Amazon Technologies, Inc.

Girish S Lakshman

Lin Wan

Michael Mahesh Bhaskaran

Joel R Spiegel

Paul G Nordstrom

Piyush Gupta

Michael Terrance McKenna

Llewellyn W Bezanson

Pradeep Desai

Eric T Ferguson

Saurabh Dileep Baji

Neha Goswami

Charles M Griffith

Richa Agarwal

Benjamin Elliott Pew

Jingqiao Zhang

M Christopher Wenneman

Matthew A Yeatts

Andrew Korytko

Michael Seifert

Gregory Ignatov

Trenton M Tatro

Karthik Balasubramanian

Marc A Onetto

Keith B Ligon

A pickup location that includes a control station and one or more storage compartment modules provides the ability for items to be ordered and delivered for pickup by a user without having to pack those items in a shipping package prior to shipping from a materials handling facility. Delivering items for pickup by a user without having to package the items prior to shipping, may provide a better experience for the customer, reduce waste in packaging material and a lower cost of delivering the ordered items to the customer.

Pickup location monitoring

Pre-order delivery of items to a pickup location

A method and system for transit characteristic prediction. In one embodiment, a method may include determining respective transit latencies from a source location to a number of destination locations, and grouping the destination locations according to a similarity criterion into a number of subsets corresponding to respective geographical regions, where transit characteristics of locations grouped into a geographical region may each satisfy the similarity criterion. The method may further include determining a respective distribution associated with each of the geographical regions, where a given respective distribution correlates a given transit characteristic from the source location to a given geographical region with a respective surety factor, where the respective surety factor includes a cumulative probability that the given transit characteristic will be achieved. The method may also include dynamically updating the respective transit characteristic, the grouping of destination locations into geographical regions and the respective distributions dependent upon collecting empirical transit data.

Method and system for transit characteristic prediction

Pickup locations

Modular station pickup locations

A method and system for anticipatory package shipping are disclosed. According to one embodiment, a method may include packaging one or more items as a package for eventual shipment to a delivery address, selecting a destination geographical area to which to ship the package, shipping the package to the destination geographical area without completely specifying the delivery address at time of shipment, and while the package is in transit, completely specifying the delivery address for the package.

Method and system for anticipatory package shipping

A method and system for transit characteristic prediction. In one embodiment, a method may include determining respective transit latencies from a source location to a number of destination locations, and grouping the destination locations according to a fitness function into a number of subsets corresponding to respective geographical regions. The grouping may involve a series of divisions and combinations of potential regions to form a plurality of sets of potential regions. Each set of potential regions may be evaluated using the fitness function, and the set with the better fitness score may be selected. The method may also include dynamically updating the respective transit characteristic, regrouping the regions, and reselecting a set of potential regions based on empirical transit data.

Adaptive regionalization for transit characteristic prediction

Various embodiments of a system and method for scheduled delivery of shipments with multiple shipment carriers are described. Various embodiments may include a scheduled delivery component configured to receive delivery availability information for multiple shipment carriers. In various embodiments, for each shipment carrier, delivery availability information may specify one or more delivery periods during which that shipment carrier is available to deliver shipments of one or more items to designated locations. This delivery availability information may be utilized to generate a scheduled delivery tool that enables users to select a particular delivery period during which an item is to be delivered. The scheduled delivery tool may provide multiple selectable delivery periods based on the delivery availability information of multiple shipment carriers. The scheduled delivery component may determine that a particular delivery period has been selected and generate a delivery instruction based on such selection.

System and method for scheduled delivery of shipments with multiple shipment carriers

Various embodiments of a system and method for constraint-based reduction of a solution space for vehicle routing are described. Embodiments may include a system configured to identify sets of redundant routes within a solution space. For each given set of redundant routes, the system may utilize one or more constraints associated with conveying shipments on routes of the given set in order to eliminate one or more routes of that set from consideration. For each of multiple routes of the given set that have not been eliminated from consideration, the system may determine a respective cost associated with utilizing that route. The system may also, in response to determining that the cost associated with a particular route that has not been eliminated from consideration is less than the cost associated with other routes that have not been eliminated from consideration, add the particular route to a reduced solution space.

System and method for constraint-based reduction of a solution space for vehicle routing

A method and system for selecting a shipping method dependent upon a dynamic model of shipping activity. In one embodiment, a method may include detecting a given shipment event for which one of a plurality of shipping methods is to be selected, determining a current output state of a discrete event model representative of shipping method selections for one or more previous shipment events, and in response to detecting the given shipping event, producing a selection of a particular one of the plurality of shipping methods that mathematically optimizes an objective function, wherein the objective function is dependent on a cost of shipping associated with the given shipment event and on a weighted deviation between the current output state of the discrete event model and a target output state of the discrete event model.

Selecting shipping methods dependent on a dynamic model of shipping activity

Growing community of inventors

Issaquah, WA, United States of America

Girish S Lakshman