Patent Publication Number: US-11040774-B2

Title: Drone authentication system

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation of U.S. Parent application Ser. No. 15/704,548 filed Sep. 14, 2017 and entitled “Drone Authentication System,” which is hereby incorporated by reference in its entirety. 
    
    
     FIELD OF THE DISCLOSURE 
     This disclosure relates generally to drones, such as unmanned aerial vehicles, and, more particularly, to authentication of users with drones. 
     BACKGROUND 
     Drones, such as unmanned aerial vehicles (UAVs), are mobile platforms capable of acquiring (e.g., sensing) information, delivering goods, manipulating objects, etc., in many operating scenarios. Drones typically have the ability to travel to remote locations that are inaccessible to manned vehicles, locations that are dangerous to humans, or any other location. Upon reaching such locations, a suitably equipped drone may perform actions, such as acquiring sensor data (e.g., audio, images, video and/or other sensor data) at a target location, delivering goods (e.g., packages, medical supplies, food supplies, engineering materials, etc.) to the target location, manipulating objects (e.g., such as retrieving objects, operating equipment, repairing equipment etc.) at the target location, etc. 
     Drones are often controlled by a remote user from a command center (e.g., using a remote control, computer device, smart phone, and/or other remote monitor) such that the remote user provides commands to the drone through a wireless communications link to perform actions. More advanced drones are also being developed that are more autonomous (e.g., fully autonomous, semi-autonomous) such that drone guidance systems may assist the remote user or remove the need for the remote user altogether. These fully autonomous drones may be configured to follow an individual or group of individuals or monitor a location where individuals may be present. The drone may then provide services to these individuals, which may require the drone to authenticate the individual as a user of the drone and/or service using a primary authentication such as a verbal username and password. However, in certain situations the primary authentication may not be appropriate such as when the individual is with other people, the individual has a medical condition that prohibits the individual from providing the primary authentication, and/or the location of the individual is prohibitive. 
     SUMMARY 
     In some embodiments in accordance with the present disclosure, a drone authentication system for use in a monitored space is disclosed. The drone authentication system may include a plurality of sensors, that includes at least an imaging sensor. The drone authentication system also includes a drone that includes: at least one communications interface; at least one non-transitory memory storing identity data associated with one or more users; and one or more processors coupled to the plurality of sensors, communications interface, and non-transitory memory and configured to execute instructions to cause the authentication system to: monitor a monitored space for a condition to provide an interaction between a drone and an individual; obtain, in response to the condition to provide the interaction between the drone and the individual being satisfied, a first set of sensor data corresponding to the individual; determine the individual requires a service based on the first set of sensor data that requires authentication of the individual as a user of the service and, in response, determine, based on the first set of sensor data, that the individual is unable to provide a primary authentication response; obtain, by the drone, a second set of sensor data corresponding to the individual unable to provide the primary authentication response; and determine, based on the second set of sensor data and stored identity data, that the individual is a user of the service and, in response, perform the service. 
     In various embodiments of the drone authentication system, the drone authentication obtains the second set of sensor data corresponding to the individual unable to provide the primary authentication response in response to determining the identity of the individual is known and the service requires authentication of the individual regardless of whether the individual is already identified. 
     In various embodiments of the drone authentication system, the one or more processors are further configured to execute instructions to cause the authentication system to autonomously navigate the drone toward the individual in response to the condition to provide the interaction between the drone and the individual being satisfied; and initiate an investigate mode of the drone when the drone is within a predetermined range of the individual. Also, the primary authentication response is an acoustic credential to be obtained by an acoustic sensor included on the drone. 
     In various embodiments of the drone authentication system, the drone includes a biometric sensor coupled to the one or more processors, and the one or more processors are further configured to execute instructions to cause the authentication system to: deploy the biometric sensor, and the second set of sensor data includes sensor data obtained by the biometric sensor. In some embodiments, the first set of sensor data does not include sensor data obtained by the biometric sensor. 
     In various embodiments of the drone authentication system, the one or more processors is further configured to execute instructions to cause the authentication system to determine, based on image sensor data captured by an image sensor and included in the first set of sensor data, one or more sensors of the plurality of sensors for use in obtaining the second set of sensor data corresponding to the individual. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic view illustrating an embodiment of a drone authentication system. 
         FIG. 2  is a schematic view illustrating an embodiment of a drone used in the drone authentication system of  FIG. 1 . 
         FIG. 3  is a schematic view illustrating an embodiment of a drone docking station used in the drone authentication system of  FIG. 1 . 
         FIG. 4  is a schematic view illustrating an embodiment of a service platform used in the drone authentication system of  FIG. 1 . 
         FIG. 5  is a schematic view illustrating an embodiment of a remote monitor used in the drone authentication system of  FIG. 1 . 
         FIG. 6  is a flow chart illustrating an embodiment of a method of providing a service that requires authentication. 
         FIG. 7  is a flow chart illustrating an embodiment of a sub-method of authenticating an individual as a user of the service provided by the drone authentication system of the method in  FIG. 6 . 
         FIG. 8  is a schematic view illustrating an embodiment of the drone authentication system during the method of  FIG. 6 . 
         FIG. 9  is a schematic view illustrating an embodiment of the drone authentication system of  FIG. 8  where an individual is present in a monitored space and a condition exists for the drone to initiate an interaction with the individual during the method of  FIG. 6 . 
         FIG. 10  is a schematic view illustrating an embodiment of the drone authentication system of  FIG. 9  where a drone is in-flight toward the individual due to the presence of the condition during the method of  FIG. 6 . 
         FIG. 11  is a schematic view illustrating an embodiment of the drone authentication system of  FIG. 10  where the drone returns to a monitoring/standby position during the method of  FIG. 6 . 
         FIG. 12  is a schematic view illustrating an embodiment of a computer system. 
     
    
    
     Embodiments of the present disclosure may be understood by referring to the detailed description that follows. It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the figures, wherein showings therein are for purposes of illustrating embodiments of the present disclosure and not for purposes of limiting the same. 
     DETAILED DESCRIPTION 
     Embodiments of the present disclosure include drone authentication systems and methods that may be used, for example, to authenticate an individual as an authenticated user of a drone and/or service provided by the drone. As discussed above, some autonomous drones need to authenticate an individual as a user of the drone and/or service provided by the drone. A primary authentication response may include an individual providing credentials to the drone via a password or passphrase. Some issues with autonomous drones are that autonomous features such as authentication of an individual as a user is not always reliable, is insufficient, and/or unavailable in numerous situations. For example, monitored spaces that are public or that include more than one individual lack privacy, the individual may have a health issue (e.g., post-surgery, Alzheimer&#39;s, dementia, a temporary memory blank, unconsciousness, trauma, shock, etc.) that prevents the user from memorizing and/or providing an authentication response, the monitored space may be a restricted space requiring extra authentication, the individual may be determined to be suspicious, the position of the individual in the monitored space may be inaccessible to the drone to obtain the primary authentication response, and/or other situations. Therefore, it may be beneficial for a drone to be configured to authenticate an individual using one or more of a plurality of authentication techniques and determining which authentication response is appropriate given the user, conditions of the user, and other conditions within the monitored space. 
     The systems and methods of the present disclosure provide for a drone authentication system that includes a drone that can be dispatched and autonomously navigated to investigate an individual within the monitored space when a condition exists to do so. Once the drone is within a predefined range of the individual and/or while the drone is in-flight toward the individual, the drone may determine whether the individual requires a service based on the conditions within the monitored space and the individual. In response to determining that the individual requires a service and that the service requires an authentication response from the individual to authenticate the individual as a user of the drone authentication system and/or service, the drone authentication system may determine whether a primary authentication response can be obtained from the individual. If the primary authentication response cannot be obtained from the individual, the drone authentication system determines one or more secondary authentication responses that can be obtained to authenticate the individual as an authenticated user of the drone and/or service provided by the drone. The drone authentication system may then determine whether the individual is an authenticated user of the drone and/or service and perform the service in response to the individual being an authenticated user. 
     Referring now to  FIG. 1 , an embodiment of a drone authentication system  100  is illustrated. In the illustrated embodiment, the drone authentication system  100  includes a drone  105  provided in a monitored space  102 . The monitored space  102  may be any indoor and/or outdoor or outside space that may be contiguous or non-contiguous. The monitored space  102  may be defined by geofencing techniques that may include specific geographic coordinates such as latitude, longitude, and/or altitude, and/or operate within a range defined by a wireless communication signal. 
     The drone  105  may be implemented by any type of drone, such as an unmanned aerial vehicle (UAV). In alternative embodiments, a robot, an unmanned vehicular device (e.g., land or water), and/or other vehicular device may be employed. In the illustrated examples of the present disclosure, the drone  105  is depicted as a UAV and includes a flight control unit and a payload unit. For example, the flight control unit of the drone  105  includes any appropriate avionics, control actuators, and/or other equipment to fly the drone. The payload unit of the drone  105  includes any equipment implementing features supported by the given drone. For example, the payload unit may include one or more sensors, such as one or more cameras and/or other imaging sensors  114 , one or more environmental sensors (e.g., such as one or more temperature sensors, pressure sensors, humidity sensors, gas sensors, altitude sensors, location sensors and the like) and/or any other sensor. In the illustrated embodiment, the drone  105  may include an acoustic sensor  115  (e.g., a microphone, a microphone array, a directionally-discriminating acoustic sensor/transducer, and other acoustic sensors for detecting acoustic energy). Additionally or alternatively, an example payload unit for the drone  105  may include tools, actuators, manipulators, etc., capable of manipulating (e.g., touching, grasping, delivering, measuring, etc.) objects. For example, as illustrated in  FIG. 1 , the drone may include a robotic arm  111  that is configured to deploy the one or more sensors include on the robotic arm  111 . For example, the one or more sensors included on the robotic arm  111  may include one or more sensors discussed above and/or a biometric sensor  113 . The biometric sensor  113  may include an ocular sensor (e.g., a retinal scanner, an iris scanner, and/or other ocular sensor), a fingerprint sensor, a blood type sensor, a DNA sensor, a temperature sensor, a blood pressure sensor, a heartbeat sensor, and/or other biometric sensors. Additionally or alternatively, an example payload unit for the drone  105  may include a portable base station, signal booster, signal repeater, etc., to provide network coverage to an area. 
     The drone authentication system  100  may optionally include or be used in connection with a drone docking station  110  for drone launching, landing, and/or storing the drone  105 . The drone docking station  110  may be located anywhere in the monitored space  102  such as a rooftop, a yard, a vehicle, a room, or elsewhere. The drone docking station  110  may be connected to an external power grid and/or receive power from a local power source such as wind, solar, and/or thermal and store this power in one or more power supplies such as batteries. In certain embodiments, a battery of the drone  105  may be charged by the drone docking station  110  through a conduction pad and/or through an inductive charging device using the power of the drone docking station  110 . The drone docking station  110  may include one or more sensors  116  such as one or more cameras and/or other imaging sensors, acoustic sensors, biometric sensors, one or more environmental sensors described above, and/or other sensors. Furthermore, the drone docking station  110  may include an autonomous docking guidance system for guiding the drone  105  to dock with the drone docking station  110 . For example, the drone docking station  110  may include at least one visual indicator (e.g., lights, reflectors) and/or acoustic indicators that are recognizable by the drone  105  to assist the drone  105  in docking with the drone docking station  110 . 
     The drone  105  and the drone docking station  110  may include communication units having one or more transceivers to enable the drone  105  to communicate with the drone docking station  110 , one or more sensors  117  and  118  located in the monitored space  102 , a remote monitor  120 , a service platform  130 , and/or to communicate among other drones. Accordingly, and as disclosed in further detail below, the remote monitor  120  may be in communication with the drone  105  directly or indirectly. As used herein, the phrase “in communication,” including variances thereof, encompasses direct communication and/or indirect communication through one or more intermediary components and does not require direct physical (e.g., wired and/or wireless) communication and/or constant communication, but rather additionally includes selective communication at periodic or aperiodic intervals, as well as one-time events. 
     For example, the drone  105  and/or the drone docking station  110  in the drone authentication system  100  of  FIG. 1  include first (e.g., long-range) transceiver(s) to permit the drone  105  and/or the drone docking station  110  to communicate with a communication network  125 . The communication network  125  may be implemented by an example mobile cellular network, such as a long term evolution (LTE) network or other third generation (3G), fourth generation (4G) wireless network, or fifth-generation (5G) wireless network. However, in some examples, the communication network  125  may be additionally or alternatively be implemented by one or more other communication networks, such as, but not limited to, a satellite communication network, a microwave radio network, and/or other communication networks. In other examples, the drone docking station  110  may maintain a network connection through a wired (e.g., Ethernet) connection. 
     The drone  105  and the drone docking station  110  additionally or alternatively may include second (e.g., short-range) transceiver(s) to permit the drone  105  and/or the drone docking station  110  to communicate with each other, the sensors  117  and  118 , other drones and/or other drone docking stations. In the illustrated example of  FIG. 1 , such second transceivers are implemented by a type of transceiver supporting short-range wireless networking. For example, such second transceivers may be implemented by Wi-Fi transceivers, Bluetooth® transceivers, infrared (IR) transceiver, and other transceivers that are configured to allow the drone  105  and/or the drone docking station  110  to intercommunicate via an ad-hoc or other wireless network. 
     The drone authentication system  100  also includes or may be used in connection with a remote monitor  120 . The remote monitor  120  may be provided by a desktop computing system, a laptop/notebook computing system, a tablet computing system, a mobile phone, a set-top box, a remote control, a wearable device, and implantable device, and/or other remote monitor for controlling drones. The remote monitor  120  may be responsible for managing the drone  105  deployed in the monitored space  102 . For example, the remote monitor  120  may communicate directly through the communication network  125  and/or indirectly through the drone docking station  110  to locate the drone  105  in the monitored space  102 , identify the drone  105  in the monitored space  102 , ascertain capabilities of the drone  105  in the monitored space  102 , monitor the operating status of the drone  105  in the monitored space  102 , receive sensor data provided by the drone  105  in the monitored space  102 , provide instructions to the drone  105 , and/or provide other functionality. 
     The drone authentication system  100  also includes or may be in connection with a service platform  130 . For example, the service platform  130  may include one or more server devices, storage systems, cloud computing systems, and/or other computing devices (e.g., desktop computing device(s), laptop/notebook computing device(s), tablet computing device(s), mobile phone(s), etc.). As discussed below, the service platform  130  may be configured to provide repositories such a user repository of user profiles and a service repository of service profiles. For example, the user repository may include a plurality of user profiles that are associated with a user of the drone and/or a service that is accessible via the drone. The service repository may include a plurality of service profiles that the service platform monitors to determine whether a condition in the monitored space exists. Also, as discussed below, the service platform  130  may be configured to provide an authentication engine that authenticates that an individual is a user of the drone and/or service being accessed via the drone. The service platform may also include and services engine for communicating instruction to the drone  105  to provide a service. 
     Referring now to  FIG. 2 , an embodiment of a drone  200  is illustrated that may be the drone  105  discussed above with reference to  FIG. 1 , and which may be provided by a UAV, a robot, an unmanned vehicular device (e.g., land or water), and/or other vehicular device. In the illustrated embodiment, the drone  200  includes a chassis  202  that houses the components of the drone  200 . Several of these components are illustrated in  FIG. 2 . For example, the chassis  202  may house a processing system (not illustrated) and a non-transitory memory system (not illustrated) that includes instructions that, when executed by the processing system, cause the processing system to provide a drone controller  204  that is configured to perform the functions of the drone controllers and/or the drones discussed below. In the specific example illustrated in  FIG. 2 , the drone controller  204  is configured to provide a sensor analysis engine  205  that computationally processes sensor signals with stored sensor signal profiles, and an authentication engine  206  that performs a primary authentication and/or a secondary authentication of a user as well as the functionality discussed below. In the specific example illustrated in  FIG. 2 , the drone controller  204  is also configured to provide a mobility controller  207  to control the example flight control unit of drone  105  and to implement any control and feedback operations appropriate for interacting with avionics, control actuators, and/or other equipment included in the flight control unit to navigate the drone  105  illustrated in  FIG. 1 . 
     The chassis  202  may further house a communication system  208  that is coupled to the drone controller  204  (e.g., via a coupling between the communication system  208  and the processing system). The communication system  208  may include software or instructions that are stored on a computer-readable medium and that allow the drone  200  to send and receive information through the communication networks discussed above. For example, the communication system  208  may include a first communication interface  210  to provide for communications through the communication network  125  as detailed above (e.g., first (e.g., long-range) transceiver(s)). In an embodiment, the first communication interface  210  may be a wireless antenna that is configured to provide communications with IEEE 802.11 protocols (Wi-Fi), cellular communications, satellite communications, other microwave radio communications and/or communications. The communication system  208  may also include a second communication interface  212  that is configured to provide direct communication with other drones, the drone docking station  110 , sensors  117  and  118 , the remote monitor  120 , and/other devices within the monitored space  102  discussed above with respect to  FIG. 1  (e.g., second (e.g., short-range) transceiver(s)). For example, the second communication interface  212  may be configured to operate according to wireless protocols such as Bluetooth®, Bluetooth® Low Energy (BLE), near field communication (NFC), infrared data association (IrDA), ANT®, Zigbee®, Z-Wave® IEEE 802.11 protocols (Wi-Fi), and other wireless communication protocols that allow for direct communication between devices. 
     The chassis  202  may also house a storage system  214  that is coupled to the drone controller  204  through the processing system. The storage system  214  may store user profiles  217  in one or more user repositories  216 . The user profiles  217  may include information associated with a user of the drone  200  and/or a service provided by the drone  200 . For example, a user profile  217  may include a user identifier that is associated with the user. For example, the user identifier may include a username, a phone number, an electronic mail address, a user device identifier (e.g., a communication interface identifier of a mobile device) and/or other identifiers that can identify the user. Each user identifier may have user information associated with the user identifier that can be used by the drone  200  to undertake various services. For example, the user information may include preselected preferences, third party data, gathered data by the drone authentication system  100  over time, identity data such as sensor signal profiles (e.g., an acoustic profile, an image profile, a blood profile, a DNA profile, a fingerprint profile, an ocular profile and/or other sensor signal profile that can be used to identify the individual and be updated from gathered data over time using machine learning techniques discussed below), and/or any other data used for authenticating an individual as a user and providing services to that user. In addition, the storage system  214  may include a service repository  218  that includes a plurality of service profiles  219 . The service repository  218  may include at least one application that provides instruction to the drone controller  204  when at least one condition is satisfied in a monitored space. In addition the at least one application may require that an individual in the monitored space to be authenticated as a user before providing the service. Each application may be associated with service profile  219  that includes sensor signal profiles of conditions that need to be satisfied before the application associated with that service profile  219  can be run on the drone controller  204 . 
     The chassis  202  may also house an acoustic sensor  220  (e.g., a microphone, a microphone array, a directionally-discriminating acoustic sensor, or other acoustic sensors), an imaging sensor  222  (e.g., a two-dimensional image capturing camera, a three-dimensional image capturing camera, an infrared image capturing camera, a depth capturing camera, similar video recorders, and/or a variety of other image or data capturing devices), a biometric sensor  226  (an ocular sensor, a fingerprint sensor, a blood type sensor, a DNA sensor, a temperature sensor, a blood pressure sensor, a heartbeat sensor, and other biometric sensors) and in some embodiments, an acoustic emitter  224 . 
     For example, the acoustic sensor  220  may include an microphone array that is configured to capture audio signals from acoustic energy in a monitored space and provide the audio signals to the sensor analysis engine  205  and/or authentication engine  206  to computationally process the audio signals against acoustic profiles associated with the user profiles  217  and/or service profiles  219  that are stored in the storage system  214  to determine whether substantial correspondence with any of the acoustic profiles exists. The acoustic sensor  220  may also be used to determine an apparent direction and/or location of the apparent source that provided the acoustic energy as discussed further below. Similarly, the acoustic emitter  224  may include a speaker array or other sound emitting device that generates and emits acoustic energy to the monitored space such that the acoustic energy is reflected off objects within the monitored space. Those objects then become apparent sources of the acoustic energy that provide unique reflected acoustic energy back to the acoustic sensor  220 . 
     The imaging sensor  222  may be a camera and/or any other sensor device that may be used to gather visual information from the monitored space surrounding the drone  200  for use in authenticating an individual and/or identifying and providing a service with the drone  200 . Imaging sensor signals may be provided to the sensor analysis engine  205  and/or authentication engine  206  to computationally process the imaging sensor signals against image profiles associated with the user profiles  217  and/or service profiles  219  that are stored in the storage system  214  to determine whether substantial correspondence with any of the image profiles exists. Similarly, the biometric sensors  226  other than the acoustic sensor  220  and the imaging sensor  222  may be used to gather biometric data from an individual  805  in the monitored space  102  for use in authenticating the individual and/or identifying and providing a service with the drone  200 . Biometric sensor signals may be provided to the sensor analysis engine  205  and/or authentication engine  206  to computationally process the biometric sensor signals against biometric profiles associated with the user profiles  217  and/or service profiles  219  that are stored in the storage system  214  to determine whether substantial correspondence with any of the biometric profiles exists. 
     The drone  200  may also include a positioning system  228  that is coupled to the drone controller  204 . The positioning system  228  may include sensors for determining the location and position of the drone in the monitored space. For example the positioning system  228  may include a global positioning system (GPS) receiver, a real-time kinematic (RTK) GPS receiver, a differential GPS receiver, a Wi-Fi based positioning system (WPS) receiver, an accelerometer, and/or other positioning systems and components. 
     Referring now to  FIG. 3 , an embodiment of a drone docking station  300  is illustrated that may be the drone docking station  110  discussed above with reference to  FIG. 1 . In the illustrated embodiment, the drone docking station  300  includes a chassis  302  that houses the components of the drone docking station  300 . Several of these components are illustrated in  FIG. 3 . For example, the chassis  302  may house a processing system (not illustrated) and a non-transitory memory system (not illustrated) that includes instructions that, when executed by the processing system, cause the processing system to provide a drone docking engine  304  that is configured to perform the functions of the drone docking engines and/or the drone docking stations discussed below. In the specific example illustrated in  FIG. 3 , the drone docking engine  304  is configured to provide a sensor analysis engine  305  that computationally processes sensor signals against stored sensor signal profiles, and an authentication engine  306  that performs a primary authentication and/or a secondary authentication of a user as well as the functionality discussed below. 
     The chassis  302  may further house a communication system  308  that is coupled to the drone docking engine  304  (e.g., via a coupling between the communication system  308  and the processing system). The communication system  308  may include software or instructions that are stored on a computer-readable medium and that allow the drone docking station  300  to send and receive information through the communication networks discussed above. For example, the communication system  308  may include a first communication interface  310  to provide for communications through the communication network  125  as detailed above (e.g., first (e.g., long-range) transceiver(s)). In a specific example, the first communication interface  310  may be a wireless antenna that is configured to provide communications with IEEE 802.11 protocols (Wi-Fi), cellular communications, satellite communications, other microwave radio communications and/or communications. In other examples, the first communication interface  310  may provide wired communications (e.g., Ethernet protocol) from the drone docking station  300  through the communication network  125 . The communication system  308  may also include a second communication interface  312  that is configured to provide direct communication with the drone  105 , other drone docking stations, sensors (e.g., sensors  117  and  118 ), monitors, and/other devices within the monitored space  102  discussed above with reference to  FIG. 1  (e.g., second (e.g., short-range) transceiver(s)). For example, the second communication interface  312  may be configured to operate according to wireless protocols such as Bluetooth®, Bluetooth® Low Energy (BLE), near field communication (NFC), infrared data association (IrDA), ANT®, Zigbee®, Z-Wave® IEEE 802.11 protocols (Wi-Fi), and other wireless communication protocols that allow for direct communication between devices. 
     The chassis  302  may also house a storage system  314  that is coupled to the drone docking engine  304  through the processing system and that is configured to store the rules and/or other data utilized by the drone docking engine  304  to provide the functionality discussed below. The storage system  314  may store user profiles  317  in one or more user repositories  316 . The user profiles  317  may include information associated with a user of the drone authentication system  100  and/or a service provided by the drone  200 . For example, a user profile  317  may include a user identifier that is associated with the user. For example, the user identifier may include a username, a phone number, an electronic mail address, a user device identifier (e.g., a communication interface identifier of a mobile device) and/or other identifiers that can identify the user. Each user identifier may have user information associated with the user identifier that can be used by the drone authentication system  100  to undertake various services. For example, the user information may include preselected preferences, third party data, gathered data by the drone authentication system  100  over time, identity data such as sensor signal profiles (e.g., an acoustic profile, an image profile, a blood profile, a DNA profile, a fingerprint profile, an ocular profile and/or other sensor signal profile that can be used to identify the individual and be updated from gathered data over time using machine learning techniques discussed below), and/or any other data used for authenticating an individual as a user and providing services to that user. In addition, the storage system  314  may include a service repository  318  that includes a plurality of service profiles  319 . The service repository  318  may include one or more applications that provide instruction to the drone controller  204  and/or drone docking engine  304  when one or more conditions are satisfied in the monitored space and that may need an individual in the monitored space to be authenticated as a user. Each application may be associated with service profile  319  that includes sensor signal profiles of conditions that need to be satisfied before the application associated with that service profile can be run on the drone controller  204  and/or drone docking engine  304 . 
     The chassis  302  may also house an acoustic sensor  320  (e.g., a microphone, a microphone array, a directionally-discriminating acoustic sensor, and other acoustic sensors), an imaging sensor  322  (e.g., a two-dimensional image capturing camera, a three-dimensional image capturing camera, an infrared image capturing camera, a depth capturing camera, similar video recorders, and/or a variety of other image or data capturing devices), and in some embodiments, an acoustic emitter and a biometric sensor (not illustrated). 
     For example, the acoustic sensor  320  may include an microphone array that is configured to capture audio signals from acoustic energy in a monitored space and provide the audio signals to the sensor analysis engine  305  and/or authentication engine  306  to computationally process the audio signals against acoustic profiles associated with the user profiles  317  and/or service profiles  319  that are stored in the storage system  314  to determine whether substantial correspondence with any of the acoustic profiles exists. The acoustic sensor  320  may also be used to determine an apparent direction and/or location of the apparent source that provided the acoustic energy as discussed further below. 
     The imaging sensor  322  may be a camera and/or any other sensor device that may be used to gather visual information from the monitored space surrounding the drone docking station  300  for use in authenticating an individual and/or identifying and providing a service with the drone docking station  300 . Imaging sensor signals may be provided to the sensor analysis engine  305  and/or authentication engine  306  to computationally process the imaging sensor signals against image profiles associated with the user profiles  317  and/or service profiles  319  that are stored in the storage system  314  to determine whether substantial correspondence with any of the image profiles exists. 
     Referring now to  FIG. 4 , an embodiment of a service platform  400  is illustrated that may be the service platform  130  discussed above with reference to  FIG. 1 . In the illustrated embodiment, the service platform  400  includes a chassis  402  that houses the components of the service platform  400 , only some of which are illustrated in  FIG. 4 . For example, the chassis  402  may house a processing system (not illustrated) and a non-transitory memory system (not illustrated) that includes instructions that, when executed by the processing system, cause the processing system to provide a services engine  404  that is configured to perform the functions of the services engines and/or service provider devices discussed below. In the specific example illustrated in  FIG. 4 , the services engine  404  is configured to provide a sensor analysis engine  405  that computationally processes sensor signals against stored sensor signal profiles, and an authentication engine  406  that performs a primary authentication and/or a secondary authentication of a user as well as the functionality discussed below. 
     The chassis  402  may further house a communication system  408  that is coupled to the services engine  404  (e.g., via a coupling between the communication system  408  and the processing system) and that is configured to provide for communication through the network as detailed below. The communication system  408  may allow the service platform  400  to send and receive information over the communication network  125  of  FIG. 1 . The chassis  402  may also house a storage system  410  that is coupled to the services engine  404  through the processing system and that is configured to store the rules and/or other data utilized by the services engine  404  to provide the functionality discussed below. The storage system  410  may store user profiles  417  in one or more user repositories  416 . The user profiles  417  may include information associated with a user of the drone authentication system  100  and/or a service provided by the drone  105 / 200  and/or service platform  400 . For example, a user profile  417  may include a user identifier that is associated with the user. For example, the user identifier may include a username, a phone number, an electronic mail address, a user device identifier (e.g., a communication interface identifier of a mobile device) and/or other identifiers that can identify the user. Each user identifier may have user information associated with the user identifier that can be used by the drone authentication system  100  to undertake various services. For example, the user information may include preselected preferences, third party data, gathered data by the drone authentication system  100  over time, identity data such as sensor signal profiles (e.g., an acoustic profile, an image profile, a blood profile, a DNA profile, a fingerprint profile, an ocular profile and/or other sensor signal profile that can be used to identify the individual and be updated from gathered data over time using machine learning techniques discussed below), and/or any other data used for authenticating an individual as a user and providing services to that user. In addition, the storage system  410  may include a service repository  418  that includes a plurality of service profiles  419 . The service repository  418  may include one or more applications that provide instruction to the services engine  404  and/or drone controller  204  when one or more conditions are satisfied in the monitored space and that may need an individual in the monitored space to be authenticated as a user. Each application may be associated with service profile  419  that includes sensor signal profiles of conditions that need to be satisfied before the application associated with that service profile can be run on the drone controller  204  and/or services engine  404 . 
     Referring now to  FIG. 5  an embodiment of a remote monitor  500  is illustrated that may be the remote monitor  120  discussed above with reference to  FIG. 1 . In the illustrated embodiment, the remote monitor  500  includes a chassis  502  that houses the components of the remote monitor  500 . Several of these components are illustrated in  FIG. 5 . For example, the chassis  502  may house a processing system (not illustrated) and a non-transitory memory system (not illustrated) that includes instructions that, when executed by the processing system, cause the processing system to provide an application engine  504  that is configured to perform the functions of the application engines, drone applications, and/or remote monitors discussed below. In the specific example illustrated in  FIG. 5 , the application engine  504  is configured to receive notifications from a drone and/or drone docking station that include audio feeds and video feeds, provide those notifications to an user through a drone application, receive instructions from the user through the drone application, and provide those instructions over a communication network to the drone and/or drone docking station as well as the functionality discussed below. 
     The chassis  502  may further house a communication system  506  that is coupled to the application engine  504  (e.g., via a coupling between the communication system  506  and the processing system) and that is configured to provide for communication through the network as detailed below. The communication system  506  may allow the remote monitor  500  to send and receive information over the communication network  125  of  FIG. 1 . The chassis  502  may also house a storage system  508  that is coupled to the application engine  504  through the processing system that is configured to store the rules, graphics, and/or other data utilized by the application engine  504  to provide the functionality discussed below. While the storage system  508  has been illustrated as housed in the chassis  502  of the remote monitor  500 , one of skill in the art will recognize that the storage system  508  may be connected to the application engine  504  through the communication network  125  via the communication system  506  without departing from the scope of the present disclosure. 
     Referring now to  FIG. 6 , an embodiment of a method  600  for user authentication is illustrated. As discussed below, the systems and methods of the present disclosure provide a drone authentication system that includes a drone and optionally a drone docking station that authenticates individuals in a monitored space as users of the drone authentication system and/or service provided via the drone authentication system. The drone authentication system may include a drone that can provide various services and/or provide various interactions with individuals such as humans, animals (e.g., livestock, pets, and wildlife), and plants (e.g., trees and crops) and/or other identifiable objects (e.g., fences, buildings, vehicles, other drones). When providing services to individuals, the service and/or drone authentication system may require that the individual is authenticated before certain services can be provided. Conventional authentication requires that the individual provide a password or passphrase. However, in certain situations, conventional authentication is not practical because the individual is experiencing a health issue preventing them from providing the authentication credential, the conditions in the monitored space require extra credentials, the conditions in the monitored space make the individual inaccessible, and/or there are privacy concerns with the individual providing the credentials in a public space where the authentication credentials may be overheard or seen by other individuals. The systems and methods of the present disclosure assess the monitored space, in which the individual requiring a service is located, to determine whether any conditions have been satisfied or exist that require a secondary authentication response from the individual and determine what the secondary authentication response should be based on those conditions. The drone authentication system then captures sensor signals from sensors that can be used to perform the secondary authentication. These systems and methods improve a drone&#39;s ability to provide services to individuals in situations where the drone would otherwise be unable to authenticate the individuals when those services require authentication. 
     The method  600  begins at block  602  where a monitored space is monitored for a presence of an individual. In an embodiment, at block  602  and with reference to the drone authentication system  800  of  FIG. 8 , the drone  105 / 200  and/or the drone docking station  110 / 300  may be in a monitoring mode, also referred herein as a standby mode, and monitor for an individual  805  (e.g., a human, an animal, and/or other living thing) in a monitored space  102  (e.g., a yard, a home, a business, a park, a stadium, a transmission line area, an access space, underground shafts, or other spaces). The monitored space  102  may be contiguous or non-contiguous. The monitored space  102  may be defined by geofencing techniques that may include specific geographic coordinates such as latitude, longitude, and/or altitude, and/or operate within a range defined by a wireless communication signal. 
     The drone  105 / 200  may include one or more sensors (e.g., an imaging sensor  114 , a biometric sensor  113 , and/or an acoustic sensor  115 ) that may generate sensor signals that can be computationally processed to determine whether an individual is present within the monitored space  102 . Likewise, the drone docking station  110 / 300  may include the sensor  116  that may generate sensor signals that can be computationally processed to determine whether an individual is present within the monitored space  102 . In another example, the drone  105  and the drone docking station  110  may be coupled (e.g., wired and/or wirelessly) with sensors  117  and  118  that are dispersed throughout the monitored space  102  that may generate sensor signals that can be computationally processed to determine whether an individual is present within the monitored space  102 . The sensor signals can be processed by one or more of the sensor analysis engines  205 ,  305 , and  405 . 
     While in the monitoring mode, the drone  105 / 200  may be docked with the drone docking station  110 . However, in other examples, the drone  105 / 200  may be at a monitor location or a standby location, the drone  105 / 200  may be proceeding along a patrol path within the monitored space  102 , or at another monitoring position such as hovering in the monitored space  102 . While the drone  105 / 200  and/or the drone docking station  110 / 300  is monitoring the monitored space  102 , the drone  105 / 200  and/or the drone docking station  110 / 300  may be generating sensor signals and or receiving sensor signals from any of the sensors  113 ,  114 ,  115 ,  116 ,  117 , and/or  118  in the monitored space  102 . While the above example for block  602  of method  600  describes the drone  105 / 200  being in a monitoring mode when monitoring the monitored space, one skilled in the art in possession of the present disclosure will recognize that at block  602  the drone  105 / 200  may be in any other mode, such as in-flight mode or in an investigate mode as described further below, and still be monitoring the monitored space  102  for sensor signals. 
     As shown in the example illustrated in  FIG. 8 , embodiments of the drone authentication system  800  are illustrated that include the drone authentication system  100  as illustrated in  FIG. 1 . As illustrated in  FIG. 8 , the drone authentication system  800  may be in a monitoring mode monitoring a monitored space  102  for an individual  805  in the monitored space  102  as described at block  602 . In the illustrated example, the drone  105  may be hovering above the drone docking station  110  awaiting sensor signals captured by any of the sensors  113 - 118  that are in the monitored space  102 . For example, the acoustic sensors  115  and  117  may be generating audio signals based on acoustic energy received at the acoustic sensors  115  and  117 . The acoustic energy may be generated by an apparent source. For example, bird vocal cords may generate a “tweet” sound or a car horn when activated may generate a “honk” sound. In other examples, the acoustic energy may be reflected acoustic energy by an apparent source. For example, a communications wire transmission pole may reflect the sound of wind to provide acoustic energy and/or reflect acoustic energy generated from an acoustic emitter  224  such that reflected acoustic energy is received by the acoustic sensors  115  and  117 . The imaging sensors  114 ,  116  and/or  118  may be generating digital images of the monitored spaces based on light radiation, infrared radiation, and other electromagnetic radiation. In another example, the communication interfaces (e.g., communication interfaces  210 ,  212 ,  310 , and/or  312 ) may be monitoring for wireless signals from user devices, which upon detection assumes a presence of an individual. The biometric sensor  113  may include a chemical sensor that can detect levels of carbon dioxide and/or include a microwave radar that may receive microwave radiation that may be computationally processed to detect a breathing pattern and/or a heartbeat of living things within the monitored space  102 . Any sensor data gathered by one or more of the sensors  113 - 118  may be converted into sensor signals which include electrical signals that can be processed by the sensor analysis engine  205 ,  305 , and/or  405  included in the drone authentication system  800 . 
     The method  600  then proceeds to block  604  where it is determined whether an individual is present in the monitored space. In an embodiment, at block  604  the sensor signals generated by at least one of the sensors  113 - 118  are computationally processed against one or more individual presences profiles included in service profile  219 ,  319 , and/or  419 . In an embodiment, at block  604  the sensor analysis engine  205 ,  305 , and/or  405  of the drone  105 / 200 , drone docking station  110 / 300 , and/or service platform  130 / 400  may computationally process the sensor signals received by the one or more sensors  113 - 118 . The sensor analysis engine  205 ,  305 , and/or  405  may determine whether any of the sensor signals have substantial correspondence with an individual presence profile stored in the respective service repositories  218 ,  318 , and/or  418 . A sensor signal profile described herein may be a digital summary of a sensor signal such as a sensor fingerprint that can be used to identify a sample of the sensor signal generated by one or more sensors that is obtained from the monitored space  102 . For example, the individual presence profile may be a digital summary indicating an individual (e.g., a human and/or a specific animal). For example, the individual presence profile may include an acoustic profile of an individual. The acoustic profile may include feature vectors that define characteristics of an audio signal such as an average zero-crossing rate, average spectrum prominent tones across a set of frequency bands, estimated tempo, spectral flatness, bandwidth, and/or other audio signal features suitable for identifying audio signals. The acoustic profile may be associated with an apparent source identifier that identifies an apparent source that provides the acoustic profile, which may be an individual. 
     Sensor profiles, such as an acoustic profile of an individual presence profile, may also be configured such that any compression and/or encoding techniques (e.g., AAC, MP3, WMA, Vorbis, and other audio compression and/or encoding techniques) performed on the sensor signal to allow an acoustic analysis engine included on the sensor analysis engine  205 ,  305 , and/or  405  to identify the sensor signal based on the sensor signal profiles. The sensor signals have substantial correspondence with a sensor signal profile when a predefined condition is satisfied. For example, the predefined condition may be that one or more feature vectors of the sensor signal match or are within a threshold of similarity (e.g., 50% similar, 60% similar, 70% similar, 80% similar, 85% similar, 90% similar, 95% similar, 99% similar, 100% similar and other thresholds of similarity) between the sensor signal and an sensor signal profile. Substantial correspondence may also include situations where unsupervised machined learning techniques (e.g., using cluster analysis), and/or supervised machine learning techniques (e.g., using statistical classification) determines that sensors signals in one group are more similar to each other than those in other groups. 
     In another example, computer vision methods (e.g., object recognition) may be used to computationally process an image against an individual presence profile. For example, the sensor analysis engine  205 ,  305 , and/or  405  may include an image analysis engine that may computationally process feature vectors from a captured image and determine whether the feature vectors from the captured image have substantial correspondence with any individual presence profile. In an example, the individual presence profile may be stored locally on the storage system  214  of the drone  105  and/or the storage system  314  of the drone docking station  110  and provided in the local service repository  218  and/or  318 , and/or stored remotely and managed at the service platform  130  to provide the remote service repository  418 . 
     In an example, if the sensor signal lacks substantial correspondence with the individual presence profiles of the local service repository  218  and/or  318 , the drone  105  or the drone docking station  110  may provide the sensor signal, feature vectors of the sensor signal, and/or a compressed and/or encoded version of the sensor signal to the service platform  130 / 400  through the communication network  125 . The sensor analysis engine  405  of the service platform  400  may computationally process the sensor signal (e.g., feature vectors of the sensor signal, the compressed and/or encoded sensor signal, and/or other variations of the sensor signal) by determining whether the sensor signal substantially corresponds with an individual presence profile stored remotely at the service repository  418  in the storage system  410 . In a specific example, the local service repositories  218  and  318  at the storage systems  214  and  314  may store a first portion of the individual presence profile and the remote service repository  418  at the storage system  410  may store a second portion of the presence indication profiles. 
     If the sensor signals indicate that the monitored space  102  does not include an individual, then the method  600  returns to block  602 . If the sensor signals indicate that an individual is present, then the method  600  proceeds to block  606  where it is determined whether a condition exists to have the drone  105  to provide an interaction with the individual that is detected. For example, the sensor analysis engine  205 ,  305 , and or  405  may determine, based on the sensor signals used to determine whether an individual is present in the monitored space  102  and/or sensor signals received after determining that the individual is present in the monitored space  102 , whether those sensor signals satisfy any conditions that are stored in the condition profile that may be included in the service profiles  219 ,  319 , and/or  419  of the storage systems  214 ,  314 , and  410  respectively. 
     In one example, the condition may be solely the detection of an individual in the monitored space. For example, the monitored space  102  may be a secured area that requires user authentication to enter. In another example, the sensor analysis engines  205 ,  305 , and/or  405  may be monitoring for various cues or actions provided by the individual via the acoustic sensors  115  and/or  117  and/or the imaging sensors  114 ,  116 , and/or  118 . As illustrated in  FIG. 9 , at least one of the imaging sensors  114 ,  116 , and/or  118  may capture an image or series of images that, when computationally processed by the sensor analysis engine  205 ,  305 , and/or  405 , indicate the individual  805  is lying on the ground and/or moved to the ground within a predefined time period indicating that individual  805  collapsed. The condition of the individual lying on the ground and/or moved to the ground within a predefined time period may substantially correspond with a condition profile stored in the service repository  218 ,  318 , and  418 . In other examples, the individual may provide an audio cue and/or a visual cue that the sensor analysis engine  205 ,  305 , and/or  405  recognizes as a condition to initiate an interaction with the individual. For example, the individual  805  may wave their hands at one or more of the imaging sensors  114 ,  116 , and/or  118  and/or the individual  805  may speak a keyword and the acoustic energy received by an acoustic sensor  115  and/or  117  may be converted to an acoustic signal that the sensor analysis engine  205 ,  305 , and/or  405  computationally processes against an acoustic profiles stored in the service profiles  219 ,  319 , and  419  included in the service repository  218 ,  318 , and  418 . In yet another example, the condition may be based on the environment of the monitored space  102  such as time of day, an event occurring in the monitored space (e.g., a concert, a race, etc.), weather conditions, and/or the location of the monitored space. 
     If a condition does not exist in the monitored space  102 , then the method  600  may return to block  602  where the drone authentication system  100  may continue to monitor the monitored space  102  in a monitor/standby mode for an individual or in a situation where the individual  805  is still present, continue to determine whether a condition exists in the monitored space  102  at block  606 . If a condition exists in the monitored space  102 , then the method  600  may continue to block  608 , where the drone  105  is switched from a standby/monitor mode to an in-flight mode. Referring to  FIG. 10 , when the drone  105  is in an in-flight mode, the drone  105  may be in-flight toward the position of the individual  805  until the drone  105  is within a predetermined distance of the individual  805 . The drone  105  may be in-flight when the drone is autonomously navigating toward an apparent position of an individual as discussed in further detail below. The drone  105  may be considered not to be in-flight when the drone  105  is hovering at a monitoring location or on a patrol path but otherwise flying in the air when the drone is a UAV. 
     In an embodiment, the sensor analysis engine  205 ,  305 , and/or  405  may determine an apparent position of the individual such as a relative direction from which acoustic energy is being provided by the individual and/or the approximate location of the individual. For example, the drone authentication system  100  may include the acoustic sensors  115  and  117  that are positioned about monitored space  102  to receive acoustic energy and capture audio signals within the monitored space  102 . The sensor analysis engines  205  and/or  305  may create a time stamp that includes the time at which each acoustic sensor  115  and  117  captured the audio signal. The sensor analysis engines  205  and/or  305  may then use known positions of the acoustic sensors  115  and  117  along with the time stamps that indicate when each acoustic sensor  115  and  117  captured the audio signal to determine the source location of the audio signal based on time-difference-of-arrival (TDOA) and triangulation techniques. In another example, the acoustic sensors  115  and  117  may be directionally-discriminating acoustic sensors that are configured to determine the general direction from which acoustic energy is being provided. The sensor analysis engine  205  and  305  may then provide the apparent position to the mobility controller  207  of the drone  105 / 200  such that the drone  105 / 200  may autonomously navigate toward the apparent position of the individual  805  providing the acoustic energy. 
     In another example, the imaging sensor  114 ,  116 , and/or  118  of the drone authentication system  100  may be used to determine the apparent position of the individual  805 . In an example, the imaging sensor  114  may be positioned at an angle while directed at the individual  805  and while the drone  105  is hovering above the ground. When an image is captured of the individual  805  by the imaging sensor  114 , other sensor data may be gathered as well, which may include position data from the positioning system  228  of the drone  105  when the image was captured, the angle of the imaging sensor  114  when the image was captured, and the distance the drone  105  is from the ground, which may be provided by an altimeter included on the drone  105 . Based on the angle of the imaging sensor  114  and the distance between the ground and the drone  105 , the sensor analysis engine  205  may determine the horizontal distance between the drone  105  and the individual  805 . Based on the horizontal distance and the positioning coordinates of the drone  105 , the sensor analysis engine  205  may determine the positioning coordinates of the individual  805 , which are then provided to the mobility controller  207  of the drone  105  such that the drone  105 / 200  may autonomously navigate toward those positioning coordinates of the individual  805 . 
     The drone  105 / 200  and/or the drone docking station  110 / 300  may be continually monitoring the monitored space  102  while the drone  105 / 200  is in-flight toward the apparent source of the individual  805 . The drone  105  may autonomously navigate toward the position of the individual  805  until the drone is within a predetermined range of that position. For example, the drone  105  may be within 1 foot, 2 feet, 5 feet, 10 feet, 20 feet, or any other predetermined range. The predetermined range may be based on the condition that caused the drone to be in the in-flight mode. For example, when the individual is on the ground, the predetermined range may be 2 feet, while if the individual  805  was waving to the drone  105 , the predetermined range may be 10 feet. When the drone  105  arrives within the predetermined range of the individual, the drone  105  may enter an investigate mode where the drone  105  hovers within the predetermined range of the individual  805 . 
     The method  600  then proceeds to block  610 , where it is determined whether the individual requires a service. While in the investigate mode, the drone  105  may provide an interaction with the individual  805  and receive sensor data based on that interaction. For example, the drone  105  may provide through the acoustic emitter  224  an audible question. The individual  805  may provide a verbal response to the question to the drone  105 , which the acoustic sensor  115  may provide as an audio signal to the sensor analysis engine  205  that may computationally process the audio signal against service profiles  219 ,  319 , and/or  419  included in the service repository  218 ,  318 , and/or  418 , respectively. Upon substantial correspondence between the audio signal and the service profiles  219 ,  319 , and/or  419  in the service repositories  218 ,  318 ,  418  that indicates no service is required, or lack of substantial correspondence with any of the service profiles  219 ,  319 , and/or  419 , the drone  105  may determine that the individual  805  does not require a service, in which case the method  600  proceeds to block  612  where the standby/monitor mode is initiated. The drone  105  may proceed back to a standby position, as shown in the example illustrated in  FIG. 11 , and/or hover at its current position until a condition exists for the drone  105  to have an interaction with an individual in the monitored space  102 . 
     The drone controller  204 , the drone docking engine  304 , and/or the services engine  404  may include machine learning/artificial intelligence algorithms to learn what conditions may require further investigation to determine whether a user requires a service and what conditions do not require investigation to determine whether an individual requires a service. If conditions are present where the drone authentication system  100  identifies that a service is required but upon further investigation and/or interaction with the individual determines that no service is required, the drone controller  204 , the drone docking engine  304 , and/or the services engine  404  may update the condition profiles, the individual presence profiles, and/or other information in the service profiles  219 ,  319 , and/or  419  to indicate that service was not needed based on the conditions that caused the drone  105  to be “in-flight” and to investigate the condition. The drone controller  204 , the drone docking engine  304 , and/or the services engine  404  may be configured with one or more machine learning algorithms to perform supervised machine learning, unsupervised machine learning (e.g., deep belief networks, neural networks, statistical pattern recognition, rule-based artificial intelligence, etc.) semi-supervised learning, reinforcement learning, deep learning, and other machine learning algorithms when updating, creating, and/or reinforcing a condition profile, an individual presence profile, a service profile,  219 ,  319 , and/or  419 , a user profile  217 ,  317 , and/or  417 , and/or any other profile stored in the user repositories  215 ,  316 , and/or  416  and service repositories  218 ,  318 , and/or  418  discussed herein that is updatable over time based on received sensor signals. 
     If the individual requires a service, for example because the user requests the service by providing a visual indication and/or acoustic indication to the drone  105  that the sensor analysis engine  205 ,  305 , and/or  405  determines is associated with an available service, then the method  600  proceeds to block  614  where the drone authentication system  100  determines whether the service requires the individual to be a user of the service. Each service profile of service profiles  219 ,  319 , and/or  419  may be associated with a service. The service profiles may indicate a level of authentication for the service. For example, the drone  105  may be configured to provide a plurality of services and/or provide services provided by a service provider through the communication network  125 . For example, the drone  105  may be configured to provide medical treatment to the individual  805 , retrieve and/or deliver an object for the individual  805 , take a photo for the individual  805 , notify emergency personnel, place an order for a good and/or service, and/or other services that a drone  105  may provide. In addition, the drone  105  may be able to access third party service through the communication network  125 . For example, a user of the drone  105  may order a pizza through the drone  105  from a pizzeria that provides an application for ordering pizzas on the service platform  130  through which the drone may communicate. Each of these services may require that the individual  805 , with which the drone  105  is interacting, be an authenticated user of the drone  105  and/or the service that is being activated. For example, if the drone  105  is asked to provide the current coordinates of the individual, the service profile associated with that service may not require that the individual  805  is an authenticated user. However, if the individual  805  is requiring medical assistance from the drone  105 , then the service profile associated with the service may require drone  105  to know the identity of the individual and authenticate the individual as a user of the service before performing a specific medical procedure as an authenticated user. 
     If the service does not require that the individual to be an authenticated user, the method  600  may continue with block  616  where the drone  105  performs the service or facilitates the performance of the service by the third party service provider through the communication network  125 . The method  600  then continues to block  612 , where the drone  105  returns to a standby/monitoring mode when the service is completed and as illustrated in  FIG. 11 . However, if the service requires that the individual be an authenticated user of the service before the drone  105  provides and/or facilitates the performance of the service for the third party service provider, then the method  600  proceeds to block  618 , where the drone authentication system determines whether the identity of the individual requiring the service is known to the drone authentication system. For example, the drone  105  may be a “follow-me” drone that is configured to follow a particular individual and/or group of individuals. Thus, the drone authentication system  100  would already know the identity of the individual  805  as a user of the drone authentication system  100 . In other situations, the drone authentication system  100  may have determined the identity of the individual  805  prior to determining whether the user required a service, for example, when the individual  805  became present in the monitored space  102  at block  604 . The drone authentication system  100 , when determining whether the sensors signals indicated that a user was present in the monitored space  102 , may have additionally performed facial recognition on captured images, voice recognition on captured audio signals, and/or determined an identity of the individual based on a user device identifier provided in a wireless signal between the individual&#39;s user device and any of the communication interfaces  210 ,  212 ,  310 , and/or  312 , as discussed in more detail below. 
     If the identity of the individual is known, the method  600  proceeds to block  620  where it is determined whether the service still requires the user of the drone authentication system  100  to be an authenticated user for the particular service. If the service does not require further authentication, the method  600  may proceed to block  616  where the service is performed by the drone  105  and/or third party service provider in conjunction with the drone  105 . However, in some situations, the service may require additional authentication or require a particular user authentication before the service is performed. Continuing with the specific example in  FIG. 10 , the individual  805  may be known to the drone authentication system  100  as a user of the drone authentication system  100 . However, because the individual  805  requires a medical service, due to the drone authentication system  100  determining that the individual has collapsed, additional and/or a particular type of user authentication may be necessary before the drone  105  provides the medical service. Thus, if the service requires authentication by the user, even if the identity of the user is known in block  620  or the identity of the individual is unknown in block  618 , the method  600  proceeds to block  622  where authentication of the individual as a user of the identified service is performed. 
     Referring now  FIG. 7 , block  622  of method is provided by sub-method  700 . At block  702 , it may be determined whether a primary authentication response required by the service is attainable. Each service profile  219 ,  319 , and/or  419  associated with the service may have a primary authentication response associated with the service profile  219 ,  319 , and/or  419  that is preferred when authenticating the individual  805  as a user of the service. For example, the service may require spoken user credentials such as a username and password that the authentication engine  206 ,  306 , and/or  406  may perform speech recognition and/or voice recognition on the spoken credentials to verify that the individual is an authenticated user. 
     However, in some situations, the primary authentication may be difficult to provide to the drone authentication system  100  (e.g., the monitored space  102  includes sufficient acoustic noise that prevents the drone authentication system  100  from capturing acoustic energy generated from the individual&#39;s vocal cords). In other situations there may be a lack of privacy in providing the primary authentication (e.g., the individual requesting the service is with one or more other individuals). In yet other scenarios, the individual may not be able to physically provide the primary authentication response. For example, the individual may be unconscious or otherwise incapacitated such that the individual is unable to provide a response, and/or the user has a medical condition (e.g., memory loss, Alzheimer&#39;s disease, recent head trauma, under the influence of drugs and/or alcohol), and/or other conditions that the drone authentication system may recognize as conditions where the primary authentication response is not attainable from the user. 
     The authentication engine  206 ,  306 , and/or  406  may determine whether the primary authentication is available based on the conditions identified in the monitored space  102  using a first set of sensors when the drone authentication system  100  is in the investigate mode. The user profiles  217 ,  317 , and/or  417  and/or services profiles  219 ,  319 , and/or  419  may include condition profiles similar to those when determining whether to perform a service and/or cause the drone  105  to be “in-flight” towards the apparent position of the individual. The authentication engine  206 ,  306 , and/or  406  may computationally process the sensor signals against condition profiles stored in the user profiles  217 ,  317 , and/or  417  for individual user&#39;s conditions and/or preferences and/or computationally process against condition profiles in the services profiles  219 ,  319 , and/or  419  for conditions that apply to all users or groups of users to determine whether there is substantial correspondence between the sensor signals and the condition profiles. 
     For example, the individual may provide an acoustic and/or visual cue to the drone authentication system  100  that the user is not going to provide the primary authentication credentials and/or that the user would like to be authenticated by a secondary authentication. In another example, the drone authentication system  100  may recognize that the individual is with another individual and automatically determines that the primary authentication is not appropriate based on the condition profiles of another individual being within a predetermined range of the individual requesting the service due to privacy concerns. In another example, a condition where the drone authentication system determines that the primary authentication is not available may be that the user is unresponsive to the request for primary authentication credentials and is lying on the ground and/or not moving. Lying on the ground motionless may be a condition that distinguishes from an individual that may be simply ignoring the drone authentication system  100  to get to the secondary authentication. 
     As discussed above, in an embodiment the authentication engine  206 ,  306  and/or  406  may include machine learning algorithms that may learn, based on conditions in the monitored space  102  and/or actions performed by the individual  805  requiring the service, that the primary authentication method is not attainable given the conditions of the monitored space  102  and/or the actions performed by the individual requiring the service. The authentication engine  206 ,  306  and/or  406  may be configured with one or more machine learning algorithms to perform supervised machine learning, unsupervised machine learning (e.g., deep belief networks, neural networks, statistical pattern recognition, rule-based artificial intelligence, etc.) semi-supervised learning, reinforcement learning, deep learning, and other machine learning algorithms when updating, creating, and/or reinforcing a condition profile for determining whether to perform a primary authentication or secondary authentication. 
     If the primary authentication response is attainable, the sub-method  700  may proceed to block  704 , where the primary authentication response is received from the individual. The authentication engine  206 ,  306 , and/or  406  may computationally process the sensor signals against sensor signal profiles stored in the user profiles  217 ,  317 , and/or  417  to determine whether there is substantial correspondence between the received primary authentication response and a stored primary authentication response profile. As discussed above, the individual  805  may provide spoken user credentials to the drone authentication system  100  that may be received by the acoustic sensor  115  and/or  117  and computationally processed (e.g., speech recognition and/or voice recognition) against acoustic profiles stored in the user profiles  217 ,  317 , and/or  417 . The sub-method  700  may then proceed to block  624  of method  600 . 
     If the primary authentication response is unattainable, then the sub-method  700  proceeds to block  706 , where a secondary authentication response to authenticate the individual  805  as an authenticated user is determined. In an embodiment, the service being provided by the drone authentication system  100  may authorize an individual as an authenticated user based on one or more secondary authentications provided by the individual. When there is a plurality of secondary authentication responses for the service as indicated in the service profiles  219 ,  319 , and/or  419 , the authentication engine  206 ,  306 , and/or  406  may determine which secondary authentication response or set of secondary authentication responses the authentication engine  206 ,  306 , and/or  406  needs to receive before authenticating an individual as a user. The determination may be based on a priority assigned to each secondary authentication response predefined by the service in the service profile  219 ,  319 , and/or  419 , the present conditions in the monitored space  102 , the actions of the individual, whether the identity of the individual is known to the drone authentication system  100  already, and/or other criteria used to determine which of one or more secondary authentication responses to obtain. 
     For example, if the individual  805  is within a predefined range of at least one other individual, such that the spoken user credentials are not appropriate for primary authentication, the authentication engine  206 ,  306 , and/or  406  may determine the identity of the individual through facial recognition as the secondary authentication. In another example, the drone authentication system  100  may already know the identity of the individual  805  because the drone is a “follow-me” drone and/or the drone authentication system  100  has already identified the individual through a first secondary authentication response (e.g., facial recognition). If the drone authentication system  100  already knows the individual  805 , the drone authentication system  100  may elicit a second secondary authentication response such as requesting a response to a question that is known to the individual  805 . For example, the question may be a preselected security question with a preselected answer stored in the user&#39;s user profile (e.g., What is the name of your first pet?). In other examples, the question may be based on information learned from the drone authentication system  100  and/or information received by the drone authentication system  100  from third party data feeds such as information from secured service providers. For example, the drone authentication system  100  may know that you went to a specific movie last week and ask you what movie you went to as the secondary authentication inquiry. 
     Continuing with the specific example illustrated in  FIGS. 8-11 , the individual  805  may be unresponsive to the drone  105  and the drone  105  may determine that the unresponsiveness of the individual  805  in addition to the initial determination that caused the drone  105  to enter into the investigate mode of the individual  805  being on the ground substantially correlates with a condition profile indicating that a primary authentication is unattainable. Therefore, because the individual  805  is unresponsive, the drone authentication system  100  may determine that a secondary authentication response is required that includes a biometric authentication to authenticate the individual  805  as a user. For example, the drone authentication system  100  may determine that the secondary authentication technique may be facial recognition, an ocular scan, a fingerprint scan, DNA analysis, and/or other biometric authentication techniques. The drone authentication system  100  may require the secondary authentication response to provide medical treatment to the individual  805 . However, if the person is unresponsive, the drone authentication system  100  may be configured to automatically provide a notification to emergency personnel that includes the coordinates of the drone  105  that is within the predefined range of the individual  805  without authentication from the user. 
     The sub-method  700  then proceeds to block  708 , where the drone  105  obtains the secondary authentication response. The drone  105  may obtain sensor signals from sensors that are relevant in authenticating the individual  805  through the secondary authentication technique to verify the individual as a user. The individual  805  may provide to the drone  105  an acoustic response to the question that the drone asked the user. In another example, the imaging sensors  114  may capture an image of the individual  805  In other examples, a biometric sensor  113  associated with the secondary authentication technique may be deployed by the robotic arm  111  of the drone  105  to obtain biometric data and/or biological material needed from the individual  805  to perform the determined secondary authentication. 
     The sub-method  700  then proceeds to block  710  where authentication is performed. In an embodiment, at block  710  the primary authentication response and/or the secondary authentication response, which includes sensor signals, may be computationally processed by the authentication engine  206 ,  306 , and/or  406  to determine whether the primary authentication response and/or the secondary authentication response substantially corresponds with user credentials that are stored and associated with a user profile  217 ,  317 , and/or  417  in the user repository  216 ,  316 , and/or  416 . 
     Referring back to  FIG. 6 , the method  600  then proceeds to block  624  where it is determined whether the individual is an authenticated user of the service from the results of the performance of the authentication performed in block  710  of sub-method  700  in  FIG. 7 . If the individual is an authenticated user, the method  600  may proceed to block  616  where the service is provided to the individual  805 . If the authentication fails, then the method  600  may proceed to block  612 , where the drone  105  and/or the drone authentication system  100  return to a standby/monitor mode of operation. As illustrated in  FIG. 11 , once the service is performed in block  616  or the authentication of the individual  805  as a user fails, then the drone  105  returns to the standby/monitoring mode at its original monitoring location. 
     Referring now to  FIG. 12 , an embodiment of a computer system  1200  suitable for implementing, for example, the control of the drones  105  and/or  200 , the drone docking stations  110  and/or  300 , the remote monitor  120  and/or  500  and the service platforms  130  and/or  400 , is illustrated. It should be appreciated that other devices utilized in the drone authentication system  100 / 800  discussed above may be implemented as the computer system  1200  in a manner as follows. 
     In accordance with various embodiments of the present disclosure, computer system  1200 , such as a computer and/or a network server, includes a bus  1202  or other communication mechanism for communicating information, which interconnects subsystems and components, such as a processing component  1204  (e.g., processor, micro-controller, digital signal processor (DSP), etc.), a system memory component  1206  (e.g., RAM), a static storage component  1208  (e.g., ROM), a disk drive component  1210  (e.g., magnetic or optical), a network interface component  1212  (e.g., modem or Ethernet card), a display component  1214  (e.g., CRT or LCD), an input component  1218  (e.g., keyboard, keypad, or virtual keyboard), a cursor control component  1220  (e.g., mouse, pointer, or trackball), and/or a location determination component  1222  (e.g., a Global Positioning System (GPS) device as illustrated, a cell tower triangulation device, and/or a variety of other location determination devices.) In one implementation, the disk drive component  1210  may comprise a database having one or more disk drive components. 
     In accordance with embodiments of the present disclosure, the computer system  1200  performs specific operations by the processing component  1204  executing one or more sequences of instructions contained in the system memory component  1206 , such as described herein with respect to the drone(s), the drone docking station(s), the service platform, and/or the remote monitor(s). Such instructions may be read into the system memory component  1206  from another computer-readable medium, such as the static storage component  1208  or the disk drive component  1210 . In other embodiments, hardwired circuitry may be used in place of or in combination with software instructions to implement the present disclosure. 
     Logic may be encoded in a computer-readable medium, which may refer to any medium that participates in providing instructions to the processing component  1204  for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and tangible media employed incident to a transmission. In various embodiments, the computer-readable medium is non-transitory. In various implementations, non-volatile media includes optical or magnetic disks and flash memory, such as the disk drive component  1210 , volatile media includes dynamic memory, such as the system memory component  1206 , and tangible media employed incident to a transmission includes coaxial cables, copper wire, and fiber optics, including wires that comprise the bus  1202  together with buffer and driver circuits incident thereto. 
     Some common forms of computer-readable media include, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, DVD-ROM, any other optical medium, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, cloud storage, or any other medium from which a computer is adapted to read. In various embodiments, the computer-readable media are non-transitory. 
     In various embodiments of the present disclosure, execution of instruction sequences to practice the present disclosure may be performed by the computer system  1200 . In various other embodiments of the present disclosure, a plurality of the computer systems  1200  coupled by a communication link  1224  to a communication network  125  (e.g., such as a LAN, WLAN, PTSN, and/or various other wired or wireless networks, including telecommunications, mobile, and cellular phone networks) may perform instruction sequences to practice the present disclosure in coordination with one another. 
     The computer system  1200  may transmit and receive messages, data, information and instructions, including one or more programs (e.g., application code) through the communication link  1224  and the network interface component  1212 . The network interface component  1212  may include an antenna, either separate or integrated, to enable transmission and reception via the communication link  1224 . Received program code may be executed by processor  1204  as received and/or stored in disk drive component  1210  or some other non-volatile storage component for execution. 
     Where applicable, various embodiments provided by the present disclosure may be implemented using hardware, software, or combinations of hardware and software. Also, where applicable, the various hardware components and/or software components set forth herein may be combined into composite components comprising software, hardware, and/or both without departing from the scope of the present disclosure. Where applicable, the various hardware components and/or software components set forth herein may be separated into sub-components comprising software, hardware, or both without departing from the scope of the present disclosure. In addition, where applicable, it is contemplated that software components may be implemented as hardware components, and vice versa. 
     Software, in accordance with the present disclosure, such as program code or data, may be stored on one or more computer-readable media. It is also contemplated that software identified herein may be implemented using one or more general-purpose or special-purpose computers and/or computer systems, networked and/or otherwise. Where applicable, the ordering of various steps described herein may be changed, combined into composite steps, and/or separated into sub-steps to provide features described herein. 
     The foregoing is not intended to limit the present disclosure to the precise forms or particular fields of use disclosed. As such, it is contemplated that various alternate embodiments and/or modifications to the present disclosure, whether explicitly described or implied herein, are possible. Persons of ordinary skill in the art in possession of the present disclosure will recognize that changes may be made in form and detail without departing from the scope of what is claimed.