Abstract: This application relates to apparatus and methods for natural language understanding in conversational systems using machine learning processes. In some examples, a computing device receives a request that identifies textual data. The computing device applies a natural language model to the textual data to generate first embeddings. In some examples, the natural language model is trained on retail data, such as item descriptions and chat session data. The computing device also applies a dependency based model to the textual data to generate second embeddings. Further, the computing device concatenates the first and second embeddings, and applies an intent and entity classifier to the concatenated embeddings to determine entities, and an intent, for the request. The computing device may generate a response to the request based on the determined intent and entities.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform receiving a voice command from a user; transforming the voice command; transforming the voice command can include using a natural language understanding and rules execution engine into (a) an intent of the user to add recipe ingredients to a cart and (b) a recipe descriptor; determining a matching recipe from a set of ingested recipes based on the recipe descriptor; determining items and quantities associated with the items that correspond to a set of ingredients included in the matching recipe using a quantity inference algorithm; and automatically adding all of the items and the quantities associated with the items to the cart. Other embodiments are disclosed.

Abstract: This application relates to apparatus and methods for natural language understanding in conversational systems using machine learning processes. In some examples, a computing device receives a request that identifies textual data. The computing device applies a natural language model to the textual data to generate first embeddings. In some examples, the natural language model is trained on retail data, such as item descriptions and chat session data. The computing device also applies a dependency based model to the textual data to generate second embeddings. Further, the computing device concatenates the first and second embeddings, and applies an intent and entity classifier to the concatenated embeddings to determine entities, and an intent, for the request. The computing device may generate a response to the request based on the determined intent and entities.

Abstract: A method including determining respective training data for each of query-type-specific answer retrieval modules. The method further can include training each of the query-type-specific answer retrieval modules. The method additionally can include determining a query type of a query from a user device for a user. The method also can include determining, in real-time and based at least in part on the query type, an answer retrieval module from the query-type-specific answer retrieval modules, as trained. Moreover, the method can include determining, in real-time by the answer retrieval module, one or more answers for the query. Then, the method can include ranking, in real-time, the one or more answers based on a user profile of the user. Finally, the method can include transmitting, via a computer network and to the user device, at least one of the one or more answers, as ranked. Other embodiments are disclosed.

Abstract: A system for generating a response to a customer query includes a computing device configured to obtain a first dataset, including a plurality of first phrase-intent pairs associated with a first domain. Each first phrase-intent pair includes a first phrase and a corresponding first intent. The computing device is configured to retrieve a set of configuration rules to configure a plurality of environments. The computing device is also configured to configure a first environment using the first dataset and the set of configuration rules to determine a result user intent based on a requested query associated with the first domain. The first environment embeds the plurality of first phrase-intent pairs in a vector space based on the set of configuration rules. The computing device is configured to perform operations based on the first environment.

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 generating training data for an intent classification machine learning model by: (a) determining, via a text-to-text machine learning model, one or more respective paraphrases for each sample phrase of training phrases; (b) generating, via a label generating machine learning model, labeled data based on unlabeled live logs by: (i) determining live-log samples from the unlabeled live logs based at least in part on: a respective timestamp of each live log of the unlabeled live logs, or random sampling; and (ii) generating, via the label generating machine learning model, the labeled data based on the live-log samples and one or more labeling functions; and (c) adding the one or more respective paraphrases for the each sample phrase of the training phrases and the labeled data to the training data.

Abstract: This application relates to systems and methods for proactively predicting user intents on personal agents. In some examples, a user intent prediction system can include a computing device configured to obtain user intent data identifying a desired action by a user on a network-enabled tool. The computing device is further configured to obtain contextual data characterizing a user's interaction with the network-enabled tool. The computing device can then determine at least one predicted future intent of the user on the network-enabled tool based on the user intent data and the contextual data and present the at least one predicted future intent to the user.

Abstract: A system for generating a response to a customer query includes a computing device configured to obtain a first dataset, including a plurality of first phrase-intent pairs associated with a first domain. Each first phrase-intent pair includes a first phrase and a corresponding first intent. The computing device is configured to retrieve a set of configuration rules to configure a plurality of environments. The computing device is also configured to configure a first environment using the first dataset and the set of configuration rules to determine a result user intent based on a requested query associated with the first domain. The first environment embeds the plurality of first phrase-intent pairs in a vector space based on the set of configuration rules. The computing device is configured to perform operations based on the first environment.

Abstract: This application relates to apparatus and methods for natural language understanding in conversational systems using machine learning processes. In some examples, a computing device receives a request that identifies textual data. The computing device applies a natural language model to the textual data to generate first embeddings. In some examples, the natural language model is trained on retail data, such as item descriptions and chat session data. The computing device also applies a dependency based model to the textual data to generate second embeddings. Further, the computing device concatenates the first and second embeddings, and applies an intent and entity classifier to the concatenated embeddings to determine entities, and an intent, for the request. The computing device may generate a response to the request based on the determined intent and entities.

Abstract: This application relates to apparatus and methods for natural language understanding in conversational systems using machine learning processes. In some examples, a computing device receives a request that identifies textual data. The computing device applies a natural language model to the textual data to generate first embeddings. In some examples, the natural language model is trained on retail data, such as item descriptions and chat session data. The computing device also applies a dependency based model to the textual data to generate second embeddings. Further, the computing device concatenates the first and second embeddings, and applies an intent and entity classifier to the concatenated embeddings to determine entities, and an intent, for the request. The computing device may generate a response to the request based on the determined intent and entities.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform receiving a voice command from a user; transforming the voice command; transforming the voice command can include using a natural language understanding and rules execution engine into (a) an intent of the user to add recipe ingredients to a cart and (b) a recipe descriptor; determining a matching recipe from a set of ingested recipes based on the recipe descriptor; determining items and quantities associated with the items that correspond to a set of ingredients included in the matching recipe using a quantity inference algorithm; and automatically adding all of the items and the quantities associated with the items to the cart. Other embodiments are disclosed.

Abstract: This application relates to systems and methods for proactively predicting user intents on personal agents. In some examples, a user intent prediction system can include a computing device configured to obtain user intent data identifying a desired action by a user on a network-enabled tool. The computing device is further configured to obtain contextual data characterizing a user's interaction with the network-enabled tool. The computing device can then determine at least one predicted future intent of the user on the network-enabled tool based on the user intent data and the contextual data and present the at least one predicted future intent to the user.

Abstract: A device receives user interface information associated with a user interface to be provided for a particular platform, and receives design information for a design of the user interface to be provided for the particular platform. The device receives a request to visually compare the user interface information and the design information, and utilizes, based on the request, a trained machine learning model to visually compare the user interface information and the design information. The device generates information, indicating defects in the user interface information, based on utilizing the trained machine learning model to visually compare the user interface information and the design information, where the defects include user interface information that does not visually match the design information. The device provides the information indicating the defects in the user interface information.

Abstract: A device receives user interface information associated with a user interface to be provided for a particular platform, and receives design information for a design of the user interface to be provided for the particular platform. The device receives a request to visually compare the user interface information and the design information, and utilizes, based on the request, a trained machine learning model to visually compare the user interface information and the design information. The device generates information, indicating defects in the user interface information, based on utilizing the trained machine learning model to visually compare the user interface information and the design information, where the defects include user interface information that does not visually match the design information. The device provides the information indicating the defects in the user interface information.

Abstract: A device receives user interface information associated with a user interface to be provided for a particular platform, and receives design information for a design of the user interface to be provided for the particular platform. The device receives a request to visually compare the user interface information and the design information, and utilizes, based on the request, a trained machine learning model to visually compare the user interface information and the design information. The device generates information, indicating defects in the user interface information, based on utilizing the trained machine learning model to visually compare the user interface information and the design information, where the defects include user interface information that does not visually match the design information. The device provides the information indicating the defects in the user interface information.

Abstract: A device receives user interface information associated with a user interface to be provided for a particular platform, and receives design information for a design of the user interface to be provided for the particular platform. The device receives a request to visually compare the user interface information and the design information, and utilizes, based on the request, a trained machine learning model to visually compare the user interface information and the design information. The device generates information, indicating defects in the user interface information, based on utilizing the trained machine learning model to visually compare the user interface information and the design information, where the defects include user interface information that does not visually match the design information. The device provides the information indicating the defects in the user interface information.