INTELLISCIENCE CORPORATION Patent applications |
Patent application number | Title | Published |
20120141021 | METHODS AND SYSTEMS FOR DATA ANALYSIS AND FEATURE RECOGNITION - Systems and methods for automated pattern recognition and object detection. The method can be rapidly developed and improved using a minimal number of algorithms for the data content to fully discriminate details in the data, while reducing the need for human analysis. The system includes a data analysis system that recognizes patterns and detects objects in data without requiring adaptation of the system to a particular application, environment, or data content. The system evaluates the data in its native form independent of the form of presentation or the form of the post-processed data. | 06-07-2012 |
20100192024 | METHODS AND SYSTEMS FOR DETECTION OF ANOMALIES IN DIGITAL DATA STREAMS - Systems and methods for determining whether or not one or pluralities of events, patterns, or data elements present within a given digital data stream should be delimited as anomalous. The system requires analyzes the data elements of the data stream using any acceptable user-specified, preset, or automatically determined analysis system. The results of the data processing, which are stored in a data storage structure such as a synaptic web or a data array for example, reveal synaptic paths (patterns) of characteristic algorithm values that function to individually define or delimit the selected data element(s) from the remainder of the original data stream. | 07-29-2010 |
20100100577 | METHODS AND SYSTEMS FOR ANALYSIS OF MULTI-SAMPLE, TWO-DIMENSIONAL DATA - The present invention utilizes a pattern extraction methodology to elucidate significant patterns and mathematical relationships that exist between and among pluralities of two-dimensional sample data sets of the same data type. In one instance, the present invention analyzes multi-sample, two-dimensional mass spectroscopy data, while in an alternate instance, another user-specified, preset, or automatically determined data type, modality, submodality, etc., is analyzed. | 04-22-2010 |
20100017353 | METHODS AND SYSTEMS FOR DATA ANALYSIS AND FEATURE RECOGNITION - Systems and methods for automated pattern recognition and object detection. The method can be rapidly developed and improved using a minimal number of algorithms for the data content to fully discriminate details in the data, while reducing the need for human analysis. The system includes a data analysis system that recognizes patterns and detects objects in data without requiring adaptation of the system to a particular application, environment, or data content. The system evaluates the data in its native form independent of the form of presentation or the form of the post-processed data. | 01-21-2010 |
20090240741 | METHODS AND SYSTEMS FOR CREATION AND USE OF RAW-DATA DATASTORE - A raw-data datastore during data analysis and feature recognition abstracts away and/or reduces dependency upon typically required components of datastore training. The datastore functions to store the original data values of a data set selection, which can represent a known feature. In some embodiments, the original data set is retained as the raw data value set referenced by the raw-data datastore. The use of this raw-data datastore eliminates the need for continued manual retraining of the original data values and patterns, which can be associated with a particular known feature, each time the pluralities of evaluation algorithms and/or the target data area are altered, changed, modified, or reconfigured. | 09-24-2009 |
20090231359 | METHODS AND SYSTEMS FOR COMPOUND FEATURE CREATION, PROCESSING, AND IDENTIFICATION IN CONJUNCTION WITH A DATA ANALYSIS AND FEATURE RECOGNITION SYSTEM - Methods and systems for creation, processing, and use of compound features during data analysis and feature recognition are disclosed herein. In a preferred embodiment, the present invention functions to apply a new level of data discrimination during data analysis and feature recognition events such that features are more easily discerned from the remainder of the data pool using processing techniques that are more conducive to human visualizations, perceptions, and/or interpretations of data. This is accomplished using an example tool that allows previously processed and identified features (hereafter “known features”) to be aggregated so as to aid the system in recognizing abstract data features, preferably using Boolean operators and user-assigned hit weight values across desired cluster ranges surrounding analyzed data elements. | 09-17-2009 |