Abstract: In some embodiments, apparatuses and methods are provided herein useful to processing captured images. In some embodiments, there is provided a system for processing captured images of objects at a product storage facility including a trained machine learning model; and a control circuit. The control circuit may identify a product identifier associated with an object in a captured image; generate predicted product identifiers associated with the object in the captured image based on text identified from the object in the captured image; aggregate the predicted product identifiers; determine a feature of the objects associated with the aggregated predicted product identifiers; determine one or more confusing product identifiers based on a determination of the aggregated predicted product identifiers being associated with the feature; and update a dataset with at least one of the one or more confusing product identifiers and images associated with the one or more confusing product identifiers.

Abstract: Provided are check-in to checkout systems and methods. The system includes a point of sale, a display, a mobile device, and a backend system. When the POS starts a transaction, such as, but not limited to scanning an item, the POS generates a transaction/basket identification. In response to starting the transaction, the POS sends the transaction/basket ID to the display in the form of a QR code or one-dimensional barcode. The mobile device operating a mobile application scans and captures the displayed code and reports to the backend systems. The backend systems then send a check-in event, which includes gathered data by the backend systems, to the POS and the POS acknowledges receipt of the message. At this point, a notification may be sent to the mobile application that the check-in was successful and a link is established for flow of information.

Abstract: Systems and methods for automatically generating electronic communications including relevant content elements are disclosed. A request to generate an electronic communication including a user identifier and a prior interaction is received. A hybrid sequential model is implemented to generate a set of hybrid sequential element recommendations selected from a catalog of items. The hybrid sequential model includes a sequential sub-model to select a set of sequential elements and a re-ranking sub-model to re-rank the sequential elements to generate the hybrid sequential elements. An affinity model is implemented to generate affinity element recommendations and a set of relevant content elements including at least one of the set of hybrid sequential element recommendations and at least one of the set of affinity element recommendations is generated. The electronic communication is generated including the set of relevant content elements and is transmitted to a user device associated with the user identifier.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to detecting a mis-scan of an item. In some embodiments, there is provided a system for detecting a mis-scan of an item for purchase comprising a checkout station; a first staging location; a second staging location; a first area of interest at the checkout station; a second area of interest at the checkout station; a camera; and a control circuit configured to: receive an identifier of a first item; detect a hand of a user purchasing the first item, the first item, and a scanner on a first image captured by the camera; in response to the detection of the hand, the first item, and the scanner, initiate detection of mis-scan items during a checkout process; determine that a payment transaction has been received; and stop the detection of mis-scan items.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions, that when executed on the one or more processors, cause the one or more processors to perform: receiving stock images comprising an anchor garment; automatically identifying the anchor garment and complementary garments within the stock images; selecting an image of the stock images in which a mask area of a first complementary garment of the complementary garments as a ratio of an area of the anchor garment is largest over other complementary garments of the complementary garments; performing an image search, using the image, in an item catalog for similar garments to the first complementary garment; and displaying, on a user interface, an avatar wearing the anchor garment and at least one of the similar garments. Other embodiments are disclosed.

Abstract: Examples provide conversational item search refinement using multi-facet filtering of items in a catalog. A multi-facet filter manager extracts facets and actions corresponding to the facets from a user utterance. The facet-actions include an entity-role and one or more filter actions associated with the facets. A facet-action includes filter actions such as exact, exclude, greater than, less than, etc. Multi-facet filters corresponding to the facet-actions are applied to a plurality of items in the catalog. The candidate items remaining after filtering are scored. The scores indicate relevance of each candidate item. One or more of the candidate items with the highest scores are selected. The selected items are added to search results which are returned to the user in response to the conversational search query. The multi-facet filter manager enables faster and more accurate search results using fewer conversational turns for reduced system resource usage and improved user efficiency.

Abstract: Systems and methods for hybrid optimization of training ranking models is disclosed. A training dataset including a plurality of anchor items, a plurality of recommended item sets, and ground truth data is obtained from a database. A base machine learning model including a step function configured to determine a relevance score is iteratively trained to generate a trained ranking model. The plurality of anchor items and the plurality of recommended item sets are provided as an input to the base machine learning model and the ground truth is provided as a target output. The step function is trained using an adaptive step size according to a first order Barzilai-Borwein (BB) method and a line search method. The trained ranking model is stored in non-transitory memory.

Abstract: Some embodiments provide retail product ordering systems comprising: a user computing device comprising an application executed by a device control circuit to: receive an audible utterance; controls a product identifier application interface to: apply a tokenizer model and obtain a set of individual search words; apply a series of featurizer models to the search words to generate features; and apply a classifier and extractor model based on the features and generate multiple requested product entities each comprising a respective sub-set of the position labeled product terms; wherein the device control circuit is further configured to access a purchase history database, confirm an accuracy of each of requested product entities relative to a purchase history, generate a listing of determined product identifiers corresponding to the confirmed set of the multiple requested product entities, and control a display system of the user computing device to render the listing of determined product identifiers.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: training, using labeled training data and a list of substitutes for an item, a machine learning algorithm; determining, using the machine learning algorithm, as trained, a respective similarity score for each substitute of the list of substitutes; ranking each substitute of the list of substitutes based on its respective similarity score; and re-training the machine learning algorithm based on at least the labeled training data and a highest ranked substitute of the list of substitutes. Other embodiments are disclosed herein.

Abstract: A computerized method trains an inventory anomaly prediction model and uses the model to predict and address inventory anomalies. Daily inventory data associated with an inventory storage location is obtained from an inventory data store and provided to a pretrained anomaly prediction model as input. The anomaly prediction model generates an anomaly indicator associated with an item type in the obtained inventory data. Corrective action is then caused to be performed in association with the item type and based on the anomaly indicator, whereby inaccuracy of an inventory count of the item type in the inventory data store is corrected. Further, in some examples, the anomaly prediction model is trained using machine learning techniques and training data that is generated and labeled using a tuned outlier detection model. Thus, the method enables the anomaly prediction model to be efficiently trained for accuracy using the described two-stage training process.

Abstract: There are provided systems and methods relating to reconciling lottery transactions. In one form, the system includes point-of-sale (POS) systems at retail store for inputting of first lottery transaction data for lottery transactions occurring at the retail stores in which the lottery transactions correspond to lottery games being operated by a third party. It includes a POS server for extracting the first lottery transaction data and a retailer server for receiving second lottery transaction data from a server of the third party. Further, it includes a control circuit that conducts one-to-one matching of entries of the two data sets, determines whether a discrepancy exists based on the one-to-one matching, conducts one-to-many matching by iteratively aggregating different combinations of entries of one data set and comparing them with individual entries of the other data set, and determines whether a discrepancy exists based on the one-to-many matching.

Abstract: Example implementations may relate to assignments. For example, a computer-implemented method may include receiving work orders and technician data. The computer implemented method can further include assigning the work orders to technicians using mixed integer linear programming comprising determining a solution to an objective function that balances objectives and constraints using an optimizer, and assigning the work orders to the technicians based on the solution. The computer-implemented method can additionally include after assigning the work orders to the technicians, scheduling the work orders. Other embodiments are described.

Abstract: A method can be implemented via execution of computing instructions configured to run at a processor and stored at a non-transitory computer-readable media. The method can include: determining one or more virtual categories associated with a search query by a user. The method can also include: classifying one or more search results for the search query into one or more category groups based on the one or more virtual categories. The method can further include: generating a respective user interface portion for each group of the one or more category groups. The method can additionally include: generating a user interface comprising the respective user interface portion for each group of the one or more category groups. The method can also include: transmitting, via a computer network, instructions to display the user interface on a user device for the user. Other embodiments are disclosed.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to displaying items for purchase. In some embodiments, there is provided a system for displaying items for purchase from a retailer including a control circuit and a retail application. In some embodiments the control circuit determines inventory items to include in a listing of selectable items for potential purchase; outputs signaling to cause a retail application to display the listing of selectable items; receives an indication of a customer selection; determines subsequent inventory items to include in an updated listing based on at least one of a corresponding dimensional space or a corresponding weight of each of the subsequent inventory items, and a remaining available storage capacity of a delivery container; and outputs subsequent signaling to cause the retail application to display the updated listing.

Abstract: A method can include determining, via a multi-channel text model, a text profanity score for a textual content for an item. The method further can include determining, via a vision model, a vision profanity score for an image content for the item. In addition, the method can include determining whether the text profanity score exceeds a text blocking score. Moreover, the method can include determining whether the vision profanity score exceeds a vision blocking score. The method further can include upon determining that the text profanity score exceeds the text blocking score or that the vision profanity score exceeds the vision blocking score, setting a blocking label for the item in an item database as blocked. Other embodiments are disclosed.

Abstract: In some implementations, a device may receive from a user device, a request. The device may determine, based on the request, a retrieval set that includes a plurality of candidate results for the request. The device may generate a plurality of request-result pairs based on the request and the retrieval set. The device may determine relevance outputs for the plurality of request-result pairs using a secondary model that is trained based on a primary model using: (1) an unlabeled dataset that is based on a candidate result set for a historical request, and (2) a margin-based loss function on all candidate result pairs of the candidate result set for the historical request. The device may transmit, to the user device and in accordance with the relevance outputs, data for presentation in a user interface of the user device based on the request.