# US 2021/0240772 A1 · Method and system for flight scheduling > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0240772 A1 - Document ID: US20210240772A1 - Status: Published application - Kind: A1 - Application number: 16/778,330 - Filed: 2020-01-31 - Published: 2021-08-05 - Earliest claimed: 2020-01-31 - Assignees: The Boeing Company - Applicants: The Boeing Company - Inventors: John W. Glatfelter; Brian D. Laughlin; Terrence J. McGowan; Donald B. Cummings - Web page: https://patentgrep.com/patent/US20210240772A1 ## CPC - G06F 16/9035: Filtering based on additional data, e.g. user or group profiles - B64D 11/00: Passenger or crew accommodation; Flight-deck installations not otherwise provided for - G06F 16/9038: Presentation of query results - G06N 20/00: Machine learning - G06Q 10/02: Reservations, e.g. for tickets, services or events - G06Q 10/0283: {for travel seating} - G08G 5/30: Flight plan management - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - B64D: EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) ## Abstract A computer-implemented method includes searching, by machine learning logic of a computer, a flight schedule database for one or more flight schedule records related to flight preference information specified by a user. The machine learning logic is trained with training data that includes flight schedules selected by passengers and flight preference information associated with the passengers. The method further includes responsive to locating the one or more flight schedule records, communicating, by the computer, flight schedules associated with the one or more flight schedule records to a terminal associated with the user. The method includes receiving, from the terminal, a booking indication associated with a particular flight schedule of the flight schedules and updating the training data to associate the particular flight schedule with the flight preference information specified by the user. ## Claims ### Claim 1 A computer-implemented method comprising: searching, by machine learning logic of a computer, a flight schedule database for one or more flight schedule records related to flight preference information specified by a user, wherein the machine learning logic is trained with training data that includes flight schedules selected by passengers and flight preference information associated with the passengers; responsive to locating the one or more flight schedule records, communicating, by the computer, flight schedules associated with the one or more flight schedule records to a terminal associated with the user; receiving, from the terminal, a booking indication associated with a particular flight schedule of the flight schedules; and updating the training data to associate the particular flight schedule with the flight preference information specified by the user. ### Claim 2 (depends on 1) The computer-implemented method according to claim 1, wherein the flight schedule records specify an aircraft equipment type, wherein searching the flight schedule database for one or more flight schedule records comprises searching, by the machine learning logic of the computer, the flight schedule database for one or more flight schedule records related to a preferred equipment type specified in the flight preference information. ### Claim 3 (depends on 1) The computer-implemented method according to claim 1, wherein the flight schedule records specify an aircraft crew experience level, wherein searching the flight schedule database for one or more flight schedule records comprises searching, by the machine learning logic of the computer, flight schedule database for one or more flight schedule records related to a preferred crew experience level specified in the flight preference information. ### Claim 4 (depends on 1) The computer-implemented method according to claim 1, wherein the flight schedule records specify an aircraft passenger seating environment that further specifies types of passengers assigned to seats of aircraft, wherein searching the flight schedule database for one or more flight schedule records comprises searching, by the machine learning logic of the computer, flight schedule database for one or more flight schedule records related to a preferred passenger seating environment specified in the flight preference information. ### Claim 5 (depends on 4) The computer-implemented method according to claim 4, wherein the preferred passenger seating environment of the flight preference information specifies a body type associated with the user, wherein the method further comprises: identifying, by the computer and based on the passenger seating environment, one or more available seats on aircraft associated with the one or more flight schedule records that accommodate passengers having the specified body type; and communicating, by the computer and to the terminal, seat availability information that specifies the one or more available seats to the terminal. ### Claim 6 (depends on 1) The computer-implemented method according to claim 1, further comprising: associating passengers with seats of aircraft that are associated with the one or more flight schedule records; scanning the passengers as they board the aircraft to classify body types of the passengers; and updating the one or more flight schedule records to relate the seats of the aircraft with body types of the passengers assigned to the seats. ### Claim 7 (depends on 1) The computer-implemented method according to claim 1, wherein the flight schedule records specify aircraft predicted flight turbulence levels, wherein searching the flight schedule database for one or more flight schedule records comprises searching, by the machine learning logic of the computer, the flight schedule database for one or more flight schedule records related to a preferred flight turbulence level specified in the flight preference information. ### Claim 8 (depends on 1) The computer-implemented method according to claim 1, further comprising: receiving, by the computer, an indication of an occurrence of a change associated with the particular flight schedule; responsive to receiving to the indication, searching, by machine learning logic of the computer, for a different flight schedule related to the flight preference information specified by the user; responsive to locating the different flight schedule, communicating, by the computer, the different flight schedule to the terminal associated with the user; receiving, from the terminal, a further booking indication associated with the different flight schedule; and updating the training data to associate the different flight schedule with the flight preference information specified by the user. ### Claim 9 A system comprising: a memory that stores instruction code; and a processor in communication with the memory, wherein the instruction code is executable by the processor to perform operations comprising: searching, by machine learning logic implemented by the instruction code, a flight schedule database for one or more flight schedule records related to flight preference information specified by a user, wherein the machine learning logic is trained with training data that includes flight schedules selected by passengers and flight preference information associated with the passengers; responsive to locating the one or more flight schedule records, communicating flight schedules associated with the one or more flight schedule records to a terminal associated with the user; receiving, from the terminal, a booking indication associated with a particular flight schedule of the flight schedules; and updating the training data to associate the particular flight schedule with the flight preference information specified by the user. ### Claim 10 (depends on 9) The system according to claim 9, wherein the flight schedule records specify an aircraft equipment type, wherein the instruction code is executable by the processor to perform operations comprising: searching, by the machine learning logic, the flight schedule database for one or more flight schedule records related to a preferred equipment type specified in the flight preference information. ### Claim 11 (depends on 9) The system according to claim 9, wherein the flight schedule records specify an aircraft crew experience level, wherein the instruction code is executable by the processor to perform operations comprising: searching, by the machine learning logic, the flight schedule database for one or more flight schedule records related to a preferred crew experience level specified in the flight preference information. ### Claim 12 (depends on 9) The system according to claim 9, wherein the flight schedule records specify an aircraft passenger seating environment that further specifies types of passengers assigned to seats of aircraft, wherein the instruction code is executable by the processor to perform operations comprising: searching, by the machine learning logic, the flight schedule database for one or more flight schedule records related to a preferred passenger seating environment specified in the flight preference information. ### Claim 13 (depends on 12) The system according to claim 12, wherein the preferred passenger seating environment of the flight preference information specifies a body type associated with the user, wherein the instruction code is executable by the processor to perform operations comprising: identifying, based on the passenger seating environment, one or more available seats on aircraft associated with the one or more flight schedule records that accommodate passengers having the specified body type; and communicating, to the terminal, seat availability information that specifies the one or more available seats to the terminal. ### Claim 14 (depends on 9) The system according to claim 9, wherein the instruction code is executable by the processor to perform operations comprising: associating passengers with seats of aircraft that are associated with the one or more flight schedule records; scanning the passengers as they board the aircraft to classify body types of the passengers; and updating the one or more flight schedule records to relate the seats of the aircraft with body types of the passengers assigned to the seats. ### Claim 15 (depends on 9) The system according to claim 9, wherein the flight schedule records specify aircraft predicted flight turbulence levels, wherein the instruction code is executable by the processor to perform operations comprising: searching, by the machine learning logic, the flight schedule database for one or more flight schedule records related to a preferred flight turbulence level specified in the flight preference information. ### Claim 16 (depends on 9) The system according to claim 9, wherein the instruction code is executable by the processor to perform operations comprising: receiving an indication of an occurrence of a change associated with the particular flight schedule; responsive to receiving to the indication, searching, by machine learning logic, for a different flight schedule related to the flight preference information specified by the user; responsive to locating the different flight schedule, communicating the different flight schedule to the terminal associated with the user; receiving, from the terminal, a further booking indication associated with the different flight schedule; and updating the training data to associate the different flight schedule with the flight preference information specified by the user. ### Claim 17 A non-transitory computer-readable medium having stored thereon instruction code, wherein the instruction code is executable by a processor of a computer to perform operations comprising: searching, by machine learning logic implemented by the instruction code, a flight schedule database for one or more flight schedule records related to flight preference information specified by a user, wherein the machine learning logic is trained with training data that includes flight schedules selected by passengers and flight preference information associated with the passengers; responsive to locating the one or more flight schedule records, communicating flight schedules associated with the one or more flight schedule records to a terminal associated with the user; receiving, from the terminal, a booking indication associated with a particular flight schedule of the flight schedules; and updating the training data to associate the particular flight schedule with the flight preference information specified by the user. ### Claim 18 (depends on 17) The non-transitory computer-readable medium according to claim 17, wherein the flight schedule records specify an aircraft equipment type, wherein the instruction code is executable by the processor of the computer to perform operations comprising: searching, by the machine learning logic, the flight schedule database for one or more flight schedule records related to a preferred equipment type specified in the flight preference information. ### Claim 19 (depends on 17) The non-transitory computer-readable medium according to claim 17, wherein the flight schedule records specify an aircraft crew experience level, wherein the instruction code is executable by the processor of the computer to perform operations comprising: searching, by the machine learning logic, the flight schedule database for one or more flight schedule records related to a preferred crew experience level specified in the flight preference information. ### Claim 20 (depends on 17) The non-transitory computer-readable medium according to claim 17, wherein the flight schedule records specify an aircraft passenger seating environment that further specifies types of passengers assigned to seats of aircraft, wherein the instruction code is executable by the processor of the computer to perform operations comprising: searching, by the machine learning logic, the flight schedule database for one or more flight schedule records related to a preferred passenger seating environment specified in the flight preference information.