Abstract: A method and system for consolidating travel records from on the one hand a source derived from a Global Distribution System or more generally a first travel database, and on the other hand a source derived from non-Global Distribution System information or more generally a second travel database. It is determined whether unique identifiers associated with the two sources match, indicating that information from the source travel records can be consolidated without compromising data integrity.

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: The disclosure is directed to an autonomous method for dynamically providing a security policy. A method for task allocation of aerial vehicles includes: generating a spot price for a task to be performed by at least one aerial vehicle; determining an expected time for completion of the task by the at least one aerial vehicle; comparing a received bid to the spot price; and performing the task using the at least one aerial vehicle if the bid is greater than the spot price.

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: A system and method that provides for the routing and delivery of services to consumers by providers is disclosed. The system enables providers to advertise and list the products and services they provide, and customers to solicit bids or to otherwise seek to engage a provider to perform a service or from whom to purchase a good. The system may store customer and provider information in databases to be searched to identify a match between customer and provider. The system transmits navigation information to the provider's device to travel to the final destination. The navigation information includes an intermediary destination the provider must reach before being presented with the final destination. The system may include a notification feature to notify a customer or third party regarding the location of the provider or the status of a requested service or delivery.

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.