PERSONALIZING USER INTERFACE DISPLAYS IN REAL-TIME

    公开(公告)号:US20240427477A1

    公开(公告)日:2024-12-26

    申请号:US18760571

    申请日:2024-07-01

    Abstract: A method can include receiving a signal from a user device of a user. The method can further include processing, via a machine learning model, user intent labels, wherein: the machine learning model is pre-trained based on historical input data and historical output data associated with multiple users comprising the user, the historical input data comprise historical feature embedding vectors associated with the multiple users, and the historical output data comprise historical intent labels based at least in part on uttered intents of the multiple users. The method can also include processing one or more user intent candidates of the user intent labels. The method can further include processing one or more user interface components for the one or more user intent candidates. Additionally, the method can include transmitting the one or more user interface components to be presented on a user interface executed on the user device of the user. Other embodiments are described.

    SYSTEMS AND METHODS FOR GENERATING A CUSTOMIZED GRAPHICAL USER INTERFACE

    公开(公告)号:US20230244866A1

    公开(公告)日:2023-08-03

    申请号:US17589143

    申请日:2022-01-31

    CPC classification number: G06F40/20 G06F3/0484 G06F40/35 G06N3/08 G06Q10/0837

    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.

    Systems and methods for generating a customized graphical user interface

    公开(公告)号:US12112127B2

    公开(公告)日:2024-10-08

    申请号:US17589143

    申请日:2022-01-31

    CPC classification number: G06F40/20 G06F3/0484 G06F40/35 G06N3/08 G06Q10/0837

    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.

    AUTOMATICALLY DETERMINING USER INTENT BY SEQUENCE CLASSIFICATION BASED ON NON-TIME-SERIES-BASED MACHINE LEARNING

    公开(公告)号:US20230244984A1

    公开(公告)日:2023-08-03

    申请号:US17589552

    申请日:2022-01-31

    CPC classification number: G06N20/00 G06K9/6282

    Abstract: A method implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include receiving, via a computer network, an intent prediction request from a frontend system. The method further can include obtaining, from a database, one or more events in a lookback period associated with one or more items ordered by a user for the intent prediction request. The method also can include determining a time-based feature encoding for the one or more events for the user by: (a) determining a feature encoding for the one or more events; (b) determining a positional encoding for the one or more events; and (c) determining the time-based feature encoding based at least in part on the feature encoding, the positional encoding, and a decay function. The positional encoding can include one or more positional vectors associated with a temporal sequence of the one or more events. The method further can include determining, in real-time via a machine learning model, a user intent for the user based on the time-based feature encoding. Other embodiments are described.

    SYSTEMS AND METHODS FOR GENERATING A CUSTOMIZED GRAPHICAL USER INTERFACE

    公开(公告)号:US20250028901A1

    公开(公告)日:2025-01-23

    申请号:US18908525

    申请日:2024-10-07

    Abstract: The system includes one or more processors and one or more non-transitory computer-readable storage devices storing instructions that, when executed, cause the one or more processors to perform receiving user utterances, and utilizing a trained natural language processing (NLP) algorithm as one or more layers in a neural network to generate from the one or more user utterances at least one first output of at least one first output layer of the neural network and at least one second output of at least one second output layer of the neural network. The instructions, when executed, also can cause the one or more processors to perform using the trained NLP algorithm to combine the at least one first output of the at least one first output layer of the neural network and the at least one second output of the at least one second output layer of the neural network to create a combined output of the neural network. The at least one first output layer of the neural network can be different than the at least one second output layer of the neural network. The instructions, when executed, also can cause the one or more processors to perform coordinating displaying a customized graphical user interface (GUI) using the combined output of the neural network. Other embodiments and variations are disclosed herein.

    PERSONALIZING USER INTERFACE DISPLAYS IN REAL-TIME

    公开(公告)号:US20220155926A1

    公开(公告)日:2022-05-19

    申请号:US17589871

    申请日:2022-01-31

    Abstract: A method implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include receiving, via a computer network, a user interaction signal from a user device for a user. The method further can include after receiving the user interaction signal, determining, in real-time via a machine learning model, a plurality of user intent labels based at least in part on transaction data, interaction data, and incident data for the user. The machine learning model can include pre-trained based on historical input data and historical output data associated with multiple users comprising the user. The historical input data can comprise historical feature embedding vectors for historical transaction data, historical interaction data, and historical incident data associated with the multiple users. The historical output data can include historical intent labels determined based at least in part on user-uttered intents by the multiple users in historical conversation data, identified by natural language understanding. In some embodiments, the method also can include, after determining the plurality of user intent labels, determining, in real-time, one or more user intent candidates of the plurality of user intent labels based on a confidence threshold. In a few embodiments, the method further can include, after determining the one or more user intent candidates, determining, in real-time, one or more user interface components for the one or more user intent candidates. After determining the one or more user interface components, the method further can include transmitting, via the computer network, the one or more user interface components to be presented on a user interface executed on the user device for the user. Other embodiments are described.

    Systems and methods for utilizing feedback data

    公开(公告)号:US12229784B2

    公开(公告)日:2025-02-18

    申请号:US17364798

    申请日:2021-06-30

    Abstract: A system can receive a first set of data. The first set of data can include information indicating a first set of user sessions and for each of the first set of user sessions having an associated summary and a corresponding agent indicated intent. The system can also, based on the first set of data, determine a set of utterances and for each of the set of utterances a corresponding set of intents. Additionally, the system can receive a second set of data. The second set of data including information indicating a second set of user sessions and for each of the second set of user sessions having an associated determined utterance and corresponding interaction of a user. Moreover, the system can validate a corresponding intent of one or more utterances of the set of utterances, based on the second set of data.

    Personalizing user interface displays in real-time

    公开(公告)号:US12026357B2

    公开(公告)日:2024-07-02

    申请号:US17589871

    申请日:2022-01-31

    CPC classification number: G06F3/0484 G10L15/18 G10L15/22 G10L2015/223

    Abstract: A method implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include receiving, via a computer network, a user interaction signal from a user device for a user. The method further can include after receiving the user interaction signal, determining, in real-time via a machine learning model, a plurality of user intent labels based at least in part on transaction data, interaction data, and incident data for the user. The machine learning model can include pre-trained based on historical input data and historical output data associated with multiple users comprising the user. The historical input data can comprise historical feature embedding vectors for historical transaction data, historical interaction data, and historical incident data associated with the multiple users. The historical output data can include historical intent labels determined based at least in part on user-uttered intents by the multiple users in historical conversation data, identified by natural language understanding. In some embodiments, the method also can include, after determining the plurality of user intent labels, determining, in real-time, one or more user intent candidates of the plurality of user intent labels based on a confidence threshold. In a few embodiments, the method further can include, after determining the one or more user intent candidates, determining, in real-time, one or more user interface components for the one or more user intent candidates. After determining the one or more user interface components, the method further can include transmitting, via the computer network, the one or more user interface components to be presented on a user interface executed on the user device for the user. Other embodiments are described.

    SYSTEMS AND METHODS FOR UTILIZING FEEDBACK DATA

    公开(公告)号:US20230004988A1

    公开(公告)日:2023-01-05

    申请号:US17364798

    申请日:2021-06-30

    Abstract: A system can receive a first set of data. The first set of data can include information indicating a first set of user sessions and for each of the first set of user sessions having an associated summary and a corresponding agent indicated intent. The system can also, based on the first set of data, determine a set of utterances and for each of the set of utterances a corresponding set of intents. Additionally, the system can receive a second set of data. The second set of data including information indicating a second set of user sessions and for each of the second set of user sessions having an associated determined utterance and corresponding interaction of a user. Moreover, the system can validate a corresponding intent of one or more utterances of the set of utterances, based on the second set of data.

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