Patent Publication Number: US-2023148289-A1

Title: Systems and methods for reducing air resistance in an electric vehicle flight

Description:
FIELD OF THE INVENTION 
     The present invention generally relates to the field of electric aircraft. In particular, the present invention is directed to systems and methods for reducing air resistance in an electric aircraft flight. 
     BACKGROUND 
     Although vertical propulsors in an eVTOL are useful for vertical landing and takeoff, vertical propulsors may not be needed during high altitude flight. Moreover, vertical propulsors may create excessive air resistance during high altitude flight. 
     SUMMARY OF THE DISCLOSURE 
     In an aspect a system for reducing air resistance in an electric aircraft flight that comprises at least a flight component connected to the electric aircraft and at least a sensor connected to the at least a flight component, wherein the at least a sensor is configured to detect a status datum of the at least a flight component and transmit the status datum to the computing device. The system further comprises a computing device communicatively connected to the electric aircraft, wherein the computing device is configured to receive the status datum from the at least a sensor, generate an optimum position of the at least a flight component as a function of the status datum and initiate the optimum position of the at least a flight component. 
     In another aspect a method for reducing air resistance in an electric aircraft flight includes detecting, by at least a sensor connected to at least a flight component, a status datum, transmitting, by the at least a sensor, the status datum to a computing device, receiving, by the computing device communicatively connected to the electric aircraft, the status datum from the at least a sensor, generating, by the computing device, an optimum position of the at least a flight component as a function of the status datum and initiating, by the computing device, the optimum position of the at least a flight component. 
     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 a system for reducing air resistance in an electric aircraft flight; 
         FIG.  2    is a flow diagram of a method for reducing air resistance in an electric aircraft flight; 
         FIG.  3    is an exemplary representation of an electric aircraft; 
         FIG.  4    is an exemplary diagram of a flight controller; 
         FIG.  5    is an illustrative diagram of a machine learning model; and 
         FIG.  6    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. 
     
    
    
     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. 
     DETAILED DESCRIPTION 
     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 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 reducing air resistance in an electric aircraft flight. In an embodiment, system includes at least a propulsor, which includes a rotor and a motor mechanically connected to the rotor, at least a sensor that is configured to detect a status datum from the propulsor and transmit the status datum to a flight controller, and a computing device connected to the at least a sensor and the at least a propulsor, where computing device is configured to receive the status datum from the at least a sensor, calculate a position datum based on the status datum, and transmit a command datum to the at least a propulsor. 
     Aspects of the present disclosure can be used to reduce air resistance by moving at least a propulsor to a position that provides the least amount of resistance, such as a position parallel to the direction of the flight. Aspects of the present disclosure can also be used to reduce possibility of damage to propulsors at higher altitude and speed. This is so, at least in part, because the system is configured to move the propulsors to an optimum position as to reduce resistance, but also may be configured to stow a portion of the propulsor, such as the rotor, when propulsor as moved to optimum position. 
     Aspects of the present disclosure allow for automatically moving propulsor to optimum position based on flight plans and/or machine learning process. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples. 
     Referring now to  FIG.  1   , an exemplary embodiment of a system  100  for reducing air resistance in an electric aircraft flight is illustrated. The configuration of system  100  is merely exemplary and should in no way be considered limiting. System  100  can include computing device, at least a flight component, sensor, status datum, optimum position, electric aircraft in communication with computing device  104 , any combination thereof, and/or the like. 
     With continued reference to  FIG.  1   , system  100  includes a computing device  104 . Computing device  104  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  104  may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Computing device  104  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. Computing device  104  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 computing device  104  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. Computing device  104  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. Computing device  104  may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Computing device 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. Computing device  104  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   , computing device  104  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, computing device  104  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. Computing device  104  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. In an embodiment, computing device  104  may be a flight controller. 
     Still referring to  FIG.  1   , system  100  includes at least a flight component  108 . As used in this disclosure a “flight component” is a component that promotes flight and guidance of an aircraft. In an embodiment, the at least a flight component  108  may be connected and/or mechanically connected to an aircraft. As used herein, a person of ordinary skill in the art would understand “mechanically connected” 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.  1   , the at least a flight component  108  may include a lift propulsor component and/or a forward propulsor component. 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. As used in this disclosure a “forward propulsor component” is a component and/or device used to propel a craft forward by exerting downward force on a fluid medium, which may include a gaseous medium such as air or a liquid medium such as water. The lift propulsor component and/or a forward propulsor component 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, the lift propulsor component and/or a forward propulsor component 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, the lift propulsor component and/or a forward propulsor component 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, the lift propulsor component 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. The blades are 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 an “fixed angle of attack” is fixed angle between the chord line of the blade and the 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 the chord line of the blade and the relative wind. As used in this disclosure a “variable angle” is an angle that is moveable from the 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 2.4°, wherein the angle adjusts and/or shifts to a second angle of 2.7° as a function of a pitch angle of 5.1° and a relative wind angle 2.4°. 
     In an embodiment, and still referring to  FIG.  1   , the lift propulsor component 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 the electric aircraft, wherein the lift force may be a force exerted in the vertical direction, directing the electric aircraft upwards. In an embodiment, and without limitation, the lift propulsor component may produce lift as a function of applying a torque to the lift propulsor component. Additionally, in an embodiment, the forward propulsor component may be configured to produce forward thrust. As used in this disclosure, “forward thrust” is a parallel force to the oncoming flow direction of fluid surrounding the surface. For example, and without limitation relative air speed may be horizontal to the electric aircraft, wherein the forward force may be a force exerted in the horizontal direction, directing the electric aircraft forwards. In an embodiment, and without limitation, the forward propulsor component may produce forward thrust as a function of applying a torque to the forward 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, the at least a flight component  108  such as a power sources may apply a torque on the lift propulsor component, or any other at least a flight component  108 , 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. 
     Continuing to refer to  FIG.  1   , the at least a flight component  108  may further include a laterally extending element. Laterally extending element may comprise controls surfaces configured to be commanded by a pilot or pilots to change a wing&#39;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. 
     With continued reference to  FIG.  1   , system  100  includes at least a sensor  112  connected to the electric aircraft. The herein disclosed system and method may comprise a plurality of sensors in the form of individual sensors or a sensor suite working in tandem or individually. Sensor  112  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. Sensor  112  may include any sensor configured to measure physical, electrical or performance related quantities from the at least a flight component  108 , wherein each signal may output to computing device  104 , a remote device, a graphical user interface, and/or any combination thereof. In a non-limiting example, sensor  112  may be housed in and/or on at least a flight component  112  measuring performance metrics such as speed, torque, rpm, force, and/or the like, electrical characteristics such as voltage, amperage, resistance, or impedance, or any other parameters and/or quantities as described in this disclosure. In an embodiment, use of a plurality of sensor  112  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 of system  100  and/or user to detect phenomenon is maintained. 
     Still referring to  FIG.  1   , sensor  112  may include any sensor suitable to measure parameters and/or quantities as described in the entirety of this disclosure. For example and without limitation, sensor  112  may include an electrical sensor. The electrical sensor may be configured to measure voltage across a component, electrical current through a component, and resistance of a component. The electrical sensor may include separate sensors to measure each of the previously disclosed electrical characteristics such as voltmeter, ammeter, and ohmmeter, respectively. Sensor  112  may include a sensor or plurality thereof that may detect voltage and/or any other electrical parameter associated with the at least a flight component  108 ; detection may be performed using any suitable component, set of components, and/or mechanism for direct or indirect measurement and/or detection of voltage levels and/or electrical parameters, including without limitation comparators, analog to digital converters, any form of voltmeter, or the like. Sensor  112  may include digital sensors, analog sensors, or a combination thereof. Sensor  112  may include digital-to-analog converters (DAC), analog-to-digital converters (ADC, A/D, A-to-D), a combination thereof. 
     Alternatively or additionally, and with continued reference to  FIG.  1   , sensor  112  may include any torque measurement sensor configured to measure the torque and/or associated performance metric of the at least a flight component  108 . The torque measurement sensor may be configured to measure toque output by the at least a flight component  108 , torque input to the at least a flight component  108 , the position of the at least a flight component  108 , the rotations per minute (rpm) of the at least a flight component  108 , and/or the like. The output measured and/or detected by torque measurement sensor and/or sensor  112  may comprise electrical signals which are transmitted to their appropriate destination wireless or through a wired connection. Sensor  112  and/or the torque measurement sensor may include any transducer, magnetic field sensor, torque meter, inertial measurement unit (IMU), force sensor, and the like. For example and without limitation sensor  112  may be configured to detect an output torque of the at least a flight component  108 , such that the output torque is 35 NKm. As a further example and without limitation, sensor  112  may be configured to detect a rpm of the at least a flight component  108 , such that the rpm of the at least a flight component is 1980 rpm. 
     With continued reference to  FIG.  1   , sensor  112  may include thermocouples, thermistors, thermometers, passive infrared sensors, resistance temperature sensors (RTD&#39;s), semiconductor based integrated circuits (IC), a combination thereof or another undisclosed sensor type, alone or in combination. Temperature, for the purposes of this disclosure, and as would be appreciated by someone of ordinary skill in the art, is a measure of the heat energy of a system. Temperature, as measured by any number or combinations of sensors present within sensor suite  200 , may be measured in Fahrenheit (° F.), Celsius (° C.), Kelvin (° K), or another scale alone or in combination. The temperature measured by sensors may comprise electrical signals which are transmitted to their appropriate destination wireless or through a wired connection. 
     Still referring to  FIG.  1   , as a further example and without limitation, sensor  112  may include a moisture sensor. “Moisture”, as used in this disclosure, is the presence of water, this may include vaporized water in air, condensation on the surfaces of objects, or concentrations of liquid water. Moisture may include humidity. “Humidity”, as used in this disclosure, is the property of a gaseous medium (almost always air) to hold water in the form of vapor. An amount of water vapor contained within a parcel of air can vary significantly. Water vapor is generally invisible to the human eye and may be damaging to electrical components. There are three primary measurements of humidity, absolute, relative, specific humidity. “Absolute humidity,” for the purposes of this disclosure, describes the water content of air and is expressed in either grams per cubic meters or grams per kilogram. “Relative humidity”, for the purposes of this disclosure, is expressed as a percentage, indicating a present stat of absolute humidity relative to a maximum humidity given the same temperature. “Specific humidity”, for the purposes of this disclosure, is the ratio of water vapor mass to total moist air parcel mass, where parcel is a given portion of a gaseous medium. 
     Continuing to refer to  FIG.  1   , sensor  112  may be configured to detect events where torque nears an upper torque threshold or lower torque threshold. The upper and/or lower torque threshold may be stores in any data storage system, such as a data storage system onboard the electric aircraft and/or a remote data storage system. The upper torque threshold may be calculated and calibrated based on factors relating to the at least a flight component  108  health, maintenance history, location within the at least a flight component, designed application, and type, among others. Sensor  112  may measure torque at an instant, over a period of time, or periodically. Sensor  112  may be configured to operate at any of these detection modes, switch between modes, or simultaneous measure in more than one mode. Computing device  104  may detect through sensor  112  events where torque nears the lower torque threshold and/or the upper torque threshold. The lower torque threshold may indicate power loss to or from the at least a flight component  108 . The upper torque threshold may indicate an excess of power and/or torque command to or from the at least a flight component  108 . Events where torque exceeds the upper and lower torque threshold may indicate the at least a flight component  108  failure or electrical anomalies that could lead to potentially dangerous situations for aircraft and personnel that may be present in or near its operation. 
     With continued reference to  FIG.  1   , sensor  112  may be configured to detect events where voltage nears an upper voltage threshold or lower voltage threshold. The upper voltage threshold may be stored in a data storage system for comparison with an instant measurement taken by any combination of sensors present within sensor  112 . The upper voltage threshold may be calculated and calibrated based on factors relating to the at least a flight component  108  health, maintenance history, location within the at least a flight component, designed application, and type, among others. Sensor  112  may measure voltage at an instant, over a period of time, or periodically. Sensor  112  may be configured to operate at any of these detection modes, switch between modes, or simultaneous measure in more than one mode. Computing device  104  may detect through sensor  112  events where voltage nears the lower voltage threshold. The lower voltage threshold may indicate power loss to or from the at least a flight component  108 . Computing device  104  may detect through sensor  112  events where voltage exceeds the upper and lower voltage threshold. Events where voltage exceeds the upper and lower voltage threshold may indicate the at least a flight component  108  failure or electrical anomalies that could lead to potentially dangerous situations for the electric aircraft and personnel that may be present in or near its operation. 
     Still referring to  FIG.  1   , in an embodiment, the at least a sensor  112  is configured to detect status datum  116 , and transmit status datum  116  to the computing device  104 . “Status datum”, for the purpose of this disclosure, is any data describing and/or identifying the position of the at least a flight component. For example, and without limitation, status datum  116  may denote one or more torques, thrusts, airspeed velocities, forces, altitudes, groundspeed velocities, directions during flight, directions facing, rpm, orientations, and the like thereof. For example and without limitation, status datum  116  may denote that the at least a flight component  108  is currently operating at a specific rpm, such as 2000 rpm, 1950 rpm, 2100 rpm, 1600 rpm and the like. As a further example and without limitation, status datum  116  may denote that the at least a flight component  108  is current in a position perpendicular to the current flight of the electric aircraft, such that the tip of a rotor is perpendicular to the boom of the electric aircraft and therefore not aligned in edgewise flight. As a further non-limiting example, status datum  116  may denote that the at least a flight component  108  is current commanded to operate at a set torque value, such as 30 Nkm. As a further example and without limitation, status datum  116  may denote that the at least a flight component  108  is currently operating at a specific torque value, such as 27 Nkm. Further, as a non-limiting example, status datum  116  may denote any metric associated to the performance of the at least a flight component  108 . 
     With continued reference to  FIG.  1   , computing device  104  is communicatively connected to the at least a sensor  112  and the at least a flight component  108 . “Communicatively connected”, for the purposes of this disclosure, is a process whereby one device, component, or circuit is able to receive data from and/or transmit data to another device, component, or circuit. Communicative connection may be performed by wired or wireless electronic communication, either directly or by way of one or more intervening devices or components. In an embodiment, communicative connection includes electrically coupling an output of one device, component, or circuit to an input of another device, component, or circuit. Communicative connecting may be performed via a bus or other facility for intercommunication between elements of a computing device. Communicative connecting may include indirect connections via “wireless” connection, low power wide area network, radio communication, optical communication, magnetic, capacitive, optical coupling, or the like. 
     Continuing to refer to  FIG.  1   , in an embodiment, computing device  104  is configured to receive status datum  116  from the at least a sensor  112 , generate optimum position  120  of the at least a flight component  108  as a function of status datum  116 , and initiate optimum position  120  of the at least a flight component  108 . As used in this disclosure, an “optimum position” is any element of data describing and/or identifying a position the at least a flight component that provides the least amount of air resistance. For example and without limitation, optimum position  120  may include data describing a position wherein the at least a flight component  108  is aligned parallel to the direction of flight, such that a first blade of the at least a flight component  108  is positioned forward and a second blade of the at least a flight component  108  is positioned backwards relative to the direction of flight of the electric aircraft. In an embodiment, and without limitation, optimum position  120  may include the at least a flight component  108  stowed within a chamber, such that the at least a flight component  108  is stored within the electric aircraft to achieve the least amount of air resistance. Stowing the at least a flight component  108  within a chamber may be useful to protect the at least a flight component  108  from the elements when not in use. As a further example and without limitation, optimum position  120  may include a position of the at least a flight component  108 , such that the position achieves breaking of the electric aircraft, wherein the at least a flight component  108  produces a reduced output, reduced torque and/or zero torque. Further, as a non-limiting example, optimum position  120  of the at least a flight component  108  may include a position of the at least a flight component  108 , wherein the at least a flight component is aligned in order to achieve the least amount of air resistance for an upcoming maneuver and/or portion of a flight plan. As a further example and without limitation, optimum position  120  of the at least a flight component  108  may include a position of the at least a flight component  108  wherein the at least a flight component  108  is less affected by an imminent weather condition, such as high winds, rain, humidity, fog, precipitation, and the like. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various positions of the at least a flight component that may be employed as optimum position as described herein. 
     With continued reference to  FIG.  1   , initiating optimum position  120  of the at least a flight component  108  may include any means of initiation as described in the entirety of this disclosure. For example and without limitation, initiating optimum position  120  of the at least a flight component  108  may include commanding the at least a flight component  108  to be held and/or fixed in optimum position  120 , such that the at least a flight component  108  is positioned by any controlling device, such as an actuator, in optimum position  120 , wherein the at least a flight component  108  is held in a fixed position. As a further example and without limitation, initiating optimum position  120  may include transmitting optimum position to a flight controller, wherein the flight controller is configured to command the at least a flight component  108  to be in optimum position  120 . Further, in a non-limiting example, computing device  104  may be configured to transmit optimum position  120  to a remote device. As used in this disclosure, a “remote device” is any computing device and/or other device that is not housed or contained within the electric aircraft. For example and without limitation, a remote device may be communicatively connected to the electric aircraft and/or computing device  104 . 
     Still referring to  FIG.  1   , in an embodiment, generating optimum position  120  of the at least a flight component  108  by computing device  104  may be configured to include calculating a position datum. As used in this disclosure, “position datum”, is any data describing and/or identifying a calculation of the number of remaining rotations by the rotor that are needed to safely reach optimum position  120 . Position datum, for example and without limitation, may include any metric of motion required in order for the at least a flight component  108  to reach optimum position  120 . In a nonlimiting example, the position datum may include a point in the rotor rotation when negative torque must be applied to reach optimum position  120 . In a nonlimiting example, the position datum may include the movement, distance, and/or length required for the at least a flight component  108  to reach optimum position  120 , such that the at least a flight component  108  is 30-degrees away from optimum position  120 . For example and without limitation, the position datum may include a number of rotations required in order for the at least a flight component  108  to reach optimum position  120 , such as 0.1 rotations, 0.25 rotations, 0.67 rotations. 0.8 rotations, and the like. As a further example and without limitation, the position datum may include any distance required in order for the at least a flight component  108  to reach optimum position  120 , such as 4.7 in, 70 cm, 147 cm, 8 in, and the like. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various datums that may be employed as the position datum as described herein. 
     With continued reference to  FIG.  1   , in an embodiment, generating optimum position  120  of the at least a flight component  108  by computing device  104  may be configured to include calculating a torque datum. As used in this disclosure, a “torque datum” is any data describing and/or identifying the negative torque that must be applied in order for the at least a flight component  108  to achieve optimum position  120 . In an embodiment, the torque datum may include any metric and/or associated unit describing negative torque that may be applied to the at least a flight component  108 . For example and without limitation, the torque datum may include a negative torque of −4 Nkm, −10 Nkm, −22 Nkm, and the like. As a further example and without limitation, the torque datum may include any value lower than the current torque output of the at least a flight component  108 , such that the torque output of the at least a flight component is 30 Nkm and the torque datum is −8 Nkm. As a further example and without limitation, the torque datum may include a negative torque required for the at least a flight component  108  to achieve a desired torque output, such that a −20 Nkm negative torque must be applied to the at least a flight component  108  in order for the at least a flight component  108  to achieve a torque degredation of 45 Nkm to 35 Nkm. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various values that may be employed as the torque datum as described herein. 
     Still referring to  FIG.  1   , in an embodiment, optimum position  120  of the at least a flight component  108  may be generated as a function of a pilot signal. As used in this disclosure a “pilot signal” is any element of datum representing one or more functions a pilot is controlling and/or adjusting. For example, the pilot signal 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, the pilot signal may include an implicit signal and/or an explicit signal. For example, and without limitation, the pilot signal 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, the pilot signal may include an explicit signal directing computing device  104  to control and/or maintain a portion of aircraft, a portion of the flight plan, the entire aircraft, the at least a flight component  108 , and/or the entire flight plan. As a further non-limiting example, the pilot signal may include an implicit signal, wherein computing device  104  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, the pilot signal 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, the pilot signal may include one or more local and/or global signals. For example, and without limitation, the pilot signal may include a local signal that is transmitted by a pilot and/or crew member. As a further non-limiting example, the pilot signal 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, the pilot signal 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.  1   , in an embodiment, optimum position  120  of the at least a flight component  108  may be generated as a function of a flight plan. As used in this disclosure, “flight plan” is any data describing and/or identifying maneuvers, flight directions, positions, commands, and/or flight paths to be performed by the electric aircraft in order for the electric aircraft to reach a set destination and/or objective. In an embodiment, and without limitation, the flight plan may be generated by a pilot, generated and/or transmitted from a fleet manager, generated and/or transmitted from an air traffic control system, and/or the like. Flight plan may be consistent with disclosure of flight plan in U.S. patent application Ser. No. 17/365,512 and titled “PILOT-CONTROLLED POSITION GUIDANCE FOR VTOL AIRCRAFT”, which is incorporated herein by reference in its entirety. For example and without limitation, the flight plan may include flight limitations, such as restricted flying zones, maximum and/or minimum flight altitudes, landing zones, and the like. As a further example and without limitation, the flight plan may include an autonomous control system for the electric aircraft. As a further example and without limitation, computing device  104  may be configured to generate optimum position  120  of the at least a flight component  104  based on an upcoming maneuver of the flight plan, such that the flight plan includes a transition from lift flight to fixed wing flight, a transition from fixed wing flight to lift flight, an upcoming landing, a required recharging of the power source and/or battery, and the like. 
     Alternatively, or additionally, and still referring to  FIG.  1   , in an embodiment, computing device  104  may be configured to generate optimum position  120  of the at least a flight component  108  as a function of a machine learning process. The machine-learning process may include any machine-learning process as described in further detail below in reference to  FIG.  5   . Machine learning process may be trained with training data that includes past calculations for the same electric aircraft or other aircrafts, such as electric aircrafts within the same fleet. For example and without limitation, training data may include simulations and/or models of simulation data for the same electric aircraft or other aircrafts, such as electric aircraft simulations within the same fleet. As a further non-limiting example, training data may include past calculations correlated to a flight plan. In a nonlimiting example, machine learning process may use past correlations of calculations for a optimum position  120  for other aircrafts following the same flight plan, such as a specific altitude at for other aircrafts at the same location, a specific maneuver, a specific weather, and the like. In an embodiment, machine learning process may be configured to use neural networks, as described in further detail below. For example and without limitation, computing device  104  may generate optimum position  120  of the at least a flight component  108  as a function of a closed loop system. In an embodiment, computing device  104  may include a proportional-integral-derivative (PID) controller. “PID controller”, as used in this disclosure, is any device configured to a control loop feedback mechanism to control process variables. In an embodiment, PID controller and/or computing device  104  may be configured to utilize a dynamic inversion design. “dynamic inversion design” as used in this disclosure, is any decoupling flight control system, such that the flight control system is nonlinear. For example and without limitation, the dynamic inversion design may include a process that decouples the control compensation design from the variations in aircraft dynamics over a wide flight envelope. As a further non-limiting example, the dynamic inversion design may include a process that decouples the plant model. 
     Now referring to  FIG.  2   , an exemplary representation of a method  200  for reducing air resistance in an electric aircraft flight is illustrated. Method  200  includes, at step  205 , detecting, by at least a sensor  112  connected to the at least a flight component  108 , status datum  116 . Detecting may include any means of detection as described in the entirety of this disclosure. Sensor may include any sensor as described above in reference to  FIG.  1   . The at least a flight component may include any flight component as described in the entirety of this disclosure. Status datum may include any status datum as described in further detail above in reference to  FIG.  1   . 
     Still referring to  FIG.  2   , method  200  includes, at step  210 , transmitting, by the at least a sensor  112 , status datum  116  to computing device  104 . Transmission may include any means and/or method of transmission as described in the entirety of this disclosure. Computing device may include any computing device as described in further detail above in reference to  FIG.  1   . For example and without limitation, computing device  104  may include a PID controller, wherein the PID controller utilizes a dynamic inversion design, as described above in further detail in reference to  FIG.  1   . 
     Continuing to refer to  FIG.  2   , at step  215 , method  200  includes receiving, by the computing device  104 , status datum  116  from at least a sensor  112 , wherein computing device  104  is communicatively connected to an electric aircraft. The electric aircraft may include any electric aircraft as described in the entirety of this disclosure. 
     With continued reference to  FIG.  2   , at step  220 , method  200  includes generating, by computing device  104 , optimum position  120  of the at least a flight component  108  as a function of status datum  116 . The optimum position may include any optimum position as described above in further detail in reference to  FIG.  1   . Generating the optimum position include any means and/or process of generation as described in the entirety of this disclosure. In an embodiment, method  200  may generate optimum position  120  of the at least a flight component  108  as a function of a machine-learning process. The machine-learning process may include any machine-learning process as described in further detail in the entirety of this disclosure. In a non-limiting embodiment, method  200  and/or generating optimum position  120  may further include calculating a position datum. The position datum may include any position datum as described above in further detail in reference to  FIG.  1   . Further, in a non-limiting embodiment, method  200  and/or generating optimum position  120  may further include calculating a torque datum. The torque datum may include any torque datum as described above in further detail in reference to  FIG.  1   . In a further non-limiting embodiment, method  200  and/or generating optimum position  120  may further include generating optimum position  120  of the at least a flight component  108  as a function of a flight plan. The flight plan may include any flight plan as described in further detail in the entirety of this disclosure. Further, in a non-limiting embodiment, method  200  and/or generating optimum position  120  may further include generating optimum position  120  of the at least a flight component  108  as a function of a pilot signal. The pilot signal may include any pilot signal as described in further detail in the entirety of this disclosure. 
     Still referring to  FIG.  2   , method  200  includes, at step  225 , initiating, by computing device  104 , optimum position  120  of the at least a flight component  108 . Initiation of the optimum position may include any initiation process as described above in further detail in reference to  FIG.  1   . For example and without limitation, method  200  and/or initiating optimum position  120  may include transmitting optimum position  120  to a remote device. The remote device may include any remote device as described in further detail in the entirety of this disclosure. As a further example and without limitation, optimum position  120  may be stored in any location, such as a data store system, as described in further detail above in reference to  FIG.  1   . 
     Referring now to  FIG.  3   , an embodiment of an electric aircraft  300  is presented. Electric aircraft  300  may include a vertical takeoff and landing aircraft (eVTOL). As used herein, a vertical take-off and landing (eVTOL) aircraft is one that can hover, take off, and land vertically. An eVTOL, as used herein, is an electrically powered aircraft typically using an energy source, of a plurality of energy sources to power the aircraft. In order to optimize the power and energy necessary to propel the aircraft. eVTOL 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. Rotor-based flight, as described herein, is where the aircraft generated lift and propulsion by way of one or more powered rotors connected with an engine, such as a “quad copter,” multi-rotor helicopter, or other vehicle that maintains its lift primarily using downward thrusting propulsors. Fixed-wing flight, as described herein, is where the aircraft is capable of flight using wings and/or foils that generate life caused by the aircraft&#39;s forward airspeed and the shape of the wings and/or foils, such as airplane-style flight. 
     With continued reference to  FIG.  3   , a number of aerodynamic forces may act upon the electric aircraft  300  during flight. Forces acting on an electric aircraft  300  during flight may include, without limitation, thrust, the forward force produced by the rotating element of the electric aircraft  300  and acts parallel to the longitudinal axis. Another force acting upon electric aircraft  300  may be, without limitation, drag, which may be defined as a rearward retarding force which is caused by disruption of airflow by any protruding surface of the electric aircraft  300  such as, without limitation, the wing, rotor, and fuselage. Drag may oppose thrust and acts rearward parallel to the relative wind. A further force acting upon electric aircraft  300  may include, without limitation, weight, which may include a combined load of the electric aircraft  300  itself, crew, baggage, and/or fuel. Weight may pull electric aircraft  300  downward due to the force of gravity. An additional force acting on electric aircraft  300  may include, without limitation, lift, which may act to oppose the downward force of weight and may be produced by the dynamic effect of air acting on the airfoil and/or downward thrust from the propulsor of the electric aircraft. Lift generated by the airfoil may depend on speed of airflow, density of air, total area of an airfoil and/or segment thereof, and/or an angle of attack between air and the airfoil. For example, and without limitation, electric aircraft  300  are designed to be as lightweight as possible. Reducing the weight of the aircraft and designing to reduce the number of components is essential to optimize the weight. To save energy, it may be useful to reduce weight of components of an electric aircraft  300 , including without limitation propulsors and/or propulsion assemblies. In an embodiment, the motor may eliminate need for many external structural features that otherwise might be needed to join one component to another component. The motor may also increase energy efficiency by enabling a lower physical propulsor profile, reducing drag and/or wind resistance. This may also increase durability by lessening the extent to which drag and/or wind resistance add to forces acting on electric aircraft  300  and/or propulsors. 
     Still referring to  FIG.  3   , electric aircraft  300  includes computing device  304 . Computing device  304  may include any computing device as described in the entirety of this disclosure. Computing device  304  may be located in any position and/or orientation on electric aircraft  300 . Electric aircraft  300  may include any number of computing device  304  connected to the aircraft. Further, electric aircraft  300  includes flight component  308 A-N. Flight component  308 A-N may include any flight component as described in the entirety of this disclosure. For example and without limitation, flight component  308 A-N may include a forward propulsor, vertical propulsor, rotor, control surface, propeller, and the like. Flight component  308 A-N may be located in any position and/or orientation on electric aircraft  300 . Electric aircraft  300  may include any number of flight component  308 A-N. 
     Now referring to  FIG.  4   , an exemplary embodiment  400  of a flight controller  404  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  404  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  404  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  404  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.  4   , flight controller  404  may include a signal transformation component  408 . 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  408  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  408  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  408  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  408  may include transforming a binary language signal to an assembly language signal. In an embodiment, and without limitation, signal transformation component  408  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.  4   , signal transformation component  408  may be configured to optimize an intermediate representation  412 . As used in this disclosure an “intermediate representation” is a data structure and/or code that represents the input signal. Signal transformation component  408  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  408  may optimize intermediate representation  412  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  408  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  408  may optimize intermediate representation to generate an output language, wherein an “output language,” as used herein, is the native machine language of flight controller  404 . 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  408  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.  4   , flight controller  404  may include a reconfigurable hardware platform  416 . 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  416  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.  4   , reconfigurable hardware platform  416  may include a logic component  420 . 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  420  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  420  may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Logic component  420  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  420  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  420  may be configured to execute a sequence of stored instructions to be performed on the output language and/or intermediate representation  412 . Logic component  420  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  404 . Logic component  420  may be configured to decode the instruction retrieved from the memory cache to opcodes and/or operands. Logic component  420  may be configured to execute the instruction on intermediate representation  412  and/or output language. For example, and without limitation, logic component  420  may be configured to execute an addition operation on intermediate representation  412  and/or output language. 
     In an embodiment, and without limitation, logic component  420  may be configured to calculate a flight element  424 . 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  424  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  424  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  424  may denote that aircraft is following a flight path accurately and/or sufficiently. 
     Still referring to  FIG.  4   , flight controller  404  may include a chipset component  428 . As used in this disclosure a “chipset component” is a component that manages data flow. In an embodiment, and without limitation, chipset component  428  may include a northbridge data flow path, wherein the northbridge dataflow path may manage data flow from logic component  420  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  428  may include a southbridge data flow path, wherein the southbridge dataflow path may manage data flow from logic component  420  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  428  may manage data flow between logic component  420 , memory cache, and a flight component  432 . 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 432  may include a component used to affect the aircrafts&#39; roll and pitch which may comprise one or more ailerons. As a further example, flight component  432  may include a rudder to control yaw of an aircraft. In an embodiment, chipset component  428  may be configured to communicate with a plurality of flight components as a function of flight element  424 . For example, and without limitation, chipset component  428  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.  4   , flight controller  404  may be configured generate an autonomous function. As used in this disclosure an “autonomous function” is a mode and/or function of flight controller  404  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  424 . 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  404  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  404  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.  4   , flight controller  404  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  424  and a pilot signal  436  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. Pilot signal  436  may include any pilot signal as described above in further detail in reference to  FIGS.  1 - 3   . For example, pilot signal  436  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  436  may include an implicit signal and/or an explicit signal. For example, and without limitation, pilot signal  436  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  436  may include an explicit signal directing flight controller  404  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  436  may include an implicit signal, wherein flight controller  404  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  436  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  436  may include one or more local and/or global signals. For example, and without limitation, pilot signal  436  may include a local signal that is transmitted by a pilot and/or crew member. As a further non-limiting example, pilot signal  436  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  436  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.  4   , 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  404  and/or a remote device may or may not use in the generation of autonomous function. Remote device may include any remote device as described in the entirety of this disclosure. For example and without limitation, remote device may include an external device to flight controller  404 . 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, naïve 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.  4   , 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  404  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.  4   , flight controller  404  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  404 . Remote device and/or FPGA may transmit a signal, bit, datum, or parameter to flight controller  404  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, a 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  404  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.  4   , flight controller  404  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.  4   , flight controller  404  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  404  may include one or more flight controllers dedicated to data storage, security, distribution of traffic for load balancing, and the like. Flight controller  404  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  404  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, Mass., 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&#39;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.  4   , 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  432 . 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.  4   , 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  404 . 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  412  and/or output language from logic component  420 , 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.  4   , 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.  4   , 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.  4   , flight controller  404  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  404  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 raining 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.  4   , 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 w i  that are derived using machine-learning processes as described in this disclosure. 
     Still referring to  FIG.  4   , flight controller may include a sub-controller  440 . 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  404  may be and/or include a distributed flight controller made up of one or more sub-controllers. For example, and without limitation, sub-controller  440  may include any controllers and/or components thereof that are similar to distributed flight controller and/or flight controller as described above. Sub-controller  440  may include any component of any flight controller as described above. Sub-controller  440  may be implemented in any manner suitable for implementation of a flight controller as described above. As a further non-limiting example, sub-controller  440  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  440  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.  4   , flight controller may include a co-controller  444 . As used in this disclosure a “co-controller” is a controller and/or component that joins flight controller  404  as components and/or nodes of a distributer flight controller as described above. For example, and without limitation, co-controller  444  may include one or more controllers and/or components that are similar to flight controller  404 . As a further non-limiting example, co-controller  444  may include any controller and/or component that joins flight controller  404  to distributer flight controller. As a further non-limiting example, co-controller  444  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  404  to distributed flight control system. Co-controller  444  may include any component of any flight controller as described above. Co-controller  444  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.  4   , flight controller  404  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  404  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.  5   , an exemplary embodiment of a machine-learning module  500  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  504  to generate an algorithm that will be performed by a computing device/module to produce outputs  508  given data provided as inputs  512 ; 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.  5   , “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  504  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  504  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  504  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  504  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  504  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  504  may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data  504  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.  5   , training data  504  may include one or more elements that are not categorized; that is, training data  504  may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and/or other processes may sort training data  504  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&#39;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  504  to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data  504  used by machine-learning module  500  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 flight elements and/or pilot signals may be inputs, wherein an output may be an autonomous function. For example and without limitation, training data  504  used by machine-learning module  500  may correlate input data, such as a current position of the flight component, to output data, such as a model of flight component position as a function of torque. As a further non-limiting example, training data  504  used by machine-learning module  500  may correlate input data, such as a desired position of the flight component, to output data, such as a required torque limit in order for the flight component to reach the desired position. For example and without limitation, training data  504  may be further used by machine-learning module  500  may correlate input data, such as a current torque of the flight component, to output data, such as a model of flight component position as a function of torque. Additionally, as a non-limiting example, training data  504  may be further used by machine-learning module  500  may correlate input data, such as a desired torque of the flight component, to output data, such as a required pilot input in order for the flight component to achieve the desired torque. 
     Further referring to  FIG.  5   , 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  516 . Training data classifier  516  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  500  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  504 . 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&#39;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  416  may classify elements of training data to sub-categories of flight elements such as torques, forces, thrusts, directions, simulation models, flight components, pilot inputs, FAA data, flight history data, degredation of flight components, any combination thereof and/or the like. 
     Still referring to  FIG.  5   , machine-learning module  500  may be configured to perform a lazy-learning process  520  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  504 . Heuristic may include selecting some number of highest-ranking associations and/or training data  504  elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a K-nearest neighbors algorithm, a lazy naïve 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.  5   , machine-learning processes as described in this disclosure may be used to generate machine-learning models  524 . 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  524  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  524  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  504  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.  5   , machine-learning algorithms may include at least a supervised machine-learning process  528 . At least a supervised machine-learning process  528 , 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 flight elements and/or pilot signals as described above as inputs, autonomous functions as outputs, 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  504 . 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  528  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.  5   , machine learning processes may include at least an unsupervised machine-learning processes  532 . 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.  5   , machine-learning module  500  may be designed and configured to create a machine-learning model  524  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.  5   , 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 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. 
     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.  6    shows a diagrammatic representation of one embodiment of a computing device in the exemplary form of a computer system  600  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  600  includes a processor  604  and a memory  608  that communicate with each other, and with other components, via a bus  612 . Bus  612  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  604  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  604  may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor  604  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  608  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  616  (BIOS), including basic routines that help to transfer information between elements within computer system  600 , such as during start-up, may be stored in memory  608 . Memory  608  may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software)  620  embodying any one or more of the aspects and/or methodologies of the present disclosure. In another example, memory  608  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  600  may also include a storage device  624 . Examples of a storage device (e.g., storage device  624 ) 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  624  may be connected to bus  612  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  624  (or one or more components thereof) may be removably interfaced with computer system  600  (e.g., via an external port connector (not shown)). Particularly, storage device  624  and an associated machine-readable medium  628  may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system  600 . In one example, software  620  may reside, completely or partially, within machine-readable medium  628 . In another example, software  620  may reside, completely or partially, within processor  604 . 
     Computer system  600  may also include an input device  632 . In one example, a user of computer system  600  may enter commands and/or other information into computer system  600  via input device  632 . Examples of an input device  632  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  632  may be interfaced to bus  612  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  612 , and any combinations thereof. Input device  632  may include a touch screen interface that may be a part of or separate from display  636 , discussed further below. Input device  632  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  600  via storage device  624  (e.g., a removable disk drive, a flash drive, etc.) and/or network interface device  640 . A network interface device, such as network interface device  640 , may be utilized for connecting computer system  600  to one or more of a variety of networks, such as network  644 , and one or more remote devices  648  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  644 , may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software  620 , etc.) may be communicated to and/or from computer system  600  via network interface device  640 . 
     Computer system  600  may further include a video display adapter  652  for communicating a displayable image to a display device, such as display device  636 . 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  652  and display device  636  may be utilized in combination with processor  604  to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system  600  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  612  via a peripheral interface  656 . 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, systems, and software 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.