Abstract: A modular vacuum plumbing assembly includes a frame, a water supply conduit attached to the frame, and a wastewater outlet conduit attached to the frame. Water travels from a water supply system to a plumbing fixture via the water supply conduit and wastewater travels away from the plumbing fixture via the wastewater outlet conduit.

Abstract: A privacy system includes a computing device configured to obtain user transactional data characterizing at least one transaction of a user on an ecommerce marketplace and to determine a privacy vulnerability score of the user by comparing the transactional data to a user vulnerability distribution. The computing device is also configured to send the privacy vulnerability score to a personalization engine.

Abstract: A system for handling hazmat products in a fulfillment center. The system includes a conveyance and packing equipment assembly that facilitates physical acquisition and consolidation of inventory items into a container, a labeling applicator that automatically applies hazmat labels, a scan tunnel, and a server. The server includes computing hardware of a processor, a memory, an input/output engine, a database, and a hazmat handling engine. The input/output engine is configured to receive a customer order and communicate between the server and the conveyance and packing equipment assembly, the labeling applicator, and the scan tunnel. The hazmat handling engine determines customer orders with exceptions requiring specialized handling. The hazmat handling engine marks determined customer orders with exceptions and forwards the remaining orders for allocation and cartonization.

Abstract: Provided are a refrigeration heat reclaim unit and method, comprising a heat exchanger, comprising a refrigerant inlet that receives a flow of refrigerant having a first state; a refrigerant outlet that outputs the flow of refrigerant having a second state; a water loop inlet that receives a flow of liquid at a first temperature; a water loop outlet that outputs the flow of liquid from the reclaim heat exchanger at a second temperature that is greater than the first temperature in response to the flow of refrigerant. The refrigeration reclaim unit also comprises a refrigerant flow control device having outputs to the refrigerant inlet and an air-cooled condenser, respectively for controlling the flow of refrigerant to at least one of the refrigerant inlet and the air-cooled condenser for maintaining a predetermined flow quality value at the refrigerant outlet.

Abstract: Described in detail herein are systems and methods for routing data in a distributed environment. A controller can maintain an inactive state. A terminal can receive a request associated with a physical object. The terminal can be communicatively coupled to a cloud computing system. The terminal can attempt to transmit the request to the cloud computing system. A router communicatively coupled to the controller and cloud computing system can attempt to initiate communication between the terminal and the cloud computing system in response to the terminal attempt to transmit the request to the cloud computing system. The router can route the request to the controller in response to failing to initiate communication between the terminal and the cloud computing system after specified amount of time. The controller can switch from an inactive state to an active state in response to receiving the request.

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, a user interaction signal from a user device for a user. The method further can include after receiving the user interaction signal, determining, in real-time via a machine learning model, a plurality of user intent labels based at least in part on transaction data, interaction data, and incident data for the user. The machine learning model can include pre-trained based on historical input data and historical output data associated with multiple users comprising the user. The historical input data can comprise historical feature embedding vectors for historical transaction data, historical interaction data, and historical incident data associated with the multiple users.

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: identifying each ingredient in an ingredient list of a recipe published on a webpage by identifying query strings from content on the webpage associated with one or more ingredients of the recipe; extracting, using an iterative process, respective ingredients from each line of text of a respective query string of the query strings, wherein each iteration of the iterative process runs until the respective ingredients of the recipe are selected; and mapping a list of one or more respective recipe products by size; and automatically adding the list of one or more respective recipe products to an electronic shopping cart. Other embodiments are disclosed.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of accessing first transaction data stored in a transaction database, the first transaction data describing first transactions for first items from first users; determining, using the first transaction data, first micro-intents associated with the first transaction data; grouping the first micro-intents into clusters; labeling each cluster of the first micro-intents with a respective label; receiving second transaction data of a user, the second transaction data describing second transactions for second items for the user; determining, using the second transaction data, second micro-intents present in the second transactions; receiving current transaction data from a user interface of an electronic device of the user; determining, using the current transaction data, that the user is expressing a curre

Abstract: A method can include determining a training dataset for a first machine learning module based on historical change data by detecting and correcting correctable label noises in the historical change data and after detecting and correcting the correctable label noises, determining the training dataset based on a label-error-free portion of the historical change data. The method further can include training the first machine learning module based on the training dataset to determine a respective risk score associated with a respective change request. The method additionally can include determining, via the first machine learning module, as trained, a first risk score associated with a first change request. Further, the method can include determining a change approval based on the first risk score and a risk threshold. Then, the method can include transmitting the change approval to cause an implementation of the first change request. Other embodiments are disclosed.

Abstract: A method including training a recurrent neural network model to create a trained model based at least 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 input image comprising a depiction of one or more items. 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.

Abstract: Systems, methods, and machine-readable media are provided for identifying violent or other specified types of incidents. An exemplary system captures social media data from at least one source relating to an incident occurring at one of a group of predetermined locations, The exemplary system processes the social media data to determine a severity and location of a violent incident and clusters multiple reports of a single incident. The exemplary system further displays a location of the incident on a map and displays details of the incident.

Abstract: This application relates to apparatus and methods for generating preference profiles that may be used to rank search results. In some examples, a computing device obtains browsing session data and determines items that were engaged, such as items that were viewed or clicked. The computing device obtains item property data, such as product descriptions, for the items, and applies a dependency parser to the item property data to identify portions that include certain words, such as nouns or adjectives, which are then identified as attributes. The computing device generates attribute data identifying portions of the item property data as item attributes. In some examples, the computing device applies one or more machine learning algorithms to the session data and/or search query to identify item attributes. The computing device may generate a profile that includes the item attributes, and may rank search results based on the attribute data, among other uses.

Abstract: Systems and methods utilizing a classification model and a ranking model are disclosed. A user identifier is received and a user profile is generated. The user profile includes a plurality of user features. The user profile is classified into a classification using a trained classification model. The trained classification model receives a first subset of the plurality of user features. A set of communication elements is ranked using a trained ranking model. The trained ranking model receives a second subset of the plurality of user features. An electronic communication including a plurality of interface elements is generated. The plurality of interface elements includes at least one communication element selected from the ranked set of communication elements in descending ranked order. A type of the electronic communication is selected based on the classification of the user profile.

Abstract: In some embodiments, systems manage databases for multiple event shopping, comprising: a plurality of distributed database management systems comprising: a management control circuit; a filter control circuit; customer event databases; and a plurality of product databases maintaining current product information sets, each product information set comprises: product information corresponding to one of the hundreds of thousands of retail products, and an association to at least one of a plurality of different event filter categorizations; wherein the filter control circuit is configured to filter product information sets, and identify a limited subset of products; wherein the management control circuit is configured to: maintain the plurality of customer event databases; cause a graphical user interface to limit a display of potential products to display a subset of product representations; and incorporate within a first event database a product identifier for each selected product.

Abstract: A systems 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, perform: receiving in-session user activity information, the in-session user activity information comprising a user search query from a user and a ranked list of products to be displayed to the user based on the user search query, and a product of the products being part of a boost category; determining a respective content model prediction score for each of the products of the ranked list of the products; determining a respective discounted cumulative gain (DCG) score for each of the products of the ranked list of the products; analyzing the product that is part of the boost category to determine if the product is to be repositioned within the ranked list of the products based on the respective DCG score for each of the products; and in response to determining that the product that is part of the boost category is to be repositione

Abstract: Examples provide a system and method for autonomously placing items into non-rigid containers. An image analysis component analyzes image data generated by one or more cameras associated with picked items ready for bagging and/or a non-rigid container, such as, but not limited to, a bag. The image analysis component generates dynamic placement data identifying how much space is available inside the bag, bag tension, and/or contents of the bag. A dynamic placement component generates a per-item assigned placement for a selected item ready for bagging based on a per-bag placement sequence and the dynamic placement data. Instructions, including the per-item assigned placement designating a location within the interior of the non-rigid container to the selected item and an orientation for the selected item after bagging, is sent to at least one robotic device. The robotic device places the selected item into the non-rigid container in accordance with the instructions.

Abstract: This application relates to employing trained machine learning processes to predict sales across various sale channels. For example, a computing device may generate features based on historical sales information, and trains the machine learning processes based on the generated features. In some examples, the computing device determines fixed cost effects from selling items across various sales channels, and adjusts the sales information based on the fixed cost effects. The computing device also generates features based on the adjusted sales. The computing device may apply the trained machine learning processes to sales information for one or more items to predict the sales of one or more items across one or more sales channels during a future temporal period. In some examples, the trained machine learning processes generate a ranking of items for a sales channel based on the output generated from the trained machine learning processes.

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: identifying a first subject matter in a first digital image; defining shapes for one or more regions in a second digital image of a second subject matter, wherein the one or more regions comprise at least one hidden area of the second subject matter that is obstructed by one or more objects; overlaying at least a first region of the one or more regions in the second digital image with another portion of the second subject matter; generating a composite image that depicts the first subject matter combined with the second subject matter having the at least the first region overlaid; and causing the composite image to be displayed. Other embodiments are described.

Abstract: In some embodiments, systems and methods are provided to control a product distribution conveyor system comprising: a conveyor control circuit; a product feed conveyor system; conveyor lane systems cooperated with the conveyor lane system; routing systems that cause movement of respective products from the feed conveyor system to one of the conveyor lane systems; and a plurality of sensor systems; wherein the conveyor control circuit is further configured to enhance throughput of the at least one feed conveyor system by associating each of the plurality of conveyor lane systems one of a plurality of retail stores such that a total volume of the products is substantially equally distributed over the plurality of conveyor lane systems comprising organizationally subdividing conveyor lane systems to define a plurality of lane subdivisions, and associating each retail store with a respective one of the plurality of lane subdivisions.

Abstract: A method including planning delivery routes using clustering. The method can include generating clusters for nodes for order data based, at least in part, on (a) location information of the nodes and (b) load capacity information of delivery vehicles. The method also can include determining a respective centroid for each of the clusters. Further, the method can include computing a node-cluster distance matrix. Additionally, the method can include reassigning the nodes to the clusters based, at least in part, on: (a) the node-cluster distance matrix; (b) load capacity information of dispatched vehicles of the delivery vehicles; and (c) a predetermined average load threshold of the dispatched vehicles. Moreover, the method can include further reassigning, in at least 2 passes, the nodes to the clusters based, at least in part, on the time window information of the nodes. Other embodiments are disclosed.