Cisco Technology, Inc.

Dmitry Goloubew

Carlos M Pignataro

Gonzalo A Salgueiro

Antonio Nucci

Hugo Latapie

Enzo Fenoglio

Andre Surcouf

Qihong Shao

Gyana Ranjan Dash

Volodymyr Iashyn

Nassim Benoussaid

Borys Viacheslavovych Berlog

Pranjal Daga

Anastasia Feygina

Methods are provided for determining decay rates and/or shelf-lives of intellectual capital (IC) detected issues for only performing actions for expired or about to expire IC detected issues. Specifically, the methods involve performing one or more data collections that include data relating to one or more of a configuration of a computing system or an operation of the computing system and detecting an issue in the computing system based on the data. The issue relates to an anomaly in one or more of the configuration of the computing system or the operation of the computing system. The methods further involve determining a shelf-life for the issue, where the shelf-life indicates an estimated duration of the issue existing in the computing system before redetecting whether the issue is still present and adjusting a next data collection of the one or more data collections based on the shelf-life of the issue.

Method and apparatus for determining shelf-life of issue detected using digitized intellectual capital

In one embodiment, an apparatus obtains unstructured text generated by a device regarding operation of the device. The apparatus identifies the unstructured text as associated with a particular command or process that generated the unstructured text. The apparatus classifies a portion of the unstructured text as anomalous by inputting the portion of the unstructured text to a machine learning-based model trained to predict text generated by the particular command or process. The apparatus provides provide the unstructured text for display that includes an indication that the portion of the unstructured text is anomalous.

Deep learning system for accelerated diagnostics on unstructured text data

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.

Extracting knowledge from collaborative support sessions

In one embodiment, a device in a network receives control plane packet data indicative of control plane packets for a control plane in the network. The device models the control plane using a machine learning model based on the control plane packet data. The device predicts an instability in the control plane using the machine learning model. The device causes performance of a mitigation action based on the predicted instability in the control plane.

Prediction of network device control plane instabilities

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Waterloo, Belgium

Dmitry Goloubew