Abstract: A method includes a server computer receiving delivery data associated with a plurality of deliveries in a geographical area. The server computer can determine a plurality of adverse delivery zones in the geographical area. The server computer can receive a fulfillment request to deliver an item to an end user at a specific location and determine if the specific location is in one of the adverse delivery zones. If the specific location is in one of the adverse delivery zones, the server computer can take one or more additional actions to ensure that the item is delivered to the end user at the specific location.

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: Provided are various systems and processes for improving last-mile delivery of real-time, on-demand orders for perishable goods. In one aspect, an automated vehicle (AV) comprises a body including a storage compartment for storing perishable goods. The storage compartment is accessible by a user upon authentication of the user. The AV further comprises a sensor module for receiving data for navigating the AV. The sensor module is positioned above the body on a support structure at a predetermined height above the ground, such as three to five feet. The data includes one or more of the following: audio data, video data, radio waves, and backscattered light waves. The AV further comprises an onboard computer system configured to process the data to navigate the AV along motor vehicle routes and pedestrian routes. The AV may be configured to interface with an automated locker system to retrieve or deposit the perishable goods.

Abstract: A method, a computer program product, and a computer system. Activities of at least one customer with at least one tagged merchandise in possession of the at least one customer are monitored. The at least one tagged merchandise includes multiple Internet-of-Things (IoT) devices communicatively coupled over local communication channels to a central IoT hub which is communicatively coupled to an IoT service over the Internet. Each IoT device is designed for a particular application. The multiple IoT devices include respective multiple sensors with each sensor being particularized for the particular application of the IoT device that includes each sensor. The multiple IoT devices include a first IoT device that includes a first sensor configured to sense a type of interaction the at least one customer has with the at least one tagged merchandise to make a determination based on a gamification analysis of a qualifying event or a non-qualifying event.

Abstract: Provided are various systems and processes for improving last-mile delivery of real-time, on-demand orders for perishable goods. In one aspect, a method is provided for aggregating on-demand deliveries using a depot dispatch protocol which may implement automated order transport and retrieval systems. The method comprises dispatching merchant couriers to transport on-demand orders from merchants to a merchant depot where the orders are aggregated and batched based on optimized delivery routes and destination proximities. Batches of orders are then transported to a customer depot corresponding to an area of delivery destinations. Orders are then assigned to delivery couriers for completion of delivery to customers. Such delivery routing systems and processes may be implemented alongside a delivery tracking system for generating estimated time of arrival predictive updates for real-time delivery of perishable goods.

Abstract: The program directs a computer processor to implement a program that selects at least one substitute ingredient for a food recipe based on a plurality of determined criteria. The program receives a food recipe comprising one or more ingredients, instructions, and preparation time. The program determines time constraints of a user. The program identifies one or more Internet of Things (IoT) enabled cooking devices that are available in a kitchen associated with the user to carry out the instructions of the food recipe. The program searches a market database in a market to identify available options for the at least one substitute ingredient found in the market database based on a similarity level of the at least one substitute ingredient compared with at least one ambiguous ingredient in the food recipe, and selects the at least one substitute ingredient according to the plurality of determined criteria.

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 computing device may receive a user request and may determine a user jurisdiction associated with the received user request. Based at least on a request type and the user jurisdiction, the computing device may select a first policy file from among a plurality of policy files, the plurality of policy files preconfigured for respective different combinations of at least the request type and the user jurisdiction 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, based on a data action included in the first policy file, at least one instruction to at least one target subsystem.

Abstract: Provided are various systems and processes for improving last-mile delivery of real-time, on-demand orders for perishable goods. In one aspect, an automated vehicle (AV) comprises a body including a storage compartment for storing perishable goods. The storage compartment is accessible by a user upon authentication of the user. The AV further comprises a sensor module for receiving data for navigating the AV. The sensor module is positioned above the body on a support structure at a predetermined height above the ground, such as three to five feet. The data includes one or more of the following: audio data, video data, radio waves, and backscattered light waves. The AV further comprises an onboard computer system configured to process the data to navigate the AV along motor vehicle routes and pedestrian routes. The AV may be configured to interface with an automated locker system to retrieve or deposit the perishable goods.

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 an approach to determining a boundary-specific offer, one or more computer processors determine a user of a mobile device is within a pre-defined physical boundary. The one or more computer processors determine a first notification of an offer corresponding to the pre-defined physical boundary. The one or more computer processors send the first notification of the offer to the mobile device. The one or more computer processors determine whether the user of the mobile device moved outside of the pre-defined physical boundary. In response to determining the user of the mobile device moved outside of the pre-defined physical boundary, the one or more computer processors invalidate the offer.

Abstract: A server computer obtains a plurality of user features, a plurality of service provider features, and a model. The server computer inputs the plurality of user features and the plurality of service provider features into the model. The server computer updates the model to form an updated model. The server computer determines a plurality of carts using the updated model. Each cart of the plurality of carts includes one or more items. The server computer provides one or more carts of the plurality of carts to an end user device. The server computer receives a selection of a cart of the one or more carts or a modification thereof. The server computer processes the selection by facilitating preparation and delivery of the one or more items in the cart. The server computer updates the updated model using data related to the cart to form a subsequent model.

Abstract: A method includes receiving, by a computer, a destination address. The computer can obtain historical fulfillment data for the destination address. The computer can then determine one or more temporary transporter locations based on the historical fulfillment data for the destination address. The computer can determine a waypoint location from the one or more temporary transporter locations. The computer can provide the waypoint location.

Abstract: In some examples, a computing device may receive data from a plurality of groups of data sources. The computing device may create a training data set from a first portion of the received data and may create a plurality of validation data sets from a second portion of the received data. For example, each validation data set may correspond to a respective one of the groups of data sources. The computing device may train, using the training data set, a plurality of machine learning models configured for synthesizing data. For instance, respective ones of the machine learning models may correspond to respective ones of the groups of data sources. Further, the computing device may validate the respective machine learning models using the respective validation data set corresponding to the respective group to which the respective machine learning model being validated corresponds.

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: Integrating a fulfillment service provider into a third-party application via an Application Programming Interface (API) is described. In an example, a computing device associated with a fulfillment service provider can determine, based at least in part on an indication of an interaction between a user and a third-party application, that the fulfillment service provider has been initialized. In one example, functionality associated with the fulfillment service provider can be accessible to the third-party application via an API. In at least one example, the computing device can generate, based at least in part on the interaction with the third-party application and while the fulfillment service provider is initialized, a recommendation of at least one of a service, a merchant, or an item associated with the fulfillment service provider. The computing device can cause the recommendation to be presented via at least one of the third-party application or the fulfillment service provider.

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: Provided are various mechanisms and processes for optimizing real-time, on-demand delivery service for perishable goods. In one aspect, a method is provided for aggregating on-demand deliveries using a depot dispatch protocol. The method comprises dispatching runners to pick up on-demand orders for drop off at a depot where the orders are aggregated and batched based on optimized delivery routes and destination proximities. Batches of orders are then assigned to couriers who may arrive at the depot to receive the batched orders without having to navigate through areas of high congestion. Such delivery routing system may be implemented alongside a delivery tracking system for generating estimated time of arrival predictive updates for real-time delivery of perishable goods.

Abstract: Provided are various mechanisms and processes for generating dynamic merchant similarity predictions. In one aspect, a system is configured for receiving historical datasets that include a series of merchants from historical browsing sessions generated by one or more users. The merchants are converted into corresponding vector representations for training a predictive model to output associated merchants based on a generated weighted vector space. Once sufficiently trained, data from a new browsing session may be received, which may include a target merchant. The target merchant is input into the predictive model as a vector to output one or more context merchants having vectors with the highest cosine similarity value to the target merchant vector. Selected context merchants may then be transmitted to the user device as targeted merchant suggestions in the new browsing session. The predictive models may be continuously trained using data received from subsequent browsing sessions.

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.