1
David Helsper, Clayton Wilkinson, Robert Zack, John T Tatum, Robert J Jannarone, Bernd Harzog: Enhanced computer performance forecasting system. Netuitive, Michael J Mehrman, Mehrman Law Office P C, April 5, 2005: US06876988 (73 worldwide citation)

A method and system for computing a performance forecast for an e-business system or other computer architecture to proactively manage the system to prevent system failure or slow response time. The system is adapted to obtain measured input values from a plurality of internal data sources and exter ...


2
John T Tatum, W Clayton Wilkinson IV, Robert J Jannarone: Automatic data extraction, error correction and forecasting system. Netuitive, Michael J Mehrman, Mehrman Law Office PC, July 8, 2003: US06591255 (50 worldwide citation)

A “Rapid Learner Client Service” (RLCS) system that allows a large number of end-users to obtain the benefits of a sophisticated neural-network forecasting system. Rather than purchasing or developing a forecasting system of their own, RLCS clients subscribe to a forecasting service performed by for ...


3
Robert Jannarone: Concurrent learning and performance information processing system. Netuitive, Michael J Mehrman, Gardner Groff Mehrman & Josephic P C, September 11, 2001: US06289330 (46 worldwide citation)

The present invention provides a system for learning from and responding to regularly arriving information at once by quickly combining prior information with concurrent trial information to produce useful learned information. A parallel embodiment of the system performs can perform updating operati ...


4

5
David Helsper, Jean Francois Huard, David Homoki, Amanda Rasmussen, Robert Jannarone: Method and system for analyzing and predicting the performance of computer network using time series measurements. Netuitive, Michael J Mehrman, Mehrman Law Office PC, October 9, 2007: US07280988 (35 worldwide citation)

A monitoring system including a baseline model that automatically captures and models normal system behavior, a correlation model that employs multivariate autoregression analysis to detect abnormal system behavior, and an alarm service that weights and scores a variety of alerts to determine an ala ...


6

7
Jean François Huard: Computer performance estimation system configured to take expected events into consideration. Netuitive, August 29, 2006: US07099799 (4 worldwide citation)

The present invention may be embodied as expected event scheduler and processor in an application performance monitoring (APM) services. The expected event scheduler and processor allows the APM system to take scheduled events into account when performing the performance forecasting for the host sys ...


8
Robert Jannarone, David Homoki, Amanda Rasmussen: Automated analyzers for estimation systems. Netuitive, Michael J Mehrman, Mehrman Law Office P C, October 24, 2006: US07127439 (4 worldwide citation)

This invention specifies analyzers to be run in conjunction with computer estimation systems, which may be applied to performance monitoring (APM) services. A semi-automated analyzer may be used by a human analyst to periodically evaluate available historical data for establishing a desired set of i ...


9
Helsper David, Zack Robert, Jannarone Robert, Wilkinson Clayton, Tatum John T, Harzog Bernd: Enhanced computer performance forecasting system. Netuitive, January 21, 2004: EP1381953-A2 (1 worldwide citation)

A method and system for computing a performance forecast for an e-business system or other computer architecture to proactively manage the system to prevent system failure or slow response time. The system is adapted to obtain measured input values from a plurality of internal data sources and exter ...


10
Jannarone Robert J: Systeme dapprentissage, de controle et de prevision concurrentiels a reseau neuronal a noyaux multiples, Multi-kernel neural network concurrent learning, monitoring, and forecasting system. Jannarone Robert J, Netuitive, FINLAYSON & SINGLEHURST, May 28, 1998: CA2272621

A multi-kernel neural network computing architecture configured to learn correlations among feature values (34, 38) as the network monitors and imputes measured input values (30) and also predicts future output values (46). This computing architecture, referred to as a concurrent-learning informatio ...