Brocade Communications Systems, Inc.

Other

Rishi Amit Sinha

Sathish Kumar Gnanasekaran

Vineet M Abraham

Badrinath Kollu

Michael W Atkinson

Michael Gee

Ria Sinha

Systems and methods used in a cognitive assistant for detecting human emotions from speech audio signals is described. The system obtains audio signals from an audio receiver and extracts human speech samples. Subsequently, it runs a machine learning based classifier to analyze the human speech signal and classify the emotion observed in it. The user is then notified, based on their preferences, with a summary of the emotion detected. Notifications can also be sent to other systems that have been configured to receive them. Optionally, the system may include the ability to store the speech sample and emotion classification detected for future analysis. The system's machine learning classifier is periodically re-trained based on labelled audio speech data and updated.

Cognitive assistant for real-time emotion detection from human speech

Systems and Methods for assisting the hearing-impaired are described. The methods rely on obtaining audio signals from the ambient environment of a hearing-impaired person. The audio signals are analyzed by a machine learning model that can classify audio signals into audio categories (e.g. Emergency, Animal Sounds) and audio types (e.g. Ambulance Siren, Dog Barking) and notify the user leveraging a mobile or wearable device. The user can configure notification preferences and view historical logs. The machine learning classifier is periodically trained externally based on labelled audio samples. Additional system features include an audio amplification option and a speech to text option for transcribing human speech to text output.

Systems and methods for assisting the hearing-impaired using machine learning for ambient sound analysis and alerts

Back pressure is mapped within a network, and primary bottlenecks are distinguished from dependent bottlenecks. Further, the presently disclosed technology is capable of performing network healing operations designed to reduce the data load on primary bottlenecks while ignoring dependent bottlenecks. Still further, the presently disclosed technology teaches identifying and/or suggesting a switch port for adding a node to the network. More specifically, various implementations analyze traffic load and back pressure in a network, identify primary and dependent bottlenecks, resolve the primary bottlenecks, collect new node parameters, and/or select a switch port for the new node. Further, a command can be sent to a selected switch to activate an indicator on the selected port. New node parameters may include new node type, maximum load, minimum load, time of maximum load, time of minimum load and type of data associated with the new node.

Back pressure remediation

Manageability tools are provided for allowing an administrator to have better control over switches in a lossless network of switches. These tools provide the ability to detect slow drain and congestion bottlenecks, detect stuck virtual channels and loss of credits, while hold times on edge ASICs to be different from hold times on core ASICs, and mitigate severe latency bottlenecks.

Manageability tools for lossless networks

Presentation of a selected port

Automatic switch port selection

Manageability tools are provided for allowing an administrator to have better control over switches in a lossless network of switches. These tools provide the ability to detect slow drain and congestion bottlenecks, detect stuck virtual channels and loss of credits, configure hold times on edge switches to be different from hold times on core switches, and mitigate severe latency bottlenecks.

Manageability tools are provided for allowing an administrator to have better control over switches in a lossless network of switches. These tools provide the ability to detect slow drain and congestion bottlenecks, detect stuck virtual channels and loss of credits, while hold times on edge ASICs to be different from hold times encore ASICs, and mitigate severe latency bottlenecks.

One embodiment of the present invention provides a system that facilitates traffic management in a network. During operation, the system detects a bottleneck in a network based on network-state information received from one or more switches. The system further identifies a data flow that contributes to the bottleneck and generates a signal to reduce the data flow from the data flow's source device.

Method and system for traffic management

Manageability tools are provided for allowing an administrator to have better control over switches in a lossless network of switches. These tools provide the ability to detect slow drain and congestion bottlenecks, detect stuck virtual channels and loss of credits, configure hold times on edge ASICs to be different from hold times on core ASICs, and mitigate severe latency bottlenecks.

Growing community of inventors

San Jose, CA, United States of America

Rishi Amit Sinha