Cisco Technology, Inc.

Enzo Fenoglio

Hugo Latapie

Joseph T Friel

Andre Surcouf

David Delano Ward

Manikandan Kesavan

Sawsen Rezig

Carlos M Pignataro

Plamen Nedeltchev

Guillaume Sauvage De Saint Marc

Pascal Thubert

Andre Jean-Marie Surcouf

Pete Rai

Thierry Gruszka

Franck Bachet

Jean-Philippe Vasseur

Nagendra Kumar Nainar

Santosh Ghanshyam Pandey

Keith Griffin

Paul G Bright-Thomas

Eric Chen

Dmitri Goloubev

Donald Mark Allen

Ronnie Dan

Kenneth Gray

Raphaël Wouters

Nathan Buckles

Didier Colens

Carole Gridley

Gonzalo A Salgueiro

Frank Brockners

David John Zacks

Shwetha Subray Bhandari

Toerless Eckert

Yoann Desmouceaux

Pierre Pfister

Monique Jeanne Morrow

Xu Zhang

Abhishek Mukherji

W Mark Townsley

Huy Phuong Tran

Altan John Stalker

Michael B Costello

Jérôme Tollet

Deon J Chatterton

Dmitry Goloubew

Wenqin Shao

Pallavi Kalapatapu

Zizhen Gao

Nham Van Le

Victor Tsekay To

Fabien Andrieux

Dmitry Goloubev

Feng Jiang

Syed Muhammad Mohsin Kazmi

In one embodiment, a first deep fusion reasoning engine (DFRE) agent in a network receives first sensor data from a first set of one or more sensors in the network. The first DFRE agent translates the first sensor data into symbolic data. The first DFRE agent applies, using a symbolic knowledge base maintained by the first DFRE agent, symbolic reasoning to the symbolic data to make an inference regarding the first sensor data. The first DFRE agent updates, based on the inference regarding the first sensor data, the knowledge base. The first DFRE agent propagates the inference to one or more other DFRE agents in the network.

Federated artificial intelligence with cumulative learning in a computer network

A method, computer system, and computer program product are provided for detecting drift in predictive models for network devices and traffic. A plurality of streams of time-series telemetry data are obtained, the time-series telemetry data generated by network devices of a data network. The plurality of streams are analyzed to identify a subset of streams, wherein each stream of the subset of streams includes telemetry data that is substantially empirically distributed. The subset of streams of time-series data are analyzed to identify a change point. In response to identifying the change point, additional time-series data is obtained from one or more streams of the plurality of streams of time-series telemetry data. A predictive model is trained using the additional time-series data to update the predictive model and provide a trained predictive model.

Drift detection for predictive network models

In one embodiment, a video analysis service receives video data captured by one or more cameras at a particular location. The service applies a neural network-based model to portions of the video data, to identify objects within the video data. The service maps outputs of the neural network-based model to symbols using a conceptual space. The outputs of the model comprise the identified objects. The service applies a symbolic reasoning engine to the symbols, to generate an alert. The service sends the alert to a user interface in conjunction with the video data.

Video analysis using a deep fusion reasoning engine (DFRE)

In one embodiment, a deep fusion reasoning engine receives network telemetry data collected from a network. The deep fusion reasoning engine learns resource utilizations for different heuristic packages that can be used in the network to evaluate operation of the network. The deep fusion reasoning engine selects one of the heuristic packages based on the resource utilizations learned for the different heuristic packages. The selected heuristic package comprises a subservice and a set of rules to be evaluated. The deep fusion reasoning engine deploys the selected heuristic package for execution by a device in the network to evaluate operation of the network using the set of rules.

Dynamic heuristic packages and metrics for network assurance using a deep fusion reasoning engine

Techniques for utilizing a communication system that provides access to a representation of a virtual environment to participants. The communication system may establish connections between personal communication bridge(s) associated with participant(s) interacting within a virtual proximity radius of one another's virtual indicator in the virtual environment. The communication system may cause conversation data to be sent each personal communication bridge associated with a participant that is within the virtual proximity radius of the sender, and cause conversation data to be received via the personal communication bridge of a participant that is within the virtual proximity radius of the sender. The communication system may also analyze data associated with the participant profile(s) and transcribed conversation data from the communication bridges(s) to recommend potential conversations of interest to participant(s).

Virtual proximity radius based web conferencing

In one embodiment, a service that monitors a network detects a plurality of anomalies in the network. The service uses data regarding the detected anomalies as input to one or more machine learning models. The service maps, using a conceptual space, outputs of the one or more machine learning models to symbols. The service applies a symbolic reasoning engine to the symbols, to rank the anomalies. The service sends an alert for a particular one of the detected anomalies to a user interface, based on its corresponding rank.

Deep fusion reasoning engine (DFRE) for prioritizing network monitoring alerts

Systems, methods, and computer-readable for multi-spatial scale object detection include generating one or more object trackers for tracking at least one object detected from on one or more images. One or more blobs are generated for the at least one object based on tracking motion associated with the at least one object. One or more tracklets are generated for the at least one object based on associating the one or more object trackers and the one or more blobs, the one or more tracklets including one or more scales of object tracking data for the at least one object. One or more uncertainty metrics are generated using the one or more object trackers and an embedding of the one or more tracklets. A training module for detecting and tracking the at least one object using the embedding and the one or more uncertainty metrics is generated using deep learning techniques.

Multi-spatial scale analytics

In one embodiment, a network quality assessment service that monitors a network obtains multimodal data indicative of a plurality of measurements from the network and subjective perceptions of the network by users of the network. The network quality assessment service uses the obtained multimodal data as input to one or more neural network-based models. The network quality assessment service maps, using a conceptual space, outputs of the one or more neural network-based models to symbols. The network quality assessment service applies a symbolic reasoning engine to the symbols, to generate a conclusion regarding the monitored network. The network quality assessment service provides an indication of the conclusion to a user interface.

Deep fusion reasoning engine (DFRE) for dynamic and explainable wireless network QoE metrics

A method comprises collecting, by a computing device located at an edge of a network, data items corresponding to information transmitted by endpoints using the network, generating, by the computing device, a probabilistic hierarchy using the data items, generating, by the computing device using the probabilistic hierarchy and natural language data, a similarity metric, generating, by the computing device using the probabilistic hierarchy, the natural language data, and the similarity metric, an ontology, detecting, by the computing device using the ontology, an anomaly, and in response to detecting the anomaly, sending a notification.

Automatic ontology generation for internet of things applications

Systems, methods, and computer-readable for multi-temporal scale analysis include obtaining two or more timescales associated with one or more images. A context associated with a monitoring objective is obtained, based on real time analytics or domain specific knowledge. The monitoring objective can include object detection, event detection, pattern recognition, or other. At least a subset of timescales for performing a differential analysis on the one or more images is determined based on the context. Multi timescale surprise detection and clustering are performed using the subset of timescales to determine whether any alerts are to be generated based on entropy based surprises. A set of rules can be created for the monitoring objective based on the differential analytics and alerts or entropy based surprises, if any.

Multi-temporal scale analytics

In one embodiment, a controller in a network trains a deep reinforcement learning-based agent to predict traffic flows in the network. The controller determines one or more resource requirements for the predicted traffic flows. The controller assigns, using the deep reinforcement learning-based agent, paths in the network to the flows based on the determined one or more resource requirements, to avoid fragmentation of a flow during transmission of the flow through the network. The controller sends, to nodes in the network, assignment instructions that cause the flows to traverse the network via their assigned paths.

Inspired path computation in a network

In one embodiment, a service obtains spatial information regarding a physical area. The service estimates locations of a device within the physical area over time, based on wireless signals sent by the device. The service generates a set of images based on the spatial information regarding the physical area and on the estimated locations of the device within the physical area over time. The service updates an estimated location of the device by inputting the generated set of images to a machine learning model trained to minimize a location estimation error.

Image-based framework for improving location estimate accuracy in indoor positioning systems

In one embodiment, a traffic analysis service obtains traffic characteristics of network traffic associated with a device in a network. The traffic analysis service uses a machine learning model to infer resource usage by the device based on the obtained traffic characteristics of the network traffic associated with the device. The traffic analysis service controls traffic flows in the network based on the inferred resource usage by the device.

Inferring device load and availability in a network by observing weak signal network based metrics

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Issy-les-Moulineaux, France

Enzo Fenoglio