System and methods for varying optimization solutions using constraints based on an endpoint

1. A system for varying optimization solutions using constraints based on an endpoint, the system comprising:

at least a processor;

a memory communicatively connected to the at least a processor, the memory containing instructions configuring the processor to:

receive process data comprising a plurality of impediments;

generate an endpoint using a module configured to:

analyze the plurality of impediments by extracting a feature from each impediment of the plurality of impediments;

classify a plurality of impediments using the extracted features to a plurality of identifiers;

rank the plurality of identifiers based on an identifier severity score;

output the endpoint based on the identifier severity score;

identify a plurality of nodes;

receive at least a constraint;

locate, in the plurality of nodes, an outlier cluster based on the endpoint, wherein locating the outlier cluster comprises:

identifying a target process;

inputting the target process and the plurality of nodes into an impact metric machine learning model;

receiving an impact metric from the impact metric machine learning model; and

determining the outlier cluster as a function of the impact metric;

determine an outlier process as a function of the outlier cluster; and

determine a visual element data structure as a function of the outlier process.

2. The system of claim 1 , wherein the module comprises a language processing model configured to identify a plurality of keywords from the plurality of impediments to output the plurality of features.

3. The system of claim 1 , wherein the module comprises a feature classifier configured to classify the plurality of impediments to the plurality of identifiers.

4. The system of claim 1 , wherein the module comprises a scoring machine learning model configured:

to receive the plurality of identifiers as an input; and

perform a scoring function to output a plurality of severity scores, wherein the scoring machine learning model is trained with datasets including weights associated with a plurality of features and identifiers.

5. The system of claim 1 , wherein the at least a constraint comprises an interface query data structure wherein the interface query data structure is at least partially based on data describing attributes of a user that is retrieved from a database including categorical information correlated to a historical range of data.

6. The system of claim 1 , wherein the impact metric indicates the degree to which the plurality of nodes supports the target process.

7. The system of claim 1 , wherein determining the outlier process as a function of the outlier cluster comprises:

inputting an outlier cluster in an outlier process machine learning model;

receiving an outlier process from the outlier machine learning model.

8. The system of claim 1 , wherein the memory contains instructions configuring the at least a processor to:

determine a visual element as a function of the visual element data structure; and

configure a user device to display the visual element to the user.

9. The system of claim 8 , wherein the visual element is configured to display the input field to the user by a Graphical User Interface (GUI), wherein the GUI is a point of interaction between the user and the remote display device.

10. A method for varying optimization solutions using constraints based on an endpoint, the method comprising:

receiving, by a computing device, process data comprising a plurality of impediments;

generating, by the computing device, an endpoint using a module configured to:

analyze the plurality of impediments by extracting a feature from each impediment of the plurality of impediments;

classify a plurality of impediments using the extracted features to a plurality of identifiers;

rank the plurality of identifiers based on an identifier severity score;

output the endpoint based on the identifier severity score;

identifying, by the computing device, a plurality of nodes;

receiving, by the computing device, at least a constraint;

locating, by the computing device, in the plurality of nodes an outlier cluster based on the endpoint, wherein locating the outlier cluster comprises:

identifying a target process;

inputting the target process and the plurality of nodes into an impact metric machine learning model;

receiving an impact metric from the impact metric machine learning model; and

determining the outlier cluster as a function of the impact metric;

determining, by the computing device, an outlier process as a function of the outlier cluster; and

determining, by the computing device, a visual element data structure as a function of the outlier process.

11. The method of claim 10 , further comprising identifying, using a language processing model, to identify a plurality of keywords from the plurality of impediments to output the plurality of features.

12. The method of claim 10 , further comprising, using a feature classifier, to classify the plurality of impediments to the plurality of identifiers.

13. The method of claim 10 , further comprising:

receiving, using a scoring machine learning model, the plurality of identifiers as an input; and

performing, using the scoring machine learning model, a scoring function to output a plurality of severity scores, wherein the scoring machine learning model is trained with datasets including weights associated with a plurality of features and identifiers.

14. The method of claim 10 , wherein the at least a constraint comprises an interface query data structure wherein the interface query data structure is at least partially based on data describing attributes of a user that is retrieved from a database including categorical information correlated to a historical range of data.

15. The method of claim 10 , wherein the impact metric indicates that the plurality of nodes supports the target process.

16. The method of claim 10 , wherein determining the outlier process as a function of the outlier cluster comprises:

inputting an outlier cluster in an outlier process machine learning model;

receiving an outlier process from the outlier machine learning model.

17. The method of claim 10 , wherein the memory contains instructions configuring the at least a processor to:

determine a visual element as a function of the visual element data structure; and

configure a user device to display the visual element to the user.

18. The method of claim 17 , wherein the visual element comprises a remote display device is configured to display the input field to the user by a Graphical User Interface (GUI), wherein the GUI is a point of interaction between the user and the remote display device.