Systems and methods for identifying a name

1. An apparatus for identifying a name, the apparatus comprising:

at least a processor; and

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

identify a plurality of attributes wherein identifying the plurality of attributes comprises:

identifying entity data using a web crawler trained to scrape the entity data from entity related platforms;

training an attribute classifier with training data comprising historical entity data correlated to historical attributes

inputting the entity data into the attribute classifier; and

outputting, using the attribute classifier, a plurality of attributes;

identify an attribute cluster using the plurality of attributes and an unsupervised clustering algorithm, wherein:

identifying the attribute cluster comprises identifying an outlier cluster; and locating in the plurality of attribute clusters an outlier cluster comprises:

identifying a target process;

inputting the target process into an impact metric machine learning model;

inputting an attribute cluster into an impact metric machine learning model;

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

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

determine a component word set as a function of an attribute cluster, using a component word set machine learning model, wherein the component word set comprises is at least one word with a desired level of relatedness with respect to the attribute cluster;

determine a candidate name set as a function of the component word set;

identify a name from the candidate name set, using a name identification machine learning model; and

determine a visual element data structure as a function of the name.

2. The apparatus of claim 1 , wherein identifying an attribute cluster comprises:

inputting a plurality of attributes into a clustering algorithm; and

receiving an attribute cluster from the clustering algorithm.

3. The apparatus of claim 1 , wherein determining a component word set as a function of the attribute cluster, using a component word set machine learning model comprises:

inputting an attribute cluster into the component word set machine learning model;

inputting a predetermined word into the component word set machine learning model;

receiving from the component word set machine learning model an association rating; and

determining whether to include the predetermined word in the component word set as a function of the association rating.

4. The apparatus of claim 1 , wherein determining a candidate name set as a function of the component word set comprises:

inputting at least a subset of the component word set into a component word combination machine learning model; and

receiving a candidate name from the component word combination machine learning model.

5. The apparatus of claim 1 , wherein identifying a name from the candidate name set using a name identification machine learning model comprises:

inputting a candidate name into an intelligibility rating machine learning model;

receiving an intelligibility rating from the intelligibility rating machine learning model; and

identifying a name from the candidate name set as a function of the intelligibility rating.

6. The apparatus of claim 1 , wherein identifying a name from the candidate name set using a name identification machine learning model comprises:

inputting a candidate name into an appeal rating machine learning model;

receiving an appeal rating from the appeal rating machine learning model; and

identifying a name from the candidate name set as a function of the appeal rating.

7. The apparatus of claim 1 , wherein identifying a name from the candidate name set using a name identification machine learning model comprises:

inputting a candidate name into an intelligibility rating machine learning model;

receiving an intelligibility rating from the intelligibility rating machine learning model;

inputting a candidate name into an appeal rating machine learning model;

receiving an appeal rating from the appeal rating machine learning model; and

identifying a name from the candidate name set as a function of the intelligibility rating, and the appeal rating.

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

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

display the visual element to a user.

9. The apparatus of claim 1 , wherein the name is a portmanteau.

10. A method of identifying a name, the method comprising:

using at least a processor, identifying a plurality of attributes wherein identifying the plurality of attributes comprises:

identifying entity data using a web crawler trained to scrape the entity data from entity related platforms;

training an attribute classifier with training data comprising historical entity data correlated to historical attributes;

inputting the entity data into the attribute classifier; and

outputting, using the attribute classifier, a plurality of attributes;

using at least a processor, identifying an attribute cluster using the plurality of attributes and an unsupervised clustering algorithm, wherein:

identifying the attribute cluster comprises identifying an outlier cluster; and

locating in the plurality of attribute clusters an outlier cluster comprises:

identifying a target process;

inputting the target process into an impact metric machine learning model;

inputting an attribute cluster into an impact metric machine learning model;

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

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

using at least a processor, determining a component word set as a function of the attribute cluster, using a component word set machine learning model, wherein the component word set comprises is at least one word with a desired level of relatedness with respect to the attribute cluster;

using at least a processor, determining a candidate name set as a function of the component word set; and

using at least a processor, identifying a name from the candidate name set, using a name identification machine learning model; and

using at least a processor, determining a visual element data structure as a function of the name.

11. The method of claim 10 , wherein identifying an attribute cluster comprises:

using at least a processor, inputting a plurality of attributes into a clustering algorithm; and

using at least a processor, receiving an attribute cluster from the clustering algorithm.

12. The method of claim 10 , wherein determining a component word set as a function of the attribute cluster, using a component word set machine learning model comprises:

using at least a processor, inputting an attribute cluster into the component word set machine learning model;

using at least a processor, inputting a predetermined word into the component word set machine learning model;

using at least a processor, receiving from the component word set machine learning model an association rating; and

using at least a processor, determining whether to include the predetermined word in the component word set as a function of the association rating.

13. The method of claim 10 , wherein determining a candidate name set as a function of the component word set comprises:

using at least a processor, inputting at least a subset of the component word set into a component word combination machine learning model; and

using at least a processor, receiving a candidate name from the component word combination machine learning model.

14. The method of claim 10 , wherein identifying a name from the candidate name set using a name identification machine learning model comprises:

using at least a processor, inputting a candidate name into an intelligibility rating machine learning model;

using at least a processor, receiving an intelligibility rating from the intelligibility rating machine learning model; and

using at least a processor, identifying a name from the candidate name set as a function of the intelligibility rating.

15. The method of claim 10 , wherein identifying a name from the candidate name set using a name identification machine learning model comprises:

using at least a processor, inputting a candidate name into an appeal rating machine learning model;

using at least a processor, receiving an appeal rating from the appeal rating machine learning model; and

using at least a processor, identifying a name from the candidate name set as a function of the appeal rating.

16. The method of claim 10 , wherein identifying a name from the candidate name set using a name identification machine learning model comprises:

using at least a processor, inputting a candidate name into an intelligibility rating machine learning model;

using at least a processor, receiving an intelligibility rating from the intelligibility rating machine learning model;

using at least a processor, inputting a candidate name into an appeal rating machine learning model;

using at least a processor, receiving an appeal rating from the appeal rating machine learning model; and

using at least a processor, identifying a name from the candidate name set as a function of the intelligibility rating, and the appeal rating.

17. The method of claim 10 , further comprising:

using at least a processor, determining a visual element as a function of the visual element data structure; and

using at least a processor, displaying the visual element to a user.

18. The method of claim 10 , wherein the name is a portmanteau.