Cognitive-based passenger selection

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.

What is claimed is: 
     
         1 . A method, comprising:
 receiving, by an application executing on a processor, a plurality of rules associated with a first seat of a plurality of seats 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, by the application, the first passenger to the first seat in the mass-transit vehicle.   
     
     
         2 . The method of  claim 1 , wherein the mass-transit vehicle comprises one of: (i) an airplane, (ii) a bus, and (iii) a train, wherein a first physical attribute of the plurality of physical attributes is received from a physical attribute service via a network, wherein the physical attribute service determines the first physical attribute based on an analysis of an image of the first passenger received from at least one of the plurality of data sources. 
     
     
         3 . The method of  claim 1 , wherein the non-physical attributes comprise: (i) emotions expressed by the first passenger, (ii) sentiment expressed by the first passenger, (iii) a preference of the first passenger, and (iv) a personality trait of the passenger, wherein a first non-physical attribute of the plurality of non-physical attributes is received from a non-physical attribute service via a network, wherein the non-physical attribute service determines the first non-physical attribute based on an analysis of: (i) blog posts generated by the first passenger, (ii) social media publications generated by the first passenger, and (iii) a profile of the first passenger received from the plurality of data sources. 
     
     
         4 . The method of  claim 1 , further comprising:
 training the application to generate the ML model based on training data, wherein the ML model specifies a respective weight for the plurality of physical attributes and the plurality of non-physical attributes, wherein the application computes the score based on the weights specified in the ML model.   
     
     
         5 . The method of  claim 4 , wherein the score comprises a composite score, wherein the composite score is computed based on: (i) a physical attribute score computed based on the weights specified in the ML model and the plurality of determined physical attributes of the first passenger, (ii) a non-physical attribute score computed based on the weights specified in the ML model and the plurality of determined non-physical attributes of the first passenger. 
     
     
         6 . The method of  claim 1 , further comprising:
 computing a respective score for each of the plurality of passengers based on the ML model and a respective plurality of physical and non-physical attributes of each passenger;   generating an ordered list of the plurality of passengers ranked based on the computed score for each passenger; and   allocating each of the plurality of passengers to a respective seat in the vehicle based on the ordered list of passengers.   
     
     
         7 . The method of  claim 1 , further comprising:
 determining a plurality of physical attributes for a second passenger, of the plurality of passengers, based on data describing the second passenger received from the plurality of data sources;   determining a plurality of non-physical attributes for the second 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 second passenger violate at least one rule associated with a second seat in the vehicle;   denoting, in a record stored in a memory, that the second seat in the vehicle is unavailable to the second passenger based on the violation of the at least one rule associated with the second seat; and   allocating the second passenger to a third seat in the vehicle.   
     
     
         8 . A computer program product, comprising:
 a non-transitory computer-readable storage medium having computer readable program code embodied therewith, the computer readable program code executable by a processor to perform an operation comprising:
 receiving a plurality of rules associated with a first seat of a plurality of seats 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. 
   
     
     
         9 . The computer program product of  claim 8 , wherein the mass-transit vehicle comprises one of: (i) an airplane, (ii) a bus, and (iii) a train, wherein a first physical attribute of the plurality of physical attributes is received from a physical attribute service via a network, wherein the physical attribute service determines the first physical attribute based on an analysis of an image of the first passenger received from at least one of the plurality of data sources. 
     
     
         10 . The computer program product of  claim 8 , wherein the non-physical attributes comprise: (i) emotions expressed by the first passenger, (ii) sentiment expressed by the first passenger, (iii) a preference of the first passenger, and (iv) a personality trait of the passenger, wherein a first non-physical attribute of the plurality of non-physical attributes is received from a non-physical attribute service via a network, wherein the non-physical attribute service determines the first non-physical attribute based on an analysis of: (i) blog posts generated by the first passenger, (ii) social media publications generated by the first passenger, and (iii) a profile of the first passenger received from the plurality of data sources. 
     
     
         11 . The computer program product of  claim 8 , the operation further comprising:
 training the application to generate the ML model based on training data, wherein the ML model specifies a respective weight for the plurality of physical attributes and the plurality of non-physical attributes, wherein the application computes the score based on the weights specified in the ML model.   
     
     
         12 . The computer program product of  claim 11 , wherein the score comprises a composite score, wherein the composite score is computed based on: (i) a physical attribute score computed based on the weights specified in the ML model and the plurality of determined physical attributes of the first passenger, (ii) a non-physical attribute score computed based on the weights specified in the ML model and the plurality of determined non-physical attributes of the first passenger. 
     
     
         13 . The computer program product of  claim 8 , the operation further comprising:
 computing a respective score for each of the plurality of passengers based on the ML model and a respective plurality of physical and non-physical attributes of each passenger;   generating an ordered list of the plurality of passengers ranked based on the computed score for each passenger; and   allocating each of the plurality of passengers to a respective seat in the vehicle based on the ordered list of passengers.   
     
     
         14 . The computer program product of  claim 8 , the operation further comprising:
 determining a plurality of physical attributes for a second passenger, of the plurality of passengers, based on data describing the second passenger received from the plurality of data sources;   determining a plurality of non-physical attributes for the second 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 second passenger violate at least one rule associated with a second seat in the vehicle;   denoting, in a record stored in a memory, that the second seat in the vehicle is unavailable to the second passenger based on the violation of the at least one rule associated with the second seat; and   allocating the second passenger to a third seat in the vehicle.   
     
     
         15 . A system, comprising:
 a processor; and   a memory storing one or more instructions which, when executed by the processor, performs an operation comprising:
 receiving a plurality of rules associated with a first seat of a plurality of seats 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. 
   
     
     
         16 . The system of  claim 15 , wherein the mass-transit vehicle comprises one of: (i) an airplane, (ii) a bus, and (iii) a train, wherein a first physical attribute of the plurality of physical attributes is received from a physical attribute service via a network, wherein the physical attribute service determines the first physical attribute based on an analysis of an image of the first passenger received from at least one of the plurality of data sources. 
     
     
         17 . The system of  claim 15 , wherein the non-physical attributes comprise: (i) emotions expressed by the first passenger, (ii) sentiment expressed by the first passenger, (iii) a preference of the first passenger, and (iv) a personality trait of the passenger, wherein a first non-physical attribute of the plurality of non-physical attributes is received from a non-physical attribute service via a network, wherein the non-physical attribute service determines the first non-physical attribute based on an analysis of: (i) blog posts generated by the first passenger, (ii) social media publications generated by the first passenger, and (iii) a profile of the first passenger received from the plurality of data sources. 
     
     
         18 . The system of  claim 17 , wherein the score comprises a composite score, wherein the composite score is computed based on: (i) a physical attribute score computed based on the weights specified in the ML model and the plurality of determined physical attributes of the first passenger, (ii) a non-physical attribute score computed based on the weights specified in the ML model and the plurality of determined non-physical attributes of the first passenger. 
     
     
         19 . The system of  claim 15 , the operation further comprising:
 computing a respective score for each of the plurality of passengers based on the ML model and a respective plurality of physical and non-physical attributes of each passenger;   generating an ordered list of the plurality of passengers ranked based on the computed score for each passenger; and   allocating each of the plurality of passengers to a respective seat in the vehicle based on the ordered list of passengers.   
     
     
         20 . The system of  claim 15 , the operation further comprising:
 determining a plurality of physical attributes for a second passenger, of the plurality of passengers, based on data describing the second passenger received from the plurality of data sources;   determining a plurality of non-physical attributes for the second 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 second passenger violate at least one rule associated with a second seat in the vehicle;   denoting, in a record stored in the memory, that the second seat in the vehicle is unavailable to the second passenger based on the violation of the at least one rule associated with the second seat; and   allocating the second passenger to a third seat in the vehicle.