Everbridge, Inc.

Emc IP Holding Company LLC

Azimuth Systems, Inc.

Prashant Darisi

Urvish Saraiya

Cory Veilleux

Christopher E Seline

Diptesh Shah

Prashant Desai

Eamon G O Neill

Jose Graziani

Jeremy Spilman

Christopher Clifton McLaren

Prashant R Juvekar

Anton Dam

Analytics dashboards for critical event management systems that include artificial-intelligence (AI) functionalities, and related software. AI functionalities disclosed include pattern recognition and predictive modelling. One or more pattern-recognition algorithms can be used, for example, to identified patterns or other groupings within stored critical events, which can then be used to improve response performance and/or to inform the generation of predictive models. One or more predictive-modeling algorithms can be used to generate one or more predictive models that can then be used, for example, to make predictions about newly arriving critical events that can then be used, among other things, to provide optimal response performance and allow users to efficiently and effectively manage responses critical events. These and other features are described in detail.

Critical event management using predictive models, and related methods and software

Analytics dashboards for critical event management software systems, and related software

A computer-implemented process for an endpoint for automated fulfillment, includes: in an Automatic Fulfillment (AF) server: creating an endpoint on the AF server; selecting supported commands; and for each command, defining required and optional parameters and endpoint type specific implementation details; and in a Compliance Manager (CM): running discovery to find new endpoint and create metadata for it in the CM; for each command to be automated, filling in mappings for at least all required parameters; and enabling the command; enabling the endpoint; binding the endpoint to a business source; and ensuring that the business source uses a fulfillment process that uses the AF server.

Identity and access management

A method and apparatus is provided that enables accurate measurement of drop rate and delay in a System Under Test (SUT) by one or more monitoring devices even when the frame error rate of the monitoring devices may be imperfect. During a packet drop measurement process, ancillary information is identified and analyzed to determine if the ancillary information can be used to infer receipt of packets when explicit information regarding receipt is not present. A delay measurement process incorporates the time required to re-transmit packets into the delay measurement to more accurately reflect SUT operation.

System and method for intelligently analyzing performance of a device under test

Growing community of inventors

Nashua, NH, United States of America

Prashant Darisi