Abstract: Techniques for optimizing a food service configured to receive food orders from multiple customers, and consolidate orders based on respective days, times, and locations associated therewith. The food orders may include deliveries, customer pick-ups, and dine-in options for eating at a restaurant. The optimized food service may be configured to assign a restaurant to the consolidated orders and send a preparation instruction to the restaurant to prepare the consolidated orders for a designated pick-up or dining time. The optimized food service may additionally instruct an order supervisor to travel to the restaurant to assist in preparation of the consolidated orders. The order supervisor may additionally serve a food order, such as for a dine-in option or full service, catered delivery. For delivery orders, the optimized food service may assign a courier and, in some examples, a delivery assistant, to deliver the orders to respective customers.

Abstract: A remote vehicle control system includes a vehicle mounted sensor system including a video camera system for producing video data and a distance mapping sensor system for producing distance map data. A data handling system is used to compress and transmit both the video and distance map data over a cellular network using feed forward correction. A virtual control system acts to receive the video and distance map data, while providing a user with a live video stream supported by distance map data. Based on user actions, control instructions can be sent to the vehicle mounted sensor system and the remote vehicle over the cellular network.

Abstract: Provided are mechanisms and processes for facilitating a real-time, on-demand delivery platform for perishable goods by determining aspects such as the location and status of a courier who is affiliated with a delivery platform. In one example, a courier carries a Bluetooth Low Energy (BLE) enabled mobile device that is configured to detect signals from beacons located at both a vehicle registered to the courier and a provider site. Each of the detected signals includes identifying information and signal strength of the detected signal. The identifying information and signal strengths are transmitted to a server that monitors the signal strengths received at the courier's BLE-enabled mobile device from the beacon devices to determine the likelihood that the courier has arrived at the provider site.

Abstract: Described herein are various systems and processes for vehicles for delivery of real-time, on-demand orders for perishable goods, and operations thereof. The automated delivery vehicle may include a food transport container and various sensors configured to determine the location and/or surroundings of the vehicle. The automated delivery vehicle may further include a display and/or other output source configured to provide outputs to persons surrounding the automated delivery vehicle. Such outputs may include, for example, an information graphical representation (e.g., provided on a display), an audio output, and/or data communicated to a wireless device.

Abstract: In some examples, a computing device may receive a user request and may determine a user type and a user location associated with the received user request. Further, based at least on a request type, the user type, and the user location, the computing device may select a first policy file from among a plurality of policy files, each policy file of the plurality of policy files preconfigured for a respective different combination of at least the request type, the user type, and the user location to contain at least one data action for instructing at least one respective target subsystem to perform the at least one data action in response to a respective user request. In addition, the computing device may send, for a respective data action included in the first policy file, a respective instruction to a respective target subsystem.

Abstract: Provided are mechanisms and processes for facilitating a real-time, on-demand delivery platform for perishable goods by determining aspects such as the location and status of a courier who is affiliated with a delivery platform. In one example, a courier carries a Bluetooth Low Energy (BLE) enabled mobile device that is configured to detect signals from beacons located at both a vehicle registered to the courier and a provider site. Each of the detected signals includes identifying information and signal strength of the detected signal. The identifying information and signal strengths are transmitted to a server that monitors the signal strengths received at the courier's BLE-enabled mobile device from the beacon devices to determine the likelihood that the courier has arrived at the provider site.

Abstract: Described are systems and processes for generating dynamic estimated time of arrival predictive updates for delivery of perishable goods. In one aspect a system is configured for generating dynamic estimated time of arrival (ETA) predictive updates between a series of successive events for real-time delivery of orders. For each order, a plurality of delivery events and corresponding timestamps are received from devices operated by customers, restaurants, and couriers. Based on the timestamps, the system generates a plurality of ETA time predictions for one or more of the delivery events with trained predictive models that use weighted factors including historical restaurant data and historical courier performance. As additional timestamps are received for a delivery event, the trained predictive models dynamically update the ETA time predictions for successive events. The predictive models may be continuously trained by updating the weighted factors based on the received timestamps.

Abstract: Provided are various mechanisms and processes for generating delivery associate incentive values. In some implementations, predicted demand can be generated based on a first set of historical data and predicted supply can be generated based on a second set of historical data. Delivery quality values can be generated based on the predicted demand and the predicted supply. The delivery quality values can be used to determine incentive values that are provided to delivery associates of an on-demand delivery platform.

Abstract: Systems and methods for universal menu integration. A digital key is issued to a vendor to access an interface. An uploaded menu is received from the vendor. The uploaded menu is in a universal format based on pre-determined criteria. The menu may be received from the vendor via HyperText Transfer Protocol (HTTP). The uploaded menu may be submitted in a format different from the universal format and then subsequently translated into the universal format. The uploaded menu is posted for receiving online delivery orders. An online delivery order is received from a user device. Last, the online delivery order is transmitted to the vendor. The interface may be an application programming interface (API) utilizing an API library including post, get, and patch functions, as well as out of stock and item availability fields. The system may be configured to receive menu updates after posting the uploaded menu.

Abstract: In some examples, a service device may receive, from buyer applications on respective buyer devices, communications indicating a number of times item information about a first item is presented in user interfaces on the buyer devices. The service device may further receive respective orders through the user interfaces, for the first item or other items. The service device may compare the number of times the item information about the first item is presented in the respective user interfaces to a number of times the buyer applications are used to place orders for the first item to determine a value for the first item. In addition, the service device may send, to a merchant device, a communication indicating an amount of the first item determined for the merchant based at least in part on the value determined for the first item.

Abstract: Described are systems and processes for generating dynamic estimated time of arrival predictive updates for delivery of perishable goods. In one aspect a system is configured for generating dynamic estimated time of arrival (ETA) predictive updates between a series of successive events for real-time delivery of orders. For each order, a plurality of delivery events and corresponding timestamps are received from devices operated by customers, restaurants, and couriers. Based on the timestamps, the system generates a plurality of ETA time predictions for one or more of the delivery events with trained predictive models that use weighted factors including historical restaurant data and historical courier performance. As additional timestamps are received for a delivery event, the trained predictive models dynamically update the ETA time predictions for successive events. The predictive models may be continuously trained by updating the weighted factors based on the received timestamps.

Abstract: In some examples, a location of a merchant is updated as the merchant moves. A server receives the location of the merchant, and compares that location to the location of a user, so as to determine whether the merchant is located within a first threshold distance or a second, smaller threshold distance from the location of the user. If the user is within the first threshold distance, the server presents a first point of sale (POS) interface to initiate an order from the merchant and present the user with an option to fulfill that order through delivery. If the merchant is located within the second, smaller threshold distance from the user, the server presents the user with a second POS interface that gives the user an option to fulfill the order through pickup instead of delivery.

Abstract: In some examples, a computing device exposes, to a merchant device, one or more Application Programming Interfaces (APIs) for accessing a delivery service. The computing device receives, from the merchant device, via the one or more APIs, a request regarding delivery of an order specified by a customer for delivery from the merchant. The computing device may send a communication to a courier to instruct the courier to deliver the order. The computing device may receive, from the merchant device, via the one or more APIs, a request for a delivery status of the order received via a user interface. The computing device may determine the delivery status of the order based at least partially on location information received from the courier, and sends the delivery status to prompt the merchant device to present the delivery status in the user interface.

Abstract: Described herein are various systems and processes for vehicles for delivery of real-time, on-demand orders for perishable goods, and operations thereof. The vehicles may each include a plurality of different modules that couple to a chassis. The modules may include battery modules, drive modules, cargo modules, and other modules. Each drive module may include wheels and motors and steering mechanisms that control the wheels. A plurality of such modules may be disposed on the chassis. The modules may be disposed on different portions of the chassis and be given different operational commands accordingly. Furthermore, operation of the vehicle may be adjusted based on the cargo carried by the vehicle.

Abstract: Described are systems and processes for generating dynamic effort-based delivery value predictions for real-time delivery of perishable goods. In one aspect, a system is configured for generating dynamic delivery value predictions for delivery opportunities provided to couriers. For each order, delivery events and corresponding timestamps are received from devices operated by customers, restaurants, and couriers. Based on the timestamps, the system generates a predicted delivery duration with trained predictive models that use weighted factors such as order data and historical restaurant data. A service value for the delivery of the order is determined based the predicted delivery duration and a predetermined active time value. The service value is then transmitted along with the corresponding delivery opportunity to a user device of a courier. The determined service values may be adjusted based on courier acceptance rates of delivery opportunities and other factors such as customer experience.

Abstract: In some examples, a service provider may determine that multiple merchants are within a threshold distance of each other. The service provider may send, to a buyer, item information about items offered by the multiple merchants. Further, based at least in part on the multiple merchants having been determined to be within the threshold distance of each other, the item information sent to the buyer may indicate that orders for items provided by the multiple merchants are available for combined delivery, e.g., as a single combined order. A first courier stationed near the merchants may pick up the items from the merchants and may handoff the items to a second courier who delivers the items to the buyer. Additionally, in some cases, the preparation of the multiple items may be timed to so that the items are ready for pickup at approximately the same time.