Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for automated pattern recognition and detection of avian influenza virus in a data set corresponding to an aspect of a biological sample. The method includes receiving a first data set corresponding to a first aspect of a first biological sample, analyzing the first data set using results of a first series of algorithms processed on a second data set corresponding to an aspect of a second biological sample known to contain avian influenza virus, generating an algorithm value cache for the first data set by running a second series of algorithms on the first data set, generating a match result by comparing the algorithm value cache with the results of the first series of algorithms, and performing a processing action based on the generated match result.

Abstract: 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.

Abstract: Systems and methods for aggregating and using data corresponding to physical samples in a virtual environment. The method includes receiving a first physical sample, sensing a first aspect of the first sample to generate first sample first aspect data, storing the first sample first aspect data in a datastore, sensing a second aspect of the first sample to generate first sample second aspect data, storing the first sample second aspect data in the datastore, generating first sample transformed data by running a first series of algorithms using at least one of the first sample first aspect data and the first sample second aspect data, and storing the first sample transformed data in the datastore.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for creating data samples for data analysis. The method includes imaging at least a portion of a first object having a target portion to generate a first image, analyzing the first image by running a first series of algorithms using the first image to generate a first algorithm value cache, removing the target portion from the first object to form a modified first object, imaging at least a portion of the modified first object to generate a second image, analyzing the second image by running the first series of algorithms using the second image to generate a second algorithm value cache, generating a transformed result by running a subtraction process using the first algorithm value cache and the second algorithm value cache, and storing the transformed result in a knowledge base.

Abstract: Systems and methods for processing a data point in a selected data file. A data file is processed and value for each data point is stored in a first system cache. A data operation using a predetermined operator is executed on at least one data point. A second system cache stores the resulting data point values. A new data file is created using the stored data point values in the second system cache.

Abstract: 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.