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 truck load configuration information corresponding to a number of items to be positioned in one or more trucks based on a delivery to be transported from a first location to a second location; analyzing, using a load generation simulator, the truck load configuration information to determine a first simulated configuration for the number of items in the one or more trucks; analyzing the first simulated configuration for the number of items in the one or more trucks to determine whether the first simulated configuration is feasible; in response to determining the first simulated configuration is not feasible, analyzing the truck load configuration information using a load shrinking model or a load expanding model to determine a second simulated configuration for the num

Abstract: A method including determining, via a machine learning model, a predicted energy load profile for a facility based at least in part on weather forecast data and sensor data for the facility. The sensor data can be received from one or more energy monitoring sensors for one or more devices in the facility. The method further can include determining one or more demand shedding time slots based at least in part on peak periods and the predicted energy load profile. Moreover, the method can include determining one or more demand shedding events for the one or more devices to be scheduled during the one or more demand shedding time slots. The method additionally can include causing a respective performance of each of the one or more demand shedding events by the one or more devices during the one or more demand shedding time slots. 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 operations: generating, using at least two different search algorithms, combined search results based on a search query, each search result of the combined search results having a respective rank based on at least one of the at least two different searching algorithms; determining a set of search results from the combined search results, wherein each result within the set of search results was respectively interacted with by at least one user; and determining a traffic impact for each result rank in the set of search results. Other embodiments are described 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 functions comprising: receiving, by an application programing interface (API), a change request for a route plan, wherein the change request is subject to a scenario specification and a load feasibility specification; determining an updated route plan based on whether the change request for the route plan is a feasible route based on the scenario specification and the load feasibility specification, and the route stop time is the route start time is flexible; and outputting the updated route plan and, when the route stop time is flexible, outputting the route start time. Other embodiments are disclosed.

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 functions comprising: generating pairs of identities from a plurality of sources; for each respective pair of identities of the pairs of identities: determining a match probability for the respective pair of identities using a deep-learning transformer-based binary classification model; and linking the respective pair of identities as nodes on a graph when the match probability meets a predetermined threshold, wherein a linkage between the nodes represents a match for the respective pair of identities; generating, using a connected component algorithm, clusters each containing identities representing a respective user; and generating a respective user profile for the respective user for each cluster. Other embodiments are disclosed.

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 first set of facet information corresponding to a first marketplace; receiving a second set of facet information corresponding to a second marketplace; analyzing the first set of facet information to reduce a number of attributes to process; building a machine learning model to map candidate facets from the first set of facet information to facets from the second set of facet information; training the machine learning model based on the first set of facet information, the second set of facet information, and the mapping of candidate facets from the first set of facet information to facets from the second set of facet information; and displaying a selected number of re-ranked facets based on an output from the machine learning model. 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 executed by the one or more processors can perform functions comprising: storing a category classification hierarchy that classifies items into a plurality of browse categories; monitoring user engagement metrics for each of the items; using the user engagement metrics to compute shelf importance signals for the items; executing a ranking engine that generates a ranked item listing for a browse category based, at least in part, on the shelf importance signals for the items; and transmitting the ranked item listing for the browse category to a computing device. Other embodiments are disclosed herein.

Abstract: A method including obtaining an optimization request at a coordinating engine. The method also can include triggering engines to process the optimization request. At least one of the engines divides the optimization request into subproblems. At least a portion of the engines solve the subproblems. Respective instances of at least one of the engines are triggered to handle respective ones of the subproblems. Each of the engines provides a dynamic algorithmic flow using modularized algorithmic solvers. The dynamic algorithmic flow is adjusted based on a respective input to each of the engines. The method additionally can include outputting, from the coordinating engine, one or more results in response to the optimization request, based on results for the subproblems generated by the engines. Other embodiments are described.

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 functions comprising: obtaining a route plan for outbound transport from a distribution center; processing multiple threads in parallel to obtain multiple improvements to the route plan, where each of the multiple threads customizes the route plan using one or more different parameters; selecting a lowest cost route solution from the multiple improvements to the route plan; and using the lowest cost route solution for the outbound transport from the distribution center. 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 marketplace information corresponding to one or more categories, wherein the historical marketplace information is for a merchant in a marketplace; determining a respective anchor for each of the one or more categories of the historical marketplace information; receiving an observed price for an item in the marketplace, wherein the observed price corresponds to an item in the one or more categories; normalizing the anchors for each of the one or more categories based on the observed price and the historical marketplace information; determining if the observed price is an anomaly based on the anchors, as normalized; and transmitting an alert to the merchant in the marketplace when an anomaly is detected. Other embodiments are disclosed.

Abstract: A computer-implemented method including determining, based on an anchor item, at least one look template from a plurality of look templates. The at least one look template can include an anchor super product type for the anchor item, one or more remaining non-accessory super product types, and one or more accessory super product types. The method also can include determining one or more respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types to generate one or more preliminary looks. The method additionally can include determining, via a machine learning module, at least one respective accessory recommendation for the anchor item for each of the one or more preliminary looks based at least in part on respective visual compatibility of the at least one respective accessory recommendation with respective existing items of each of the one or more preliminary looks to create one or more looks.

Abstract: A method can include determining a translatability class label and a class-associated search query for a cross-lingual search query. Determining the translatability class label and the class-associated search query can include determining whether a first class-translation pair for the cross-lingual search query exists in a memory module. If the first class-translation pair exists in the memory module, the method further can include retrieving the first class-translation pair for the cross-lingual search query from the memory module. When no class-translation pair for the cross-lingual search query exists in the memory module, the method additionally can include: determining the translatability class label, and determining the class-associated search query based on the translatability class label.

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 interaction information corresponding to a user in a marketplace; identifying a shopping journey and a basket type for the user based on the cart context and items in a cart for the user for a current user session; identifying a price threshold for the cart for the user; building a machine learning model for the current user session in real-time based on the historical interaction information, the cart context, the basket type and the price threshold to determine a ranking of new items to display to the user to add to the cart for the current user session, wherein the new items satisfy the price threshold; re-ranking the ranking of the new items to display to the user in the current user session based on item attributes of the new items; and transmitting the

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 workload information corresponding to a workload, driver information corresponding to drivers, and one or more constraints; building a coordinate system based on the workload information, the driver information and the one or more constraints; analyzing the coordinate system to determine a respective efficiency metric for each driver of the drivers for the workload; identifying a driver of the drivers in which the respective efficiency metric for the driver satisfies an efficiency metric threshold; and assigning the workload to the driver to reduce driver workload waste. Other embodiments are disclosed.

Abstract: A method including obtaining an index message at both (a) an index updater for an index and (b) a subindex updater for a subindex. The method also can include updating, by the index updater, the index based on the index message. The method additionally can include determining, at the subindex updater, whether the index message is eligible for the subindex. The method further can include updating, by the subindex updater, the subindex based on the index message when the index message is eligible for the subindex. Other embodiments are described.

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 certain operations. The operations can include generating a first specificity score for a first query. The operations also can include propagating the first specificity score for the first query to generate a second specificity score for a second query. The operations additionally can include training a machine-learning classifier at least based on the first query and the second query. The operations further can include generating, using the machine-learning classifier, a third specificity score for a third query. Other embodiments are described.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to projecting action indicators on items in a retail facility. In some embodiments, a system comprises an image capture device configured to capture an image of a shelving unit, a control circuit configured to identify items located in the shelving unit, determine an action item for an item located in the shelving unit, identify boundaries of the item, and generate an action image including an action indicator based on the action item for the item, wherein the action indicator is located within the boundaries of the item, and a display device configured to present the action image on the shelving unit, wherein the action indicator is presented on the item.

Abstract: An system and a method for scoring products viewed by a user prior to making a purchase decision and also identify the products considered most before ultimately purchasing. The system uses sensors placed on the shelves to identify the item viewed before making a purchase decision and also track the time spent per decision. The tracking will allow viewed and ultimately bought scores and item similarity scores to be determined. The scores in conjunction with point-of-sale terminal data to identify times determinative of users' interest levels prior to a purchase. The system predicatively replenishes inventory of high interest items based on historical patterns of interest and historical point-of-sale terminal data.

Abstract: A method for tracking an item in a distributed environment is provided. At least one node in a network adds a new block to a first cryptographically verifiable ledger represented by a first sequence of blocks that is stored in one or more non-transitory computer-readable media. The new block added to the first cryptographically verifiable ledger contains a component identifier and a hash of a previous block in the first sequence of blocks. The at least one node in the network adds a new block to a second cryptographically verifiable ledger represented by a second sequence of blocks that is stored in the one or more non-transitory computer-readable media. The new block added to the second cryptographically verifiable ledger contains a destination identifier, the first sequence of blocks, and a hash of a previous block in the second sequence of blocks.

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 cause the one or more processors to perform receiving one or more orders comprising a list of one or more pallets; grouping each pallet of the one or more pallets into one or more groups using one or more respective qualities of each respective pallet; for each group of the one or more groups, building one or more stacks of pallets using at least three different stacking models; selecting a final stack list of a model of the at least three different stacking models having a lowest number of stacks; and optimizing the final stack list using a probabilistic model. Other embodiments are disclosed herein.