Patent classifications
G06Q10/0837
Systems and methods for initiating returns over a network
The present invention provides systems and methods for processing return transactions over a network. An embodiment of the invention discloses an online return application that generates an electronic return shipping label that can be delivered to a browser of a customer that wishes to make a return. Also, disclosed is the creation and transmission of label delivery links, which provide for dynamic generation and delivery of shipping labels.
Systems and methods for initiating returns over a network
The present invention provides systems and methods for processing return transactions over a network. An embodiment of the invention discloses an online return application that generates an electronic return shipping label that can be delivered to a browser of a customer that wishes to make a return. Also, disclosed is the creation and transmission of label delivery links, which provide for dynamic generation and delivery of shipping labels.
Artificial intelligence system employing graph convolutional networks for analyzing multi-entity-type multi-relational data
Respective initial feature sets are obtained for the nodes of a graph in which the nodes represent instances of entity types and edges represent relationships. Using the initial feature sets and the graph, a graph convolutional model is trained to generate one or more types of predictions. In the model, a representation of a particular node at a particular hidden layer is based on aggregated representations of neighbor nodes, and an embedding produced at a final hidden layer is used as input to a prediction layer. The trained model is stored.
Artificial intelligence system employing graph convolutional networks for analyzing multi-entity-type multi-relational data
Respective initial feature sets are obtained for the nodes of a graph in which the nodes represent instances of entity types and edges represent relationships. Using the initial feature sets and the graph, a graph convolutional model is trained to generate one or more types of predictions. In the model, a representation of a particular node at a particular hidden layer is based on aggregated representations of neighbor nodes, and an embedding produced at a final hidden layer is used as input to a prediction layer. The trained model is stored.
SYSTEMS AND METHODS FOR RANKING POTENTIAL ATTENDED DELIVERY/PICKUP LOCATIONS
A computer system for ranking potential attended delivery/pickup locations is disclosed. In various embodiments, a user or computer system identifies an area in which to establish at least one attended delivery/pickup location. In a particular embodiment, the system receives data associated with potential attended delivery/pickup locations (e.g., attended delivery/pickup location candidates), including the specific characteristics of those candidates. The system then, based at least in part on the characteristics of each of the attended delivery/pickup location candidates, ranks the attended delivery/pickup location candidates and displays the rankings to a user for use in selecting the most suitable candidate for the area.
Management device, management method, and program
Provided is a management device for managing an operation of a package delivery vehicle configured to travel autonomously on a road without accommodating a driver, and capable of storing a package into each of a plurality of storages each covered by an openable and closable door, the management device including: a storage device that stores a program; and a hardware processor, in which the hardware processor is configured to execute the program stored in the storage device to: receive application information on delivery of the package from a user; and determine an operation of the package delivery vehicle, in which the hardware processor is configured to cause the package delivery vehicle during delivery of a package to collect a new package.
Modular mobility base for a modular autonomous logistics vehicle transport apparatus
A modular mobility base for a modular autonomous bot apparatus transporting an item being shipped including a mobile base platform, a component alignment interface, a mobility controller, a propulsion and steering system, and sensors. The component alignment interface provides an alignment channel into which another modular component can be placed and secured on the platform. The mobility controller generates propulsion control signals for controlling speed of the modular mobility base and steering control signals for navigation of the modular mobility base. The propulsion system is connected to the platform and responsive to the propulsion control signal. The steering system is connected to the mobile base platform and is responsive to the steering control signal to cause changes to directional movement of the modular mobility base. The sensors are disposed on the platform provide feedback sensor data to the mobility controller about a condition of the modular mobility base.
Modular mobility base for a modular autonomous logistics vehicle transport apparatus
A modular mobility base for a modular autonomous bot apparatus transporting an item being shipped including a mobile base platform, a component alignment interface, a mobility controller, a propulsion and steering system, and sensors. The component alignment interface provides an alignment channel into which another modular component can be placed and secured on the platform. The mobility controller generates propulsion control signals for controlling speed of the modular mobility base and steering control signals for navigation of the modular mobility base. The propulsion system is connected to the platform and responsive to the propulsion control signal. The steering system is connected to the mobile base platform and is responsive to the steering control signal to cause changes to directional movement of the modular mobility base. The sensors are disposed on the platform provide feedback sensor data to the mobility controller about a condition of the modular mobility base.
Systems and methods for electronically processing pickup of return items from a customer
Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of receiving a return request for an item from a customer electronic device of a customer, determining that the return request is available for a pickup return option for (1) pickup, by a driver, of the item at an address associated with the customer and (2) transportation, by the driver, of the item from the address to the store, and, if the customer selects a pickup return option: assigning the driver to pick up the item from the address and transport the item to the store, receiving a return scan for the item from a store electronic device at the store or from a driver electronic device, and initiating a refund to the customer for the item.
Systems and methods for electronically processing pickup of return items from a customer
Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of receiving a return request for an item from a customer electronic device of a customer, determining that the return request is available for a pickup return option for (1) pickup, by a driver, of the item at an address associated with the customer and (2) transportation, by the driver, of the item from the address to the store, and, if the customer selects a pickup return option: assigning the driver to pick up the item from the address and transport the item to the store, receiving a return scan for the item from a store electronic device at the store or from a driver electronic device, and initiating a refund to the customer for the item.