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 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: An online concierge system receives orders and allocates orders to pickers who obtain items in an order from a retailer and deliver the items to a customer from whom the order was received. When an item included in an order is unavailable, the online concierge system suggests one or more replacement items for the item. To select a replacement item for an unavailable item, the online concierge system generates replacement scores for each of a set of candidate replacement items. A replacement score for a candidate replacement item is generated from a probability of the customer performing a positive action when the unavailable item is replaced by the candidate replacement item and a predicted probability of a picker finding the candidate replacement item at the retailer. Based on replacement scores for various candidate replacement items for an unavailable item, the online concierge system selects one or more candidate replacement items.

Abstract: An online system leverages a cart issue prediction model trained as a machine-learning model to identify predicted issues with the operation of a smart cart based on sensor data captured by sensors on the smart cart and/or feedback from a user of the smart cart. The machine-learning model is trained on historical data related to the operation of a fleet of smart carts. In response to identifying any cart issues, the online system can trigger a remedial workflow to remedy the predicted issues. The online system may transmit command signals to the smart cart to calibrate the sensors, may transmit remedial tasks to a client device of the user for prompting the user to aid in remedying the predicted issues, or may schedule a service appointment with a technician, including an issue report indicating the predicted issues.

Abstract: An online system leverages a cart issue prediction model trained as a machine-learning model to identify predicted issues with the operation of a smart cart based on sensor data captured by sensors on the smart cart and/or feedback from a user of the smart cart. The machine-learning model is trained on historical data related to the operation of a fleet of smart carts. In response to identifying any cart issues, the online system can trigger a remedial workflow to remedy the predicted issues. The online system may transmit command signals to the smart cart to calibrate the sensors, may transmit remedial tasks to a client device of the user for prompting the user to aid in remedying the predicted issues, or may schedule a service appointment with a technician, including an issue report indicating the predicted issues.

Abstract: An online system trains a multimodal machine-learning model to predict a rate of using an item that can be ordered at the online system by a user. The machine-learning model is trained by using a plurality of training examples, where each training example includes training images associated with a respective training user that are related to a respective item from the collection of items, and data related to conversion of the respective item by the respective training user. Upon receiving images of user's physical spaces that store items, the online system applies the trained machine-learning model to the images to output a rate of using a specific item by the user. Based on the predicted rate, the online system generates a user interface signal causing a device associated with the user to display a user interface with a user interface element for use by the user to restock the item.

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: 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: Use of a language model to automatically perform visual assessment of quality of an item being fulfilled by a picker. The online system receives an image of the item and identifies a set of potential problems associated with the item. The online system generates a plurality of prompts for input into the language model including the image and one or more questions each corresponding to a respective potential problem of the set potential problems. The online system requests the language model to generate, based on the plurality of prompts, a feedback response for each potential problem. The online system generates an aggregated output by aggregating the feedback response for each potential problem, and based on the aggregated output, a second message that identifies one or more relevant problems associated with the item. The online system causes a device of the picker to display the second message.

Abstract: A shopping cart includes sensors configured to collect data about a physical interaction of a user with a product in a retail store. A set of features, such as product quality score, interaction duration, sequence of product interaction, and/or whether the product was added to the cart, are extracted from the data. These features are fed into a machine learning model to determine the user's quality preference score, indicating a likelihood that the user would be dissatisfied with the quality of the product. If a user orders online and their quality preference score surpasses a threshold, a notification is sent to the picker fulfilling the order. Furthermore, the user may send in satisfaction feedback via a client device of the user. Such feedback may subsequently be used to retrain the machine learning model.

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: A trained model is used to generate action recommendations by predicting different metrics related to items ordered at an online system. The online system gathers replacement data related to replacement of an item either online or at a location of a retailer when a user uses a physical receptacle in communication with the online system. The online system applies a metric prediction model trained to predict, based on the replacement data, an irreplaceability score indicative of an item's irreplaceability feature and/or a brand stickiness score indicative of an item's brand stickiness feature. The online system applies an item replacement model trained to identify, based in part on the irreplaceability score and/or the brand stickiness score, a list of candidate replacement items. A device associated with the user displays a user interface with a replacement item from the list prompting the user to include the replacement item to a cart.

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: An online system performs item redesign and engagement prediction. The system obtains item data describing characteristics of a target item for redesign. The system generates a prompt including the characteristics and directions to redesign at least one of them. The system executes the prompt on a generative model to output redesigns. Each redesign includes a modification to at least one characteristic of the target item. The system inputs features of variants, each variant include one store location and one redesign, into an engagement prediction model to output an engagement score for the variant. The engagement prediction model is trained on historical data describing levels of user engagement with items in association with the many store locations. The system identifies candidate variants based on the user engagement scores for further testing. The system transmits the candidate variants to a testing system to assess viability of the redesign.

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.