Airline seat brand identification and fare grouping system

A first network node device, comprising:

• processing circuitry operatively coupled to memory and network interface circuitry, the network interface circuitry being configured to communicatively couple the first network node device, over a network, to a set of airline network node devices, each airline network node device comprising computing infrastructure corresponding to a respective one of a set of airlines, wherein the set of airlines includes at least one contributing airline and at least one non-contributing airline;
• wherein the memory stores instructions that, when executed by the processing circuitry, cause the processing circuitry to:
• receive, via the network interface circuitry, from the set of airline network node devices, in accordance with standardized transport protocols or secure message queues, ticketing data from each of the set of airline network node devices and brand reference data from one or more contributing airline network node devices of the set of airline network node devices, the ticketing data including structured ticketing records having fare basis information, routing identifiers, booking class codes, cabin designators, or ancillary attributes, and the brand reference data including airline supplied brand definitions or airline contributed brand identifiers that associate a fare or product with a branded fare family;
• determine, for each respective airline of the set of airlines, whether the respective airline corresponding to one of the set of airline network node devices is a contributing airline that provides the brand reference data or a non-contributing airline that does not provide the brand reference data;
• in response to determining that the respective airline is the contributing airline, execute a first processing sequence including converting the brand reference data associated with the contributing airline into standardized brand reference records, applying mapping logic, based on the standardized brand reference records maintained in the memory of the first network node device, to assign a standardized airline brand identifier to ticketing data associated with the contributing airline based on at least one airline contributed brand identifier and fare basis information, and invoking a first artificial intelligence (AI) model executed by first AI circuitry of the first network node device or one or more AI network node devices, wherein the first AI model includes a generative AI model or semantic numerical representation AI model trained on airline or travel commerce datasets to identify brand amenity attributes missing from the brand reference data and the ticketing data and associate the brand amenity attributes with the assigned standardized airline brand identifier based on the ticketing data associated with the contributing airline, wherein invoking the first AI model includes providing, to the first AI model, a first AI invocation indication including the assigned standardized airline brand identifier and receiving a first AI result indication including a structured brand amenity feature vector including brand amenity attributes associated with the assigned standardized airline brand identifier, the structured brand amenity feature vector including categorical fields corresponding to baggage allowance, refundability, seat selection capability, upgrade eligibility, or change fee flexibility;
• in response to determining that the respective airline is the non-contributing airline, execute a second processing sequence including generating a set of ticketing-related variables from the ticketing data associated with the non-contributing airline by parsing fare basis strings using association rules derived from ticketing data received from the set of airline network node devices and selecting input variables using correlation analysis and feature importance processing to form a finalized model input vector including a fare basis string variable and one or more of a route pattern, carrier code, cabin indicator, ticket designator, or fare-related value, invoking a second AI model executed by second AI circuitry of the first network node device or the one or more AI network node devices, wherein the second AI model includes a deep learning neural network model trained, based on labeled brand examples derived from contributing airlines or other training sources, to infer an airline brand based on the set of ticketing-related variables to generate an inferred airline brand identifier missing from the brand reference data and the ticketing data and a confidence score or feature importance metric, wherein invoking the second AI model includes transmitting a second AI invocation indication including the finalized model input vector and receiving a second AI result indication including the inferred airline brand identifier and the confidence score or feature importance metric, and invoking the first AI model to obtain a structured brand amenity feature vector including brand amenity attributes associated with the inferred airline brand identifier;
• process the structured brand amenity feature vectors associated with the assigned standardized airline brand identifiers and the inferred airline brand identifiers generated in the respective first and second processing sequences through a multidimensional clustering algorithm based on mixed numerical and categorical similarity metrics by generating a mixed-type feature matrix including categorical brand amenity attributes and numerical fare-related values and applying the multidimensional clustering algorithm or, by an adaptive mixed-type clustering framework, selecting and applying one of a plurality of mixed-type clustering techniques based on one or more clustering validation metrics to obtain benchmark groups of airline brands and corresponding benchmark data;
• and
• output, via the network interface circuitry, a brand-based benchmarking dataset derived from the benchmark groups of airline brands and the corresponding benchmark data, wherein the brand-based benchmarking dataset includes structured records stored in the memory or transmitted via the network interface circuitry, the structured records being formatted as JSON records or relational database records for delivery to at least one airline network node device, a downstream analytical system, visualization dashboard, or reporting application, the structured records identifying, for each assigned standardized airline brand identifier and each inferred airline brand identifier, at least a benchmark group assignment, a relative position score, an average fare to amenity ratio, and, for each structured record associated with an inferred airline brand identifier, the confidence score or feature importance metric.

A system, comprising:

• a set of airline network node devices, each configured to communicate over a network and to provide to the network ticketing data, wherein one or more contributing airline network node devices of the set of airline network node devices are further configured to provide brand reference data, the ticketing data including fare basis information and the brand reference data including airline contributed brand identifiers, each airline network node device comprising computing infrastructure corresponding to a respective one of a set of airlines, wherein the set of airlines includes at least one contributing airline and at least one non-contributing airline;
• and
• a first network node device including processing circuitry operatively coupled to memory and network interface circuitry, the network interface circuitry being configured to communicatively couple the first network no de device to the set of airline network node devices over the network;
• wherein the memory stores instructions that, when executed by the processing circuitry, cause the processing circuitry to:
• receive, via the network interface circuitry, from the set of airline network node devices, in accordance with standardized transport protocols or secure message queues, the ticketing data from each of the set of airline network node devices and the brand reference data from one or more contributing airline network node devices of the set of airline network node devices, the ticketing data including structured ticketing records having fare basis information, routing identifiers, booking class codes, cabin designators, or ancillary attributes, and the brand reference data including airline supplied brand definitions or airline contributed brand identifiers that associate a fare or product with a branded fare family;
• determine, for each respective airline of the set of airlines, whether the respective airline corresponding to one of the set of airline network node devices is a contributing airline that provides the brand reference data or a non-contributing airline that does not provide the brand reference data;
• in response to determining that the respective airline is the contributing airline, execute a first processing sequence including converting the brand reference data associated with the contributing airline into standardized brand reference records, applying mapping logic, based on the standardized brand reference records maintained in the memory of the first network node device, to assign a standardized airline brand identifier to ticketing data associated with the contributing airline based on at least one airline contributed brand identifier and fare basis information, and invoking a first artificial intelligence (AI) model executed by first AI circuitry of the first network node device or one or more AI network node devices, wherein the first AI model includes a generative AI model or semantic numerical representation AI model trained on airline or travel commerce datasets to identify brand amenity attributes missing from the brand reference data and the ticketing data and associate the brand amenity attributes with the assigned standardized airline brand identifier based on the ticketing data associated with the contributing airline, wherein invoking the first AI model includes providing, to the first AI model, a first AI invocation indication including the assigned standardized airline brand identifier and receiving a first AI result indication including a structured brand amenity feature vector including brand amenity attributes associated with the assigned standardized airline brand identifier, the structured brand amenity feature vector including categorical fields corresponding to baggage allowance, refundability, seat selection capability, upgrade eligibility, or change fee flexibility;
• in response to determining that the respective airline is the non-contributing airline, execute a second processing sequence including generating a set of ticketing-related variables from ticketing data associated with the non-contributing airline by parsing fare basis strings using association rules derived from ticketing data received from the set of airline network node devices and selecting input variables using correlation analysis and feature importance processing to form a finalized model input vector including a fare basis string variable and one or more of a route pattern, carrier code, cabin indicator, ticket designator, or fare-related value, invoking a second AI model executed by second AI circuitry of the first network node device or the one or more AI network node devices, wherein the second AI model includes a deep learning neural network model trained, based on labeled brand examples derived from contributing airlines or other training sources, to infer an airline brand based on the set of ticketing-related variables to generate an inferred airline brand identifier missing from the brand reference data and the ticketing data and a confidence score or feature importance metric, wherein invoking the second AI model includes transmitting a second AI invocation indication including the finalized model input vector and receiving a second AI result indication including the inferred airline brand identifier and the confidence score or feature importance metric, and invoking the first AI model to obtain a structured brand amenity feature vector including brand amenity attributes associated with the inferred airline brand identifier;
• process the structured brand amenity feature vectors associated with the assigned standardized airline brand identifiers and the inferred airline brand identifiers generated in the respective first and second processing sequences through a multidimensional clustering algorithm based on mixed numerical and categorical similarity metrics by generating a mixed-type feature matrix including categorical brand amenity attributes and numerical fare-related values and applying the multidimensional clustering algorithm or, by an adaptive mixed-type clustering framework, selecting and applying one of a plurality of mixed-type clustering techniques based on one or more clustering validation metrics to obtain benchmark groups of airline brands and corresponding benchmark data;
• and
• output, via the network interface circuitry, a brand-based benchmarking dataset derived from the benchmark groups of airline brands and the corresponding benchmark data, wherein the brand-based benchmarking dataset includes structured records stored in the memory or transmitted via the network interface circuitry, the structured records being formatted as JSON records or relational database records for delivery to at least one airline network node device, a downstream analytical system, visualization dashboard, or reporting application, the structured records identifying, for each assigned standardized airline brand identifier and each inferred airline brand identifier, at least a benchmark group assignment, a relative position score, an average fare to amenity ratio, and, for each structured record associated with an inferred airline brand identifier, the confidence score or feature importance metric.