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    Granular Cluster Generation for Real-Time Processing
    2.
    发明公开
    Granular Cluster Generation for Real-Time Processing 审中-公开

    公开(公告)号：US20230267176A1

    公开(公告)日：2023-08-24

    申请号：US18167297

    申请日：2023-02-10

    申请人： Google LLC

    发明人： Xuerui Wang ,  Daniel Li ,  Xiaodan Song ,  Jie Han ,  Rahul Sharma

    IPC分类号： G06F18/23211 ,  G06F16/906 ,  G06F16/215 ,  G06F18/23213

    CPC分类号： G06F18/23211 ,  G06F16/906 ,  G06F16/215 ,  G06F18/23213

    摘要： Generating granular clusters for real-time processing is provided. The systems can identify tokens based on aggregating input from computing devices over a time interval. The systems can identify, based on metrics, a subset of tokens for cluster generation. The systems can generate, via a clustering technique, token clusters from the subset of the tokens, each of the token clusters comprising two or more tokens from the subset of the tokens. The systems can apply a de-duplication technique to each of the token clusters. The systems can apply a filtering technique to the token clusters to remove tokens erroneously grouped in a token cluster. The systems can assign, based on a selection process, a label for each of the token clusters. The systems can activate, based on a number of remaining tokens in each of the token clusters, a subset of the token clusters for real-time content selection.

    SECURITY IN COMMUNICATION NETWORKS
    3.
    发明公开
    SECURITY IN COMMUNICATION NETWORKS 审中-公开

    公开(公告)号：US20230254329A1

    公开(公告)日：2023-08-10

    申请号：US18161292

    申请日：2023-01-30

    申请人： Nokia Solutions and Networks Oy

    发明人： Mehrnoosh MONSHIZADEH ,  Vikramajeet Khatri ,  Marah Gamdou

    IPC分类号： H04L9/40 ,  G06F18/23211

    CPC分类号： H04L63/1425 ,  G06F18/23211

    摘要： According to an example aspect of the present invention, there is provided an apparatus, comprising means for performing, receiving input data comprising data points, applying N initial clustering algorithms at least to a subset of said data points to generate N initial clustering matrices, generating a co-association matrix from the N initial clustering matrices, generating a distance matrix from the co-association matrix, applying a density based clustering algorithm to the distance matrix to generate data clusters, determining a subset of the generated data clusters as anomalous clusters, wherein at least some of the data points in each anomalous cluster are anomalous data points and performing at least one action based on the anomalous clusters.

    Data test method, electronic device and storage medium
    5.
    发明授权
    Data test method, electronic device and storage medium 有权

    公开(公告)号：US11971959B2

    公开(公告)日：2024-04-30

    申请号：US17333472

    申请日：2021-05-28

    申请人： HON HAI PRECISION INDUSTRY CO., LTD.

    发明人： Cheng-Feng Wang

    IPC分类号： G06F11/30 ,  G06F18/23 ,  G06F18/23211 ,  G06F18/2413

    CPC分类号： G06F18/23211 ,  G06F18/23 ,  G06F18/24137 ,  G06F18/24147

    摘要： A data test method, an electronic device, and a storage medium are provided. In the data test method, based on a Density-Based Spatial Clustering of Applications with Noise (DBSCAN), at least one cluster is obtained by removing discrete points in the target data and performing clustering, an calculation result is obtained by performing a regression analysis on the target data with the objective function, and parameters to be tested are verified according to the calculation result. Utilizing the data test method, objective function can be used to perform verification and residual analysis on the target data, related descriptions are be repeated here.

    System and methods for scoring telecommunications network data using regression classification techniques
    7.
    发明授权
    System and methods for scoring telecommunications network data using regression classification techniques 有权

    公开(公告)号：US11763215B2

    公开(公告)日：2023-09-19

    申请号：US17929057

    申请日：2022-09-01

    申请人： Verizon Patent and Licensing Inc.

    发明人： Kishore K. Guntuku ,  Vamsi Krishna Boyapati

    IPC分类号： G06Q10/04 ,  G06N10/00 ,  G01W1/10 ,  G06F18/22 ,  G06F18/23211

    CPC分类号： G06Q10/04 ,  G01W1/10 ,  G06F18/22 ,  G06F18/23211 ,  G06N10/00

    摘要： Systems and methods provide a demand forecasting and network optimization for telecommunications services in a network. The systems and methods use classical and quantum computing devices. The computing devices evaluate data types using statistical symmetry recognition and operate between classical and quantum environments. Computing devices receive deposited data, batch data, and streamed data that relates to telecommunications services and segregate the data into spatial and temporal factors. The computing devices receive an analytic request for a forecast of the telecommunications services and conduct a multi-class plural-factored elastic cluster (MPEC) analysis for the telecommunications services using the segregated data. The MPEC analysis includes generating vectors comprised of slopes from plural coefficients to determine demand elasticity from plural features. The computing devices generate, based on the multi-class plural-factored elastic cluster model, a real-time demand-based forecast for the telecommunications services, and output the demand-based forecast.

    Communication network data fault detection and mitigation
    10.
    发明授权
    Communication network data fault detection and mitigation 有权

    公开(公告)号：US11860744B2

    公开(公告)日：2024-01-02

    申请号：US17199141

    申请日：2021-03-11

    申请人： AT&T Intellectual Property I, L.P.

    发明人： Ke Xu ,  Weihua Ye ,  Hyun Ok Lee

    IPC分类号： G06F11/14 ,  G06N20/00 ,  G06F18/23211

    CPC分类号： G06F11/1469 ,  G06F18/23211 ,  G06N20/00 ,  G06F2201/82

    摘要： A processing system may apply a binary classifier to detect whether a first data pattern of a first data source associated with a communication network performance indicator is consistent with prior data patterns of the first data source that are labeled as correct data patterns, determine, via the binary classifier, that the first data pattern is not consistent, apply a clustering model to a first input data set comprising the first data pattern and invalid data patterns of the first data source to obtain a first plurality of clusters, verify that the first data pattern is an invalid data pattern when the first plurality of clusters is the same as a second plurality of clusters generated by applying the clustering model to a second input data set comprising the invalid data patterns, and replace the first data source with a replacement data source as an active data source in response.