Abstract: An embodiment of a perforating gun system deployable in a wellbore extending through a subterranean earthen formation includes a tandem sub including a sub housing and a signal pod receivable in the sub housing, wherein the signal pod includes a detonator and a receptacle; and a perforating gun including a charge carrier configured to receive one or more shaped charges, the charge carrier including a detonator cord housing configured to receive a terminal end of a detonator cord, and wherein the detonator cord housing is receivable in the receptacle of the signal pod to ballistically couple the detonator cord with the detonator.

Abstract: A method for optimizing order fulfillment in a warehouse by an online system. The system receives an item catalog from an inventory system, which includes item locations. The system also obtains a taxonomy that organizes items into hierarchical levels based on attributes. When an order is placed by a user, the system checks whether the item catalog contains location data for each item. If an item's location is missing, the system identifies alternative items from the taxonomy with shared attributes and uses the location of a selected alternative item as a proxy. The system determines a picking sequence for the items in the order, optimized to minimize travel distance within the warehouse, and transmits the sequence to a shopper's device for fulfillment.

Abstract: A system receives a request for a set of items at a warehouse from a user device, and determines a set of candidate items responsive to the request. The system applies a trained item availability model to each candidate item to determine a prediction of a likelihood that the candidate item is available for pickup at the warehouse. A subset of candidate items that have a prediction below a threshold is classified as low availability. The computer system also determines a cap of low availability items to present to a user based on a user utility curve. The user utility curve is modeled based on user utility associated with amounts of low availability items presented. The low availability items are filtered to an amount within the determined cap. The filtered low availability items are sent to the user device for presentation in a user interface.

Abstract: A swivel assembly includes a first member having a first throughbore, a second member having a second throughbore, a flexible joint coupled between the first member and the second member that permits at least one of the first member and the second member to rotate an unlimited number of rotations relative to the other and that also connects the first throughbore and the second throughbore such that an electrical signal pathway for receiving an electrical signal conductor is formed that extends continuously through the flexible joint and between the first throughbore and the second throughbore, and a pressure bulkhead positioned along the electrical signal pathway that divides the electrical signal pathway into a pressure exposed zone that is exposed to external pressure from the surrounding environment and a pressure isolated zone that is isolated from the external pressure.

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: Based on orders fulfilled by shoppers of an online concierge system, the online concierge system identifies items in an order that are difficult to find in a warehouse in which the order is fulfilled. When a shopper obtains a difficult to find item from the warehouse, the online concierge system prompts the shopper to provide information for finding the difficult to find item in the warehouse. The online concierge system stores the information for finding the difficult to find item from the shopper in association with the difficult to find item and with the warehouse. Subsequently, when a different shopper is fulfilling an order from the warehouse including the difficult to find item, the online concierge system displays the information for finding the difficult to find item in the warehouse to the different shopper.

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 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: A system for setting a plug in a wellbore includes a setting tool configured, upon activation, to displace the shift the plug in the wellbore from a run-in configuration permitting fluid flow within the wellbore around the plug to a set configuration restricting fluid flow in the wellbore around the plug, an initiator including an igniter switch and an igniter assembly including an igniter energetic element, the initiator including a signal interrupter configured to shift automatically from a first state in which signal communication is provided through the signal interrupter between the igniter switch and the igniter assembly to a second state in which signal communication is restricted through the signal interrupter between the igniter switch and the igniter assembly in response to exposing the initiator to a predefined toolstring condition and whereby a surface indication is provided of the shifting of the plug to the set configuration.

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 performs asynchronous automated correction handling of incorrectly sorted items using point-of-sale data. The online concierge system receives orders from customer client devices and determines a batched order based on the received orders. The online concierge system sends the batched order to a shopper client device for fulfillment. The online concierge system receives transaction data associated with the batched order from a third party system. The online concierge system determines whether a sorting error occurred based on the transaction data and the batched order. In response to determining that a sorting error occurred, the online concierge system sends an instruction to correct the sorting error to the shopper client device.

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: Based on orders fulfilled by shoppers of an online concierge system, the online concierge system identifies items in an order that are difficult to find in a warehouse in which the order is fulfilled. When a shopper obtains a difficult to find item from the warehouse, the online concierge system prompts the shopper to provide information for finding the difficult to find item in the warehouse. The online concierge system stores the information for finding the difficult to find item from the shopper in association with the difficult to find item and with the warehouse. Subsequently, when a different shopper is fulfilling an order from the warehouse including the difficult to find item, the online concierge system displays the information for finding the difficult to find item in the warehouse to the different shopper.

Abstract: A system for setting a plug in a wellbore includes a setting tool configured, upon activation, to displace the shift the plug in the wellbore from a run-in configuration permitting fluid flow within the wellbore around the plug to a set configuration restricting fluid flow in the wellbore around the plug, an initiator including an igniter switch and an igniter assembly including an igniter energetic element, the initiator including a signal interrupter configured to shift automatically from a first state in which signal communication is provided through the signal interrupter between the igniter switch and the igniter assembly to a second state in which signal communication is restricted through the signal interrupter between the igniter switch and the igniter assembly in response to exposing the initiator to a predefined toolstring condition and whereby a surface indication is provided of the shifting of the plug to the set configuration.

Abstract: A toolstring for plugging and perforating a wellbore includes a perforating gun extending between an uphole end and a downhole end longitudinally opposed to the uphole end, a plugging assembly extending between an uphole end and a downhole end longitudinally opposed to the uphole end of the plugging assembly, the plug assembly including a setting tool physically attached directly to the perforating gun, the setting tool including an elongate housing extending between an uphole end and a downhole end opposite the uphole end of the housing, an open passageway extending from the uphole end to the downhole end of the housing, and wherein the setting tool further includes an elongate mandrel positioned in the open passageway and having an uphole end and a downhole end opposite the uphole end of the mandrel, and a combustion chamber located within the elongate housing for receiving an energetic charge.

Abstract: An online concierge system performs asynchronous automated correction handling of incorrectly sorted items using point-of-sale data. The online concierge system receives orders from customer client devices and determines a batched order based on the received orders. The online concierge system sends the batched order to a shopper client device for fulfillment. The online concierge system receives transaction data associated with the batched order from a third party system. The online concierge system determines whether a sorting error occurred based on the transaction data and the batched order. In response to determining that a sorting error occurred, the online concierge system sends an instruction to correct the sorting error to the shopper client device.

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: A toolstring for plugging and perforating a wellbore includes a perforating gun extending between an uphole end and a downhole end longitudinally opposed to the uphole end, a plugging assembly extending between an uphole end and a downhole end longitudinally opposed to the uphole end of the plugging assembly, the plug assembly including a setting tool physically attached directly to the perforating gun, the setting tool including an elongate housing extending between an uphole end and a downhole end opposite the uphole end of the housing, an open passageway extending from the uphole end to the downhole end of the housing, and wherein the setting tool further includes an elongate mandrel positioned in the open passageway and having an uphole end and a downhole end opposite the uphole end of the mandrel, and a combustion chamber located within the elongate housing for receiving an energetic charge.

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.