Netuitive, Inc.

Brainlike, Inc.

Robert J Jannarone

Jennifer A Gibson

David Helsper

David Homoki

John Tyler Tatum

Amanda Rasmussen

John T Tatum

J Tyler Tatum

Clayton Wilkinson, Iv

Leronzo Lidell Tatum

Jean-Francois Huard

Robert Zack

Bernd Harzog

Thuy Xuan Cox

W Clayton Wilkinson, Iv

An auto-adaptive system is provided that includes a template builder that allows weighted templates to be created for computing auto-adaptive features, an auto-adaptive event locator that analyzes a data set to identify events, an event extractor that locates and extracts identified events and provides events for review by an event analyzer (operator or programmed module) to distinguish clutter data from target data, and an auto-adaptive risk analyzer that processes data related to hit rates, false alarm rates, alarm costs, and risk factors to determine return on investment information and receiver operator characteristic curves.

System and method for auto-adaptive network

Feature values, which may be multi-dimensional, collected over successive time slices, are efficiently processed for use, for example, in known adaptive learning functions and event detection. A Markov chain in a recursive function to calculate imputed values for data points by use of a 'nearest neighbor' matrix. Only data for the time slices currently required to perform computations must be stored. Earlier data need not be retained. A data selector, referred to herein for convenience as a window driver, selects successive cells of appropriate adjacent values in one or more dimensions to comprise an estimation set. The window driver effectively indexes tables of data to efficiently deliver input data to the matrix. In one form, feature inputs are divided into subgroups for parallel, pipelined processing.

Efficient processing in an auto-adaptive network

In an auto-adaptive system, efficient processing generates predicted values in an estimation set in at least one dimension for a dependent data location. The estimation set comprises values for a dependent data point and a preselected number of spatial nearest neighbor values surrounding the dependent data point in a current time slice; The prediction may be made for time slices, seconds, hours or days into the future, for example. Imputed values may also be generated. A mean value sum of squares and cross product MVSCP matrix, inverse, and other learned parameters are used. The present embodiments require updating only one MVSCP matrix and its inverse per time slice. A processing unit may be embodied with selected modules each calculating a component function of feature value generation. Individual modules can be placed in various orders. More than one of each type of module may be provided.

Auto-adaptive network for sensor data processing and forecasting

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 alarm status and implement appropriate response actions. The baseline model decomposes the input variables into a number of components representing relatively predictable behaviors so that the erratic component e(t) may be isolated for further processing. These components include a global trend component, a cyclical component, and a seasonal component. Modeling and continually updating these components separately permits a more accurate identification of the erratic component of the input variable, which typically reflects abnormal patterns when they occur.

Method and system for analyzing and predicting the performance of computer network using time series measurements

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 input measurements, model parameters, and reporting criteria, collectively called configuration parameters, to be used by an estimation system. In addition, a fully automated analyzer may periodically and automatically reevaluate such configuration parameters and automatically reconfigure the estimation system accordingly. For both types of analyzer, the active set of input measurements for the computerized estimation system can be initially established or periodically updated to perform any variety of configuration tuning operations, including the following: removing linearly redundant variables; removing inputs showing no variation; removing unnecessary or non-estimable inputs; tuning estimation system operating parameters that govern learning rates and the use of recent trends; occasional model accuracy assessment; and tuning monitoring alarm thresholds.

Automated analyzers for estimation systems

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 external data sources to predict a system's performance especially under unpredictable and dramatically changing traffic levels in an effort to proactively manage the system to avert system malfunction or slowdown. The performance forecasting system can include both intrinsic and extrinsic variables as predictive inputs. Intrinsic variables include measurements of the systems own performance, such as component activity levels and system response time. Extrinsic variables include other factors, such as the time and date, whether an advertising campaign is underway, and other demographic factors that may effect or coincide with increased network traffic.

Enhanced computer performance forecasting system

A multi-kernel neural network computing architecture configured to learn correlations among feature values as the network monitors and imputes measured input values and also predicts future output values . This computing architecture includes a multi-kernel neural network array with the capability to learn and predict in real time. The CIP also includes a manager and an input-output transducer that may be used for input-output refinement. These components allow the computing capacity of the multi-kernel array to be reassigned in response to measured performance or other factors. The output feature values computed by the multi-kernel array and processed by an output processor of the transducer are supplied to a response unit that may be configured to perform a variety of monitoring, forecasting, and control operations in response to the computed output values.

Multi-kernel neural network concurrent learning, monitoring, and forecasting system

A &ldquo;Rapid Learner Client Service&rdquo; (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 forecasting equipment located at a remote site. This allows a single highly sophisticated forecasting system to meet the forecasting needs of a large number of subscribers. This forecasting service is performed by an RLCS server that periodically and automatically accesses the subscriber's computer to obtain a fresh set of input data. Alternatively, the subscriber's computer may contact the RLCS server to initiate the process. This input data is then downloaded to the RLCS server, where it is checked and corrected for errors by imputing values for missing or deviant input values. The error-corrected input data is then used to compute a forecast of output values, which are downloaded to the client's computer. The RLCS server also computes and downloads a set accuracy statistics for the client's review.

Automatic data extraction, error correction and forecasting system

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 operations for memory elements of matrix through the coordinated use of parallel feature processors and a joint access memory, which contains weighted values and provision for connecting feature processors pairwise. The parallel version also performs feature function monitoring, interpretation and refinement operations promptly and in concert with concurrent operation. A non-parallel embodiment of the system uses a single processor to perform the above operations however, more slowly than the parallel embodiment, yet faster than available alternatives.

Concurrent learning and performance information processing system

A multi-kernel neural network computing architecture configured to learn correlations among feature values,as the network monitors and imputes measured input values,and also predicts future output values,This computing architecture, referred to as a concurrent-learning information processor (CIP,), includes a multi-kernel neural network array,with the capability to learn and predict in real time. The CIP,also includes a manager,and an input-output transducer,that may be used for input-output refinement. These components allow the computing capacity of the multi-kernel array,to be reassigned in response to measured performance or other factors. The output feature values,computed by the multi-kernel array,and processed by an output processor,of the transducer,are supplied to a response unit,that may be configured to perform a variety of monitoring, forecasting, and control operations in response to the computed output values. Important characteristics of the CIP,such as feature function specifications,and,connection specifications,learning weight schedules,and the like may be set by a technician through a graphical user interface,Refinement processes also allow the CIP,be reconfigured in accordance with user commands for application to different physical applications.

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San Diego, CA, United States of America

Robert J Jannarone