFICIAL INTELLIGENCE
    3.
    发明公开
    METHOD AND SYSTEM FOR CLASSIFYING ENCRYPTED NETWORK TRAFFIC USING ARTIFICIAL INTELLIGENCE 审中-公开

    公开(公告)号：US20240356814A1

    公开(公告)日：2024-10-24

    申请号：US18622929

    申请日：2024-03-30

    申请人： Solana Networks Inc.

    发明人： Biswajit NANDY ,  Nabil SEDDIGH ,  Donald William Arthur BENNETT ,  Jingyi SHEN ,  Manjender NIR

    IPC分类号： H04L41/16

    CPC分类号： H04L41/16

    摘要： A system for labelling and classification of encrypted network traffic is disclosed. The system employs a Labeler, having a semi-supervised machine learning module for semi-automated labeling of encrypted network traffic, with an initial involvement of a human-in-the-loop intelligence for rapid training of the Labeler. The Labeler produces a labeled training set of encrypted network traffic flows. The system further includes a Modeler having a genetic algorithm module, for automatically selecting a list of network traffic features for further use in real-time classification of the encrypted network traffic, and outputting a corresponding classification model. The system further includes a Classifier for real-time classification of the encrypted network traffic using the classification model. Corresponding methods for labeling and classifying the encrypted network traffic are also provided.

    AUTOMATING A CONFIGURATION OF AN INFRASTRUCTURE FOR CLOUD APPLICATIONS
    5.
    发明公开
    AUTOMATING A CONFIGURATION OF AN INFRASTRUCTURE FOR CLOUD APPLICATIONS 审中-公开

    公开(公告)号：US20240356807A1

    公开(公告)日：2024-10-24

    申请号：US18304535

    申请日：2023-04-21

    申请人： International Business Machines Corporation

    发明人： Srikumar Venugopal ,  Christian Pinto ,  Michele Gazzetti ,  Alessandro Pomponio

    IPC分类号： H04L41/08 ,  H04L41/0894 ,  H04L41/16

    CPC分类号： H04L41/0886 ,  H04L41/0894 ,  H04L41/16

    摘要： Embodiments of the present invention provide an approach for automating a configuration of a server infrastructure for cloud applications by leveraging monitoring data from both the infrastructure and the applications that run on it. Specially, input information including an application which has been submitted is received along with a target dataset, a cloud provider and values for a specific performance measure. The application is mapped to a specific class and a performance model is selected based on the class. A set of resource configurations is generated and estimates of a target measure (e.g., run time) are provided for each configuration option using the selected model. A resource configuration option that provides either the best value of the measure or closest to the application objectives is selected and committed to an application deployment file.

    System for monitoring and controlling a dynamic network
    7.
    发明授权
    System for monitoring and controlling a dynamic network 有权

    公开(公告)号：US12126508B2

    公开(公告)日：2024-10-22

    申请号：US17787250

    申请日：2020-03-01

    申请人： Dubai Electricity & Water Authority PJSC

    发明人： Jacob Jacob ,  Kemal Labbaci ,  Abdullah Saeed Ali Obaidullah Alnuaimi

    IPC分类号： H04L41/16 ,  H04L41/12 ,  H04L43/065 ,  H04L43/20

    CPC分类号： H04L43/065 ,  H04L41/12 ,  H04L41/16

    摘要： The invention relates to a system for monitoring and controlling a dynamic network such as an oil, gas, or water pipeline. The system includes a plurality of sensors for measuring aspects of a state of the network with each sensor being associated with a segment of the network and connected to a virtual sensor which accumulates and pre-processes measurements from the sensors for each segment of the network. The system further includes a network topology processor for storing the topology of the network and relating sensors and virtual sensors to segments of the network and neighbouring sensors and virtual sensors in accordance with the topology and a reinforcement learning artificial neural network (ANN) based nonlinear state estimation and predictive control model which uses measurements from the sensors and virtual sensors to model the state of the network and estimate sequential states of the network.

    System and Method for Artificial Intelligence based Threat Modeling
    8.
    发明公开
    System and Method for Artificial Intelligence based Threat Modeling 审中-公开

    公开(公告)号：US20240348638A1

    公开(公告)日：2024-10-17

    申请号：US18135688

    申请日：2023-04-17

    申请人： Tejvir

    发明人： Tejvir

    IPC分类号： H04L9/40 ,  H04L41/16

    CPC分类号： H04L63/1433 ,  H04L41/16 ,  H04L63/1441

    摘要： The present invention discloses a system and method for providing automated threat modeling for cloud-based security powered by AI. Specifically, the disclosed invention utilizes advanced AI-based techniques and tools to automate the identification of potential security threats, provide traceability and compliance mapping, and generate cloud-specific security policies for improved efficiency and scalability. The disclosed invention leverages AI to analyze vast amounts of data to identify potential security risks and automatically generate appropriate security measures, significantly reducing the time and effort required to develop comprehensive security policies. The disclosed invention is designed to meet the needs of modern enterprises that require fast and reliable security solutions to protect their cloud-based infrastructure. By automating the threat modeling process, this invention enables businesses to scale their security operations and maintain compliance with industry regulations while ensuring that their systems are adequately protected against potential cyber threats.

    MACHINE LEARNING SEGMENT ROUTING FOR MULTIPLE TRAFFIC MATRICES
    9.
    发明公开
    MACHINE LEARNING SEGMENT ROUTING FOR MULTIPLE TRAFFIC MATRICES 审中-公开

    公开(公告)号：US20240348547A1

    公开(公告)日：2024-10-17

    申请号：US18298660

    申请日：2023-04-11

    申请人： Nokia Solutions and Networks Oy

    发明人： Muralidharan Kodialam ,  TV Lakshman

    IPC分类号： H04L47/12 ,  H04L41/16 ,  H04L45/00

    CPC分类号： H04L47/12 ,  H04L41/16 ,  H04L45/34

    摘要： In some embodiments, there may be provided a method that includes receiving a first traffic matrix; receiving information regarding links associated with each segment of the network; determining a total amount of segment flow using the at least one non-linear deflection parameter applied to the traffic demand of the first traffic matrix; determining a link flow for each of the links using the total amount of segment flow and the second input to the machine learning model; determining link utilization for each of the links using the link flows and a capacity for each of the links; learning, by the machine learning model using a gradient descent, a minimum of a maximum amount of the link utilization over the links by at least adjusting a value of the at least one non-linear deflection parameter. Related systems, methods, and articles of manufacture are also disclosed.