Abstract: An online system predicts time to park at a fulfillment location in fulfillment of an order by a fulfillment user. The online system receives an order from a requesting user, and applies a timeliness prediction model to the order, the parking configuration of the corresponding fulfillment location, to other contextual factors, or some combination thereof to predict the time to park at the fulfillment location. The timeliness prediction model is trained on historical orders with their associated completion times and known parking configurations of the respective fulfillment locations. The online system may batch orders together to optimize fulfillment efficiency in consideration of the predicted lag time for the order. The online system assigns and transmits the batches to fulfillment users to fulfill at the fulfillment locations.

Abstract: A machine-learned predictive model is trained to predict potential for customer complaint. The model is part of an online concierge system. The online concierge system accesses a customer order that includes one or more items. The online concierge system determines input data for an item of the one or more items. The online concierge system determines a prediction value associated with potential for customer complaint for the item by applying the machine-learned prediction model to the input data. The online concierge system provides the prediction value to a picker client device associated with a picker who is assigned the item. The picker client device presents an alert to the picker based in part on the prediction value, and the alert includes a message that is customized to mitigate a cause of potential customer complaint for the item.

Abstract: An online system uses a computer-vision item identification model to identify items and physical containers storing those items to detect sorting errors of the physical containers. The online system receives a first image from a client device that depicts a set of physical containers that contain items for a batch of orders that the online system has received. The online system identifies items in those physical containers by applying a contained-item identification model to the first image. The online system uses the output of this model to determine which visible items are in each physical container and uses that information plus order data for the batch of orders to determine which physical containers are associated with each order. The online system compares this first image to a subsequently received image to determine whether the correct physical containers were delivered by the user.

Abstract: An online system predicts whether a user will be at a delivery location at a delivery time for an attended delivery of an order using a machine-learned model. The online system receives the order from a client device of a user and a request by the user for an attended delivery of the order where the user will be at the delivery location at the delivery time of the order. The machine-learned model predicts that the user will not be at the delivery location at the delivery time based on user attributes of the user and order attributes of the order that are input into the machine-learned model. The online system performs a remedial action including transmitting a notification to the client device of the user to provide additional instructions for the attended delivery responsive to the determination that the user is not likely to be at the delivery location.

Abstract: An online concierge system receives item data for an item included among an inventory at a retailer location, in which the item data includes a set of real-time item data for the item and a set of constraints. The system accesses and applies a first machine-learning model to predict a freshness satisfaction score for the item based at least in part on the item data. The system updates the item data to include the score and accesses and applies a second machine-learning model to predict an elasticity of demand for the item based at least in part on the updated item data. The system determines an optimal value associated with the item based at least in part on the freshness satisfaction score, the elasticity of demand, and the set of constraints. A value associated with the item is then adjusted based at least in part on the optimal value.

Abstract: An online system receives orders from users and dispatches pickers to fulfill the orders by obtaining ordered items at a retailer. If an ordered item cannot be found by a picker, the picker may refund the item or attempt to find a replacement item. While obtaining a replacement item may increase revenue to the online system, it can also cause a bad outcome for user experience (e.g., an unacceptable replacement item, a refund request of the replacement item, etc.). To balance these interests, the online system trains a model to predict an outcome metric comprising a likelihood of a bad outcome from replacing an item or an expected amount of profit to the online system from a replacement item. The online system compares the outcome metric to a threshold to determine whether to promote or dissuade the picker from replacing a not-found item.

Abstract: An online concierge system receives, from a user client device associated with a user of the online concierge system, a request to access a user interface including information describing one or more items included among an inventory at a retailer location. The system then retrieves a set of item data for an item included among the inventory at the retailer location. The system accesses and applies a machine-learning model to predict a freshness satisfaction score for the item based at least in part on the set of item data for the item. The system then generates the user interface including the information describing the item(s) based at least in part on the freshness satisfaction score for the item and sends the user interface to the user client device, causing the user client device to display the user interface.

Abstract: An online system receives a request to access an interactive geographical map of sources from a client device associated with a user. The system retrieves data describing a geographical location associated with the user. The system identifies one or more sources within a threshold distance of the location and retrieves data including information describing items available at each source. For each source, the system accesses and applies a machine-learning model to predict a user engagement score indicating a likelihood of one or more interactions by the user with a set of items available at the source if the source is included in the map. Based on the score for each source, the system selects a set of sources and generates the map, in which the map indicates the geographical location of each selected source. The map is then sent to the client device, causing the device to display the map.

Abstract: A machine-learned predictive model is trained to predict potential for customer complaint. The model is part of an online concierge system. The online concierge system accesses a customer order that includes one or more items. The online concierge system determines input data for an item of the one or more items. The online concierge system determines a prediction value associated with potential for customer complaint for the item by applying the machine-learned prediction model to the input data. The online concierge system provides the prediction value to a picker client device associated with a picker who is assigned the item. The picker client device presents an alert to the picker based in part on the prediction value, and the alert includes a message that is customized to mitigate a cause of potential customer complaint for the item.

Abstract: An online system includes an interface which facilitates communication between customers and pickers who are servicing the user's order. The customer may request a modification to their order through the interface. The online system performs an inference task in conjunction with the model serving system or the interface system to continuously monitor conversations between users and pickers to infer whether a customer requested to modify their order to maintain an updated order and an updated in-store transaction estimate for the order. The online system determines if the order has been updated to account for the requested changes. If the order has not been updated, the online system automatically updates the customer's order and computes an updated in-store transaction estimate based on the changes made.

Abstract: An online system receives information describing a set of items requested by a user and an indication via a chat interface that a particular item needs replacement. The online system generates one or more prompts configured to request a machine learned language model to identify the particular item that needs replacement and to identify one or more replacement items for the particular item. The online system receives a set of item identifiers from the machine learned language model and selects a replacement item from a database based on the set of item identifiers. The online system may also receive an order and a communication history associated with a user including a message with a request to modify the a. The online uses the machine-learning language model to map the request type to the set of API requests for updating the order to reflect the request from the user.

Abstract: A machine-learned predictive model is trained to predict potential for customer complaint. The model is part of an online concierge system. The online concierge system accesses a customer order that includes one or more items. The online concierge system determines input data for an item of the one or more items. The online concierge system determines a prediction value associated with potential for customer complaint for the item by applying the machine-learned prediction model to the input data. The online concierge system provides the prediction value to a picker client device associated with a picker who is assigned the item. The picker client device presents an alert to the picker based in part on the prediction value, and the alert includes a message that is customized to mitigate a cause of potential customer complaint for the item.

Abstract: An online system generates text-based representations of various types of data for processing using a large language model. The online system extracts location data from a map of a source location and converts the location data into a text-based representation of the location data. The online system receives a set of item identifiers from a client device of a user and generates an LLM prompt based on the set of item identifiers and the text-based representations of the location data. The online system receives a response from the LLM and parses the response for a text-based description of related items. The online system maps the text-based description of the related items to item identifiers and transmits a notification to the client device that includes item data associated with the related items.

Abstract: An online system receives orders from users and dispatches pickers to fulfill the orders by obtaining ordered items at a retailer. If an ordered item cannot be found by a picker, the picker may refund the item or attempt to find a replacement item. While obtaining a replacement item may increase revenue to the online system, it can also cause a bad outcome for user experience (e.g., an unacceptable replacement item, a refund request of the replacement item, etc.). To balance these interests, the online system trains a model to predict an outcome metric comprising a likelihood of a bad outcome from replacing an item or an expected amount of profit to the online system from a replacement item. The online system compares the outcome metric to a threshold to determine whether to promote or dissuade the picker from replacing a not-found item.

Abstract: An online system may prompt a shopper to capture one or more images of items on a checkout belt of a retailer, wherein the items are for fulfilling orders for one or more users of an online service. An online system may provide the one or more images to a machine learning model configured to classify an item as a product. An online system may classify the items to one or more products by applying the machine learning model to the images. An online system may for each user, matching the classified products to the user's order. An online system may obtain an annotated image of the items highlighting classified products which do not match the user's order. An online system may provide to the shopper the annotated image with a notification of a potential discrepancy.

Abstract: An online system detects an anomaly associated with an item selection made by a picker for fulfilling an order of a user of an online system. The system generates a prompt for execution by a machine-learned model trained as a large language model. The prompt comprises a chat log between the picker and the user. The system provides the prompt to the machine-learned model for execution. The system receives, as output from the machine-learned model and based on the chat log, a description indicating whether the anomaly is attributable to the user. The system determines, based on the output from the machine-learned model, that the item selection is not attributable to the user. Responsive to determining that the item selection is not attributable to the user, the system provides a notification to a client device of the user to confirm whether the item selection is approved by the user.

Abstract: A method of monitoring skin abnormalities on a skin portion of a patient includes the steps of: receiving a first image data from an image capture device, wherein the first image data includes the first skin portion; identifying a first skin mask corresponding to the first skin portion; identifying one or more first keypoints within the first skin mask, wherein each first keypoint comprises an abnormality on a first body region within the first skin portion; receiving a second image data from an image capture device, wherein the second image data includes a second body region within a second skin portion, and wherein the first and second image data are sequentially captured; and comparing the first and second body regions of the first and second image data to match the skin mask of the first and second image data.

Abstract: A method of monitoring skin abnormalities on a skin portion of a patient including the steps of receiving a first image data from an image capture device and identifying a first skin mask corresponding to the skin portion. The image data includes the skin portion and a first scaling element on the skin portion. The method further includes the steps of identifying one or more first keypoints within the first skin mask and classifying the one or more keypoints. Each first keypoint comprises an abnormality on the skin portion.

Abstract: A method of monitoring skin abnormalities on a skin portion of a patient including the steps of receiving a first image data from an image capture device and identifying a first skin mask corresponding to the skin portion. The image data includes the skin portion and a first scaling element on the skin portion. The method further includes the steps of identifying one or more first keypoints within the first skin mask and classifying the one or more keypoints. Each first keypoint comprises an abnormality on the skin portion.