# US 12,112,127 B2 · Systems and methods for generating a customized graphical user interface > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,112,127 B2 - Document ID: US12112127B2 - Status: Granted - Kind: B2 - Application number: 17/589,143 - Filed: 2022-01-31 - Published: 2023-08-03 - Granted: 2024-10-08 - Earliest claimed: 2022-01-31 - Assignees: Walmart Apollo, LLC - Applicants: Walmart Apollo, LLC - Inventors: Neeraj Agrawal; Priyanka Bhatt; Tanishka Agarwal - Web page: https://patentgrep.com/patent/US12112127B2 ## CPC - G06F 40/20: Natural language analysis (semantic analysis of natural language G06F40/30) - G06F 3/0482: Interaction with lists of selectable items, e.g. menus - G06F 3/0484: for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range - G06F 3/04895: Guidance during keyboard input operation, e.g. prompting - G06F 40/30: Semantic analysis - G06F 40/35: Discourse or dialogue representation - G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/045: Combinations of networks - G06N 3/08: Learning methods - G06N 3/084: Backpropagation, e.g. using gradient descent - G06Q 10/0837: Return transactions - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract 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. ## Claims ### Claim 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. ### Claim 2 (depends on 1) The system of claim 1, wherein the customized GUI comprises a reply from a chat bot. ### Claim 3 (depends on 1) The system of claim 1, wherein the one or more user utterances comprise messages entered into a chat bot. ### Claim 4 (depends on 1) 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. ### Claim 5 (depends on 1) 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. ### Claim 6 (depends on 1) 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. ### Claim 7 (depends on 1) 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. ### Claim 8 (depends on 1) 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. ### Claim 9 (depends on 1) The system of claim 1, wherein the one or more user utterances comprise requests to return, exchange, or refund one or more items. ### Claim 10 (depends on 1) 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. ### Claim 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. ### Claim 12 (depends on 11) The method of claim 11, wherein the customized GUI comprises a reply from a chat bot. ### Claim 13 (depends on 11) The method of claim 11, wherein the one or more user utterances comprise messages entered into a chat bot. ### Claim 14 (depends on 11) 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. ### Claim 15 (depends on 11) 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. ### Claim 16 (depends on 11) 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. ### Claim 17 (depends on 11) 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. ### Claim 18 (depends on 11) 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. ### Claim 19 (depends on 11) The method of claim 11, wherein the one or more user utterances comprise requests to return, exchange, or refund one or more items. ### Claim 20 (depends on 11) 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.