Methods for discovery of new automation routine types
A system to create new automation routines includes a processor to: Over time, store a group of actions taken by a group of agents within applications. From the stored group of actions, identify a repeating pattern of actions in a subset of the applications, and construct a binary vector, each position within the binary vector storing a 1 for an occurrence of any action within any application of the subset, and a 0 otherwise. From the binary vector and the repeating pattern, extract a sentence including at least one action within at least one application. Based on the repeating pattern, an application type, or a business goal, create a constraint. If the sentence meets the constraint, accept the sentence and add it to a pool of accepted sentences. From the pool of accepted sentences, identify a pattern of occurrences of the accepted sentence, and create a new automation routine.
1. A system adapted to automatically create new automation routines, the system comprising:
a fraud management server and a non-transitory computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the fraud management server, to perform operations which comprise:
over a period of time, storing a plurality of user inputs inputted into a user interface by a plurality of agents, wherein the user inputs occur within a plurality of applications; and
in near-real time:
from the stored plurality of user inputs, automatically identifying and storing a repeating pattern of user inputs, wherein the user inputs of the repeating pattern of user inputs take place in a subset of the plurality of applications;
from the stored plurality of user inputs, automatically constructing and storing a binary vector, each position within the binary vector comprising a 1 for an occurrence of any user input within any application of the subset of the plurality of applications, and a 0 for a non-occurrence of any user input within any application of the subset of the plurality of applications;
from the binary vector, the repeating pattern of user inputs, and a look-ahead window of a specified number of user inputs, automatically extracting and storing a sentence, wherein the sentence comprises at least one user input within at least one application of the subset of the plurality of applications;
based on the repeating pattern of user inputs, an application type of an application of the subset of the plurality of applications, or a business goal associated with the repeating pattern of user inputs, creating a constraint;
in response to the sentence meeting the constraint, accepting the sentence;
in response to the sentence being accepted, adding the sentence to a pool of accepted sentences;
from the pool of accepted sentences, identifying a pattern of occurrences of the accepted sentence;
from the pattern of occurrences of the accepted sentence, creating a new automation routine usable by a user to replace a sequence of user inputs into the user interface; and
reporting, to a user, via a display in communication with the fraud management server, the new automation routine.
2. The system of claim 1 , wherein the routine is a search routine, a document processing routine, or a focused routine.
3. The system of claim 1 , wherein the pattern of occurrences comprises a number of times the sentence has been used, a number of agents using the sentence, or the number of times the application has been used.
4. The system of claim 1 , wherein the constraint comprises constraint properties comprising an item constraint, a length constraint, a price constraint, a return on investment constraint, a non-empty sentence constraint, an application usage time constraint, a number of times switching between applications constraint, a number of copy user inputs constraint, a number of scroll user inputs constraint, a number of text input user inputs constraint, or combinations of any of the foregoing.
5. The system of claim 1 , wherein the constraint comprises a constraint metric comprising an average, a median, a percentage, a minimum, a maximum, a sum, a count, or a duration, wherein the constraint metric is compared against a configurable threshold.
6. The system of claim 1 , wherein the operations further comprise converting the new automation routine into code.
7. The system of claim 6 , wherein the operations further comprise making the code available on an automation finder application.
8. The system of claim 1 , wherein extracting the sentence involves identifying start points and end points of the sentence based on a maximum number of 1s, a minimum number of 1s, an average number of 1s, or based on a maximum number of 0s, a minimum number of 0s, or an average number of 0s, or based on the look-ahead window of the specified number of user inputs.
9. The system of claim 1 , wherein creating the constraint involves evaluating, for each user input within the sentence, the values of constraint properties against constraint metrics, and creating a Boolean expression of the evaluations, wherein the Boolean expression comprises the constraint.
10. The system of claim 1 , wherein determining whether the sentence meets the constraint involves matching user inputs within the sentence to parameters of the constraint.
11. A computer-implemented method adapted to automatically create new automation routines, which method comprises:
with a fraud management server having a memory:
over a period of time, storing a plurality of user inputs inputted into a user interface by a plurality of agents, wherein the user inputs occur within a plurality of applications; and
in near-real time:
from the stored plurality of user inputs, automatically identifying and storing a repeating pattern of user inputs, wherein the user inputs of the repeating pattern of user inputs take place in a subset of the plurality of applications;
from the stored plurality of user inputs, automatically constructing and storing a binary vector, each position within the binary vector comprising a 1 for an occurrence of any user input within any application of the subset of the plurality of applications, and a 0 for a non-occurrence of any user input within any application of the subset of the plurality of applications;
from the binary vector, the repeating pattern of user inputs, and a look-ahead window of a specified number of user inputs, automatically extracting and storing a sentence, wherein the sentence comprises at least one user input within at least one application of the subset of the plurality of applications;
based on the repeating pattern of user inputs, an application type of an application of the subset of the plurality of applications, or a business goal associated with the repeating pattern of user inputs, creating a constraint;
in response to the sentence meeting the constraint, accepting the sentence;
in response to the sentence being accepted, adding the sentence to a pool of accepted sentences;
from the pool of accepted sentences, identifying a pattern of occurrences of the accepted sentence;
from the pattern of occurrences of the accepted sentence, creating a new automation routine usable by a user to replace a sequence of user inputs into the user interface; and
reporting, to a user, via a display in communication with the fraud management server, the new automation routine.
12. The method of claim 11 , wherein the routine is a search routine, a document processing routine, or a focused routine.
13. The method of claim 11 , wherein the pattern of occurrences comprises a number of times the sentence has been used, a number of agents using the sentence, or the number of times the application has been used.
14. The method of claim 11 , wherein the constraint comprises constraint properties comprising an item constraint, a length constraint, a price constraint, a return on investment constraint, a non-empty sentence constraint, an application usage time constraint, a number of times switching between applications constraint, a number of copy user inputs constraint, a number of scroll user inputs constraint, a number of text input user inputs constraint, or combinations of any of the foregoing.
15. The method of claim 11 , wherein the constraint comprises a constraint metric comprising an average, a median, a percentage, a minimum, a maximum, a sum, a count, or a duration, wherein the constraint metric is compared against a configurable threshold.
16. The method of claim 11 , further comprising converting the new automation routine into code.
17. The method of claim 16 , further comprising making the code available on an automation finder application.
18. The method of claim 11 , wherein extracting the sentence involves identifying start points and end points of the sentence based on a maximum number of 1s, a minimum number of 1s, an average number of 1s, or based on a maximum number of 0s, a minimum number of 0s, or an average number of 0s, or based on the look-ahead window of the specified number of user inputs.
19. The method of claim 11 , wherein creating the constraint involves evaluating, for each user input within the sentence, the values of constraint properties against constraint metrics, and creating a Boolean expression of the evaluations, wherein the Boolean expression comprises the constraint.
20. The method of claim 11 , wherein determining whether the sentence meets the constraint involves matching user inputs within the sentence to parameters of the constraint.