Patent ID: 11893508
Assignee: DIGITAL.AI SOFTWARE, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 8:
9. The method of claim 8, wherein the first predictive analysis parameters are determined by:
implementing input data analysis techniques to analyze the first input data associated with selected entities to determine at least one of (i) a number and proportion of nulls or unspecified values for each parameter, (ii) a number and proportion of distinct values in each parameter, (iii) parameters with minimal or no variance in values, (iv) outliers for each parameters, (v) functional dependencies between the one or more first predictive analysis parameters within and across the entities, (vi) correlations between the one or more first predictive analysis parameters as determined using a statistical technique, (vii) categorical parameters with one or more category frequencies greater than a prespecified maximum percentage of observations or less than a prespecified minimum percentage of observations, (viii) a statistical distribution that matches actual parameter values along with support for under sampling or oversampling for adjustment of value distribution as required for numerical parameters, (ix) a minimum, a maximum, a median, a first quartile, and a third quartile for the numerical parameters, or (x) skewness for the numerical parameters;
automatically filtering parameters that have outliers from the first predictive analysis parameters; and
automatically implementing user defined rules on the first predictive analysis parameters for at least one of (i) discarding of parameters which meet user defined criterion, (ii) filtering of data values for user specified parameters, (iii) replacing of data values for the first predictive analysis parameters, (iv) standardizing the data types and type specific derivations comprising at least one of (a) hour-of-day or (b) day-of-week for datetime types or reducing scale for the numerical parameters, (v) implementing a regular expression style transformation on the first predictive analysis parameters or concatenating parameters, (vi) performing numeric transformations, (vii) consolidating excessive number of categories or categories with very few observations for categorical parameters, (viii) skewness reducing transforms on the first predictive analysis parameters, (ix) performing transformations for modeling algorithms that are sensitive to variable scales comprising k-means, or (x) extracting principal components for dimensionality reduction.