Abstract: An online system uses a trained machine-learning model to detect errors in catalog data based on interactions of users of the online system with physical carts. Upon receiving an interaction signal indicating an interaction by the user with a device in a location of a source or an action signal indicating an action in the location of the source, the online system applies the trained model to the interaction signal and/or the action signal to generate an error score for an item that indicates a likelihood of an error in relation to the item. Responsive to the error score being above a threshold score, the online system generates an error checking signal for confirming that the error is present. Responsive to the confirmation of the error, the online system generates a user interface that alerts about the error and requests an action to correct the error.

Abstract: The present disclosure is directed to determining purchase suggestions for an online shopping concierge platform. In particular, the methods and systems of the present disclosure may receive, from a computing device associated with a customer of an online shopping concierge platform, data indicating one or more interactions of the customer with the online shopping concierge platform; determine, based at least in part on one or more machine learning (ML) models and the data indicating the interaction(s), a likelihood that the customer will purchase a particular item if presented, at a specific time, with a suggestion to purchase the particular item; and generate and communicate data describing a graphical user interface (GUI) comprising at least a portion of a listing of one or more purchase suggestions including the suggestion to purchase the particular item.

Abstract: A system receives real-time sensor data from sensors of a smart cart. The system identifies a triggering event based on the sensor data. The system obtains a template for the triggering event, wherein the template comprises instructions for generating suggestions for the user to augment smart cart operation. The system may obtain other contextual information, e.g., order data, user data, source data about a source location, etc. The system generates a prompt by modifying the template to include the sensor data or the contextual information. The system causes execution of the prompt by a language model, which outputs a response based on the prompt. The system generates augmented content including the suggestions for the user by parsing the response. The augmented content may be multimodal, combining multiple forms of data. The system transmits the augmented content for presentation to the user to augment operation of the smart cart.

Abstract: A trained model of an online system is used to generate action recommendations by predicting future demands. The online system gathers in-store data by receiving, from a device of a picker and/or a computing system of an in-store physical receptacle, data with information about an inventory of an item. The online system estimates, based on conversion data for the item, a level of inventory for the item. The trained model is then applied to predict, based on the in-store data and the estimated level of inventory, a demand prediction score indicative of a future demand for the item. The online system generates, based on the estimated level of inventory and the demand prediction score, a depletion metric indicative of a time period until the inventory of the item is depleted. Based on the depletion metric, the online system triggers an action in relation to the inventory of the item.

Abstract: An online system uses a trained model for intelligent handling of unclaimed online pickup orders. After identifying that an order placed by a user of the online system is unclaimed at a location of a source, the online system obtains, from a device of a picker associated with the online system and/or a device associated with the source, signals with information about each item in each bundle of the unclaimed order. The online system applies the trained model to identify, based on the obtained signals, a preferred method for disposal of each bundle. Based on the identified preferred method for disposal of each bundle, the online system generates a disposal decision signal and communicates the disposal decision signal to the device associated with the source that prompts personnel at the location of the source to dispose each bundle of the unclaimed order using the identified preferred disposal method.

Abstract: An online system receives a request from a client device associated with a user to place an order for pickup from a source location during a timeframe and identifies candidate remedial actions associated with the order based on the timeframe and a current time. The system retrieves user data for the user and accesses a machine-learning model. For each candidate remedial action, the system applies the model to predict, based on the user data and order data for the order, a likelihood the user will pick up the order if the candidate remedial action is taken and computes an associated value based on the likelihood. The system selects a remedial action from the candidate remedial actions based on the values, generates, based on the selected remedial action, a message associated with the order that includes a set of selectable options, and sends the message to the client device.

Abstract: A device interfaced with an online system detects, via a physical sensor, item removal and generates a user interface with an alternative option for conversion. Upon receiving a signal from the device indicating the item removal, the online system selects a set of candidate items for replacement of the removed item, wherein each candidate item has a conversion value that is less than a conversion value of the removed item. The online system applies a trained machine-learning model to generate a conversion score for each candidate item that indicates a likelihood of conversion by the user of each candidate item. The online system selects, based on the conversion score for each candidate item, a replacement item from the set of candidate items, and generates a user interface signal that causes a user interface of the device to prompt the user to convert the replacement item.

Abstract: An online concierge system dynamically determines types of shopping events. The types may be used in various ways to increase efficiency of an item pipeline. The system may monitor interactions of a customer with an ordering interface on a customer client device associated with the customer. The monitoring may be during a shopping event that is categorized by a type, wherein the type describes a purpose of the shopping event. Responsive to a monitored interaction being an interaction from a set of trigger interactions, the system may determine a type of shopping event by applying the monitored interaction and content of a shopping cart of the ordering interface to a type prediction model. The system may assign an updated type to be the determined type, and perform an action based in part on the updated type.

Abstract: A trained model is used to predict a scheduled delivery for a self-picked order. Responsive to receiving an indication from a device associated with a user of an online system that the device is either within a defined vicinity from a location of a retailer or physically present at the location of the retailer, the online system applies a user targeting computer model trained to generate, based on user data and ordering data, a score for the user indicative of a likelihood of the user accepting an offer for the scheduled delivery of the order. Responsive to the score being greater than a threshold score, the online system generates a list of service options for the scheduled delivery of the order and displays the list of service options at a user interface of the device prompting the user to select a service option for the scheduled delivery of the order.

Abstract: An online system receives orders from users and fulfills the orders by dispatching a picker to a physical source to obtain the items for delivery.  Some items in an order may be considered “foundational,” meaning that a user who ordered the items may wish to cancel one or more other items in the order if the foundational item is unavailable (e.g., the item is a critical ingredient for a recipe).  The online system predicts items in the order that are foundational using a trained machine-learning model.  The online system presents the items to the picker in a sequence so the foundational items are obtained earlier by the picker. This enables the picker to observe whether the determined foundational item is available sooner in the picking process, allowing earlier performance of a remedial action and possibly avoiding replacing previously obtained items affected by the unavailability of the foundational item.

Abstract: A cart management system generates an error priority assignment for smart cart systems based on device error predictions for those smart cart systems. An error priority assignment is an assignment of the relative priority of servicing or providing maintenance to a set of smart cart systems. To generate the error priority assignment, the cart management system applies an error detection model to cart data received from the set of smart cart systems. The cart data has measurements captured by sensors coupled to the smart cart systems, and the error detection model uses the cart data to generate device error predictions. Each of these predictions represents a likelihood that a smart cart system will experience a device error within some time period. The cart management system uses the device error predictions to generate the error priority assignment and selects which smart cart system to service based on the error priority assignment.

Abstract: An online system uses a trained machine-learning model to detect errors in catalog data based on interactions of users of the online system with physical carts. Upon receiving an interaction signal indicating an interaction by the user with a device in a location of a source or an action signal indicating an action in the location of the source, the online system applies the trained model to the interaction signal and/or the action signal to generate an error score for an item that indicates a likelihood of an error in relation to the item. Responsive to the error score being above a threshold score, the online system generates an error checking signal for confirming that the error is present. Responsive to the confirmation of the error, the online system generates a user interface that alerts about the error and requests an action to correct the error.

Abstract: An online system uses a trained machine-learning model to create an online cart or a physical cart for a user of the online system. Upon receiving a signal with an indication about an interaction by the user with one or more items via a first conversion channel of the online system, the online system retrieves one or more candidate items for the user to convert via a second conversion channel of the online system that is different from the first conversion channel. The online system applies the machine-learning model to output a conversion score for each retrieved candidate item that indicates a likelihood of conversion. Responsive to the conversion score being above a threshold score, the online system generates a user interface at a device associated with the user prompting the user to use the second conversion channel for conversion of each retrieved candidate item.

Abstract: A client device, or an online system communicating with the device, receives video data depicting a field of view of a display area of the device and applies machine-learning algorithms to the video data to detect objects, including portions of a body of a user of the device, within the field of view and to determine a series of body poses. The device/system uses machine-learning models to predict an action performed by the user based on the series of poses and to predict a recipe being prepared based on the objects and a predicted series of actions performed by the user. The device/system selects a suggestion associated with preparing the recipe based on candidate suggestions associated with preparing the recipe, the objects, or the predicted series of actions, and generates an augmented reality element describing the suggestion. The augmented reality element is displayed in the display area of the device.

Abstract: An online system uses a machine-learning model to identify servicing agents suited to perform tasks of new types. The online system maintains a list of tuples for servicing agents, each tuple including a score for a servicing agent and an identifier of a task type, the score indicating a level of aptitude of the servicing agent to perform a task of the task type. Upon obtaining a description for a task of a new type, the online system applies the machine-learning model to the list of tuples and the description for the task to generate a task score for each servicing agent that is indicative of a level of aptitude of each servicing agent for performing the task of the new type. The online system selects, using the task score for each servicing agent, servicing agents to whom the online system offers the task of the new type.

Abstract: An online system trains a ceiling prediction model to determine a user's ceiling for one or more item categories. The user's ceiling for an item category is a maximum amount of an item within the item category the user is likely to include in an order. Based on previously fulfilled orders for the user, information describing a current order from the user, and contextual information about the order, the ceiling prediction model determines the user's ceiling for an item category. The online system leverages the user's ceiling for an item category to refine content about different items that is selected for presentation to a user. For example, the online system determines whether the order includes a quantity of items from an item category that equals the user's ceiling for the item category when determining which items to present to the user.

Abstract: A smart cart system accounts for edge cases in user interactions by leveraging sensor data and machine-learning models of a smart cart system. For example, a smart cart system uses sensor data to detect when a user removes an item from the smart cart system and presents content to the user on a display of the smart cart system based on the removed item. The smart cart system captures images of the storage area and applies an item identification model to the images to identify the item removed from the storage area. The smart cart system identifies a set of candidate items based on location sensor data describing a location of the smart cart system when the item was removed and computes presentation scores for each of the set of candidate items based on item data for each item the removed item.

Abstract: An online system uses a trained machine-learning model for efficient packing of items. Upon receiving, from a device of an agent or a device of a source via a network, a signal indicating that a set of items are ready for packing, the online system applies the machine-learning model to identify, based at least in part on input data, a packing order for one or more items of the set of items. Based on the identified packing order for the one or more items, the online system generates a packing interface signal. The online system sends the packing interface signal, wherein sending the packing interface signal causes the one or more items to be packed according to the identified packing order. This process is repeated until it is confirmed that all items from the set of items were packed.

Abstract: An online system utilizes a generative machine-learning model to generate a user interface of the online system with visualization of items of specific quantities. Upon receiving an interaction with an item on the user interface, the online system identifies a quantity of the item to show in the user interface. Responsive to identifying the quantity of the item, the online system generates a prompt for the generative model, the prompt including the identified quantity of the item, information about a reference object, and a request for generating an image of the identified quantity of the item in the reference object. The online system requests the generative model to generate, by providing the prompt to the generative model, the image of the identified quantity of the item. The online system updates the user interface to display the generated image of the identified quantity of the item in the reference object.

Abstract: The present disclosure is directed to determining purchase suggestions for an online shopping concierge platform. In particular, the methods and systems of the present disclosure may receive, from a computing device associated with a customer of an online shopping concierge platform, data indicating one or more interactions of the customer with the online shopping concierge platform; determine, based at least in part on one or more machine learning (ML) models and the data indicating the interaction(s), a likelihood that the customer will purchase a particular item if presented, at a specific time, with a suggestion to purchase the particular item; and generate and communicate data describing a graphical user interface (GUI) comprising at least a portion of a listing of one or more purchase suggestions including the suggestion to purchase the particular item.