Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. The embodiments may employ machine learning processes to detect fraudulent activity. In some examples, a computing device determines customer data and device data for a customer and device involved in a transaction. The customer data may include previous transactions by the customer, and the device data may include previous transactions involving the device. The computing device generates features based on the customer data and the device data, and applies one or more machine learning models to the generated features to generate a trust score. The trust score is indicative of how likely a transaction is to be fraudulent. In some examples, the transaction is not allowed if the trust score is beyond a threshold. In some examples, the computing device trains the machine learning models based on customer data and device data for a plurality of customers and devices.

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: displaying, one or more instructions on a user interface of a mobile device to instruct a user to capture each respective image of a prescription label; determining, using contrast, a location of each respective image in the set of images that is associated with the respective different portions of the prescription label on the medication package; reconstructing each of the respective different portions of the prescription label on the medication package as a flattened reconstruction of the respective different portions of the prescription label; and providing for display on the user interface a reconstructed prescription label. Other embodiments are disclosed herein.

Abstract: There is provided a conveyor assembly for sorting merchandise items. In one form, the conveyor assembly includes: a merchandise identification module; sortation modules each comprising a multi-directional sorting portion with two sets of rollers, a first wing including a third set of rollers, a second wing including a fourth set of rollers, and a controller configured to receive an IP address; a first, disassembled state of the sortation modules; a second, assembled state of the sortation modules; a merchandise database; and a control circuit configured to receive identifying information about an unsorted merchandise item, assign a unique IP address to each sortation module corresponding to the position of the sortation module, determine a sortation destination for the unsorted merchandise item, determine the sortation module corresponding to the sortation destination; and instruct the controller of the corresponding sortation module to divert the unsorted merchandise item to the sortation destination.

Abstract: This application relates to apparatus and methods for automatically grouping the same or similar items provided by various suppliers that may use various supplier identification systems to identify the items to a retailer. In some examples, a computing device receives a supplier identifier from a supplier. The supplier identifier identifies an item that the supplier provides to the retailer for sale. The computing device may determine a group identifier for the item, where the group identifier identifies the item within a group of the retailer. The computing device may update the supplier identifier's association to the retailer identifier based on whether the group identifier is also associated with the retailer identifier. In some examples, the computing device executes a machine learning model to determine anomalies within a current database of supplier identifiers and associated retailer identifiers. In some examples, the computing device indicates changes to associations and any detected anomalies.

Abstract: Examples provide an item storage cart for verifying correct item placement into containers on the item storage cart. A cart manager component outputs planogram data identifying a location of a selected item. Weight data generated by one or more weight sensors associated with the set of item storage containers is analyzed to verify whether a correct item is placed in a given storage container on the cart and/or verify a selected item is placed into a correct storage container. The cart manager component determines if an unselected item is removed from a storage container and/or if an unselected item is added to a storage container. A notification is output if an incorrect item is added to a cart, an incorrect item is removed from the cart, an item is added to the wrong container or removed from the wrong container.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to the sortation of products using a conveyor assembly.

Abstract: A data retrieval system can include a computing device that is configured to, in response to receiving a uniform resource locator from a user device, parse the uniform resource locator to identify a dynamic path variable. The computing device can be configured to generate a set of execution steps based on the received uniform resource locator. The computing device can also be configured to execute the set of execution steps to retrieve a set of data, the set of execution steps being executed based on the dynamic path variable in response to the dynamic path variable being identified. The computing device can further be configured to transform a user interface of the user device based on the set of data.

Abstract: This application relates to systems and methods for automatically correcting labels in untrusted data based on a small sample of trusted data in a training database. In some examples, training data may be divided into a trusted dataset and an untrusted dataset using stratified sampling. An adversarial algorithm may be used to reassign labels in the data samples associated with the untrusted data based on a set of features in the data and labels in the trusted dataset. The untrusted dataset with the reassigned labels may then be used to train a machine learning model.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to ensuring quality of meat cuts. In some embodiments, a system for ensuring quality of meat cuts comprises a capture device comprising an image capture device configured to capture an image of a cut of meat, a depth sensor configured to capture depth data, a transceiver configured to transmit the image and the depth data, a microcontroller configured to control the image capture device, the depth sensor, and the transceiver, a database configured to store meat cut specifications, and the control circuit configured to receive, from the capture device, the image and the depth data, retrieve, from the database, a meat cut specification, evaluate the image of the cut of meat and the depth data associated with the cut of meat, and classify the cut of meat.

Abstract: A system for generating a geographically-clustered routing solution includes a computer system configured to receive a plurality of orders. Each order in the plurality of orders comprises a geographic location. The plurality of orders are clustered into a plurality of clusters based on the geographic location of each order and a routing solution is generated for each of the plurality of clusters. Each routing solution is transmitted to at least one vehicle system.

Abstract: An analysis of the scans is performed to determine whether the selected item was moved from the back room to the shelf, then returned from the shelf to the back room. The analysis also determines a re-bin time when the selected product was returned to the back room. An adjustment to the PI is blocked when last PI adjustment time is later than the re-bin time. An adjustment to the PI is also blocked when an audit indicator is received at the interface indicating that the retail store is under an audit. An adjusted PI value is determined when the analysis indicates that the selected item was moved from the back room to the shelf, then returned from the shelf, the adjusted PI value being at least one more than the shelf capacity value.

Abstract: An automated warehouse storage system including a multilevel storage array is provided. Each aisle has a set of storage levels and each level has storage locations distributed along the aisle. The guideway network extending through the multilevel storage array is configured for autonomous vehicles to move along the guideway network within the multilevel storage array. The guideway network including an inter-aisle guideway spanning at least two of the multiple aisles and a set of guideway levels extending in an aisle of the multiple aisles and disposed so that each guideway level is at a different one of the storage levels and the vehicles on the guideway level can access the storage locations distributed along the aisle. Each set of guideway levels is connected to the inter-aisle guideway forming a common guideway path so that a vehicle can move between inter-aisle guideway and each guideway level along the common guideway path.

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 respective number of totes for each respective order of one or more orders; determining, using a first set of rules, when the respective number of totes for a respective order of the one or more orders can be reduced; when the respective number of totes is determined to be able to be reduced, reducing, using a second set of rules, the respective number of totes for the respective order to a respective reduced number of totes for the respective order of the one or more orders; comparing the respective number of totes for the respective order to the respective reduced number of totes for the respective order; and when the respective number of totes for the respective order is compared to be equal to the respective reduced number of totes for the respective order, facilitating displaying, on an electronic device

Abstract: In some embodiments, apparatuses and methods are provided herein useful to delivering merchandise using autonomous ground vehicles (AGVs) in cooperation with unmanned aerial vehicles (UAVs). In some embodiments, the system includes: an AGV having a motorized locomotion system, a storage area to hold merchandise, a sensor to detect obstacles, a transceiver, and a control circuit to operate the AGV; a UAV having a motorized flight system, a gripper mechanism to grab merchandise, a transceiver, an optical sensor to capture images; and a control circuit to operate the UAV. In some forms, the system may include a control circuit that instructs movement of the AGV along a delivery route; determines if the AGV has stopped due to an obstacle; and in certain circumstances, instructs the UAV to retrieve merchandise from the AGV, calculates a delivery route for the UAV to the delivery location, and instructs the UAV to deliver the merchandise.

Abstract: An automated order fulfillment system and mobile robot are disclosed, where the mobile robot includes a compliant drive for moving between levels of a multilevel storage structure.

Abstract: A system including one or more processors and one or more non-transitory media storing computer instructions configured to run on the one or more processors and perform: identifying a first sub-population of case individuals from a gross population of the case individuals; presenting first test content to a first test sub-population of the case individuals, the first test content is selected according to a first statistical model; measuring a first test sub-population average feedback metric based on first test content feedback provided from the first test sub-population of the case individuals; determining that the first test sub-population average feedback metric exceeds a first control population average feedback metric of a first control population of the case individuals; and determining that a probability value for a difference between the first test sub-population average feedback metric and the first control population average feedback metric is less than a predetermined significance level value.

Abstract: A mobile assembly apparatus and a method of assembling a retail product utilizing a retail assembly are discussed. The mobile assembly apparatus may include a cabinet structure, an expandable work surface, a frame, wheels, an articulated arm, outrigger supports, a power supply, sensors, a hardware dispenser, and a computing device. The mobile assembly apparatus scans a machine readable identifier and retrieves assembly instructions for an item associated with the identifier. The computing device controls the outrigger supports and extendable surface based on the instructions. The computing device monitors the progress of the assembly of the time and validates the assembly based on assembly data from the sensors.

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 comprising: calculating a first user propensity score and a second user propensity score for a user based on at least one feature vector of historical data of the user; using the first user propensity score to place the user into a first segment; using the second user propensity score to place the user into a second segment different than the first segment; and facilitating delivery of a message to an electronic device of the user based on the first segment and the second segment. 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: bundling multiple registered queries of a dataset using a scheduling technique, wherein the dataset is homogenous in schema; running a single table scan of the dataset to process the multiple registered queries of the dataset in parallel; and generating a respective output responsive to each of the multiple registered queries. Other embodiments are disclosed.