Abstract: Aspects of the invention perform an operation comprising receiving a plurality of rules associated with a first seat in a mass-transit vehicle, determining a plurality of physical attributes for a first passenger, of a plurality of passengers, based on data describing the first passenger received from a plurality of data sources, determining a plurality of non-physical attributes for the first passenger based on the received data describing the first passenger, determining that at least one of the plurality of physical attributes and the plurality of non-physical attributes of the first passenger satisfy each rule in the set of rules associated with the first seat, computing, based on a machine learning (ML) model a score for the first passenger, determining that the score exceeds a threshold score associated with the first seat, and allocating the first passenger to the first seat in the mass-transit vehicle.

Abstract: Aspects of the invention perform an operation comprising receiving a plurality of rules associated with a first seat in a mass-transit vehicle, determining a plurality of physical attributes for a first passenger, of a plurality of passengers, based on data describing the first passenger received from a plurality of data sources, determining a plurality of non-physical attributes for the first passenger based on the received data describing the first passenger, determining that at least one of the plurality of physical attributes and the plurality of non-physical attributes of the first passenger satisfy each rule in the set of rules associated with the first seat, computing, based on a machine learning (ML) model a score for the first passenger, determining that the score exceeds a threshold score associated with the first seat, and allocating the first passenger to the first seat in the mass-transit vehicle.

Abstract: Aspects of the invention perform an operation comprising receiving a plurality of rules associated with a first seat in a mass-transit vehicle, determining a plurality of physical attributes for a first passenger, of a plurality of passengers, based on data describing the first passenger received from a plurality of data sources, determining a plurality of non-physical attributes for the first passenger based on the received data describing the first passenger, determining that at least one of the plurality of physical attributes and the plurality of non-physical attributes of the first passenger satisfy each rule in the set of rules associated with the first seat, computing, based on a machine learning (ML) model a score for the first passenger, determining that the score exceeds a threshold score associated with the first seat, and allocating the first passenger to the first seat in the mass-transit vehicle.

Abstract: A method controls execution of an intended application. One or more processors receive a first formatted character string that is in a first format. The processor(s) determine which application from multiple applications is an intended application that is to use the first formatted character string as a basis of an input to the intended application. The processor(s) convert the first formatted character string into a second formatted character string by applying a second format that is used by the intended application, and then execute the intended application by using the second formatted character string as an input to the intended application.

Abstract: Embodiments generally relate an intelligent automated trolley system. In some embodiments, a method includes receiving a trolley request. The method further includes determining one or more luggage characteristics of one or more luggage items. The method further includes determining one or more trolleys to send based on the luggage characteristics of the one or more luggage items. The method further includes assigning the one or more trolleys to assist the user based on the one or more characteristics of the one or more luggage items. The method further includes sending the one or more trolleys to a meeting location.

Abstract: A method controls execution of an intended application. One or more processors receive a first formatted character string that is in a first format. The processor(s) determine which application from multiple applications is an intended application that is to use the first formatted character string as a basis of an input to the intended application. The processor(s) convert the first formatted character string into a second formatted character string by applying a second format that is used by the intended application, and then execute the intended application by using the second formatted character string as an input to the intended application.

Abstract: Aspects of the invention perform an operation comprising receiving a plurality of rules associated with a first seat in a mass-transit vehicle, determining a plurality of physical attributes for a first passenger, of a plurality of passengers, based on data describing the first passenger received from a plurality of data sources, determining a plurality of non-physical attributes for the first passenger based on the received data describing the first passenger, determining that at least one of the plurality of physical attributes and the plurality of non-physical attributes of the first passenger satisfy each rule in the set of rules associated with the first seat, computing, based on a machine learning (ML) model a score for the first passenger, determining that the score exceeds a threshold score associated with the first seat, and allocating the first passenger to the first seat in the mass-transit vehicle.

Abstract: Embodiments generally relate an intelligent automated trolley system. In some embodiments, a method includes receiving a trolley request. The method further includes determining one or more luggage characteristics of one or more luggage items. The method further includes determining one or more trolleys to send based on the luggage characteristics of the one or more luggage items. The method further includes assigning the one or more trolleys to assist the user based on the one or more characteristics of the one or more luggage items. The method further includes sending the one or more trolleys to a meeting location.

Abstract: Systems, methods, and computer program products to perform an operation comprising receiving a plurality of rules associated with a first seat in a mass-transit vehicle, determining a plurality of physical attributes for a first passenger, of a plurality of passengers, based on data describing the first passenger received from a plurality of data sources, determining a plurality of non-physical attributes for the first passenger based on the received data describing the first passenger, determining that at least one of the plurality of physical attributes and the plurality of non-physical attributes of the first passenger satisfy each rule in the set of rules associated with the first seat, computing, based on a machine learning (ML) model a score for the first passenger, determining that the score exceeds a threshold score associated with the first seat, and allocating the first passenger to the first seat in the mass-transit vehicle.

Abstract: A method is disclosed for retrieving the reservation status information of a storage area network (SAN) device, a host transmits a persistent reservation in command with service action setting of ‘read reservation’ to a first LUN, wherein the host is connected to a port of the data storage server to which the LUN belongs. The host receives a message from the LUN. The host determines that the message is a success. The host sends to the LUN a persistent reservation in command with service action setting of ‘read keys’, responsive to a success message. The host determines that the LUN responds with a zero data length. The host determines the LUN is reserved with type 2 reservation, responsive to a determination that the LUN responds with a non-zero data length.

Abstract: A method is disclosed for retrieving the reservation status information of a storage area network (SAN) device, a host transmits a persistent reservation in command with service action setting of ‘read reservation’ to a first LUN, wherein the host is connected to a port of the data storage server to which the LUN belongs. The host receives a message from the LUN. The host determines that the message is a success. The host sends to the LUN a persistent reservation in command with service action setting of ‘read keys’, responsive to a success message. The host determines that the LUN responds with a zero data length. The host determines the LUN is reserved with type 2 reservation, responsive to a determination that the LUN responds with a non-zero data length.

Abstract: A method is disclosed for retrieving the reservation status information of a storage area network (SAN) device, a host transmits a persistent reservation in command with service action setting of ‘read reservation’ to a first LUN, wherein the host is connected to a port of the data storage server to which the LUN belongs. The host receives a message from the LUN. The host determines that the message is a success. The host sends to the LUN a persistent reservation in command with service action setting of ‘read keys’, responsive to a success message. The host determines that the LUN responds with a zero data length. The host determines the LUN is reserved with type 2 reservation, responsive to a determination that the LUN responds with a non-zero data length.

Abstract: A system and computer program product are disclosed for retrieving the reservation status information of a storage area network (SAN) device, a host transmits a persistent reservation in command with service action setting of ‘read reservation’ to a first LUN, wherein the host is connected to a port of the data storage server to which the LUN belongs. The host receives a message from the LUN. The host determines that the message is a success. The host sends to the LUN a persistent reservation in command with service action setting of ‘read keys’, responsive to a success message. The host determines that the LUN responds with a zero data length. The host determines the LUN is reserved with type 2 reservation, responsive to a determination that the LUN responds with a non-zero data length.