Abstract: 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.

Abstract: The present invention is be directed to systems and methods that efficiently reduce cluttered data and identify useful information, in real-time. The disclosed auto-adaptive system distinguishes target data in data sets from clutter data that causes low target hit rates and high false alarm rates. Data set features may then be modified to account for changes over time, resulting in auto-adaptive alarm thresholds, higher target hits rates, and lower false alarm rates. In addition, data may be reduced to snip containing target information, while excluding large amounts of clutter data. Thereby, real-time data can be more readily understood and transmitted data can be reduced.

Abstract: A computer implemented method for processing audio data communicated between a first device and a second device over a data communication network, where one or more processors are programmed to perform steps include at a first device: receiving time series audio data comprising audio data over a time period; partitioning the audio data in a plurality of time segments; transforming the audio data in the plurality of time segments into a plurality of feature values; transmitting a subset of plurality of feature values over a data communication network; and at a second device: receiving the transmitted plurality of feature values from the data communication network; and transforming the feature values into the time domain to reproduce the time series audio data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 includes a multi-kernel neural network array 14 with the capability to learn and predict in real time. The CIP 10 also includes a manager 16 and an input-output transducer 12 that may be used for input-output refinement. These components allow the computing capacity of the multi-kernel array 14 to be reassigned in response to measured performance or other factors. The output feature values 46 computed by the multi-kernel array 14 and processed by an output processor 44 of the transducer 12 are supplied to a response unit 18 that may be configured to perform a variety of monitoring, forecasting, and control operations in response to the computed output values.

Abstract: 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 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.

Abstract: 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.

Abstract: 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 information processor (CIP 10), includes a multi-kernel neural network array 14 with the capability to learn and predict in real time. The CIP 10 also includes a manager 16 and an input-output transducer 12 that may be used for input-output refinement. These components allow the computing capacity of the multi-kernel array 14 to be reassigned in response to measured performance or other factors. The output feature values 46 computed by the multi-kernel array 14 and processed by an output processor 44 of the transducer 12 are supplied to a response unit 18 that may be configured to perform a variety of monitoring, forecasting, and control operations in response to the computed output values.

Abstract: 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 information processor (CIP 10), includes a multi-kernel neural network array 14 with the capability to learn and predict in real time. The CIP 10 also includes a manager 16 and an input-output transducer 12 that may be used for input-output refinement. These components allow the computing capacity of the multi-kernel array 14 to be reassigned in response to measured performance or other factors. The output feature values 46 computed by the multi-kernel array 14 and processed by an output processor 44 of the transducer 12 are supplied to a response unit 18 that may be configured to perform a variety of monitoring, forecasting, and control operations in response to the computed output values.

Abstract: 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. At the beginning of each time trial a vector of measurement values and a vector of measurement plausibility values are supplied to the system, and a learning weight is either supplied to or generated by the system.