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 perform: inputting one or more pairs of digital images into a neural network; determining, using the neural network, a respective contrastive loss for each respective pair of digital images of the one or more pairs of digital images; receiving one or more new digital images; and determining, using the neural network, the one or more new digital images and the respective contrastive loss, at least one image to which the one or more new digital images is similar. Other embodiments are disclosed herein.

Abstract: There are provided systems and methods for the purchase of age-restricted merchandise without requiring the transmission of biometric information. In one form, the system includes: age-restricted merchandise items offered for sale to customers, a server that receives orders from customer devices, and a customer database containing a plurality of customer records.

Abstract: A control circuit obtains a first set of rules that define a conveyability range for containers as a function of conveyability constraints pertaining to an intermediary distribution facility. The control circuit also accesses conveyability constraint information for the intermediary distribution facility and generates a conveyability range for that facility by evaluating the constraint information against the first set of rules. The control circuit obtains a second set of rules that define an objective function that uses the conveyability range as a constraint to determine an optimal container pack size for the given item. (By one approach these teachings will also accommodate using at least two additional constraints, such as a so-called days-of-supply (DOS) constraint and a so-called pack-and-a-half constraint.) The control circuit then generates an optimal container pack size for the given item by evaluating the objective function against the conveyability range (and other selected constraints).

Abstract: In some embodiments, apparatuses and methods provide long term scheduling at a plurality of retail stores including storing a forecasted data over at least eight weekly scheduling periods; determining based on the forecasted data a first baseline hours demand value for each day of a week for each job function of a first set of job functions at a particular retail store; determining based on the forecasted data a second baseline hours demand value for each day of the week for each job function of a second set of job functions at the particular retail store; based on the first and second baseline hours demand values, automatically generating a plurality of fixed schedules for a set of task resources at each of the plurality of retail stores; accessing a first fixed schedule associated with the first task resource; and displaying the first fixed schedule.

Abstract: A universal gripper is disclosed for transferring totes and sub-totes, for example in an order fulfillment center. The universal gripper includes adjustable tines and pins capable of being positioned to engage within slots in the different configurations of totes and sub-totes.

Abstract: In some examples, a system may include a memory resource storing instructions, and one or more processors. In various examples the one or more processors may be configured to execute the instructions to receive data indicating that an order has been dropped by a driver of a first computing system. The order may include a set of order parameters that may include a first order parameter and a second order parameter. Additionally, the one or more processors may be configured to execute the instructions to determine a remaining delivery time period based at least a time when the order was dropped and the first order parameter. Moreover, the one or more processors may be configured to execute the instructions to obtain profile data of a set of drivers that includes availability data indicating the corresponding driver is available for assignment.

Abstract: Systems and methods are disclosed for brand and license hierarchy management to improve brand governance. A hierarchal data structure permits tracking of products, associating items separately with brands and licenses, so that a subset of items associated with a particular brand may have differing license associations. The hierarchal structure advantageously facilitates nesting of primary and secondary (or more) brands and licenses to provide for more reliable automated royalty payment management that can reduce risks of missing payments or making unnecessary duplicate payments. A factory tracking functionality assists compliance with regulatory and ethical sourcing requirements to help reduce reputational and financial risks, for example with private brands, exclusive brands, and imported 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 operations comprising: identifying a segment of users who are value conscious about a product by: evaluating a number of activities of the users indicating whether or not the users are value conscious for the product; and generating, using a conditional probability equation, a probability that a user of the users will show interest in the product; and transmitting instructions to display, for viewing by the user, a recommendation for the product, wherein the recommendation is based on the probability being above a threshold that the user will show interest in the product. Other embodiments are disclosed herein.

Abstract: A method including determining a preferred communication channel for a user by: (a) determining a respective normalized propensity score for each subscribed communication channel to which the user subscribes, based at least in part on a respective propensity score for the each subscribed communication channel; and (b) determining the preferred communication channel from among subscribed communication channels based on the respective normalized propensity score. The method further can include transmitting, via the preferred communication channel, a message to a user device of the user. Other embodiments are disclosed.

Abstract: An apparatus is disclosed including a robotic system having a robotic picking workstation, tote storage and retrieval and transit decks. The system has bots that autonomously transport totes from the storage and retrieval system to the robotic picking workstation via the transit decks. The robotic picking workstation may have a picking lane where a robotic handler transports eaches from totes on the bots to order totes in the workstation. The robotic picking workstation further has a queuing buffer where bots are cued for the picking lane.

Abstract: A light includes a hub and an arm extending radially outward from the hub. The arm includes a light source configured to emit light from the arm. The arm is rotatably connected to the hub such that the arm is configured to rotate about a longitudinal axis of the hub between an extended angular position and a folded angular position. The arm extends radially outward from the hub in a different radial direction in the extended angular position as compared to the folded angular position.

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 perform: receiving a request to generate one or more schedules for one or more drivers; determining one or more respective day cab schedules for each respective day cab driver of the one or more drivers; assigning one or more permanent drivers of the one or more drivers to one or more permanent tractors; assigning at least one driver of one or more remaining drivers of the one or more drivers to at least one tractor using a first set of rules; generating the one or more schedules for the one or more drivers; and coordinating displaying the one or more schedules for the one or more drivers on an electronic device of a user. Other embodiments are disclosed herein.

Abstract: This application relates to apparatus and methods for automatically determining and providing prices for items for sale in stores or online such as to clear inventor of the items. In some examples, a computing device employs artificial intelligence, such as machine learning models, to determine the pricing of the items. For example, the computing device may employ a forecasting model that determines a forecasted demand for an item based on store inventory and experimental sales of the item. The computing device may also employ an item pricing optimization model that determines a clearance price for the item based at least on the forecasted demand as well as historical data indicating previous price adjustments and sales of the item. In some examples, the item pricing optimization model determines a time period to apply the clearance price to the item. The item may then be placed on sale for the clearance price.

Abstract: Examples provide an electronic shelf label (ESL) removal tool for removing an electronic shelf label from a shelf. The tool includes a central body member which is substantially cylindrical. A gripping member is included at a first end of the central body enabling a user to grip the tool. An expansion member at a second end of the central body slides between a shelf rail to expand or increase a distance between the rails. The tool enables the user to remove the ESL from between the rails more easily. A wedge-shaped tab at the second end of the central body fits between one edge of the shelf rail and the edge of the ESL to pry the ESL out from between the rails.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to automatically annotating images. In some embodiments, a system for automatically annotating images comprises a database, wherein the database is configured to store images and annotations for the images and a control circuit, wherein the control circuit is communicatively coupled to the database, and wherein the control circuit is configured to retrieve, from the database, an image, generate, based on the image, a collection of augmented images, generate segmentation maps for each image in the collection of augmented images, wherein each of the segmentation maps include segments, select, based on a threshold, ones of the segments above a threshold, merge the ones of the segments above the threshold to create a segmented image, and generate, for each segment of the segmented image, classifications, wherein an annotation for the image includes the segmented images and the classifications.

Abstract: The system captures portions of a label on a package in a set of images, reconstructs the label based on the set of images, identifies text in the label, determines associations of identified text and types of information, and stores the set of images, the reconstructed label, the identified text in the label, and the determined associations as, for example, a batch in a review queue. During a review process, the batch is reviewed and a structured prescription record is determined for the batch which is further used by the system and user of the system associated with the batch to provide various features to the user.

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 perform: receiving in-session user activity entered into on an initial graphical user interface (GUI) from a user electronic device of a user; pre-processing the in-session user activity to determine one or more intents of the in-session user activity; comparing the one or more intents of the in-session user activity with one or more complementary intents; and coordinating displaying a complimentary GUI on the user device of the user based on the one or more complementary intents. Other embodiments are disclosed herein.

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: generating, using one or more store signals, a lifecycle metric for each respective item sold in a respective storefront of multiple storefronts, wherein the multiple storefronts are located in one or more geographic locations, and wherein each respective item is concurrently listed online via a website; determining offer listing data for the respective item; generating an offer listing metric by reconciling, using a reconciliation algorithm, the offer listing data for the respective item with an offer store data; and transmitting the offer listing metric to user interfaces, wherein the offer listing metric indicates that the respective item is available for sale at the respective storefront. Other embodiments are disclosed.

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 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 ad request. The method also can include retrieving ad candidates from an ad database. The method further can include determining a respective ad ranking score for each of the ad candidates, based at least in part on the ad request and respective historical retrieval scores for each of the ad candidates.