Disclosed is a method, performed by an electronic device, for assigning items into one or more containers. The method comprises obtaining a plurality of attributes associated with a corresponding item. The method comprises obtaining a set of container parameters associated with a corresponding container. The method comprises obtaining one or more constraints, wherein the one or more constraints limit assigning items in a same container. The method comprises determining an assignment of the items to the one or more containers, based on the attributes, the set of container parameters and the one or more constraints. The method comprises outputting, based on the assignment, an assignment plan of the items into the one or more containers.

Rule data sets are received. These rule sets are associated with constraints controlling how records that are associated with the goods are consolidated. These goods are to be received for importing. An estimate score indicative of the risk for inspection for a first set of goods that are to be imported is generated. Based at least in part on the rule data sets and the generated estimate, a plurality of records are consolidated to a single instance for the first set of goods. Based on the consolidating, a user interface is caused to be generated that renders information associated with the consolidating.

Rule data sets are received. These rule sets are associated with constraints controlling how records that are associated with the goods are consolidated. These goods are to be received for importing. An estimate score indicative of the risk for inspection for a first set of goods that are to be imported is generated. Based at least in part on the rule data sets and the generated estimate, a plurality of records are consolidated to a single instance for the first set of goods. Based on the consolidating, a user interface is caused to be generated that renders information associated with the consolidating.

Rule data sets are received. These rule sets are associated with constraints controlling how records that are associated with the goods are consolidated. These goods are to be received for importing. An estimate score indicative of the risk for inspection for a first set of goods that are to be imported is generated. Based at least in part on the rule data sets and the generated estimate, a plurality of records are consolidated to a single instance for the first set of goods. Based on the consolidating, a user interface is caused to be generated that renders information associated with the consolidating.

Devices and techniques are generally described for cross-domain machine learning. A first machine learning model may be trained using first data of a first domain. Predictions may be generated by inputting a plurality of domain data from other domains apart from the first domain into the first machine learning model. For each of the predictions, a prediction error may be determined. A grouping of similar domains from among the other domains may be determined based on the prediction errors. A second machine learning model may be trained for the grouping of similar domains.

A method at a computing device for attributing a cause to a border delay, the method including determining that a vehicle has entered a border geofence; obtaining sensor data from the vehicle; and correlating the sensor data with the determining that the vehicle has entered the geofence to attribute the cause to the border delay.

The disclosure is related to monitoring integrity of cargo in a container on a trans-oceanic voyage and cargo on truck or trains. The method includes determining, by a trigger from at least one remote processor, that the system is activated by a change associated with a bio-element of at least one tag on cargo in a shipping process. At least one environment camera is activated to provide an environment media. At least one data retrieving component is activated to retrieve and to provide data associated with a tracking marker of cargo associated with the container. At least one satellite receiver is activated to receive location information for the at least one local processor. One or more of the environment media, the data, and the location information is packaged for a bit stream. The bit stream is communicated to a remote device.

Disclosed is a method process of online rating for international import and export shipments. The rating combines multiple carriers together in order to provide one cohesive international service. The consumer can obtain a door to port or door to door rate when employing a pick up carrier, such as a trucking company, and an airline or ocean carrier as well as a destination delivery company. The method provide a real time monetary rate costs based upon the closest terminal and determines which terminal to terminal rate to use for the air or ocean portion as well as providing a pick up rate for that service. The process further estimates the multiple ocean and air rate costs from various vendors associated with a shipment and stores these rate costs through a database to be used at a later date for verification.

An example operation may include one or more of querying, via an application programming interface (API), a blockchain ledger for attributes of a shipment by a carrier from an origin location to a destination location, predicting, via an artificial intelligence (AI) model, one or more future events that will occur during the shipment based on the attributes of the shipment retrieved from querying the blockchain ledger, generating, via a smart contract, an accelerated e-invoice based on the one or more future events predicted by the AI model, and storing the accelerated e-invoice on the blockchain ledger.

An example operation may include one or more of querying, via an application programming interface (API), a blockchain ledger for attributes of a shipment by a carrier from an origin location to a destination location, predicting, via an artificial intelligence (AI) model, one or more future events that will occur during the shipment based on the attributes of the shipment retrieved from querying the blockchain ledger, generating, via a smart contract, an accelerated e-invoice based on the one or more future events predicted by the AI model, and storing the accelerated e-invoice on the blockchain ledger.