Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for analysis of images including wearable items. For example, a method may include obtaining a first set of images, each of the first set of images depicting a product; obtaining a first set of labels associated with the first set of images; training an image segmentation neural network based on the first set of images and the first set of labels; obtaining a second set of images, each of the second set of images depicting a known product; obtaining a second set of labels associated with the second set of images; training an image classification neural network based on the second set of images and the second set of labels; receiving a query image depicting a product that is not yet identified; and performing image segmentation of the query image and identifying the product in the image by performing image analysis.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.

Abstract: An example computer-implemented method includes receiving agricultural data records comprising a first set of yield properties for a first set of seeds grown in a first set of environments, and receiving genetic feature data related to a second set of seeds. The method further includes generating a second set of yield properties for the second set of seeds associated with a second set of environments by applying a model using the genetic feature data and the agricultural data records. In addition, the method includes determining predicted yield performance for a third set of seeds associated with one or more target environments by applying the second set of yield properties, and generating seed recommendations for the one or more target environments based on the predicted yield performance for the third set of seeds. In the present example, the method also includes causing display of the seed recommendations.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically assorting merchandise. For example, a method may include receiving or generating a plurality of cells defined by one or more dimensions, receiving a total number of resources for allocation to the plurality of cells, allocating the total number of resources among the plurality of cells, determining one or more stock keeping units for each cell, determining a quantity of each of the one or more stock keeping units in each cell, assigning one or more launch dates for each of the one or more stock keeping units for each cell, and stocking the one or more inventories with one or more articles corresponding to the stock keeping units in each cell, based on the determined quantity and the assigned one or more launch dates of each of the stock keeping units in each cell.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for generating recommendations regarding products. A method may include determining a set of content features including one or more product attributes; determining a set of latent features; receiving a query user identifier and a query product identifier; determining a feature vector associated with the query user identifier and the query product identifier based on the set of content features and the set of latent features; determining one or more model coefficients for a linear model; and utilizing the linear model to determine a fit score for the query user identifier and the query product identifier.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for generating recommendations regarding products. A method may include determining a set of content features including one or more product attributes; determining a set of latent features; receiving a query user identifier and a query product identifier; determining a feature vector associated with the query user identifier and the query product identifier based on the set of content features and the set of latent features; determining one or more model coefficients for a linear model; and utilizing the linear model to determine a fit score for the query user identifier and the query product identifier.

Abstract: An example computer-implemented method includes receiving a plurality of agricultural data records including yield properties of one or more products grown in a given field and continuous data indicative of multiple raw field features and specific to the given field. The method also includes transforming the raw field features into distinct feature classes and generating, using data from the plurality of agricultural data records and the distinct feature classes, genomic-by-environmental relationships between the one or more products. Further, the method includes generating, based at least in part on the genomic-by-environmental relationships, predicted yield performance for a set of products associated with one or more target environments, generating product recommendations for the one or more target environments based on the predicted yield performance for the set of products, and providing one or more instructions configured to cause display of the product recommendations.

Abstract: Systems and methods are provided for managing hybrid seeds for planting. One example computer-implemented method includes receiving a first dataset of hybrid seeds for planting on a target field, where the first dataset includes probability of success values and historical agricultural data for the hybrid seeds, and selecting a subset of hybrid seeds of the first dataset based on the probability of success values. The method also includes generating representative yield values for the subset of hybrid seeds based on the historical agricultural data, generating risk values for the subset of hybrid seeds based on the historical agricultural data, and generating a second dataset of hybrid seeds for planting based on the risk values, the representative yield values, and properties for the target field. The method further includes causing displaying the representative yield values and the risk values related to the second dataset of hybrid seeds for planting.

Abstract: An example computer-implemented method includes receiving agricultural data records comprising a first set of yield properties for a first set of seeds grown in a first set of environments, and receiving genetic feature data related to a second set of seeds. The method further includes generating a second set of yield properties for the second set of seeds associated with a second set of environments by applying a model using the genetic feature data and the agricultural data records. In addition, the method includes determining predicted yield performance for a third set of seeds associated with one or more target environments by applying the second set of yield properties, and generating seed recommendations for the one or more target environments based on the predicted yield performance for the third set of seeds. In the present example, the method also includes causing display, on a display device communicatively coupled to the server computer system, the seed recommendations.

Abstract: An example computer-implemented method includes receiving a plurality of agricultural data records including yield properties of products grown in fields and raw field features of the fields. The method also includes transforming the raw field features into distinct feature classes that characterize key features affecting yield of the one or more products, and generating, using data from the plurality of agricultural data records and the distinct feature classes, genomic-by-environmental relationships between one or more products, yield properties of the one or more products, and field features associated with the one or more products.

Abstract: Techniques are provided for generating target success group of hybrid seeds for target fields include a server receiving agricultural data records that represent crop seed data describing seed and yield properties of hybrid seeds and first field geo-location data for agricultural fields where the hybrid seeds were planted. The server receives second geo-locations data for target fields where hybrid seeds are to be planted. The server generates a dataset of hybrid seed properties that include yield values and environmental classifications for hybrid seeds and then a dataset of success probability scores that describe the probability of a successful yield on the target fields based on the dataset of hybrid seed properties and the second geo-location data. The server generates target success yield group of hybrid seeds and probability of success values based on success probability scores and a yield threshold. The server causes display of the target success yield group.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically assorting merchandise. For example, a method may include receiving or generating a plurality of cells defined by one or more dimensions, receiving a total number of resources for allocation to the plurality of cells, allocating the total number of resources among the plurality of cells, determining one or more stock keeping units for each cell, determining a quantity of each of the one or more stock keeping units in each cell, assigning one or more launch dates for each of the one or more stock keeping units for each cell, and stocking the one or more inventories with one or more articles corresponding to the stock keeping units in each cell, based on the determined quantity and the assigned one or more launch dates of each of the stock keeping units in each cell.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for generating recommendations regarding products. A method may include determining a set of content features including one or more product attributes; determining a set of latent features; receiving a query user identifier and a query product identifier; determining a feature vector associated with the query user identifier and the query product identifier based on the set of content features and the set of latent features; determining one or more model coefficients for a linear model; and utilizing the linear model to determine a fit score for the query user identifier and the query product identifier.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically assorting merchandise. For example, a method may include receiving or generating a plurality of cells defined by one or more dimensions, receiving a total number of resources for allocation to the plurality of cells, allocating the total number of resources among the plurality of cells, determining one or more stock keeping units for each cell, determining a quantity of each of the one or more stock keeping units in each cell, assigning one or more launch dates for each of the one or more stock keeping units for each cell, and stocking the one or more inventories with one or more articles corresponding to the stock keeping units in each cell, based on the determined quantity and the assigned one or more launch dates of each of the stock keeping units in each cell.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for generating recommendations regarding products. A method may include determining a set of content features including one or more product attributes; determining a set of latent features; receiving a query user identifier and a query product identifier; determining a feature vector associated with the query user identifier and the query product identifier based on the set of content features and the set of latent features; determining one or more model coefficients for a linear model; and utilizing the linear model to determine a fit score for the query user identifier and the query product identifier.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for analysis of images including wearable items. For example, a method may include obtaining a first set of images, each of the first set of images depicting a product; obtaining a first set of labels associated with the first set of images; training an image segmentation neural network based on the first set of images and the first set of labels; obtaining a second set of images, each of the second set of images depicting a known product; obtaining a second set of labels associated with the second set of images; training an image classification neural network based on the second set of images and the second set of labels; receiving a query image depicting a product that is not yet identified; and performing image segmentation of the query image and identifying the product in the image by performing image analysis.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.