Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In one embodiment, a device obtains an output of a machine learning-based anomaly detector for unstructured text. The output of the anomaly detector includes a sequence of text analyzed by the detector and an indication that a portion of the sequence of text was flagged by the detector as an anomaly. The device extracts a context for the anomaly as an n-gram of portions of the sequence of text surrounding the anomaly. The device identifies a structure of the anomaly by identifying anchor portions of the extracted context. The device generates, based on the identified structure, an expression that represents the structure of the anomaly within the unstructured text.

Abstract: 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.

Abstract: 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.

Abstract: 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.