Systems and methods for digital communication of flight plan

FIELD OF THE INVENTION

The present invention generally relates to the field of digital communication. In particular, the present invention is directed to systems and methods for digital communication of flight plan to air traffic control.

BACKGROUND

Flight plans for aircraft are generally managed by an air traffic control service. This process can become quite involved with multiple exchanges of information via a series of communications between the pilots and air traffic controllers. The constant bidirectional communication between a pilot and an air traffic control operator over radio requires ample time for both operators to send and receive communications regarding the validity of a flight plan for an aircraft. This mode of communication can allow for a large amount of time being wasted while also being inefficient.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for digital communication of a flight plan for an electric aircraft, the system including a sensor coupled to an electric vertical takeoff and landing aircraft, wherein the sensor is configured to detect a plurality of measured flight data. The system further including a flight controller, wherein the flight controller is configured to receive the plurality of measured flight data from the sensor, generate a proposed flight plan as a function of the plurality of measured flight data, transmit the proposed flight plan to a remote device, and receive a confirmation flight plan from the remote device. The system further including a pilot display, wherein the pilot display is configured to receive the confirmation flight plan from the flight controller and display the confirmation flight plan to a pilot.

In another aspect, a method for digital communication of a flight plan for use in an electric aircraft, the method including detecting, at a sensor, a plurality of measured flight data, receiving, at a flight controller, the plurality of measured flight data from the sensor, generating, at a flight controller, a proposed flight plan as a function of the plurality of measured flight data, transmitting, at the flight controller, the proposed flight plan to a remote device, receiving, from the remote device, a confirmation flight plan, receiving, at a pilot display, the confirmation flight plan from the flight controller and displaying, at the pilot display, the confirmation flight plan to a pilot.

These and other aspects and features of non-limiting embodiments of the present invention will become apparent to those skilled in the art upon review of the following description of specific non-limiting embodiments of the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of a system for digital communication of a flight plan to an air traffic control;

FIG. 2 is a block diagram illustrating exemplary embodiments of fuzzy sets for a separation element;

FIG. 3 is a block diagram of an exemplary embodiment of a method for digital communication of a flight plan to an air traffic control;

FIG. 4 is a block diagram of another exemplary embodiment of a method for digital communication of a flight plan to an air traffic control;

FIG. 5 is a diagrammatic representation of an exemplary embodiment of an electric aircraft;

FIG. 6 is a block diagram of an exemplary embodiment of a flight controller;

FIG. 7 is a block diagram of an exemplary embodiment of a machine-learning module; and

FIG. 8 is a block diagram of a computing system that can be used to implement any one or more of the methodologies disclosed herein and any one or more portions thereof.

DETAILED DESCRIPTION

The drawings are not necessarily to scale and may be illustrated by phantom lines, diagrammatic representations and fragmentary views. In certain instances, details that are not necessary for an understanding of the embodiments or that render other details difficult to perceive may have been omitted.

In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, that the present invention may be practiced without these specific details. As used herein, the word “exemplary” or “illustrative” means “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” or “illustrative” is not necessarily to be construed as preferred or advantageous over other implementations. All of the implementations described below are exemplary implementations provided to enable persons skilled in the art to make or use the embodiments of the disclosure and are not intended to limit the scope of the disclosure, which is defined by the claims. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply exemplary embodiments of the inventive concepts defined in the appended claims. Hence, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise.

At a high level, aspects of the present disclosure are directed to systems and methods for digital communication of a flight plan to air traffic control configured for use in an electric aircraft. In some embodiment, systems and methods for digital communication of a flight plan to an air traffic control operator for use in an electric vertical take-off and landing (eVTOL) aircraft are provided. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples.

Aspects of the present disclosure can be used as a buffer between an electric aircraft pilot and an air traffic control operator. Aspects of the present disclosure can also be used to determine or select a flight plan expeditiously for an electric aircraft to follow such that little time is wasted on selecting, determining, modifying, confirming, verifying, sending, receiving, or the like, a flight plan for an electric aircraft pilot to command. Aspects of the present disclosure can also be used to store and retrieve multiple flight plans and communicate them to an electric aircraft pilot and/or air traffic control operator. This is so that a human operator may at least be able to have the liberty to choose a base flight plan to the operator’s preferences.

Aspects of the present disclosure can advantageously allow for bypassing of the typical instrument approach utilized by aircraft which can involve timely processing through some type of central government regulatory and control system, and can desirably allow verification or confirmation of a proposed flight plan, or the like, to directly communicate with a remote device, site or facility such as a relevant central or local air traffic control (ATC) authority, another fleet management site, or another flight plan that is modified by the ATC authority.

For purposes of this disclosure, in aviation, an “instrument approach”, instrument approach plan or instrument approach procedure (IAP) is a series of predetermined maneuvers for the orderly transfer of an aircraft operating under instrument flight rules from the beginning of the initial approach to a landing or to a point from which a landing may be made visually. Instrument flight rules (IFR) is one of two sets of regulations governing all aspects of civil aviation aircraft operations; the other is visual flight rules (VFR). The U.S. Federal Aviation Administration’s (FAA) Instrument Flying Handbook defines IFR as: “Rules and regulations established by the FAA to govern flight under conditions in which flight by outside visual reference is not safe. IFR flight depends upon flying by reference to instruments in the flight deck, and navigation is accomplished by reference to electronic signals.” It is also a term used by pilots and controllers to indicate the type of flight plan an aircraft is flying, such as an IFR or VFR flight plan.

Aspects of the present disclosure can assist with and/or substitute for air traffic control (ATC) instrument approach for electric aircraft seeking to verify or confirm a proposed or potential flight plan. Typically, instrument flight plane pilots provide information such as type of aircraft, start and departure airport, end airport, current path they want to fly (low/high altitude airways), safety information (people on board, equipment and the like) which is filed through a central government system. Any central or local ATC receives a copy of the intended flight plan. When the pilot is ready to fly, he or she typically uses a radio and requests permission for the intended flight plan. In response, the pilot receives back either the original flight plan for execution or a modified one.

However, many current flying profiles, such as flight plans with electric and eVTOL aircraft which may be manned or unmanned, may involve considerations which are different from typical instrument approach plans. For example, such instrument approach plans may not be viable to be executed at some or many recharging infrastructures, and the like. Thus, in accordance with some aspects of the present disclosure, an optimized safe approach plan for a recharging infrastructure is provided to overcome some or all of these challenges. For example, and without limitation, a safe approach plan may take into consideration environmental or ambient conditions as well as other aircraft (e.g. unmanned aerial vehicles (UVAs)) in the vicinity. Such a safe approach plan may be unique to the location, environment and logistics of each individual recharging infrastructure and could be communicated to the relevant ATC facility, so that, if needed, they can route other aircraft accordingly.

In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, that the present invention may be practiced without these specific details. As used herein, the word “exemplary” or “illustrative” means “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” or “illustrative” is not necessarily to be construed as preferred or advantageous over other implementations. All of the implementations described below are exemplary implementations provided to enable persons skilled in the art to make or use the embodiments of the disclosure and are not intended to limit the scope of the disclosure, which is defined by the claims. For purposes of description herein, the terms “upper”, “lower”, “left”, “rear”, “right”, “front”, “vertical”, “horizontal”, and derivatives thereof shall relate to embodiments oriented as shown for exemplary purposes in FIG. 6. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply embodiments of the inventive concepts defined in the appended claims. Hence, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise.

Referring now to FIG. 1, a block diagram of an exemplary embodiment of a system 100 for digital communication of a flight plan to an air traffic control is illustrated. System 100 includes an electric vertical take-off and landing (eVTOL) aircraft which includes at least a drone and at least an unmanned aerial vehicle as further described in FIG. 2. System 100 includes a sensor 104 configured to a detect a plurality of measured flight data comprising a flight plan datum 108, a safety datum 112, and an input datum 116. Sensor 104 may include a sensor suite comprising of a plurality of individual sensors 104 communicatively connected to a flight controller 120. Sensor 104 may be mechanically and/or communicatively connected a flight controller 120. Sensor 104 may be mechanically and/or electronically connected an electric aircraft or a plurality of aircraft actuators and/or components. Sensor 104 may include a plurality of physical controller are network bus units that may be configured to detect a plurality of measured flight data. A “sensor,” for the purposes of this disclosure, is an electronic device configured to detect, capture, measure, or combination thereof, a plurality of external and electric vehicle component quantities. Sensor 104 may be integrated and/or connected to at least an actuator, a portion thereof, or any subcomponent thereof. Sensor 104 may include a photodiode configured to convert light, heat, electromagnetic elements, and the like thereof, into electrical current for further analysis and/or manipulation. Sensor 104 may include circuitry or electronic components configured to digitize, transform, or otherwise manipulate electrical signals. Electrical signals may include analog signals, digital signals, periodic or aperiodic signal, step signals, unit impulse signal, unit ramp signal, unit parabolic signal, signum function, exponential signal, rectangular signal, triangular signal, sinusoidal signal, sinc function, or pulse width modulated signal. The plurality of datum captured by sensor 104 may include circuitry, computing devices, electronic components or a combination thereof that translates into at least an electronic signal configured to be transmitted to another electronic component.

With continued reference to FIG. 1, sensor 104 may include a motion sensor. A “motion sensor”, for the purposes of this disclosure is a device or component configured to detect physical movement of an object or grouping of objects. One of ordinary skill in the art would appreciate, after reviewing the entirety of this disclosure, that motion may include a plurality of types including but not limited to: spinning, rotating, oscillating, gyrating, jumping, sliding, reciprocating, or the like. Sensor 104 may include, but not limited to, torque sensor, gyroscope, accelerometer, magnetometer, inertial measurement unit (IMU), pressure sensor, force sensor, proximity sensor, displacement sensor, vibration sensor, LIDAR sensor, and the like. In a non-limiting embodiment sensor 104 ranges may include a technique for the measuring of distances or slant range from an observer including sensor 104 to a target which may include a plurality of outside parameters. “Outside parameter,” for the purposes of this disclosure, refer to environmental factors or physical electric vehicle factors including health status that may be further be captured by a sensor 104. Outside parameter may include, but not limited to air density, air speed, true airspeed, relative airspeed, temperature, humidity level, and weather conditions, among others. Outside parameter may include velocity and/or speed in a plurality of ranges and direction such as vertical speed, horizontal speed, changes in angle or rates of change in angles like pitch rate, roll rate, yaw rate, or a combination thereof, among others. Outside parameter may further include physical factors of the components of the electric aircraft itself including, but not limited to, remaining fuel or battery. Outside parameter may include at least an environmental parameter. Environmental parameter may be any environmentally based performance parameter as disclosed herein. Environment parameter may include, without limitation, time, pressure, temperature, air density, altitude, gravity, humidity level, airspeed, angle of attack, and debris, among others. Environmental parameters may be stored in any suitable datastore consistent with this disclosure. Environmental parameters may include latitude and longitude, as well as any other environmental condition that may affect the landing of an electric aircraft. Technique may include the use of active range finding methods which may include, but not limited to, light detection and ranging (LIDAR), radar, sonar, ultrasonic range finding, and the like. In a non-limiting embodiment, sensor 104 may include at least a LIDAR system to measure ranges including variable distances from the sensor 104 to a potential landing zone or flight path. LIDAR systems may include, but not limited to, a laser, at least a phased array, at least a microelectromechanical machine, at least a scanner and/or optic, a photodetector, a specialized GPS receiver, and the like. In a non-limiting embodiment, sensor 104 including a LIDAR system may targe an object with a laser and measure the time for at least a reflected light to return to the LIDAR system. LIDAR may also be used to make digital 4-D representations of areas on the earth’s surface and ocean bottom, due to differences in laser return times, and by varying laser wavelengths. In a non-limiting embodiment the LIDAR system may include a topographic LIDAR and a bathymetric LIDAR, wherein the topographic LIDAR that may use near-infrared laser to map a plot of a land or surface representing a potential landing zone or potential flight path while the bathymetric LIDAR may use water-penetrating green light to measure seafloor and various water level elevations within and/or surrounding the potential landing zone. In a non-limiting embodiment, electric aircraft may use at least a LIDAR system as a means of obstacle detection and avoidance to navigate safely through environments to reach a potential landing zone. Sensor 104 may include a sensor suite which may include a plurality of sensors that may detect similar or unique phenomena. For example, in a non-limiting embodiment, sensor suite may include a plurality of accelerometers, a mixture of accelerometers and gyroscopes, or a mixture of an accelerometer, gyroscope, and torque sensor.

With continued reference to FIG. 1, sensor 104 may be communicatively connected to at least a pilot control that may send a plurality of pilot inputs to the sensor 104, the manipulation of which, may constitute at least an aircraft command. “Communicatively connected”, for the purposes of this disclosure, is two or more components electrically, or otherwise connected and configured to transmit and receive signals from one another. Signals may include electrical, electromagnetic, visual, audio, radio waves, or another undisclosed signal type alone or in combination. Any datum or signal herein may include an electrical signal. Electrical signals may include analog signals, digital signals, periodic or aperiodic signal, step signals, unit impulse signal, unit ramp signal, unit parabolic signal, signum function, exponential signal, rectangular signal, triangular signal, sinusoidal signal, sinc function, or pulse width modulated signal. Sensor 104 may include circuitry, computing devices, electronic components or a combination thereof that translates input datum 108 into at least an electronic signal configured to be transmitted to another electronic component. Sensor communicatively connected to at least a pilot control may include a sensor disposed on, near, around or within at least pilot control. Input datum 108 may include a plurality of pilot inputs. An “input datum,” for the purposes of this disclosure, is at least an element of data identifying and/or a pilot input or command. At least pilot control may be communicatively connected to any other component presented in system, the communicative connection may include redundant connections configured to safeguard against single-point failure. Pilot input may indicate a pilot’s desire to change the heading or trim of an electric aircraft. Pilot input may indicate a pilot’s desire to change an aircraft’s pitch, roll, yaw, or throttle. Aircraft trajectory is manipulated by one or more control surfaces and propulsors working alone or in tandem consistent with the entirety of this disclosure, hereinbelow. Pitch, roll, and yaw may be used to describe an aircraft’s attitude and/or heading, as they correspond to three separate and distinct axes about which the aircraft may rotate with an applied moment, torque, and/or other force applied to at least a portion of an aircraft. “Pitch”, for the purposes of this disclosure is an aircraft’s angle of attack, that is the difference between the aircraft’s nose and the horizontal flight trajectory. For example, an aircraft pitches “up” when its nose is angled upward compared to horizontal flight, like in a climb maneuver. In another example, the aircraft pitches “down”, when its nose is angled downward compared to horizontal flight, like in a dive maneuver. When angle of attack is not an acceptable input to any system disclosed herein, proxies may be used such as pilot controls, remote controls, or sensor levels, such as true airspeed sensors, pitot tubes, pneumatic/hydraulic sensors, and the like. “Roll” for the purposes of this disclosure, is an aircraft’s position about its longitudinal axis, that is to say that when an aircraft rotates about its axis from its tail to its nose, and one side rolls upward, like in a banking maneuver. “Yaw”, for the purposes of this disclosure, is an aircraft’s turn angle, when an aircraft rotates about an imaginary vertical axis intersecting the center of the earth and the fuselage of the aircraft. “Throttle”, for the purposes of this disclosure, is an aircraft outputting an amount of thrust from a propulsor. Pilot input, when referring to throttle, may refer to a pilot’s desire to increase or decrease thrust produced by at least a propulsor.

With continued reference to FIG. 1, at least an input datum 108 may include an electrical signal. At least an input datum 108 may include mechanical movement of any throttle consistent with the entirety of this disclosure. Electrical signals may include analog signals, digital signals, periodic or aperiodic signal, step signals, unit impulse signal, unit ramp signal, unit parabolic signal, signum function, exponential signal, rectangular signal, triangular signal, sinusoidal signal, sinc function, or pulse width modulated signal. Sensor may include circuitry, computing devices, electronic components or a combination thereof that translates pilot input into at least an input datum 108 configured to be transmitted to any other electronic component. Any pilot input as described herein may be consistent with any pilot input as described in U.S. Pat. App. No. 17/218,387 filed on Mar. 31, 2021, and titled, “METHOD AND SYSTEM FOR FLY-BY-WIRE FLIGHT CONTROL CONFIGURED FOR USE IN ELECTRIC AIRCRAFT,” which is incorporated herein in its entirety by reference. Pilot input may include a pilot control which may include a throttle wherein the throttle may be any throttle as described herein, and in non-limiting examples, may include pedals, sticks, levers, buttons, dials, touch screens, one or more computing devices, and the like. Additionally, a right-hand floor-mounted lift lever may be used to control the amount of thrust provided by the lift fans or other propulsors. The rotation of a thumb wheel pusher throttle may be mounted on the end of this lever and may control the amount of torque provided by the pusher motor, or one or more other propulsors, alone or in combination. Any throttle as described herein may be consistent with any throttle described in U.S. Pat. App. No. 16/929,206 filed on Jul. 15, 2020, and titled, “A HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”, which is incorporated herein in its entirety by reference. Sensor 104 may be mechanically and communicatively connected to an inceptor stick. The pilot input may include a left-hand strain-gauge style STICK for the control of roll, pitch and yaw in both forward and assisted lift flight. A 4-way hat switch on top of the left-hand stick enables the pilot to set roll and pitch trim. Any inceptor stick described herein may be consistent with any inceptor or directional control as described in U.S. Pat. App. No. 17/001,845 filed on Aug. 25, 2020, and titled, “A HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”, which is incorporated herein in its entirety by reference. At least an input datum 108 may include a manipulation of one or more pilot input controls as described above that correspond to a desire to affect an aircraft’s trajectory as a function of the movement of one or more flight components and one or more actuators, alone or in combination. “Flight components”, for the purposes of this disclosure, includes components related to, and mechanically connected to an aircraft that manipulates a fluid medium in order to propel and maneuver the aircraft through the fluid medium. The operation of the aircraft through the fluid medium will be discussed at greater length hereinbelow.

Still referring to FIG. 1, sensor may include a plurality of sensors in the form of individual sensors or a sensor suite working in tandem or individually. A sensor suite may include a plurality of independent sensors, as described herein, where any number of the described sensors may be used to detect any number of physical or electrical quantities associated with an aircraft power system or an electrical energy storage system. Independent sensors may include separate sensors measuring physical or electrical quantities that may be powered by and/or in communication with circuits independently, where each may signal sensor output to a control circuit such as a user graphical interface. In an embodiment, use of a plurality of independent sensors may result in redundancy configured to employ more than one sensor that measures the same phenomenon, those sensors being of the same type, a combination of, or another type of sensor not disclosed, so that in the event one sensor fails, the ability to detect phenomenon is maintained and in a non-limiting example, a user alter aircraft usage pursuant to sensor readings. Sensor may be configured to detect pilot input from at least pilot control. At least pilot control may include a throttle lever, inceptor stick, collective pitch control, steering wheel, brake pedals, pedal controls, toggles, joystick. One of ordinary skill in the art, upon reading the entirety of this disclosure would appreciate the variety of. Collective pitch control may be consistent with disclosure of collective pitch control in U.S. Pat. App. Ser. No. 16/929,206 and titled “HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”, which is incorporated herein by reference in its entirety.

With continued reference to FIG. 1, sensor 104 is configured to capture at least a flight plan datum 112. A “flight plan datum,” for the purposes of this disclosure, is an element or signal of data that represents an electric aircraft route and various environmental or outside parameters. Flight plan datum may include an element of that representing the safest, most efficient, shortest, or a combination thereof, flight path. In a non-limiting embodiment, flight controller 120 may be configured to generate a flight path towards a closest recharging pad when the controller detects the electric aircraft is low on power. In a non-limiting embodiment, an optimal flight path may include the path to a closest recharging pad. Sensors, as described herein, are any device, module, and/or subsystems, utilizing any hardware, software, and/or any combination thereof to detect events and/or changes in the instant environment and communicate the information to the vehicle controller. Sensor 104 may be part of a sensor suite wherein individual sensors may include separate sensors measuring physical or electrical quantities that may be powered by and/or in communication with circuits independently, where each may signal sensor output to a control circuit such as a user graphical interface. As a further example a degree of torque may be sensed, without limitation, using load sensors deployed at and/or around a propulsor and/or by measuring back electromotive force (back EMF) generated by a motor driving the propulsor. In an embodiment, use of a plurality of independent sensors may result in redundancy configured to employ more than one sensor that measures the same phenomenon, those sensors being of the same type, a combination of, or another type of sensor not disclosed, so that in the event one sensor fails, the ability to detect phenomenon is maintained and in a non-limiting example, a user alter aircraft usage pursuant to sensor readings. One of ordinary skill in the art will appreciate, after reviewing the entirety of this disclosure, that motion may include a plurality of types including but not limited to: spinning, rotating, oscillating, gyrating, jumping, sliding, reciprocating, or the like. The flight plan datum 112 may include a flight plan that may be a proposed flight path 124 for the flight control 120 to communicate with at least an air traffic control operator 148. In a non-limiting embodiment, the flight plan datum 112 may include various elements of data that may include a separation element 132.

With continued reference to FIG. 1, sensor 104 is configured to capture at least a safety datum 116. A “safety datum,” for the purposes of this disclosure, is an element or signal of data that represents physical parameters of individual actuators and/or flight components of an electric aircraft or logistical parameters of the electric aircraft. Safety datum 116 may include a measured torque parameter that may include the remaining vehicle torque of a flight component among a plurality of flight components. A “measured torque parameter,” for the purposes of this disclosure, refer to a collection of physical values representing a rotational equivalence of linear force. A person of ordinary skill in the art, after viewing the entirety of this disclosure, would appreciate the various physical factors in measuring torque of an object. For instance and without limitation, remaining vehicle torque may be consistent with disclosure of remaining vehicle torque in U.S. Pat. App. Ser. No. 17/197,427 and titled “SYSTEM AND METHOD FOR FLIGHT CONTROL IN ELECTRIC AIRCRAFT”, which is incorporated herein by reference in its entirety. Remaining vehicle torque may include torque available at each of a plurality of flight components at any point during an aircraft’s entire flight envelope, such as before, during, or after a maneuver. For example, and without limitation, torque output may indicate torque a flight component must output to accomplish a maneuver; remaining vehicle torque may then be calculated based on one or more of flight component limits, vehicle torque limits, environmental limits, or a combination thereof. Vehicle torque limit may include one or more elements of data representing maxima, minima, or other limits on vehicle torques, forces, attitudes, rates of change, or a combination thereof. Vehicle torque limit may include individual limits on one or more flight components, structural stress or strain, energy consumption limits, or a combination thereof. Remaining vehicle torque may be represented, as a non-limiting example, as a total torque available at an aircraft level, such as the remaining torque available in any plane of motion or attitude component such as pitch torque, roll torque, yaw torque, and/or lift torque. The flight controller 120 may mix, refine, adjust, redirect, combine, separate, or perform other types of signal operations to translate pilot desired trajectory into aircraft maneuvers. In a nonlimiting embodiment a pilot may send a pilot input at a press of a button to capture current states of the outside environment and subsystems of the electric aircraft to be displayed onto an output device in pilot view. The captured current state may further display a new focal point based on that captured current state. Flight controller 120 may condition signals such that they can be sent and received by various components throughout the electric vehicle.

With continued reference to FIG. 1, the sensor 104 may include an IMU wherein IMU may be an IMU as described herein to capture the at least a safety datum 116. Capturing the safety datum 116 may include the IMU to detect at least an aircraft angle. Safety datum 116 may include a desired attitude or rate of attitude change. At least an aircraft angle may include any information about the orientation of the aircraft in three-dimensional space such as pitch angle, roll angle, yaw angle, or some combination thereof. In non-limiting examples, at least an aircraft angle may use one or more notations or angular measurement systems like polar coordinates, cartesian coordinates, cylindrical coordinates, spherical coordinates, homogenous coordinates, relativistic coordinates, or a combination thereof, among others. IMU is configured to detect at least an aircraft angle rate. At least an aircraft angle rate may include any information about the rate of change of any angle associated with an electrical aircraft as described herein. Any measurement system may be used in the description of at least an aircraft angle rate.

With continued reference to FIG. 1, the safety datum 116 may include logistical information regarding an electric aircraft. The logistical information may include, but not limited to, information about the type of electric aircraft, an estimated departure and/or arrival time, an airport or landing infrastructure location for a departure and an arrival, a number of passengers or cargo on board the electric aircraft, health status information of the passengers or cargo, and the like. In a non-limiting embodiment, the safety datum 116 may independently be transmitted via radio frequency signals to at least an air traffic control operator. Safety datum 116 may include a plurality of data signals detailing a control to one or more actuators communicatively connected to the aircraft. Safety datum 116 may include a plurality of data entries relating aircraft pitch, roll, yaw, torque, angular velocity, climb, speed, performance, lift, thrust, drag, battery charge, fuel level, location, and the like. The safety datum 116 may include a plurality of data communicating the status of flight control devices such as proportional-integral-derivative controller, fly-by-wire system functionality, aircraft brakes, impeller, artificial feel devices, stick shaker, power-by-wire systems, active flow control, thrust vectoring, alerion, landing gear, battery pack, propulsor, management components, control surfaces, sensors/sensor suites, creature comforts, inceptor, throttle, collective, cyclic, yaw pedals, MFDs, PFDs, and the like. A person of ordinary skill in the art, after viewing the entirety of this disclosure, would appreciate a requirement of logistical data for an ATC authority to consider in verifying a flight plan for the electric aircraft.

With continued reference to FIG. 1, the sensor 104 may transmit the plurality of measured flight data including the input datum 108, flight plan datum 112, and safety datum 116 as a function of a digital communication. A “digital communication,” for the purposes of this disclosure, refer to a mode of transfer and reception of data over a communication channel via digital signals. Digital signals may include, but not limited to, audio signals, electrical signals, video signals, radar signals, radio signals, sonar signals, transmission signals, LIDAR signals and the like thereof. In a non-limiting embodiment, the transmission of any element of data including the plurality of measured flight data, a proposed flight plan 124, a confirmation flight plan 136, and the like, may be conducted as a function of a digital communication. Digital communication may include, but not limited to, data transmission, data reception, a communication system, and the like. A communication system that may support digital communication may include a plurality of individual telecommunications networks, transmission systems, relay stations, tributary stations, and the like. In a non-limiting embodiment, the system 100 may transmit the plurality of measured flight data over a point-to-point or point-to-multipoint communication channels which may include, but not limited to, copper wires, optical fibers, wireless communication channels, storage media, computer buses and the like. The data being transmitted may be represented as, but not limited to, electromagnetic signals, electrical voltage, radio wave, microwave, infrared signals, and the like. In a non-limiting embodiment, transmission of data via digital communication may be conducted using any network methodology. A person of ordinary skill in the art, after viewing the entirety of this disclosure, would appreciate the transmission of data in the context of network methodologies and digital communication.

With continued reference to FIG. 1, system 100 includes flight controller 120 which is configured to receive the plurality of measured flight data from the sensor 104, wherein the flight controller is described in further detail in FIG. 6. Flight controller 120 may include a computing device described in further detail in FIG. 8. Flight controller 120 may include a plurality of physical controller area network buses communicatively connected to the aircraft and the sensor 104. A “physical controller area network bus,” as used in this disclosure, is vehicle bus unit including a central processing unit (CPU), a CAN controller, and a transceiver designed to allow devices to communicate with each other’s applications without the need of a host computer which is located physically at the aircraft. Physical controller area network (CAN) bus unit may include physical circuit elements that may use, for instance and without limitation, twisted pair, digital circuit elements/FGPA, microcontroller, or the like to perform, without limitation, processing and/or signal transmission processes and/or tasks. For instance and without limitation, CAN bus unit may be consistent with disclosure of CAN bus unit in U.S. Pat. App. Ser. No. 17/218,342 and titled “METHOD AND SYSTEM FOR VIRTUALIZING A PLURALITY OF CONTROLLER AREA NETWORK BUS UNITS COMMUNICATIVELY CONNECTED TO AN AIRCRAFT,” which is incorporated herein by reference in its entirety. In a non-limiting embodiment, the flight controller 120 may receive the plurality of measured flight data from the sensor 104 by a physical CAN bus unit and/or transmit a proposed flight plan 124 to a second physical CAN bus unit of the flight controller 120 which may be configured to send and receive a plurality of signals from the at least an air traffic control operator 148. In a non-limiting embodiment, the sensor 104 may include a physical CAN bus unit to detect the plurality of measured flight data in tandem with a plurality of individual sensors from a sensor suite. Physical CAN bus unit may include multiplex electrical wiring for transmission of multiplexed signaling. Physical CAN bus unit 104 may include message-based protocol(s), wherein the invoking program sends a message to a process and relies on that process and its supporting infrastructure to then select and run appropriate programing. A plurality of physical CAN bus units may be located physically at the aircraft may include mechanical connection to the aircraft, wherein the hardware of the physical CAN bus unit is integrated within the infrastructure of the aircraft.

With continued reference to FIG. 1, digital communication of any signal, data, flight plan, and the like thereof, may be conducted via a plurality of transmission signals. Digital communication may include using any device that is capable for communicating with a virtual CAN bus unit, flight controller 120, at least an air traffic control operator 148 via a ground station 144, or able to receive data, retrieve data, store data, and/or transmit data, for instance via a data network technology such as 3G, 4G/LTE, 5G, Wi-Fi, IEEE 802.11 family standards, IEEE 802.1aq standards, and the like. For instance and without limitation, Shortest Path Bridging (SPB), specified in the IEEE 802.1aq standard, is a computer networking technology intended to simplify the creation and configuration of networks, while enabling multipath routing. It may include a proposed replacement for Spanning Tree Protocol (SPB) which blocks any redundant paths that could result in a layer 2 loop. SPB may allow all paths to be active with multiple equal-cost paths. SPB may also increase the number of VLANs allowed on a layer-2 network. Bridging between devices may also include devices that communicate using other mobile communication technologies, or any combination thereof, for instance and without limitation, short-range wireless communication for instance, using Bluetooth and/or Bluetooth LE standards, AirDrop, near-field (NFC), and the like. Bridging between devices may be performed using any wired, optical, or wireless electromagnetic transmission medium, as described herein. Transmission signal may include radio frequency transmission signal. A “radio frequency transmission signal,” as used in this disclosure, is an alternating electric current or voltage or of a magnetic, electric, or electromagnetic field or mechanical system in the frequency range from approximately 20 kHz to approximately 300 GHz. Radio frequency (RF) transmission signal may compose analogue and/or digital signal received, from instance via the network gateway and transmitted using functionality of output power of radio frequency from a transmitter to an antenna, and/or any RF receiver. RF transmission signal may use longwave transmitter device for transmission of signals. RF transmission signal may include a variety of frequency ranges, wavelength ranges, ITU designations, and IEEE bands including HF, VHF, UHF, L, S, C, X, Ku, K, Ka, V, W, mm, among others. Radio frequency transmission signal 124 may be generated by and/or from network switch. Signals received by network switch 116 from CAN gateway may be transmitted, for instance and without limitation as multiplexed by way of a multiplexor and/or selected by some logic at network switch, as a radio frequency transmission signal from network switch. Network switch may include a physical layer defining electrical and/or optical properties of a physical connection between a device, such as a CAN gateway, and a communication device such as without limitation a radiating antenna used to convert a time-varying electric current into an electromagnetic wave or field. In a non-limiting example, transmission signal of measured state data originating from physical CAN bus unit may be transmitted to a virtual CAN bus, and/or virtual CAN bus unit, as a radio wave-transmissible signal. Measured state data relating to a variety of flight information concerning an aircraft may be signaled to a virtual bus via a transmitting antenna and/or encoder and received by a receiving antenna and/or receiver at bus unit; transmission may be relayed by one or more intervening devices such as network hubs and/or nodes, satellites, or the like. Radio frequency signal transmission may be sent to a virtual bus unit and the virtual bus unit may correspondingly transmit back to physical CAN bus unit through network switch.

With continued reference to FIG. 1, flight controller 120 is configured to generate a proposed flight plan 124. The proposed flight plan 124 may be generated as a function of the plurality of measured flight data from the sensor 104 and/or a plurality of physical CAN bus units communicatively connected to the sensor 104. A “proposed flight plan,” for the purposes of this disclosure, refer to an element of data representing a physical path for an electric aircraft to follow wherein the path ends at a desired location. The proposed flight plan 124 may include an optimal flight plan which may include a path that is the safest, most efficient, the fastest, or combination thereof. Flight controller may further be configured to generate a flight path towards a closest recharging pad when the controller detects the electric aircraft is low on power. The proposed flight plan 124 may be configured to be generated manually by a pilot of the electric aircraft. For instance, a pilot may physically select a departure and arrival endpoints for the electric aircraft and connect the endpoints by a line representing a flight path in any form to the pilot’s preference. The flight controller 120 is further configured to generate the proposed flight plan 124 as a function of at least an air traffic database 128. A plurality of flight plans may be stored and/or retrieved in air traffic database 128. The plurality of measured flight data, which may be used for generating a training data, may also be stored and/or retrieved from air traffic database 128. Flight controller 120 may receive, store, and/or retrieve the training data, the plurality of flight plans, and the like, from air traffic database 128. Flight controller 120 may store and/or retrieve machine-learning models, classifiers, among other determinations, I/O data, heuristics, algorithms, and the like, from air traffic database 128. Air traffic database 128 may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NOSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Air traffic database 128 may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table and the like. Air traffic database 128 may include a plurality of data entries and/or records, as described above. Data entries in air traffic database 128 may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and/or reflect data and/or records as used herein, as well as categories and/or populations of data consistent with this disclosure.

Further referring to FIG. 1, air traffic database 128 may include, without limitation, a heuristic table. Determinations by a machine-learning process, machine-learning model, ranking function, and/or classifier, may also be stored and/or retrieved from the air traffic database 128. As a non-limiting example, air traffic database 128 may organize data according to one or more instruction tables. One or more air traffic database 128 tables may be linked to one another by, for instance in a non-limiting example, common column values. For instance, a common column between two tables of air traffic database 128 may include an identifier of a submission, such as a form entry, textual submission, accessory device tokens, local access addresses, metrics, and the like, for instance as defined herein; as a result, a search by the flight controller 120 may be able to retrieve all rows from any table pertaining to a given submission or set thereof. Other columns may include any other category usable for organization or subdivision of data, including types of data, names and/or identifiers of individuals submitting the data, times of submission, and the like; persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data from one or more tables may be linked and/or related to data in one or more other tables.

Continuing in reference to FIG. 1, in a non-limiting embodiment, one or more tables of air traffic database 128 may include, as a non-limiting example, flight plan table 308, which may include categorized identifying data, as described above, including a plurality of flight plans including distinct individual flight plans representing a plurality of alternative flight plans for distinct and separate electric aircraft, and the like. Flight plan table may include flight plan categories according to aircraft destination, type of aircraft, weight of cargo of the aircraft, and the like, categories, and may include linked tables to mathematical expressions that describe the impact of each alternative flight plan. One or more tables may include, without limitation, a heuristic table, which may organize rankings, scores, models, outcomes, functions, numerical values, scales, arrays, matrices, and the like, that represent determinations, probabilities, metrics, parameters, values, standards, indexes, and the like, include one or more inputs describing potential mathematical relationships, as described herein. In a non-limiting embodiment, the flight controller 120 may retrieve a flight plan from the air traffic database 128 to be confirmed or modified by at least an air traffic control operator 148 as a function of a digital communication, a physical CAN bus unit. In a non-limiting embodiment, air traffic database 128 may be accessed by the at least an air traffic control operator 148 by digital communication via a ground station 144 and a surveillance or broadcast system. In a non-limiting embodiment, the pilot and/or flight controller 120 may retrieve an alternative flight plan which may represent a new proposed flight plan to be confirmed by the at least an air traffic control operator 148. The proposed flight plan 124 may be generated as a function of a machine-learning model using a training set that may include a plurality of flight plans and flight plan data from the air traffic database 128 and use the plurality of measured flight data from the sensor 104 and/or the at least a physical CAN bus unit as inputs. A person of ordinary skill in the art, after viewing the entirety of this disclosure, would appreciate the incorporation of a machine-learning model in the context of generating a flight plan.

Continuing in reference to FIG. 1, generating the proposed flight plan 124 may include generating a proposed flight plan training data using the plurality of measured flight data detected by the sensor 104 and the at least a physical CAN bus unit wherein the plurality of measured flight data includes the at least an input datum 108, the at least a flight plan datum 112, and the at least a safety datum 116, and training a proposed flight plan machine-learning model with the proposed flight plan training data that includes a plurality of data entries wherein each entry correlates the plurality of measured flight data to a plurality of flight plans which may be retrieved from the air traffic database 128, and generating the proposed flight plan 124 as a function of the proposed flight plan machine-learning model and the plurality of measured flight data.

Continuing in reference to FIG. 1, proposed flight plan training data may be received from the plurality of measured flight data from a sensor 104. Proposed flight plan training data may be received from an air traffic database 128 which may include a plurality of database tables configured to retrieve/store input datum 108, flight plan datum 112, and a safety datum 116. Such training data may include a plurality of data entries of flight plan parameters correlated to types of flight plans. Training data may originate as analysis from previous flights and/or flight plans of the electric aircraft, previous flights and/or flight plans of different electric aircrafts distinct from one another, and the like, from one or more electric aircrafts. Proposed flight plan training data may originate from one or more electric aircraft pilots and/or at least air traffic control operators, for instance via a user interface with a flight controller to provide flight history, air traffic history, weather condition information, and the like. Flight controller 120, which may include a remote computing device, may use any machine-learning algorithm to train a machine-learning model such as a flight plan machine-learning model using the proposed flight plan training data. The flight plan machine-learning model may take the plurality of measured flight data from the sensors as inputs and output a confirmation flight plan. Flight plan machine-learning model may take data from an air traffic database as inputs and output a confirmation flight plan. It is important to note that training data for machine-learning processes, algorithms, and/or models used herein may originate from any source described for proposed flight plan training data.

Continuing in reference to FIG. 1, a proposed flight plan machine-learning model may include any machine-learning algorithm such as K-nearest neighbors algorithm, a lazy naive Bayes algorithm, and the like, machine-learning process such as supervised machine-learning, unsupervised machine-learning, or method such as neural nets, deep learning, and the like, as described in further detail below. Proposed flight plan machine-learning model may be trained to derive an algorithm, function, series of equations, or any mathematical operation, relationship, or heuristic, that can automatedly accept an input of the plurality of measured flight data and generate an output of a proposed flight plan 124. Proposed flight plan machine-learning model 116 may derive individual functions describing unique relationships observed from the proposed flight plan training data for each input datum 108, at least a flight plan datum 112, and at least a safety datum 116, wherein different relationships may emerge between different pilots, electric aircrafts, type of cargo and/or number of passengers in an electric aircraft, flight priority of an electric aircraft, and the like. Proposed flight plan machine-learning model may derive relationships from the training data which indicate patterns in estimated flight duration of different flight plans or proposed flight plans according to where an electric aircraft is departing from and/or arriving to, and the like Proposed flight plan 124 may include any number of parameters, numerical values, strings, functions, mathematical expressions, text, and the like. Proposed flight plan 124 and at least an air traffic database 128 may become increasingly more complete, and more robust, with larger sets of plurality of measured flight data.

With continued reference to FIG. 1, flight controller 120 is configured to transmit the proposed flight plan 124 and at least a separation element 132 to at least an air traffic control operator 148. A “Separation element,” for the purposes of this disclosure, is an element of data representing a physical value of a distance an electric aircraft should maintain from a particular location. Particular location may include, but not limited to, a surface of the ground, a destination and/or arrival location, environmental obstacles, and the like. The separation element 132 may include a distance threshold the electric aircraft must maintain to avoid at least a collision with another aircraft, obstacle, the surface, and the like. The separation element 132 may include a safety parameter that may be a function of a safety datum 116. In a non-limiting embodiment, safety element 136 may include a safety comparison described in further detail in FIG. 2.

Now referring to FIG. 2, an exemplary embodiment of fuzzy set for safety comparison 200 for a separation element 132 is illustrated. A first fuzzy set 204 may be represented, without limitation, according to a first membership function 208 representing a probability that an input falling on a first range of values 212 is a member of the first fuzzy set 204, where the first membership function 208 has values on a range of probabilities such as without limitation the interval [0,1], and an area beneath the first membership function 208 may represent a set of values within first fuzzy set 204. Although first range of values 212 is illustrated for clarity in this exemplary depiction as a range on a single number line or axis, first range of values 212 may be defined on two or more dimensions, representing, for instance, a Cartesian product between a plurality of ranges, curves, axes, spaces, dimensions, or the like. First membership function 208 may include any suitable function mapping first range 212 to a probability interval, including without limitation a triangular function defined by two linear elements such as line segments or planes that intersect at or below the top of the probability interval. As a non-limiting example, triangular membership function may be defined as:

$y (x, a, b, c) = \{\begin{matrix} 0, f o r x > c a n d x < a \\ \frac{x - a}{b - a}, f o r a \leq x < b \\ \frac{c - x}{c - b}, i f b < x \leq c \end{matrix})$

a trapezoidal membership function may be defined as:

$y (x, a, b, c, d) = m a x (m i n (\frac{x - a}{b - a}, 1, \frac{d - x}{d - c}), 0)$

a sigmoidal function may be defined as:

$y (x, a, c) = \frac{1}{1 - e^{- a (x - c)}}$

a Gaussian membership function may be defined as:

$y (x, c, σ) = e^{- \frac{1}{2} {(\frac{x - c}{σ})}^{2}}$

and a bell membership function may be defined as:

$y (x, a, b, c,) = {[1 + {|\frac{x - c}{a}|}^{2 b}]}^{- 1}$

Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various alternative or additional membership functions that may be used consistently with this disclosure.

First fuzzy set 204 may represent any value or combination of values as described above, including predictive prevalence value, probabilistic outcome, any resource datum, any niche datum, and/or any combination of the above. A second fuzzy set 216, which may represent any value which may be represented by first fuzzy set 204, may be defined by a second membership function 220 on a second range 224; second range 224 may be identical and/or overlap with first range 212 and/or may be combined with first range via Cartesian product or the like to generate a mapping permitting evaluation overlap of first fuzzy set 204 and second fuzzy set 216. Where first fuzzy set 204 and second fuzzy set 216 have a region 328 that overlaps, first membership function 208 and second membership function 220 may intersect at a point 232 representing a probability, as defined on probability interval, of a match between first fuzzy set 204 and second fuzzy set 216. Alternatively or additionally, a single value of first and/or second fuzzy set may be located at a locus 236 on first range 212 and/or second range 224, where a probability of membership may be taken by evaluation of first membership function 208 and/or second membership function 220 at that range point. A probability at 228 and/or 232 may be compared to a threshold 240 to determine whether a positive match is indicated. Threshold 240 may, in a non-limiting example, represent a degree of match between first fuzzy set 204 and second fuzzy set 216, and/or single values therein with each other or with either set, which is sufficient for purposes of the matching process; for instance, threshold may indicate a sufficient degree of overlap between a plurality of data including a first distance, wherein the first distance may include a radial distance from an electric aircraft representing the midpoint of the radial distance that is to not be overlapped by an outside landmark including, but not limited to, any type of infrastructure that is not a landing zone for the electric aircraft, environmental terrain, one or more potentially intervening flight paths of other aircrafts, and the like. There may be multiple thresholds; for instance, a second threshold may include a threshold for a different measurement unit for plurality of data unique to the first threshold which may include infrared mapping. Second threshold may include measurements of level of green-light for the mapping and measuring of seafloor, riverbed elevation, water level, and the like. Each threshold may be established by one or more user inputs or automatically by a flight controller. Each threshold and a safety parameter may be determined using training data that correlates the separation element 132 and the plurality of measured flight data including a safety datum 116 with degrees of safety including at least a safety threshold level as a function of a machine-learning model as described in further detail below.

Still referring to FIG. 2, in an embodiment, a degree of match between fuzzy sets may be used to rank one distance from another. For instance, a sensor 104 including a LIDAR system may detect more than one distances an electric aircraft should maintain from a particular location if two potential distances have fuzzy sets closely matching an ideal distance from a particular location or above a safety threshold level fuzzy set by having a degree of overlap exceeding a threshold, wherein flight controller 120 may further rank the two resources by ranking a resource having a higher degree of match more highly than a resource having a lower degree of match. Where multiple fuzzy matches are performed, degrees of match for each respective fuzzy set may be computed and aggregated through, for instance, addition, averaging, or the like, to determine an overall degree of match, which may be used to rank resources; selection between two or more matching resources may be performed by selection of a highest-ranking resource, and/or multiple potential landing zones may be presented to a user in order of ranking.

Referring back to FIG. 1, flight controller 120 is configured to transmit the proposed flight plan 124 and the at least a separation element 132 at least an air traffic control operator 148. flight controller 120 may include an automated broadcaster 140 configured to receive the proposed flight plan 124 and the at least a separation element 132 and transmit them to the at least an air traffic control operator 148 as a function of a digital communication and at least a ground station 144. An “automated broadcaster,” for the purposes of this disclosure, refer to a computing device that represents a hub for the transmission and receiving of signals. Automated broadcaster may include an Automatic Dependent Surveillance-Broadcast (ADS-B) which includes a surveillance technology in which an electric aircraft may determine its position via satellite navigation, the sensor 104, the at least a physical CAN bus unit, or combination thereof, and periodically broadcasts it, enabling the electric aircraft to be tracked. In a non-limiting embodiment, the automated broadcaster may transmit the proposed flight plan 124 and the at least a separation element 132 to at least an air traffic control operator 152 by a buffer including a ground station148 as a replacement for secondary surveillance radar, as no interrogation signal is needed from the ground. It can also be received by other aircrafts to provide situational awareness and allow self-separation. ADS-B is “automatic” in that it requires no pilot or external input. It is “dependent” in that it depends on data from the aircraft’s navigation system. The automated broadcaster 140 may be configured to be a hub for digital communication with at least an air traffic control operator 148 to determine a confirmation flight plan 136. The automated broadcaster may transmit a proposed flight plan 124 to a remote device 152 may include any suitable device or facility to which an aircraft’s approach plan would be of interest for safety, planning and logistics purposes. For example, and without limitation, remote device 152 may be an air traffic control device, such as an air traffic control computing device, that is operated by an air traffic control site such as, without limitation, one located in an ATC tower or at an airport, and the like, among others. In another example, and without limitation, remote device may be another recharging site or platform or a fleet management facility, or a device such as a computing device at these locations. Remote device 152 may include any of the computing devices as disclosed herein.

With continued reference to FIG. 1, flight controller 120 may be configured to loop transmission signals between a pilot of the electric aircraft and at least an air traffic control operator 148. The at least an air traffic control operator 148 may include any ground-based controller. The at least an air traffic control operator 148 may include a human controller or an automated controller. the at least an air traffic control operator 148 may transmit a traffic separation rules via digital communication to the flight controller 120. Flight controller 120 may serve as an autopilot system for the electric aircraft. In a non-limiting embodiment, at least an air traffic control operator 148 may communicate via digital communication with the automated broadcaster 140 as a function of the flight controller 120 wherein the automated broadcaster transmits the proposed flight plan 124 and the at least a separation element 132 to the at least an air traffic control operator 148 to confirm, verify, validate, reject, or modify the proposed flight plan. The resulting flight plan may include a confirmation flight plan that is configured to be received via digital communication by the automated broadcaster 140 to identify and/or determine the confirmation flight plan 136.

With continued reference to FIG. 1, flight controller 120 may use an assessment classification machine-learning process to generate and transmit a new proposed flight plan which may include a flight plan assessment using at least a traffic separation rule. A “traffic separation rule,” for the purposes of this disclosure, refer to a minimum distance from another aircraft to reduce the risk of a collision. Separation element 132 may be configured to transmit a minimum distance to the at least air traffic control operator 148 as a traffic separation rule. In a non-limiting embodiment, the flight controller 120 may transmit the separation element 132 to bypass a loop of transmission signals that may comprise a traffic separation rule from the at least an air traffic control operator 148 to optimize digital communication and reduce time wasted between transmission of signals. For instance, the flight control module 132 may incorporate a separation element 132 with the proposed flight plan 124 to increase the probability of the proposed flight plan to be approved, confirmed, verified, and/or acknowledged by the at least air traffic control operator 148 that complies with air traffic control clearance. In a non-limiting embodiment, an air traffic control may transmit a modified flight plan along with the at least a traffic separation rule in which the flight controller 120 may use the at least a traffic separation rule in tandem with an alternative or new proposed flight plan selected manually by the pilot of the electric aircraft or automatically by the flight controller 120 from the air traffic database 128 to generate a new proposed flight plan in the form of a flight plan assessment. A “flight plan assessment,” for the purposes of this disclosure, refer to a determination about a current proposed flight plan according to a classification of the at least a traffic separation rule. Flight plan assessment may include a proposed flight plan distinct from the initial proposed flight plan 124. Assigning the proposed flight plan 124 to a flight plan assessment may include classifying the proposed flight plan 124 to the flight plan assessment using the assessment classification machine-learning process, and assigning the proposed flight plan 124 as a function of the classifying. Assessment classification machine-learning process may include any machine-learning process, algorithm, and/or model performed by a machine-learning module, as described in further detail below Assessment classification machine-learning process 124 may generate a classifier using training data that may include any training data described in the entirety of this disclosure. A “classifier” may include a machine-learning model, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below. Classifier may provide “classification” by sorting inputs, such as the data in the proposed flight plan 124, into categories or bins of data, such as classifying the data into a flight plan assessment. Classifier may output the bins of data and/or labels associated therewith.

Continuing in reference to FIG. 1, assessment classification machine-learning process 124 may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher’s linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a non-limiting example, a classifier may classify elements of training data to elements that characterizes a sub-population, such as a subset of input datum 108, at least a flight plan datum 112, and at least a safety datum 116 and/or other analyzed items and/or phenomena for which a subset of training data may be selected, for generating specified training data sets for subsequent process(es) described herein. Classification may include identifying which set of flight plan assessment a proposed flight plan 124 observation, or set of observations, belongs. Classification may include clustering based on pattern recognition, wherein the presence of the plurality of measured flight data identified in proposed flight plan 124 relate to a particular flight plan assessment. Such classification methods may include binary classification, where the proposed flight plan 124 is simply matched to each existing flight plan assessment and sorted into a category based on a “yes”/“no” match. Classification may include weighting, scoring, or otherwise assigning a numerical valuation to data elements in proposed flight plan 124 as it relates to each flight plan assessment 120. Such a score may represent a likelihood, probability, or other statistical identifier that relates to the classification into flight plan assessment, where the highest score may be selected depending on the definition of “highest”. In this way, assessment classification machine-learning process may be free to create new classification categories as a function of how well a user may be categorized to existing categories.

With continued reference to FIG. 1, the automated broadcaster 140, after at least a loop of transmission signals with at least an air traffic control operator 148, may determine a confirmation flight plan 136. In a non-limiting embodiment, confirmation flight plan 136 may include the proposed flight plan 124 without any modifications. In a non-limiting embodiment, confirmation flight plan 136 may be determined and/or generated by the flight plan assessment as a function of a new proposed flight plan. In a non-limiting embodiment, the confirmation flight plan may include a modification of the proposed flight plan 124 as a function of the at least traffic separation rule from the at least an air traffic control operator 148. In a non-limiting embodiment, the confirmation flight plan may include an optimal flight plan that at least an air traffic control operator 148 has confirmed of a proposed flight plan 124. The flight controller 120 may automatically execute the confirmation flight plan 136 which may include an autopilot system.

With continued reference to FIG. 1, flight controller 120 is configured to transmit the confirmation flight plan 136 to an outside device such as pilot display 156. The pilot display may comprise an output device. “Output device”, for the purposes of this disclosure, is a visual apparatus that is comprised of compact flat panel designs, liquid crystal display, organic light-emitting diode, or combination thereof to present visual information superimposed on spaces. Display 160 may include a graphical user interface (GUI), multi-functional display (MFD), primary flight display (PFD), gages, dials, screens, touch screens, speakers, haptic feedback device, live feed, window, combination thereof, or another display type not listed here. In a nonlimiting embodiment, the pilot display 156 may include a mobile computing device like a smartphone, tablet, computer, laptop, client device, server, a combination thereof, or another undisclosed display alone or in combination. The pilot display 156 may be disposed in at least a portion of a cockpit of an electric aircraft. The Pilot display 156 may be a heads-up display (HUD) disposed in goggles, glasses, eye screen, or other headwear a pilot or user may be wearing. The pilot display 156 may include augmented reality, virtual reality, or combination thereof.

With continued reference to FIG. 1, the pilot display 156 may include monitor display that may display information in pictorial form. Monitor display may include visual display, computer, and the like. For example, monitors display may be built using liquid crystal display technology that displays to the pilot information from a computer’s user interface. Output device may include any processor and/or computing device containing any processor suitable for use in and/or with an augmented reality device. Output device may include any component and/or element suitable for use with augmented reality over-head display. The display may further include at least a peripheral display. The peripheral display may further be mounted to a pilot’s head that is in the peripheral of the user’s field of view. In a non-limiting embodiment, the pilot interface may view the outside environment as a function of the sensors and flight controller and generate a focal point as a dot on the at least peripheral display. Output device may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, pilot display 156 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Pilot display 156 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, pilot display 156 cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing. In a non-limiting embodiment, pilot display 156 may be viewed by a pilot to view the confirmation flight plan 136 and command the electric aircraft as a function of the confirmation flight plan 136.

With continued reference to FIG. 1, an embodiment of the pilot display 156 may display a focal point that indicates the desired landing location for the VTOL aircraft as an arrival destination for the electric aircraft. “Focal point”, for the purposes of this disclosure, is a piece of data that represents an electronic symbol that is trailed by a guidance symbol representing the optimal flight path. The focal point may be determined by at least a predetermined flight plan. “Guidance symbol”, for the purposes of this disclosure, is a pattern, indicum, or array of symbols indicating a direction or position to be traversed by a vehicle on the way to the desired location indicated by the focal point. For example, the pilot may follow the flight path the guidance symbol is protruding to the desired location indicated by the focal point. The pilot display 156 may display an estimated time of arrival that may alter during the course of a flight for the VTOL aircraft to arrive at the focal point. The estimated time of arrival may comprise at least a digital clock.

With continued reference to FIG. 1, an embodiment of the pilot display 156 may display at least a warning symbol to the pilot. The warning symbol may include an abbreviation, a sign, or combination thereof. The warning symbol may highlight itself in blinking form, different colors, or combination thereof. Examples of warning symbols may indicate, but not limited to, a malfunction or failure of at least a flight component, flight controller, software relating to generating focal point or guidance symbol, unfavorable landing location, and the like. The warning symbol or plurality of warning symbols may dissuade the pilot from undertaking a disadvantageous action. Examples of disadvantageous actions include, but not limited to, at least actions that may harm the VTOL aircraft or flight components, actions that may hard the pilot, actions that may produce collateral damage, and the like. The pilot display 156 may display a status symbol of the landing of the VTOL aircraft. The status symbol may comprise a status of landing zone. “Status of landing zone”, for the purposes of this disclosure, is a piece of data that represents a physical symbol, electronic symbol, or combination thereof. The status of landing zone may include an abbreviation, sign, or combination thereof. For example, the status of landing zone may inform the pilot at least within a proximity of the focal point 304 that the landing zone predetermined by the focal point is at least valid if the VTOL aircraft is cleared to land safely or invalid if the VTOL aircraft is not cleared to land. The flight mode symbol may be displayed on the pilot display 156. The flight mode symbol may be determined by a feedback loop that may include a process whereby a pilot takes some action, causing flight components to respond; system may sense or recalculating one or more of the data described above, and then update the display. As described above, aircraft may be moving in a given direction on a path to a destination, and flight controller may update the path; update may be based on a torque output as function of a sensor, new aircraft position, velocity or acceleration vectors, or a combination thereof. Feedback loop may further include an updated optimal torque output that is based on a new path, new torque output as a function of a sensor, or combination thereof. Flight symbol may include a feedback loop may further include a change depending on how the accurately the aircraft or pilot is complying with recommendations in the previous iteration of the display. For example, pilot display 156 may display feedback loop in a form of the aircraft’s actual or currently projected path along with a recommended one. Pilot display 156 may further display feedback loop with a new optimal torque output and an actual torque output. In a non-limiting embodiment, the flight controller may command a sensor which may include at least an IMU and configure it to be a closed-loop accelerometer in the instances of flight disturbances. In a non-limiting embodiment, a pilot may command aircraft to achieve a new optimal torque based on information that is displayed on pilot display 156. New optimal torque may be updated based on detections captured by a sensor and the like. A further example may include the use of colors that may include, but not limited to, a red color that may be used if the pilot and/or aircraft is pursuing a course that is not the recommended one.

With continued reference to FIG. 1, the pilot display 156 may include a GUI. As described above, GUI may display the current flight plan and/or optimal flight path in graphical form. Graphical form may include a two-dimensional plot of two variables that represent data received by the controller, such as past maneuvers and predicted future maneuvers. In one embodiment, GUI may also display the user’s input in real-time. The GUI may further include to display the velocity and position of the electric aircraft based on provided future inputs. In another embodiment, GUI may display the maneuver that was just performed by the user, the suggested maneuver to be performed and the maneuver being currently performed by the user. In one embodiment, GUI will display a different suggested maneuver upon deviation by the user from flight plan. In a non-limiting example, GUI may display different color schemes for immediate past maneuver, suggested immediate future maneuver, and the like. In one embodiment, additionally to the flight plan, GUI may display objective and a directional line once objective is nearby. In one embodiment, GUI may display a directional path to the objective when flight plan is set for an intermediate objective. In a nonlimiting example, GUI may display a dotted path additionally to the suggested maneuvers and a graphical representation of the objective one user gets near the objective as to assist user when landing or reaching objective. In another nonlimiting example, GUI may display a dotted line connected to the final objective as to keep user informed of direction of final objective when flight plan is set for an intermediate objective.

Referring now to FIG. 3, a block diagram of an exemplary embodiment of a method 300 for digital communication of a flight plan to an air traffic control is illustrated. Method 300 includes step 305 which includes detecting a plurality of measured flight data comprising a flight plan datum, a safety datum, and an input datum by a sensor. The flight plan datum may include any flight plan datum described herein. The input datum may include any input datum as described herein. The safety datum may include any safety datum described herein. The sensor may include any sensor described herein. Step 305 may include detecting a plurality of outside parameters of an outside environment. Outside parameter may include any outside parameter as described herein.

Still referring to FIG. 3, method 300 includes step 310 which includes receiving, by a flight controller, the plurality of measured flight data from the sensor. The flight controller may include any flight controller as described in the entirety of this disclosure.

Still referring to FIG. 3, method 300 includes step 315 which includes generating a proposed flight plan. Generating a proposed flight plan may include using a machine-learning model and training the machine-learning model with training data wherein the training data may include any training data described herein. Step 315 may include using an air traffic database in the generating of the proposed flight plan. Air traffic database may include any database described herein.

Still referring to FIG. 3, method 300 includes step 320 which includes transmitting the proposed flight plan to a remote device. Remote device may include any remote device as described herein. Remote device may include at least an ATC and/or ATC operator. Step 320 may include transmitting at least a separation element to the at least an air traffic control operator. The least an air traffic control operator may include any air traffic control operator as described herein. The at least a separation element may include any separation element described herein. Transmitting may include digital communication and a plurality of transmission signals. Transmission signals may include any transmission signal described herein. Step 320 may include transmitting to at least a remote device. Transmitting the proposed flight plan further comprises transmitting the proposed flight plan to at least an air traffic control operator, at least the pilot, and at least an autopilot system of the electric aircraft. In a non-limiting embodiment, the transmitted proposed flight plan is configured to be selected from an air traffic database. In a non-limiting embodiment, digital communication may be conducted via a ground station as a hub for transfer of signals between the at least an air traffic control operator and the flight controller.

Still referring to FIG. 3, method 300 includes step 325 which includes receiving a confirmation flight plan as a function of remote device. The remote device may include at least an ATC operator. The least an air traffic control operator may include any air traffic control operator as described herein. Step 325 may include the flight controller to digitally communicate with at least an air traffic control operator continuously via the plurality of radio frequency transmission signals and a ground station. Step 325 may further include the at least an air traffic control operator to receive a constant flow of radio frequency transmission signals to remain informed of the electric aircraft’s flight path without directly communicating to the pilot. Determining the confirmation flight plan further includes using a machine-learning process and the separation element as training classifier. Training classifier may be any classifier as described herein. Determining the confirmation flight plan may include transmitting a plurality of alternative proposed flight plans to the pilot display, wherein the pilot display is further configured to display the plurality of alternative proposed flight plans at least a pilot or at least a flight controller may select as a proposed flight plan and potentially command in the event at least an air traffic control operator rejects the proposed flight plan. A person of ordinary skill in the art, after viewing the entirety of this disclosure, would appreciate a plurality of transmission of signals in determining a confirmation in the context of flight plans and air traffic control clearance.

Still referring to FIG. 3, method 300 includes step 330 which includes receiving the confirmation flight plan by a pilot display. Receiving may include receiving by a GUI. The pilot display may include any pilot display as described herein. GUI may include any GUI as described herein.

Still referring to FIG. 3, method 300 includes step 335 which includes displaying the confirmation flight plan to a pilot, wherein the confirmation flight plan is configured to be commanded by the pilot. Displaying the confirmation flight plan may be done automatically by the flight controller.

Referring now to FIG. 4, a block diagram of another exemplary embodiment of a method 400 for digital communication of a flight plan to an air traffic control performed by the flight controller is provided. In a non-limiting embodiment, method 400 may describe a feedback loop of signals between a flight controller and an at least air traffic control operator to reduce time wasted between communication of human controllers in requesting flight plans and confirming flight plans. A person of ordinary skill in the art, after viewing the entirety of this disclosure, would appreciate the communication loop in the context of obtaining ATC clearance for a proposed flight plan.

Still referring to FIG. 4, method 400 includes step 405 which includes selecting a proposed flight plan from at least an air traffic database. Step 405 may include the flight controller to access the air traffic database and select a flight plan from the plurality of flight plans as the proposed flight plan. In a non-limiting embodiment, if at least an air traffic control operator rejects the proposed flight plan via digital communication, the flight controller may select an alternative flight plan as a new proposed flight plan to be sent to the at least air traffic control operator for another round of verification.

Still referring to FIG. 4, method 400 may include step 410 which includes transmitting a proposed flight plan to at least an air traffic control operator. Step 410 may include transmitting the proposed flight plan to a buffer first such as a ground station and/or a remote device.

Still referring got FIG. 4, method 400 includes step 415 which includes receiving the proposed flight plan by at least an air traffic control operator. Receiving the proposed flight plan may be done through the ground station via digital communication and/or remote device. The at least air traffic control operator may either confirm the proposed flight plan and transmit a confirmation flight plan back to the flight controller as described in step 420 or modify the proposed flight plan and transmit the modified flight plan back to the flight controller as described in step 425.

Still referring to FIG. 4, method 400 includes step 420 which includes confirming the proposed flight plan. Confirming may include an air traffic authority to confirm or verify the flight plan wherein the air traffic authority includes a human operator or an automated operator. Method 400 may include step 425 which includes transmitting the confirmation flight plan back to the flight controller.

Still referring to FIG. 4 method 400 includes step 425 which includes modifying the proposed flight plan. Step 425 may include the at least air traffic control operator rejecting the proposed flight plan. Method 400 may include step 435 which includes transmitting the modified flight plan back to the flight controller.

Still referring to FIG. 4, method 400 may include 440 which includes displaying the confirmation flight plan to a pilot display. Step 440 may include the flight controller determining the confirmation flight plan using an assessment classification machine-learning process. In a non-limiting embodiment, the flight controller may perform a final review of the confirmation flight plan received from at least an air traffic control operator to verify if it has been modified or is still satisfactory to a pilot.

Still referring to FIG. 4, method 400 may include step 445 which includes a conditional step that the flight controller, as a function of an autopilot system or a pilot of an electric aircraft, may conclude to either accept the modified flight plan the at least air traffic control operator has modified or generate a new proposed flight plan. The generating of a new proposed flight plan may include selecting an alternative flight plan or a second flight plan from the air traffic database. If the flight management system or pilot accepts the modified flight plan as a confirmation flight plan, the flight controller may display the modified flight plan to a pilot display. If the flight controller or pilot does not accept the modified flight plan, the flight controller may perform another loop of digital communication and select a new proposed flight plan as described in step 405 with at least a traffic separation rule as a factor.

Referring now to FIG. 5, an exemplary embodiment of an aircraft 500, which may include, or be incorporated with, a system for optimization of a recharging flight plan is illustrated. As used in this disclosure an “aircraft” is any vehicle that may fly by gaining support from the air. As a non-limiting example, aircraft may include airplanes, helicopters, commercial and/or recreational aircrafts, instrument flight aircrafts, drones, electric aircrafts, airliners, rotorcrafts, vertical takeoff and landing aircrafts, jets, airships, blimps, gliders, paramotors, and the like thereof.

Still referring to FIG. 5, aircraft 500 may include an electrically powered aircraft. In embodiments, electrically powered aircraft may be an electric vertical takeoff and landing (eVTOL) aircraft. Aircraft 500 may include an unmanned aerial vehicle and/or a drone. Electric aircraft may be capable of rotor-based cruising flight, rotor-based takeoff, rotor-based landing, fixed-wing cruising flight, airplane-style takeoff, airplane-style landing, and/or any combination thereof. Electric aircraft may include one or more manned and/or unmanned aircrafts. Electric aircraft may include one or more all-electric short takeoff and landing (eSTOL) aircrafts. For example, and without limitation, eSTOL aircrafts may accelerate the plane to a flight speed on takeoff and decelerate the plane after landing. In an embodiment, and without limitation, electric aircraft may be configured with an electric propulsion assembly. Electric propulsion assembly may include any electric propulsion assembly as described in U.S. Nonprovisional App. Ser. No. 16/703,225, filed on Dec. 4, 2019, and entitled “AN INTEGRATED ELECTRIC PROPULSION ASSEMBLY,” the entirety of which is incorporated herein by reference. For purposes of description herein, the terms “upper”, “lower”, “left”, “rear”, “right”, “front”, “vertical”, “horizontal”, “upward”, “downward”, “forward”, “backward” and derivatives thereof shall relate to the invention as oriented in FIG. 5.

Still referring to FIG. 5, aircraft 500 includes a fuselage 504. As used in this disclosure a “fuselage” is the main body of an aircraft, or in other words, the entirety of the aircraft except for the cockpit, nose, wings, empennage, nacelles, any and all control surfaces, and generally contains an aircraft’s payload. Fuselage 504 may include structural elements that physically support a shape and structure of an aircraft. Structural elements may take a plurality of forms, alone or in combination with other types. Structural elements may vary depending on a construction type of aircraft such as without limitation a fuselage 504. Fuselage 504 may comprise a truss structure. A truss structure may be used with a lightweight aircraft and comprises welded steel tube trusses. A “truss,” as used in this disclosure, is an assembly of beams that create a rigid structure, often in combinations of triangles to create three-dimensional shapes. A truss structure may alternatively comprise wood construction in place of steel tubes, or a combination thereof. In embodiments, structural elements may comprise steel tubes and/or wood beams. In an embodiment, and without limitation, structural elements may include an aircraft skin. Aircraft skin may be layered over the body shape constructed by trusses. Aircraft skin may comprise a plurality of materials such as plywood sheets, aluminum, fiberglass, and/or carbon fiber, the latter of which will be addressed in greater detail later herein.

In embodiments, and with continued reference to FIG. 5, aircraft fuselage 504 may include and/or be constructed using geodesic construction. Geodesic structural elements may include stringers wound about formers (which may be alternatively called station frames) in opposing spiral directions. A “stringer,” as used in this disclosure, is a general structural element that includes a long, thin, and rigid strip of metal or wood that is mechanically coupled to and spans a distance from, station frame to station frame to create an internal skeleton on which to mechanically couple aircraft skin. A former (or station frame) may include a rigid structural element that is disposed along a length of an interior of aircraft fuselage 504 orthogonal to a longitudinal (nose to tail) axis of the aircraft and may form a general shape of fuselage 504. A former may include differing cross-sectional shapes at differing locations along fuselage 504, as the former is the structural element that informs the overall shape of a fuselage 504 curvature. In embodiments, aircraft skin may be anchored to formers and strings such that the outer mold line of a volume encapsulated by formers and stringers comprises the same shape as aircraft 500 when installed. In other words, former(s) may form a fuselage’s ribs, and the stringers may form the interstitials between such ribs. The spiral orientation of stringers about formers may provide uniform robustness at any point on an aircraft fuselage such that if a portion sustains damage, another portion may remain largely unaffected. Aircraft skin may be mechanically coupled to underlying stringers and formers and may interact with a fluid, such as air, to generate lift and perform maneuvers.

In an embodiment, and still referring to FIG. 5, fuselage 504 may include and/or be constructed using monocoque construction. Monocoque construction may include a primary structure that forms a shell (or skin in an aircraft’s case) and supports physical loads. Monocoque fuselages are fuselages in which the aircraft skin or shell is also the primary structure. In monocoque construction aircraft skin would support tensile and compressive loads within itself and true monocoque aircraft can be further characterized by the absence of internal structural elements. Aircraft skin in this construction method is rigid and can sustain its shape with no structural assistance form underlying skeleton-like elements. Monocoque fuselage may comprise aircraft skin made from plywood layered in varying grain directions, epoxy-impregnated fiberglass, carbon fiber, or any combination thereof.

According to embodiments, and further referring to FIG. 5, fuselage 504 may include a semi-monocoque construction. Semi-monocoque construction, as used herein, is a partial monocoque construction, wherein a monocoque construction is describe above detail. In semi-monocoque construction, aircraft fuselage 504 may derive some structural support from stressed aircraft skin and some structural support from underlying frame structure made of structural elements. Formers or station frames can be seen running transverse to the long axis of fuselage 504 with circular cutouts which are generally used in real-world manufacturing for weight savings and for the routing of electrical harnesses and other modern on-board systems. In a semi-monocoque construction, stringers are thin, long strips of material that run parallel to fuselage’s long axis. Stringers may be mechanically coupled to formers permanently, such as with rivets. Aircraft skin may be mechanically coupled to stringers and formers permanently, such as by rivets as well. A person of ordinary skill in the art will appreciate, upon reviewing the entirety of this disclosure, that there are numerous methods for mechanical fastening of the aforementioned components like screws, nails, dowels, pins, anchors, adhesives like glue or epoxy, or bolts and nuts, to name a few. A subset of fuselage under the umbrella of semi-monocoque construction includes unibody vehicles. Unibody, which is short for “unitized body” or alternatively “unitary construction”, vehicles are characterized by a construction in which the body, floor plan, and chassis form a single structure. In the aircraft world, unibody may be characterized by internal structural elements like formers and stringers being constructed in one piece, integral to the aircraft skin as well as any floor construction like a deck.

Still referring to FIG. 5, stringers and formers, which may account for the bulk of an aircraft structure excluding monocoque construction, may be arranged in a plurality of orientations depending on aircraft operation and materials. Stringers may be arranged to carry axial (tensile or compressive), shear, bending or torsion forces throughout their overall structure. Due to their coupling to aircraft skin, aerodynamic forces exerted on aircraft skin will be transferred to stringers. A location of said stringers greatly informs the type of forces and loads applied to each and every stringer, all of which may be handled by material selection, cross-sectional area, and mechanical coupling methods of each member. A similar assessment may be made for formers. In general, formers may be significantly larger in cross-sectional area and thickness, depending on location, than stringers. Both stringers and formers may comprise aluminum, aluminum alloys, graphite epoxy composite, steel alloys, titanium, or an undisclosed material alone or in combination.

In an embodiment, and still referring to FIG. 5, stressed skin, when used in semi-monocoque construction is the concept where the skin of an aircraft bears partial, yet significant, load in an overall structural hierarchy. In other words, an internal structure, whether it be a frame of welded tubes, formers and stringers, or some combination, may not be sufficiently strong enough by design to bear all loads. The concept of stressed skin may be applied in monocoque and semi-monocoque construction methods of fuselage 504. Monocoque comprises only structural skin, and in that sense, aircraft skin undergoes stress by applied aerodynamic fluids imparted by the fluid. Stress as used in continuum mechanics may be described in pound-force per square inch (lbf/in²) or Pascals (Pa). In semi-monocoque construction stressed skin may bear part of aerodynamic loads and additionally may impart force on an underlying structure of stringers and formers.

Still referring to FIG. 5, it should be noted that an illustrative embodiment is presented only, and this disclosure in no way limits the form or construction method of a system and method for loading payload into an eVTOL aircraft. In embodiments, fuselage 504 may be configurable based on the needs of the eVTOL per specific mission or objective. The general arrangement of components, structural elements, and hardware associated with storing and/or moving a payload may be added or removed from fuselage 504 as needed, whether it is stowed manually, automatedly, or removed by personnel altogether. Fuselage 504 may be configurable for a plurality of storage options. Bulkheads and dividers may be installed and uninstalled as needed, as well as longitudinal dividers where necessary. Bulkheads and dividers may be installed using integrated slots and hooks, tabs, boss and channel, or hardware like bolts, nuts, screws, nails, clips, pins, and/or dowels, to name a few. Fuselage 504 may also be configurable to accept certain specific cargo containers, or a receptable that can, in turn, accept certain cargo containers.

Still referring to FIG. 5, aircraft 500 may include a plurality of laterally extending elements attached to fuselage 504. As used in this disclosure a “laterally extending element” is an element that projects essentially horizontally from fuselage, including an outrigger, a spar, and/or a fixed wing that extends from fuselage. Wings may be structures which include airfoils configured to create a pressure differential resulting in lift. Wings may generally dispose on the left and right sides of the aircraft symmetrically, at a point between nose and empennage. Wings may comprise a plurality of geometries in planform view, swept swing, tapered, variable wing, triangular, oblong, elliptical, square, among others. A wing’s cross section geometry may comprise an airfoil. An “airfoil” as used in this disclosure is a shape specifically designed such that a fluid flowing above and below it exert differing levels of pressure against the top and bottom surface. In embodiments, the bottom surface of an aircraft can be configured to generate a greater pressure than does the top, resulting in lift. Laterally extending element may comprise differing and/or similar cross-sectional geometries over its cord length or the length from wing tip to where wing meets the aircraft’s body. One or more wings may be symmetrical about the aircraft’s longitudinal plane, which comprises the longitudinal or roll axis reaching down the center of the aircraft through the nose and empennage, and the plane’s yaw axis. Laterally extending element may comprise controls surfaces configured to be commanded by a pilot or pilots to change a wing’s geometry and therefore its interaction with a fluid medium, like air. Control surfaces may comprise flaps, ailerons, tabs, spoilers, and slats, among others. The control surfaces may dispose on the wings in a plurality of locations and arrangements and in embodiments may be disposed at the leading and trailing edges of the wings, and may be configured to deflect up, down, forward, aft, or a combination thereof. An aircraft, including a dual-mode aircraft may comprise a combination of control surfaces to perform maneuvers while flying or on ground.

Still referring to FIG. 5, aircraft 500 includes a plurality of flight components 508. As used in this disclosure a “flight component” is a component that promotes flight and guidance of an aircraft. In an embodiment, flight component 508 may be mechanically coupled to an aircraft. As used herein, a person of ordinary skill in the art would understand “mechanically coupled” to mean that at least a portion of a device, component, or circuit is connected to at least a portion of the aircraft via a mechanical coupling. Said mechanical coupling can include, for example, rigid coupling, such as beam coupling, bellows coupling, bushed pin coupling, constant velocity, split-muff coupling, diaphragm coupling, disc coupling, donut coupling, elastic coupling, flexible coupling, fluid coupling, gear coupling, grid coupling, hirth joints, hydrodynamic coupling, jaw coupling, magnetic coupling, Oldham coupling, sleeve coupling, tapered shaft lock, twin spring coupling, rag joint coupling, universal joints, or any combination thereof. In an embodiment, mechanical coupling may be used to connect the ends of adjacent parts and/or objects of an electric aircraft. Further, in an embodiment, mechanical coupling may be used to join two pieces of rotating electric aircraft components.

Still referring to FIG. 5, plurality of flight components 508 may include at least a lift propulsor component 512. As used in this disclosure a “lift propulsor component” is a component and/or device used to propel a craft upward by exerting downward force on a fluid medium, which may include a gaseous medium such as air or a liquid medium such as water. Lift propulsor component 512 may include any device or component that consumes electrical power on demand to propel an electric aircraft in a direction or other vehicle while on ground or in-flight. For example, and without limitation, lift propulsor component 512 may include a rotor, propeller, paddle wheel and the like thereof, wherein a rotor is a component that produces torque along the longitudinal axis, and a propeller produces torquer along the vertical axis. In an embodiment, lift propulsor component 512 includes a plurality of blades. As used in this disclosure a “blade” is a propeller that converts rotary motion from an engine or other power source into a swirling slipstream. In an embodiment, blade may convert rotary motion to push the propeller forwards or backwards. In an embodiment lift propulsor component 512 may include a rotating power-driven hub, to which are attached several radial airfoil-section blades such that the whole assembly rotates about a longitudinal axis. Blades may be configured at an angle of attack, wherein an angle of attack is described in detail below. In an embodiment, and without limitation, angle of attack may include a fixed angle of attack. As used in this disclosure a “fixed angle of attack” is fixed angle between a chord line of a blade and relative wind. As used in this disclosure a “fixed angle” is an angle that is secured and/or unmovable from the attachment point. For example, and without limitation fixed angle of attack may be 3.2° as a function of a pitch angle of 9.7° and a relative wind angle 6.5°. In another embodiment, and without limitation, angle of attack may include a variable angle of attack. As used in this disclosure a “variable angle of attack” is a variable and/or moveable angle between a chord line of a blade and relative wind. As used in this disclosure a “variable angle” is an angle that is moveable from an attachment point. For example, and without limitation variable angle of attack may be a first angle of 4.7° as a function of a pitch angle of 7.1° and a relative wind angle 5.4°, wherein the angle adjusts and/or shifts to a second angle of 5.7° as a function of a pitch angle of 5.1° and a relative wind angle 5.4°. In an embodiment, angle of attack be configured to produce a fixed pitch angle. As used in this disclosure a “fixed pitch angle” is a fixed angle between a cord line of a blade and the rotational velocity direction. For example, and without limitation, fixed pitch angle may include 18°. In another embodiment fixed angle of attack may be manually variable to a few set positions to adjust one or more lifts of the aircraft prior to flight. In an embodiment, blades for an aircraft are designed to be fixed to their hub at an angle similar to the thread on a screw makes an angle to the shaft; this angle may be referred to as a pitch or pitch angle which will determine a speed of forward movement as the blade rotates.

In an embodiment, and still referring to FIG. 5, lift propulsor component 512 may be configured to produce a lift. As used in this disclosure a “lift” is a perpendicular force to the oncoming flow direction of fluid surrounding the surface. For example, and without limitation relative air speed may be horizontal to aircraft 500, wherein lift force may be a force exerted in a vertical direction, directing aircraft 500 upwards. In an embodiment, and without limitation, lift propulsor component 512 may produce lift as a function of applying a torque to lift propulsor component. As used in this disclosure a “torque” is a measure of force that causes an object to rotate about an axis in a direction. For example, and without limitation, torque may rotate an aileron and/or rudder to generate a force that may adjust and/or affect altitude, airspeed velocity, groundspeed velocity, direction during flight, and/or thrust. For example, one or more flight components 108 such as a power sources may apply a torque on lift propulsor component 512 to produce lift. As used in this disclosure a “power source” is a source that that drives and/or controls any other flight component. For example, and without limitation power source may include a motor that operates to move one or more lift propulsor components, to drive one or more blades, or the like thereof. A motor may be driven by direct current (DC) electric power and may include, without limitation, brushless DC electric motors, switched reluctance motors, induction motors, or any combination thereof. A motor may also include electronic speed controllers or other components for regulating motor speed, rotation direction, and/or dynamic braking.

Still referring to FIG. 5, power source may include an energy source. An energy source may include, for example, an electrical energy source a generator, a photovoltaic device, a fuel cell such as a hydrogen fuel cell, direct methanol fuel cell, and/or solid oxide fuel cell, an electric energy storage device (e.g., a capacitor, an inductor, and/or a battery). An electrical energy source may also include a battery cell, or a plurality of battery cells connected in series into a module and each module connected in series or in parallel with other modules. Configuration of an energy source containing connected modules may be designed to meet an energy or power requirement and may be designed to fit within a designated footprint in an electric aircraft in which aircraft 500 may be incorporated.

In an embodiment, and still referring to FIG. 5, an energy source may be used to provide a steady supply of electrical power to a load over the course of a flight by a vehicle or other electric aircraft. For example, an energy source may be capable of providing sufficient power for “cruising” and other relatively low-energy phases of flight. An energy source may also be capable of providing electrical power for some higher-power phases of flight as well, particularly when the energy source is at a high SOC, as may be the case for instance during takeoff. In an embodiment, an energy source may be capable of providing sufficient electrical power for auxiliary loads including without limitation, lighting, navigation, communications, de-icing, steering or other systems requiring power or energy. Further, an energy source may be capable of providing sufficient power for controlled descent and landing protocols, including, without limitation, hovering descent or runway landing. As used herein an energy source may have high power density where electrical power an energy source can usefully produce per unit of volume and/or mass is relatively high. “Electrical power,” as used in this disclosure, is defined as a rate of electrical energy per unit time. An energy source may include a device for which power that may be produced per unit of volume and/or mass has been optimized, at the expense of the maximal total specific energy density or power capacity, during design. Non-limiting examples of items that may be used as at least an energy source may include batteries used for starting applications including Li ion batteries which may include NCA, NMC, Lithium iron phosphate (LiFePO4) and Lithium Manganese Oxide (LMO) batteries, which may be mixed with another cathode chemistry to provide more specific power if the application requires Li metal batteries, which have a lithium metal anode that provides high power on demand, Li ion batteries that have a silicon or titanite anode, energy source may be used, in an embodiment, to provide electrical power to an electric aircraft or drone, such as an electric aircraft vehicle, during moments requiring high rates of power output, including without limitation takeoff, landing, thermal de-icing and situations requiring greater power output for reasons of stability, such as high turbulence situations, as described in further detail below. A battery may include, without limitation a battery using nickel based chemistries such as nickel cadmium or nickel metal hydride, a battery using lithium ion battery chemistries such as a nickel cobalt aluminum (NCA), nickel manganese cobalt (NMC), lithium iron phosphate (LiFePO4), lithium cobalt oxide (LCO), and/or lithium manganese oxide (LMO), a battery using lithium polymer technology, lead-based batteries such as without limitation lead acid batteries, metal-air batteries, or any other suitable battery. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various devices of components that may be used as an energy source.

Still referring to FIG. 5, an energy source may include a plurality of energy sources, referred to herein as a module of energy sources. A module may include batteries connected in parallel or in series or a plurality of modules connected either in series or in parallel designed to deliver both the power and energy requirements of the application. Connecting batteries in series may increase the voltage of at least an energy source which may provide more power on demand. High voltage batteries may require cell matching when high peak load is needed. As more cells are connected in strings, there may exist the possibility of one cell failing which may increase resistance in the module and reduce an overall power output as a voltage of the module may decrease as a result of that failing cell. Connecting batteries in parallel may increase total current capacity by decreasing total resistance, and it also may increase overall amp-hour capacity. Overall energy and power outputs of at least an energy source may be based on individual battery cell performance or an extrapolation based on measurement of at least an electrical parameter. In an embodiment where an energy source includes a plurality of battery cells, overall power output capacity may be dependent on electrical parameters of each individual cell. If one cell experiences high self-discharge during demand, power drawn from at least an energy source may be decreased to avoid damage to the weakest cell. An energy source may further include, without limitation, wiring, conduit, housing, cooling system and battery management system. Persons skilled in the art will be aware, after reviewing the entirety of this disclosure, of many different components of an energy source.

In an embodiment and still referring to FIG. 5, plurality of flight components 508 may be arranged in a quad copter orientation. As used in this disclosure a “quad copter orientation” is at least a lift propulsor component oriented in a geometric shape and/or pattern, wherein each of the lift propulsor components are located along a vertex of the geometric shape. For example, and without limitation, a square quad copter orientation may have four lift propulsor components oriented in the geometric shape of a square, wherein each of the four lift propulsor components are located along the four vertices of the square shape. As a further non-limiting example, a hexagonal quad copter orientation may have six lift propulsor components oriented in the geometric shape of a hexagon, wherein each of the six lift propulsor components are located along the six vertices of the hexagon shape. In an embodiment, and without limitation, quad copter orientation may include a first set of lift propulsor components and a second set of lift propulsor components, wherein the first set of lift propulsor components and the second set of lift propulsor components may include two lift propulsor components each, wherein the first set of lift propulsor components and a second set of lift propulsor components are distinct from one another. For example, and without limitation, the first set of lift propulsor components may include two lift propulsor components that rotate in a clockwise direction, wherein the second set of lift propulsor components may include two lift propulsor components that rotate in a counterclockwise direction. In an embodiment, and without limitation, the first set of propulsor lift components may be oriented along a line oriented 45° from the longitudinal axis of aircraft 500. In another embodiment, and without limitation, the second set of propulsor lift components may be oriented along a line oriented 135° from the longitudinal axis, wherein the first set of lift propulsor components line and the second set of lift propulsor components are perpendicular to each other.

Still referring to FIG. 5, plurality of flight components 508 may include a pusher component 516. As used in this disclosure a “pusher component” is a component that pushes and/or thrusts an aircraft through a medium. As a non-limiting example, pusher component 516 may include a pusher propeller, a paddle wheel, a pusher motor, a pusher propulsor, and the like. Additionally, or alternatively, pusher flight component may include a plurality of pusher flight components. Pusher component 516 is configured to produce a forward thrust. As used in this disclosure a “forward thrust” is a thrust that forces aircraft through a medium in a horizontal direction, wherein a horizontal direction is a direction parallel to the longitudinal axis. As a non-limiting example, forward thrust may include a force of 1145 N to force aircraft to in a horizontal direction along the longitudinal axis. As a further non-limiting example, forward thrust may include a force of, as a non-limiting example, 300 N to force aircraft 500 in a horizontal direction along a longitudinal axis. As a further non-limiting example, pusher component 516 may twist and/or rotate to pull air behind it and, at the same time, push aircraft 500 forward with an equal amount of force. In an embodiment, and without limitation, the more air forced behind aircraft, the greater the thrust force with which the aircraft is pushed horizontally will be. In another embodiment, and without limitation, forward thrust may force aircraft 500 through the medium of relative air. Additionally or alternatively, plurality of flight components 508 may include one or more puller components. As used in this disclosure a “puller component” is a component that pulls and/or tows an aircraft through a medium. As a non-limiting example, puller component may include a flight component such as a puller propeller, a puller motor, a tractor propeller, a puller propulsor, and the like. Additionally, or alternatively, puller component may include a plurality of puller flight components.

In an embodiment and still referring to FIG. 5, aircraft 500 may include a flight controller located within fuselage 504, wherein a flight controller is described in detail below, in reference to FIG. 6. In an embodiment, and without limitation, flight controller may be configured to operate a fixed-wing flight capability. As used in this disclosure a “fixed-wing flight capability” is a method of flight wherein the plurality of laterally extending elements generate lift. For example, and without limitation, fixed-wing flight capability may generate lift as a function of an airspeed of aircraft 100 and one or more airfoil shapes of the laterally extending elements, wherein an airfoil is described above in detail. As a further non-limiting example, flight controller may operate the fixed-wing flight capability as a function of reducing applied torque on lift propulsor component 512. For example, and without limitation, flight controller may reduce a torque of 9 Nm applied to a first set of lift propulsor components to a torque of 5 Nm. As a further non-limiting example, flight controller may reduce a torque of 12 Nm applied to a first set of lift propulsor components to a torque of 0 Nm. In an embodiment, and without limitation, flight controller may produce fixed-wing flight capability as a function of increasing forward thrust exerted by pusher component 516. For example, and without limitation, flight controller may increase a forward thrust of 500 kN produced by pusher component 516 to a forward thrust of 569 kN. In an embodiment, and without limitation, an amount of lift generation may be related to an amount of forward thrust generated to increase airspeed velocity, wherein the amount of lift generation may be directly proportional to the amount of forward thrust produced. Additionally or alternatively, flight controller may include an inertia compensator. As used in this disclosure an “inertia compensator” is one or more computing devices, electrical components, logic circuits, processors, and the like there of that are configured to compensate for inertia in one or more lift propulsor components present in aircraft 500. Inertia compensator may alternatively or additionally include any computing device used as an inertia compensator as described in U.S. Nonprovisional App. Ser. No. 17/106,557, filed on Nov. 30, 2020, and entitled “SYSTEM AND METHOD FOR FLIGHT CONTROL IN ELECTRIC AIRCRAFT,” the entirety of which is incorporated herein by reference.

In an embodiment, and still referring to FIG. 5, flight controller may be configured to perform a reverse thrust command. As used in this disclosure a “reverse thrust command” is a command to perform a thrust that forces a medium towards the relative air opposing aircraft 100. For example, reverse thrust command may include a thrust of 180 N directed towards the nose of aircraft to at least repel and/or oppose the relative air. Reverse thrust command may alternatively or additionally include any reverse thrust command as described in U.S. Nonprovisional App. Ser. No. 17/319,155, filed on May 13, 2021, and entitled “AIRCRAFT HAVING REVERSE THRUST CAPABILITIES,” the entirety of which is incorporated herein by reference. In another embodiment, flight controller may be configured to perform a regenerative drag operation. As used in this disclosure a “regenerative drag operation” is an operating condition of an aircraft, wherein the aircraft has a negative thrust and/or is reducing in airspeed velocity. For example, and without limitation, regenerative drag operation may include a positive propeller speed and a negative propeller thrust. Regenerative drag operation may alternatively or additionally include any regenerative drag operation as described in U.S. Nonprovisional App. Ser. No. 17/319,155.

In an embodiment, and still referring to FIG. 5, flight controller may be configured to perform a corrective action as a function of a failure event. As used in this disclosure a“ corrective action” is an action conducted by the plurality of flight components to correct and/or alter a movement of an aircraft. For example, and without limitation, a corrective action may include an action to reduce a yaw torque generated by a failure event. Additionally or alternatively, corrective action may include any corrective action as described in U.S. Nonprovisional App. Ser. No. 17/222,539, filed on Apr. 5, 2021, and entitled “AIRCRAFT FOR SELF-NEUTRALIZING FLIGHT,” the entirety of which is incorporated herein by reference. As used in this disclosure a “failure event” is a failure of a lift propulsor component of the plurality of lift propulsor components. For example, and without limitation, a failure event may denote a rotation degradation of a rotor, a reduced torque of a rotor, and the like thereof. Additionally or alternatively, failure event may include any failure event as described in U.S. Nonprovisional App. Ser. No. 17/113,647, filed on Dec. 7, 2020, and entitled “IN-FLIGHT STABILIZATION OF AN AIRCAFT,” the entirety of which is incorporated herein by reference.

Now referring to FIG. 6, an exemplary embodiment 600 of a flight controller 604 is illustrated. As used in this disclosure a “flight controller” is a computing device of a plurality of computing devices dedicated to data storage, security, distribution of traffic for load balancing, and flight instruction. Flight controller 604 may include and/or communicate with any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Further, flight controller 604 may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. In embodiments, flight controller 604 may be installed in an aircraft, may control the aircraft remotely, and/or may include an element installed in the aircraft and a remote element in communication therewith.

In an embodiment, and still referring to FIG. 6, flight controller 604 may include a signal transformation component 608. As used in this disclosure a “signal transformation component” is a component that transforms and/or converts a first signal to a second signal, wherein a signal may include one or more digital and/or analog signals. For example, and without limitation, signal transformation component 608 may be configured to perform one or more operations such as preprocessing, lexical analysis, parsing, semantic analysis, and the like thereof. In an embodiment, and without limitation, signal transformation component 608 may include one or more analog-to-digital convertors that transform a first signal of an analog signal to a second signal of a digital signal. For example, and without limitation, an analog-to-digital converter may convert an analog input signal to a 10-bit binary digital representation of that signal. In another embodiment, signal transformation component 608 may include transforming one or more low-level languages such as, but not limited to, machine languages and/or assembly languages. For example, and without limitation, signal transformation component 608 may include transforming a binary language signal to an assembly language signal. In an embodiment, and without limitation, signal transformation component 608 may include transforming one or more high-level languages and/or formal languages such as but not limited to alphabets, strings, and/or languages. For example, and without limitation, high-level languages may include one or more system languages, scripting languages, domain-specific languages, visual languages, esoteric languages, and the like thereof. As a further non-limiting example, high-level languages may include one or more algebraic formula languages, business data languages, string and list languages, object-oriented languages, and the like thereof.

Still referring to FIG. 6, signal transformation component 608 may be configured to optimize an intermediate representation 612. As used in this disclosure an “intermediate representation” is a data structure and/or code that represents the input signal. Signal transformation component 608 may optimize intermediate representation as a function of a data-flow analysis, dependence analysis, alias analysis, pointer analysis, escape analysis, and the like thereof. In an embodiment, and without limitation, signal transformation component 608 may optimize intermediate representation 612 as a function of one or more inline expansions, dead code eliminations, constant propagation, loop transformations, and/or automatic parallelization functions. In another embodiment, signal transformation component 608 may optimize intermediate representation as a function of a machine dependent optimization such as a peephole optimization, wherein a peephole optimization may rewrite short sequences of code into more efficient sequences of code. Signal transformation component 608 may optimize intermediate representation to generate an output language, wherein an “output language,” as used herein, is the native machine language of flight controller 604. For example, and without limitation, native machine language may include one or more binary and/or numerical languages.

In an embodiment, and without limitation, signal transformation component 608 may include transform one or more inputs and outputs as a function of an error correction code. An error correction code, also known as error correcting code (ECC), is an encoding of a message or lot of data using redundant information, permitting recovery of corrupted data. An ECC may include a block code, in which information is encoded on fixed-size packets and/or blocks of data elements such as symbols of predetermined size, bits, or the like. Reed-Solomon coding, in which message symbols within a symbol set having q symbols are encoded as coefficients of a polynomial of degree less than or equal to a natural number k, over a finite field F with q elements; strings so encoded have a minimum hamming distance of k+1, and permit correction of (q-k-1)/2 erroneous symbols. Block code may alternatively or additionally be implemented using Golay coding, also known as binary Golay coding, Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-check coding, and/or Hamming codes. An ECC may alternatively or additionally be based on a convolutional code.

In an embodiment, and still referring to FIG. 6, flight controller 604 may include a reconfigurable hardware platform 616. A “reconfigurable hardware platform,” as used herein, is a component and/or unit of hardware that may be reprogrammed, such that, for instance, a data path between elements such as logic gates or other digital circuit elements may be modified to change an algorithm, state, logical sequence, or the like of the component and/or unit. This may be accomplished with such flexible high-speed computing fabrics as field-programmable gate arrays (FPGAs), which may include a grid of interconnected logic gates, connections between which may be severed and/or restored to program in modified logic. Reconfigurable hardware platform 616 may be reconfigured to enact any algorithm and/or algorithm selection process received from another computing device and/or created using machine-learning processes.

Still referring to FIG. 6, reconfigurable hardware platform 616 may include a logic component 620. As used in this disclosure a “logic component” is a component that executes instructions on output language. For example, and without limitation, logic component may perform basic arithmetic, logic, controlling, input/output operations, and the like thereof. Logic component 620 may include any suitable processor, such as without limitation a component incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; logic component 620 may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Logic component 620 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC). In an embodiment, logic component 620 may include one or more integrated circuit microprocessors, which may contain one or more central processing units, central processors, and/or main processors, on a single metal-oxide-semiconductor chip. Logic component 620 may be configured to execute a sequence of stored instructions to be performed on the output language and/or intermediate representation 612. Logic component 620 may be configured to fetch and/or retrieve the instruction from a memory cache, wherein a “memory cache,” as used in this disclosure, is a stored instruction set on flight controller 604. Logic component 620 may be configured to decode the instruction retrieved from the memory cache to opcodes and/or operands. Logic component 620 may be configured to execute the instruction on intermediate representation 612 and/or output language. For example, and without limitation, logic component 620 may be configured to execute an addition operation on intermediate representation 612 and/or output language.

In an embodiment, and without limitation, logic component 620 may be configured to calculate a flight element 624. As used in this disclosure a “flight element” is an element of datum denoting a relative status of aircraft. For example, and without limitation, flight element 624 may denote one or more torques, thrusts, airspeed velocities, forces, altitudes, groundspeed velocities, directions during flight, directions facing, forces, orientations, and the like thereof. For example, and without limitation, flight element 624 may denote that aircraft is cruising at an altitude and/or with a sufficient magnitude of forward thrust. As a further non-limiting example, flight status may denote that is building thrust and/or groundspeed velocity in preparation for a takeoff. As a further non-limiting example, flight element 624 may denote that aircraft is following a flight path accurately and/or sufficiently.

Still referring to FIG. 6, flight controller 604 may include a chipset component 628. As used in this disclosure a “chipset component” is a component that manages data flow. In an embodiment, and without limitation, chipset component 628 may include a northbridge data flow path, wherein the northbridge dataflow path may manage data flow from logic component 620 to a high-speed device and/or component, such as a RAM, graphics controller, and the like thereof. In another embodiment, and without limitation, chipset component 628 may include a southbridge data flow path, wherein the southbridge dataflow path may manage data flow from logic component 620 to lower-speed peripheral buses, such as a peripheral component interconnect (PCI), industry standard architecture (ICA), and the like thereof. In an embodiment, and without limitation, southbridge data flow path may include managing data flow between peripheral connections such as ethernet, USB, audio devices, and the like thereof. Additionally or alternatively, chipset component 628 may manage data flow between logic component 620, memory cache, and a flight component 108. As used in this disclosure a “flight component” is a portion of an aircraft that can be moved or adjusted to affect one or more flight elements. For example, flight component 108 may include a component used to affect the aircrafts’ roll and pitch which may comprise one or more ailerons. As a further example, flight component 108 may include a rudder to control yaw of an aircraft. In an embodiment, chipset component 628 may be configured to communicate with a plurality of flight components as a function of flight element 624. For example, and without limitation, chipset component 628 may transmit to an aircraft rotor to reduce torque of a first lift propulsor and increase the forward thrust produced by a pusher component to perform a flight maneuver.

In an embodiment, and still referring to FIG. 6, flight controller 604 may be configured generate an autonomous function. As used in this disclosure an “autonomous function” is a mode and/or function of flight controller 604 that controls aircraft automatically. For example, and without limitation, autonomous function may perform one or more aircraft maneuvers, take offs, landings, altitude adjustments, flight leveling adjustments, turns, climbs, and/or descents. As a further non-limiting example, autonomous function may adjust one or more airspeed velocities, thrusts, torques, and/or groundspeed velocities. As a further non-limiting example, autonomous function may perform one or more flight path corrections and/or flight path modifications as a function of flight element 624. In an embodiment, autonomous function may include one or more modes of autonomy such as, but not limited to, autonomous mode, semi-autonomous mode, and/or non-autonomous mode. As used in this disclosure “autonomous mode” is a mode that automatically adjusts and/or controls aircraft and/or the maneuvers of aircraft in its entirety. For example, autonomous mode may denote that flight controller 604 will adjust the aircraft. As used in this disclosure a “semi-autonomous mode” is a mode that automatically adjusts and/or controls a portion and/or section of aircraft. For example, and without limitation, semi-autonomous mode may denote that a pilot will control the propulsors, wherein flight controller 604 will control the ailerons and/or rudders. As used in this disclosure “non-autonomous mode” is a mode that denotes a pilot will control aircraft and/or maneuvers of aircraft in its entirety.

In an embodiment, and still referring to FIG. 6, flight controller 604 may generate autonomous function as a function of an autonomous machine-learning model. As used in this disclosure an “autonomous machine-learning model” is a machine-learning model to produce an autonomous function output given flight element 624 and a pilot signal 636 as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. As used in this disclosure a “pilot signal” is an element of datum representing one or more functions a pilot is controlling and/or adjusting. For example, pilot signal 636 may denote that a pilot is controlling and/or maneuvering ailerons, wherein the pilot is not in control of the rudders and/or propulsors. In an embodiment, pilot signal 636 may include an implicit signal and/or an explicit signal. For example, and without limitation, pilot signal 636 may include an explicit signal, wherein the pilot explicitly states there is a lack of control and/or desire for autonomous function. As a further non-limiting example, pilot signal 636 may include an explicit signal directing flight controller 604 to control and/or maintain a portion of aircraft, a portion of the flight plan, the entire aircraft, and/or the entire flight plan. As a further non-limiting example, pilot signal 636 may include an implicit signal, wherein flight controller 604 detects a lack of control such as by a malfunction, torque alteration, flight path deviation, and the like thereof. In an embodiment, and without limitation, pilot signal 636 may include one or more explicit signals to reduce torque, and/or one or more implicit signals that torque may be reduced due to reduction of airspeed velocity. In an embodiment, and without limitation, pilot signal 636 may include one or more local and/or global signals. For example, and without limitation, pilot signal 636 may include a local signal that is transmitted by a pilot and/or crew member. As a further non-limiting example, pilot signal 636 may include a global signal that is transmitted by air traffic control and/or one or more remote users that are in communication with the pilot of aircraft. In an embodiment, pilot signal 636 may be received as a function of a tri-state bus and/or multiplexor that denotes an explicit pilot signal should be transmitted prior to any implicit or global pilot signal.

Still referring to FIG. 6, autonomous machine-learning model may include one or more autonomous machine-learning processes such as supervised, unsupervised, or reinforcement machine-learning processes that flight controller 604 and/or a remote device may or may not use in the generation of autonomous function. As used in this disclosure “remote device” is an external device to flight controller 604. Additionally or alternatively, autonomous machine-learning model may include one or more autonomous machine-learning processes that a field-programmable gate array (FPGA) may or may not use in the generation of autonomous function. Autonomous machine-learning process may include, without limitation machine learning processes such as simple linear regression, multiple linear regression, polynomial regression, support vector regression, ridge regression, lasso regression, elasticnet regression, decision tree regression, random forest regression, logistic regression, logistic classification, K-nearest neighbors, support vector machines, kernel support vector machines, naive bayes, decision tree classification, random forest classification, K-means clustering, hierarchical clustering, dimensionality reduction, principal component analysis, linear discriminant analysis, kernel principal component analysis, Q-learning, State Action Reward State Action (SARSA), Deep-Q network, Markov decision processes, Deep Deterministic Policy Gradient (DDPG), or the like thereof.

In an embodiment, and still referring to FIG. 6, autonomous machine learning model may be trained as a function of autonomous training data, wherein autonomous training data may correlate a flight element, pilot signal, and/or simulation data to an autonomous function. For example, and without limitation, a flight element of an airspeed velocity, a pilot signal of limited and/or no control of propulsors, and a simulation data of required airspeed velocity to reach the destination may result in an autonomous function that includes a semi-autonomous mode to increase thrust of the propulsors. Autonomous training data may be received as a function of user-entered valuations of flight elements, pilot signals, simulation data, and/or autonomous functions. Flight controller 604 may receive autonomous training data by receiving correlations of flight element, pilot signal, and/or simulation data to an autonomous function that were previously received and/or determined during a previous iteration of generation of autonomous function. Autonomous training data may be received by one or more remote devices and/or FPGAs that at least correlate a flight element, pilot signal, and/or simulation data to an autonomous function. Autonomous training data may be received in the form of one or more user-entered correlations of a flight element, pilot signal, and/or simulation data to an autonomous function.

Still referring to FIG. 6, flight controller 604 may receive autonomous machine-learning model from a remote device and/or FPGA that utilizes one or more autonomous machine learning processes, wherein a remote device and an FPGA is described above in detail. For example, and without limitation, a remote device may include a computing device, external device, processor, FPGA, microprocessor and the like thereof. Remote device and/or FPGA may perform the autonomous machine-learning process using autonomous training data to generate autonomous function and transmit the output to flight controller 604. Remote device and/or FPGA may transmit a signal, bit, datum, or parameter to flight controller 604 that at least relates to autonomous function. Additionally or alternatively, the remote device and/or FPGA may provide an updated machine-learning model. For example, and without limitation, an updated machine-learning model may be comprised of a firmware update, a software update, an autonomous machine-learning process correction, and the like thereof. As a non-limiting example a software update may incorporate a new simulation data that relates to a modified flight element. Additionally or alternatively, the updated machine learning model may be transmitted to the remote device and/or FPGA, wherein the remote device and/or FPGA may replace the autonomous machine-learning model with the updated machine-learning model and generate the autonomous function as a function of the flight element, pilot signal, and/or simulation data using the updated machine-learning model. The updated machine-learning model may be transmitted by the remote device and/or FPGA and received by flight controller 604 as a software update, firmware update, or corrected autonomous machine-learning model. For example, and without limitation autonomous machine learning model may utilize a neural net machine-learning process, wherein the updated machine-learning model may incorporate a gradient boosting machine-learning process.

Still referring to FIG. 6, flight controller 604 may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Further, flight controller may communicate with one or more additional devices as described below in further detail via a network interface device. The network interface device may be utilized for commutatively connecting a flight controller to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. The network may include any network topology and can may employ a wired and/or a wireless mode of communication.

In an embodiment, and still referring to FIG. 6, flight controller 604 may include, but is not limited to, for example, a cluster of flight controllers in a first location and a second flight controller or cluster of flight controllers in a second location. Flight controller 604 may include one or more flight controllers dedicated to data storage, security, distribution of traffic for load balancing, and the like. Flight controller 604 may be configured to distribute one or more computing tasks as described below across a plurality of flight controllers, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. For example, and without limitation, flight controller 604 may implement a control algorithm to distribute and/or command the plurality of flight controllers. As used in this disclosure a “control algorithm” is a finite sequence of well-defined computer implementable instructions that may determine the flight component of the plurality of flight components to be adjusted. For example, and without limitation, control algorithm may include one or more algorithms that reduce and/or prevent aviation asymmetry. As a further non-limiting example, control algorithms may include one or more models generated as a function of a software including, but not limited to Simulink by MathWorks, Natick, Massachusetts, USA. In an embodiment, and without limitation, control algorithm may be configured to generate an auto-code, wherein an “auto-code,” is used herein, is a code and/or algorithm that is generated as a function of the one or more models and/or software’s. In another embodiment, control algorithm may be configured to produce a segmented control algorithm. As used in this disclosure a “segmented control algorithm” is control algorithm that has been separated and/or parsed into discrete sections. For example, and without limitation, segmented control algorithm may parse control algorithm into two or more segments, wherein each segment of control algorithm may be performed by one or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 6, control algorithm may be configured to determine a segmentation boundary as a function of segmented control algorithm. As used in this disclosure a “segmentation boundary” is a limit and/or delineation associated with the segments of the segmented control algorithm. For example, and without limitation, segmentation boundary may denote that a segment in the control algorithm has a first starting section and/or a first ending section. As a further non-limiting example, segmentation boundary may include one or more boundaries associated with an ability of flight component 108. In an embodiment, control algorithm may be configured to create an optimized signal communication as a function of segmentation boundary. For example, and without limitation, optimized signal communication may include identifying the discrete timing required to transmit and/or receive the one or more segmentation boundaries. In an embodiment, and without limitation, creating optimized signal communication further comprises separating a plurality of signal codes across the plurality of flight controllers. For example, and without limitation the plurality of flight controllers may include one or more formal networks, wherein formal networks transmit data along an authority chain and/or are limited to task-related communications. As a further non-limiting example, communication network may include informal networks, wherein informal networks transmit data in any direction. In an embodiment, and without limitation, the plurality of flight controllers may include a chain path, wherein a “chain path,” as used herein, is a linear communication path comprising a hierarchy that data may flow through. In an embodiment, and without limitation, the plurality of flight controllers may include an all-channel path, wherein an “all-channel path,” as used herein, is a communication path that is not restricted to a particular direction. For example, and without limitation, data may be transmitted upward, downward, laterally, and the like thereof. In an embodiment, and without limitation, the plurality of flight controllers may include one or more neural networks that assign a weighted value to a transmitted datum. For example, and without limitation, a weighted value may be assigned as a function of one or more signals denoting that a flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 6, the plurality of flight controllers may include a master bus controller. As used in this disclosure a “master bus controller” is one or more devices and/or components that are connected to a bus to initiate a direct memory access transaction, wherein a bus is one or more terminals in a bus architecture. Master bus controller may communicate using synchronous and/or asynchronous bus control protocols. In an embodiment, master bus controller may include flight controller 604. In another embodiment, master bus controller may include one or more universal asynchronous receiver-transmitters (UART). For example, and without limitation, master bus controller may include one or more bus architectures that allow a bus to initiate a direct memory access transaction from one or more buses in the bus architectures. As a further non-limiting example, master bus controller may include one or more peripheral devices and/or components to communicate with another peripheral device and/or component and/or the master bus controller. In an embodiment, master bus controller may be configured to perform bus arbitration. As used in this disclosure “bus arbitration” is method and/or scheme to prevent multiple buses from attempting to communicate with and/or connect to master bus controller. For example and without limitation, bus arbitration may include one or more schemes such as a small computer interface system, wherein a small computer interface system is a set of standards for physical connecting and transferring data between peripheral devices and master bus controller by defining commands, protocols, electrical, optical, and/or logical interfaces. In an embodiment, master bus controller may receive intermediate representation 612 and/or output language from logic component 620, wherein output language may include one or more analog-to-digital conversions, low bit rate transmissions, message encryptions, digital signals, binary signals, logic signals, analog signals, and the like thereof described above in detail.

Still referring to FIG. 6, master bus controller may communicate with a slave bus. As used in this disclosure a “slave bus” is one or more peripheral devices and/or components that initiate a bus transfer. For example, and without limitation, slave bus may receive one or more controls and/or asymmetric communications from master bus controller, wherein slave bus transfers data stored to master bus controller. In an embodiment, and without limitation, slave bus may include one or more internal buses, such as but not limited to a/an internal data bus, memory bus, system bus, front-side bus, and the like thereof. In another embodiment, and without limitation, slave bus may include one or more external buses such as external flight controllers, external computers, remote devices, printers, aircraft computer systems, flight control systems, and the like thereof.

In an embodiment, and still referring to FIG. 6, control algorithm may optimize signal communication as a function of determining one or more discrete timings. For example, and without limitation master bus controller may synchronize timing of the segmented control algorithm by injecting high priority timing signals on a bus of the master bus control. As used in this disclosure a “high priority timing signal” is information denoting that the information is important. For example, and without limitation, high priority timing signal may denote that a section of control algorithm is of high priority and should be analyzed and/or transmitted prior to any other sections being analyzed and/or transmitted. In an embodiment, high priority timing signal may include one or more priority packets. As used in this disclosure a “priority packet” is a formatted unit of data that is communicated between the plurality of flight controllers. For example, and without limitation, priority packet may denote that a section of control algorithm should be used and/or is of greater priority than other sections.

Still referring to FIG. 6, flight controller 604 may also be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of aircraft and/or computing device. Flight controller 604 may include a distributer flight controller. As used in this disclosure a “distributer flight controller” is a component that adjusts and/or controls a plurality of flight components as a function of a plurality of flight controllers. For example, distributer flight controller may include a flight controller that communicates with a plurality of additional flight controllers and/or clusters of flight controllers. In an embodiment, distributed flight control may include one or more neural networks. For example, neural network also known as an artificial neural network, is a network of “nodes,” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes may be organized in a network, such as without limitation a convolutional neural network, including an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 6, a node may include, without limitation a plurality of inputs x_i that may receive numerical values from inputs to a neural network containing the node and/or from other nodes. Node may perform a weighted sum of inputs using weights w_i that are multiplied by respective inputs x_i. Additionally or alternatively, a bias b may be added to the weighted sum of the inputs such that an offset is added to each unit in the neural network layer that is independent of the input to the layer. The weighted sum may then be input into a function φ, which may generate one or more outputs y. Weight w_i applied to an input x_i may indicate whether the input is “excitatory,” indicating that it has strong influence on the one or more outputs y, for instance by the corresponding weight having a large numerical value, and/or a “inhibitory,” indicating it has a weak effect influence on the one more inputs y, for instance by the corresponding weight having a small numerical value. The values of weights w_i may be determined by training a neural network using training data, which may be performed using any suitable process as described above. In an embodiment, and without limitation, a neural network may receive semantic units as inputs and output vectors representing such semantic units according to weights wi that are derived using machine-learning processes as described in this disclosure.

Still referring to FIG. 6, flight controller may include a sub-controller 640. As used in this disclosure a “sub-controller” is a controller and/or component that is part of a distributed controller as described above; for instance, flight controller 604 may be and/or include a distributed flight controller made up of one or more sub-controllers. For example, and without limitation, sub-controller 640 may include any controllers and/or components thereof that are similar to distributed flight controller and/or flight controller as described above. Sub-controller 640 may include any component of any flight controller as described above. Sub-controller 640 may be implemented in any manner suitable for implementation of a flight controller as described above. As a further non-limiting example, sub-controller 640 may include one or more processors, logic components and/or computing devices capable of receiving, processing, and/or transmitting data across the distributed flight controller as described above. As a further non-limiting example, sub-controller 640 may include a controller that receives a signal from a first flight controller and/or first distributed flight controller component and transmits the signal to a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 6, flight controller may include a co-controller 644. As used in this disclosure a “co-controller” is a controller and/or component that joins flight controller 604 as components and/or nodes of a distributer flight controller as described above. For example, and without limitation, co-controller 644 may include one or more controllers and/or components that are similar to flight controller 604. As a further non-limiting example, co-controller 644 may include any controller and/or component that joins flight controller 604 to distributer flight controller. As a further non-limiting example, co-controller 644 may include one or more processors, logic components and/or computing devices capable of receiving, processing, and/or transmitting data to and/or from flight controller 604 to distributed flight control system. Co-controller 644 may include any component of any flight controller as described above. Co-controller 644 may be implemented in any manner suitable for implementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 6, flight controller 604 may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, flight controller 604 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Flight controller may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing.

Referring now to FIG. 7, an exemplary embodiment of a machine-learning module 700 that may perform one or more machine-learning processes as described in this disclosure is illustrated. Machine-learning module may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. A “machine learning process,” as used in this disclosure, is a process that automatedly uses training data 704 to generate an algorithm that will be performed by a computing device/module to produce outputs 708 given data provided as inputs 712; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language.

Still referring to FIG. 7, “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements. For instance, and without limitation, training data 704 may include a plurality of data entries, each entry representing a set of data elements that were recorded, received, and/or generated together; data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data 704 may evince one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data 704 according to various correlations; correlations may indicate causative and/or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine-learning processes as described in further detail below. Training data 704 may be formatted and/or organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data 704 may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data 704 may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data 704 may be provided in fixed-length formats, formats linking positions of data to categories such as comma-separated value (CSV) formats and/or self-describing formats such as extensible markup language (XML), JavaScript Object Notation (JSON), or the like, enabling processes or devices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 7, training data 704 may include one or more elements that are not categorized; that is, training data 704 may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and/or other processes may sort training data 704 according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and/or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person’s name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data 704 to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data 704 used by machine-learning module 700 may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a non-limiting illustrative example a plurality of measured flight data including an input datum, a safety datum, and a flight plan datum may be inputs and a proposed flight plan may be in an output. In a non-limiting illustrative example, the proposed flight plan and a separation element may be inputs used to output a flight assessment or a confirmation flight plan].

Further referring to FIG. 7, training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier 716. Training data classifier 716 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. Machine-learning module 700 may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 704. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher’s linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a non-limiting example, training data classifier 716 may classify elements of training data to [such as a cohort of flight plan types that include different flight times, flight priorities, cargo and/or number passengers of a flight, and/or other analyzed items and/or phenomena for which a subset of training data may be selected].

Still referring to FIG. 7, machine-learning module 700 may be configured to perform a lazy-learning process 720 and/or protocol, which may alternatively be referred to as a “lazy loading” or “call-when-needed” process and/or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining the input and training set to derive the algorithm to be used to produce the output on demand. For instance, an initial set of simulations may be performed to cover an initial heuristic and/or “first guess” at an output and/or relationship. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data 704. Heuristic may include selecting some number of highest-ranking associations and/or training data 704 elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a K-nearest neighbors algorithm, a lazy naive Bayes algorithm, or the like; persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various lazy-learning algorithms that may be applied to generate outputs as described in this disclosure, including without limitation lazy learning applications of machine-learning algorithms as described in further detail below.

Alternatively or additionally, and with continued reference to FIG. 7, machine-learning processes as described in this disclosure may be used to generate machine-learning models 724. A “machine-learning model,” as used in this disclosure, is a mathematical and/or algorithmic representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above, and stored in memory; an input is submitted to a machine-learning model 724 once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum. As a further non-limiting example, a machine-learning model 724 may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training data 704 set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 7, machine-learning algorithms may include at least a supervised machine-learning process 728. At least a supervised machine-learning process 728, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include inputs as described in the entirety of this disclosure and outputs as described in the entirety of this disclosure and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 704. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various possible variations of at least a supervised machine-learning process 728 that may be used to determine relation between inputs and outputs. Supervised machine-learning processes may include classification algorithms as defined above.

Further referring to FIG. 7, machine learning processes may include at least an unsupervised machine-learning processes 732. An unsupervised machine-learning process, as used herein, is a process that derives inferences in datasets without regard to labels; as a result, an unsupervised machine-learning process may be free to discover any structure, relationship, and/or correlation provided in the data. Unsupervised processes may not require a response variable; unsupervised processes may be used to find interesting patterns and/or inferences between variables, to determine a degree of correlation between two or more variables, or the like.

Still referring to FIG. 7, machine-learning module 700 may be designed and configured to create a machine-learning model 724 using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 7, machine-learning algorithms may include, without limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminate analysis. Machine-learning algorithms may include kernel ridge regression. Machine-learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include various forms of latent space regularization such as variational regularization. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may include cross-decomposition algorithms, including partial least squares and/or canonical correlation analysis. Machine-learning algorithms may include naïve Bayes methods. Machine-learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine-learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized tress, AdaBoost, gradient tree boosting, and/or voting classifier methods. Machine-learning algorithms may include neural net algorithms, including convolutional neural net processes.

Referring now to FIG. 8, an exemplary embodiment of a flight controller 800 for digital communication of a flight plan to air traffic control is illustrated. System includes a computing device. Flight controller may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Flight controller may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices, flight controller may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting flight controller to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and/or from a computer and/or a computing device, flight controller may include but is not limited to, for example, a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. flight controller may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. flight controller may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices, flight controller may be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of system 100 and/or computing device.

With continued reference to FIG. 1, flight controller may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, flight controller may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. flight controller may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing.

It is to be noted that any one or more of the aspects and embodiments described herein may be conveniently implemented using one or more machines (e.g., one or more computing devices that are utilized as a user computing device for an electronic document, one or more server devices, such as a document server, etc.) programmed according to the teachings of the present specification, as will be apparent to those of ordinary skill in the computer art. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those of ordinary skill in the software art. Aspects and implementations discussed above employing software and/or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and/or software module.

Such software may be a computer program product that employs a machine-readable storage medium. A machine-readable storage medium may be any medium that is capable of storing and/or encoding a sequence of instructions for execution by a machine (e.g., a computing device) and that causes the machine to perform any one of the methodologies and/or embodiments described herein. Examples of a machine-readable storage medium include, but are not limited to, a magnetic disk, an optical disc (e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-only memory “ROM” device, a random access memory “RAM” device, a magnetic card, an optical card, a solid-state memory device, an EPROM, an EEPROM, and any combinations thereof. A machine-readable medium, as used herein, is intended to include a single medium as well as a collection of physically separate media, such as, for example, a collection of compact discs or one or more hard disk drives in combination with a computer memory. As used herein, a machine-readable storage medium does not include transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as a data signal on a data carrier, such as a carrier wave. For example, machine-executable information may be included as a data-carrying signal embodied in a data carrier in which the signal encodes a sequence of instruction, or portion thereof, for execution by a machine (e.g., a computing device) and any related information (e.g., data structures and data) that causes the machine to perform any one of the methodologies and/or embodiments described herein.

Examples of a computing device include, but are not limited to, an electronic book reading device, a computer workstation, a terminal computer, a server computer, a handheld device (e.g., a tablet computer, a smartphone, etc.), a web appliance, a network router, a network switch, a network bridge, any machine capable of executing a sequence of instructions that specify an action to be taken by that machine, and any combinations thereof. In one example, a computing device may include and/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of a computing device in the exemplary form of a computer system 800 within which a set of instructions for causing a control system to perform any one or more of the aspects and/or methodologies of the present disclosure may be executed. It is also contemplated that multiple computing devices may be utilized to implement a specially configured set of instructions for causing one or more of the devices to perform any one or more of the aspects and/or methodologies of the present disclosure. Computer system 800 includes a processor 804 and a memory 808 that communicate with each other, and with other components, via a bus 812. Bus 812 may include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures.

Processor 804 may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor 804 may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor 804 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC).

Memory 808 may include various components (e.g., machine-readable media) including, but not limited to, a random-access memory component, a read only component, and any combinations thereof. In one example, a basic input/output system 816 (BIOS), including basic routines that help to transfer information between elements within computer system 800, such as during start-up, may be stored in memory 808. Memory 808 may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software) 820 embodying any one or more of the aspects and/or methodologies of the present disclosure. In another example, memory 808 may further include any number of program modules including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combinations thereof.

Computer system 800 may also include a storage device 824. Examples of a storage device (e.g., storage device 824) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device 824 may be connected to bus 812 by an appropriate interface (not shown). Example interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device 824 (or one or more components thereof) may be removably interfaced with computer system 800 (e.g., via an external port connector (not shown)). Particularly, storage device 824 and an associated machine-readable medium 828 may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system 800. In one example, software 820 may reside, completely or partially, within machine-readable medium 828. In another example, software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In one example, a user of computer system 800 may enter commands and/or other information into computer system 800 via input device 832. Examples of an input device 832 include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device 832 may be interfaced to bus 812 via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus 812, and any combinations thereof. Input device 832 may include a touch screen interface that may be a part of or separate from display 836, discussed further below. Input device 832 may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.

A user may also input commands and/or other information to computer system 800 via storage device 824 (e.g., a removable disk drive, a flash drive, etc.) and/or network interface device 840. A network interface device, such as network interface device 840, may be utilized for connecting computer system 800 to one or more of a variety of networks, such as network 844, and one or more remote devices 848 connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network 844, may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software 820, etc.) may be communicated to and/or from computer system 800 via network interface device 840.

Computer system 800 may further include a video display adapter 852 for communicating a displayable image to a display device, such as display device 836. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter 852 and display device 836 may be utilized in combination with processor 804 to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system 800 may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus 812 via a peripheral interface 856. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof.

The foregoing has been a detailed description of illustrative embodiments of the invention. Various modifications and additions can be made without departing from the spirit and scope of this invention. Features of each of the various embodiments described above may be combined with features of other described embodiments as appropriate in order to provide a multiplicity of feature combinations in associated new embodiments. Furthermore, while the foregoing describes a number of separate embodiments, what has been described herein is merely illustrative of the application of the principles of the present invention. Additionally, although particular methods herein may be illustrated and/or described as being performed in a specific order, the ordering is highly variable within ordinary skill to achieve methods and systems according to the present disclosure. Accordingly, this description is meant to be taken only by way of example, and not to otherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in the accompanying drawings. It will be understood by those skilled in the art that various changes, omissions and additions may be made to that which is specifically disclosed herein without departing from the spirit and scope of the present invention.

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20200105146	Schwartz et al.	Apr 2020	A1
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	Number	Date	Country
Parent	17406252	Aug 2021	US
Child	17855734		US

Systems and methods for digital communication of flight plan

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