User interfaces and rule discovery and automation

1 . A system comprising:

a non-transitory computer readable medium configured to store instructions thereon; and

a processor connected to the non-transitory computer readable medium, wherein the processor is configured to execute the instructions for:

instructing a display to display a first rule definition graphical user interface (GUI) displaying first attributes associated with a first rule;

receiving a first multi-source dataset, wherein the first multi-source dataset comprises:

the first set of attributes associated with the first rule;

a first set of requests comprising a first set of characteristics;

training a machine learning tool using the first multi-source dataset;

wherein the first set of attributes associated with the rule is used as input features for the machine learning model;

dividing the first multi-source dataset into a set of clusters using a first unsupervised clustering technique, wherein the set of clusters comprises:

a first cluster;

a second cluster;

determining second attributes associated with a second rule based on the set of clusters;

determining a similarity of the first attributes of the first rule and the second attributes of the second rule;

delivering, in response to the similarity exceeding a first threshold, a first recommendation, wherein the delivering the first recommendation comprises:

instructing the display to display a second rule definition graphical user interface (GUI) displaying the second attributes associated with the second rule; and

receiving an indication to approve or commit the second rule.

2 . The system of claim 1 , wherein the processor is further configured to execute the instructions for:

updating the first multi-source dataset to include the second set of attributes associated with the second rule; or

storing user feedback and using the user feedback or domain specific KPIs to further train the machine learning tool.

3 . The system of claim 1 wherein the processor is further configured to execute the instructions for:

delivering the first recommendation by providing a universal resource link (URL) to the second rule definition GUI displaying the second attributes associated with the second rule.

4 . The system of claim 3 , wherein the processor is further configured to execute the instructions for:

instructing the display to display a form comprising dropdown form fields wherein options for each of the dropdown form fields are pre-selected to reflect the second attributes of the second rule.

5 . The system of claim 4 , wherein a first subset of the options that contain differences between the first attributes of the first rule and the second attributes of the second rule are highlighted.

6 . The system of claim 1 , wherein the processor is further configured to execute the instructions for:

determining a cosine similarity.

7 . The system of claim 1 , wherein first multi-source dataset comprises:

data derived from process ticket requests or rule-approver preferences for approving rules.

8 . A method comprising:

instructing a display to display a first rule definition graphical user interface (GUI) displaying first attributes associated with a first rule;

receiving a first multi-source dataset, wherein the first multi-source dataset comprises:

the first set of attributes associated with the first rule;

a first set of requests comprising a first set of characteristics;

training a machine learning tool using the first multi-source dataset;

wherein the first set of attributes associated with the rule is used as input features for the machine learning model;

dividing the first multi-source dataset into a set of clusters using a first unsupervised clustering technique, wherein the set of clusters comprises:

a first cluster;

a second cluster;

determining second attributes associated with a second rule based on the set of clusters;

determining a similarity of the first attributes of the first rule and the second attributes of the second rule;

delivering, in response to the similarity exceeding a first threshold, a first recommendation, wherein the delivering the first recommendation comprises:

instructing the display to display a second rule definition graphical user interface (GUI) displaying the second attributes associated with the second rule; and

receiving an indication to approve or commit the second rule.

9 . The method of claim 8 , further comprising:

updating the first multi-source dataset to include the second set of attributes associated with the second rule; or

storing user feedback and using the user feedback or domain specific KPIs to further train the machine learning tool.

10 . The method of claim 8 , further comprising:

delivering the first recommendation by providing a universal resource link (URL) to the second rule definition GUI displaying the second attributes associated with the second rule.

11 . The method of claim 10 , further comprising:

instructing the display to display a form comprising dropdown form fields wherein options for each of the dropdown form fields are pre-selected to reflect the second attributes of the second rule.

12 . The method of claim 11 , wherein a first subset of the options that contain differences between the first attributes of the first rule and the second attributes of the second rule are highlighted.

13 . The method of claim 8 , further comprising:

determining a cosine similarity.

14 . The method of claim 8 , wherein first multi-source dataset comprises:

data derived from process ticket requests or rule-approver preferences for approving rules.

15 . A non-transitory computer readable medium configured to store instructions that when executed cause a processor to perform operations comprising:

instructing a display to display a first rule definition graphical user interface (GUI) displaying first attributes associated with a first rule;

receiving a first multi-source dataset, wherein the first multi-source dataset comprises:

the first set of attributes associated with the first rule;

a first set of requests comprising a first set of characteristics;

training a machine learning tool using the first multi-source dataset;

wherein the first set of attributes associated with the rule is used as input features for the machine learning model;

dividing the first multi-source dataset into a set of clusters using a first unsupervised clustering technique, wherein the set of clusters comprises:

a first cluster;

a second cluster;

determining second attributes associated with a second rule based on the set of clusters;

determining a similarity of the first attributes of the first rule and the second attributes of the second rule;

delivering, in response to the similarity exceeding a first threshold, a first recommendation, wherein the delivering the first recommendation comprises:

instructing the display to display a second rule definition graphical user interface (GUI) displaying the second attributes associated with the second rule; and

receiving an indication to approve or commit the second rule.

16 . The non-transitory computer readable medium of claim 15 , wherein the instructions are configured to cause the processor to perform operations further comprising:

updating the first multi-source dataset to include the second set of attributes associated with the second rule; or

storing user feedback and using the user feedback or domain specific KPIs to further train the machine learning tool.

17 . The non-transitory computer readable medium of claim 15 , wherein the instructions are configured to cause the processor to perform operations further comprising:

delivering the first recommendation by providing a universal resource link (URL) to the second rule definition GUI displaying the second attributes associated with the second rule.

18 . The non-transitory computer readable medium of claim 17 , wherein the instructions are configured to cause the processor to perform operations further comprising:

instructing the display to display a form comprising dropdown form fields wherein options for each of the dropdown form fields are pre-selected to reflect the second attributes of the second rule.

19 . The non-transitory computer readable medium of claim 18 , wherein a first subset of the options that contain differences between the first attributes of the first rule and the second attributes of the second rule are highlighted.

20 . The non-transitory computer readable medium of claim 15 , to perform operations further comprising:

determining a cosine similarity.