# US 2020/0401906 A1 · Crowd sourced maintenance systems and methods for transportation vehicles > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401906 A1 - Document ID: US20200401906A1 - Status: Published application - Kind: A1 - Application number: 16/449,260 - Filed: 2019-06-21 - Published: 2020-12-24 - Earliest claimed: 2019-06-21 - Assignees: Panasonic Avionics Corporation - Applicants: Panasonic Avionics Corporation - Inventors: Philip Watson; Steven Bates - Web page: https://patentgrep.com/patent/US20200401906A1 ## CPC - G06N 5/02: Knowledge representation; Symbolic representation - G07C 5/006: {Indicating maintenance} - G06N 20/00: Machine learning - G07C 5/008: {communicating information to a remotely located station (transmission systems for measured values G08C)} - G07C 5/02: Registering or indicating driving, working, idle, or waiting time only (apparatus forming part of taximeters G07B13/00) - G07C 5/08: Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time - G07C 5/0808: {Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005)} - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE ## Abstract Methods and systems are provided for communicating an announcement to passengers on a transportation vehicle. One method includes receiving by a first device, an input from a passenger of a transportation vehicle, the input reporting a problem at the transportation vehicle; transmitting the input by the first device to a second device; evaluating by the second device whether the problem identified by the input is to be resolved at the transportation vehicle; generating a response by the second device to address the problem, when the evaluation indicates that the problem can be resolved at the transportation vehicle; and updating a data structure by the second device for addressing the problem after the transportation vehicle has reached a destination, when the evaluation indicates that the problem cannot be resolved at the transportation vehicle. ## Claims ### Claim 1 A method, comprising: receiving by a first device, an input from a passenger of a transportation vehicle, the input reporting a problem at the transportation vehicle transmitting the input by the first device to a second device; evaluating by the second device whether the problem identified by the input is to be resolved at the transportation vehicle; wherein the second device accesses a portion of a machine learned model to evaluate whether the problem identified by the input is to be resolved at the transportation vehicle; generating a response by the second device to address the problem, when the evaluation indicates that the problem can be resolved at the transportation vehicle; and updating a data structure by the second device for addressing the problem after the transportation vehicle has reached a destination, when the evaluation indicates that the problem cannot be resolved at the transportation vehicle. ### Claim 2 (depends on 1) The method of claim 1, further comprising: forwarding the input to a third computing device remote to the transportation vehicle; and updating the machine learned model by the third computing device, based on the input. ### Claim 3 (depends on 2) The method of claim 2, wherein the third computing device uses the input to update a training dataset of the machine learned model. ### Claim 4 (depends on 1) The method of claim 1, wherein the machine learned model is used to generate a predictive response for the input for predicting failure of a device of the transportation vehicle. ### Claim 5 (depends on 1) The method of claim 1, wherein the machine learned model is used to generate a preventive response for the input to prevent failure of a device of the transportation vehicle. ### Claim 6 (depends on 1) The method of claim 1, wherein the machine learned model is used to generate a prescriptive response for a device of the transportation vehicle. ### Claim 7 (depends on 1) The method of claim 1, wherein the transportation vehicle is one of an aircraft, a train, a ship and a bus. ### Claim 8 A method, comprising: receiving an input from a passenger of an aircraft at a passenger electronic device (PED), indicating a problem at the aircraft, the PED being in communication with an in-flight entertainment (IFE) system computing device; transmitting the input by the PED to the IFE system computing device; determining by the IFE computing device whether the problem identified by the input can be resolved at the aircraft; wherein the IFE system computing device uses a portion of a machine learned model to evaluate whether the problem identified by the input can be resolved at the aircraft; generating a response by the IFE system computing device to address the problem, when the evaluation indicates that the problem can be resolved at the aircraft; and transmitting the input from the aircraft to a ground computing system to address the problem after the aircraft has landed, when it is determined that the problem cannot be resolved at the aircraft. ### Claim 9 (depends on 1) The method of claim 1, further comprising: updating a training dataset and the machine learned model by the ground computing device, based on the input. ### Claim 10 (depends on 1) The method of claim 1, wherein the PED transmits the input to a crew member device in communication with the IFE system and the crew member device makes the determination whether the problem identified by the input can be resolved at the aircraft. ### Claim 11 (depends on 10) The method of claim 10, wherein the crew member device transmits an explanation for the problem to the PED. ### Claim 12 (depends on 10) The method of claim 10, wherein the crew member device transmits a solution to the PED to mitigate the problem. ### Claim 13 (depends on 8) The method of claim 8, wherein the machine learned model is used to generate a predictive response for the input for predicting failure of a device of the aircraft. ### Claim 14 (depends on 1) The method of claim 1, wherein the machine learned model is used to generate a preventive response for the input to prevent failure of a device of the aircraft. ### Claim 15 A non-transitory machine readable medium having stored thereon instructions for performing a method comprising machine executable code which when executed by at least one machine, causes the machine to: receive an input from a passenger of an aircraft at a passenger electronic device (PED), indicating a problem at the aircraft, the PED being in communication with an in-flight entertainment (IFE) system computing device; transmit the input by the PED to the IFE system computing device; determine by the IFE computing device whether the problem identified by the input can be resolved at the aircraft; wherein the IFE system computing device uses a portion of a machine learned model to evaluate whether the problem identified by the input can be resolved at the aircraft; generate a response by the IFE system computing device to address the problem, when the evaluation indicates that the problem can be resolved at the aircraft; and transmit the input from the aircraft to a ground computing system to address the problem after the aircraft has landed, when it is determined that the problem cannot be resolved at the aircraft. ### Claim 16 (depends on 15) The storage medium of claim 15, wherein the machine executable code further causes the machine to: update a training dataset and the machine learned model by the ground computing device, based on the input. ### Claim 17 (depends on 15) The storage medium of claim 15, wherein the PED transmits the input to a crew member device in communication with the IFE system and the crew member device makes the determination whether the problem identified by the input can be resolved at the aircraft. ### Claim 18 (depends on 17) The storage medium of claim 17, wherein the crew member device transmits an explanation for the problem to the PED. ### Claim 19 (depends on 17) The storage medium of claim 17, wherein the crew member device transmits a solution to the PED to mitigate the problem. ### Claim 20 (depends on 15) The storage medium of claim 15, wherein the machine learned model is used to generate a predictive response for the input for predicting failure of a device of the aircraft.