Systems and methods for generating a customized graphical user interface
Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to perform receiving one or more user utterances; re-training a pre-trained natural language processing (NLP) algorithm on the one or more user utterances; using the pre-trained NLP algorithm, as re-trained, as one or more layers in a neural network; combining at least one first output of at least one first output layer of the neural network with at least one second output of at least one second output layer of the neural network to create a final output of the neural network, wherein: the at least one first output layer of the neural network is different than the at least one second output layer of the neural network; and wherein the pre-trained NLP algorithm, as re-trained, is used to determine the final output of the neural network; and coordinating displaying a customized graphical user interface (GUI) using the final output of the neural network. Other embodiments are disclosed herein.
1. A system comprising:
one or more processors; and
one or more non-transitory computer-readable storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to perform:
receiving one or more user utterances;
re-training a pre-trained natural language processing (NLP) algorithm on the one or more user utterances;
using the pre-trained NLP algorithm, as re-trained, as one or more layers in a neural network;
combining at least one first output of at least one first output layer of the neural network with at least one second output of at least one second output layer of the neural network to create a final output of the neural network, wherein:
the at least one first output layer of the neural network is different than the at least one second output layer of the neural network; and
wherein the pre-trained NLP algorithm, as re-trained, is used to determine the final output of the neural network; and
coordinating displaying a customized graphical user interface (GUI) using the final output of the neural network.
2. The system of claim 1 , wherein the customized GUI comprises a reply from a chat bot.
3. The system of claim 1 , wherein the one or more user utterances comprise messages entered into a chat bot.
4. The system of claim 1 , wherein the pre-trained NLP algorithm, as re-trained and used in the neural network, feeds into:
the first output layer of the neural network and the second output layer of the neural network through two different pathways.
5. The system of claim 1 , wherein:
the at least one first output of the at least one first output layer comprises a first cross entropy loss;
the at least one second output of the at least one second output layer comprises a second cross entropy loss; and
the final output of the neural network comprises mean squared error.
6. The system of claim 1 , wherein:
the computing instructions are further configured to run on the one or more processors and cause the one or more processors to perform:
after using the pre-trained NLP algorithm, as re-trained, as one or more layers in the neural network, receiving a new user utterance from a user; and
the final output of the neural network is correlated with an intent of the new user utterance.
7. The system of claim 1 , wherein the neural network comprises a hybrid neural network comprising at least a portion of two different types of hierarchical multi-label classification networks.
8. The system of claim 1 , wherein the neural network comprises a hybrid neural network comprising at least one chained portion and at least one unchained portion.
9. The system of claim 1 , wherein the one or more user utterances comprise requests to return, exchange, or refund one or more items.
10. The system of claim 1 , wherein coordinating displaying the customized GUI using the final output of the neural network comprises:
calculating a total loss using the at least one first output of the at least one first output layer, the at least one second output of the at least one second output layer, and the final output of the neural network; and
coordinating displaying the customized GUI using the total loss.
11. A method implemented via execution of computing instructions configured to run at one or more processors and configured to be stored at non-transitory computer-readable media, the method comprising:
receiving one or more user utterances;
re-training a pre-trained natural language processing (NLP) algorithm on the one or more user utterances;
using the pre-trained NLP algorithm, as re-trained, as one or more layers in a neural network;
combining at least one first output of at least one first output layer of the neural network with at least one second output of at least one second output layer of the neural network to create a final output of the neural network, wherein:
the at least one first output layer of the neural network is different than the at least one second output layer of the neural network; and
wherein the pre-trained NLP algorithm, as re-trained, is used to determine the final output of the neural network; and
coordinating displaying a customized graphical user interface (GUI) using the final output of the neural network.
12. The method of claim 11 , wherein the customized GUI comprises a reply from a chat bot.
13. The method of claim 11 , wherein the one or more user utterances comprise messages entered into a chat bot.
14. The method of claim 11 , wherein the pre-trained NLP algorithm, as re-trained and used in the neural network, feeds into:
the first output layer of the neural network and the second output layer of the neural network through two different pathways.
15. The method of claim 11 , wherein:
the at least one first output of the at least one first output layer comprises a first cross entropy loss;
the at least one second output of the at least one second output layer comprises a second cross entropy loss; and
the final output of the neural network comprises mean squared error.
16. The method of claim 11 , wherein:
the method further comprises:
after using the pre-trained NLP algorithm, as re-trained, as one or more layers in the neural network, receiving a new user utterance from a user; and
the final output of the neural network is correlated with an intent of the new user utterance.
17. The method of claim 11 , wherein the neural network comprises a hybrid neural network comprising at least a portion of two different types of hierarchical multi-label classification networks.
18. The method of claim 11 , wherein the neural network comprises a hybrid neural network comprising at least one chained portion and at least one unchained portion.
19. The method of claim 11 , wherein the one or more user utterances comprise requests to return, exchange, or refund one or more items.
20. The method of claim 11 , wherein coordinating displaying the customized GUI using the final output of the neural network comprises:
calculating a total loss using the at least one first output of the at least one first output layer, the at least one second output of the at least one second output layer, and the final output of the neural network; and
coordinating displaying the customized GUI using the total loss.