Robotic process automation driven conditional formatting rules for user interfaces

1 . A computer program product for automated conditional formatting of a user interface, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the program instructions when executed by a processor cause the processor to:

receive, by a robotic process automation (RPA) agent, user activity via a dashboard user interface;

automatically determine, by the processor, data engaged by a user from data associated with the user activity;

identify a role of the user during an engagement with the dashboard user interface;

identify, by the RPA agent, one or more data segments of interest from the data engaged by the user;

forward the one or more data segments of interest and the identified role of the user to a machine learning module;

predict, by the machine learning module, an intent of the user in a future session of engaging with new data on the dashboard user interface, wherein the prediction of the intent is based on the user activity and the identified role of the user;

predict, by the machine learning module, a set of data segments in the dashboard user interface that will be of interest to the user in the future session of engaging with the new data on the dashboard user interface, wherein the prediction of the set of data segments is based on: the predicted intent of the user and a reception of a notification of an opening of the dashboard user interface;

send the predicted set of data segments to the RPA agent;

apply, by the RPA agent, conditional formatting rules to the predicted set of data segments; and

display the predicted set of data segments on the dashboard user interface by using the applied conditional formatting rules.

2 . The computer program product of claim 1 , wherein the program instructions further cause the processor to:

continuously monitor, by the RPA agent, the user activity with the dashboard user interface;

store an updated history of the user activity with the dashboard user interface; and

update, the display of the predicted set of data segments, based on identifying of a change in the user activity with the dashboard user interface from the updated history.

3 . The computer program product of claim 2 , wherein the program instructions further cause the processor to:

forward the updated history to the machine learning module; and

update the predicted set of data segments based on the updated history.

4 . The computer program product of claim 3 , wherein the program instructions further cause the processor to:

generate, by the machine learning module, a user profile based on the user activity with the dashboard user interface and the identified role of the user; and

update the user profile based on the updated history.

5 . The computer program product of claim 1 , wherein the program instructions further cause the processor to:

determine a role-based access restriction rule associated with the user; and

adjust the prediction of the set of data segments to include only data segments that are available to the user based on the role-based access restriction rule.

6 . The computer program product of claim 1 , wherein the automated conditional formatting displays the predicted set of data segments using a visual indicator that is distinguishable from a default formatting of data segments, other than the predicted set of data segments, displayed in the dashboard user interface.

7 . A computer-implemented method for automated conditional formatting of user interfaces, the computer-implemented method comprising:

receiving, by a robotic process automation (RPA) agent, user activity via a dashboard user interface;

automatically determining, by the RPA agent, data engaged by a user from data associated with the user activity;

identifying a role of the user during an engagement with the dashboard user interface;

identifying, by the RPA agent, one or more data segments predicted to be of interest based on the data engaged by the user;

forwarding the one or more data segments of interest and the identified role of the user, to a machine learning module;

predicting, by the machine learning module, an intent of the user in a future session of engaging with new data on the dashboard user interface, wherein the prediction of the intent is based on the user activity and the identified role of the user;

predicting, by the machine learning module, a set of data segments in the dashboard user interface that will be of interest to the user in the future session of engaging with the new data on the dashboard user interface, wherein the prediction of the set of data segments is based on: the predicted intent of the user and a reception of a notification of an opening of the dashboard user interface;

sending the predicted set of data segments to the RPA agent;

applying, by the RPA agent, conditional formatting rules to the predicted set of data segments; and

displaying the predicted set of data segments on the dashboard user interface by using the applied conditional formatting rules.

8 . The computer-implemented method of claim 7 , further comprising:

continuously monitoring, by the RPA agent, the user activity with the dashboard user interface;

storing an updated history of the user activity with the dashboard user interface; and

updating, the display of the predicted set of data segments, based on identifying of a change in the user activity with the dashboard user interface from the updated history.

9 . The computer-implemented method of claim 8 , further comprising:

forwarding the updated history to the machine learning module; and

updating the predicted set of data segments based on the updated history.

10 . The computer-implemented method of claim 9 , further comprising:

generating, by the machine learning module, a user profile based on the user activity with the dashboard user interface and the identified role of the user; and

updating the user profile based on the updated history.

11 . The computer-implemented method of claim 7 , further comprising:

determining a role-based access restriction rule associated with the user; and

adjusting the prediction of the set of data segments to include only data segments that are available to the user based on the role-based access restriction rule.

12 . The computer-implemented method of claim 7 , wherein the automated conditional formatting comprises displaying the predicted set of data segments using a visual indicator that is distinguishable from a default formatting of data segments, other than the predicted set of data segments, displayed in the dashboard user interface.

13 . A computing device, comprising:

a processor operating a dashboard user interface displaying data; and

a memory coupled to the processor, the memory storing instructions to cause the processor to perform operations comprising:

receiving, by a robotic process automation (RPA) agent, user activity via the dashboard user interface;

automatically determining, by the processor, data engaged by a user from the user activity;

identifying a role of the user during an engagement with the dashboard user interface;

identifying, by the RPA agent, one or more data segments of interest from the data engaged by the user;

forwarding the one or more data segments of interest and the identified role of the user to a machine learning module;

predicting, by the machine learning module, an intent of the user in a future session of engaging with new data on the dashboard user interface, wherein the prediction of the intent is based on the user activity and the identified role of the user;

predicting, by the machine learning module, a set of data segments in the dashboard user interface that are identified to be of interest to the user in the future session of engaging with the new data on the dashboard user interface, wherein the prediction of the set of data segments is based on: the predicted intent of the user and a reception of a notification of an opening of the dashboard user interface;

sending the predicted set of data segments to the RPA agent;

applying, by the RPA agent, conditional formatting rules to the predicted set of data segments; and

displaying the predicted set of data segments on the dashboard user interface by using the applied conditional formatting rules.

14 . The computing device of claim 13 , wherein the instructions further cause the processor to perform the operations comprising:

continuously monitoring, by the RPA agent, the user activity with the dashboard user interface;

storing an updated history of the user activity with the dashboard user interface; and

updating, the display of the predicted set of data segments, based on identifying of a change in the user activity with the dashboard user interface from the updated history.

15 . The computing device of claim 14 , wherein the instructions further cause the processor to perform the operations comprising:

forwarding the updated history to the machine learning module; and

updating the predicted set of data segments based on the updated history.

16 . The computing device of claim 15 , wherein the instructions further cause the processor to perform the operations comprising:

generating, by the machine learning module, a user profile based on the user activity with the dashboard user interface and the identified role of the user; and

updating the user profile based on the updated history.

17 . The computing device of claim 13 , wherein the instructions further cause the processor to perform the operations comprising:

determining a role-based access restriction rule associated with the user; and

adjusting the prediction of the set of data segments to include only data segments that are available to the user based on the role-based access restriction rule.