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 functions including: receiving a respective item description and at least one respective attribute value for each item of a set of items; generating at least one respective text embedding; generating a graph of the set of items based on at least co-view data to create pairs of items that are co-viewed by joining respective pairs of items; training the text embedding model and a machine learning model using a neural loss function based on the graph; and automatically determining, using the machine learning model, as trained, a label for each item of the set of items. Other embodiments are disclosed.

Abstract: A check-in system for an dispensing items includes a computing device having an application installed thereon. The computing device is configured to check in a customer to notify the kiosk of a time range during which an order of the customer is picked up. The kiosk is configured to receive a check-in message from the computing device, receive the location of the computing device, receive a message indicative of the arrival of the customer at the automated tower, retrieve information of the order from an order database stored on a remote server, assemble the order based on the check-in message and the information of the order, and move the assembled order close to a pick-up location of the automated tower to facilitate pick-up of the assembled order.

Abstract: In some embodiments, systems and methods are provided a retail point-of-sale (POS) hardware management system, comprising: an intermediary system control circuit configured to execute computer program modules comprising a hardware management module configured to operate as an intermediary module between multiple different sets of client POS systems, a plurality of different sets of hardware systems configured to be engaged by the client POS systems, and a plurality of shared resource applications; the hardware management module comprising a plurality of hardware manager sub-modules each corresponding to one of the sets of hardware systems; each of the hardware manager sub-module is configured to output communications and receive communications in accordance with a single predefined hardware communications protocol, of a set of a plurality of predefined hardware communications protocols, corresponding to the set of hardware systems regardless of a type of hardware system or manufacturer.

Abstract: Some embodiments product identifying systems comprising: a scanner control circuit; an imaging system; a display; and a memory storing a product identifier module executed by the control circuit to: detect, through a series of images, a machine-readable identifier pattern; control the display to render the series of images on the display; activate a scan timer having a scan duration; render, on the display, a scan timer graphic overlaid on a subset of the series of images and corresponding to a changing remaining time of the scan duration as the remaining time expires; confirm that the identifier pattern was maintained within a threshold distance of the center of the field of view over at least a threshold read duration of the scan duration; extract identifying information; and obtain a product identifier corresponding to the identifying information and add the product identifier to a virtual shopping cart.

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 a feasible dock-out plan designed to deliver one or more orders to one or more physical stores based on a feasible route plan by: generating a preliminary dock-out time boundary based on one or more delivery time windows; determining an earliest dock-out time based at least in part on the preliminary dock-out time boundary and a backward search; and determining a latest dock-out time based at least in part on the preliminary dock-out time boundary and a forward search; and transmitting a range of dock-out times comprising (i) the earliest dock-out time and (ii) the latest dock-out time to a user. Other embodiments are described.

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: A computerized method, system and computer program product may comprise: generating or receiving a search result in response to a user query by a user that searches for an item, the search result depicting the item; and modifying the search result by generating a combination image that depicts user-defined subject matter engaging with the item. The present invention may provide other and/or additional benefits or advantages.

Abstract: A method including extracting queries from historical search query data. The method also can include spellchecking each of the queries (i) using a first spellcheck model and (ii) using a second spellcheck model. A latency of the first spellcheck model is lower than a latency of the second spellcheck model, and an overall accuracy of the second spellcheck model is higher than an overall accuracy of the first spellcheck model. The method additionally can include determining first queries of the queries in which, for each of the first queries, a first respective spellcheck output from the first spellcheck model is different from a second respective spellcheck output from the second spellcheck model. The method further can include adding at least a portion of the first queries to a spellcheck cache for runtime spellchecking. Other embodiments are described.

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 historical driver search information corresponding to a first offer publish time criterion, the first offer publish criterion including a driver lag time; building a machine learning model based on the driver search information to determine a first metric and a second metric; analyzing the first metric and the second metric with an optimization model to determine a second offer publish time criterion that reduces the driver lag time; receiving an order for a delivery for an item, the order including a delivery time window; transmitting the order to a driver search platform subject to the second offer publish time criterion to reduce the driver lag time and mitigate delivery outside of the delivery time window. Other embodiments are disclosed herein.

Abstract: A computer-implemented method including determining a feature-embedding vector for a listing item based on textual feature data and imagery feature data for the listing item. The method also can include determining, via a machine learning module, an intellectual property infringement prediction associated with a genuine item based on a feature-embedding vector for the genuine item and the feature-embedding vector for the listing item. Furthermore, the method can include upon determining that the intellectual property infringement prediction is positive, causing a take-down of the listing item from a retailer platform. Other embodiments are described.

Abstract: A method can include upon receiving, from a computer network, a conversational input from a user device for a user, determining a context based on one or more contextual units. The one or more contextual units can be associated with immediate prior one or more conversational inputs relative to the conversational input. The method further can include determining an intent associated with the conversational input based on the context. Moreover, the method can include determining one or more entities associated with the conversational input based on the context and one or more expected entities determined based on one or more predefined conversation flows. The method additionally can include determining an output based on the intent and the one or more entities. The method also can include transmitting, via the computer network, the output to be displayed on the user device. Other embodiments are disclosed.

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: obtaining a first load plan, wherein the first load plan comprises a set of stacks assigned to floor spots of a trailer; determining that at least one of (a) there is an empty floor spot in the first load plan for the trailer or (b) the first load plan is infeasible; determining a target number of stacks for an updated load plan; iterating, via simulated annealing, through modifications to the updated load plan; terminating the iterating when a predetermined termination criteria is satisfied; and outputting the updated load plan. Other embodiments are disclosed.

Abstract: Systems and methods 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: receiving historical engagement information for products in a marketplace; clustering a first subset of the products based on at least one clustering criterion and based on a set of attributes; identifying a second subset of the products that are similar to the first subset of the products based on at least one similarity criterion; determining a third subset of the products by filtering the second subset of the products based on the historical engagement information for the second subset of the products; determining at least one benchmark for the first subset of the products based on the historical engagement information for the first subset of the products; and determining a lift score for the third subset of the products based on the at least one benchmark.

Abstract: A computer-implemented method including determining an anchor product type for an anchor item. The method further can include determining at least one associated product type for the anchor product type.

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 functions relating to detecting trending search queries that are popular in a short-term time period. User engagement metrics are derived from historical search engine data. A trending analysis function executes statistical analyses on the user engagement to identify the trending search queries. Other embodiments are disclosed herein.

Abstract: A method can include determining, via a translatability classifier module, a translatability class label for a cross-lingual search query received, via a computer network, from a user device for a user. The translatability classifier module can be trained to determine the translatability class label among multiple translatability class labels associated with user intentions for the cross-lingual search query based on a respective probability of the cross-lingual search query being associated with each of the multiple translatability class labels. The method further can include determining, via a language translator module, a class-associated search query for the cross-lingual search query based on the translatability class label, as determined. The method additionally can include transmitting, via the computer network, the class-associated search query to a monolingual search engine. Other embodiments are disclosed.

Abstract: A method including receiving a request from a user to view a page. The page is one of a search results page or a browse shelf page. The method also can include obtaining a respective brand affinity score for the user for each of one or more product types associated with the request. The method additionally can include generating a respective brand affinity signal for the user for each respective item in a baseline list of items to be displayed on the page, based on the request and the respective brand affinity score for the user for a product type of the one or more product types associated with the respective item. The method further can include generating a reranking of the items to be displayed on the page, based on a machine learning model and based on factors comprising the respective brand affinity signals for the user for the items and other rerank signals. The method additionally can include outputting the reranking of the items. Other embodiments are described.

Abstract: A system including one or more processors and one or more non-transitory computer-readable storage devices storing computing instructions that, when run on the one or more processors, cause the one or more processors to execute operations: storing image hashes corresponding to images for items offered via an electronic platform; receiving, from a computing device, a search query via a search engine included on the electronic platform; executing a relevance function; executing a visual diversity ranking function that reorders the relevant set of search results to generate a ranked set of search results, wherein the visual diversity ranking function utilizes the image hashes corresponding to the subset of the items to reorder the relevant set of search results; and transmitting the ranked set of search results to the computing device. Other embodiments are disclosed herein.

Abstract: A computer-implemented method including detecting respective one or more text boxes in each of multiple partial images of a text-bearing area. The method also can include determining respective one or more edge text boxes of the respective one or more text boxes in each of overlapping partial images of the multiple partial images, wherein each of the respective one or more edge text boxes comprise a respective incomplete text. The method additionally can include matching one or more pairs of corresponding edge text boxes from the respective one or more edge text boxes of two adjacent images of the overlapping partial images of the multiple partial images. The method also can include determining cross-image texts in the one or more pairs of the corresponding edge text boxes.

Abstract: Systems and methods 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: receiving historical marketplace information for a user in a marketplace corresponding to products previously purchased by the user; processing the products to group the products into one or more product-type clusters; analyzing the one or more product-type clusters to determine respective inter-purchase interval (IPI) likelihood scores for each product in each of the one or more product-type clusters; identifying one or more candidate products from the one or more product-type clusters that have respective IPI likelihood scores that satisfy one or more thresholds; determining a respective time and a respective duration for a respective re-purchase notification for the user based on the respective IPI likelihood score for each of the one or more candidate products; ranking the o