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: 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 a processor and a non-transitory computer-readable media storing computing instructions that, when executed on the processor, cause the processor to perform certain operations. The operations can include identifying a 2D silo image of a geometric item based on a probability value exceeding a predetermined probability threshold. The operations also can include segmenting artifacts from the 2D silo image to isolate first pixels of a border of the geometric item. The operations further can include trimming second pixels along the border of the geometric item. The operations also can include performing an aspect ratio validation on the 2D silo image to validate that the 2D silo image corresponds to a shape of the geometric item. The operations additionally can include auto-validating that a visual resolution level of the 2D silo image falls within a predetermined acceptance rate.

Abstract: A system including a processor and a non-transitory computer-readable media storing computing instructions that, when executed on the processor, cause the processor to perform certain operations: obtaining an image of a digital space; extracting a depth map and a segmentation map of the image; passing each of the depth map and the segmentation map through a respective model of two parallel image diffusion models using stable diffusion with controlled image generation; prompting a selection of a target style for the digital space; segmenting, using image segmentation, the image in a target stylized digital space; and determining, using dominant color filtering, visual images of complementary items. Other embodiments are described.

Abstract: A system including a processor and a non-transitory computer-readable media storing computing instructions that, when executed on the processor, cause the processor to perform certain operations. The operations can include obtaining a rendered image for a 3D-asset generated from a reference image of an object. The operations also can include generating, using a machine learning model, a color score for the rendered image based on a first color histogram for the rendered image and a second color histogram for the reference image. The operations additionally can include generating, using a deep learning model and a slice loss function, a texture score for the rendered image. The acts operations can include determining a quality score for the rendered image based on a predetermined quality threshold and a combination of the color score and the texture score. Other embodiments are described.

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 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: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed configured to run on the one or more processors, cause the one or more processors to and perform operations: receiving a command from a user; transforming, using a trained machined learning model, the command by detecting an intent to add one or more recipe ingredients; determining a recipe from a set of recipes; determining one or more items and one or more quantities or sizes of the one or more items; selecting a respective quantity or size from quantities or sizes of available items of the one or more items, wherein the respective quantity or size, as selected, is overruled when a different quantity or size preference is indicated by the user; and automatically adding to the shopping cart the one or more items. Other embodiments are described.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed configured to run on the one or more processors, cause the one or more processors to and perform operations: receiving a command from a user; transforming, using a trained machined learning model, the command by detecting an intent to add one or more recipe ingredients; determining a recipe from a set of recipes; determining one or more items and one or more quantities or sizes of the one or more items; selecting a respective quantity or size from quantities or sizes of available items of the one or more items, wherein the respective quantity or size, as selected, is overruled when a different quantity or size preference is indicated by the user; and automatically adding to the shopping cart the one or more items. Other embodiments are described.

Abstract: In various embodiments, systems and methods for augmented reality guidance are disclosed. A first position of a local device and a second position of a first item within a predetermined locale are determined. Route data from the first position to the second position is generated and a position of the local device is tracked. In response to the position of the local device being within a predetermined distance of the second position, an image capture element of the local device is activated. Image data is received. When the first item is within the image data, an item highlighter indicating a position of the first item within the image data is generated and when the first item is not within the image data, a direction indicator indicating a position of the first item relative to a field of view of the image data is generated.

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.