Apparatus and method for generating a solution

1. An apparatus for generating a solution, the apparatus comprising:

a user interface configured to receive user data;

at least a processor communicatively connected to the user interface; and

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

analyze a user interaction received through the user interface based on the user data;

identify a problem as a function of the user interaction, wherein identifying the problem comprises classifying the problem to a problem category, and wherein classifying the problem to the problem category comprises:

training a problem classifier using a problem training dataset, wherein the problem training dataset correlates causes of problems to problem categories;

classifying the problem to the problem category using the trained problem classifier based at least on the user interaction; and

determining an accuracy score, wherein the accuracy score is a numerical value representing the accuracy of outputs of the problem classifier; and

retraining the problem classifier with an additional problem training dataset based at least on the accuracy score;

generate a solution based on the user interaction and the user data received from the user interface, wherein the solution comprises a plurality of resources for addressing the problem, wherein generating the solution comprises training a web crawler configured to retrieve and index a plurality of resources; and

track user progress with the solution.

2. The apparatus of claim 1 , wherein the at least a processor is configured to utilize a chatbot to receive the user data.

3. The apparatus of claim 1 , wherein the user interface comprises a video streaming system configured to connect a plurality of users through live video and receive the user interaction.

4. The apparatus of claim 1 , wherein the at least a processor is further configured to retrieve the user data by training a second web crawler to retrieve the user data.

5. The apparatus of claim 1 , wherein the user data comprises audio data of a user's voice.

6. The apparatus of claim 1 , wherein analyzing the user interaction comprises identifying keywords in the user data indicating the problem.

7. The apparatus of claim 6 , wherein identifying the keywords comprises utilizing a language model to extract the keywords from audio data.

8. The apparatus of claim 1 , wherein analyzing the user interaction comprises using a facial landmark detection technique to classify a facial expression to a positive, neutral, or negative impact, wherein the negative impact is classified as a contributing cause.

9. The apparatus of claim 1 , wherein identifying the problem comprises identifying a pattern in the user data indicating negativity, wherein the pattern comprises similar indicators of a negative impact.

10. The apparatus of claim 1 , wherein generating the solution further comprises:

receiving a solution training dataset comprising data correlating main causes to solutions;

training a solution classifier using the solution training dataset; and

outputting, using the solution classifier, the solution.

11. A method for generating a solution, the method comprising:

receiving, through a user interface, user data, wherein the user interface is communicatively connected to a computing device;

analyzing, by the computing device, a user interaction received through the user interface based on the user data;

identifying, by the computing device, a problem as a function of the user interaction, wherein identifying the problem comprises classifying the problem to a problem category, and wherein classifying the problem to the problem category comprises:

training a problem classifier using a problem training dataset, wherein the problem training dataset correlates causes of problems to problem categories;

classifying the problem to the problem category using the trained problem classifier based at least on the user interaction; and

determining an accuracy score, wherein the accuracy score is a numerical value representing the accuracy of outputs of the problem classifier; and

retraining the problem classifier with an additional problem training dataset based at least on the accuracy score;

generating, by the computing device, a solution based on the user interaction and the user data received from the user interface, wherein the solution comprises a plurality of resources for addressing the problem, wherein generating the solution comprises:

training a web crawler configured to retrieve and index a plurality of resources; and

tracking, by the computing device, user progress with the solution.

12. The method of claim 11 , wherein receiving the user data further comprises using a chatbot.

13. The method of claim 11 , wherein analyzing the user interaction further comprises connecting a plurality of users through live video using a video streaming system operating on the user interface, wherein the video streaming system is configured to receive the user interaction.

14. The method of claim 11 , wherein receiving the user data further comprises training a second web crawler to retrieve the user data.

15. The method of claim 11 , wherein the user data comprises audio data of a user's voice.

16. The method of claim 11 , wherein analyzing the user interaction comprises identifying keywords in the user data indicating the problem.

17. The method of claim 16 , wherein identifying the keywords comprises utilizing a language model to extract keywords from audio data.

18. The method of claim 11 , wherein analyzing the user interaction comprises using a facial landmark detection technique to classify a facial expression to a positive, neutral, or negative impact, wherein the negative impact is classified as a contributing cause.

19. The method of claim 11 , wherein identifying the problem comprises identifying a pattern in the user data indicating negativity, wherein the pattern comprises similar indicators of a negative impact.

20. The method of claim 11 , wherein generating the solution further comprises:

receiving a solution training dataset comprising data correlating main causes to solutions;

training a solution classifier using the solution training dataset; and

outputting, using the solution classifier, the solution.