Abstract: An online concierge system receives, from a client device associated with a user of the online concierge system, order data associated with an order placed with the online concierge system, in which the order data describes a delivery location for the order. The online concierge system receives information describing a set of attributes associated with the delivery location and accesses a machine learning model trained to predict a difference between an arrival time and a delivery time for the delivery location. The online concierge system applies the model to the set of attributes associated with the delivery location to predict the difference between the arrival time and the delivery time for the delivery location and determines an estimated delivery time for the order based at least in part on the predicted difference. The online concierge system sends the estimated delivery time for the order for display to the client device.

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 concierge system receives, from a client device associated with a user of the online concierge system, order data associated with an order placed with the online concierge system, in which the order data describes a delivery location for the order. The online concierge system receives information describing a set of attributes associated with the delivery location and accesses a machine learning model trained to predict a difference between an arrival time and a delivery time for the delivery location. The online concierge system applies the model to the set of attributes associated with the delivery location to predict the difference between the arrival time and the delivery time for the delivery location and determines an estimated delivery time for the order based at least in part on the predicted difference. The online concierge system sends the estimated delivery time for the order for display to the client device.

Abstract: Certain embodiments herein disclose a cannula assembly. The cannula assembly comprises a cannula, having a proximal end and a distal end. The cannula comprises a head at the proximal end, the head having one or more wings. The cannula also comprises a hollow rod extending from the head to the distal end. The cannula assembly also comprises a hub couplable to the head and having a housing with an inner wall. The inner wall comprises an undercut, and one or more distal ends of the corresponding one or more wings are configured to fit into the undercut and couple the hub to the head upon insertion of the head into the hub.

Abstract: An online concierge system trains a computer model to map receipt item labels to order item identifiers, enabling the online concierge system to identify discrepancies between receipt items and customer order items. The online concierge system identifies a training set of data comprising quantities of receipt item labels and corresponding orders having quantities of order item identifiers and trains the computer model to predict quantities of order item identifiers based on the set of training data. The online concierge system applies a one-hot encoding for a receipt item label to determine predicted order item identifiers and maps the receipt item label to order item identifiers based on the predictions.

Abstract: A trained model is used to predict and prevent a failed delivery of an order placed by a user of an online system. The online system accesses a delivery prediction model trained to predict a likelihood of a delivery for the order ending up as a failed delivery as the order would not be delivered at a location associated with the user. The online system applies the delivery prediction model to predict, based on order data, user data and fulfillment data, the likelihood of the failed delivery for the order. Responsive to the predicted likelihood of the failed delivery being greater than a threshold value, the online system identifies one or more actions associated with the order to prevent an occurrence of the failed delivery for the order. The online system applies the one or more actions to prevent the occurrence of the failed delivery for the order.

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 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 uses a set of models trained to predict a probability or each of a set of events, including one or more negative events, for a candidate replacement item. The online concierge system generates a score for a candidate replacement item as a weighted combination of the predicted probabilities, with negative weights applied to the probabilities for negative events. Based on the scores for various candidate replacement products, the online concierge system selects one or more candidate replacement items for an item.

Abstract: Embodiments disclosed herein provide devices, systems, and methods for instrument exchanges during ophthalmic surgery. More particularly, the present disclosure relates to valved cannula assemblies and methods of use thereof. A valved cannula assembly includes a cannula having a head at a proximal end of the cannula and a hollow rod extending from the head to a distal end of the cannula. The valved cannula assembly includes a valved hub coupled to the head and including a septum having two or more curved flaps configured to provide an opening for an instrument.

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: Certain aspects of the present disclosure generally relate to ophthalmic surgery, and more particularly, to methods and apparatuses for controlling intraocular pressure (IOP) and administering ocular tamponades during or after ophthalmic surgery. An exemplary apparatus generally includes a proximal segment having a first inner diameter (ID), a first proximal end, and a first distal end. The apparatus further includes a distal segment having a second ID smaller than the first ID, a second proximal end, and a second distal end configured to be disposed inside a hub or a cannula transition of a valved cannula comprising the hub, a shaft, and the cannula transition between the hub and the shaft.

Abstract: An online system receives a request to confirm a transaction that is associated with an order. The system accepts or declines the transaction based on whether an amount associated with the pending transaction is likely to exceed an expected amount of the order by more than a threshold value. To determine the threshold, the system trains a first model to predict an overspend for an order and then trains a second model to predict an amount of error associated with the predictions from the first model. The outputs of the first model and the second model provide a mean and a variance for an expected distribution of the overspend. If the actual overspend amount for the transaction exists in too high of a percentile of the distribution, the transaction may be flagged for review or declined.

Abstract: Certain aspects of the present disclosure generally relate to ophthalmic surgery, and more particularly, to methods and apparatuses for controlling intraocular pressure (IOP) and administering ocular tamponades during or after ophthalmic surgery. An exemplary apparatus generally includes a proximal segment having a first inner diameter (ID), a first proximal end, and a first distal end. The apparatus further includes a distal segment having a second ID smaller than the first ID, a second proximal end, and a second distal end configured to be disposed inside a hub or a cannula transition of a valved cannula comprising the hub, a shaft, and the cannula transition between the hub and the shaft.

Abstract: An online concierge system receives, from a client device associated with a user of the online concierge system, order data associated with an order placed with the online concierge system, in which the order data describes a delivery location for the order. The online concierge system receives information describing a set of attributes associated with the delivery location and accesses a machine learning model trained to predict a difference between an arrival time and a delivery time for the delivery location. The online concierge system applies the model to the set of attributes associated with the delivery location to predict the difference between the arrival time and the delivery time for the delivery location and determines an estimated delivery time for the order based at least in part on the predicted difference. The online concierge system sends the estimated delivery time for the order for display to the client device.

Abstract: Certain aspects of the present disclosure generally relate to ophthalmic surgery, and more particularly, to methods and apparatuses for controlling intraocular pressure (IOP). An exemplary apparatus generally includes a proximal segment comprising a first inner diameter (ID), an intermediate segment coupled to the proximal segment, the intermediate segment comprising a second ID smaller than the first ID, and a distal segment coupled to the intermediate segment, the distal segment comprising a third ID smaller than the second ID.

Abstract: An online concierge system receives data points associated with picking up and delivering orders and arriving at retailer/delivery locations from picker client devices and executes a clustering process on one or more sets of the data points. The system determines a geolocation associated with each location based on the clustering process and identifies one or more points of interest associated with each location based on rules applied to the data points. The system receives information describing an order, identifies pairs of data points associated with a location associated with the order, and determines a navigation path including a sequence of points of interest for servicing the order based on points of interest associated with the location and a difference between times associated with each pair of data points. The system sends the geolocation and navigation path to a picker client device associated with a picker servicing the order.

Abstract: Certain embodiments herein disclose a cannula assembly. The cannula assembly comprises a cannula, having a proximal end and a distal end. The cannula comprises a head at the proximal end, the head having one or more wings. The cannula also comprises a hollow rod extending from the head to the distal end. The cannula assembly also comprises a hub couplable to the head and having a housing with an inner wall. The inner wall comprises an undercut, and one or more distal ends of the corresponding one or more wings are configured to fit into the undercut and couple the hub to the head upon insertion of the head into the hub.

Abstract: A trocar-cannula insertion tool for ophthalmic procedures may include a cap with a body having a proximal end and a distal end opposite the proximal end. A marking element extends in a distal direction from the distal end of the body and includes at least one marking tip for forming one or more indentations on a patient's eye during an ophthalmic procedure. A manipulation element extends from the body in a direction different from the marking element and includes at least one manipulation tip for gripping a tissue of the patient's eye during the ophthalmic procedure. The cap may further include at least one window for exposing a photoluminescent cannula of the trocar-cannula insertion tool to light, enabling the cannula to absorb photons prior to insertion thereof.