Apparatus and methods for multiple stage process modeling
An apparatus and method for multiple stage process modeling is provided. The apparatus includes a processor and a memory connected to the processor. The memory containing instructions configuring the a processor to receive process data sets, each process data set representing a progression stage that describes a sequence of activities performed by an entity device, generate, using the process data sets and a machine learning algorithm, a progression outlook profile including progression stage profiles, each progression stage profile representative of a respective progression stage and may generate progression actions describing progression from a first progression stage to a second progression stage based on input data, and a progression stage profile classifier that may use input data and identify a progression stage currently occupied by a process based on input data. The processor may receive process data describing a process to classify received process data to a progression stage profile.
1 . An apparatus for multiple stage process modeling, the apparatus comprising:
a reconfigurable hardware module;
at least a processor communicatively connected to the reconfigurable hardware module; and
a memory connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
receive a plurality of process data sets, each process data set representing a progression stage, wherein the progression stage describes a sequence of activities;
instantiate, at the reconfigurable hardware module, a progression stage profile classifier, wherein the progression stage profile classifier is generated, by a machine-learning module of the at least a processor, using a linear regression technique wherein the machine-learning module is configured to iteratively retrain the progression stage profile classifier based on user inputs indicating sub-optimal performance by performing an auditing process, wherein the progression stage profile classifier comprises a machine learning model and wherein iteratively retraining the progression stage profile classifier comprises:
sanitizing a training data of the progression stage profile classifier by eliminating training examples of the training data in order to reduce an interference of a convergence of the machine learning model in order to increase an accuracy of the machine learning model, and wherein the training examples comprise exemplary inputs and exemplary outputs;
generate, using at least some of the plurality of process data sets and the instantiated progression stage profile classifier, a progression outlook profile comprising:
a plurality of progression stage profiles, each progression stage profile representative of a respective progression stage and configured to generate progression actions describing progression from a first progression stage to a second progression stage based on input data;
receive current process data describing at least a process to be analyzed, wherein the process includes a current assessment of the sequence of activities;
classify received current process data to a progression stage profile using the progression stage profile classifier, wherein classifying comprises classifying the current assessment to at least the first progression stage;
output at least a current action datum using the progression stage profile, wherein output comprises at least a recommended action for an entity device;
generate an interface data structure including an input field, wherein the interface data structure configures a remote display device to:
display at least an input field;
receive at least a user-input datum into the input field, wherein the user-input datum describes data for updating at least the sequence of activities;
generate an activity sequence summary based on the updated sequence of activities;
display the recommended action for the entity device including data based on the user-input datum and the activity sequence summary; and
display at least a vector from the current assessment to the second progression stage, wherein the vector represents a divergence value, and wherein the divergence value describes a divergence between a first numerical classification of the current assessment and a second numerical classification of the second progression stage.
2 . The apparatus of claim 1 , wherein generating the interface data structure further comprises:
retrieving data describing attributes of the entity device from a database communicatively connected to the processor; and
generating the interface data structure based on the data describing attributes of the entity device.
3 . The apparatus of claim 1 , wherein generating the recommended action for the entity device comprises:
retrieving data describing current preferences of the entity device between a minimum value and a maximum value from a database communicatively connected to the processor, wherein retrieving data further comprises receiving at least a form element input into the input field.
4 . The apparatus of claim 1 , further comprising generating at least an additional input field based on a divergence value that describes divergence between the current assessment to the second progression stage.
5 . The apparatus of claim 1 , wherein generating the recommended action for the entity device comprises:
classifying at least an instance of the current assessment to the second progression stage;
determining a proximity of a respective current assessment to the second progression stage calculated based on at least the user-input datum; and
adjusting the recommended action to reduce the proximity.
6 . The apparatus of claim 1 , wherein generating the recommended action for the entity device further comprises:
classifying the current assessment to the second progression stage, wherein classifying the current assessment further comprises:
comparing the current assessment to the second progression stage; and
determining a parity value based on comparison of the current assessment to the second progression stage, wherein the parity value is included within the recommended action.
7 . The apparatus of claim 4 , wherein generating the recommended action for the entity device further comprises:
determining a pattern, wherein the pattern describes entity interaction with a database communicatively connected to the processor;
classifying at least an element of the pattern to the divergence value; and
adjusting the pattern based on a magnitude of the divergence value.
8 . The apparatus of claim 1 , wherein generating the recommended action for the entity device further comprises:
classifying one or more new instances of the user-input datum to at least the second progression stage;
generating at least a divergence value between the user-input datum and at least the second progression stage based on the classification; and
displaying the divergence value.
9 . A method for multiple stage process modeling, the method comprising:
receiving, by a computing device incorporating a reconfigurable hardware module, a plurality of process data sets, each process data set representing a progression stage, wherein the progression stage describes a sequence of activities performed by an entity device;
instantiating, at the reconfigurable hardware module, a progression stage profile classifier, wherein the progression stage profile classifier is generated, by a machine-learning module of the at least a processor, using a linear regression technique wherein the machine-learning module is configured to iteratively retrain the progression stage profile classifier based on user inputs indicating sub-optimal performance by performing an auditing process, wherein the progression stage profile classifier comprises a machine learning model and wherein iteratively retraining the progression stage profile classifier comprises:
sanitizing a training data of the progression stage profile classifier by eliminating training examples of the training data in order to reduce an interference of a convergence of the machine learning model to increase an accuracy of the machine learning model, and wherein the training examples comprise exemplary inputs and exemplary outputs;
generating, using at least some of the plurality of process data sets and the instantiated progression stage profile classifier, a progression outlook profile comprising:
a plurality of progression stage profiles, each progression stage profile representative of a respective progression stage and configured to generate progression actions describing progression from a first progression stage to a second progression stage based on input data;
receiving, by the computing device, current process data describing at least a process to be analyzed, wherein the process includes a current assessment of the sequence of activities performed by an entity device;
classifying, by the computing device, received current process data to a progression stage profile using the progression stage profile classifier, wherein classifying comprises classifying the current assessment to at least the first progression stage;
outputting, by the computing device, at least a current action datum using the progression stage profile, wherein output comprises at least a recommended action for the entity device;
generating, by the computing device, an interface data structure including an input field, wherein the interface data structure configures a remote display device to:
display at least an input field;
receive at least a user-input datum into the input field, wherein the user-input datum describes data for updating at least the sequence of activities performed by the entity device;
generate an activity sequence summary based on the updated sequence of activities performed by the entity device;
display the recommended action for the entity device including data based on the user-input datum and the activity sequence summary; and
display at least a vector from the current assessment to the second progression stage, wherein the vector represents a divergence value, wherein the divergence value describes a divergence between a first numerical classification of the current assessment and a second numerical classification of the second progression stage.
10 . The method of claim 9 , wherein generating the interface data structure further comprises:
retrieving data describing attributes of the entity device from a database communicatively connected to the computing device; and
generating the interface data structure based on the data describing attributes of the entity device.
11 . The method of claim 9 , further comprising generating at least an additional input field based on a divergence value that describes divergence between the current assessment to the second progression stage.
12 . The method of claim 9 , wherein generating the recommended action for the entity device comprises:
classifying at least an instance of the current assessment to the second progression stage;
determining a proximity of a respective current assessment to the second progression stage calculated based on at least the user-input datum; and
adjusting the recommended action to reduce the proximity.
13 . The method of claim 9 , wherein generating the recommended action for the entity device further comprises:
classifying the current assessment to the second progression stage, wherein classifying the current assessment further comprises:
comparing the current assessment to the second progression stage; and
determining a parity value based on comparison of the current assessment to the second progression stage, wherein the parity value is included within the recommended action.
14 . The method of claim 11 , wherein generating the recommended action for the entity device further comprises:
determining a pattern, wherein the pattern describes entity interaction with a database communicatively connected to the computing device;
classifying at least an element of the pattern to the divergence value; and
adjusting the pattern based on a magnitude of the divergence value.
15 . The method of claim 9 , wherein generating the recommended action for the entity device further comprises:
classifying one or more new instances of the user-input datum to at least the second progression stage;
generating at least a divergence value between the user-input datum and at least the second progression stage based on the classification; and
displaying the divergence value.
16 . The apparatus of claim 1 , wherein the vector is positioned and arranged to connect a first numerical classification of the current assessment to a second numerical classification of the second progression stage.
17 . The method of claim 9 , wherein the vector is positioned and arranged to connect a first numerical classification of the current assessment to a second numerical classification of the second progression stage.