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: 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 “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.