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: 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.