Cisco Technology, Inc.

Qihong Shao

Xinjun Zhang

David John Zacks

Gurvinder Singh

David C White, Jr

Doosan Jung

Matthew R Engle

Qixu Gong

Josh Viktorov

Pengfei Sun

Yue Liu

Carlos M Pignataro

Antonio Nucci

Derek William Engi

Gyana Ranjan Dash

Dmitry Goloubew

Anastasia Feygina

Benjamin L Chang

Jaime Madruga Rita

Daniel Shan-Shea Chen

Kevin Broich

Pranjal Daga

Kenneth Charles Croley

Methods are provided in which a computing device obtains user data and network data associated with one or more assets used in an enterprise network of a user. The computing device further determines an identity of the user based on the user data and the network data and generates a task recommendation based on the identity of the user. The task recommendation includes one or more tasks having a plurality of operations that are to be performed within a predetermined time interval. The computing device further provides the task recommendation for performing one or more actions associated with configuring the enterprise network.

Personas detection and task recommendation system in network

A platform dynamically detects a user persona and facilitates a user objective in a user session. The platform obtains user activity of users across multiple historical sessions. The platform clusters similar user activity across the historical sessions to determine personas being used in the historical sessions. The platform mines sequential patterns in a set of user activity data associated with one of the personas and determines at least one predictive rule associated with that persona. Each predictive rule includes an initial activity among the set of user activity data and at least one subsequent activity in the set of user activity data.

Dynamically detecting user personas of network users for customized suggestions

Methods are provided for generating digests of network-related notifications specifically tailored to user's personas and adaptable across multiple timescale frequencies. Specifically, the methods involve obtaining user data of a user associated with an enterprise network and a plurality of network-related notifications. Each of the plurality of network-related notifications relates to network operations or network configurations. The methods further involve determining a network persona of the user in a context of the enterprise network based on the user data and generating a digest of the plurality of network-related notifications based on the network persona. The digest includes a semantic summary for each of the plurality of network-related notifications that is specific to the network persona. The methods further involve providing the digest for performing one or more actions associated with the enterprise network.

Persona-based multi-scale network related digest generation

Methods are provided for generating hierarchical summaries with actionable recommendations having various granularities. Specifically, the methods involve obtaining notifications related to network issues and generating meta-semantic data that includes a summary of each of the notifications. The methods further involve obtaining inventory data of network devices in a plurality of domains of a network. The inventory data includes configuration information of the network devices. The methods further involve generating a multi-level hierarchical summary specific to the network based on the inventory data and the meta-semantic data. The multi-level hierarchical summary includes a first level specific to one or more affected network devices and a second level specific to a group of network devices. The methods further involve providing the multi-level hierarchical summary for performing one or more actions associated with the network.

Hierarchical auto summary generation with multi-task learning in networking recommendations

A heterogeneous graph learning system generates and analyzes network implementations. The heterogeneous graph learning system includes obtaining information describing multiple network implementations including heterogeneous nodes. The heterogeneous graph learning system also includes generating a one-hop graph connecting a particular node of the heterogeneous nodes with a set of related nodes. The one-hop graph connects the particular node with the set of related nodes via corresponding edges. The heterogeneous graph learning system further includes transforming the one-hop graph into a weighted graph based on a Dynamic Meta Path Transformation (DMPT). In the DMPT, each of the corresponding edges connecting the particular node to a corresponding related node among the set of related nodes is associated with a corresponding weight.

Heterogeneous graph learning-based unified network representation

A method, computer system, and computer program product are provided for mitigating network risk. A plurality of risk reports corresponding to a plurality of network devices in a network are processed to determine a multidimensional risk score for the network. The plurality of risk reports are analyzed using a semantic analysis model to identify one or more factors that contribute to the multidimensional risk score. One or more actions are determined using a trained learning model to mitigate one or more dimensions of the multidimensional risk score. The outcomes of applying the one or more actions are presented to a user to indicate an effect of each of the one or more actions on the multidimensional risk score for the network.

Interpreting and remediating network risk using machine learning

A method, computer system, and computer program product are provided for network risk analysis. A plurality of risk reports relating to a network device in a network are obtained, wherein each risk report is associated with a particular dimension of a plurality of dimensions of risk for the network device in the network. A count of the plurality of risk reports is determined for each dimension of the plurality of dimensions of risk. A regression model is applied to determine a risk value for the network device in the network based on the count of the plurality of risk reports for each dimension and based a role of the network device in the network.

Tailored network risk analysis using deep learning modeling

A method, computer system, and computer program product are provided for peer risk benchmarking. Customer data for a first network is obtained, wherein the customer data comprises a role of one or more network devices in the first network and a plurality of risk reports corresponding to the one or more network devices, and wherein each risk report is associated with a particular dimension of a plurality of dimensions of risk for the one or more network devices. A network profile image is generated by processing the plurality of risk reports. A generative adversarial network generates a synthetic network profile image from the network profile image, wherein the synthetic network profile image does not include the customer data. A second network is evaluated using the synthetic network profile image to identify differences between the first network and the second network.

Peer risk benchmarking using generative adversarial networks

At a communication server, a first computer device and a second computer device are connected to a collaborative support session configured to support audio communications, screen sharing, and control of the first computer device by the second computer device. Screen sharing video image content is converted to a text sequence with timestamps. A text log with timestamps is generated from the text sequence. Using a command-based machine learning model, a sequence of commands and associated parameters, with timestamps, are determined from the text log. Audio is analyzed to produce speech-based information with timestamps. The command sequence is time-synchronized with the speech-based information based on the timestamps of the command sequence and the timestamps of the speech-based information. A knowledge report for the collaborative support session is generated. The knowledge report includes entries each including a timestamp, commands and associated parameters, and speech-based information that are time-synchronized to the timestamp.

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Clyde Hill, WA, United States of America

Qihong Shao