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公开(公告)号：US20180278972A1
公开(公告)日：2018-09-27
申请号：US15992015
申请日：2018-05-29
Applicant: CABLE TELEVISION LABORATORIES, INC.
Inventor： JAMES KIM , KARTHIK SUNDARESAN , THOMAS KEE
IPC: H04N21/231 , H04N21/61 , H04N21/24 , H04N21/238
CPC classification number: H04N21/23103 , H04N21/238 , H04N21/2385 , H04N21/2402 , H04N21/241 , H04N21/6118 , H04N21/643
Abstract: Systems and methods presented herein provide for a software defined network (SDN) controller in a cable television system that virtualizes network elements in the cable television system to provide content delivery and data services through the virtualized network elements. In one embodiment, the SDN controller is operable in a cloud computing environment to balance data traffic through the virtualized network elements. For example, the SDN controller may abstract Layer 2 Control Protocol (L2CP) frame processing of the network elements into the cloud computing environment to relieve the network elements from the burdens of Ethernet frame processing. In this regard, the SDN controller comprises a L2CP decision module that determines how L2CP should be processed for the network elements in the cable television system.
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2.发明申请公开(公告)号：US20200097775A1
公开(公告)日：2020-03-26
申请号：US16577304
申请日：2019-09-20
Applicant: CABLE TELEVISION LABORATORIES, INC.
Inventor： JINGJIE ZHU , KARTHIK SUNDARESAN
Abstract: The present disclosure generally relates to apparatus, software and methods for detecting and classifying anomalous features in one-dimensional data. The apparatus, software and methods disclosed herein use a YOLO-type algorithm on one-dimensional data. For example, the data can be any one-dimensional data or time series, such as but not limited to be power over time data, signal to noise ratio (SNR) over time data, modulation error ratio (MER) data, full band capture data, radio frequency data, temperature data, stock data, or production data. Each type of data may be susceptible to repeating phenomena that produce recognizable anomalous features. In some embodiments, the features can be characterized or labeled as known phenomena and used to train a machine learning model via supervised learning to recognize those features in a new data series.
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公开(公告)号：US20200097852A1
公开(公告)日：2020-03-26
申请号：US16577699
申请日：2019-09-20
Applicant: CABLE TELEVISION LABORATORIES, INC.
Inventor： JINGJIE ZHU , KARTHIK SUNDARESAN
IPC: G06N20/00
Abstract: The present disclosure generally relates to apparatus, software and methods for detecting anomalous elements in data. For example, the data can be any time series, such as but not limited to radio frequency data, temperature data, stock data, or production data. Each type of data may be susceptible to repeating phenomena that produce recognizable features of anomalous elements. In some embodiments, the features can be characterized as known patterns and used to train a machine learning model via supervised learning to recognize those features in a new data series.
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公开(公告)号：US20190288874A1
公开(公告)日：2019-09-19
申请号：US16358663
申请日：2019-03-19
Applicant: CABLE TELEVISION LABORATORIES, INC.
Inventor： GREGORY C. WHITE , KARTHIK SUNDARESAN
Abstract: Methods, systems, and devices for Content-Centric Networking (CCN) are described. In some cases, a node may receive a CCN packet from an upstream node and communicate the CCN packet to one or more downstream nodes (e.g., that previously requested the CCN packet). In a first case, the node may establish a persistent internet protocol (IP) tunnel with the downstream node and communicate the CCN packet to the downstream node by the persistent IP tunnel. Here, a cable modem between the node and the downstream node may not decode the CCN packet. In a second case, the node may append an identifier to the CCN packet prior to communicating the CCN packet to the one or more downstream nodes. Here, the identifier may indicate to the downstream nodes which CCN packets are relevant to the downstream node.
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