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 generating personalized product-type metrics for the user based at least in part on a user embedding for the user and product-type embedding Gaussian mixture distributions; determining top product types based at least in part on personalized product-type complementarity metrics generated using the personalized product-type metrics and cosine similarity measurements; generating a set of first items associated with the top product-types; ranking each respective item in the set of first items generated using an item-level embedding Gaussian distribution for the anchor item and a respective item-level embedding Gaussian distribution for the each respective item; and selecting a set of top items as the personalized complementary item recommendations based on the ranking. Other embodiments are disclosed.

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: generating linkage scores between nodes at least based on a machine learning model; creating links between the nodes to form connected components based on the linkage scores exceeding a predetermined threshold; generating an actual matching linkage set of the nodes linked in the connected components by using a relaxed blocking criteria; and generating a quality score for the connected components. Other embodiments are disclosed.

Abstract: A method including obtaining information about a trailer that has been partially loaded with preloaded stacks in a manner that deviates from an original load design. The trailer is loaded with stacks of pallets including (i) the preloaded stacks that have already been loaded in the trailer and (ii) unloaded stacks that have not yet been loaded into the trailer. The method also can include determining positions of empty floor spots remaining in the trailer. The method additionally can include determining a first portion of an incremental load design for the unloaded stacks using a gap-filling pattern behind an uneven rear edge of the preloaded stacks in the trailer. The method further can include determining a second portion of the incremental load design.

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: segmenting each respective user of one or more users into a respective user state of one or more respective user states; predicting, using a machine learning LTV update model, a respective LTV for each respective user of the one or more users; determining a respective transition path from the respective user state of the respective user to a different respective user state; determining a respective online advertisement to transition a respective user of the one or more users from a respective transition state to the different respective user state; and displaying the respective online advertisement for the respective user of the one or more users. Other embodiments are disclosed herein.

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: Systems and methods for improved Spoken Language Understanding (“SLU”) are provided. The methods may comprise receiving an utterance from a user, contextualizing a plurality of words in the utterance, providing the contextualized words to the slot detector to determine the probability of a word forming the beginning or end of a slot to determine slots and nested slots, an intent classifier to determine the probability of a word conveying a user intent, and a slot classifier that applies specific labels to each slot and nest slot. The SLU method may employ a model and jointly trains the model for each task (determining beginning and end of slots, intents, and slot classifications) using a combined loss function.

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, from one or more electronic devices of one or more users, one or more orders for pickup at a location during a first window of time; at a first time during the first window of time, determining when a designated pickup area at the location exceeds a predetermined level of congestion using: (1) continuously received mobile check-ins at the location; and (2) congestion data of the designated pickup area at the location; and when the designated pickup area at the location does not exceed the predetermined level of congestion during the first window of time, as determined at the first time, causing a pickup electronic device of a pickup to display a pickup request at the location to fulfill at least one order of the one or more orders during the first window of time. Other embodiments are disclosed herein.

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 acts of receiving one or more digital images; identifying a foreground of the one or more digital images; analyzing the foreground of the one or more digital images to identify a skin region in the foreground of the one or more digital images; when the skin region is identified, clustering a non-skin remainder of the foreground of the one or more digital images into one or more clusters; extracting one or more patches of the one or more digital images from the one or more clusters of the foreground of the one or more digital images; determining one or more scores for the one or more patches of the one or more digital images; and coordinating displaying a patch of the one or more patches on an electronic display based on the one or more scores for the one or more patches. Other embodiments are disclosed herein.

Abstract: A method including training a recurrent neural network model to create a trained model based at least in part on: (a) first images associated with first items on a website, (b) first search terms used by users of the website to search for the first items on the website, and (c) personal features of the users. The method also can include receiving an input image that was uploaded by a current user. The method additionally can include obtaining a user encoded representation vector for the current user based on a set of personal features of the current user. The method further can include generating an image encoded representation vector for the input image. The method additionally can include deriving search terms that are personalized to the current user for the one or more items depicted in the input image, using the trained model and based on the user encoded representation vector for the current user and the image encoded representation vector for the input image. Other embodiments are disclosed.

Abstract: This application relates to systems and methods for automatically generating fraud strategies. A system may comprise customer computing devices, a database, and retailer computing device(s). The retailer computing device(s) are configured to receive data related to customer transactions, calculate a negative reaction score, negative resolution score, anomaly score, and impact score for at least one value of the received data, determine whether the calculated scores are greater than or equal to respective thresholds, identify a value of the received data as a potentially fraudulent data value when one or more of the calculated scores is greater than or equal to its respective threshold, generate an output comprising a list of potentially fraudulent data value(s) and impact scores, identify area(s) of high impact leaks based on the output, and determine which customer computing devices are executing fraudulent transactions based on the identification of areas of high impact leaks.

Abstract: Systems and methods for facilitating computer-vision-based detection and identification of consumer products comprise a support surface for supporting thereon one or more consumer products to be detected and a plurality of digital cameras each arranged at a different height relative to the support surface and positioned such that a field of view of each of the digital cameras includes the consumer product(s) located on the support surface. Movement of the support surface and/or the digital cameras permits the digital cameras to capture images of the consumer product from different angles of view and up to a full 360 degree of rotation around the at least one consumer product. A computing device uses at least the image data from such images to train a computer vision consumer product identification model to generate a reference image data model to be used for recognition of the at least one consumer product.

Abstract: Systems and methods for generating and implementing reusable interface components is disclosed. A schema is loaded from a repository. The schema defines one or more containers configured to receive a component therein. At least one component is selected for insertion into a first container defined by the schema. The at least one component includes a frontend portion and a backend portion. The fronted portion defines at least one customization of the component. The at least one customization of the at least one component is defined and the at least one component is implemented using a predetermined decoding process.

Abstract: This application relates to systems and methods for facilitating online search based on offline transaction data.

Abstract: In some examples, a system may include a first computing device communicatively coupled to a second computing device. Additionally, the first computing device is configured to obtain, from the second computing device, check-in data indicating an arrival of the user of the second computing device at a first location, and in response to obtaining the check-in data, determine current wait times. Moreover, the first computing device is configured to determine a first number of customers waiting for service, determine a first number of associates available to assist the first number of customers, and determine an expected wait time for the user operating the second computing device based at least on the current wait times. In some examples, the first number of customers waiting for service, and the first number of associates available. Further, the first computing device is configured to transmit the expected wait time to the second computing device.

Abstract: This application relates to apparatus and methods for determining data outputs to advertise on a platform such as a website. A computing device receives a website search request and determines a search term keyword. The computing device also determines a plurality of item accounts, such as sponsor campaigns, based on the search term keyword and a corresponding keyword of each item account. Each item account also includes a corresponding data output, such as a digital advertisement. The computing device identifies one of the item accounts based on determining an engagement probability for the digital advertisement of each item account. The engagement probability is determined based on aggregated impression and engagement data for the digital advertisement. The computing device provides the digital advertisement for the identified item account to a server, which may display the digital advertisement on the platform.

Abstract: A disclosed system for transport asset monitoring, for example monitoring truck trailer unloading progress at large retail locations, includes an artificial intelligence (AI) solution for managing operations at a facility. The AI solution analyzes current load percentage and other data to predict availability for moving the transport asset and ability to accept a new incoming transport asset. Predictions of availability can reduce response times, resulting in higher utilization rates for assets, thereby improving efficiency. An exemplary system includes a sensor configured to sense operation progress parameter data for a transport asset; and logic to receive the operation progress parameter data from the sensor; determine, using the AI solution and based at least on the operation progress parameter data, a predicted milestone parameter; and report the predicted milestone parameter to a remote node.

Abstract: Approaches are provided that determine and fine-tune product assortments carried by retail stores. In aspects, the number of stores that carry the item, the sales performance of the item, the loyalty of customers to the item, whether substitutes for the item can be found, and other factors are used to determine an actions such as increasing or decreasing the shelf space for an item.

Abstract: Systems and methods are provided herein for producing and vending ice in an energy-efficient manner. A system for producing and vending ice comprises: an ice-making device configured to manufacture ice; a reservoir coupled to the ice-making device and configured to store the manufactured ice manufactured; and a control circuit communicatively coupled to the ice-making device, the control circuit configured to: determine a beginning ice inventory at a first time; determine a predicted ice demand for a first period, wherein the predicted ice demand is a function of at least a historical factor, a weather factor, a customer social event factor, and a public social event factor; determine an ice manufacturing quantity based on the beginning ice inventory, the predicted ice demand, and a manufacturing capacity of the ice-making device; and cause the ice-making device to produce an amount of ice consistent with the determined ice manufacturing quantity.