Systems, methods, and graphical user interfaces for configuring design of experiments

A system, method, and computer-program product includes displaying a plurality of factor-setting user interface (UI) control elements configured to receive an input of characters for specifying a set of design of experiment factors for creating a design of experiment (DOE), displaying a plurality of factor type UI control elements configured to receive input for specifying a factor type of a plurality of factor types, displaying a plurality of dynamic rows of editable UI control elements configured to receive inputs of experimental values for the set of DOE factors, and displaying a composite factor UI control component configured to receive inputs for generating one or more control signals that add or remove one or more DOE factors of the set of DOE factors.

TECHNICAL FIELD

This invention relates generally to the field of experimental design and, more specifically, to new and useful systems and methods for configuring design of experiments.

BACKGROUND

Design of Experiments (DoE) are typically configured using computer tools. Users may interact with such computer tools to define inputs of the design of experiments (commonly referred to as “factors”) and outputs of the design of experiments (commonly referred to as “responses”). However, these computer tools may not allow a user to modify factors of the design of experiments without discarding previous user work. Furthermore, these computer tools do not intelligently adapt a model underlying the design of experiments as factors are added, removed, or as attributes of the factors change. Such limitations and inefficiencies are not only frustrating to the user, but can also cause inaccuracies in the design of experiments model if not corrected.

The embodiments of the present application provide technical solutions that address, at least, the needs described above, as well as the deficiencies of the start of the art.

BRIEF SUMMARY OF THE INVENTION(S)

In some embodiments, a computer-program product embodied in a non-transitory machine-readable storage medium stores computer instructions that, when executed by one or more processors, perform operations that provide a graphical user interface comprising a plurality of factor-setting user interface (UI) control elements configured to receive an input of characters for specifying a set of design of experiment factors for creating a design of experiment (DOE), a plurality of factor type UI control elements, each respective factor type UI control element of the plurality of factor type UI control elements being configured to receive input for specifying a factor type of a plurality of factor types displayed by the respective factor type UI control element for a corresponding DOE factor of the set of DOE factors, a plurality of dynamic rows of editable UI control elements configured to receive inputs of experimental values for the set of DOE factors, wherein one or more dynamic rows of editable UI control elements of the plurality of dynamic rows of editable UI control elements are dynamically reconfigured from a current format of control elements to a new format of control elements based on one or more input operations to one or more of the plurality of factor type UI control elements, and a composite factor UI control component configured to receive inputs for generating one or more control signals that add or remove one or more DOE factors of the set of DOE factors.

In some embodiments, the graphical user interface further comprises a factor-setting grid container control, wherein the factor-setting grid container control includes: one or more columns of the plurality of factor-setting UI control elements, a column of the plurality of factor type UI control elements, and one or more columns of the plurality of dynamic rows of editable UI control elements.

In some embodiments, the factor-setting grid container control includes a plurality of rows of factor-setting composition components, and each row of factor-setting composition components of the plurality of factor-setting composition components includes: a factor-setting UI control element of the plurality of factor-setting UI control elements, a factor type UI control element of the plurality of factor type UI control elements, and a dynamic row of editable UI control elements of the plurality of dynamic rows of editable UI control elements.

In some embodiments, the graphical user interface further comprises a plurality of DOE platform configuration UI control elements being configured to receive input for configuring the plurality of factor type UI control elements to a DOE platform of a plurality of DOE platforms, wherein each DOE platform of the plurality DOE platforms defines a distinct number of factor types that is selectably available for a respective factor type UI control element of the plurality of factor type UI control elements.

In some embodiments, a respective DOE platform configuration UI control element of the plurality of DOE platform configuration UI control elements, when operated, changes a current DOE platform of a target factor type UI control element of the plurality of factor type UI control elements to a selected DOE platform, and the selected DOE platform increases or decreases a number of factor type UI control elements available for selection when operating the target factor type UI control element.

In some embodiments, the composite factor UI control component comprises a plurality of UI control buttons native to an operating system implementing the GUI including: a factor addition control button that, when operated, adds one or more DOE factors to an end of the set of DOE factors, a factor subtraction control button that, when operated, removes one or more DOE factors from the end of the set of DOE factors, a factor deletion control button that, when operated, removes one or more selected DOE factors from an arbitrary position within the set of DOE factors; a factor control operation undo button that, when operated, reverts the DOE or the GUI to a previous state that includes erasing a last set of one or more changes to the DOE or the GUI; and a factor control operation redo button that, when operated, reverses one or more most recent in time operations of the factor control operation undo button.

In some embodiments, the composite factor UI control component further includes a sub-composite factor UI control component comprising a number edit control element that, when operated, receives an input of a number identifying a number of DOE factors to append to the end of the set of DOE factors and a drop-down menu control element that, when operated, displays the plurality of factor type UI control elements that are available for selection, wherein in response to an input of a target number into the number edit control element and a selection of a target factor type from the plurality of factor type UI control elements that are selectably available via the drop-down menu control element, a control signal is generated that causes an automatically appending of the number of DOE factors to the end of the set of DOE of factors.

In some embodiments, the plurality of UI control buttons of the composite factor UI control component further includes a factor set reset control button that, when operated, deletes the set of DOE factors.

In some embodiments, the plurality of UI control buttons of the composite factor UI control component further includes a factor dialog control button that, when operated, displays a dialog menu element that includes: a first grid container control comprising a plurality of rows of factor-setting composition components, each row of factor-setting composition components of the plurality of rows of factor-setting composition components includes: a plurality of UI control elements for creating a distinct DOE factor of the set of DOE factors, and a DOE factor instantiation control button arranged in-line with the plurality of UI control elements, wherein the DOE factor instantiation control button, when operated, automatically appends one or more target DOE factors to the set of DOE factors, and a second grid container control comprising a further plurality of rows of factor-setting composition components, each row of factor-setting composition components of the further plurality of rows of factor-setting composition components includes a further plurality of UI control elements for creating a plurality of DOE factors of the set of DOE factors; a further composite factor UI control component arranged with the second grid container for controlling an addition to and a subtraction from of one or more DOE factors from a further set of DOE factors of the second grid container; and a further DOE factor instantiation control button arranged with the second grid container control that, when operated, automatically appends the plurality of DOE factors to the set of DOE factors.

In some embodiments, the current format of control elements corresponds to an incumbent factor type UI control element of the plurality of factor type UI control elements and the new format of control elements corresponds to a succeeding factor type UI control element of the plurality of factor type UI control elements that is selected in place of the incumbent factor type UI control element.

In some embodiments, the plurality of factor-setting UI control elements comprise a plurality of character edit boxes arranged in a column, and each of the plurality of character edit boxes is configured to receive input of one or more characters that define a target DOE factor of the set of DOE factors.

In some embodiments, the plurality of factor type UI control elements comprise a plurality of drop-down menu elements arranged in a column, an input operation to a respective drop-down menu element of the plurality of drop-down menu elements causes the respective drop-down menu element to expand and display a set of factor type UI control elements.

In some embodiments, an order of the plurality of factor-setting UI control elements is rearranged within the GUI based on: executing a selection of a target factor-setting UI control element of the plurality of factor-setting UI control elements, moving the target factor-setting UI control element from a current position within the order, and placing the target factor-setting UI control element within a new position within the order of the plurality of factor-setting UI control elements.

In some embodiments, the set of DOE factors is configured with a plurality of sequential groups of DOE factors, each of the plurality of sequential groups of DOE factors having a maximum group size not exceeding a specified number of DOE factors, an operation to rearrange a target DOE factor from a first group to a second group of the plurality of sequential groups of DOE factors automatically updates a group identification value associated with the target DOE factor and one or more group identification values of other DOE factors moved into one or more other groups of DOE factors of the plurality of sequential groups of DOE factors based on the rearrangement of the target DOE factor.

In some embodiments, when one or more DOE factors are appended to an end of the set of DOE factors causing a size of the set of DOE factors to exceed a display area of the graphical UI, the graphical UI is automatically scrolled to a section of the set of DOE factors displaying at least the one or more DOE factors.

In some embodiments, a computed-implemented method comprises displaying a plurality of factor-setting user interface (UI) control elements configured to receive an input of characters for specifying a set of design of experiment factors for creating a design of experiment (DOE); displaying a plurality of factor type UI control elements, each respective factor type UI control element of the plurality of factor type UI control elements being configured to receive input for specifying a factor type of a plurality of factor types displayed by the respective factor type UI control element for a corresponding DOE factor of the set of DOE factors; displaying a plurality of dynamic rows of editable UI control elements configured to receive inputs of experimental values for the set of DOE factors, wherein one or more dynamic rows of editable UI control elements of the plurality of dynamic rows of editable UI control elements are dynamically reconfigured from a current format of control elements to a new format of control elements based on one or more input operations to one or more of the plurality of factor type UI control elements; and displaying a composite factor UI control component configured to receive inputs for generating one or more control signals that add or remove one or more DOE factors of the set of DOE factors.

In some embodiments, the computer-implemented method further comprises displaying a factor-setting grid container control, wherein the factor-setting grid container control includes: one or more columns of the plurality of factor-setting UI control elements, a column of the plurality of factor type UI control elements, and one or more columns of the plurality of dynamic rows of editable UI control elements.

In some embodiments, the factor-setting grid container control includes a plurality of rows of factor-setting composition components, and each row of factor-setting composition components of the plurality of factor-setting composition components includes: a factor-setting UI control element of the plurality of factor-setting UI control elements, a factor type UI control element of the plurality of factor type UI control elements, and a dynamic row of editable UI control elements of the plurality of dynamic rows of editable UI control elements.

In some embodiments, the computer-implemented method further comprises displaying a plurality of DOE platform configuration UI control elements being configured to receive input for configuring the plurality of factor type UI control elements to a DOE platform of a plurality of DOE platforms, wherein each DOE platform of the plurality DOE platforms defines a distinct number of factor types that is selectably available for a respective factor type UI control element of the plurality of factor type UI control elements.

In some embodiments, a respective DOE platform configuration UI control element of the plurality of DOE platform configuration UI control elements, when operated, changes a current DOE platform of a target factor type UI control element of the plurality of factor type UI control elements to a selected DOE platform, and the selected DOE platform increases or decreases a number of factor type UI control elements available for selection when operating the target factor type UI control element.

In some embodiments, the composite factor UI control component comprises a plurality of UI control buttons native to an operating system implementing the GUI including: a factor addition control button that, when operated, adds one or more DOE factors to an end of the set of DOE factors, a factor subtraction control button that, when operated, removes one or more DOE factors from the end of the set of DOE factors, a factor deletion control button that, when operated, removes one or more selected DOE factors from an arbitrary position within the set of DOE factors; a factor control operation undo button that, when operated, reverts the DOE or the GUI to a previous state that includes erasing a last set of one or more changes to the DOE or the GUI; and a factor control operation redo button that, when operated, reverses one or more most recent in time operations of the factor control operation undo button.

In some embodiments, the composite factor UI control component further includes a sub-composite factor UI control component comprising a number edit control element that, when operated, receives an input of a number identifying a number of DOE factors to append to the end of the set of DOE factors and a drop-down menu control element that, when operated, displays the plurality of factor type UI control elements that are available for selection, wherein in response to an input of a target number into the number edit control element and a selection of a target factor type from the plurality of factor type UI control elements that are selectably available via the drop-down menu control element, a control signal is generated that causes an automatically appending of the number of DOE factors to the end of the set of DOE of factors.

In some embodiments, a computer-implemented system comprises: one or more processors; a memory; a computer-readable medium operably coupled to the one or more processors, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the one or more processors, cause a computing device to perform operations comprising: displaying a plurality of factor-setting user interface (UI) control elements configured to receive an input of characters for specifying a set of design of experiment factors for creating a design of experiment (DOE); displaying a plurality of factor type UI control elements, each respective factor type UI control element of the plurality of factor type UI control elements being configured to receive input for specifying a factor type of a plurality of factor types displayed by the respective factor type UI control element for a corresponding DOE factor of the set of DOE factors; displaying a plurality of dynamic rows of editable UI control elements configured to receive inputs of experimental values for the set of DOE factors, wherein one or more dynamic rows of editable UI control elements of the plurality of dynamic rows of editable UI control elements are dynamically reconfigured from a current format of control elements to a new format of control elements based on one or more input operations to one or more of the plurality of factor type UI control elements; and displaying a composite factor UI control component configured to receive inputs for generating one or more control signals that add or remove one or more DOE factors of the set of DOE factors.

In some embodiments, the computer-implemented system further comprises computer-readable instructions that, when executed, cause the computing device to perform operations comprising: displaying a factor-setting grid container control, wherein the factor-setting grid container control includes: one or more columns of the plurality of factor-setting UI control elements, a column of the plurality of factor type UI control elements, and one or more columns of the plurality of dynamic rows of editable UI control elements.

In some embodiments, the factor-setting grid container control includes a plurality of rows of factor-setting composition components, and each row of factor-setting composition components of the plurality of factor-setting composition components includes: a factor-setting UI control element of the plurality of factor-setting UI control elements, a factor type UI control element of the plurality of factor type UI control elements, and a dynamic row of editable UI control elements of the plurality of dynamic rows of editable UI control elements.

In some embodiments, the computer-implemented system further comprises computer-readable instructions that, when executed, cause the computing device to perform operations comprising: displaying a plurality of DOE platform configuration UI control elements being configured to receive input for configuring the plurality of factor type UI control elements to a DOE platform of a plurality of DOE platforms, wherein each DOE platform of the plurality DOE platforms defines a distinct number of factor types that is selectably available for a respective factor type UI control element of the plurality of factor type UI control elements.

In some embodiments, a respective DOE platform configuration UI control element of the plurality of DOE platform configuration UI control elements, when operated, changes a current DOE platform of a target factor type UI control element of the plurality of factor type UI control elements to a selected DOE platform, and the selected DOE platform increases or decreases a number of factor type UI control elements available for selection when operating the target factor type UI control element.

In some embodiments, the composite factor UI control component comprises a plurality of UI control buttons native to an operating system implementing the GUI including: a factor addition control button that, when operated, adds one or more DOE factors to an end of the set of DOE factors, a factor subtraction control button that, when operated, removes one or more DOE factors from the end of the set of DOE factors, a factor deletion control button that, when operated, removes one or more selected DOE factors from an arbitrary position within the set of DOE factors; a factor control operation undo button that, when operated, reverts the DOE or the GUI to a previous state that includes erasing a last set of one or more changes to the DOE or the GUI; and a factor control operation redo button that, when operated, reverses one or more most recent in time operations of the factor control operation undo button.

In some embodiments, the current format of control elements corresponds to an incumbent factor type UI control element of the plurality of factor type UI control elements and the new format of control elements corresponds to a succeeding factor type UI control element of the plurality of factor type UI control elements that is selected in place of the incumbent factor type UI control element.

In some embodiments, the plurality of factor-setting UI control elements comprise a plurality of character edit boxes arranged in a column, and each of the plurality of character edit boxes is configured to receive input of one or more characters that define a target DOE factor of the set of DOE factors.

In some embodiments, a computer-program product embodied in a non-transitory machine-readable storage medium stores computer instructions that, when executed by one or more processors, perform operations comprising: receiving factor specification data that specifies a plurality of factors and one or more factor parameters of the plurality of factors for configuring a design of experiments; executing a factor type conversion for a target factor of the plurality of factors based on the factor specification data indicating a selection of a successor factor type selected from a plurality of factor types, wherein executing the factor type conversion includes: replacing, at a graphical user interface, a set of factor specification user interface elements that correspond to an incumbent factor type previously selected for the target factor with a second set of factor specification user interface elements that correspond to the successor factor type; and updating the design of experiments with an experiment design policy for the successor factor type, wherein the experiment design policy for the successor factor type controls a transformation of an underlying model of the design of experiments; executing a model transformation of the underlying model of the design of experiments based on the experiment design policy for the successor factor type, wherein executing the model transformation includes modifying one or more operational parameters of the underlying model to satisfy the experimental design policy for the successor factor type; and executing the design of experiments based at least on the factor specification data, the execution of the factor type conversion, and the execution of the model transformation.

In some embodiments, the set of factor specification user interface elements indicates a continuous range associated with the target factor, executing the factor type conversion for the target factor further includes: converting a minimum value and a maximum value of the continuous range to a character representation, defining a first categorical level based on the character representation of the minimum value, and defining a second categorical level based on the character representation of the maximum value, and the second set of factor specification user interface elements indicates a plurality of categorical levels associated with the target factor, including the first categorical level and the second categorical level.

In some embodiments, the set of factor specification user interface elements indicates a continuous range associated with the target factor, executing the factor type conversion for the target factor further includes: defining a first discrete numeric level based on a minimum value of the continuous range, and defining a second discrete numeric level based on a maximum value of the continuous range, and the second set of factor specification user interface elements indicates a plurality of discrete numeric levels associated with the target factor, including the first discrete numeric level and the second discrete numeric level.

In some embodiments, the set of factor specification user interface elements indicates a plurality of categorical levels associated with the target factor, executing the factor type conversion for the target factor further includes: identifying, from the plurality of categorical levels, a first categorical level and a second categorical level that satisfies one or more factor type conversion criteria, converting the first categorical level and the second categorical level to a numerical value, defining a minimum continuous value based on the numerical value of the first categorical level, and defining a maximum continuous value based on the numerical value of the second categorical level, and the second set of factor specification user interface elements indicates a continuous range associated with the target factor, including the minimum continuous value and the maximum continuous value of the target factor.

In some embodiments, the first categorical level and the second categorical level satisfy the one or more factor type conversion criteria when a value of the first categorical level and a value of the second categorical level correspond to a numerical string, and the first categorical level and the second categorical level do not satisfy the one or more factor type conversion criteria when the value of the first categorical level and the value of the second categorical level does not include the numerical string.

In some embodiments, the first categorical level and the second categorical level are not identified when the plurality of categorical levels do not include at least two categorical levels that satisfy the one or more factor type conversion criteria, when the first categorical level and the second categorical level are identified, executing the factor type conversion for the target factor includes: the converting of the first categorical level and the second categorical level to the numerical value, the defining of the minimum continuous value based on the numerical value of the first categorical level, and the defining of the maximum continuous value based on the numerical value of the second categorical level, and when the first categorical level and the second categorical level are not identified, executing the factor type conversion for the target factor includes: defining the minimum continuous value based on a pre-determined minimum value, and defining the maximum continuous value based on a pre-determined maximum value.

In some embodiments, the set of factor specification user interface elements indicates a plurality of categorical levels associated with the target factor, executing the factor type conversion for the target factor further includes: converting each of the plurality of categorical levels to a discrete numeric level, wherein the converting includes: in accordance with a determination that one or more factor type conversion criteria are satisfied: converting each of the plurality of categorical levels to a numerical value, and defining a first plurality of discrete numeric levels that each correspond to the numerical value of a respective one of the plurality of categorical levels, and the second set of factor specification user interface elements indicates a plurality of discrete numeric levels associated with the target factor, including the discrete numeric level corresponding to each of the plurality of categorical levels.

In some embodiments, the one or more factor type conversion criteria are satisfied when the plurality of categorical levels comprise a plurality of numerical strings, and the one or more factor type conversion criteria are not satisfied when the plurality of categorical levels do not comprise a plurality of numerical strings.

In some embodiments, converting each of the plurality of categorical levels to the discrete numeric level further includes: in accordance with a determination that the one or more factor type conversion criteria are not satisfied: mapping each of the plurality of categorical levels to a pre-determined discrete numeric value, and defining a second plurality of discrete numeric levels that each correspond to the pre-determined discrete numeric value mapped to a respective one of the plurality of categorical levels.

In some embodiments, the set of factor specification user interface elements indicates a plurality of discrete numeric levels associated with the target factor, including a minimum discrete numeric level and a maximum discrete numeric level of the target factor, executing the factor type conversion for the target factor further includes: defining a minimum continuous value based on the minimum discrete numeric level, and defining a maximum continuous value based on the maximum discrete numeric level, and the second set of factor specification user interface elements indicates a continuous range of the target factor, including the minimum continuous value and the maximum continuous value of the target factor.

In some embodiments, the set of factor specification user interface elements indicates a plurality of discrete numeric levels associated with the target factor, executing the factor type conversion for the target factor further includes: converting the plurality of discrete numeric levels to a character representation, and transforming each of the plurality of discrete numeric levels to a categorical level based on the converting, wherein each categorical level corresponds to the character representation of a respective one of the plurality of discrete numeric levels, and the second set of factor specification user interface elements indicates a plurality of categorical levels associated with the target factor, including the categorical level corresponding to each of the plurality of discrete numeric levels.

In some embodiments, executing the model transformation includes: adding, to the underlying model, a main effect model term that comprises the target factor when one or more first model transformation criteria are satisfied, adjusting, in the underlying model, one or more continuous model terms to have a single degree of freedom, preserving one or more model interaction terms present in the underlying model, preserving a power of one or more model terms in the underlying model, and adding, to underlying model, an intercept model term when one or more second model transformation criteria are satisfied.

In some embodiments, the one or more first model transformation criteria are satisfied when the main effect model term is not already included in the underlying model, and the one or more second model transformation criteria are satisfied when: the incumbent factor type corresponds to a mixture factor type, and a remainder of the plurality of factors in the factor specification data have a factor type that is different from the mixture factor type.

In some embodiments, executing the model transformation includes: adding, to the underlying model, a main effect model term that comprises the target factor when one or more first model transformation criteria are satisfied, removing, from the underlying model, one or more model terms that comprise the target factor with a power greater than one, setting, in the underlying model, the main effect model term to have a value of k−1 degrees of freedom, wherein k corresponds to a total number of categorical levels associated with the target factor, and adding, to the underlying model, an intercept model term when one or more second model transformation criteria are satisfied.

In some embodiments, executing the model transformation includes: adjusting, in the underlying model, one or more model terms that have a power to have a second power that is equivalent to a total number of discrete numeric levels associated with the target factor, adjusting, in the underlying model, one or more second model terms that do not have a power, wherein adjusting a target model term of the one or more second model terms includes: setting the target model term to have a first level of estimability when the target model term corresponds to a main effects model term, and setting the target model term to have a second level of estimability when the target model term corresponds to a model interaction term, preserving one or more model interaction terms present in the underlying model, and adding, to the underlying model, an intercept model term when one or more model transformation criteria are satisfied.

In some embodiments, a computed-implemented method comprises: receiving factor specification data that specifies a plurality of factors and one or more factor parameters of the plurality of factors for configuring a design of experiments; executing a factor type conversion for a target factor of the plurality of factors based on the factor specification data indicating a selection of a successor factor type selected from a plurality of factor types, wherein executing the factor type conversion includes: replacing, at a graphical user interface, a set of factor specification user interface elements that correspond to an incumbent factor type previously selected for the target factor with a second set of factor specification user interface elements that correspond to the successor factor type; and updating the design of experiments with an experiment design policy for the successor factor type, wherein the experiment design policy for the successor factor type controls a transformation of an underlying model of the design of experiments; executing a model transformation of the underlying model of the design of experiments based on the experiment design policy for the successor factor type, wherein executing the model transformation includes modifying one or more operational parameters of the underlying model to satisfy the experimental design policy for the successor factor type; and executing the design of experiments based at least on the factor specification data, the execution of the factor type conversion, and the execution of the model transformation.

In some embodiments, the set of factor specification user interface elements indicates a continuous range associated with the target factor, executing the factor type conversion for the target factor further includes: converting a minimum value and a maximum value of the continuous range to a character representation, defining a first categorical level based on the character representation of the minimum value, and defining a second categorical level based on the character representation of the maximum value, and the second set of factor specification user interface elements indicates a plurality of categorical levels associated with the target factor, including the first categorical level and the second categorical level.

In some embodiments, the set of factor specification user interface elements indicates a continuous range associated with the target factor, executing the factor type conversion for the target factor further includes: defining a first discrete numeric level based on a minimum value of the continuous range, and defining a second discrete numeric level based on a maximum value of the continuous range, and the second set of factor specification user interface elements indicates a plurality of discrete numeric levels associated with the target factor, including the first discrete numeric level and the second discrete numeric level.

In some embodiments, the set of factor specification user interface elements indicates a plurality of categorical levels associated with the target factor, executing the factor type conversion for the target factor further includes: identifying, from the plurality of categorical levels, a first categorical level and a second categorical level that satisfies one or more factor type conversion criteria, converting the first categorical level and the second categorical level to a numerical value, defining a minimum continuous value based on the numerical value of the first categorical level, and defining a maximum continuous value based on the numerical value of the second categorical level, and the second set of factor specification user interface elements indicates a continuous range associated with the target factor, including the minimum continuous value and the maximum continuous value of the target factor.

In some embodiments, the first categorical level and the second categorical level satisfy the one or more factor type conversion criteria when a value of the first categorical level and a value of the second categorical level correspond to a numerical string, and the first categorical level and the second categorical level do not satisfy the one or more factor type conversion criteria when the value of the first categorical level and the value of the second categorical level does not include the numerical string.

In some embodiments, the first categorical level and the second categorical level are not identified when the plurality of categorical levels do not include at least two categorical levels that satisfy the one or more factor type conversion criteria, when the first categorical level and the second categorical level are identified, executing the factor type conversion for the target factor includes: the converting of the first categorical level and the second categorical level to the numerical value, the defining of the minimum continuous value based on the numerical value of the first categorical level, and the defining of the maximum continuous value based on the numerical value of the second categorical level, and when the first categorical level and the second categorical level are not identified, executing the factor type conversion for the target factor includes: defining the minimum continuous value based on a pre-determined minimum value, and defining the maximum continuous value based on a pre-determined maximum value.

In some embodiments, the set of factor specification user interface elements indicates a plurality of categorical levels associated with the target factor, executing the factor type conversion for the target factor further includes: converting each of the plurality of categorical levels to a discrete numeric level, wherein the converting includes: in accordance with a determination that one or more factor type conversion criteria are satisfied: converting each of the plurality of categorical levels to a numerical value, and defining a first plurality of discrete numeric levels that each correspond to the numerical value of a respective one of the plurality of categorical levels, and the second set of factor specification user interface elements indicates a plurality of discrete numeric levels associated with the target factor, including the discrete numeric level corresponding to each of the plurality of categorical levels.

In some embodiments, executing the model transformation includes: adjusting, in the underlying model, one or more model terms that have a power to have a second power that is equivalent to a total number of discrete numeric levels associated with the target factor, adjusting, in the underlying model, one or more second model terms that do not have a power, wherein adjusting a target model term of the one or more second model terms includes: setting the target model term to have a first level of estimability when the target model term corresponds to a main effects model term, and setting the target model term to have a second level of estimability when the target model term corresponds to a model interaction term, preserving one or more model interaction terms present in the underlying model, and adding, to the underlying model, an intercept model term when one or more model transformation criteria are satisfied.

In some embodiments, a computer-implemented system comprises: one or more processors; a memory; a computer-readable medium operably coupled to the one or more processors, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the one or more processors, cause a computing device to perform operations comprising: receiving factor specification data that specifies a plurality of factors and one or more factor parameters of the plurality of factors for configuring a design of experiments; executing a factor type conversion for a target factor of the plurality of factors based on the factor specification data indicating a selection of a successor factor type selected from a plurality of factor types, wherein executing the factor type conversion includes: replacing, at a graphical user interface, a set of factor specification user interface elements that correspond to an incumbent factor type previously selected for the target factor with a second set of factor specification user interface elements that correspond to the successor factor type; and updating the design of experiments with an experiment design policy for the successor factor type, wherein the experiment design policy for the successor factor type controls a transformation of an underlying model of the design of experiments; executing a model transformation of the underlying model of the design of experiments based on the experiment design policy for the successor factor type, wherein executing the model transformation includes modifying one or more operational parameters of the underlying model to satisfy the experimental design policy for the successor factor type; and executing the design of experiments based at least on the factor specification data, the execution of the factor type conversion, and the execution of the model transformation.

In some embodiments, the set of factor specification user interface elements indicates a plurality of discrete numeric levels associated with the target factor, including a minimum discrete numeric level and a maximum discrete numeric level of the target factor, executing the factor type conversion for the target factor further includes: defining a minimum continuous value based on the minimum discrete numeric level, and defining a maximum continuous value based on the maximum discrete numeric level, and the second set of factor specification user interface elements indicates a continuous range of the target factor, including the minimum continuous value and the maximum continuous value of the target factor.

In some embodiments, the set of factor specification user interface elements indicates a plurality of discrete numeric levels associated with the target factor, executing the factor type conversion for the target factor further includes: converting the plurality of discrete numeric levels to a character representation, and transforming each of the plurality of discrete numeric levels to a categorical level based on the converting, wherein each categorical level corresponds to the character representation of a respective one of the plurality of discrete numeric levels, and the second set of factor specification user interface elements indicates a plurality of categorical levels associated with the target factor, including the categorical level corresponding to each of the plurality of discrete numeric levels.

In some embodiments, executing the model transformation includes adding, to the underlying model, a main effect model term that comprises the target factor when one or more first model transformation criteria are satisfied, adjusting, in the underlying model, one or more continuous model terms to have a single degree of freedom, preserving one or more model interaction terms present in the underlying model, preserving a power of one or more model terms in the underlying model, and adding, to underlying model, an intercept model term when one or more second model transformation criteria are satisfied.

In some embodiments, the one or more first model transformation criteria are satisfied when the main effect model term is not already included in the underlying model, and the one or more second model transformation criteria are satisfied when: the incumbent factor type corresponds to a mixture factor type, and a remainder of the plurality of factors in the factor specification data have a factor type that is different from the mixture factor type.

In some embodiments, executing the model transformation includes: adding, to the underlying model, a main effect model term that comprises the target factor when one or more first model transformation criteria are satisfied, removing, from the underlying model, one or more model terms that comprise the target factor with a power greater than one, setting, in the underlying model, the main effect model term to have a value of k−1 degrees of freedom, wherein k corresponds to a total number of categorical levels associated with the target factor, and adding, to the underlying model, an intercept model term when one or more second model transformation criteria are satisfied.

In some embodiments, executing the model transformation includes: adjusting, in the underlying model, one or more model terms that have a power to have a second power that is equivalent to a total number of discrete numeric levels associated with the target factor, adjusting, in the underlying model, one or more second model terms that do not have a power, wherein adjusting a target model term of the one or more second model terms includes: setting the target model term to have a first level of estimability when the target model term corresponds to a main effects model term, and setting the target model term to have a second level of estimability when the target model term corresponds to a model interaction term, preserving one or more model interaction terms present in the underlying model, and adding, to the underlying model, an intercept model term when one or more model transformation criteria are satisfied.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventions are not intended to limit the inventions to these preferred embodiments, but rather to enable any person skilled in the art to make and use these inventions.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of embodiments of the technology. However, it will be apparent that various embodiments may be practiced without these specific details. The figures and description are not intended to be restrictive.

Example Systems

FIG.1illustrates an example network100including an example set of devices communicating with each other (e.g., over one or more of an exchange system or a network), according to embodiments of the present technology. Network100includes network devices configured to communicate with a variety of types of client devices, for example, client devices140, over a variety of types of communication channels. A client device140may be configured to communicate over a public or private network (e.g., client device140B is configured to support a browser for computing requests or providing authentication).

Network devices and client devices can transmit a communication over a network100. Network100may include one or more of different types of networks, including a wireless network, a wired network, or a combination of a wired and wireless network. Examples of suitable networks include the Internet, a personal area network, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), cloud network, or a cellular network. A wireless network may include a wireless interface or combination of wireless interfaces. As an example, a network in the one or more networks may include a short-range communication channel, such as a Bluetooth or a Bluetooth Low Energy channel. A wired network may include a wired interface. The wired and/or wireless networks may be implemented using routers, access points, base stations, bridges, gateways, or the like, to connect devices in the network. The one or more networks can be incorporated entirely within or can include an intranet, an extranet, or a combination thereof. In one embodiment, communications between two or more systems and/or devices can be achieved by a secure communications protocol, such as secure sockets layer (SSL) or transport layer security (TLS), or other available protocols such as according to an Open Systems Interaction model. In addition, data and/or transactional details may be encrypted. Networks may include other devices for infrastructure for the network. For example, a cloud network may include cloud infrastructure system on demand. As another example, one or more client devices may utilize an Internet of Things (IoT) infrastructure where things (e.g., machines, devices, phones, sensors) can be connected to networks and the data from these things can be collected and processed within the things and/or external to the things. IoT may be implemented with various infrastructure such as for accessibility (technologies that get data and move it), embed-ability (devices with embedded sensors), and IoT services. Industries in the IoT space may include automotive (connected car), manufacturing (connected factory), smart cities, energy and retail.

Network devices and client devices can be different types of devices or components of devices. For example, client device140is shown as a laptop and balancer160is shown as a processor. Client devices and network devices could be other types of devices or components of other types of devices such as a mobile phone, laptop computer, tablet computer, temperature sensor, motion sensor, and audio sensor. Additionally, or alternatively, the network devices may be or include sensors that are sensitive to detecting aspects of their environment. For example, the network devices may include sensors such as water sensors, power sensors, electrical current sensors, chemical sensors, optical sensors, pressure sensors, geographic or position sensors (e.g., GPS), velocity sensors, acceleration sensors, and flow rate sensors. Examples of characteristics that may be sensed include force, torque, load, strain, position, temperature, air pressure, fluid flow, chemical properties, resistance, electromagnetic fields, radiation, irradiance, proximity, acoustics, moisture, distance, speed, vibrations, acceleration, electrical potential, and electrical current. The sensors may be mounted to various components used as part of a variety of different types of systems (e.g., an oil drilling operation). The network devices may detect and record data related to the environment that it monitors, and transmit that data to network100.

As noted, one type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment (not shown) according to certain embodiments includes an oil drilling system. For example, the one or more drilling operation sensors may include, for example, surface sensors that measure a standpipe pressure, a surface torque, and a rotation speed of a drill pipe, and downhole sensors that measure a rotation speed of a bit and fluid densities. Besides the raw data collected directly by the sensors, other data may include parameters either developed by the sensors or assigned to the system by a client or other controlling device. For example, one or more drilling operation control parameters may control settings such as a mud motor speed to flow ratio, a bit diameter, a predicted formation top, seismic data, weather data, etc. Other data may be generated using physical models such as an earth model, a weather model, a seismic model, a bottom hole assembly model, a well plan model, an annular friction model, etc. In addition to sensor and control settings, predicted outputs, of for example, the rate of penetration and pump pressure may also be stored and used for modeling, prediction, or classification.

In another example, another type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment according to certain embodiments includes a home automation or similar automated network in a different environment, such as an office space, school, public space, sports venue, or a variety of other locations. Network devices in such an automated network may include network devices that allow a user to access, control, and/or configure various home appliances located within the user's home (e.g., a television, radio, light, fan, humidifier, sensor, microwave, iron, and/or the like), or outside of the user's home (e.g., exterior motion sensors, exterior lighting, garage door openers, sprinkler systems, or the like). For example, network device or client device may include a home automation switch that may be coupled with a home appliance. In another embodiment, a network or client device can allow a user to access, control, and/or configure devices, such as office-related devices (e.g., copy machine, printer, or fax machine), audio and/or video related devices (e.g., a receiver, a speaker, a projector, a DVD player, or a television), media-playback devices (e.g., a compact disc player, a CD player, or the like), computing devices (e.g., a home computer, a laptop computer, a tablet, a personal digital assistant (PDA), a computing device, or a wearable device), lighting devices (e.g., a lamp or recessed lighting), devices associated with a security system, devices associated with an alarm system, devices that can be operated in an automobile (e.g., radio devices, navigation devices), and/or the like. Data may be collected from such various sensors in raw form, or data may be processed by the sensors to create parameters or other data either developed by the sensors based on the raw data or assigned to the system by a client or other controlling device.

In another example, another type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment (e.g., computing environment or another computing environment not shown) according to certain embodiments includes a manufacturing environment (e.g., manufacturing products or energy). A variety of different network devices may be included in an energy pool, such as various devices within one or more power plants, energy farms (e.g., wind farm, and solar farm) energy storage facilities, factories, homes and businesses of consumers. One or more of such devices may include one or more sensors that detect energy gain or loss, electrical input or output or loss, and a variety of other efficiencies. These sensors may collect data to inform users of how the energy pool, and individual devices within the pool, may be functioning and how they may be made more efficient. In a manufacturing environment, image data can be taken of the manufacturing process or other readings of manufacturing equipment. For example, in a semiconductor manufacturing environment, images can be used to track, for example, process points (e.g., movement from a bonding site to a packaging site), and process parameters (e.g., bonding force, electrical properties across a bond of an integrated circuit).

Network device sensors may also perform processing on data it collects before transmitting the data to a computing environment, or before deciding whether to transmit data to a computing environment. For example, network devices may determine whether data collected meets certain rules, for example by comparing data or values calculated from the data and comparing that data to one or more thresholds. The network device may use this data and/or comparisons to determine if the data should be transmitted to a computing environment for further use or processing.

Devices in computing environment114may include specialized computers, servers, or other machines that are configured to individually and/or collectively process large amounts of data (e.g., using a session pool102). The computing environment114may also include storage devices (e.g., data stores120) that include one or more databases of structured data, such as data organized in one or more hierarchies, or unstructured data. The databases may communicate with the processing devices within computing environment114to distribute data to them and store data used in the computing environment114. Computing environment114may collect, analyze and/or store data from or pertaining to communications, client device operations, client rules, and/or user-associated actions stored at one or more devices in computing environment114. Such data may influence communication routing to the devices within computing environment114, and how data is stored or processed within computing environment114, among other actions.

Network100may also include one or more network-attached data stores120. Network-attached data stores120are used to store data to be processed by the computing environment114as well as any intermediate or final data generated by the computing system in non-volatile memory. For instance, data stores120can perform functions such as writing and copying data and can provide data storage for network functions such as sessions, authorization, publishing and retrieving packages. In certain embodiments, the configuration of the computing environment114allows its operations to be performed such that intermediate and final data results can be stored solely in volatile memory (e.g., RAM), without a requirement that intermediate or final data results be stored to non-volatile types of memory (e.g., disk). This can be useful in certain situations, such as when the computing environment114receives ad hoc queries from a user and when responses, which are generated by processing large amounts of data, need to be generated on-the-fly. In this non-limiting situation, the computing environment114may be configured to retain the processed information within memory so that responses can be generated for the user at different levels of detail as well as allow a user to interactively query against this information.

Network-attached data stores120may store a variety of different types of data organized in a variety of different ways and from a variety of different sources. For example, network-attached data stores120may include storage other than primary storage located within computing environment114that is directly accessible by processors located therein. Network-attached data stores120may include secondary, tertiary, auxiliary, or back-up storage (e.g., data storage120B), such as large hard drives, servers, and virtual memory, among other types. Storage devices may include portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing and containing data (e.g., computer a machine-readable storage medium or computer-readable storage medium such as computer readable medium210inFIG.2).

Furthermore, the data stores may hold a variety of different types of data. For example, network-attached data stores120may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as performance metrics or criteria) or product sales databases (e.g., a database containing individual data records identifying details of individual product performance).

The unstructured data may be presented to the computing environment114in different forms such as a flat file or a conglomerate of data records and may have data values and accompanying time stamps. The computing environment114may be used to analyze the unstructured data in a variety of ways to determine the best way to structure (e.g., hierarchically) that data, such that the structured data is tailored to a type of further analysis on the data. For example, after being processed, the unstructured time stamped data may be aggregated by time (e.g., into daily time period units) to generate time series data and/or structured hierarchically according to one or more dimensions (e.g., parameters, attributes, and/or variables). For example, data may be stored in a hierarchical data structure, such as a ROLAP OR MOLAP database, or may be stored in another tabular form, such as in a flat-hierarchy form.

Other devices can further be used to influence communication routing and/or processing between devices within computing environment114and with devices outside of computing environment114. For example, as shown inFIG.1, computing environment114may include a device130supporting a web application. Thus, computing environment114can retrieve data of interest, such as client information (e.g., product information, client rules, etc.), technical product details, news, current or predicted weather, and so on. Balancer160can be used to balance and direct load within the computing environment114. Authentication device150can be used to provide authentication or other security protocols for a client device, user or group accessing computing environment114.

In addition to computing environment114collecting data (e.g., as received from network devices, such as sensors, and client devices or other sources) to be processed as part of a big data analytics project, it may also receive data in real time as part of a streaming analytics environment. As noted, data may be collected using a variety of sources as communicated via different kinds of networks or locally. Such data may be received on a real-time streaming basis. For example, network devices may receive data periodically from sensors as the sensors continuously sense, monitor and track changes in their environments. Devices within computing environment114may also perform pre-analysis on data it receives to determine if the data received should be processed as part of an ongoing project. The data received and collected by computing environment114, no matter what the source or method or timing of receipt, may be processed over a period of time for a client to determine results data based on the client's needs and rules.

FIG.1includes a pool of devices with a pool manager104and session pool102. Network100includes a variety of pool managers (e.g., pool manager104) and worker nodes110(e.g., devices, servers, or server farms of session pool102), according to embodiments of the present technology. Devices of session pool102are communicatively connected (e.g., via communication path108and communication path106). Therefore, the pool manager may transmit information (e.g., related to the session pool102or notifications), to and receive information from each other. Although only one pool manager104is shown inFIG.1, the network100may include more pool managers or a different kind of device manager (e.g., a dedicated resource manager).

Session pool102includes one or more worker nodes (e.g., worker node110A). Shown inFIG.1are three worker nodes110A-C merely for illustration, more or less worker nodes could be present. For instance, the pool manager104may itself be a worker node and may not need further worker nodes to complete a task. A given worker node could include dedicated computing resources or allocated computing resources as needed to perform operations as directed by the pool manager104. The number of worker nodes included in a session pool102may be dependent, for example, upon how large the project or data set is being processed by the session pool102, the capacity of each worker node, and the time designated for the session pool102to complete the project. Each worker node within the session pool102may be connected (wired or wirelessly, and directly or indirectly) to pool manager104. Therefore, each worker node may receive information from the pool manager104(e.g., an instruction to perform work on a project) and may transmit information to the pool manager104(e.g., a result from work performed on a project). Furthermore, worker nodes110may communicate with each other (either directly or indirectly). For example, worker nodes110may transmit data between each other related to a job being performed or an individual task within a job being performed by that worker node. However, in certain embodiments, worker nodes110may not, for example, be connected (communicatively or otherwise) to certain other worker nodes. In an embodiment, worker nodes may only be able to communicate with the pool manager104that controls it, and may not be able to communicate with other worker nodes in the session pool102.

The pool manager104may connect with other devices of network100or an external device (e.g., a pool user, such as a server or computer). For example, a server or computer may connect to pool manager104and may transmit a project or job to the node. The project may include a data set. The data set may be of any size. Once the pool manager104receives such a project including a large data set, the pool manager104may distribute the data set or projects related to the data set to be performed by worker nodes110. Alternatively, for a project including a large data set, the data set may be received or stored by a machine other than a pool manager104or worker node110(e.g., a Hadoop data node).

Pool manager may maintain knowledge of the status of the worker nodes110in the session pool102(i.e., status information), accept work requests from clients, subdivide the work across worker nodes110, and coordinate the worker nodes110, among other responsibilities. Worker nodes110may accept work requests from a pool manager104and provide the pool manager104with results of the work performed by the worker nodes110. A session pool102may be started from a single node (e.g., a machine, computer, server, etc.). This first node may be assigned or may start as the primary pool manager104that will control any additional nodes that enter the session pool102.

When a project is submitted for execution (e.g., by a client or a pool manger104), it may be assigned to a set of nodes. After the nodes are assigned to a project, a data structure (i.e., a communicator) may be created. The communicator may be used by the project for information to be shared between the project code running on each node. A communication handle may be created on each node. A handle, for example, is a reference to the communicator that is valid within a single process on a single node, and the handle may be used when requesting communications between nodes.

A pool manager may be designated as the primary pool manager among multiple pool managers. A server, computer or other external device may connect to the primary pool manager. Once the pool manager receives a project, the primary pool manager may distribute portions of the project to its worker nodes for execution. For example, when a project is initiated on session pool102, primary pool manager104controls the work to be performed for the project to complete the project as requested or instructed. The primary pool manager may distribute work to the worker nodes110based on various factors, such as which subsets or portions of projects may be completed most efficiently and in the correct amount of time. For example, a worker node may perform analysis on a portion of data that is already local (e.g., stored on) the worker node. The primary pool manager also coordinates and processes the results of the work performed by each worker node after each worker node executes and completes its job. For example, the primary pool manager may receive a result from one or more worker nodes, and the pool manager may organize (e.g., collect and assemble) the results received and compile them to produce a complete result for the project received from the end user.

Any remaining pool manager (not shown) may be assigned as backup pool manager for the project. In an embodiment, backup pool manager may not control any portion of the project. Instead, backup pool manager may serve as a backup for the primary pool manager and take over as primary pool manager if the primary pool manager were to fail.

To add another node or machine to the session pool102, the primary pool manager may open a pair of listening sockets, for example. A socket may be used to accept work requests from clients, and the second socket may be used to accept connections from other pool nodes. The primary pool manager may be provided with a list of other nodes (e.g., other machines, computers, servers) that will participate in the pool, and the role that each node will fill in the pool. Upon startup of the primary pool manager (e.g., the first node on the pool), the primary pool manager may use a network protocol to start the server process on every other node in the session pool102. Command line parameters, for example, may inform each node of one or more pieces of information, such as: the role that the node will have in the pool, the host name of the primary pool manager, and the port number on which the primary pool manager is accepting connections from peer nodes. The information may also be provided in a configuration file, transmitted over a secure shell tunnel, and recovered from a configuration server. While the other machines in the pool may not initially know about the configuration of the pool, that information may also be sent to each other node by the primary pool manager. Updates of the pool information may also be subsequently sent to those nodes.

For any pool manager other than the primary pool manager added to the pool, the pool manager may open multiple sockets. For example, the first socket may accept work requests from clients, the second socket may accept connections from other pool members, and the third socket may connect (e.g., permanently) to the primary pool manager. When a pool manager (e.g., primary pool manager) receives a connection from another pool manager, it first checks to see if the peer node is in the list of configured nodes in the pool. If it is not on the list, the pool manager may clear the connection. If it is on the list, it may then attempt to authenticate the connection. If authentication is successful, the authenticating node may transmit information to its peer, such as the port number on which a node is listening for connections, the host name of the node, and information about how to authenticate the node, among other information. When a node, such as the new pool manager, receives information about another active node, it will check to see if it already has a connection to that other node. If it does not have a connection to that node, it may then establish a connection to that pool manager.

Any worker node added to the pool may establish a connection to the primary pool manager and any other pool manager on the pool. After establishing the connection, it may authenticate itself to the pool (e.g., any pool manager, including both primary and backup, or a server or user controlling the pool). After successful authentication, the worker node may accept configuration information from the pool manager.

When a node joins a session pool102(e.g., when the node is powered on or connected to an existing node on the pool or both), the node is assigned (e.g., by an operating system of the pool) an identifier (e.g., a universally unique identifier (UUID)). This identifier may help other nodes and external entities (devices, users, etc.) to identify the node and distinguish it from other nodes. When a node is connected to the pool, the node may share its identifier with the other nodes in the pool. Since each node may share its identifier, each node may know the identifier of every other node on the pool. Identifiers may also designate a hierarchy of each of the nodes (e.g., backup pool manager) within the pool. For example, the identifiers of each of the backup pool manager may be stored in a list of backup pool manager to indicate an order in which the backup pool manager will take over for a failed primary pool manager to become a new primary pool manager. However, a hierarchy of nodes may also be determined using methods other than using the unique identifiers of the nodes. For example, the hierarchy may be predetermined, or may be assigned based on other predetermined factors.

The pool may add new machines at any time (e.g., initiated from any pool manager). Upon adding a new node to the pool, the pool manager may first add the new node to its table of pool nodes. The pool manager may also then notify every other pool manager about the new node. The nodes receiving the notification may acknowledge that they have updated their configuration information.

Primary pool manager104may, for example, transmit one or more communications to backup pool manager or other control or worker nodes within the session pool102). Such communications may be sent using protocols such as periodically, at fixed time intervals, or between known fixed stages of the project's execution. The communications transmitted by primary pool manager104may be of varied types and may include a variety of types of information. For example, primary pool manager104may transmit snapshots (e.g., status information) of the session pool102so that backup pool manager104always has a recent snapshot of the session pool102. The snapshot or pool status may include, for example, the structure of the pool (including, for example, the worker nodes in the pool, unique identifiers of the nodes, or their relationships with the primary pool manager) and the status of a project (including, for example, the status of each worker node's portion of the project). The snapshot may also include analysis or results received from worker nodes in the session pool102. The backup pool manager may receive and store the backup data received from the primary pool manager. The backup pool manager may transmit a request for such a snapshot (or other information) from the primary pool manager, or the primary pool manager may send such information periodically to the backup pool manager.

As noted, the backup data may allow the backup pool manager to take over as primary pool manager if the primary pool manager fails without requiring the pool to start the project over from scratch. If the primary pool manager fails, the backup pool manager that will take over as primary pool manager may retrieve the most recent version of the snapshot received from the primary pool manager and use the snapshot to continue the project from the stage of the project indicated by the backup data. This may prevent failure of the project as a whole.

A backup pool manager may use various methods to determine that the primary pool manager has failed. In one example of such a method, the primary pool manager may transmit (e.g., periodically) a communication to the backup pool manager that indicates that the primary pool manager is working and has not failed, such as a heartbeat communication. The backup pool manager may determine that the primary pool manager has failed if the backup pool manager has not received a heartbeat communication for a certain predetermined period of time. Alternatively, a backup pool manager may also receive a communication from the primary pool manager itself (before it failed) or from a worker node that the primary pool manager has failed, for example because the primary pool manager has failed to communicate with the worker node.

Different methods may be performed to determine which backup pool manager of a set of backup pool manager will take over for failed primary pool manager104and become the new primary pool manager. For example, the new primary pool manager may be chosen based on a ranking or “hierarchy” of backup pool manager based on their unique identifiers. In an alternative embodiment, a backup pool manager may be assigned to be the new primary pool manager by another device in the session pool102or from an external device (e.g., a system infrastructure or an end user, such as a server or computer, controlling the session pool102). In another alternative embodiment, the backup pool manager that takes over as the new primary pool manager may be designated based on bandwidth or other statistics about the session pool102.

A worker node within the session pool102may also fail. If a worker node fails, work being performed by the failed worker node may be redistributed amongst the operational worker nodes. In an alternative embodiment, the primary pool manager may transmit a communication to each of the operable worker nodes still on the session pool102that each of the worker nodes should purposefully fail also. After each of the worker nodes fail, they may each retrieve their most recent saved checkpoint of their status and re-start the project from that checkpoint to minimize lost progress on the project being executed.

While each device inFIG.1is shown as a single device, it will be appreciated that multiple devices may instead be used.FIG.2shows an example computing structure for a device inFIG.2.FIG.2includes a computing device202. The computing device202has a computer-readable medium210and a processor208. Computer-readable medium210is an electronic holding place or storage for information so the information can be accessed by processor208. The computer readable medium210is a non-transitory medium in which data can be stored and that does not include carrier waves and/or transitory electronic signals. Examples of a non-transitory medium may include, for example, a magnetic disk or tape, optical storage media such as compact disk or digital versatile disk, flash memory, memory or memory devices. A computer-program product may include code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including, for example, memory sharing, message passing, token passing, and network transmission. Computer-readable medium210can include, but is not limited to, any type of random-access memory (RAM), any type of read only memory (ROM), any type of flash memory, etc. such as magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips), optical disks (e.g., compact disc (CD), digital versatile disc (DVD)), smart cards, flash memory devices, etc.

Processor208executes instructions (e.g., stored at the computer-readable medium210). The instructions can be carried out by a special purpose computer, logic circuits, or hardware circuits. In one or more embodiments, processor208is implemented in hardware and/or firmware. Processor208executes an instruction, meaning it performs or controls the operations called for by that instruction. The term “execution” is the process of running an application or the carrying out of the operation called for by an instruction. The instructions can be written using one or more programming language, scripting language, assembly language, etc. Processor208in one or more embodiments can retrieve a set of instructions from a permanent memory device and copy the instructions in an executable form to a temporary memory device that is generally some form of RAM, for example. Processor208operably couples with components of computing device202(e.g., input/output interface204and with computer readable medium210) to receive, to send, and to process information.

For instance, in one or more embodiments, computing device202sends and/or receives information from one or more of databases230, cloud sources232, application programming interfaces236(API's), graphical user interfaces240(GUIs), printers242, webpages244, and computing systems246. The input/output interface204may be configured to receive languages238(e.g., to communicate with other computing systems246) or specific electronic files or documents234(e.g., inputs for building models or designing experiments). The input/output interface204may be a single interface (e.g., an output interface only to output reports to a printer242), multiple interface (e.g., a graphical user interface240may be interactive and send and receive data over input/output interface204), or a set of interfaces (e.g., to connect with multiple devices).

In one or more embodiments, computer-readable medium210stores instructions for execution by processor208. In one or more embodiments, one or more applications stored on computer-readable medium210are implemented in software (e.g., computer-readable and/or computer-executable instructions) stored in computer-readable medium210and accessible by processor208for execution of the instructions.

FIG.3illustrates a system300including a control node (e.g., pool manager104ofFIG.1) and a worker node (e.g., worker nodes110ofFIG.1), according to embodiments of the present technology. System300includes one control node (control node302) and one worker node (worker node310) for purposes of illustration but may include more worker and/or control node. The control node302is communicatively connected to worker node310via communication path350. Therefore, control node302may transmit information (e.g., related to the session pool102or notifications), to and receive information from worker node310via path350.

System300includes data processing nodes (e.g., control node302and worker node310). Control node302and worker node310can include multi-core data processors. Each control node302and worker node310in this example includes a grid-enabled software component (GESC)320that executes on the data processor associated with that node and interfaces with buffer memory322also associated with that node. Each control node302and worker node310in this example includes a database management software (DBMS)328that executes on a database server (not shown) at control node302and on a database server (not shown) at worker node310.

Each control node302and worker node310in this example also includes a data storage324. Data storage324, similar to network-attached data stores120inFIG.1, are used to store data to be processed by the nodes in the computing environment. Data storage324may also store any intermediate or final data generated by the computing system after being processed, for example in non-volatile memory. However, in certain embodiments, the configuration of the system300allows its operations to be performed such that intermediate and final data results can be stored solely in volatile memory (e.g., RAM), without a requirement that intermediate or final data results be stored to non-volatile types of memory. Storing such data in volatile memory may be useful in certain situations, such as when the pool receives queries (e.g., ad hoc) from a client device330and when responses, which are generated by processing large amounts of data, need to be generated quickly or on-the-fly. In such a situation, the pool may be configured to retain the data within memory so that responses can be generated at different levels of detail and so that a client may interactively query against this information.

Each control node302and worker node310in this example also includes a user-defined function (UDF)326. The UDF326provides a mechanism for the DBMS328to transfer data to or receive data from the database stored in the data storage324that are managed by the DBMS. For example, UDF326can be invoked by the DBMS328to provide data to the GESC320for processing. The UDF326may establish a socket connection (not shown) with the GESC320to transfer the data. Alternatively, the UDF326can transfer data to the GESC320by writing data to shared memory accessible by both the UDF326and the GESC320.

The GESC320at the control node302and worker node310may be connected via a network. Therefore, control node302and worker node310can communicate with each other via the network using a predetermined communication protocol such as, for example, the Message Passing Interface (MPI). Each GESC320can engage in point-to-point communication with the GESC at another node or in collective communication with multiple GESCs via the network. The GESC320at each node may contain identical (or nearly identical) software instructions. Each control node302and worker node310may be configured to operate as either a pool manager or a worker node. The GESC320B at the control node302can communicate, over a communication path352, with a client device330. More specifically, control node302may communicate with client application332hosted by the client device330to receive queries and to respond to those queries after processing large amounts of data.

DBMS328may control the creation, maintenance, and use of database or data structure (not shown) within control node302and worker node310. The database may organize data stored in data storage324. The DBMS328at control node302may accept requests for data and transfer the appropriate data for the request. With such a process, collections of data may be distributed across multiple physical locations. In this example, each control node302and worker node310stores a portion of the total data managed by the management system in its associated data storage324.

Furthermore, the DBMS328may be responsible for protecting against data loss using replication techniques. Replication includes providing a backup copy of data stored on one node on one or more other nodes. Therefore, if one node fails, the data from the failed node can be recovered from a replicated copy residing at another node. Data or status information for each node in the session pool102may also be shared with each node on the pool.

FIG.4provides example applications400(e.g., applications executed by a computing device202, worker node310, or control node302) for performing one or more tasks or operations.

For example, data access operations402can be used for accessing data from different sources (e.g., importing and/or reading Excel files, flat files, relational databases, APIs, R, Python, and SAS® files and databases). For instance, data can be imported for data visualization, exploration and analysis. Data can be formatted or optimized. For instance, data blending and cleanup operations404can be used to remove complexity (e.g., in text, images and functions data) and for screening data (e.g., screening data for outliers, entry errors, missing values and other inconsistencies that can compromise data analysis). This can be useful for visual and interactive tools. Data can also be transformed, blended, grouped, filtered, merged into a single table or into subsets, or otherwise arranged for a particular scenario.

In one or more embodiments, one or more applications400include data exploration and visualization operations406that can be used to support plot and profiler tools. For instance, plot tools can be used to create data plots (e.g., to plot data to spot patterns and patterns that do not fit a trend). Some example plots include bubble plots, scatter plots (matrix and 3D), parallel plots, cell plots, contour plots, ternary plots, and surface plots. Profilers are tools that can be used to create a specialized set of plots in which changing one plot changes the other plots. For instance, profiling is an approach to generate visualizations of response surfaces by seeing what would happen if a user changed just one or two factors at a time. Profiler tools can be used to create interactive profiles of data (e.g., to explore and graph data dynamically and uncover hidden relationships between graphed data or interface with linked data, to interpret and understand the fit of equations to data, and to find factor values to optimize responses). Some example profiler tools include prediction profiler, contour profiler, surface profiler, mixture profiler, custom profiler, and excel profiler. A prediction profiler can be used to show vertical slices across each factor, holding other factors at a current value. A contour profiler allows horizontal slices showing contour lines for two factors at a time. A surface profiler generates three-dimensional plots for two factors at a time, or contour surface plot for 3 factors at a time. A mixture profiler is a contour profiler for mixture of factors. A custom profiler is a numerical optimizer. An excel profiler allows for visualization of models or formulas stored in electronic worksheets. Accordingly, profiler tools can allow for one or more of simulation, surface visualization, optimization, and desirability studies. Graphs (e.g., from plot or profiler tools) can be exported to electronic or print reports for presenting findings. Further, data exploration and visualization operations406can include text exploration such as computer extraction of symbols, characters, words and phrases; or computer visualization such as to organize symbols, characters, words and phrases to uncover information regarding a text or classify the text.

In one or more embodiments, one or more applications400include data analysis and modeling operations408can be used to analyze one or many variables or factors in linked analysis. Analysis results may be linked with specific graphs designed for different types of data or metrics (e.g., graphs related to histograms, regression modeling and distribution fitting). Data analysis and modeling can be performed real-time (or just-in-time). For instance, applications400can included statistical modeling operations410. For instance, statistical modeling operations410can be used for a diversity of modeling tasks such as univariate, multivariate and multifactor. Data can be transformed from its collected form (e.g., text or functional form) and data can be used for building models for better insights (e.g., discovery trends or patterns in data). As another example, one or more applications400can include predictive modeling and machine learning operations412to build models using predictive modeling techniques, such as regression, neural networks and decision trees. The operations412can be used to fit multiple predictive models and determine the best performing model with model screening. Validation (e.g., cross-validation and k-fold cross-validation) can be used (e.g., to prevent over-fitting or to select a best model). Machine learning methods can be used by the user without having to write code and tune algorithms. Examples of machine learning techniques are described in more detail with respect toFIGS.5and6).

In one or more embodiments, one or more applications400include design of experiments (DOE) operations414used to create designs for experiments that provide test conditions for one or more factors tested in the experiment. For example, the design of experiments operations414can be used to create optimally designed experiments, efficient experiments to meet constraints, process limitations and budget, and/or screening designs to untangle important effects between multiple factors. DOE operations414can also be used for evaluating designs (e.g., design diagnostic measures such as efficiency metrics).

In one or more embodiments, one or more applications400include quality and process engineering operations416to track and visualize quality and processes. For instance, the quality and process engineering operations416can generate charts to explore root causes of quality or process problems (e.g., causes of variation in manufacturing processes and drill down into problem processes). Additionally, or alternatively, they can be used to generate notifications for metrics that exceed a threshold such as an out-of-control signal or a control chart warning. Additionally, or alternatively, they can be used to study the capability and performance of one or more variables to identify processes that are not meeting user-defined goals. Objective data from processes or consumer data can be used to release better products and react to market trends.

In one or more embodiments, one or more applications400include reliability analysis operations418. For example, in manufacturing, reliability analysis tools can be used to prevent failure, improve warranty or product performance, find and address important design vulnerabilities, and pinpoint defects in materials or processes. Reliability analysis tools can also be used to determine how to reduce or improve these issues (e.g., by identifying trends and outliers in data and model predictions). What-if Analysis operations422can be used to demonstrate patterns of predicted responses and the effect of each factor on the response with scenario analysis. For example, a graphical user interface can be used for a user to put in different inputs, assumptions or constraints for a system and observe responses or effects. For instance, in a measurement system analysis analyzing whether parts would be in-specification, different estimated variances between parts and operators testing the parts could be varied to determine the effect on modeled output for the measurement system analysis.

In one or more embodiments, one or more applications400include automation and scripting operations420. For example, automation can allow code-free access for a user to automation routines all the way up to completely customized applications (e.g., code free access to SAS®, MATLAB®, Python® and R routines). For example, a design created for experiments can be automated such that automatic testing is performed for the design.

In one or more embodiments, one or more applications400include operations for greater user control and interaction. For instance, customization operations424can be used for user customization (e.g., mass customizations, and customizations of graphics, statistics, and default views). As another example, content organization operations426can be used to organize data (e.g., translate statistical results to a simplified view to communicate findings and organize, summarize, and document content to better aid the accountability and reproducibility of projects). As another example, the communicating results operations428can be used for presentation of results, models, or other output from one or more applications400(e.g., presented in print, graphical user interface, or web-based versions).

In one or more embodiments, fewer, different, and additional components can be incorporated into computing device202. In one or more embodiments, the input/output interface has more than one interface that uses the same or different interface technology.

In one or more embodiments, the one or more applications400can be integrated with other analytic or computing tools not specifically shown here. For instance, one or more applications are implemented using or integrated with one or more software tools such as JMP®, Base SAS, SAS® Enterprise Miner™, SAS/STAT®, SAS® High Performance Analytics Server, SAS® Visual Data Mining and Machine Learning, SAS® LASR™ SAS® In-Database Products, SAS® Scalable Performance Data Engine, SAS® Cloud Analytic Services, SAS/OR®, SAS/ETS®, SAS® Inventory Optimization, SAS® Inventory Optimization Workbench, SAS® Visual Analytics, SAS® Viya™, SAS In-Memory Statistics for Hadoop®, SAS® Forecast Server, and SAS/IML®.

One or more embodiments are useful for generating and using machine-learning models.FIG.5is a flow chart of an example of a process for generating and using a machine-learning model according to some aspects. Machine learning is a branch of artificial intelligence that relates to mathematical models that can learn from, categorize, and make predictions about data. Such mathematical models, which can be referred to as machine-learning models, can classify input data among two or more classes; cluster input data among two or more groups; predict a result based on input data; identify patterns or trends in input data; identify a distribution of input data in a space; or any combination of these. Examples of machine-learning models can include (i) neural networks; (ii) decision trees, such as classification trees and regression trees; (iii) classifiers, such as Naïve bias classifiers, logistic regression classifiers, ridge regression classifiers, random forest classifiers, least absolute shrinkage and selector operator (LASSO) classifiers, and support vector machines; (iv) clusterers, such as k-means clustering, mean-shift clusterers, and spectral clusterers; (v) factorizers, such as factorization machines, principal component analyzers and kernel principal component analyzers; and (vi) ensembles or other combinations of machine-learning models. In some examples, neural networks can include deep neural networks, feed-forward neural networks, recurrent neural networks, convolutional neural networks, radial basis function (RBF) neural networks, echo state neural networks, long short-term memory neural networks, bi-directional recurrent neural networks, gated neural networks, hierarchical recurrent neural networks, stochastic neural networks, modular neural networks, spiking neural networks, dynamic neural networks, cascading neural networks, neuro-fuzzy neural networks, or any combination of these.

Different machine-learning models may be used interchangeably to perform a task. Examples of tasks that can be performed at least partially using machine-learning models include various types of scoring; bioinformatics; cheminformatics; software engineering; fraud detection; customer segmentation; generating online recommendations; adaptive websites; determining customer lifetime value; search engines; placing advertisements in real time or near real time; classifying DNA sequences; affective computing; performing natural language processing and understanding; object recognition and computer vision; robotic locomotion; playing games; optimization and metaheuristics; detecting network intrusions; medical diagnosis and monitoring; or predicting when an asset, such as a machine, will need maintenance.

Any number and combination of tools can be used to create machine-learning models. Examples of tools for creating and managing machine-learning models can include SAS® Enterprise Miner, SAS® Rapid Predictive Modeler, and SAS® Model Manager, SAS Cloud Analytic Services (CAS)®, SAS Viya® of all which are by SAS Institute Inc. of Cary, North Carolina.

Machine-learning models construction can be at least partially automated (e.g., with little or no human involvement) in a training process. During training, input data can be iteratively supplied to a machine-learning model to enable the machine-learning model to identify patterns related to the input data or to identify relationships between the input data and output data. With training, the machine-learning model can be transformed from an untrained state to a trained state. Input data can be split into one or more training sets and one or more validation sets, and the training process may be repeated multiple times. The splitting may follow a k-fold cross-validation rule, a leave-one-out-rule, a leave-p-out rule, or a holdout rule. An overview of training and using a machine-learning model is described below with respect to the flow chart ofFIG.5.

In block504, training data is received. In some examples, the training data is received from a remote database or a local database, constructed from various subsets of data, or input by a user. The training data can be used in its raw form for training a machine-learning model or pre-processed into another form, which can then be used for training the machine-learning model. For example, the raw form of the training data can be smoothed, truncated, aggregated, clustered, or otherwise manipulated into another form, which can then be used for training the machine-learning model.

In block506, a machine-learning model is trained using the training data. The machine-learning model can be trained in a supervised, unsupervised, or semi-supervised manner. In supervised training, each input in the training data is correlated to a desired output. This desired output may be a scalar, a vector, or a different type of data structure such as text or an image. This may enable the machine-learning model to learn a mapping between the inputs and desired outputs. In unsupervised training, the training data includes inputs, but not desired outputs, so that the machine-learning model has to find structure in the inputs on its own. In semi-supervised training, only some of the inputs in the training data are correlated to desired outputs.

In block508, the machine-learning model is evaluated. For example, an evaluation dataset can be obtained, for example, via user input or from a database. The evaluation dataset can include inputs correlated to desired outputs. The inputs can be provided to the machine-learning model and the outputs from the machine-learning model can be compared to the desired outputs. If the outputs from the machine-learning model closely correspond with the desired outputs, the machine-learning model may have a high degree of accuracy. For example, if 90% or more of the outputs from the machine-learning model are the same as the desired outputs in the evaluation dataset, the machine-learning model may have a high degree of accuracy. Otherwise, the machine-learning model may have a low degree of accuracy. The 90% number is an example only. A realistic and desirable accuracy percentage is dependent on the problem and the data.

In some examples, if the machine-learning model has an inadequate degree of accuracy for a particular task, the process can return to block506, where the machine-learning model can be further trained using additional training data or otherwise modified to improve accuracy. If the machine-learning model has an adequate degree of accuracy for the particular task, the process can continue to block510.

In block510, new data is received. In some examples, the new data is received from a remote database or a local database, constructed from various subsets of data, or input by a user. The new data may be unknown to the machine-learning model. For example, the machine-learning model may not have previously processed or analyzed the new data.

In block512, the trained machine-learning model is used to analyze the new data and provide a result. For example, the new data can be provided as input to the trained machine-learning model. The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these.

In block514, the result is post-processed. For example, the result can be added to, multiplied with, or otherwise combined with other data as part of a job. As another example, the result can be transformed from a first format, such as a time series format, into another format, such as a count series format. Any number and combination of operations can be performed on the result during post-processing.

A more specific example of a machine-learning model is the neural network600shown inFIG.6. The neural network600is represented as multiple layers of interconnected neurons, such as neuron608, that can exchange data between one another. The layers include an input layer602for receiving input data, a hidden layer604, and an output layer606for providing a result. The hidden layer604is referred to as hidden because it may not be directly observable or have its input directly accessible during the normal functioning of the neural network600. Although the neural network600is shown as having a specific number of layers and neurons for exemplary purposes, the neural network600can have any number and combination of layers, and each layer can have any number and combination of neurons.

The neurons and connections between the neurons can have numeric weights, which can be tuned during training. For example, training data can be provided to the input layer602of the neural network600, and the neural network600can use the training data to tune one or more numeric weights of the neural network600. In some examples, the neural network600can be trained using backpropagation.

Backpropagation can include determining a gradient of a particular numeric weight based on a difference between an actual output of the neural network600and a desired output of the neural network600. Based on the gradient, one or more numeric weights of the neural network600can be updated to reduce the difference, thereby increasing the accuracy of the neural network600. This process can be repeated multiple times to train the neural network600. For example, this process can be repeated hundreds or thousands of times to train the neural network600.

In some examples, the neural network600is a feed-forward neural network. In a feed-forward neural network, every neuron only propagates an output value to a subsequent layer of the neural network600. For example, data may only move one direction (forward) from one neuron to the next neuron in a feed-forward neural network.

In other examples, the neural network600is a recurrent neural network. A recurrent neural network can include one or more feedback loops, allowing data to propagate in both forward and backward through the neural network600. This can allow for information to persist within the recurrent neural network. For example, a recurrent neural network can determine an output based at least partially on information that the recurrent neural network has seen before, giving the recurrent neural network the ability to use previous input to inform the output.

In some examples, the neural network600operates by receiving a vector of numbers from one layer; transforming the vector of numbers into a new vector of numbers using a matrix of numeric weights, a nonlinearity, or both; and providing the new vector of numbers to a subsequent layer of the neural network600. Each subsequent layer of the neural network600can repeat this process until the neural network600outputs a final result at the output layer606. For example, the neural network600can receive a vector of numbers as an input at the input layer602. The neural network600can multiply the vector of numbers by a matrix of numeric weights to determine a weighted vector. The matrix of numeric weights can be tuned during the training of the neural network600. The neural network600can transform the weighted vector using a nonlinearity, such as a sigmoid tangent or the hyperbolic tangent. In some examples, the nonlinearity can include a rectified linear unit, which can be expressed using the following equation: y=max(x, 0), where y is the output and x is an input value from the weighted vector. The transformed output can be supplied to a subsequent layer, such as the hidden layer604, of the neural network600. The subsequent layer of the neural network600can receive the transformed output, multiply the transformed output by a matrix of numeric weights and a nonlinearity, and provide the result to yet another layer of the neural network600. This process continues until the neural network600outputs a final result at the output layer606.

Other examples of the present disclosure may include any number and combination of machine-learning models having any number and combination of characteristics. The machine-learning model(s) can be trained in a supervised, semi-supervised, or unsupervised manner, or any combination of these. The machine-learning model(s) can be implemented using a single computing device or multiple computing devices, such as the session pool102discussed above.

Implementing some examples of the present disclosure at least in part by using machine-learning models can reduce the total number of processing iterations, time, memory, electrical power, or any combination of these consumed by a computing device when analyzing data. For example, a neural network may more readily identify patterns in data than other approaches. This may enable the neural network to analyze the data using fewer processing cycles and less memory than other approaches, while obtaining a similar or greater level of accuracy.

Some machine-learning approaches may be more efficiently and speedily executed and processed with machine-learning specific processors (e.g., not a generic CPU). Such processors may also provide an energy savings when compared to generic CPUs. For example, some of these processors can include a graphical processing unit (GPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), an artificial intelligence (AI) accelerator, a neural computing core, a neural computing engine, a neural processing unit, a purpose-built chip architecture for deep learning, and/or some other machine-learning specific processor that implements a machine learning approach or one or more neural networks using semiconductor (e.g., silicon (Si), gallium arsenide (GaAs)) devices. Furthermore, these processors may also be employed in heterogeneous computing architectures with a number of and a variety of different types of cores, engines, nodes, and/or layers to achieve various energy efficiencies, chip-level thermal processing considerations, processing speed improvements, data communication speed improvements, and/or data efficiency targets and improvements throughout various parts of the system when compared to a homogeneous computing architecture that employs CPUs for general purpose computing.

Associated Processes

FIG.7illustrates one embodiment of a method700for displaying a design of experiments (DoE) factor specification user interface. It should be appreciated that other examples contemplated within the scope of the present disclosure may involve more operations, fewer operations, different operations, or a different order of operations than as shown inFIG.7.

As will be described in more detail herein, the embodiments of the method700may result in numerous technical advantages and technical improvements-a subset of which will now be described. Firstly, the user interface elements described with reference to processes710-740may reduce a total number of inputs required for specifying and/or manipulating factors involved in a design of experiments. Secondly, the dynamic reconfiguration of rows described with reference to processes720and730may provide a user with an efficient way to adjust experimental values (e.g., levels) of one or more design of experiment factors when such design of experiment factors change to a new factor type. Thirdly, the composite factor user interface control component described with reference to process740may enable a user to efficiently add or remove factors in the design of experiment, thus providing a high level of control and customization when configuring the design of experiments.

Overall, these technical advantages and improvements provide a more intuitive, flexible, and user-friendly interface for designing experiments, thereby reducing a cognitive burden on the user when configuring one or more design of experiments. Furthermore, providing efficient ways for managing factors in a design of experiments may improve a user's experience and decrease user interaction time, which is particularly important in instances where the design of experiments factor specification user interface is displayed on battery-operated devices. Additionally, in some embodiments, the method700described herein may accelerate a technical configuration of one or more design of experiments and the underlying computer systems that execute the design of experiments created via the design of experiments factor specification user interface.

In some embodiments, the method700may include a process710that functions to display a plurality of factor-setting user interface (UI) control elements configured to receive an input of characters for specifying a set of design of experiment factors for creating a design of experiments (DoE). An example of the process710displaying a plurality of factor-setting user interface control elements is illustrated inFIG.8. Specifically, inFIG.8, the process710is causing a plurality of factor-setting user interface control elements802a-802hto be displayed in a design of experiments factor specification user interface800. It shall be noted that, in some embodiments, the set of design of experiment factors specified by the plurality of factor-setting user interface control elements802a-802hmay relate to variables in the design of experiments that may be manipulated during one or more runs of the design of experiments—as will described in more detail in the method3900.

In some embodiments, the plurality of factor-setting user interface control elements displayed by the process710may each specify (e.g., indicate) a name of a target factor in the design of experiments. For instance, in the example illustrated inFIG.8, the plurality of factor-setting user interface control elements802a-802hare specifying a name of eight (8) factors in the design of experiments. Specifically, in this example, the factor-setting user interface control elements802a-802hspecify a plurality of design of experiment factors named “Wing Length,” “Paper Type,” “Drop Height,” “Paper Clip,” “Extra fold at bottom,” “X,” “X2,” and “X3,” respectively. It shall be noted that the above example is not intended to be limiting and that the design of experiments factor specification user interface800may specify additional, fewer, or different design of experiment factors without departing from the scope of the disclosure.

Additionally, in the example ofFIG.8, each of the plurality of factor-setting user interface control elements802a-802hcomprise a character edit box that is configured to update a name of a target factor based on receiving an input of one or more characters (e.g., alphabetic characters, numeric characters, alphanumeric characters, etc.). For instance, as illustrated inFIGS.9and10, an input862is received for replacing the selected character ‘X’ (illustrated inFIG.9) with the characters for “Factor 1” (illustrated inFIG.10) and, as a result, causes the name of the design of experiment factor corresponding to the factor-setting user interface control element802fto be changed from “X” to “Factor 1.”

It shall be noted the above example is not intended to be limiting and that the plurality of factor-setting user interface control elements displayed by the process710may comprise different types of user interface elements without departing from the scope of the disclosure, such as text edit boxes or the like.

In some embodiments, the names specified by the plurality of factor-setting user interface control elements802a-802hmay be automatically generated. In one or more examples of such embodiments, the names specified by the plurality of factor-setting user interface control elements802a-802hmay be automatically generated based on an order of the plurality factor-setting user interface control elements802a-802hin the design of experiments factor specification user interface800(e.g., the names specified by the factor-setting user interface control elements802a-802hinFIG.8may instead be “X1,” “X2,” “X3,” “X4,” “X5,” “X6,” “X7,” “X8,” respectively).

In some embodiments, the process710may display the plurality of factor-setting user interface control elements within a factor-setting grid container control that includes (e.g., arranges content along) one or more rows and one or more columns. An example of the process710displaying the plurality of factor-setting user interface control elements within a factor-setting grid container control is also illustrated inFIG.8. Specifically, inFIG.8, the process710is displaying the plurality of factor-setting user interface control elements802a-802hwithin a factor-setting grid container control804that includes a column806a, a column806blabeled “Name” (also referred to herein as a “factor-setting UI control elements column”), a column806clabeled “Role” (also referred to herein as a “factor type UI control elements column”), a column806dlabeled “Changes”, a dynamic column806e(also referred to herein as a “dynamic column of editable UI control elements), and a column806flabeled “Unit.” Example user interface elements that may be included in the plurality of columns806a-806fwill now be described.

Additionally, as also shown inFIG.8, the column806aincludes a plurality of factor re-ordering user interface elements826a-826h. The column806blabeled “Name” includes the plurality of factor-setting user interface control elements802a-802h. The column806clabeled “Role” includes a plurality of factor type user interface control elements810a-810h. The column806dlabeled “Changes” includes a plurality of design of experiments platform configuration user interface control elements812a-812h. The dynamic column806eincludes a plurality of dynamic rows of editable user interface control elements814a-814h. Lastly, the column806flabeled “Unit” includes a plurality of factor unit user interface control elements816a-816h.

Furthermore, in the example illustrated inFIG.8, the factor-setting grid container control804also includes a plurality of factor-setting composition component rows808a-808h. Specifically, inFIG.8, the plurality of factor-setting composition component rows808a-808hinclude the plurality of factor re-ordering user interface elements826a-826h, the plurality of factor-setting user interface control elements802a-802h, the plurality of factor type user interface control elements810a-810h, the plurality of design of experiments platform configuration user interface control elements812a-812h, the plurality of dynamic rows of editable user interface control elements814a-814h, and the plurality of factor unit user interface control elements816a-816h, respectively. It shall be noted that the characteristics and operations of the user interface control elements810a-810h,812a-812h,814a-814h, and816a-816hwill be described in more detail in processes720-740.

In some embodiments, a respective factor-setting composition component row in the factor-setting grid container control804may be re-ordered based on input(s) directed to the column806a. For instance, in the example ofFIG.23, an input828selecting the factor re-ordering user interface element826ais detected. Further, inFIG.24, the input828moves from a position corresponding to the factor re-ordering user interface element826ato a position corresponding to the factor re-ordering user interface element826h. Thus, as a result of detecting the input828, as illustrated inFIG.25, the positions of the factors named “Wing Length” and “X3” are swapped in the design of experiments factor specification user interface800.

Referring toFIG.7, in some embodiments, the method700may include a process720that functions to display a plurality of factor type UI control elements, each respective factor type UI control element of the plurality of factor type UI control elements being configured to receive input for specifying a factor type of a plurality of factor types displayed by the respective factor type UI control element for a corresponding DOE factor of the set of DOE factors. As will be described in more detail herein, in some embodiments, displaying the plurality of factor type user interface control elements may enable a user interacting with the design of experiments factor specification user interface to specify whether a target design of experiment factor relates to a continuous factor type, a discrete numeric factor type, a categorical factor type, a blocking factor type, a mixture factor type, a constant factor, an uncontrolled factor type, or the like. An example of the process720displaying a plurality of factor type user interface control elements will now be described with reference toFIG.11.

InFIG.11, the process720is displaying a plurality of factor type user interface control elements810a-810hthat each include a selectable combo box user interface element (also referred to herein as a “dropdown menu element”). Specifically, inFIG.11, the selectable combo box user interface elements associated with the plurality of factor type user interface control elements810a-810hare specifying (e.g., indicating) that that the design of experiment factors named “Wing Length,” “Paper Type,” “Drop Height,” “Paper Clip,” “Extra fold at bottom,” “Factor 1,” “X2,” and “X3” are currently of a discrete numeric factor type, a categorical factor type, a continuous factor type, a categorical factor type, a categorical factor type, a categorical factor type, a categorical factor type, and a categorical factor type, respectively.

In some embodiments, an input operation to a respective combo box user interface element may cause the respective combo box user interface element to expand and display a set of factor type user interface control elements. For instance, as additionally illustrated inFIG.11, the process720is detecting an input820selecting the factor type user interface control element810f, and in response, as illustrated inFIG.12, a plurality of factor type user interface control elements822a-822gare displayed in the design of experiments factor specification user interface800.

In some embodiments, the plurality of factor type user interface control elements822a-822gare selectable and, when selected, updates a factor type associated with a corresponding factor. For instance, as also shown inFIG.12, the process720is detecting an input824selecting the factor type user interface control element822a, and in response, as shown inFIG.13, the design of experiment factor named “Factor 1” is updated from being a categorical factor type to a continuous factor type.

It shall be noted that when the factor type user interface control elements822b-822gare selected, the design of experiment factor named “Factor 1” may be analogously updated to a discrete numeric factor type, a categorical factor type, a blocking factor type, a mixture factor type, a constant factor type, or an uncontrolled factor type, respectively.

Referring toFIG.7, in some embodiments, the method700may include a process730that functions to display a plurality of dynamic rows of editable UI control elements configured to receive inputs of experimental values for the set of DOE factors, wherein one or more dynamic rows of editable UI control elements of the plurality of dynamic rows of editable UI control elements are dynamically reconfigured from a current format of control elements to a new format of control elements based on one or more input operations to one or more of the plurality of factor type UI control elements. Examples of the process730displaying a plurality of dynamic rows will now be described with reference toFIG.13.

In some embodiments, a dynamic row of editable user interface control elements displayed for a discrete numeric factor may enable a user to define one or more discrete numeric levels (e.g., experimental values) for the discrete numeric factor. For instance, in the example ofFIG.13, the dynamic row of editable user interface control elements814adisplayed for the discrete numeric factor “Wing Length” includes a plurality of number edit boxes830aand830b. Specifically, inFIG.13, the plurality of number edit boxes830aand830bspecify (e.g., indicate) that the factor “Wing Length” is currently associated with two (2) discrete numeric levels having the values two (2) and six (6), respectively. It shall be noted that if the factor “Wing Length” was associated with more, fewer, or different discrete numeric levels, the dynamic row of editable user interface control elements814amay correspondingly include more, fewer, or different number edit boxes.

Additionally, as shown inFIG.13, the dynamic row of editable user interface control elements814aincludes button control user interface elements830cand830d. The button control user interface element830cmay be selectable and, when selected, may function to remove one or more of the plurality of number edit boxes830aand830bfrom the design of experiments factor specification user interface800and dissociate the discrete numeric levels associated with such number edit boxes from the factor “Wing Length.”

Conversely, in some embodiments, the button control user interface element830dmay be selectable and, when selected, may function to display one or more additional number edit boxes in the dynamic row of editable user interface control elements814afor adding one or more discrete numeric levels to the factor “Wing Length.” That is, in some embodiments, numeric input received by the plurality of number edit boxes830a,830b, and the number edit boxes added via button control user interface element830c-830dmay be used to specify or update one or more discrete numeric levels associated with the factor “Wing Length.”

In some embodiments, a dynamic row of editable user interface control elements displayed for a continuous factor may include a plurality of user interface control elements that enable a user to define a continuous range for the continuous factor (e.g., experimental values). For instance, in the example ofFIG.13, the dynamic rows of editable user interface control elements814cand814fdisplayed for the continuous factors “Drop Height” and “Factor 1” include a plurality of number edit boxes832a-832band834a-834b, respectively.

In some embodiments, numeric input received by the plurality of number edit boxes832aand834amay be used to define a minimum value of the continuous range associated with the factors “Drop Height” and “Factor 1,” respectively. For instance, in the example illustrated inFIG.13, the number edit boxes832aand834aspecify (e.g., indicate) that the minimum value of the continuous range associated with factor “Drop Height” and “Factor 1” is the value one (1) and negative one (−1), respectively.

Conversely, in some embodiments, numeric input received by the plurality of number edit boxes832band834bmay be used to define a maximum value of the continuous range associated with the factors “Drop Height” and “Factor 1,” respectively. For instance, in the example illustrated inFIG.13, the number edit boxes832band834bspecify (e.g., indicate) that the maximum value of the continuous range associated with factor “Drop Height” and “Factor 1” is the value twenty (20) and one (1), respectively.

In some embodiments, a dynamic row of editable user interface control elements displayed for a categorical factor may include a plurality of user interface control elements that are configured to receive input for defining one or more categorical levels (e.g., experimental values) of the categorical factor. For instance, in the example ofFIG.13, the dynamic rows of editable user interface control elements814b,814d,814e,814g, and814hinclude a plurality of text edit boxes840a-840b,840c-840d,840e-840f,840g-840h, and840i-840j, respectively.

Specifically, inFIG.13, the plurality of text edit boxes840aand840bare specifying (e.g., indicating) that the factor “Paper Type” is current associated with two (2) categorical levels having the values “Cardstock” and “Regular,” respectively. The plurality of text boxes840cand840dare specifying that the factor “Paper Clip” is currently associated with two (2) categorical levels having the values “Small” and “Big,” respectively. The plurality of text boxes840eand840fare specifying that the factor “Extra fold at bottom” is currently associated with two (2) categorical levels having the values “Yes” and “No,” respectively. The plurality of text boxes840gand840hare specifying that the factor “X2” is currently associated with two (2) categorical levels having the values “Yes” and “No,” respectively. Lastly, the plurality of text boxes840iand840jare specifying that the factor “X3” is currently associated with two (2) categorical levels having the values “Yes” and “No,” respectively.

In some embodiments, a dynamic row of editable user interface control elements displayed for a mixture factor may include a plurality of user interface control elements that are configured to receive input for defining experimental values of the mixture factor. For instance, in the example ofFIG.14A, the dynamic row of editable user interface control elements814idisplayed for the mixture factor “X1” includes a plurality of number edit boxes842aand842b. Example operations of the plurality of number edit boxes842aand842bwill now be described. However, it shall be noted that these examples are not intended to be limiting and that the dynamic row of editable user interface control elements814imay include fewer, additional, or different items without departing from the scope of the disclosure. It shall also be noted that the control elements842c-842gmay have similar characteristics to the control elements802a-802h,810a-810h,812a-812h, and816a-816hdescribed above.

In some embodiments, numerical input provided to the number edit box842amay be used to define a lower mixture value for the factor “X1.” For instance, in the example illustrated inFIG.14A, the number edit box842ais specifying (e.g., indicating) that the lower mixture value of the factor “X1” is zero (0). Conversely, in some embodiments, numerical input provided to the number edit box842bmay be used to define an upper mixture value for the factor “X1.” For instance, as also illustrated inFIG.14A, the number edit box842bis specifying (e.g., indicating) that the upper mixture value of the factor “X1” is one (1).

In some embodiments, a dynamic row of editable user interface control elements displayed for a blocking factor may include a plurality of user interface control elements that are configured to receive input for defining experimental values of the blocking factor. For instance, in the example ofFIG.14B, the dynamic row of editable user interface control elements814jdisplayed for the blocking factor “X1” includes a number edit box844aand a text edit box844b. Example operations of the number edit box844aand text edit box844bwill now be described. However, it shall be noted that these examples are not intended to be limiting and that the dynamic row of editable user interface control elements814jmay include fewer, additional, or different items without departing from the scope of the disclosure. It shall also be noted that the control elements844c-844gmay have similar characteristics to the control elements802a-802h,810a-810h,812a-812h, and816a-816hdescribed above.

In some embodiments, numerical input provided to the number edit box844amay be used to define a number of runs per block. For instance, in the example illustrated inFIG.14B, the number edit box844ais specifying (e.g., indicating) that the factor “X1” is associated with four (4) runs per block. Analogously, in some embodiments, text input provided to the text edit box844bmay be used to define a block label for the factor “X1.” For instance, in the example illustrated inFIG.14B, the text edit box844bis specifying (e.g., indicating) that the block label associated with the factor “X1” is “Block.”

In some embodiments, a dynamic row of editable user interface control elements displayed for a covariate factor may include a plurality of user interface control elements that are configured to receive input for defining experimental values of the covariate factor. For instance, in the example ofFIG.14C, the dynamic row of editable user interface control elements814kdisplayed for the covariate factor “X1” includes a plurality of number edit boxes846aand846b. Example operations of the number edit boxes846aand846bwill now be described. However, it shall be noted that these examples are not intended to be limiting and that the dynamic row of editable user interface control elements814kmay include fewer, additional, or different items without departing from the scope of the disclosure. It shall also be noted that the control elements846c-846gmay have similar characteristics to the control elements802a-802h,810a-810h,812a-812h, and816a-816hdescribed above.

In some embodiments, numerical input provided to the number edit box846amay be used to define a minimum value for the factor “X1.” For instance, in the example illustrated inFIG.14C, the number edit box846ais specifying (e.g., indicating) that the minimum value of the factor “X1” is negative 1 (−1). Conversely, in some embodiments, numerical input provided to the number edit box846bmay be used to define a maximum value for the factor “X1.” For instance, as also illustrated inFIG.14C, the number edit box846bis specifying (e.g., indicating) that the maximum value of the factor “X1” is one (1).

In some embodiments, a dynamic row of editable user interface control elements displayed for a constant factor may include one or more user interface control elements that are configured to receive input for defining an experimental value of the constant factor. For instance, in the example ofFIG.14D, the dynamic row of editable user interface control elements814ldisplayed for the constant factor “X1” includes a number edit box848a. Example operations of the number edit box848awill now be described. However, it shall be noted that these examples are not intended to be limiting and that the dynamic row of editable user interface control elements814lmay include fewer, additional, or different items without departing from the scope of the disclosure. It shall also be noted that the control elements848c-848gmay have similar characteristics to the control elements802a-802h,810a-810h,812a-812h, and816a-816hdescribed above.

In some embodiments, numerical input provided to the number edit box848amay be used to define a constant value for the factor “X1.” For instance, in the example illustrated inFIG.14D, the number edit box848ais specifying (e.g., indicating) that the constant value of the factor “X1” is zero (0).

In some embodiments, one or more of the dynamic rows of editable user interface control elements displayed via the process730may be dynamically reconfigured when a factor type of a corresponding factor changes. Specifically, in some embodiments, when a factor changes to a new factor type (e.g., succeeding factor type), the dynamic row of editable user interface control elements may be reconfigured to cease displaying user interface control elements associated with a previous factor type (e.g., the incumbent factor type) and start displaying user interface control elements associated with the new factor type (e.g., the succeeding factor type). For instance, as shown inFIG.13, when the design of experiment factor named “Factor 1” changed—for reasons previously described in process720—from being a categorical factor (as illustrated inFIG.11) to being a continuous factor (as shown inFIG.13), the dynamic row of editable user interface control elements814fwas reconfigured from displaying the user interface control elements848a-848dassociated with defining experimental values for the categorical factor type (as illustrated inFIG.11) to displaying the user interface control elements834aand834bassociated with defining experimental values for the continuous factor type (as shown inFIG.13).

It shall be noted that, in some embodiments, if the design of experiment factor named “Factor 1” was instead changed from being of a categorical factor type to a discrete numeric factor type, blocking factor type, mixture factor type, constant factor type, or uncontrolled factor type, the dynamic row814fwould have instead been dynamically reconfigured to display user interface control elements associated with defining experimental values for the discrete numeric factor (e.g., elements similar to830a-830dinFIG.13), the blocking factor type (e.g., elements similar to844a-844binFIG.14B), the mixture factor type (e.g., elements similar to846a-846binFIG.14A), the constant factor type (e.g., element similar to848ainFIG.14D), or the uncontrolled factor type, respectively. It shall also be noted that, in some embodiments, “dynamically reconfiguring a row” as referred to herein may additionally, or alternatively, include performing one or more of the operations described with reference to process3924in the method3900.

Stated another way, the dynamic reconfiguration of a row, as referred to herein, may be initiated in response to detecting a user input selecting a new factor type for a target factor. Accordingly, in response to detecting the user input, the method700may function to dynamically (e.g., automatically) reconfigure a row of user interface control elements corresponding to the target factor by ceasing a display of user interface control elements associated with a previous factor type of the target factor and initiating a display of user interface control elements associated with the new factor type of the target factor.

Referring toFIG.7, in some embodiments, the method700may include a process740that functions to display a composite factor UI control component configured to receive inputs for generating one or more control signals that add or remove one or more DOE factors of the set of DOE factors. An example of the process740displaying a composite factor user interface control component is illustrated inFIG.15. Specifically, inFIG.15, the process740is causing the composite factor user interface control component850to be displayed in the design of experiments factor specification user interface800. Example control elements that may be included in the composite factor user interface control component850will now be described.

In some embodiments, the composite factor user interface control component850may include a button control element850a. The button control element850a, in some embodiments, may be configured to remove one or more previously selected factors from the design of experiments. For instance, as also illustrated inFIG.15, an input852directed towards the button control element850ais detected while the factor-setting composition component row808gis selected. In response, as illustrated inFIG.16, the factor-setting composition component row808gis removed from the factor-setting grid container control804. It shall be noted that the above example is not intended to be limiting and that if additional or different factor-setting composition component rows were selected than illustrated, the additional or different factor-setting composition components rows would be deleted in analogous ways.

Additionally, or alternatively, in some embodiments, the composite factor user interface control component850may include a button control element850b. The button control element850b, in some embodiments, may be configured to remove one or more design of experiment factors displayed at an end of the factor-setting grid container control804. For instance, as illustrated inFIG.17, an input854selecting the button control element850bis detected and, in response, as illustrated inFIG.18, the factor-setting composition component row826his removed from the end of the factor-setting grid container control804.

Additionally, or alternatively, in some embodiments, the composite factor user interface control component850may include a button control element850c. The button control element850c, in some embodiments, may be configured to add one or more design of experiment factors to the design of experiments. For instance, as also illustrated inFIG.18, an input858selecting the button control element850cis detected and, in response, as illustrated inFIG.19, the factor-setting composition component row826his added (e.g., appended) to the end of the factor-setting grid container control804.

It shall be noted that, in some instances, a factor-setting composition component row being added to the design of experiments factor specification user interface may exceed a current display area of the design of experiments factor specification user interface. Accordingly, in some examples of such embodiments, the design of experiments factor specification user interface may be automatically scrolled to a section of the design of experiments factor specification user interface that at least includes the recently added factor-setting composition component row.

Additionally, or alternatively, in some embodiments, the composite factor user interface control component850may include a button control element850d. The button control element850d, in some embodiments, may be configured to undo one or more operations performed by a user interacting with the design of experiments graphical user interface800. Specifically, in some embodiments, the button control element850dmay undo user input(s) directed to the control elements802a-802h,810a-810h,812a-812h,814a-814h,816a-816h, and850a-850h, and/or may undo operations performed by the control elements802a-802h,810a-810h,812a-812h,814a-814h,816a-816h, and/or850a-850h. For instance, in the example ofFIG.20, an input860selecting the button control element850dis detected and, in response, as illustrated inFIG.21, the number edit box830billustrated inFIG.20is no longer being displayed in the design of experiments factor specification user interface800(e.g., the discrete numeric level ‘6’ is no longer associated with the factor named “Wing Length”).

Additionally, or alternatively, in some embodiments, the composite factor user interface control component850may include a button control element850e. The button control element850e, in some embodiments, may be configured to reverse one or more most recent in time operations of the button control element850d. For instance, as also illustrated inFIG.21, an input862selecting the button control element850eis detected and, in response, as illustrated inFIG.22, the number edit box830bis re-displayed in the design of experiments factor specification user interface800(e.g., the discrete numeric level ‘6’ is re-associated with the factor named “Wing Length”).

Additionally, or alternatively, in some embodiments, the composite factor user interface control component850may include a sub-composite factor user interface control component850ffor adding design of experiment factors of a respective factor type in bulk. As shown inFIG.26, in some embodiments, the sub-composite factor user interface850fmay include a number edit control element850f-1and a drop-down menu element850f-2. Example operations and characteristics of the number edit control element850f-1and the drop-down menu element850f-2will now be described.

In some embodiments, numeric input received by the number edit control element850f-1may control a total number of design of experiment factors to add to the design of experiments factor specification user interface800. For instance, in the example ofFIG.26, the numeric input being displayed within the number edit control element850f-1is indicating that, when the drop-down menu element850f-2is operated, three (3) design of experiment factors will be added (e.g., appended) to the design of experiments factor specification user interface800. It shall be noted that the above example is not intended to be limiting and that the number edit control component850f-1may specify fewer or additional design of experiments to the to the design of experiments factor specification user interface800without departing from the scope of the disclosure.

In some embodiments, a selection of the drop-down menu element850f-2may cause a plurality of factor type user interface control elements to be displayed in the design of experiments factor specification user interface800. For instance, as also shown inFIG.26, an input864selecting the drop-down menu element850f-2is detected and, in response, as illustrated inFIG.27, the drop-down menu element expands to display a plurality of factor type user interface control elements866-880. Specifically, inFIG.27, the plurality of factor type user interface control elements correspond to a continuous factor type, a discrete numeric factor, a categorical factor type, a blocking factor type, a covariate factor type, a mixture factor type, a constant factor type, and an uncontrolled factor type, respectively.

In some embodiments, the plurality of factor type user interface control elements866-880may be selectable and, when selected, control the type of design of experiment factors added to the design of experiments factor specification user interface800. For instance, as also shown in the example ofFIG.27, an input882selecting the factor type user interface control element866is detected and, in response, as illustrated inFIG.28, three (3) factor-setting composition component rows808f-808hcorresponding to the continuous factor type are added to the design of experiments factor specification user interface800. Specifically, inFIG.28, the control elements802f-802h,810f-810h,812f-812h,814f-814h, and888-898are indicating that the design of experiment factors corresponding to the newly added factor-setting are named X, X2, X3, are of the continuous factor type, have changes set to ‘easy’, have a lower limit of negative one (−1), and have an upper limit of one (1), respectively.

It shall be noted that, in some embodiments, if the number edit box control element850f-1specified a different number of design of experiment factors to add than as illustrated (e.g., a number other than three (3)), a different number of factor-setting composition component rows would have instead been added to the design of experiments factor specification user interface800.

Additionally, as illustrated inFIG.27, the factor type user interface control elements868and870are being displayed in associated with visual indications902,904, respectively. Specifically, inFIG.27, the visual indications902and904are indicating that input may cause the drop-down menu element850f-2to be further expanded for defining a total number of discrete numeric levels and categorical levels associated with the design of experiment factors being added to the design of experiments factor specification user interface800. For instance, as illustrated inFIG.29, an input882directed to the factor type user interface control element868is detected and, in response, as also illustrated inFIG.29, a plurality of factor level user interface control elements906-920are displayed for specifying a total number of discrete numeric factor levels to define for the design of experiment factors being added to the design of experiments factor specification user interface800.

Furthermore, as also illustrated inFIG.29, an input884selecting the factor level user interface control element912is detected. In response, as illustrated inFIG.30, three design of experiment factors are added to the design of experiments factor specification user interface800(indicated by the factor-setting composition component rows808f-808h). Specifically, inFIG.28, the control elements802f-802h,810f-810h,812f-812h,814f-814h, and922-948are indicating that the design of experiment factors corresponding to the newly added factor-setting are named X, X2, X3, are of the discrete numeric factor type, have changes set to ‘easy,’ and are associated with five discrete numeric levels having values one (1), two (2), three (3), four (4), and five (5), respectively.

Additionally, or alternatively, in some embodiments, the composite factor user interface control component850may include a factor dialog control button850gthat, when selected, causes a dialog menu element to be displayed. For instance, as also shown inFIG.30, an input952selecting the factor dialog control button850gis detected and, in response, as shown inFIG.31, a dialog menu element954is displayed. It shall be noted that, while not explicitly illustrated inFIG.31, the dialog menu element954may be displayed in a variety of ways including, but not limited to, overlaid on the design of experiments factor specification user interface800, in a new user interface (e.g., distinct from the design of experiments factor specification user interface800), or the like.

In some embodiments, the dialog menu element954may include a first grid container control956comprising one or more rows and one or more columns. For instance, in the example ofFIG.31, the first grid container control956includes a column958anamed “Role,” a column958bnamed “Changes,” a dynamic column958c, a column958dnamed “N Factors,” and a column958enamed “Action.” Specifically, inFIG.31, the columns958a-958einclude a plurality of factor text labels960a-960c, a plurality of combo box user interface control elements962a-962c(similar to elements812a-812hillustrated inFIG.8), a plurality of dynamic rows of editable user interface control components964a-964c, a plurality of number input fields966a-966c, and a plurality of button control elements968a-968c, respectively.

Furthermore, as also illustrated inFIG.31, the first grid container control956includes a plurality of factor-setting composition component rows959a-959c. Specifically, inFIG.31, the factor-setting composition component rows959a-959cinclude the user interface elements960a-968a,960b-968b, and960c-968c, respectively. Example characteristics and operations of the above-mentioned user interface elements will now be described.

In some embodiments, the plurality of factor text labels960a-960cmay indicate the type of factors that will be added to the design of experiments factor specification user interface800when a selection of the button control elements968a-968care detected. For instance, in the example ofFIG.31, the plurality of factor text labels960a-960care indicating that the button control elements968a-968c, when selected, will add one or more continuous factors, one or more discrete numeric factors, and one or more categorical factors to the design of experiments factor specification user interface800, respectively.

In some embodiments, the plurality of combo box user interface control elements962a-962cmay indicate a change difficultly level of the continuous factors, discrete numeric factors, and categorical factors that will be added to the design of experiments factor specification user interface800when a selection of the button control elements968a-968cis detected, respectively. For instance, in the example ofFIG.31, the plurality of combo box user interface control elements962a-962care indicating that continuous, discrete numeric, and categorical factors added via button controls968a-968cwill have a change difficulty level set “Easy,” “Hard,” and “Very Hard,” respectively. It shall be noted that, in some embodiments, input directed to the plurality of combo box user interface control elements962a-962cmay cause the plurality of combo box user interface control elements962a-962cto expand for enabling a user interacting with the design of experiments graphical user interface800to select a factor change difficulty level for the continuous factor role, discrete numeric factor role, and categorical factor role, respectively.

In some embodiments, the rows964a-964cof editable user interface control elements may include user interface control elements for specifying attributes of factors that will be added to the design of experiments factor specification user interface800when a selection of the button control elements968a-968cis detected. Examples of items/content that may be displayed within the rows964a-964cof editable user interface control element will now be described below.

In some embodiments, the row964aof editable user interface control elements may include one or more user interface control elements that are configured to receive input for defining a continuous range of the continuous factor(s) added via the button control element968a. For instance, in the example ofFIG.31, the row964aof editable user interface control elements includes number edit boxes970and972that are specifying that the continuous factors added via button control element968awill have a minimum value zero (0) and a maximum value of one (1), respectively.

In some embodiments, the row964bof editable user interface control elements may include one or more user interface control elements that are configured to receive input for defining a number of levels for the discrete numeric factors added via the button control element968b. For instance, as also illustrated in the example ofFIG.31, the row964bof editable user interface control elements includes a number input field974that is specifying that the discrete numeric factors added via button control element968bwill have five (5) discrete levels.

In some embodiments, the row964cof editable user interface control elements may include one or more user interface control elements that are configured to receive input for defining a number of levels for the categorical factors added via the button control element968c. For instance, as also illustrated in the example ofFIG.31, the row964cof editable user interface control elements includes a number input field976that is specifying that the categorical factors added via button control element968cwill have four (4) categorical levels.

In some embodiments, the plurality of number input fields966a-966cmay be configured to receive input for specifying a total number of factors to add to the design of experiments factor specification user interface800when a selection of the button control elements968a-968cis detected, respectively. For instance, in the example ofFIG.31, the plurality of number input fields966a-966care specifying that five (5) continuous factors, six (6) discrete numeric factors, and three (3) categorical factors will be added to the design of experiments factor specification user interface800when the button control elements968a-968care selected, respectively.

In some embodiments, the button control element968a, when selected, may add one or more factors to the design of experiments factor specification user interface800in a manner defined by the elements960a-966a. An example result of selecting the button control element968ais illustrated inFIG.32. Specifically, inFIG.32and because the number input field966aillustrated inFIG.31includes the numerical value five (5), a corresponding number of factor-setting composition component rows808i-808mare added to the design of experiments factor specification user interface800.

Additionally, as shown inFIG.32, the plurality of factor-setting user interface control elements802i-802m, the plurality of factor-type user interface control elements810i-810m, the plurality of design of experiments platform configuration user interface control elements812i-812m, and the plurality of number input fields970-988included in the plurality of dynamic rows of editable user interface control elements814i-814mare indicating that the design of experiment factors corresponding to the factor-setting composition component rows808i-808mare named “X4”-“X8”, respectively, have a factor type based on the text label960aillustrated inFIG.31(e.g., continuous factor type), have a change difficulty level based on the combo box user interface control element962aillustrated inFIG.31(e.g., Easy), and have a continuous range based on the number input fields970-972illustrated inFIG.31(e.g., a continuous range between zero (0) and one (1)).

Analogously, in some embodiments, the button control element968b, when selected, may add one or more factors to the design of experiments factor specification user interface800in a manner defined by the elements960b-966b. An example result of selecting the button control element968bis illustrated inFIG.33. Specifically, inFIG.33and because the number input field966billustrated inFIG.31includes the numerical value six (6), a corresponding number of factor-setting composition component rows808i-808nare added to the design of experiments factor specification user interface800.

Additionally, as shown inFIG.33, the plurality of factor-setting user interface control elements802i-802n, the plurality of factor-type user interface control elements810i-810n, the plurality of design of experiments platform configuration user interface control elements812i-812n, and the plurality of text input fields990-1048included in the plurality of dynamic rows of editable user interface control elements814i-814mare indicating that the design of experiment factors corresponding to the factor-setting composition component rows808i-808mare named “X4”-“X9”, respectively, have a factor type based on the text label960aillustrated inFIG.31(e.g., discrete numeric factor type), have a change difficulty level based on the combo box user interface control element962aillustrated inFIG.31(e.g., Hard), and have a number of discrete levels based on the number input fields974illustrated inFIG.31(e.g., five (5) discrete levels).

Similarly, in some embodiments, the button control element968c, when selected, may add one or more factors to the design of experiments factor specification user interface800in a manner defined by the elements960c-966c. An example result of selecting the button control element968cis illustrated inFIG.34. Specifically, inFIG.34and because the number input field966cillustrated inFIG.31includes the numerical value three (3), a corresponding number of factor-setting composition component rows808i-808kare added to the design of experiments factor specification user interface800.

Additionally, as shown inFIG.34, the plurality of factor-setting user interface control elements802i-802k, the plurality of factor-type user interface control elements810i-810k, the plurality of design of experiments platform configuration user interface control elements812i-812k, and the plurality of text input fields1050-1070included in the plurality of dynamic rows of editable user interface control elements814i-814kare indicating that the design of experiment factors corresponding to the factor-setting composition component rows808i-808mare named “X4”-“X6”, respectively, have a factor type based on the text label960cillustrated inFIG.31(e.g., categorical factor type), have a change difficulty level based on the combo box user interface control element962cillustrated inFIG.31(e.g., Very Hard), and have a number of categorical levels based on the number input fields976illustrated inFIG.31(e.g., four (4) discrete levels).

Furthermore, in some embodiments, the dialog menu element displayed by the process740may include a second grid container control element. For instance, in the example ofFIG.31, the process740is causing a second grid container control element1074to be displayed in the dialog menu element954. Example control elements that may be displayed within the second grid container control element1074will now be described.

In some embodiments, as illustrated inFIG.31, the second grid container control element1074may include a composite factor user interface control component1076. The composite factor user interface control component1076, in some embodiments, may include one or more button control elements for modifying design of experiment factors displayed within the second grid container control1074. For instance, in the example illustrated inFIG.31, the composite factor user interface control component1076includes a plurality of button control elements1076a-1076e. It shall be noted that the above example is not intended to be limiting and that the composite factor user interface control component1076may include additional, different, or fewer button control elements than illustrated inFIG.31without departing from the scope of the disclosure. Example operations of the button control elements1076a-1076ewill now be described.

The button control element1076a-1076e, in some embodiments, may perform operations similar to button control element850a-850eillustrated inFIG.22. That is, in some embodiments, the button control element1076amay be configured to remove one or more previously selected factor-setting composition component rows1078a-1078ffrom the second grid container control1074. The button control element1076bmay be configured to remove a factor-setting composition component row displayed at an end of the factor-setting grid container control1074(e.g., row1078f). The button control element1076cmay be configured to add one or more factor-setting composition component rows to the second grid container control1074. The button control element1076dmay be configured to undo one or more operations performed within the second grid container control1074. The button control element1076emay be configured to reverse one or more most recent in time operations of the button control element850d.

Additionally, in some embodiments, the second grid container control element1074may include one or more rows and one or more columns. For instance, as also illustrated in the example ofFIG.31, the second grid container control element1074includes a plurality of factor-setting composition component rows1078a-1078fand a plurality of columns1078a-1078e. Specifically, inFIG.31, the plurality of factor-setting composition component rows1078a-1078finclude the control elements1080a-1088a,1080b-1088b,1080c-1088c,1080d-1088d,1080e-1088e, and1080f-1088f, respectively.

It shall be noted that the control elements1080a-1080f, in some embodiments, may be configured to operate in a same or similar manner as the plurality of factor re-ordering user interface elements826a-826hillustrated inFIGS.23-25. The control elements1082a-1082f, in some embodiments, may be configured to operate in a same or similar manner as the plurality of factor-setting user interface control elements802a-802hillustrated inFIG.8. The control elements1082a-1082f, in some embodiments, may be configured to operate in a same or similar manner as the plurality of factor type user interface control elements810a-810hillustrated inFIG.8. The control elements1084a-1084f, in some embodiments, may be configured to operate in a same or similar manner as the plurality of combo box user interface control elements962a-962calso illustrated inFIG.31. The rows of control elements1086a-1086fmay have similar operating characteristics as the plurality of rows of control elements964a-964calso illustrated inFIG.31. The control elements1088a-1088f, in some embodiments, may be configured to operate in a same or similar manner as the plurality of number input fields966a-966calso illustrated inFIG.31.

Furthermore, in some embodiments, the dialog menu element displayed by the process740may include one or more button control elements. For instance, in the example ofFIG.31, the dialog menu element954includes button control elements1090aand1090b. The button control element1090a, when selected, in some embodiments, may cause an addition of one or more factor-setting composition component rows to the design experiments factor specification user interface800. Conversely, in some embodiments, the button control element1090b, when selected, in some embodiments, may forgo an addition of one or more factor-setting composition component rows to the design experiments factor specification user interface800.

An example result of selecting the button control element1090ais illustrated inFIG.35. Particularly, inFIG.35, a plurality of factor-setting composition component rows808i-808m,808n-808s,808t-808z,808aa-808bb,808cc-808dd, and808ee-808ggare added to the design of experiments factor specification user interface800based on the factor-setting composition component rows1078a,1078b,1078c,1078d,1078e, and1078f, respectively.

The user interface control elements displayed with rows808i-808mwill now be described. Additionally, as shown inFIG.35, the plurality of factor-setting user interface control elements802i-802mare indicating the design of experiment factors corresponding to the rows808i-808mare named “X4”-“X8,” respectively. The plurality of factor type user interface control elements810i-810mare indicating that the design of experiment factors corresponding to the rows808i-808mhave a factor type based on the combo box user interface control element1082a(e.g., continuous factor type). The plurality of design of experiments platform configuration user interface control elements812i-812mare indicating that the design of experiment factors corresponding to the rows808i-808mhave a change difficulty level based on the combo box user interface control element1084a(e.g., “Easy”). The plurality of rows of editable user interface control elements814i-814mare indicating that the design of experiment factors corresponding to the rows808i-808mhave a minimum continuous value corresponding to number input field1090(e.g., 0) and a maximum continuous value corresponding to number input field1092(e.g., 1).

The user interface control elements displayed with rows808n-808swill now be described. Additionally, as shown inFIG.35, the plurality of factor-setting user interface control elements802n-802sare indicating the design of experiment factors corresponding to the rows808n-808sare named “X9”-“X14,” respectively. The plurality of factor type user interface control elements810n-810sare indicating that the design of experiment factors corresponding to the rows808n-808shave a factor type based on the combo box user interface control element1082b(e.g., continuous factor type). The plurality of design of experiments platform configuration user interface control elements812n-812sare indicating that the design of experiment factors corresponding to the rows808n-808shave a change difficulty level based on the combo box user interface control element1084b(e.g., “Hard”). The plurality of rows of editable user interface control elements814n-814sare indicating that the design of experiment factors corresponding to the rows808n-808shave a minimum continuous value corresponding to number input field1094(e.g., −1) and a maximum continuous value corresponding to number input field1096(e.g., 1).

The user interface control elements displayed with rows808t-808zwill now be described. Additionally, as shown inFIG.35, the plurality of factor-setting user interface control elements802t-802zare indicating the design of experiment factors corresponding to the rows808t-808zare named “X15”-“X21,” respectively. The plurality of factor type user interface control elements810t-810zare indicating that the design of experiment factors corresponding to the rows808t-808zhave a factor type based on the combo box user interface control element1082c(e.g., discrete numeric factor type). The plurality of design of experiments platform configuration user interface control elements812t-812zare indicating that the design of experiment factors corresponding to the rows808t-808zhave a change difficulty level based on the combo box user interface control element1084c(e.g., “Very Hard”). The plurality of rows of editable user interface control elements814t-814zare indicating that the design of experiment factors corresponding to the rows808n-808shave a total number of discrete levels based on the number input control element1098(e.g., five (5) discrete levels).

The user interface control elements displayed with rows808aa-808bbwill now be described. Additionally, as shown inFIG.35, the plurality of factor-setting user interface control elements802aa-80bbare indicating the design of experiment factors corresponding to the rows808aa-808bbare named “X22”-“X23,” respectively. The plurality of factor type user interface control elements810aa-810bbare indicating that the design of experiment factors corresponding to the rows808aa-808bbhave a factor type based on the combo box user interface control element1082d(e.g., discrete numeric factor type). The plurality of design of experiments platform configuration user interface control elements812aa-812bbare indicating that the design of experiment factors corresponding to the rows808aa-808bbhave a change difficulty level based on the combo box user interface control element1084d(e.g., “Easy”). The plurality of rows of editable user interface control elements814aa-814bbare indicating that the design of experiment factors corresponding to the rows808aa-808bbhave a total number of discrete levels based on the number input control element1100(e.g., eight (8) discrete levels).

The user interface control elements displayed with rows808cc-808ddwill now be described. Additionally, as shown inFIG.35, the plurality of factor-setting user interface control elements802cc-802ddare indicating the design of experiment factors corresponding to the rows808cc-808ddare named “X24”-“X25,” respectively. The plurality of factor type user interface control elements810cc-810ddare indicating that the design of experiment factors corresponding to the rows808cc-808ddhave a factor type based on the combo box user interface control element1082e(e.g., categorical factor type). The plurality of design of experiments platform configuration user interface control elements812cc-812ddare indicating that the design of experiment factors corresponding to the rows808cc-808ddhave a change difficulty level based on the combo box user interface control element1084e(e.g., “Hard”). The plurality of rows of editable user interface control elements814cc-814ddare indicating that the design of experiment factors corresponding to the rows808cc-808ddhave a total number of categorical levels based on the number input control element1102(e.g., three (3) categorical levels).

The user interface control elements displayed with rows808ee-808ggwill now be described. Additionally, as shown inFIG.35, the plurality of factor-setting user interface control elements802ee-802ggare indicating the design of experiment factors corresponding to the rows808ee-808ggare named “X26”-“X28,” respectively. The plurality of factor type user interface control elements810ee-810ggare indicating that the design of experiment factors corresponding to the rows808ee-808gghave a factor type based on the combo box user interface control element1082f(e.g., categorical factor type). The plurality of design of experiments platform configuration user interface control elements812ee-812ggare indicating that the design of experiment factors corresponding to the rows808ee-808gghave a change difficulty level based on the combo box user interface control element1084f(e.g., “Very Hard”). The plurality of rows of editable user interface control elements814ee-814ggare indicating that the design of experiment factors corresponding to the rows808ee-808gghave a total number of categorical levels based on the number input control element1104(e.g., four (4) categorical levels).

In some embodiments, the above-described factor-setting component composition rows may be extensible to include additional, fewer, or different user interface control elements based on a DoE platform associated with the design of experiments factor specification user interface800. For instance, in the example illustrated inFIG.36, the design of experiments factor specification user interface800is being displayed for configuring a GO-SSD design of experiments. Specifically, inFIG.36, the design of experiments factor specification user interface800includes a plurality of factor-setting composition component rows808hh-808kkthat belong to design of experiment group 1 (e.g., as indicated by the design of experiments group user interface elements1106hh-1106kk), a plurality of factor-setting composition component rows808ll-808oothat belong to design of experiment group 2 (e.g., as indicated by the design of experiments group user interface elements1106ll-1106oo), and a plurality of factor-setting composition component rows808pp-808ssthat belong to design of experiment group 3 (e.g., as indicated by the design of experiments group user interface elements1106pp-1106ss). It shall be noted that a design of experiment group, as generally referred to herein, may have a maximum group size (e.g., four (4) factors) that is controlled by a user or by a system implementing the method700.

In some embodiments, the design of experiment factor group associated with a respective factor-setting composition component row may be updated during a factor rearrangement operation. For instance, as also illustrated inFIG.36, an input1108selecting the factor re-ordering user interface element826hhis detected. Further, inFIG.37, the input1108moves from a position corresponding to the factor re-ordering user interface elements826hhto a position corresponding to the factor re-ordering user interface elements826ll. Thus, as a result, as illustrated inFIG.38, the factor-setting composition component rows826lland826hhswap positions within the design of experiments factor specification user interface800. Additionally, as result of the input1108, the design of experiment group associated with the factor-setting composition component rows826llis updated from being associated group two (2) to being associated with group one (1) (e.g., as indicated by the design of experiments group user interface element1106ll), and the design of experiment group associated with the factor-setting composition component rows826hhis updated from being associated group one (1) to being associated with group two (2) (e.g., as indicated by the design of experiments group user interface element1106hh).

FIG.39illustrates one embodiment of a method3900for performing factor type conversion(s) and model transformation(s) in a design of experiments. It should be appreciated that other examples contemplated within the scope of the present disclosure may involve more operations, fewer operations, different operations, or a different order of operations than as shown inFIG.39.

As will be described in more detail herein, the embodiments of the method3900may result in numerous technical advantages and technical improvements-a subset of which will now be described. Firstly, in some embodiments, when converting a target design of experiment factor from a previous factor type to a new factor type, the processes3910-3928may enable experimental values required by the new factor type to be automatically defined (e.g., without user input) based on experimental values previously associated with the target design of experiments factor when such factor was of the previous factor type. Secondly, the processes3928-3940may enable a design of experiments model to flexibly adapt (e.g., without user input) as factors are added, removed, or as attributes of the factors change.

Overall, these technical advantages and improvements may reduce user error when configuring a design of experiments, thereby creating a more efficient and accurate design of experiments. It shall be appreciated that the processes3910-3940may be particularly beneficial for users with limited experience in configuring a design of experiments and for users that may not fully understand the ramifications of converting a design of experiment factor to a new factor type, including the changes required to a design of experiment model underlying the design of experiments.

In some embodiments, the method3900may include a process3910that functions to receive factor specification data that specifies a plurality of factors and one or more factor parameters of the plurality of factors for configuring a design of experiments. As will be described in more detail herein, in some embodiments, the plurality of factors specified in the factor specification data may relate to independent variables that may be manipulated during the design of experiments to measure their respective effect on one or more dependent variables (e.g., response variables). Additionally, in some embodiments, as will also be described in more detail herein, the factor parameters defined within the factor specification data may relate to specific values (e.g., levels) or settings that the plurality of factors may take during the design of experiments. Thus, in some embodiments, the combination of the factor parameters defined for each of the plurality of factors may represent the experimental space of the design of experiments.

In some embodiments, the process3910may function to receive the factor specification data via the design of experiments factor specification user interface800illustrated and described inFIGS.8-38. As previously mentioned above, the factor specification user interface800, in some embodiments, may enable a user to add one or more factors to the design of experiments, remove one or more factors from the design of experiments, and/or update one or more factors in the design of experiments.

An example of factor specification data received by the design of experiments factor specification user interface800is illustrated inFIG.8. Specifically, inFIG.8, the design of experiments factor specification user interface800is receiving, via user input, factor specification data801that includes eight (8) design of experiment factors—as indicated by the eight (8) factor-setting component composition rows808a-808h. It shall be noted that the above example is not intended to be limiting and that the factor specification data801may specify additional, different, or fewer design of experiment factors than illustrated without departing from the scope of the disclosure. Various non-limiting factor parameters that may be specified in the factor specification data801will now be described.

In some embodiments, the factor specification data801may specify a name of each factor in the factor specification data801(e.g., a factor name parameter). For instance, in the example ofFIG.8, the factor specification data801is specifying that the eight (8) design of experiment factors are named “Wing Length,” “Paper Type,” “Drop Height,” “Paper Clip,” “Extra fold at bottom,” “X,” “X2,” and “X3,” respectively (as indicated by the factor-setting user interface control elements802a-802h). It shall be noted that the name of a respective factor may be modified in ways previously described in the method700.

Additionally, or alternatively, in some embodiments, the factor specification data801may specify a type of each factor in the factor specification data801(e.g., a factor type parameter). For instance, as also shown in the example ofFIG.8, the factor specification data801is specifying that the design of experiment factors named “Wing Length,” “Paper Type,” “Drop Height,” “Paper Clip,” “Extra fold at bottom,” “X,” “X2,” and “X3” correspond to a discrete numeric factor type, a categorical factor type, a continuous factor type, a categorical factor type, a categorical factor type, a categorical factor type, a categorical factor type, and a categorical factor type, respectively (as indicated by the plurality of factor type user interface control elements810a-810h). It shall be noted that, in some embodiments, the type of a respective factor may be changed in ways previously described in the method700.

Additionally, or alternatively, in some embodiments, the factor specification data801may specify a change difficulty level of each factor in the factor specification data801(e.g., a factor change parameter). For instance, in the example ofFIG.8, the factor specification data801is specifying that the design of experiment factors named “Wing Length,” “Paper Type,” “Drop Height,” “Paper Clip,” “Extra fold at bottom,” “X,” “X2,” and “X3” have a change difficulty of “Easy” (as indicated by the plurality of design of experiments platform configuration user interface control elements812a-812h). It shall be noted that, in some embodiments, the change difficultly level of a respective factor may be modified in ways previously described in the method700.

In some embodiments, the factor specification data801may specify the values (e.g., levels) that each factor in the factor specification data801may take during an execution of the design of experiments. For instance, in the example ofFIG.8, the factor specification data801is specifying that the design of experiment factor named “Wing Length” is associated with the discrete numeric levels two (2) and six (6)—as indicated by the dynamic row814a. The factor specification data801is also specifying that the design of experiment factor named “Paper Type” is associated with the categorical levels “Cardstock” and “Regular” (as indicated by the dynamic row814b). The factor specification data801is also specifying that the design of experiment factor named “Drop Height” has a continuous range between one (1) and twenty (20) (as indicated by the dynamic row814c). The factor specification data801is also specifying that the design of experiment factor named “Paper Clip” is associated with categorical levels “Small” and “Big” (as indicated by the dynamic row814d). The factor specification data801is also specifying that the design of experiment factors named “Extra fold at bottom,” “X,” “X2,” and “X3” are associated with categorical levels “Yes” and “No” (as indicated by the dynamic rows814e-814h). It shall be noted that the values (e.g., levels) of a respective factor may be modified in ways previously described in the method700.

In some embodiments, the factor specification data801may specify a unit of measurement (UoM) associated each factor in the factor specification data801(e.g., a factor change parameter). For instance, in the example ofFIG.8, the factor specification data801is specifying that the design of experiment factors named “Wing Length,” “Paper Type,” “Drop Height,” “Paper Clip,” “Extra fold at bottom,” “X,” “X2,” and “X3” are not currently associated with a unit of measurement (as indicated by the “empty” plurality of factor unit user interface control elements816a-816h). It shall be noted that the UoM of a respective factor may be modified in ways previously described in the method700.

Referring toFIG.39, in some embodiments, the method3900may include a process3920that functions to execute a factor type conversion for a target factor of the plurality of factors based on the factor specification data indicating a selection of a successor factor type selected from a plurality of factor types. As will be described in more detail herein, executing a factor type conversion may cause the target to be converted from a current factor type (e.g., a first factor type) to a successor factor type (e.g., a second factor type). Examples of factor type conversions performed by the process3920may include, but should not be limited to, converting a continuous factor to a categorical factor, converting a categorical factor to a discrete numeric factor, converting a discrete numeric factor to a continuous factor, and/or the like. Additional non-limiting examples will be further described herein.

In some embodiments, as indicated by step4002inFIG.40, the process3920may function to receive the selection of the successor factor type via a graphical user interface (e.g., the design of experiments factor specification user interface800illustrated in FIGS. 8-38). An example of the process3920receiving the selection of a successor factor type via a graphical user interface is illustrated inFIGS.11-13. Specifically, inFIGS.11-13, for reasons previously described, the selection input824illustrated inFIG.12causes “Factor 1” to change from a categorical factor type as indicated by the user interface control element810finFIG.11(e.g., a current factor type, an incumbent factor type, etc.) to a continuous factor type as indicated by the user interface control element810finFIG.13(e.g., a successor factor type). It shall be noted that the above example is not intended to be limiting and that the successor factor type of “Factor 1” may be different than as illustrated when the input824selects a different one of the plurality of factor type user interface control element822a-822h.

Additionally, as indicated by the step4004inFIG.40, in response to receiving the selection of a successor factor type, the process3920may function to convert the target factor to the successor factor type in accordance with pre-defined factor type conversion rules. The pre-defined factor type conversion rules, in some embodiments, may relate to rules for dynamically updating values (e.g., attributes, parameters, levels, etc.) associated with the target factor as the target factor changes from a current factor type to a succeeding factor type and/or may relate to rules for updating the design of experiment factor specification user interface800as the target factor changes from a current factor type to a succeeding factor type. Examples of such pre-defined factor type conversion rules will be described in more detail with respect processes3920and3924.

Referring toFIG.39, in some embodiments, executing the factor type conversion may include a process3924that functions to replace, at a graphical user interface, a set of factor specification user interface elements that correspond to an incumbent factor type previously selected for the target factor with a second set of factor specification user interface elements that correspond to the successor factor type. Various examples of factor type conversions that may be performed by such processes will now be described.

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a continuous factor type to a categorical factor type. An example of the processes3920and3924converting a target factor from the continuous factor type to the categorical factor type will now be described with reference toFIGS.41and42.

FIG.41illustrates an example design of experiment factor4100.

Specifically, inFIG.41, the factor-setting user interface control element802hhis indicating that the design of experiment factor4100is named “X1.” The factor type user interface control element810hhis indicating that the design of experiment factor4100is currently a continuous factor type. The design of experiments platform configuration user interface control element812hhis indicating that the design of experiment factor4100has an “Easy” change difficulty setting. The dynamic row814hhis displaying a plurality of number edit boxes4102and4104that are specifying the minimum continuous range and the maximum continuous range of the design of experiment factor4100(e.g., one (1) and two (2)), respectively.

In some embodiments, converting the design of experiment factor4100from a continuous factor type to a categorical factor type may include updating the row814hhillustrated inFIG.41to include one or more user interface elements that are configured to receive input for defining one or more categorical levels for the design of experiments factor4100. For instance, as shown inFIG.42, when converting the design of experiment factor4100from the continuous factor type (indicated by810hhinFIG.41) to the categorical factor type (indicated by810hhinFIG.42), the process4324replaces the number input fields4102and4104illustrated inFIG.41with user interface elements4202-4208. It shall be noted that the user interface elements4202-4208may have similar characteristics as elements836a-836d,838a-838d, and840g-840jillustrated inFIG.13.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor4100from the continuous factor type to the categorical factor type may include defining a first categorical level for the design of experiment factor4100based on a character representation of the minimum continuous value currently associated with the design of experiment factor4100. For instance, in the example ofFIGS.41and42, when converting the design of experiment factor4100from the continuous factor type to the categorical factor type, the process4320converts the minimum continuous value indicated by the number edit field4102to a character representation (e.g., 1→“L1”) and, in turn, defines a first categorical level for the design of experiment factor4100with a value corresponding to the character representation of the minimum continuous value—as indicated by element4206.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor4100from the continuous factor type to the categorical factor type may include defining a second categorical level for the design of experiment factor4100based on a character representation of the maximum continuous value currently associated with the design of experiment factor4100. For instance, in the example ofFIGS.41and42, when converting the design of experiment factor4100from the continuous factor type to the categorical factor type, the process4320converts the maximum continuous value indicated by the number edit field4104to a character representation (e.g., 2→“L2”) and, in turn, defines a second categorical level for the design of experiment factor4100with a value corresponding to the character representation of the maximum continuous value—as indicated by element4208.

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a continuous factor type to a discrete numeric factor type. Various examples of such processes converting the design of experiment factor4100from the continuous factor type to the discrete numeric factor type will now be described with reference toFIGS.41and43.

In some embodiments, converting the design of experiments factor4100from the continuous factor type to the discrete numeric factor type may include updating the row814hhillustrated inFIG.41to include one or more user interface elements that are configured to receive input for defining one or more discrete numeric levels of the design of experiment factor4100. For instance, as shown inFIG.43, when converting the design of experiment factor4100from the continuous factor type (as indicated by810hhinFIG.41) to the discrete numeric factor type (as indicated by810hhinFIG.43), the process4324replaces the number input fields4102and4104illustrated inFIG.41with user interface elements4302-4308. It shall be noted that the user interface elements4302-4308may have similar characters as the user interface elements830a-830dillustrated inFIG.13.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor4100from the continuous factor type to the discrete numeric factor type may include defining a discrete numeric level for the design of experiment factor4100based on a minimum value of a continuous range currently associated with the design of experiment factor4100. For instance, as also shown inFIG.43, when converting the design of experiment factor4100from the continuous factor type to the discrete numeric factor type, the process3920defines a first discrete numeric level with a value that corresponds to minimum continuous value associated with the design of experiment factor4100inFIG.41(as indicated by user interface element4306).

Additionally, or alternatively, in some embodiments, converting the design of experiment factor4100from the continuous factor type to the discrete numeric factor type may include defining a discrete numeric level for the design of experiment factor4100based on a maximum value of a continuous range currently associated with the design of experiment factor4100. For instance, as also shown inFIG.43, when converting the design of experiment factor4100from the continuous factor type to the discrete numeric factor type, the process3920defines a second discrete numeric level with a value that corresponds to the maximum continuous value associated with the design of experiment factor4100inFIG.41(as indicated by user interface element4308).

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a continuous factor type to a mixture factor type. Various examples of such processes converting the design of experiment factor4100from the continuous factor type to the mixture factor type will now be described with reference toFIGS.41and44.

In some embodiments, converting the design of experiments factor4100from the continuous factor type to the mixture factor type may include updating the row814hhillustrated inFIG.41to include one or more user interface elements that are configured to receive input for defining mixture parameters (e.g., values) of the design of experiment factor4100. For instance, in the example ofFIGS.41and44, when converting the design of experiment factor4100from the continuous factor type (as illustrated by810hhinFIG.41) to the mixture factor type (as illustrated by810hhinFIG.44), the process4324replaces the number input fields4102and4104illustrated inFIG.41with the number input fields4402and4404. It shall be noted that, in some embodiments, the number input fields4402and4404may be configured to receive user input for setting a minimum mixture value and a maximum mixture value of design of experiment factor4100.

Additionally, or alternatively, in some embodiments, converting the design of experiments factor4100from the continuous factor type to the mixture factor type may include defining a minimum mixture value for the design of experiment factor4100based on a minimum value of a continuous range currently associated with the design of experiment factor4100. For instance, as also shown inFIG.44, when converting the design of experiment factor4100from the continuous factor to the mixture factor, the process3924sets a minimum mixture value of the design of experiment factor4100to correspond to the minimum continuous value specified by number input field4102inFIG.41(as indicated by the user interface control element4402).

It shall be noted that, in some embodiments, the minimum mixture value may be different from the minimum continuous value. For instance, in some embodiments, the process4320may set the minimum mixture value to zero (0) when the minimum continuous value indicated by number input field4102is less than zero (0) and/or when the maximum value indicated by number input field4104is less than zero (0).

Additionally, or alternatively, in some embodiments, converting the design of experiments factor4100from the continuous factor type to the mixture factor type may include defining a maximum mixture value for the design of experiment factor4100based on a maximum value of a continuous range currently associated with the design of experiment factor4100. For instance, as also shown inFIG.44, when converting the design of experiment factor4100from the continuous factor type to the mixture factor type, the process3924sets a maximum mixture value of the design of experiment factor4100to correspond to the maximum continuous value specified by the number input field4104inFIG.41(as indicated by the user interface control element4404).

It shall be noted that, in some embodiments, the maximum mixture value may be different from the maximum continuous value. For instance, in some embodiments, the process4320may set the maximum mixture value to one (1) when the maximum continuous value indicated by number input field4104is less than zero (0).

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a continuous factor type to a blocking factor type. Various non-limiting example of such processes converting the design of experiment factor4100illustrated inFIG.41from the continuous factor type to the blocking factor type will now be described with reference toFIGS.41and45.

In some embodiments, converting the design of experiments factor4100from the continuous factor type to the blocking factor type may include updating the row814hhillustrated inFIG.41to include one or more user interface elements that are configured to receive input for defining one or more blocking levels of the design of experiments factor4100. For instance, as shown inFIG.45, when converting the design of experiment factor4100from the continuous factor type (as illustrated by810hhinFIG.41) to the blocking factor type (as indicated by810hhinFIG.45), the number input fields4102and4104illustrated inFIG.41are replaced with user interface elements4502-4504for defining block levels of the design of experiment factor4100.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor4100from the continuous factor type to the blocking factor type may include defining a block level based on the minimum continuous value currently associated with the design of experiment factor4100. For instance, as also illustrated inFIG.45, when converting design of experiment factor4100from the continuous factor type to the blocking factor type, the process3920defines a first block level with a value that corresponds to the minimum continuous value specified by the number input field4102illustrated inFIG.41(as indicated by the user interface element4502).

Additionally, or alternatively, in some embodiments, converting the design of experiment factor4100from the continuous factor type to the blocking factor type may include defining a block level based on the maximum continuous value currently associated with the design of experiment factor4100. For instance, as also illustrated inFIG.45, when converting design of experiment factor4100from the continuous factor type to the blocking factor type, the process3920defines a first block level with a value that corresponds to the maximum continuous value specified by the number input field4104illustrated inFIG.41(as indicated by the user interface element4504).

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a continuous factor type to a covariate factor type. Various non-limiting example of such processes converting the design of experiment factor4100illustrated inFIG.41from the continuous factor type to the covariate factor type will now be described with reference toFIGS.41and46.

In some embodiments, converting the design of experiments factor4100from the continuous factor type to the covariate factor type may include updating the row814hhillustrated inFIG.41to include one or more user interface elements that are configured to receive input for defining (or specifying) one or more covariate levels of the design of experiment factor4100. For instance, as shown inFIG.46, when converting the design of experiment factor4100from the continuous factor type (as illustrated by810hhinFIG.41) to the covariate factor type (as illustrated by810hhinFIG.46), the process3920replaces the number input fields4102and4104illustrated inFIG.41with user interface elements4602-4604for defining covariate levels of the design of experiment factor4100. It shall be noted that, in some embodiments, the covariate levels may be specified via user interaction with a design of experiments covariate dialog column or the like.

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a continuous factor type to a constant factor type. Various examples of such processes converting the design of experiment factor4100illustrated inFIG.41from the continuous factor type to the constant factor type will now be described with reference toFIGS.41and47.

In some embodiments, converting the design of experiments factor4100from the continuous factor type to the constant factor type may include updating the row814hhillustrated inFIG.41to include one or more user interface elements that are configured to receive input for defining a constant value of the design of experiment factor4100. For instance, as shown inFIG.47, when converting the design of experiment factor4100from the continuous factor type (as illustrated by810hhinFIG.41) to the constant factor type (as indicated by810hhinFIG.47), the process3920replaces the number input fields4102and4104illustrated inFIG.41with number edit box4702for defining a constant value of the design of experiment factor4100.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor4100from the continuous factor type to the constant factor type may include defining a constant value for the design of experiment factor4100based on the minimum continuous value currently associated with the design of experiment factor4100. For instance, as also shown inFIG.47, when converting the design of experiment factor4100from the continuous factor type to the constant factor type, the process3920defines a constant value for the design of experiment factor4100that corresponds to the minimum continuous value indicated by number input field4102illustrated inFIG.41(as indicated by the number edit box4702).

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a categorical factor type to a continuous factor type. An example of the processes3920and3924converting a target factor from the categorical factor type to the continuous factor type will now be described with reference toFIGS.48and49.

FIG.48illustrates an example design of experiment factor4800. Specifically, in the example ofFIG.48, the factor-setting user interface control element802iiis indicating that the design of experiment factor4800is named “X3.” The factor type user interface control element810iiis indicating that the design of experiment factor4800is currently a categorical factor type. The design of experiments platform configuration user interface control element812iiis indicating that the design of experiment factor4800has an “Easy” change difficulty setting. The dynamic row814iiis displaying a plurality of button control elements4802-4804and a plurality of text edit boxes4806-4812. It shall be noted that elements4802-4812may operate in a similar manner as button control elements836a-838dand text edit boxes840a-840jillustrated inFIG.13.

In some embodiments, converting the design of experiment factor4800from the categorical factor type to the continuous factor type may include updating the row814iiillustrated inFIG.48to include one or more user interface elements that are configured to receive input for defining a continuous range of the design of experiment factor4800. For instance, as shown inFIG.49, when converting the design of experiment factor4800from the categorical factor type (as indicated by810iiinFIG.48) to the continuous factor type (as indicated by810iiinFIG.49), the process4324replaces the user interface elements4802-4812illustrated inFIG.48with the number edit boxes4902-4904for defining a continuous range of the design of experiment factor4800.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor4800from the categorical factor type to the continuous factor type may include defining a minimum continuous value and a maximum continuous value for the design of experiment factor4800based on the categorical levels currently associated with the design of experiment factor4800. The minimum continuous value and the maximum continuous value, in such embodiments, may be defined based on a first and a second categorical level identified, by the process4320, as including a numerical string (e.g., a string that includes one or more numbers). For instance, in the example illustrated inFIG.48, the process4320may identify that the categorial levels “L1” and “L2” corresponding to the text input boxes4806and4808relate to numerical strings.

In some embodiments, based on the process4320identifying a set of categorical levels that each include a numerical string, the process4320may function to extract the numbers included in such numerical strings and, in turn, use the extracted numbers to define the minimum continuous value and the maximum continuous value for the design of experiment factor4800. For instance, in the example illustrated inFIGS.48and49, when converting the design of experiment factor4800from the categorical factor type to the continuous factor type, the process4320extracts the numbers included in the categorical levels specified by the text edit boxes4806and4808(e.g., extracting one (1) from “L1” and two (2) from “L2”) and, in turn, uses the extracted numbers as the minimum continuous value and maximum continuous value for the design of experiment factor4800(as indicated by the number input boxes4902and4904), respectively. It shall be noted that, in some embodiments, if the process4320determines the design of experiment factor4800does not include at two categorial levels that comprise a numerical string, the process4320may function to set default values for the minimum and maximum continuous values (e.g., such as negative one (−1) and positive one (+1), or the like).

In some embodiments, the factor type conversion executed by the process3920may cause a target factor to be converted from the categorical factor type to the discrete numeric factor type. Various examples of such processes converting the design of experiment factor4800illustrated inFIG.48from the categorical factor type to the discrete numeric factor type will now be described with reference toFIGS.48and50.

In some embodiments, converting the design of experiment factor4800from the categorical factor type to the discrete numeric factor type may include updating the row814iiillustrated inFIG.48to include one or more user interface elements that are configured to receive input for defining one or more discrete numeric levels of the design of experiment factor4800. For instance, as shown inFIG.50, when converting the design of experiment factor4800from the categorical factor type (as indicated by810iiinFIG.48) to the discrete numeric factor type (as indicated by810iiinFIG.50), the process4324replaces the user interface control elements4802-4812with the button control elements5002-5004and the number edit boxes5006-5012. It shall be noted that the button control elements5002-5004and the number edit boxes5006-5012may operate in a similar to the button control elements830c-830dand the number edit boxes830a-830billustrated inFIG.13.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor4800from the categorical factor type to the discrete numeric factor type may include defining a plurality of discrete numeric levels based on the categorical levels currently associated with the design of experiments factor4800. Defining the plurality of discrete numeric levels, in some embodiments, may include converting each of the plurality of categorical values associated with the design of experiment factor4800to a numerical value. For instance, in the example ofFIG.48, the process4320may function to convert the categorical levels “L1,” “L2,” “L3,” and “L4” (indicated by the text edit boxes4806-4812inFIG.48) to corresponding numerical values one (1), two (2), three (3), and four (4), respectively.

In turn, in some embodiments, the process4320may use the numerical values extracted by the process4320to define a plurality discrete numeric levels for the design of experiments factor4800. For instance, as also shown inFIG.50, based on the extracting, the process4320creates a plurality of discrete numeric levels for the design of experiment factor4800(as indicated by the number edit boxes5006-5012). Specifically, inFIG.50, the process4320uses the number extracted from the categorical value “L1” (e.g., one (1)) to define the discrete numeric level corresponding to number edit box5006. The process4320uses the number extracted from the categorical value “L2” (e.g., two (2)) to define the discrete numeric level corresponding to number edit box5008. The process4320uses the number extracted from the categorical value “L3” (e.g., three (3)) to define the discrete numeric level corresponding to number edit box5010. The process4320uses the number extracted from the categorical value “L4” (e.g., four (4)) to define the discrete numeric level corresponding to number edit box5012.

It shall be noted that, in some embodiments, the discrete numeric levels indicated by the plurality of number edit boxes5006-5012may correspond to default values (and, optionally, not the numerical values extracted by the process4320). For instance, when the process4320determines that the plurality of categorical levels do not include numerical strings, the process4320may instead function to map each of the plurality of categorical levels to a pre-determined value (e.g., a default value). In turn, based on the mapping, the process4320may function to define a plurality of discrete numeric levels for the design of experiments factor4800that each correspond to one of the pre-determined values.

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from the categorical factor type to the mixture factor type. Various examples of such processes converting the design of experiment factor4800illustrated inFIG.48from the categorical factor type to the mixture factor type will now be described with reference toFIGS.48and51.

In some embodiments, converting the design of experiments factor4800from the categorical factor type to the mixture factor type may include updating the row814iiillustrated inFIG.48to include one or more user interface elements that are configured to receive input for defining mixture parameters (e.g., values) of the design of experiment factor4800. For instance, in the example ofFIGS.48and51, when converting the design of experiment factor4800from the categorical factor type (as illustrated by810iiinFIG.48) to the mixture factor type (as illustrated by810iiinFIG.51), the process4324replaces the user interface control elements4802-4812illustrated inFIG.41with the number input fields5102and5104. It shall be noted that, in some embodiments, the number input fields5102and5104may be configured to receive input for setting a minimum mixture value and a maximum mixture value of the design of experiment factor4800.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor4800from the categorical factor type to the mixture factor type may include defining a minimum mixture value and a maximum mixture value for the design of experiment factor4800based on the categorical levels currently associated with the design of experiments factor4800. The minimum mixture value and the maximum mixture value, in some examples, may be defined based on a first and a second categorical level identified, by the process4320, as including a numerical string (e.g., a string that includes one or more numbers). For instance, in the example illustrated inFIG.48, the process4320may identify that the categorial levels “L1” and “L2” corresponding to the text input boxes4806and4808relate to numerical strings.

In some embodiments, based on the process4320identifying a set of categorical levels that each include a numerical string, the process4320may function to extract the numbers included in such numerical strings and, in turn, use the extracted numbers to define the minimum mixture value and the maximum mixture value. For instance, in the example illustrated inFIGS.48and51, when converting the design of experiment factor4800from the categorical factor type to the mixture factor type, the process4320extracts the numbers included in the categorical levels specified by text input boxes4806and4808(e.g., extracts the number one (1) from “L1” and the number two (2) from “L2”), sets the minimum mixture value to correspond the number extracted from the categorical level “L1” (as indicated by the number input box5102), and sets the maximum mixture value to correspond the number extracted from the categorical level “L2” (as indicated by the number input box5104).

It shall be noted that, in some embodiments, if the process4320determines the design of experiment factor4800does not include at two categorial levels that comprise a numerical string, the process4320may function to set default values for the minimum and maximum mixture values (e.g., such as zero (0) and positive one (+1), or the like). Similarly, it shall also be noted that, in some embodiments, the minimum mixture value and the maximum mixture value set for the design of experiments factor4100may be different from the number extracted from a categorical level. For instance, in some embodiments, the process4320may set the minimum mixture value to zero (0) when the number extracted from the categorical level “L1” is less than zero (0) and the set the maximum mixture value to one (1) when the number extracted from the categorical level “L2” is less than zero (0).

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a categorical factor type to a blocking factor type. Various non-limiting example of such processes converting the design of experiment factor4800illustrated inFIG.48from the categorical factor type to the blocking factor type will now be described with reference toFIGS.48and52.

In some embodiments, converting the design of experiments factor4800from the categorical factor type to the blocking factor type may include updating the row814iiillustrated inFIG.48to include one or more user interface elements that are configured to receive input for defining one or more blocking levels of the design of experiments factor4800. For instance, as shown inFIG.52, when converting the design of experiment factor4800from the continuous factor type (as illustrated by810iiinFIG.48) to the blocking factor type (as illustrated by810iiinFIG.52), the process3920replaces the user interface elements4802-4812illustrated inFIG.48with user interface elements5202-5208for defining block levels of the design of experiment factor4100.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor4800from the categorical factor type to the blocking factor type may include defining a plurality of blocking levels for the design of experiment factor4800based on the categorical levels currently associated with the design of experiment factor4800. Defining the plurality of blocking levels, in some embodiments, may include converting each of the plurality of categorical values associated with the design of experiments factor4800to a numerical value. For instance, in the example ofFIG.48, the process4320may function to convert the categorical levels “L1,” “L2,” “L3,” and “L4” (indicated by the text edit boxes4806-4812inFIG.48) to corresponding numerical values one (1), two (2), three (3), and four (4), respectively.

In turn, in some embodiments, the process4320may use the extracted numerical values to define a plurality blocking levels for the design of experiment factor4800. For instance, as also shown inFIG.52, based on the extracting, the process4320creates a plurality of blocking levels for the design of experiment factor4800(as indicated by the number edit boxes5202-5208). Specifically, inFIG.52, the process4320uses the number extracted from the categorical value “L1” (e.g., one (1)) to define the blocking level corresponding to number edit box5202. The process4320uses the number extracted from the categorical value “L2” (e.g., two (2)) to define the blocking level corresponding to number edit box5204. The process4320uses the number extracted from the categorical value “L3” (e.g., three (3)) to define the blocking level corresponding to number edit box5206. The process4320uses the number extracted from the categorical value “L4” (e.g., four (4)) to define the blocking level corresponding to number edit box5208.

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a categorical factor type to a covariate factor type. Various non-limiting examples of such processes converting the design of experiment factor4800illustrated inFIG.48from the categorical factor type to the covariate factor type will now be described with reference toFIGS.48and53.

In some embodiments, converting the design of experiment factor4800from the categorical factor type to the covariate factor type may include updating the row814iiillustrated inFIG.48to include one or more user interface elements that are configured to receive input for defining (or specifying) one or more covariate levels of the design of experiment factor4800. For instance, as shown inFIG.53, when converting the design of experiment factor4800from the categorical factor type (as illustrated by810iiinFIG.48) to the covariate factor type (as illustrated by810iiinFIG.53), the process3920replaces user interface elements4802-4812illustrated inFIG.48with user interface elements5304-5306for defining covariate levels of the design of experiment factor4800. It shall be noted that, in some embodiments, the covariate levels may be specified via user interaction with a design of experiments covariate dialog column or the like.

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a categorical factor type to a constant factor type. Various non-limiting examples of such processes converting the design of experiment factor4800illustrated inFIG.48from the categorical factor type to the constant factor type will now be described with reference toFIGS.48and54.

In some embodiments, converting the design of experiments factor4800from the categorical factor type to the constant factor type may include updating the row814iiillustrated inFIG.48to include one or more user interface elements that are configured to receive input for defining (or specifying) a constant value of the design of experiment factor4800. For instance, as shown inFIG.54, when converting the design of experiment factor4800from the categorical factor type (as illustrated by810iiinFIG.48) to the constant factor type (as indicated by810iiinFIG.54), the process3920replaces the user interface elements4802-4812illustrated inFIG.48with number edit box5402for defining a constant value of the design of experiment factor4800.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor4800to the constant factor type may include defining a constant value for the design of experiment factor4100based on a respective categorical level associated with the design of experiment factor4800. Defining the constant value, in such embodiments, may include converting the respective categorical level to a numerical value (e.g., by extracting the number included from the respective categorical level). For instance, in the example ofFIG.48, the process4320may function to convert the categorical level “L1” to the numerical value one (1).

In turn, in some embodiments, the process4320may use the extracted numerical value to define a constant value for the design of experiments factor4800. For instance, as shown inFIG.54, when converting the design of experiment factor4800from the categorical factor type to the constant factor type, the process4320uses the constant value extracted from the categorical level “L1” (e.g., one (1)) as the constant value for the design of experiment factor4800(as indicated by element5402).

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a discrete numeric factor type to a continuous factor type. An example of such processes converting a target factor from the discrete numeric factor type to the continuous factor type will now be described with reference toFIGS.55and56.

FIG.55illustrates an example design of experiment factor5500.

Specifically, in the example ofFIG.55, a factor-setting user interface control element802jjis indicating that the design of experiment factor5500is named “X2.” The factor type user interface control element810jjis indicating that the design of experiment factor4800is currently a discrete numeric factor type. The design of experiment platform configuration user interface control element812jjis indicating that the design of experiment factor4800has an “Easy” change difficulty setting. The dynamic row814jjis displaying a plurality of button control elements5502-5504and a plurality of number edit boxes5508-5514. It shall be noted that, in some embodiments, the control elements5502-5514may have characteristics similar to the button control elements830c-830dand the number edit boxes830a-830billustrated inFIG.13.

In some embodiments, converting the design of experiment factor5500from the discrete numeric factor type to the continuous factor type may include updating the row814jjillustrated inFIG.55to include one or more user interface elements that are configured to receive input for defining a continuous range of the design of experiment factor4800. For instance, as shown inFIG.56, when converting the design of experiment factor5500from the discrete numeric factor type (as indicated by810jjinFIG.55) to the continuous factor type (as indicated by810jjinFIG.56), the process4324replaces the user interface control elements5502-5514illustrated inFIG.55with the number edit boxes5602-5604for defining a continuous range of the design of experiment factor5500.

Additionally, or alternatively, in some embodiments, converting the design of experiments factor5500from the discrete numeric factor type to the continuous factor type may include defining a minimum continuous value and a maximum continuous value for the design of experiment factor5500based on a minimum discrete numeric level and a maximum discrete numeric level associated with the design of experiment factor5500. For instance, in the example ofFIG.56, the process3920identifies (e.g., determines) that the minimum discrete numeric level and the maximum discrete numeric level associated with the design of experiment factor5500inFIG.55corresponds to one (1) and five (5), respectively. Accordingly, as also shown inFIG.56, the process3920sets the minimum continuous value to correspond to the minimum discrete numeric value (indicated by number input box5602) and sets the maximum continuous value to correspond to the maximum discrete numeric value (indicated by number input box5604).

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from the discrete numeric factor type to the categorical factor type. Various examples of such process converting the design of experiment factor5500illustrated inFIG.55from the discrete numeric factor type to the categorical factor type will now be described with reference toFIGS.55and57.

In some embodiments, converting the design of experiment factor5500from the discrete numeric factor type to the categorical factor type may include updating the row814jjillustrated inFIG.55to include one or more user interface elements that are configured to receive input for defining one or more categorical levels of the design of experiment factor5500. For instance, as shown inFIG.50, when converting the design of experiment factor5500from the discrete numeric factor type (as indicated by810jjinFIG.55) to the categorical factor type (as indicated by810jjinFIG.57), the process4324replaces the user interface control elements5502-5514with the button control elements5702-5704and the text edit boxes5706-5714. It shall be noted that the control elements5702-5714, in some embodiments, may be configured to define categorical levels in a similar manner as the elements836a-836d,838a-838d, and840g-840jillustrated inFIG.13.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor5500from the discrete numeric factor type to the categorical factor type may include defining a plurality of categorical levels for the design of experiment factor5500based on the discrete numeric levels currently associated with the design of experiments factor5500. Defining the plurality of categorical levels, in some embodiments, may include converting each of the plurality of discrete numeric levels associated with the design of experiments factor5500to a character representation. For instance, in the example ofFIG.55, the process4320may function to convert the discrete numeric levels one (1), two (2), three (3), four (4), and five (5) (as indicated by the number edit boxes5506-5514inFIG.55) to corresponding character values “L1,” “L2,” “L3,” “L4,” and “L5”, respectively.

In turn, in some embodiments, the process4320may use the character representations to define a plurality categorical levels for the design of experiments factor5500. For instance, as also shown inFIG.57, based on the converting, the process4320creates a plurality of categorical levels that each correspond to the character representation of a respective one of the discrete numeric levels associated with the design of experiment factor5500(as indicated by the number edit boxes5704-5714). Specifically, in the example ofFIG.57, the categorical level indicated by the number edit box5706corresponds to the character representation of the discrete numeric level associated with input text edit box5506. The categorical level indicated by the number edit box5708corresponds to the character representation of the discrete numeric level associated with input text edit box5508. The categorical level indicated by the number edit box5710corresponds to the character representation of the discrete numeric level associated with input text edit box5510. The categorical level indicated by the number edit box5712corresponds to the character representation of the discrete numeric level associated with text edit box5512. The categorical level indicated by the number edit box5714corresponds to the character representation the discrete numeric level associated input text edit box5514.

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from the discrete numeric factor type to the mixture factor type. Various examples of such processes converting the design of experiment factor5500illustrated inFIG.55from the discrete numeric factor type to the mixture factor type will now be described with reference toFIGS.55and58.

In some embodiments, converting the design of experiments factor5500from the discrete numeric factor type to mixture factor type may include updating the row814jjillustrated inFIG.55to include one or more user interface elements that are configured to receive input for defining mixture parameters (e.g., values) of the design of experiment factor5500. For instance, in the example ofFIGS.55and58, when converting the design of experiment factor5500from the discrete numeric factor type (as illustrated by810jjinFIG.55) to the mixture factor type (as illustrated by810jjinFIG.58), the process4324replaces the control elements5502-5514illustrated inFIG.55with the number input fields5802and5804. It shall be noted that, in some embodiments, the number input fields5802and5804may be configured to receive input for setting a minimum mixture value and a maximum mixture value of design of experiment factor5500.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor5500from the discrete numeric factor type to the mixture factor type may include defining a minimum mixture value and a maximum mixture value based on a minimum discrete numeric level and a maximum discrete numeric level associated with the design of experiment factor5500. For instance, in the example ofFIG.58, when converting the design of experiment factor5500from the discrete numeric factor type to the mixture factor type, the process3920identifies (e.g., determines) that the minimum discrete numeric level and the maximum discrete numeric level associated with the design of experiment factor5500inFIG.55corresponds to one (1) and five (5), respectively. Accordingly, as also shown inFIG.58, the process3920sets the minimum mixture value to correspond to the minimum discrete numeric value (indicated by number edit box5802) and sets the maximum mixture value to correspond to the maximum discrete numeric value (indicated by number edit box5804).

It shall be noted that, in some embodiments, the minimum mixture value set for the design of experiments factor5500may be different from the minimum discrete numeric value. For instance, in some embodiments, the process4320may set the minimum mixture value to zero (0) when the minimum discrete numeric value is less than zero (0) and/or when the maximum discrete value is less than zero (0). Similarly, it shall also be noted that, in some embodiments, the maximum mixture value set for the design of experiments factor5500may be different from the maximum discrete numeric value. For instance, in some embodiments, the process4320may set the maximum mixture value to one (1) when the maximum discrete numeric value is less than zero (0).

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a discrete numeric factor type to a blocking factor type. Various non-limiting example of such processes converting the design of experiment factor5500illustrated inFIG.55from the discrete numeric factor type to the blocking factor type will now be described with reference toFIGS.55and59.

In some embodiments, converting the design of experiments factor5500from the discrete numeric factor type to the blocking factor type may include updating the row814jjillustrated inFIG.55to include one or more user interface elements that are configured to receive input for defining one or more blocking levels of the design of experiments factor5500. For instance, as shown inFIG.59, when converting the design of experiment factor5500from the discrete numeric factor type (as illustrated by810jjinFIG.55) to the blocking factor type (as indicated by810jj), the process3924replaces the user interface elements5502-5514illustrated inFIG.55with the user interface elements5902-5910for defining block levels of the design of experiment factor5500.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor5500from the discrete numeric factor type to the blocking factor type may include defining a plurality of blocking levels for the design of experiment factor5500based on the discrete numeric levels currently associated with the design of experiment factor5500. The plurality of blocking levels, in some embodiments, may each correspond to one of the discrete numeric levels associated with the design of experiment factor5500and, optionally, may have a value based on the discrete numeric level to which it corresponds. For instance, in the example illustrated inFIG.59, when converting the design of experiment factor5500from the discrete numeric factor type to the blocking factor type, the process4320creates a plurality of blocking levels (e.g., as indicated by the number input boxes5902-5910). Specifically, in the example ofFIG.59, the blocking level indicated by the number edit box5902corresponds to the discrete numeric level associated number edit box5506. The blocking level indicated by the number edit box5904corresponds to the discrete numeric level associated number input box5508. The blocking level indicated by the number edit box5908corresponds to the discrete numeric level associated with number input box5512. The blocking level indicated by the number edit box5910corresponds to the discrete numeric level associated with number input box5514.

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a discrete numeric factor type to a covariate factor type. Various non-limiting examples of such processes converting the design of experiment factor5500illustrated inFIG.55from the discrete numeric factor type to the covariate factor type will now be described with reference toFIGS.55and60.

In some embodiments, converting the design of experiment factor5500from the discrete numeric factor type to the covariate factor type may include updating the row814jjillustrated inFIG.55to include one or more user interface elements that are configured to receive input for defining (e.g., specifying) one or more covariate levels of the design of experiment factor5500. For instance, as shown inFIG.60, when converting the design of experiment factor5500from the discrete numeric factor type (as illustrated by810jjinFIG.55) to the covariate factor type (as illustrated by801jjinFIG.60), the process3924replaces the control elements5502-5514illustrated inFIG.55with user interface elements6002-6004for defining covariate levels of the design of experiment factor5500. It shall be noted that, in some embodiments, the covariate levels may be specified based on user interaction received at a design of experiments covariate dialog or the like.

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a discrete numeric factor type to a constant factor type. Various non-limiting examples of such processes converting the design of experiment factor5500illustrated inFIG.55from the discrete numeric factor type to the constant factor type will now be described with reference toFIGS.55and61.

In some embodiments, converting the design of experiments factor5500from the discrete numeric factor type to the constant factor type may include updating the row814jjillustrated inFIG.55to include one or more user interface elements that are configured to receive input for defining (or specifying) a constant value of the design of experiment factor5500. For instance, as shown inFIG.61, when converting the design of experiment factor from the discrete numeric factor type (as illustrated by810jjinFIG.55) to the constant factor type (as indicated by810jjinFIG.61), the process4324replaces the user interface elements5502-5514illustrated inFIG.55with the number edit box6102for defining a constant value of the design of experiment factor4100.

In some embodiments, the constant value of the design of experiment factor4100may be automatically set based on the minimum discrete value currently associated with the design of experiment factor4100. For instance, as also shown inFIG.61, when converting the design of experiment factor from the discrete numeric factor type (as illustrated by810jjinFIG.55) to the constant factor type (as indicated by810jjinFIG.61), the process4320sets the constant value to one (1) because the minimum discrete numeric level associated with the design of experiment factor5500inFIG.55has a value of one (1) (as indicated by number edit box6102).

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a mixture factor type to a continuous factor type. An example of such processes converting a target factor from the mixture factor type to the continuous factor type will now be described with reference toFIGS.62and63.

FIG.62illustrates an example design of experiment factor6200.

Specifically, inFIG.62, the factor-setting user interface control element802kkis indicating that the design of experiment factor6200is named “X.” The factor type user interface control element810kkis indicating that the design of experiment factor6200is currently a mixture factor type. The design of experiments platform configuration user interface control element812kkis indicating that the design of experiment factor6200has an “Easy” change difficulty setting. The dynamic row814kkis displaying a plurality of number edit boxes6202and6204that are specifying the minimum mixture value and a maximum mixture value of the design of experiment factor6200(e.g., one (1) and two (2)), respectively.

In some embodiments, converting the design of experiment factor6200from the mixture factor type to the continuous factor type may include updating the row814kkillustrated inFIG.62to include one or more user interface elements that are configured to receive input for defining a continuous range of the design of experiment factor6200. For instance, in the example ofFIGS.62and63, when converting the design of experiment factor6200from the mixture factor type (as illustrated by810kkinFIG.62) to the continuous factor type (as illustrated by810kkinFIG.63), the process4324replaces the number input fields6202and6204illustrated inFIG.62with the number input fields6302and6304. It shall be noted that, in some embodiments, the number input fields6302and6304may have similar characters as number input fields834aand834billustrated inFIG.13.

Additionally, or alternatively, in some embodiments, converting the design of experiments factor6200from the mixture factor type to the continuous factor type may include defining a minimum continuous value for the design of experiment factor4100based on a minimum mixture currently associated with the design of experiment factor6200. For instance, as also shown inFIG.44, when converting the design of experiment factor6200from the mixture factor type to the continuous factor type, the process4320sets the minimum continuous value to correspond to the minimum mixture value associated with the design of experiments factor6200inFIG.62(as indicated by the number input fields6202and6504inFIGS.62and65).

Additionally, or alternatively, in some embodiments, converting the design of experiments factor6200from the mixture factor type to the continuous factor type may include defining a maximum mixture value for the design of experiment factor6200based on a maximum value of a continuous range currently associated with the design of experiment factor6200. For instance, as also shown inFIG.63, when converting the design of experiment factor6200from the mixture factor type to the continuous factor type, the process4320sets the maximum mixture value of the design of experiment factor6200to correspond to the maximum continuous value associated with the design of experiment factor6200inFIG.62(as indicated by the number input fields6204and6508inFIGS.62and65).

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a mixture factor type to a categorical factor type. Various non-limiting example of such processes converting the design of experiment factor6200illustrated inFIG.62from the mixture factor type to the categorical factor type will now be described with reference toFIGS.62and64.

In some embodiments, converting the design of experiment factor6200from a mixture factor type to a categorical factor type may include updating the row814kkillustrated inFIG.62to include one or more user interface elements that are configured to receive input for defining one or more categorical levels for the design of experiment factor6200. For instance, as shown inFIG.64, when converting the design of experiment factor6200from the mixture factor type (indicated by810kkinFIG.62) to the categorical factor type (indicated by810kkinFIG.64), the process4324replaces the number input fields6202and6204illustrated inFIG.62with user interface elements6402-6408. It shall be noted that the user interface elements6402-6408may have similar characteristics to the elements836a-836d,838a-838d, and840g-840jillustrated inFIG.13.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor6200from the mixture factor type to the categorical factor type may include defining a first categorical level for the design of experiment factor6200based on a character representation of the minimum mixture value currently associated with the design of experiment factor6200. For instance, as also shown inFIG.64, when converting the design of experiment factor6200from the mixture factor type to the categorical factor type, the process4320converts the minimum mixture value indicated by the number input box6202(e.g., one (1)) to a corresponding character representation (e.g., 1→“L1”) and, in turn, defines a categorical level with a value corresponding the character representation of the minimum mixture value—as indicated by element6406.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor6200from the mixture factor type to the categorical factor type may include defining a second categorical level based on a character representation of the maximum mixture value currently associated with the design of experiment factor6200. For instance, as also shown inFIG.64, when converting the design of experiment factor6200from the mixture factor type to the categorical factor type, the process4320converts the maximum mixture value indicated by the number input box6204to a corresponding character representation (e.g., 2→“L2”) and, in turn, defines a categorical level with a value corresponding the character representation of the maximum mixture value—as indicated by element6408.

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a mixture factor type to a discrete numeric factor type. Various non-limiting examples of such processes converting the design of experiment factor6200from the mixture factor type to the discrete numeric factor type will now be described with reference toFIGS.62and65.

In some embodiments, converting the design of experiment factor6200from the mixture factor type to the discrete numeric factor type may include updating the row814kkillustrated inFIG.62to include one or more user interface elements that are configured to receive input for defining one or more discrete numeric levels of the design of experiment factor6200. For instance, as shown inFIG.65, when converting the design of experiment factor6200from the mixture factor type (as indicated by810kkinFIG.62) to the discrete numeric factor type (as indicated by the810kkinFIG.65), the process4324replaces the number input fields6202and6204illustrated inFIG.62with the user interface elements6502-6508. It shall be noted that the user interface elements6502-6508may have similar characteristics as elements830a-830dillustrated inFIG.13.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor6200from the mixture factor type to the discrete numeric factor type may include defining a discrete numeric level for the design of experiment factor4100based on a minimum mixture currently associated with the design of experiment factor6200. For instance, as also shown inFIG.65, when converting the design of experiment factor6200from the mixture factor type to the discrete numeric factor type, the process4320defines a first discrete numeric level for the design of experiment factor6200(as indicated by the user interface control element6506) with a value that corresponds to the minimum mixture value indicated by the number input box6202inFIG.62.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor6200from the mixture factor type to the discrete numeric factor type may include defining a discrete numeric level for the design of experiment factor6200based on a maximum mixture value currently associated with the design of experiment factor6200. For instance, as also shown inFIG.43, when converting the design of experiment factor6200from the mixture factor type to the discrete numeric factor type, the process4320further defines a second discrete numeric level for the design of experiment factor6200(as indicated by the user interface control element6508) with a value that corresponds to the maximum mixture value indicated by the number input box6204inFIG.62.

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a mixture factor type to a blocking factor type. Various non-limiting examples of such processes converting the design of experiment factor6200illustrated inFIG.62from the mixture factor type to the blocking factor type will now be described with reference toFIGS.62and66.

In some embodiments, converting the design of experiment factor6200from the mixture factor type to the blocking factor type may include updating the row814kkillustrated inFIG.62to include one or more user interface elements that are configured to receive input for defining one or more blocking levels of the design of experiments factor6200. For instance, as shown inFIG.66, when converting the design of experiment factor6200from the mixture factor type (as illustrated by810kkinFIG.62) to the blocking factor type (as indicated by810kkinFIG.66), the process3924replaces the number input fields6202and6204illustrated inFIG.62with user interface elements6602-6604for defining block levels of the design of experiment factor6200.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor6200from the mixture factor type to the blocking factor type may include defining a block level for the design of experiment factor6200based on the minimum mixture value currently associated with the design of experiment factor6200. For instance, as also illustrated inFIG.66, when converting the design of experiment factor6200from the mixture factor type to the blocking factor type, the process3920defines a first block level for the design of experiment factor6200(as indicated by number input box6602) with a value that corresponds to the minimum mixture value indicated by number input field6202inFIG.62.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor6200to the blocking factor type may include defining a block level based on the maximum mixture value currently associated with the design of experiment factor6200. For instance, as also illustrated inFIG.66, when converting the design of experiment factor6200from the mixture factor type to the blocking factor type, the process3920also defines a second block level for the design of experiment factor6200(as indicated by number input box6604) with a value that corresponds to the maximum mixture value indicated by number input field6204inFIG.62.

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a mixture factor type to a covariate factor type. Various non-limiting example of such processes converting the design of experiment factor6200illustrated inFIG.62from the mixture factor type to the covariate factor type will now be described with reference toFIGS.62and67.

In some embodiments, converting the design of experiments factor6200from the mixture factor type to the covariate factor type may include updating the row814kkillustrated inFIG.62to include one or more user interface elements that are configured to receive input for defining (or specifying) one or more covariate levels of the design of experiment factor6200. For instance, as shown inFIG.67, when converting the design of experiment factor6200from the mixture factor type (as indicated by810kkinFIG.62) to the covariate factor type (as indicated by810kkinFIG.67), the process3924replaces the number input fields6202and6204illustrated inFIG.62with user interface elements6702-6704for defining covariate levels of the design of experiment factor6200. It shall be noted that, in some embodiments, the covariate levels of the design of experiments factor6200may be defined based on user input received via a design of experiments covariate dialog column or the like.

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a mixture factor type to a constant factor type. Various non-limiting example of such processes converting the design of experiment factor6200illustrated inFIG.62from the mixture factor type to the constant factor type will now be described with reference toFIGS.62and68.

In some embodiments, converting the design of experiments factor4100from the mixture factor type to the constant factor type may include updating the row814kkillustrated inFIG.62to include one or more user interface elements that are configured to receive input for defining (or specifying) a constant value of the design of experiment factor6200. For instance, as shown inFIG.68, when converting the design of experiment factor6200from the mixture factor type (as illustrated by810kkinFIG.62) to the constant factor type (as indicated by810kkinFIG.68), the process3924replaces the number input fields6202and6204illustrated inFIG.62with number input user interface element6802for defining a constant value of the design of experiment factor6200.

Additionally, or alternatively, in some embodiments, converting the design of experiment factor6200to the constant factor type may include defining a constant value for the design of experiment factor6200based on the minimum mixture value currently associated with the design of experiment factor6200. For instance, as also illustrated inFIG.68, when converting the design of experiment factor6200from the mixture factor type to the constant factor type, the process3920defines a constant value for the design of experiment factor6200(as indicated by element6802) that is equivalent to the minimum mixture value indicated by number input field6202illustrated inFIG.62.

In some embodiments, the factor type conversion executed by the processes3920and3924may cause a target factor to be converted from a blocking factor type to a continuous factor type, a categorical factor type, a discrete numeric factor type, a mixture factor type, a constant factor type, a covariate factor type, and/or the like. When converting a target factor from a blocking factor type to one of the above-mentioned factor types, the process3920, in some embodiments, may function to perform the conversion based on default type conversion rules (e.g., such as when the blocking factor specifies ‘runs per block’). It shall also be noted that, in some embodiments, the process3920may additionally, or alternatively, function to convert constant factors to the continuous factor type, the categorical factor type, the discrete numeric factor type, the mixture factor type, the covariate factor type, the blocking factor type, and/or the like based on similar default type conversion rules.

In some embodiments, the default type conversion rules may define the default values (e.g., levels) of a respective factor type. For instance, in a non-limiting example, the default type conversion rules for the continuous factor type may define that, by default, the minimum continuous value of a continuous factor is negative (−1), and the maximum continuous value of a continuous factor is positive one (+1). Thus, in some embodiments, when converting a target factor from a blocking factor type to a continuous factor type, the process3920may set the values of the target factor according to the default type conversion rules (e.g., set the minimum continuous value and the maximum continuous value to negative one (−1) and positive one (1), respectively). Other default type conversions for other factors type may be analogously defined.

An example of the process3920converting a blocking factor type based on default type conversion rules is illustrated inFIGS.69and70. InFIGS.69and70, the process3920is converting the design of experiment factor6900from a blocking factor type to the discrete numeric factor type. As illustrated, when converting the design of experiment factor6900from the blocking factor type to the discrete numeric factor type, the process3920adds, based on default settings, five (5) discrete numeric levels to the design of experiment factor6900. Specifically, in the example ofFIG.70, the five discrete have default values of one (1), two (2), three (3), four (4), and five (5), respectively.

It shall be noted that the above example is not intended to be limiting and that, in other embodiments, the default type conversion rules may include additional, fewer, or different discrete numeric levels than illustrated. Alternatively, in some embodiments, the process3920may function to convert a target factor from a blocking factor type to a continuous factor type, discrete numeric factor type, mixture factor type, covariate factor type, and/or a constant factor based on the same factor type conversion rules described inFIGS.48-54for converting a categorical factor to a continuous factor, a discrete numeric factor, a mixture factor, a covariate factor, and/or a constant factor.

Referring toFIG.39, in some embodiments, the process3920may include a process3928that functions to update the design of experiments with an experiment design policy for the successor factor type, wherein the experiment design policy for the successor factor type controls a transformation of an underlying model of the design of experiments. The experiment design policy, as generally referred to herein, may refer to heuristics (e.g., rules) that enable the model underlying the design of experiments to flexibly adapt as factors are added to the design of experiments, removed from the design of experiments, and/or as attributes of the factors in the design of experiments change (e.g., type, levels, etc.).

In some embodiments, when the process3920is converting a target factor to a continuous factor type (e.g., the successor factor type), the process3928may function to update the design of experiments with a continuous factor experiment design policy. The continuous factor experiment design policy, in some embodiments, may comprise a main effect model term heuristic, a model power heuristic, a degrees of freedom heuristic, a term interaction heuristic, and/or a mixture source heuristic. The main effect model term heuristic, in some embodiments, may cause the process3930(described later) to add a main effect model term for the target factor when one or more model transformation criteria are satisfied (e.g., if the model underlying the design of experiments does not already include such a main effect model term). The model power heuristic, in some embodiments, may cause the process3930to maintain (e.g., preserve) powers of one or more terms in a design of experiment model. The degrees of freedom heuristic, in some embodiments, may cause the process3930to set terms associated with continuous factor to one (1) degree of freedom. The term interaction heuristic, in some embodiments, may cause the process3930to preserve (e.g., maintain) model interaction terms currently present in the design of experiments model. The mixture source heuristic, in some embodiments, may cause the process3930to add an intercept term to the design of experiments model when the target factor is being converted from a mixture factor type and/or when the design of experiments model does not include other mixture factors.

In some embodiments, when the process3920is converting a target factor to a categorical factor type (e.g., the successor factor type), the process3928may function to update the design of experiments with a categorical factor experiment design policy. The categorical factor experiment design policy, in some embodiments, may comprise a main effect model term heuristic, a model power heuristic, a degrees of freedom heuristic, a term interaction heuristic, and/or a mixture source heuristic. The main effect model term heuristic, in some embodiments, may cause the process3930(described later) to add a main effect model term for the target factor when one or more model transformation criteria are satisfied (e.g., if the model underlying the design of experiments does not already include such main effect model term). The model power heuristic, in some embodiments, may cause the process3930to remove terms in the design of experiment model that include the target factor with a power greater than one. The degrees of freedom heuristic, in some embodiments, may cause the process3930to set term corresponding to the target factor to k−1 degrees of freedom (where k is the number of levels associated with the target factor). The term interaction heuristic, in some embodiments, may cause the process3930to preserve (e.g., maintain) model interaction terms currently present in the design of experiments model. The mixture source heuristic, in some embodiments, may cause the process3930to add a model intercept term to the design of experiments model when the target factor is being converted from a mixture factor type and/or when the design of experiments model does not include any other mixture factors.

In some embodiments, when the process3920is converting a target factor to a discrete numeric factor type (e.g., the successor factor type), the process3928may function to update the design of experiments with a discrete numeric factor experiment design policy. The discrete numeric factor experiment design policy, in some embodiments, may comprise a model power heuristic, a term interaction heuristic, and/or a mixture source heuristic. The main effect model term heuristic, in some embodiments, may cause the process3930(illustrated inFIG.39) to add a main effect model term for the target factor when one or more model transformation criteria are satisfied (e.g., if the model underlying the design of experiments does not already include such a main effect model term). The model power heuristic, in some embodiments, may cause the process3930to set main effect terms to have a first level of estimability (e.g., “Necessary” estimability) and/or may cause terms with powers up to k−1 (where k is the number of levels associated with the target factor) to have the first level of estimability or a second level of estimability (e.g., “If possible” estimability) based on system or user preferences. The term interaction heuristic, in some embodiments, may cause the process3930to preserve (e.g., maintain) model interaction terms currently present in the design of experiments model. The mixture source heuristic, in some embodiments, may cause the process3930to add an intercept term to the design of experiments model when the target factor is being converted from a mixture factor type and when the design of experiments model does not include any other mixture factors.

In some embodiments, when the process3920is converting a target factor to a mixture factor type (e.g., the successor factor type), the process3928may function to update the design of experiments with a mixture factor experiment design policy. The mixture factor experiment design policy, in some embodiments, may comprise a model intercept heuristic and/or a mixture term interaction heuristic. The model intercept heuristic, in some embodiments, may cause the process3930to remove a model intercept term from the design of experiment model if such term exists in the design of experiment model. The mixture term interaction heuristic may cause, the process3930to remove main effect model terms associated with other variables (e.g., process variables) and/or may cause the process3930remove interaction model terms that comprise mixture factors from the design of experiments model.

In some embodiments, when the process3920is converting a target factor to a blocking factor type (e.g., the successor factor type), the process3928may function to update the design of experiments with a blocking factor experiment design policy. The blocking factor experiment design policy, in some embodiments, may comprise a blocking factor term interaction heuristic, a degree of freedom heuristic, and/or a mixture source heuristic. The blocking factor term interaction heuristic, in some embodiments, may cause the process3930to remove interactions terms from the design of experiment model that involve the target factor. The degree of freedom heuristic, in some embodiments, may cause the process3930to update a main effect model term associated with the target factor to have k−1 degrees of freed (where k is the number of blocking levels associated with the target factor). The mixture source heuristic, in some embodiments, may cause the process3930to add an intercept term to the design of experiments model when the target factor is being converted from a mixture factor type and when the design of experiments model does not include any other mixture factors (e.g., the target factor corresponds to the last mixture factor in the design of experiments).

In some embodiments, when the process3920is converting a target factor to a constant factor type (e.g., the successor factor type), the process3928may function to update the design of experiments with a constant factor experiment design policy. The constant factor experiment design policy, in some embodiments, may comprise a model term heuristic, and/or a mixture source heuristic. The model term heuristic, in some embodiments, may cause the process3930to remove one or more model terms from the design of experiment model that involve the target factor. The mixture source heuristic, in some embodiments, may cause the process3930to add an intercept term to the design of experiments model when the target factor is being converted from a mixture factor type and/or when the design of experiments model does not include any other mixture factors (e.g., the target factor corresponds to the last mixture factor in the design of experiments).

It shall be noted that, in some embodiments, when the process3920is converting a target factor to a covariate factor type (e.g., the successor factor type), the process3928may function to update the design of experiments according to the categorical experiment design policy, the continuous experiment design policy, and/or the mixture experiment design. That is, the process3930may convert the target factor from an incumbent factor type (e.g., source factor type) to the covariate factor type according to the heuristics defined in the categorical experiment design policy, the continuous experiment design policy, and/or the mixture experiment design policy.

In some embodiments, when the process3920is converting a target factor to the successor factor type, the process3928may function to update the design of experiments with a run size experiment design policy. The run size experiment design policy, in some embodiments, may comprise a minimum run size heuristic, a default run size heuristic, and a user specified run size heuristic. The minimum run size heuristic, in some embodiments, may cause the process3930to update the minimum number of runs for the design of experiment to correspond to the sum of the degrees of freedom associated with each design of experiment factor. The default run size heuristic, in some embodiments, may cause the process3930to update the default run size to a value corresponding to a product of the two design of experiment factors with the largest number of levels plus at least an additional four (4) degrees of freedom for error. The user specified run size heuristic, in some embodiments, may cause the process3930to update the user specified run size to the minimum design of experiment run size when the user specified run size is less than the minimum design of experiment run size.

In some embodiments, when the process3920is converting a target factor to the successor factor type, the process3928may function to update the design of experiments with a space filling experiment design policy. The space filling experiment design policy, in some embodiments, may comprise an available design choice heuristic that causes the process3930to restrict design choices when a continuous factor is converted to a categorical factor type, a discrete numeric factor type, a mixture factor type. Additionally, or alternatively, in some embodiments, the available design choice heuristic may cause the process3930to make a plurality of design choices available for selection when a respective design of experiment factor is converted to a continuous factor type.

In some embodiments, when the process3920is converting a target factor to the successor factor type, the process3928may function to update the design of experiments with a definitive screening design policy. The definitive screening design policy, in some embodiments, may comprise a run size heuristic that causes the process3930to set the run size of the design of experiments to an even number when the continuous factor is converted to a categorical factor and/or that causes the process3930to set the run size of the design of experiments to an odd number when design of experiments do not include a categorical factor.

In some embodiments, when the process3920is converting a target factor to the successor factor type, the process3928may function to update the design of experiments with a screening design policy. The screening design policy, in some embodiments, may comprise an available screening type heuristic that causes the process3930to present options for selecting fractional factorial designs or main effects screening designs when a continuous factor is converted to a two-level factor.

Referring toFIG.39, in some embodiments and as previously mentioned above, the method3900may include the process3930. The process3930, in some embodiments, may function to execute a model transformation of the underlying model of the design of experiments based on the experiment design policy for the successor factor type, wherein executing the model transformation includes modifying one or more operational parameters of the underlying model to satisfy the experimental design policy for the successor factor type.

As will be described in more detail herein, in some embodiments, modifying operational parameters of the underlying model to satisfy the experimental design policy may include, but should be limited to, adding one or more model terms to the design of experiments model to satisfy the heuristics (e.g., rules) associated with the experiment design policy, removing one or more model terms from the design of experiments model to satisfy the heuristics associated with the experiment design policy, updating attributes of one or more model terms in the design of experiments model to satisfy the heuristics associated with the experiment design policy, updating a run size of the design of experiment, and/or the like.

It shall be noted that, in some embodiments, as illustrated inFIG.40, the process3930may perform a determination4006to determine if transformation of the underlying design of experiments model is required. If the process3930determines that the factor conversion executed by the step4004(e.g., process3920) does not require transformation of the underlying design of experiments model, the method3900may forgo performing steps4010-4016and perform step4008which functions to update the graphical user interface based on the converting of the target factor at step4004(e.g., update the design of experiment factor specification user interface800illustrated inFIGS.8-38).

Conversely, as also shown inFIG.40, if the process3930determines that the factor conversion executed by the process3920requires transformation of the underlying design of experiments model, the process3930may function to perform a determination4010to determine if the underlying model of the design of experiments relates to a special class of designs (e.g., space filling design platform, screening design platform, and/or the like). If the process3930determines that the underlying model does not relate to a special class of design, the process3930may perform step4012and/or4014. Various non-limiting examples of the process3930transforming terms of a design of experiment model (4012) and updating runtime parameters of the design of experiment model (4014) will now be described.

An example of the process3930transforming a design of experiment model to satisfy the continuous factor experiment design policy (described in the process3928) is illustrated inFIGS.71and72. In the example ofFIGS.71and72, the process3930is updating a design of experiment model7100based on (e.g., in response to) the process3920converting a design of experiment factor7104from a constant factor type to a continuous factor type. In particular, as illustrated inFIG.71, the design of experiments model7100includes a model intercept term7102. InFIG.72, the process3930determines that the design of experiment model7100illustrated inFIG.71does not currently include a main effect term corresponding to the design of experiment factor7104and, in response, adds such a main effect model term7202to the design of experiment model7100for satisfying the main effect model term heuristic defined in process3928. Additionally, in the example ofFIG.74, the process3930sets the main effect model term7306to one (1) degree of freedom to satisfy the degrees of freedom heuristic described in the process3928.

It also be noted that if the above example included model terms with powers and/or model interaction terms, the process3930may have additionally functioned to preserve the powers and the model interaction terms to satisfy the model power heuristic and the term interaction heuristic described in the process3928.

Another example of the process3930transforming a design of experiment model to satisfy the continuous factor experiment design policy (described in the process3928) is illustrated inFIGS.73and74. In the example ofFIGS.73and74, the process3930is updating a design of experiment model7300based on (e.g., in response to) the process3920converting a design of experiment factor7302from a mixture factor type to a continuous factor type. In particular, in the example ofFIG.73, the design of experiments model7100includes a main effects model term7303that corresponds to the design of experiment factor7302. InFIG.72, the process3930determines that the design of experiment factor7302was the last mixture factor in the design of experiments and, in response, adds a model intercept term7304to the design of experiment model7300to satisfy the mixture source heuristic described in the process3928.

An example of the process3930transforming a design of experiment model to satisfy the categorical factor experiment design policy (described in the process3928) is illustrated inFIGS.75and76. In the example ofFIGS.75and76, the process3930is updating a design of experiment model7500based on (e.g., in response to) the process3920converting a design of experiment factor7502from a discrete factor type to a categorical factor type. In particular, in the example ofFIG.75, the design of experiments model7100includes a model intercept term7304, a main effects model term7306, and model power terms7308-7312. InFIG.72, the process3930removes the model interaction terms7308-7312to satisfy the model power heuristic described in the process3298. It shall be noted that the process3930may perform similar transformation to satisfy the other experimental design policies described with reference to the process3928.

An example of the process3930transforming a design of experiment model to satisfy the run size experiment design policy (described in the process3928) is illustrated inFIGS.77and78. In the example ofFIGS.77and78, the process3930is updating a design of experiment model7700based on (e.g., in response to) the process3930converting the design of experiment factor7722from a continuous factor type to a categorical factor type with four categorical levels. Specifically, inFIG.77, the design of experiments model7700includes a model intercept term7702and a plurality of main effect model terms7704-7710corresponding to the plurality of design of experiment factors7718-7724. Additionally, as shown inFIG.77, the design of experiment model7700currently has a minimum run size7712of five (5) runs, a default run size7714of twelve (12) runs, and a user specified run size of twelve (12) runs.

InFIG.78, based on converting the design of experiment factor7722from a continuous factor type to a categorical factor type, the process3930updates the design of experiment model7700to include the model power terms7726-7730. Additionally, as shown inFIG.78, based on converting the design of experiment factor7722from a continuous factor type to a categorical factor type, the process3930updates the minimum run size7712to seven (7) runs to satisfy the run size experiment design policy described in the process3928.

Conversely, as also shown inFIG.40, in some embodiments, if the process3930determines via determination4010that the underlying model relates to a special class of designs, the process3930may perform step4016. Various non-limiting examples of the process3930updating available design choices for the design of experiments (4016) will now be described.

An example of the process3930updating available design choices to satisfy the space filling experiment design policy (described in the process3928) is illustrated inFIGS.79and80. In the example ofFIGS.79and80, the process3930is updating design choices available for selection based on (e.g., in response to) the process3930converting a design of experiment factor from a continuous factor type to a categorical factor type. Specifically, inFIG.79, the space filling design choices available for selection includes a sphere packing design choice7902, a Latin hypercube design choice7904, a uniform design choice7906, a minimum potential design choice7908, a maximum entropy design choice7910, a gaussian process IMSE Optimal design choice7912, and a fast flexible filling design choice7914.

InFIG.78, based on converting the design of experiment factor from the continuous factor type to the categorical factor type, the process3930updates the space filing design choices available for selection to satisfy the space filling experiment design policy described in the process3928. Specifically, inFIG.80, the process3930causes the sphere packing design choice7902, the Latin hypercube design choice7904, the uniform design choice7906, the minimum potential design choice7908, the maximum entropy design choice7910, and the gaussian process IMSE Optimal design choice7912to longer be available for selection.

Referring toFIG.39, in some embodiments, the method3900may include a process3940that functions to execute the design of experiments based at least on the factor specification data, the execution of the factor type conversion, and the execution of the model transformation. In some embodiments, executing the design of experiments may include computing a plurality of design of experiment runs based on the processes3910-3930. Each design of experiment run, in such embodiments, may refer to a single iteration of the design of experiment and/or may define specific levels or values that each factor may take during a respective design of experiment run. Furthermore, in some embodiments, once executed, the process3940may present the results of the design of experiment runs in a graphical user interface for enabling a user to assess the effect of the factors on the outcome of the experiment.

It shall also be noted that the system and methods of the embodiment and variations described herein can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions are preferably executed by computer-executable components preferably integrated with the system and one or more portions of the processors and/or the controllers. The computer-readable medium can be stored on any suitable computer-readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, memory sticks (e.g., SD cards, USB flash drives), cloud-based services (e.g., cloud storage), magnetic storage devices, Solid-State Drives (SSDs), or any suitable device. The computer-executable component is preferably a general or application-specific processor, but any suitable dedicated hardware or hardware/firmware combination device can alternatively or additionally execute the instructions.

The systems and methods of the preferred embodiments may additionally, or alternatively, be implemented on an integrated data analytics software application and/or software architecture such as that are offered by SAS Institute Inc. or JMP Statistical Discovery LLC of Cary, N.C., USA. Merely for illustration, the systems and methods of the preferred embodiments may be implemented using or integrated with one or more software tools such as JMP®, which is developed and provided by JMP Statistical Discovery LLC.

Although omitted for conciseness, the preferred embodiments include every combination and permutation of the implementations of the systems and methods described herein.

As a person skilled in the art will recognize from the previous detailed description and from the figures and claims, modifications and changes can be made to the embodiments of the disclosure without departing from the scope of the various described embodiments.