System and method for protecting wildlife and enabling remote wildlife sightseeing

Abstract

To provide a system and method for protecting wildlife and enabling remote wildlife sightseeing.SOLUTION: A data processing system for remote wildlife viewing includes a memory and a processor 120. The processor controls a set of one or more camera-equipped drones 101 to 106, a user logs onto one or more of the drones, captures one or more wildlife images with one or more of the cameras 122, and displays the one or more wildlife images on a user device 111 to 113.SELECTED DRAWING: Figure 1B

Description

[Technical Field]

[0001] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 024,695, filed May 14, 2020, and U.S. Provisional Patent Application No. 63 / 023,524, filed May 12, 2020, which are incorporated herein by reference in their entireties.

[0002] FIELD OF THE INVENTION Embodiments of the present invention relate to nature conservation, and more particularly to protecting wildlife and enabling remote wildlife tourism. [Background technology]

[0003] Generally, wildlife conservation refers to the act of protecting wild species and their habitats in order to maintain healthy wildlife species and populations and to restore, protect, and / or improve natural ecosystems. Major threats to wildlife include habitat destruction, degradation, and fragmentation by humans, overexploitation, poaching, pollution, and climate change. Habitat destruction and fragmentation can make wildlife populations more vulnerable by reducing the space and resources available to wildlife and increasing the likelihood of conflict with humans. An increasing number of species are at risk of extinction in Earth's ecosystems. Overexploitation occurs when animals and plants are harvested at a rate that exceeds their ability to recover. Overexploitation leads to a decline in the size and abundance of species.

[0004] Poaching for the illegal wildlife trade is a major threat to certain species, particularly those with high economic value and endangered species. Such species include large mammals such as African elephants, tigers, and rhinoceroses, which are traded for their tusks, skins, and horns, respectively. A lesser known form of poaching involves the capture of protected plants and animals for souvenirs, food, skins, and pets. Because poachers tend to target endangered species, poaching further reduces already low populations. National and international government efforts are underway to protect wildlife and wildlife habitats.

[0005] In general, an unmanned aerial vehicle (UAV), commonly known as a drone, is an aircraft without a human pilot on board. UAVs can fly under remote control by a human operator or with varying degrees of autonomy, from autopilot-assisted to fully autonomous aircraft where no human intervention is possible. Summary of the Invention

[0006] Systems and methods for protecting wildlife and enabling remote wildlife tourism are described. In at least some embodiments, a system for remote wildlife viewing includes a memory and a processor. The processor is configured to control a set of one or more camera-equipped drones. The processor is configured to enable a user to log on to one or more of the drones and capture one or more wildlife images using one or more cameras of the drones. The processor is configured to display the one or more wildlife images on a user device.

[0007] The processor may be further configured to receive one or more commands from a user to control at least one of one or more of the drones and one or more of the cameras. The processor may be further configured to identify wildlife based on one or more wildlife images using neural network technology. The processor may be configured to allow a user to log on to one or more of the drones for a predetermined period of time. The processor may be configured to assign a score to a user based on the one or more wildlife images. The processor may be configured to control the location of the drone using geofencing technology. The processor may be configured to monitor the power of the drone. The processor may be configured to monitor the allocated flight time of the drone. The processor may be configured to receive information identifying the user. The processor may be configured to receive recorded wildlife images from a user to prevent illegal activity. The processor may be configured to generate a map showing at least one of the user's location, the locations of other users, and locations where one or more wildlife images were captured. The processor may be configured to display educational material related to the one or more wildlife images on the user device. The processor may be configured to share revenue with a conservation agency from users who reserve time on the system. The processor may be configured to transmit data including at least one of the location and status of one or more drones to a conservation agency. The processor may be configured to generate a map including wildlife movement patterns in an area based on data from a user. The processor may be configured to receive a request to book a flight at a predetermined time, accept payment for the flight, and generate and send notifications regarding the flight to the user. The processor may be configured to generate a user interface on a user device for providing remote wildlife viewing. The processor may be configured to generate a flight tutorial video on the user device. The processor may be configured to determine that a user is authorized to use one or more of the drones. The processor may be configured to generate a map showing the location of drones assigned to the user relative to a geofenced area.The processor may be configured to generate a profile associated with the user and store the profile in memory. The processor may be configured to calculate flight statistics and store the flight statistics in memory. The processor may be configured to share a user stream associated with the flight with other users. The processor may be configured to determine / select a drone for the user from one or more drones.

[0008] In at least some embodiments, an apparatus for remote wildlife viewing may include one or more cameras and a processor coupled to the one or more cameras. The processor may be configured to receive an indication that a user may log on to the apparatus, receive one or more commands for capturing wildlife images for the user, capture wildlife images using one or more of the cameras in response to the one or more commands, and transmit the one or more wildlife images for display on a user device. The apparatus may include a housing coupled to the processor. The apparatus may include a helium balloon coupled to the housing. The apparatus may include a mesh surrounding the housing to protect wildlife. The apparatus may include one or more speakers coupled to the processor to generate destructive interference of sounds emanating from the apparatus. The apparatus may include a microphone coupled to the processor. The apparatus may include a docking port coupled to the processor.

[0009] In at least some embodiments, a device for detecting wildlife interdictions may include a memory and a processor coupled to the memory. The processor may be configured to collect data related to wildlife, identify the wildlife interdiction based on the collected data, and generate a notification regarding the wildlife interdiction. The processor may include one or more sensors coupled to the processor for detecting molecules associated with wildlife. The wildlife interdiction may be identified based on the detected wildlife-associated molecules. The data may be online sales data. The wildlife interdiction may be identified based on the online sales data.

[0010] In at least some embodiments, a system for monitoring individual animals within a protected area may include a memory and a processor coupled to the memory. The processor may be configured to receive a selection of an animal, retrieve data associated with the selected animal from a database, and generate a map including one or more routes showing the movement of the selected animal. The processor may be configured to collect data from rangers including at least one of an animal's location and an identifier, store the data in a database, and generate a profile of the animal based on the data. The processor may be configured to receive a selection of at least one of an area and a data range associated with the animal. The processor may be configured to compare captured wildlife images of the animal with stored wildlife images of the animal using image recognition technology. The processor may be configured to generate a notification when the animal moves out of the area.

[0011] A system for monitoring protected area boundaries may include one or more sensors and a processor coupled to the one or more sensors. The processor may be configured to scan the protected area boundary to capture wildlife imagery using the one or more sensors, identify illegal boundary crossings from the wildlife imagery, and generate notifications regarding the illegal boundary crossings. The processor may be configured to scan the protected area boundary using a satellite-telescope boundary protection mechanism coupled to the one or more sensors. The one or more sensors may include one or more LiDAR sensors.

[0012] In at least some embodiments, a method for remote wildlife viewing is described. A set of one or more camera-equipped drones is controlled. A user is able to log on to one or more of the drones and capture one or more wildlife images using one or more of the cameras. The one or more wildlife images are displayed on a user device. In at least some embodiments, one or more commands may be received from a user to control at least one of the one or more of the drones and one or more of the cameras. In response to the one or more commands, wildlife image data captured by one or more of the cameras may be received. Wildlife may be identified based on the one or more wildlife images using neural network technology. A user may be allowed to log on to one or more of the drones for a predetermined period of time. A score may be assigned to the user based on the one or more wildlife images. The location of the drone may be controlled using geofencing technology. The power of the drone may be monitored. The drone's allocated flight time may be monitored. Information identifying the user may be received. Recorded wildlife images may be received from the user to prevent illegal activity. A map may be generated showing at least one of the user's location, the locations of other users, and locations where one or more wildlife images were captured. Educational material related to the one or more wildlife images may be displayed on the user device. Revenue from users reserving time with the system may be shared with a conservation agency. Data comprising at least one of the location and status of one or more drones is transmitted to the conservation agency. A map may be generated based on the data from the user, comprising wildlife movement patterns in an area. A request to book a flight at a predetermined time is received. A selection of a conservation location may be received. Payment for the flight may be received. A notification regarding the flight is generated for sending to the user. A user interface for providing remote wildlife viewing may be generated on the user device. A flight tutorial video may be generated on the user device. It may be determined that the user is authorized to use one or more of the drones. A map may be generated showing the location of drones assigned to the user relative to a geofenced area.A profile associated with the user may be generated and stored in memory, one or more flight statistics may be calculated and stored in memory, a user stream associated with the flight may be shared with other users, and a drone may be determined / selected for the user from one or more drones.

[0013] In at least some embodiments, a method for remote wildlife viewing is provided. An indication that a user is able to log on to the drone is received. One or more commands are received to capture wildlife images for the user. In response to the one or more commands, wildlife images are captured using one or more of the cameras. The one or more wildlife images are transmitted for display on a user device. In at least some embodiments, the drone may include a processor, a housing coupled to the processor, a helium balloon coupled to the housing, a mesh surrounding the housing to protect wildlife, one or more speakers coupled to the processor to generate destructive interference of sounds emanating from the drone, a microphone coupled to the processor, and a docking port coupled to the housing.

[0014] In at least some embodiments, a method for detecting a wildlife prohibition is provided. Data related to wildlife is detected. A wildlife prohibition is detected based on the collected data. A notification about the wildlife prohibition is generated. One or more sensors may be used to detect molecules related to wildlife. A wildlife prohibition may be identified based on the detected wildlife related molecules. The data may be online sales data. A wildlife prohibition may be identified based on the online sales data.

[0015] In at least some embodiments, a method for monitoring individual animals within a protected area is described. A selection of an animal is received. Data associated with the selected animal is retrieved from a database. A map is generated comprising one or more routes illustrating the movement of the selected animal. Data including at least one of a location and an identifier of the animal may be collected from rangers. The collected data may be stored in a database. A profile of the animal may be generated based on the collected data. A selection of at least one of an area and a data range associated with the animal may be received. Image recognition technology may be used to compare captured wildlife images of the animal with stored wildlife images of the animal. A notification may be generated if the animal moves out of the area.

[0016] In at least some embodiments, a method of monitoring a protected area boundary is described. The protected area boundary is scanned to capture wildlife imagery using one or more sensors. Illegal boundary crossings are identified from the wildlife imagery. Notifications are generated regarding the illegal boundary crossings. The protected area boundary may be scanned using a satellite-telescope boundary protection mechanism coupled to one or more sensors. The one or more sensors may include one or more LiDAR sensors.

[0017] In at least some embodiments, a non-transitory machine-readable medium comprises instructions that cause a data processing system to perform the methods for remote wildlife viewing described herein.

[0018] In at least some embodiments, a non-transitory machine-readable medium comprises instructions that cause a data processing system to perform the methods for detecting wildlife interdictions described herein.

[0019] In at least some embodiments, a non-transitory machine-readable medium comprises instructions that cause a data processing system to perform the methods for monitoring individual animals in a protected area described herein.

[0020] In at least some embodiments, a non-transitory machine-readable medium comprises instructions that cause a data processing system to perform the methods of monitoring protected area boundaries described herein.

[0021] Other systems, methods, and machine-readable media for protecting wildlife and enabling remote wildlife tourism are also described.

[0022] Embodiments of the present invention may be best understood by referring to the following description and accompanying drawings that are used to illustrate embodiments of the invention. [Brief explanation of the drawings]

[0023] [Figure 1A] FIG. 1 illustrates a system for remote wildlife viewing according to one embodiment. [Figure 1B] FIG. 1 is a block diagram illustrating a data processing system for remote wildlife viewing according to one embodiment. [Figure 1C] FIG. 1 is a block diagram illustrating a system for remote wildlife viewing according to one embodiment. [Figure 2A] 1 is a flowchart of a method for providing remote wildlife viewing according to one embodiment. [Figure 2B] 2 is a flowchart of a method 210 for providing remote wildlife viewing according to one embodiment. [Figure 3A] 1 is a flowchart of a method for providing remote wildlife viewing according to one embodiment. [Figure 3B] FIG. 1 illustrates a user interface for providing remote wildlife viewing according to one embodiment. [Figure 4] 1 is a flowchart of a method for detecting wildlife prohibitions according to one embodiment. [Figure 5] 1 is a flowchart of a method for monitoring individual animals in a protected area according to one embodiment. [Figure 6] 1 is an example of a data structure containing animal-related data collected from security personnel, according to one embodiment. [Figure 7] 1 is a flowchart of a method for monitoring individual animals in a protected area according to one embodiment. [Figure 8] FIG. 1 illustrates a map showing animal movements, according to one embodiment. [Figure 9] 1 is a flowchart of a method for monitoring protected area boundaries according to one embodiment. [Figure 10] 1 is a block diagram of a data processing system according to one embodiment. [Figure 11] 1 illustrates a setup for a simulation of active noise cancellation to determine loudspeaker amplitude and phase, according to one embodiment. [Figure 12A] 10 shows a mesh plot amplitude of a signal on a sphere without a compensation signal as a function of spherical coordinate angles phi and theta, according to one embodiment. [Figure 12B] 1 shows a mesh plot amplitude of a signal on a sphere as a function of spherical coordinate angles phi and theta, with a near-optimal compensated signal, A=3.71 and p=3.14, according to one embodiment. [Figure 13A] 10 shows a mesh plot of sound amplitude on the xy plane without a compensation signal, according to one embodiment. [Figure 13B] 10 shows a mesh plot of sound amplitude on the xy plane with a compensation signal according to one embodiment. [Figure 14A] 10 shows a mesh plot of sound amplitude on the xz plane without a compensation signal, according to one embodiment. [Figure 14B] 10 shows a mesh plot of sound amplitude on the xz plane with a compensation signal according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0024] A system and method for conserving wildlife and enabling remote wildlife tourism is described.

[0025] In at least one embodiment, a system for remote wildlife viewing includes a memory and a processor. The processor is configured to control a set of one or more camera-equipped drones. The processor is configured to allow a user to log on to one or more of the drones to capture one or more wildlife images using one or more of the cameras. The processor is configured to display the one or more wildlife images on a user device. The processor is advantageously coupled to the set of one or more camera-equipped drones to enable online viewing of animals by a user who logs on to the system and can control the drone, the camera, or both the drone and the camera.

[0026] In at least some embodiments, a system for remote wildlife viewing allows people or groups of people, such as parents with children, to rent time on a camera they can control online from anywhere to observe, zoom in on, or monitor animals. This is especially important when conservation authorities are limiting tourism due to travel restrictions caused by, for example, the spread of COVID-19. In one embodiment, a system for remote wildlife viewing allows people with an internet connection to explore animals in protected areas by renting time on a remote-controlled drone or blimp, a drone that uses gas balloons to stay aloft with less power and noise than traditional drones.

[0027] In this application, the terms "drone," "blimp," and "vehicle" are used interchangeably. Systems for remote wildlife viewing advantageously enable conservation agencies to increase revenue, raise awareness worldwide, and provide parents with something to enjoy and learn about with their children.

[0028] In at least some embodiments, the described systems and methods advantageously prevent the destruction and sale of wildlife and allow for monetary gain from conservation efforts. In at least some embodiments, the disclosed technology operates automatically, without human intervention. While the following examples and embodiments address protecting wildlife and enabling remote wildlife tourism, such technology may be applied to any type of environment that benefits from remote viewing.

[0029] Various embodiments and aspects will be described with reference to the details discussed below, and the accompanying drawings illustrate various embodiments. The following description and drawings are illustrative and should not be construed as limiting. Numerous specific details are set forth to provide a thorough understanding of the various embodiments. However, in certain instances, well-known or conventional details are not set forth in order to concisely discuss the embodiments.

[0030] References herein to "one embodiment" or "embodiments" mean that a particular feature or characteristic described in connection with an embodiment may be included in at least one embodiment. The appearances of the phrases "in one embodiment," "at least some embodiments," and "in an embodiment" in various places within this specification do not necessarily all refer to the same embodiment(s). The processes illustrated in the following figures are performed by processing logic comprising hardware (e.g., circuitry, dedicated logic, etc.), software, or a combination of both. While the processes below are described in terms of several sequential operations, it should be recognized that some of the described operations may occur in different orders. Also, some operations may occur in parallel rather than sequentially.

[0031] In this application, the term "and / or" generally describes a conjunctive relationship between related objects and indicates that a three-way relationship may exist. For example, A and / or B may refer to only A, only B, or both A and B. Each of A and B may refer to a single object or multiple objects.

[0032] 1A is a diagram 10 illustrating a system for remote wildlife viewing, according to one embodiment. A person (virtual tourist) 11 anywhere in the world can select a preserve 14 from a list of preserves and reserve a time slot (e.g., about 30 minutes or other time slot) to fly a drone 13 at the selected location. When the virtual tourist 11 connects to the reserved drone via a website, a signal is transmitted via the cloud 12 to a communications hub 15 at the preserve 14 and to the drone 13. The virtual tourist can command the drone for a predetermined portion of the flight time (e.g., about 25 minutes or other predetermined time), record footage with a high-quality zoom camera coupled to the drone 13, and take photos.

[0033] In one embodiment, the video provided by the drone is in a format suitable for delivery to a web browser (e.g., Chrome®, Firefox®, Safari®, Edge®, or other web browser) via video embedding. In one embodiment, the video has the appropriate format, video encoding, audio encoding, frame rate, and resolution suitable for the web browser. In one embodiment, the video provided by the drone is transcoded into a format suitable for the web browser. For the remainder of the flight time, the drone 13 automatically returns to the base station to recharge.

[0034] 1B is a block diagram illustrating a data processing system 100 for remote wildlife viewing, according to one embodiment. Data processing system 100 includes a set of autonomously driving (AD) vehicles. In one embodiment, the set of vehicles includes drones, such as drone 101, drone 102, drone 103, drone 104, and drone 105, and other vehicles, such as cars, trucks, trains, boats, spacecraft, or any other AD vehicle.

[0035] As shown in FIG. 1 , drone 101 includes memory (not shown), processor 120 coupled to the memory, and one or more cameras (e.g., camera 122) coupled to processor 201 to perform methods for protecting wildlife and / or enabling remote wildlife tourism as described in more detail herein. In at least some embodiments, the drone with the camera is remotely controlled. In at least some embodiments, the drone with the camera has pointing and zoom capabilities. In at least some embodiments, the drone with the camera has location capabilities using Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), or other location technologies. In at least some embodiments, the drone with the camera is remotely controlled to enable online wildlife (e.g., animals, plants) viewing and / or crowdsourced tracking. In at least some embodiments, the drone has one or more sensors, such as sensor 106 coupled to one or more cameras, such as camera 107. In at least some embodiments, the sensors are Light Detection and Ranging (LiDAR) sensors, radar sensors, ultrasonic sensors, Global Positioning System (GPS) sensors, other sensors, or any combination thereof. In at least some embodiments, one or more sensors, such as, for example, sensor 106, are coupled to geostationary satellite passive or active sensors to monitor the protection perimeter, as described in more detail below with respect to FIG.

[0036] In at least some embodiments, the drone has a flight time that allows a user to direct the drone to a viewing location of interest, spend sufficient time there, and return the drone to a location where it will recharge. In at least some embodiments, the drone has a flight time that exceeds 15 minutes. In at least some embodiments, the drone makes as little noise as possible because noise can disturb animals and humans in the location. For example, a drone's sound resembling a bee can disturb animals such as elephants. In at least some embodiments, the drone's sound is reduced at the location using active sound-reducing speakers. In at least some embodiments, the drone's sound is digitally filtered for users who wish to hear sounds other than the drone, as described in more detail below. In at least some embodiments, the drone is managed by a collision avoidance system. In at least some embodiments, the drone is automatically geofenced. In at least some embodiments, the drone includes a cage that covers the propeller to prevent it from harming wildlife, such as birds.

[0037] As shown in FIG. 1B , system 100 includes a communications hub 108 that connects drones 101-106 via communications links (e.g., communications link 118) within the protected area. Communications hub 108 is coupled to data storage device 117 via a network. In at least some embodiments, storage device 117 is computer memory, a database, a cloud, or a combination thereof. Communications hub 108 includes memory (not shown) and one or more processors (e.g., processor 119) coupled to the memory for performing methods for protecting wildlife and enabling remote wildlife tourism, as described in more detail below. In at least some embodiments, communications between drones 101-106 and hub 108 occur using Wi-Fi, IEEE 802.11, or other communications protocols, generally at frequencies in the approximate range of 2.4 GHz, 5 GHz, or other frequency ranges. In at least some embodiments, the communications hub 108 broadcasts at a power greater than that typically permitted by the 802.11 protocol (e.g., greater than 10 watts) to achieve coverage over an approximate range of 5 km to 10 km, or distances greater than 10 km. In at least some embodiments, communications between the drones 101-106 and the hub 108 are performed using the WiMax or IEEE 802.16 standard, which allows for communications links of over one mile and link speeds in the approximate range of 40 Mbit / s to 1 Gbit / s with approximately 1 ms latency. In at least some embodiments, communications between the drones 101-106 and the hub 108 are performed using 3G, 4G, or 5G modems on the drones that connect to cellular base stations. In at least some embodiments, the communications hub 108 has a high-speed, low-latency connection to communicate to application servers over the Internet. In at least some embodiments, the communications hub 108 uses a satellite link, such as the Ka-Ku band, in areas without Internet access to connect to application servers. In at least some embodiments, the communication link (eg, communication link 118) has low latency and sufficient bandwidth for high quality video.

[0038] 1B , system 100 includes application server system 109 coupled to communication hub 108 and storage device 117. Application server system 109 is coupled to user devices (e.g., user device 111, user device 112, user device 113, user device 114, and user device 115) via computer network 116. In one embodiment, network 116 is the Internet. In one embodiment, network 116 is a local area network (LAN) or other communication network. In one embodiment, network 116 is a wireless network. In one embodiment, application server system 109 is coupled to communication hub 108 and storage device 117 via a computer network, e.g., a local area network (LAN), an intranet, an extranet, or the Internet. Application server system 109 includes memory (not shown) and one or more processors (e.g., processor 121) coupled to the memory that manage applications for performing methods for protecting wildlife and enabling remote wildlife tourism, as described in more detail below.

[0039] In some embodiments, processor 121 is configured to allow online users to fly drones. In at least some embodiments, processor 121 is configured to geofence the drone to prevent it from going outside a conservation boundary and / or above or below a certain altitude. In at least some embodiments, processor 121 is configured to provide flight planning and collision avoidance for the drone. In at least some embodiments, processor 121 is configured to fly the drone automatically back to a base station for recharging. In at least some embodiments, processor 121 is configured to allow people to log on to the drone, schedule time with the drone, and collect and verify users' government identification, such as passports or driver's licenses, addresses, and other information to reduce the risk of users using the system for illegal purposes, such as poaching. In at least some embodiments, processor 121 is configured to keep track of which users were within a particular animal's territory at what time to prevent poaching. In at least some embodiments, processor 121 is configured to communicate with conservation authorities, manage payments to conservation authorities, send communications, such as emails, to users, and / or perform other actions.

[0040] 1B , system 100 includes user devices (e.g., user devices 111-115). User device 111 includes a memory (not shown) and one or more processors (e.g., processor 124) coupled to the memory for performing methods for conserving wildlife and enabling remote wildlife tourism, as described in more detail below. A web user interface (e.g., web user interface 123) coupled to the processor on the user device can be used to perform one or more of the following functions: remotely controlling a drone; viewing, directing, and zooming a camera; taking photos and / or video of wildlife; comparing discovered species, individuals, or both species and individuals with animal lists at conservation agencies; contributing to research, such as recording animal locations; identifying animals, species, or both animals and species using automated image identification tools; reporting suspicious activity, such as poachers; and other functions that enable contributions to the online community. In at least some embodiments, the user device may be a laptop computer, a netbook computer, a notebook computer, an ultrabook computer, a smartphone, a tablet, a personal digital assistant (PDA), an ultra-mobile PC, a mobile phone, a desktop computer, or any other electronic device that processes data.

[0041] FIG. 1C is a block diagram illustrating a system 1100 for remote wildlife viewing, according to one embodiment. As shown in FIG. 1C, the system 1100 includes a client, which is a client Javascript® 1107 running on a user browser 1101, and a control system 1102 coupled to a drone registry 1108. In one embodiment, the drone registry 1108 represents a database comprising a registry of drones. The control system 1102 is coupled to a drone control bridge, such as drone control bridge 1104, via a network 1103, such as the Internet. The drone control bridge is coupled to drones, such as drone 1106, via drone controllers, such as drone controller 1105. In one embodiment, the drone control system is a system coupled to one or more backend application servers that enable users to control the drones. In one embodiment, the drone control bridge is configured to accept drone commands and control the drones. In one embodiment, the system for remote wildlife viewing includes a reservation system comprising a processor (not shown) coupled to an application server configured to enable users to schedule or book flights. In one embodiment, a flight is associated with an end user, a time, a drone, and a location reservation. In one embodiment, the drone comprises physical drone hardware, as described in more detail below. In one embodiment, an end user refers to a human controlling the drone using a processor. In one embodiment, a system for remote wildlife viewing comprises one or more authentication systems comprising one or more processors configured to provide authentication services for users, as described in more detail below.

[0042] 2A is a flowchart of a method 200 for providing remote wildlife viewing, according to one embodiment. In one embodiment, method 200 is performed in an application server system, such as application server system 109. In another embodiment, method 200 is performed in a communications hub, such as communications hub 108. Method 200 begins at operation 201, which includes controlling a set of one or more camera-equipped drones used to capture images. In one embodiment, the drones' positions are controlled using geofencing technology. In one embodiment, a processor of the remote wildlife viewing system generates a map showing the drones' positions relative to the geofenced area, as described in more detail below.

[0043] In one embodiment, the drone's power is monitored, and in one embodiment, the drone's flight is monitored to determine a malfunction of the drone, such as a collision between the drone and another object (e.g., another drone, a tree, or any other object), loss of communication, or other malfunction, and generate an alarm to notify the user.

[0044] In operation 202, a request is received from a user to use one or more of the drones for a predetermined amount of time. In one embodiment, the request includes information identifying the user. In one embodiment, the user is coupled to one or more drones via application server 109, communications hub 108, or both the application server and communications hub. In one embodiment, the request for remote wildlife viewing is received from the user via a user interface displayed on the user device. In one embodiment, a processor of the remote wildlife viewing system generates a flight tutorial video on the user interface.

[0045] In one embodiment, the request comprises a request to book a flight for a predetermined date and / or time. In one embodiment, a processor of the remote wildlife viewing system receives a selection of a preserve from a user via a user interface. In one embodiment, a processor of the remote wildlife viewing system receives payment for the flight via a user interface. In one embodiment, payment for the flight is received using the Stripe® payment system. In another embodiment, payment for the flight is received using the Paypal® payment system or other payment system. In one embodiment, a notification (alert) is generated and sent to the user regarding the flight for the predetermined date and time.

[0046] In operation 203, it is determined whether the user is authorized to use one or more of the drones. In one embodiment, the processor of the remote wildlife viewing system authenticates the user using one or more user authentication technologies, such as Auth0 authentication, PSportJS® authentication, Google® login, other user authentication technologies, or any combination thereof. If it is determined that the user is not authorized to use one or more of the drones, method 200 returns to operation 201. If it is determined that the user is authorized to use one or more of the drones, in operation 204, the user is allowed to log on to one or more of the drones to capture one or more wildlife images using one or more of the cameras. In one embodiment, the processor of the remote wildlife viewing system determines a drone of the one or more drones for the user. In one embodiment, the number and type of drones in a preserve selected by the user are determined. In one embodiment, available time slots for the selected preserve are determined. In one embodiment, drones are assigned to the user based on the determined number and type of drones and available time slots. In one embodiment, the user is allowed to log on to one or more of the drones for a predetermined time. In one embodiment, the drone's allotted flight time is monitored.

[0047] At operation 205, one or more commands are received from a user to control at least one of one or more of the drones and one or more of the cameras. At operation 206, wildlife image data captured by the one or more cameras in response to the one or more commands is received. In one embodiment, wildlife is identified based on the one or more wildlife images using neural network techniques. In one embodiment, recorded wildlife images are received from a user to prevent illegal activity. At operation 207, the one or more wildlife images are displayed based on the wildlife image data on a user device, such as user device 111. In one embodiment, a score is assigned to the user based on the one or more wildlife images.

[0048] In one embodiment, a processor of the drone control system (e.g., processor 119, processor 120, or another processor) stores raw media (e.g., images, video, or both images and video) from the flight in permanent storage (e.g., storage device 117) accessible to a processor of the application server system (e.g., processor 12). In one embodiment, a processor of the remote wildlife viewing system (e.g., processor 119, processor 120, processor 121, or another processor) generates post-flight statistics comprising one or more flight parameters. In one embodiment, the flight parameters are flight altitude, flight speed, flight distance, flight time, other flight parameters, or any combination thereof. In one embodiment, a total flight parameter is calculated as the sum of the values ​​of the flight parameters associated with the individual flights.

[0049] In one embodiment, the post-flight statistics comprise individual flight distances, individual flight times, a sum of individual flight distances (total flight distance), a sum of individual flight times (total flight time), a maximum flight parameter, a median flight parameter, other flight statistics, or any combination thereof. In one embodiment, the post-flight statistics are stored in memory. In one embodiment, one or more individual post-flight statistics are tracked.

[0050] In one embodiment, the processor of the remote wildlife viewing system generates a profile associated with the user and stores the profile in memory. In one embodiment, the profile associated with the user comprises data related to user information, flight information, media information, or any combination thereof. In one embodiment, the user information comprises the user's email, phone number, name, password, avatar, or any combination thereof. In one embodiment, the flight information comprises flight count, distance traveled, flight time, other flight statistics, or any combination thereof. In one embodiment, the media information comprises photo count, video count, other media statistics, or any combination thereof. In one embodiment, updated user information is received and the user profile is adjusted based on the updated user information. In one embodiment, updated flight information is received and the user profile is adjusted based on the updated flight information. In one embodiment, updated media information is received and the user profile is adjusted based on the updated media information.

[0051] In one embodiment, the processor of the remote wildlife viewing system shares the user stream associated with the flight with one or more other users. In one embodiment, the processor of the remote wildlife viewing system is configured to generate a shareable URL where a given flight is publicly streamed. In one embodiment, the processor of the remote wildlife viewing system is configured to create a public stream page or share the page to various social applications. In one embodiment, the processor of the remote wildlife viewing system is configured to provide a guided experience in which a guide views the user's stream and interacts with the user using text chat, voice chat, or both voice chat and text chat. In one embodiment, the processor of the remote wildlife viewing system is configured to generate a user interface for receiving public reviews of the flight. The public reviews may include, for example, star ratings and / or a comment section.

[0052] In one embodiment, a map is generated showing at least one of the user's location, the locations of other users, and the locations where one or more wildlife images were captured. In one embodiment, a map showing wildlife movement patterns in an area is generated based on data from the user. In one embodiment, educational material related to the one or more captured wildlife images is displayed on the user device. At operation 208, it is determined whether the allotted time exceeds a predetermined time threshold. If the allotted time does not exceed the predetermined time threshold, method 200 returns to operation 205. If the allotted time exceeds the predetermined time threshold, method 200 ends. In one embodiment, revenue generated from users reserving time on the system is shared with a conservation authority. In one embodiment, data including the location of one or more drones, the status of one or more drones, or both the location and status is transmitted to a conservation authority, as described in further detail herein.

[0053] 2B is a flowchart of a method 210 for providing remote wildlife viewing, according to one embodiment. At operation 211, the method begins. At operation 212, login information is received from a user. In one embodiment, the login information comprises a user identifier (ID), a password, other user login information, or any combination thereof. At operation 213, it is determined whether the user is authorized to access the system for remote wildlife viewing based on the login information. If it is determined that the user is not authorized to access the system, it is determined whether the user has attempted to log in to the system more than a predetermined number (e.g., three, or any other number). If the user's number of attempts does not exceed the predetermined number, method 210 returns to operation 212. If the user's number of attempts exceeds the predetermined number, method 210 ends. If it is determined that the user is authorized, at operation 214, access to the system for remote wildlife viewing is granted. At operation 215, it is determined whether an instruction for the user to exit the system has been received. If it is determined that an instruction for the user to exit the system has not been received, method 210 returns to operation 214. If it is determined that an instruction for the user to exit the system has been received, method 210 ends at operation 214.

[0054] FIG. 3 is a flowchart of a method 300 for providing remote wildlife viewing, according to one embodiment. In one embodiment, method 300 is performed on a drone device, e.g., drone 111. In another embodiment, method 300 is performed on a communications hub, e.g., communications hub 108. Method 300 begins at operation 301, which includes receiving an indication from an application server that a user is able to log on to the device. At operation 302, one or more commands to capture wildlife images for the user are received. At operation 303, in response to the one or more commands, wildlife images are captured using one or more of the cameras. At operation 304, the one or more wildlife images are transmitted to the application server via the communications hub for display on the user device. In an embodiment, an apparatus for remote wildlife viewing includes one or more cameras and a processor coupled to the one or more cameras and configured to perform method 300. In an embodiment, an apparatus for providing remote wildlife viewing includes a housing coupled to the processor, a helium balloon coupled to the housing, and a mesh surrounding the housing to protect the wildlife. In an embodiment, a device for providing remote wildlife viewing includes one or more speakers coupled to a housing and a processor for generating destructive interference for sounds emanating from the device, a microphone coupled to the processor, and a docking port coupled to the processor, as described in further detail below.

[0055] In one embodiment, users keep track of animals by digitally tagging individual animals and digitally marking locations where animals have been observed. In at least some embodiments, the photographs users take are combined with image recognition to maintain species and individual animal counts, monitor animal movements, detect poachers, and perform other investigations.

[0056] In one embodiment, the process of landing drones for recharging is automated or largely automated. In one embodiment, humans at the reserve maintain the system for the large number of drones. Flashing lights, infrared sources, or other radio beacons at specific frequencies are used to precisely steer drones to charging stations. Magnets and / or physical guide funnels at charging stations are used to automatically dock drones to recharge their batteries.

[0057] In one embodiment, users pay for a specific amount of drone time at the time of booking, and revenue is shared between the system provider and wildlife agencies.

[0058] In one embodiment, the remote drone control and nature viewing system is used for gaming. A user logs on to one or more drones and sees how many different species of animals they can spot within an allotted time. In one embodiment, one or more automatic image recognition techniques, such as deep learning neural network image recognition techniques used to categorize photos of dogs and cats on the internet, are used for species classification. In an embodiment, individual animals, such as elephants, are identified based on identifying features unique to each individual animal using one or more automatic image recognition techniques.

[0059] In one embodiment, a user first reviews images and classifies whether a unique species or individual animal is found in each image. The classified image bank is then used as a database for training machine learning image recognition. The user attempts to obtain a large number of images of animal or plant species for recognition. The user who finds and photographs the most species wins, for example, based on a points system. In an embodiment, the user attempts to identify the discovered species. In an embodiment, educational materials about various species of animals, birds, and / or plants are provided to the user for species identification. Games involving finding and photographing animals, such as birds, can thrill birdwatchers around the world, for example. In one embodiment, drone flight is restricted to prevent the user from chasing birds or other animals.

[0060] In one embodiment, the drones are covered with a protective mesh to prevent harm to animals in the event of a collision. The lightweight protective mesh currently weighs less than 50 grams and does not significantly affect the flight time of commercially available drones, which cost around $1,000.

[0061] In one embodiment, drones are made quieter to avoid disturbing animals and humans, and / or geofenced to maintain a distance that prevents certain animals from hearing them. In one embodiment, drone noise is reduced by using larger propellers that operate at a lower frequency than conventional propellers. In another embodiment, drone noise is reduced by using propellers / rotors that operate at different frequencies so that there is no single high-intensity tone. This can be achieved by having one engine on the drone drive gears with different ratios, or by having multiple engines operate at different frequencies. If the engine is the dominant noise component, using a single engine at a single frequency at a single point source can facilitate active sound cancellation. In one embodiment, drones are designed to avoid the sound frequencies of bees, which disturb certain animals.

[0062] In one embodiment, a drone is designed to destructively interfere with the sound generated by its propeller and / or engine to cancel noise over a wide angular range. This involves the propeller and / or engine moving at a frequency slow enough to minimize the distance between the noisy engine or propeller and the speaker that emits the sound. In this way, destructive interference can be achieved for animals or humans at all angles around the drone. For example, if the noisy propeller is 10 cm away from the speaker, the sound waves will be approximately 10 times this length, or 1 m, and in this case, the rotor frequency may be 300 m / s / 1 m = 300 Hz or less. Additionally, the speaker can be positioned to optimally reduce noise in a cone below the drone for creatures closest to the drone. Additionally, a microphone can be used to sense noise emanating from the drone or part of the drone and emit a sound designed to be 180 degrees out of phase with the noise to cancel it out.

[0063] In one embodiment, the drone includes a directional microphone that allows the user to hear animal sounds, such as bird calls. In one embodiment, signal processing on the digitized audio signal is used to filter the drone's sounds. This may be based on a template of a drone's typical sound in the time or frequency domain, or may include a separate microphone that measures the sounds produced by the drone in real time.

[0064] In one embodiment, the drone includes a microphone and automatic voice recognition to identify the sound of gunfire. Multiple drones may have this capability. In one embodiment, all drones synchronize to a standardized clock, such as a GPS, and have their own location, allowing the drones to triangulate the location of the gunfire. This approach uses the multiplication of the reception timing of the sound and the speed of sound to determine the location. More specifically, the angle to the sound source can be determined by determining the difference in distance to the sound source between two receivers, and the sound source can be triangulated using three receivers. One or more drones can then be directed to this location to detect possible poaching activity.

[0065] In one embodiment, users report any suspicious activity they witness to a central control station to prevent illegal activities, such as poaching. Users can volunteer to patrol for poachers, and conservation staff can use specific notifications to indicate they are not poachers, such as a bean that generates a signal at a specific frequency. Platform for remote wildlife viewing according to one embodiment

[0066] In at least some embodiments, the platform allows for flying drones and remotely controlling drone cameras from anywhere in the world with a robust internet connection. In at least some embodiments, the platform for remote wildlife viewing is the NatureEye® platform.

[0067] The stages of using the platform for remote wildlife viewing, and the platform automated interactions and functions for each stage, may be as follows:

[0068] Stage 1: Book a flight 1. The user selects a conservation area. 2. The user selects a flight time and books a single appointment (e.g., a 25-minute appointment) or a bundle linked to their online calendar (e.g., Google® Calendar, Yahoo® Calendar, Outlook® Calendar, or any other online calendar). 3. When booking a flight, users are asked to create an account (e.g., a NatureEye® account). 4. Credit card payment processing via online credit card payment systems, such as Paypal®, Google Pay® credit card payments, or other online credit card payment systems. Gift card options are offered. 5. At the appointed time, the user receives an email notification and clicks on a website (e.g., the NatureEye® website) or a link in the email to begin the appointment.

[0069] Stage 2: Fly the drone 1. The user watches a video (e.g., a 5-minute tutorial) on how to control the drone. 2. User Interface (UI) Desktop Options 1. The user controls the drone's horizontal movement using the keyboard arrow keys or WASD keys, controls the drone's vertical movement by pressing the spacebar to move up and the shift key to move down, and controls the drone's gimbal by dragging the mouse where they want to view. In at least some embodiments, when the drone hits a geofence, a message appears in or next to the video link with an arrow indicating the direction in which further movement is not possible. In at least some embodiments, an outline of the geofence is displayed on a display device, as described in more detail below. In at least some embodiments, the geofence is checked approximately every few seconds, more frequently as the drone approaches the vicinity of the geofence. In at least some embodiments, a map is generated on the display device showing the drone's position relative to the geofenced area. Mobile Options 2. The user controls the drone's horizontal movement by joystick movement, the drone's vertical movement by up and down arrow icons, and the drone's gimbal by dragging on the screen in the direction the user wants to see. 3. Pre-flight testing (to test the controls). 4. To prevent accidents, the drone's collision detection and avoidance system is automatically enabled and kept active throughout the entire flight. 5. Once the time expires (say after about 25 minutes), the drone will land on autopilot, although the user can continue to watch the footage. 6. Once the drone lands, the user is thanked and the video feed ends.

[0070] Stage 3: Capture and Share 1. Users have the option to zoom in 2x optically or 4x digitally using the drone's lens. 2. The UI allows users to take photos and record video of their flight. 3. Provide a shareable stream link with friends and family where they can watch the streamed flight. In at least some embodiments, sharing is possible on social media applications (e.g., Facebook®, Whatsapp®, or other social media applications). 4. At the end of the flight, users have the option to quickly preview the photos and videos they have taken and also download and share them. 5. Provide a donation button that allows users to donate to the conservation site. The button will link to the conservation site's donation page on the site.

[0071] Table 1 provided below lists actions for building a platform for remote wildlife viewing according to one embodiment. [Table 1]

[0072] In at least some embodiments, a platform for remote wildlife viewing may include one or more of the following modules: 1. Booking Module

[0073] In at least some embodiments, the reservation module comprises a user sign-up module, a user login module, a module representing various protection agencies to visit, and a calendar schedule module. In at least some embodiments, the reservation module is implemented using HTML / JavaScript website building software. 1.1.User Signup In at least some embodiments, a sign-up page is displayed on the NatureEye® website written in HTML / JavaScript® language. 1.2.User Login In at least some embodiments, user login is implemented using an off-the-shelf website package. 1.3. Modules representing the various protection agencies to visit In at least some embodiments, the protection agency is represented by a photograph, a web link to its location, and a video. The embedded video is in Moving Picture Experts Group (MPEG) format and is played using off-the-shelf website packages. In at least some embodiments, a descriptive article about the protection agency is displayed on a website (e.g., the NatureEye® website). 1.4.Calendar Scheduler A calendar showing available time slots is displayed on the NatureEye® website. In at least some embodiments, reservations are made using a pre-built website package.

[0074] In at least some embodiments, an email is sent to the user with a link to integrate with Google Calendar®. 2. Notification Module

[0075] In at least some embodiments, a notification module is coupled to the calendar scheduler to send reminder emails to users. In at least some embodiments, once a day, all appointments due the next day are reviewed and a reminder email is sent to users with a link to a drone feed that will be active at a specific time. 3. Payment Module

[0076] In at least some embodiments, payments are processed using a Google Play® plugin, Square®, or other payment plugin. 4. User Interface (UI)

[0077] FIG. 3B illustrates a user interface (UI) 310 for remote wildlife viewing, according to one embodiment. As shown in FIG. 3B, user interface 310 includes an image portion 311 and a map portion 312. In at least some embodiments, image portion 311 displays video associated with remote wildlife viewing. In at least some embodiments, image portion 311 displays one or more still images associated with remote wildlife viewing. In at least some embodiments, image portion 311 displays both one or more still images and video. Image portion 311 includes a direction indicator 313, a position / speed indicator 314, a camera direction control 315, and a camera zoom control 316, as shown in FIG. 3B. UI 310 displays icons representing the most recent snapshots or videos 317, 318, 319. In at least some embodiments, position / speed indicator 314 indicates at least one of drone location latitude, drone location longitude, drone location altitude, or drone speed.

[0078] In at least some embodiments, if a user logs on to the drone early, an indicator showing a countdown to the start time is displayed on the UI 310. Once the user begins flying, video is sourced from the drone and displayed in the image portion 311. The drone's direction, altitude, and speed are also shown on the UI 310. In at least some embodiments, the video is sourced from a DJI® application and, in at least some embodiments, can be streamed to the user. In at least some embodiments, the drone's location relative to the geofenced area is displayed in the map portion 312. In at least some embodiments, the map is displayed using HTML / Jscript®. 5.Share

[0079] In at least some embodiments, once the session is complete, an icon is displayed representing all images / videos taken during the flight. The icon can be clicked to expand it. Image / video sharing options are available upon clicking. Sharing options include Facebook®, Whatsapp®, text messaging, email, and other sharing options. 6. Flight Control Software

[0080] In at least some embodiments, the flight control software is implemented using the Flightbase® application. In at least some embodiments, the flight control software runs on an application server, in the cloud, on an on-site base station, or any combination thereof. In at least some embodiments, a JAVA / JAVAScript® API provided by Flightbase® is accessed to enable website functionality. 7. Field base station

[0081] In at least some embodiments, the on-site base station provides communication with both the drone and the internet. In at least some embodiments, the base station comprises a DJI® remote control device attached to a cell phone and / or laptop. In at least some embodiments, the application on the cell phone / laptop is DJI® Base Station software. The DJI® remote control connects to the drone via long-range Wi-Fi. In at least some embodiments, an outdoor, elevated remote control antenna is coupled to the base station. In at least some embodiments, the cell phone / laptop connects to Wi-Fi or Ethernet if present at the site. Otherwise, the cell phone connects to the internet via 4G (in at least some embodiments, StarLink® is used to provide low-latency connectivity to Wi-Fi).

[0082] Referring again to FIG. 1C, the system for remote wildlife viewing comprises a number of systems as follows: Client - Javascript (registered trademark) that runs on the browser (client). Application Server - The application logic (application server) that manages the system to provide remote wildlife viewing. Drone Control System - A system that allows a user to control a drone. This may be a FlytBase® feature (Drone Control System). Drone Control Bridge—A system for accepting drone commands and controlling drones (Drone Control Bridge). This may be a FlytBase® feature. Reservation System - A system that allows users to schedule or book flights (reservation system). Authentication Service (Authentication Service).

[0083] Authentication and Identity Verification 1. Authentication

[0084] In embodiments, end users are authenticated using industry standard technologies and practices, including OAuth 2.0 and JSON Web Tokens (JWT). Authenticating an end user involves three main components: End users can be authenticated by two different means: username and password authentication or an external identity service.

[0085] In an embodiment, an unauthenticated client is redirected to the system's authentication service to sign in. The user is presented with the option to sign in with a username and password or with an external identity service (e.g., a Google® or Facebook® account). If the user chooses to sign in with an external identity service, the user is redirected to the external identity service using an OAuth 2.0 flow. If the user chooses to sign in with a username and password, the user is validated against a stored username and salted and hashed password. In an embodiment, passwords are salted and hashed with bcrypt, and all user data is encrypted at rest with a minimum of 128-bit AES encryption. If the authentication service can sufficiently authenticate the end user, the authentication service returns a JWT to the client, which is stored on the client. In an embodiment, the JWT contains information to identify the user (e.g., a stable unique identifier for the user) and is cryptographically signed by the authentication service.

[0086] In an embodiment, when an authenticated client attempts to access a protected resource from an application server, the client includes a JWT with the request. When the application server processes a request that requires authentication, it ensures that the request includes a JWT. The application server verifies the cryptographic signature of the JWT to ensure that it was generated by an authentication service and has not been tampered with. If the JWT is verified, the application server uses the unique user identifier.

[0087] In an embodiment, if the application server receives a request requiring authentication that does not include a JWT or that includes an invalid JWT, the application server rejects the request and returns an error code instead of processing the request.

[0088] In embodiments, as an anti-poaching measure, the authentication service is configured to require two-factor authentication (2FA). It can be configured to require 2FA in various scenarios. Scenarios in which 2FA may be required include when any user signs into the application, when any user signs in for the first time from a new IP address or geographic region, immediately before a user begins any flight within the system, or immediately before a user begins a flight at a specific location. Each of these scenarios can be individually configured and tailored to the anti-poaching measure. 2. Identity Verification

[0089] In embodiments, in situations where drones operating over nature reserves are controlled by external users, identity verification is a component to support anti-poaching measures. In at least some embodiments, completing identity verification is required to access certain components of the application, such as booking a flight or initiating a flight. When a user makes an initial reservation, after selecting a time slot and before payment, the user is prompted to complete an identity verification action. This action requires the user to submit an image of a government-issued identification card (e.g., a passport or driver's license). The user then proceeds with payment. In one embodiment, the system verifies that the identification card is valid, verifies that the identification card is from an authorized country, and stores the identification information for a prescribed period of time. This allows for retracing in the event of an adverse event (such as poaching).

[0090] In an embodiment, the system requires identification upon a user's first flight booking. Subsequent bookings may not require identification. In an embodiment, the identifying characteristics may be configurable on a location-by-location basis, since not all locations pose a risk of poaching. User Profile

[0091] In an embodiment, the system's application server stores information about the user's past and upcoming flights in a user profile, which also includes saved media from past flights, saved payment details, the status of identity verification, and any other information the system requires about the user to operate. role

[0092] In an embodiment, the system for remote wildlife viewing provides multiple user roles that authorize users for specific tasks. Example roles are shown in Table 2 below. [Table 2] Purchasing and Flight Reservation System

[0093] There may be four main components to a flight reservation system. In an embodiment, the payment processor is an external system exposed in the HTTPS request to process credit cards or other payment mechanisms to bill users. In an embodiment, the drone inventory management system is a system that stores information about which drones are available at which locations. The drone inventory management system also stores flight reservations for each drone and can create a list of flight reservation availability times that other systems can use. In an embodiment, the list of availability times for drones at a location takes into account operational downtime required by the drones (e.g., short downtimes for simple drone round trip times after each flight, longer downtimes for each drone to charge after several flights, or occasional downtimes for inspection, maintenance, and service windows).

[0094] In an embodiment, the application servers are a set of servers available to clients over the public internet that manage authentication and authorization of individual users and facilitate interaction between clients and the payment processor and drone inventory management system.

[0095] In an embodiment, the client is a set of Javascript that runs on the user's browser when the user visits a flight booking web page that interacts with the application server based on the user's selections.

[0096] In an embodiment, when a client initiates the flight booking process, the application server verifies that the user's account has completed the identity verification process. If the end user is authenticated, the user first selects the location where they wish to book a flight. After the user selects the location, the application server retrieves a list of drones available at that location from the drone inventory management system. As described above, the availability list takes into account the drone's operational downtime. Once the user selects the date and time of the flight, the application server requests the drone inventory management system to create a temporary reservation for the selected flight time.

[0097] In an embodiment, after creating a temporary reservation for flight time, the user is presented with a payment screen. If the user has not already completed identity verification, the end user is prompted to complete the identity verification process. The user enters their payment details. If the user enters a credit card payment method, those payment details are sent directly to a PCI-compliant payment processor without passing through the application server. The payment processor returns a payment token representing the payment details. The client then sends the payment token to the application server. The application server then creates a charge by sending the payment token and the charge amount to the payment processor. If the payment processor successfully creates the charge, the application server converts the temporary reservation to a permanent reservation in the drone inventory management system. The application server returns a confirmation code to the client, which is displayed on-screen for the end user. If the user does not complete the purchase within the temporary reservation window, the temporary reservation is released. Flight Control System Overview

[0098] There can be four main components in a drone flight control system:

[0099] In one embodiment, the drone control bridge is an application running on hardware that bridges drone control to the internet. The purpose of the drone control bridge is to translate commands received from the drone control system over the internet into controls on the drone controller. In one embodiment, the drone control bridge comprises a microcontroller and / or processor configured to execute software running on an Android OS-based device that communicates with the drone control system over the internet. In one embodiment, the drone control bridge comprises a microcontroller and / or processor configured to execute software running on an Android OS-based device that communicates with the drone controller over USB. In one embodiment, the drone control bridge registers itself and the drone's identifier with the drone control system. In one embodiment, the drone controller provides a USB-based API for passing commands and receiving telemetry and video streaming data. The drone control bridge streams the telemetry and video data back to the drone control system.

[0100] In at least some embodiments, the drone control bridge communicates with the drone controller using a drone controller-specific API via direct cable, Wifi, Bluetooth, or other transport protocol. In at least some embodiments, the drone controller may run on general-purpose hardware (e.g., an Android or iOS-based phone), allowing the bridge software to be installed directly on the drone controller. In at least some embodiments, the drone controller provides an API to the internet, allowing it to be bridged directly to the drone control system without the need for a direct connection with the drone controller.

[0101] In embodiments, the drone control system provides an interface to external applications to manage the sending of control commands to specific drones via protocols such as https. In at least some embodiments, the drone control system communicates with the drone control bridge using TCP and UDP-based protocols over the internet. These protocols define both the commands sent from the drone control system to the drone control bridge and the streaming data, including telemetry and video, sent from the drone control bridge back to the drone control system. The drone control system API is described below.

[0102] In an embodiment, the application server is a system of servers available to clients over the public internet that manages authentication and authorization for individual users and distributes authorized commands to the drone control system. The application server communicates with the drone control system over an SSL channel (both HTTPS and secure web sockets).

[0103] In an embodiment, the client is a set of Javascript™ that runs on the user's browser when the user visits a flight control web page that interacts with the application server and issues control commands. Drone Registration

[0104] In an embodiment, when a drone and associated drone control bridge are turned on and enabled, the drone controller bridge registers itself with the drone control system, making the drone available to remotely control. In one embodiment, the drone control system updates the drone registry to note that the associated drone is available and to indicate which drone control bridge should be used to send control commands to the drone. Start of flight

[0105] In an embodiment, at the start of a flight, an end user visits a flight control page. The client makes an authenticated request to the application server identifying the end user and the flight the end user wishes to control. The application server validates the request by ensuring that the authentication is valid using the authentication process described above. Thus, the end user has identified an existing flight scheduled to begin in the near future. Once the request is validated, the application server and client open a reliable and secure duplex communication channel (a "message channel"), for example, a secure WebSocket connection.

[0106] In an embodiment, when a message channel is first opened, the application server should send a flight status message to the client, which in an embodiment includes the status of the flight (e.g., pre-flight, in-flight, post-flight, or error) and other metadata about the current flight (e.g., current flight duration, remaining flight time, any notifications that need to be presented to the user).

[0107] In an embodiment, the client then makes a separate authenticated request to the application server to retrieve the video stream. The application server invokes a set of code on the drone control system that generates a URL to a video stream available on the public internet that displays a live feed from the drone's camera. The client then displays the video in a browser as an underlay for one of the control user interfaces. In an embodiment, the video stream is in MP4 format and the quality of the video stream is 720p. Flight Control

[0108] In an embodiment, when a flight state allows the client to control the drone, the client sends the desired state of the drone to the application server via a drone control command message. The client includes settings related to the drone operation aspects that the client is allowed to control (e.g., vertical speed setting, forward speed setting, horizontal speed setting, and yaw angular velocity setting). Each setting is expressed in a unitless range from -1 to 1.

[0109] The drone control interprets each unitless setpoint and converts it to physical units (e.g., 3 m / s or 0.2 rad / s), ensuring that the overall setpoints represent a safe state for the entire drone (e.g., a certain distance from the ground, within a geofenced area, etc.). If the converted overall setpoints represent an unsafe state, the application server lowers the setpoints until the entire system represents a safe state for the entire drone. Finally, the application server system invokes a code set on the drone control system that applies the converted safe setpoints to the drones. In an embodiment, the code set on the drone control system distributes commands to the actual drones using a drone registry and drone control bridge.

[0110] In an embodiment, to ensure safe drone operation, the client frequently and periodically sends drone control command messages to the control system (e.g., one message per second). The application server verifies that the drone is actively controlled by the client by verifying that drone control command messages are periodically received from the client. If a certain period (e.g., three seconds) passes without receiving a drone control command message from the client, the application server automatically updates the drone's setpoints to safe values ​​(e.g., all zero speed setpoints). If drone control command messages are again received from the client, the application server resumes applying command messages to the drone. If a longer period (e.g., one minute) passes without receiving a drone control command message from the client, the application server automatically invokes code on the drone control system to return the drone to its home position and end the flight. If a drone control command message is received at this time, the application server discards the message and instructs the client to establish a new connection to resume the flight.

[0111] In embodiments, to ensure responsive operation of the drone, the client sends an additional drone control command message to the application server each time the user begins or ends an action. For example, if the end user presses a key indicating that the drone should have a positive forward speed setpoint, the client immediately sends a drone control command message with the updated forward speed setpoint. Then, when the end user releases the key, the client immediately sends a drone control command message with a zero forward speed setpoint. In embodiments, this control mechanism is applicable to any remotely controlled machine capable of streaming video and operating by setting a speed setpoint (e.g., boats, underwater vehicles, submarines, quadcopters, etc.). Drone parameters, geofencing, and points of interest

[0112] In an embodiment, each drone references certain parameters, which may be configurable by location. Examples of parameters include: 1. Available dates and times. This is a. Time ranges by day of the week for regular time slots b. Additional time ranges available for specific dates c. Exceptions to the normal time range in (a) (e.g., holidays, maintenance windows, guide vacations) Equipped with. 2. Flight time. This indicates the flight time allowed for the drone. In an embodiment, the flight time is less than the expected battery life by a predetermined safety margin. The reservation is provided for this period. 3. Points of Interest. This is a set of points (e.g., GPS coordinates) and descriptive text associated with each point. These points are presented to the user as points of interest available during flight for a particular drone. Optionally, each point of interest includes a URL that can provide sufficient details and media to describe each point. 4. Geofencing Volume. This parameter describes a volume of space outside of which drones are not allowed. The volume is described as a set of 3D GPS coordinate vertices connected in a graph. The drone control system will not allow the drone to be outside of a predefined volume during flight. If the user attempts to fly outside of this volume, the drone controller will return an error code that is displayed to the user, and the drone will stop at the boundary. Because geofencing is described as a 3D volume, it can be used to keep the drone roughly along a specific path, prevent the drone from colliding with obstacles, and / or prevent the drone from flying too low and disturbing wildlife. Additionally, because geofencing is a configurable 3D volume, the minimum height can be adjusted to accommodate varying terrain. 5. Media Capabilities. Different drones may have different recording capabilities. For example, some drones may provide 4K footage, while others may provide 1080p or lower. This field provides the system with the media capabilities per drone so that the user knows in advance what to expect.

[0113] Drone Control Hibernation API Below is a functional example of the drone control hibernation API, with exemplary code in places. Acquiring flight operation information

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[0114] In an embodiment, all messages sent over a WebSocket connection are single serialized JSON objects, each of which has a message type defined below. Client → Server Drone Control Type: Drone Control Direction: Client → Server This message conveys the client's desired settings for the drone at any given time. Upon receiving this message, the server will apply the given settings to the drone if they have changed since the last update. This message has a 1-second heartbeat and can be sent immediately after the user makes a control change.

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[0115] 4 is a flowchart of a method 400 for detecting wildlife prohibitions, according to one embodiment. Method 400 begins at operation 401, which includes collecting data related to wildlife 401. At operation 402, a wildlife prohibition is identified based on the collected data. In one embodiment, wildlife products are detected using one or more sensors. The wildlife prohibition is identified based on the detected molecules. In one embodiment, the collected data is online sales data, and the wildlife prohibition is identified based on the online sales data. At operation 403, a notification of the wildlife prohibition is generated, as described in more detail below.

[0116] In one embodiment, one or more sensors are wildlife interdiction detectors. In one embodiment, the one or more sensors are configured to detect molecules of wildlife commodities, such as rhino horn, ivory, tiger bone, and / or other wildlife. In one embodiment, the wildlife interdiction detector is approximately 1000 times more sensitive than a sniffer dog. In one embodiment, the conductivity of a molecular wildlife interdiction sensor changes uniquely based on the molecular composition of the wildlife commodity (e.g., ivory, rhino horn, tiger bone). In one embodiment, the detector sensor may be used at ports for drugs and explosives. The detector sensor is adapted to detect molecular wildlife interdiction. In one embodiment, a taste and smell sensor incorporating human olfactory and taste receptors on a disposable biochip, such as those manufactured by Aromyx Corporation® of Palo Alto, California, is adapted to detect wildlife interdiction. In one embodiment, one or more wildlife interdiction detector sensors are plugged into a smartphone. Providing IP addresses of illegal URLs on the surface web to law enforcement agencies

[0117] In at least some embodiments, leads related to wildlife prohibitions are collected and provided to law enforcement. In at least some embodiments, a fraudulent prosecution fee is collected for any lead that results in a fine or arrest. In embodiments, collecting leads involves running an algorithm that analyzes online sales on websites such as E-Bay and Amazon and identifies those sales that are likely to be illegal sales of wildlife products. The algorithm then queries law enforcement for those URLs hosting the products for sale.

[0118] Prohibited trade continues on the surface web largely because of the difficulty of sifting through and identifying prohibited items. In the case of surface websites, data can be automatically downloaded and analyzed using machine learning. One study, Hernandez-Castro et al., PeerJ Computer Science, July 2015, 29;1(4):e10 (DOI: 10.7717 / peerj-cs.10), incorporated herein by reference in its entirety, used a machine learning online prohibited item identification system implemented by two professors at the University of Leeds to achieve 93% accuracy in automatically detecting illegal ivory sales on UK eBay by mining product metadata.

[0119] In one embodiment, the system includes a database of prohibited items that can be searched by law enforcement, or an ongoing data feed mechanism, possibly as simple as email, to send law enforcement agencies the web locations / IP addresses of URLs selling wildlife prohibited items. These may be law enforcement agencies that are financially incentivized to make more arrests or collect more fines. In one embodiment, the system facilitates the collection of fraudulent whistle-blowing fees paid by law enforcement agencies. Examples of such relevant agencies in the United States are: US Fish & Wildlife Service Office of Law Enforcement 5275 Leesburg Pike, MS: OLE Falls Church, VA 22041 Cell: 703-819-2875 Work: 703-358-2520. Websites selling wildlife contraband on the dark web to catch buyers

[0120] As shown by a study in Conservation Biology, in 2016, many wildlife contraband items were traded on the surface web, with relatively few on the dark web. See Harrison et al., Conserv Biol. 2016 Aug;30(4):900-4 (doi:10.1111 / cobi.12707), incorporated herein by reference in its entirety. However, trading on the dark web is increasing due to the expansion of password-protected dark web markets, fueled largely by drugs and other contraband. One approach to expanding dark web channels is to set up multiple dark web sites selling wildlife contraband and provide information to law enforcement. Many dark web commerce sites are low-density and simple. See, for example, LaCapria Snopes, “Have 3D Printed Rhino Horns Been Developed to Stop Poaching?” July 11, 2016 (available at https: / / www.snopes.com / 3d-printed-rhino-horn-developed / ), which is incorporated herein by reference in its entirety, whereby counterfeit rhino horns can be arranged for sale by or in association with undercover police officers, and shipping addresses can be provided to law enforcement for search warrants.

[0121] 5 is a flowchart of a method 500 for monitoring individual animals in a protected area, according to one embodiment. Method 500 begins at operation 501, which involves collecting data related to the animal from rangers. In one embodiment, the data comprises the animal's location, the animal's identifier, or both the animal's identifier and location. In operation 502, the collected data is stored in a database. In operation 503, a profile of the animal is generated based on the collected data, as described in more detail below.

[0122] FIG. 6 is an example of a data structure 600 containing data related to animals collected from security personnel, according to one embodiment. As shown in FIG. 6, data structure 600 is represented by a table. In one embodiment, the data structure is created and stored in a storage device, such as storage device 117 or another storage device. As shown in FIG. 6, the table includes an animal identifier (ID) column 601 containing an identifier (e.g., ID1, ID2, ID3, etc.) that uniquely identifies the animal. The table includes an animal location column 602 indicating coordinates X1, Y1, Z1, X2, Y2, Z2, . . . Xn, Yn, Zn (e.g., GPS coordinates or other coordinates) for the animal. The table includes a date / time column 603 indicating the date and / or time the animal was observed (e.g., 02 / 25 / 2021 10:00 PM, 12 / 31 / 2021 8:30 AM, 01 / 25 / 21 3:00 PM). The table includes an animal profile column 604 that includes animal photo(s), unique animal features (e.g., F1, F2, F3, F4, F5, F6, which may be associated with the animal's face, tusks, body, and tail), footprint(s), etc. The table includes an animal category column 605 that indicates animal categories C1, C2, Cn (e.g., breeding herd(s), other animal categories). The table includes one or more rows (e.g., row 606, row 607, row 608). Each row includes an animal identifier and a location, date / time, profile, and category corresponding to the animal identifier.

[0123] FIG. 7 is a flowchart of a method 700 for monitoring individual animals in a protected area, according to one embodiment. At operation 701, a selection of an animal is received. In an embodiment, one or more animal IDs are determined based on the selection. In one embodiment, captured wildlife images of the animal are compared to stored wildlife images of the animal to identify the animal using image recognition techniques. At operation 702, data related to the selected animal is retrieved from a database based on the animal ID. In an embodiment, data corresponding to the selected animal ID (e.g., location, date / time, profile, category, or any combination thereof) is retrieved from the database. In one embodiment, a selection of at least one of an area and a data range related to the selected animal is received. At operation 703, a map is generated based on the retrieved data, including one or more routes showing the movement of the selected animal within the selected area and data range. In one embodiment, a notification is generated and sent to a user when the animal moves out of the area.

[0124] Figure 8 illustrates a map 800 showing animal movement, according to one embodiment. Map 800 shows the locations of animals in location coordinates (e.g., x, y coordinates) 801. As shown in Figure 8, the animals are categorized into groups, such as herd 802, herd 803, and herd 804. Map 800 includes paths 807, 808, 809, 811, and 812, which show the movement of animals (e.g., animal 805 and animal 806) within an area, as described in more detail below.

[0125] For example, a system for monitoring individual animals within a protected area may be used for conservation organizations such as Elephant Human Relations Aid (EHRA) in Namibia, as well as other conservation organizations. The system may be used to reliably determine how many native animals (e.g., Desert African Elephants, or other native animals) are in an area and how the migration paths of individual animals and herds overlap on a map, such as Google Maps®. In one embodiment, the system uses GPS-tagged data and photographs collected by rangers and volunteers. In other embodiments, the system includes image recognition to automate animal identification. For example, for elephants, unique identifying features may include tusks, ears, face, tail, and footprints.

[0126] Generally, EHRA operates in an area of ​​Namibia approximately 0-100 km north of the Ugbu River and 0-100 km east of the sea. The conservation organization's goal is to protect endemic species, such as the desert African elephant, a species of elephant adapted to the desert environment, and allow them to continue to roam freely within the area. To achieve this goal, EHRA seeks to protect the livelihoods and economic interests of farmers and settlements within the area by preventing them from capturing elephants. Reports are prepared for the Namibian government and conservation organizations to document the number of elephants remaining within the area. Recent government reports have stated that the elephant population is in the hundreds, but currently, the number remaining is approximately 30. One reason for this overestimate is that multiple conservation agencies around the area submit elephant observation counts, which are then aggregated. However, as elephants move from one area to another, the same elephant is counted multiple times. With ongoing conservation measures, the elephant population may increase again as they roam freely within the area.

[0127] A system for monitoring individual animals within a protected area may be used to provide one or more of the following functions: 1. It allows for accurate estimation of the number of endemic animals within protected areas. 2. Generate a report showing the movement patterns of one or more animals. 3. Allow rangers to track wildlife in many areas. The system may be accessible online at a conservation organization's website (eg, the EHRA website). Specification 1

[0128] In one embodiment, the data is generated by rangers working for a conservation organization (e.g., EHRA) who record the GPS location of each elephant or other animal's location. The rangers have learned to identify the unique markings on each animal (e.g., elephant or other animal), and can include the animal's unique ID along with the location information.

[0129] In one embodiment, data from approximately 10 years of security personnel is recorded in XLS spreadsheets. These XLS spreadsheets can be imported into a database.

[0130] In one embodiment, new data recorded by rangers and volunteers is stored as GPS waypoints in GPX format. In one embodiment, the waypoint description includes the animal ID. If the animal ID is not available, the data may be stored in an unclassified bucket. In one embodiment, the waypoint is also associated with a photo.

[0131] In one embodiment, the database is queried via a web interface by selecting one or more animals, regions, and date ranges. In one embodiment, animals are placed in breeding herds, so the entire breeding herd may be selected. A map 800 may be generated showing a series of different colored lines indicating where each of the animals has moved over a specified time frame. In one embodiment, a color swatch is generated to indicate the identity of the individual animals 805 represented by the lines (e.g., paths 807) on the map. In one embodiment, the map 800 is generated on Google Maps®, Yahoo® Maps, or other maps.

[0132] In one embodiment, a profile is created for each animal that includes a set of photos depicting the animal's unique features, such as the animal's face, tusks, body, and tail. In one embodiment, photos of the animal's footprints are also included in the profile. In one embodiment, the profile also includes all sightings of the animal associated with that animal ID. In one embodiment, each colored line showing an animal's path (e.g., path 807) on the map is clickable to show the animal's profile.

[0133] In one embodiment, animals are categorized into breeding groups, which can be a single animal or a group of animals. In one embodiment, this clustering of animals is created and editable by users with editing privileges. In one embodiment, lines drawn on the map are categorized into breeding groups, with each group represented by a different line style, for example, solid, dashed, dot-dash, dotted, or other styles. Clicking on a line style in the legend takes the user to the breeding group, which lists all the animals in that group. Every animal profile includes a link to that animal's breeding group.

[0134] In one embodiment, a separate set of dots is recorded on the map for the locations of animals for which no ID is available, and these dots are clickable so that the information can be edited, for example to add an animal ID.

[0135] In one embodiment, the database is queried from a tab on the conservation organization's website, which may be implemented in a content management system, such as Joomla®, or other content management system. In one embodiment, the conservation organization's website is an EHRA implemented in Joomla®, which requires password access to prevent unauthorized access by poachers.

[0136] In one embodiment, the database is manually editable via a web interface for users with editing privileges, including all information related to specific animals, for example, adding or removing photos, changing data associated with each location, grouping animals into breeding herds, and changing animal IDs associated with specific locations. In one embodiment, the database is archived every 2-6 months, and older versions are searchable. Specification 2

[0137] In one embodiment, map 800 is editable to include specific locations of importance, such as water sources, wells, and particular farms. In one embodiment, regions on the map are delineated that can be designated for specific features, such as vegetation. In one embodiment, breeding flocks are generated automatically by a clustering algorithm that considers the proximity of groups of individual animal lineages to each other.

[0138] In one embodiment, photographs of animals taken are automatically compared to photos in the animal's profile using image recognition machine learning technology. Officers are asked to take photos of animals when recording their location, if possible. These photos are used to verify that the animal ID provided by the officer is correct. Image recognition can be tested using a database, such as the database by Elephants Alive®, which contains approximately 1,500 animal photos, each with a unique ID. In one embodiment, image recognition works with different lighting (shadow / sun), different settings (dark / light), different angles of the animal's ears, etc. In one embodiment, image recognition is reconfigurable / retrainable for any animal as the animal's markings change. Image recognition functionality can include a precise specification of the type of image taken (e.g., frontal view of face or side view of ears).

[0139] In one embodiment, the system provides a notification (e.g., a flag) when an animal is spotted far from the animal's expected movement pattern, based on automatic animal ID or data entered into a database.

[0140] In one embodiment, for example, conservation volunteers in the area can use a smartphone app to take photos of animals and record their whereabouts, which can be submitted to a database.

[0141] 9 is a flowchart of a method 900 for monitoring a protected area boundary, according to one embodiment. In operation 901, the protected area boundary is scanned to capture imagery using one or more sensors. In one embodiment, the protected area boundary is scanned using a satellite-telescope boundary protection mechanism coupled to the one or more sensors. In one embodiment, the one or more sensors include one or more LiDAR sensors, radar sensors, ultrasonic sensors, Global Positioning System (GPS) sensors, other sensors, or any combination thereof. In operation 902, illegal boundary crossings are identified from the imagery. In operation 903, a notification is generated regarding the illegal boundary crossing, as described in more detail below. Passive Sensing

[0142] In one embodiment, a system for monitoring protected area boundaries includes a boundary protection mechanism comprising one or more cameras coupled to a satellite telescope to detect humans illegally crossing the area boundary. In one embodiment, the camera is an infrared (IR) camera with a resolution of 10 cm per pixel to identify humans in IR images. In one embodiment, the camera is a radiation-hardened IR camera. The camera telescope system takes approximately 100MP photographs covering approximately 1 square kilometer for human detection. This resolution is also available in cameras that are not radiation-hardened, such as Canon DSLR cameras. 100MP covers approximately 1 square kilometer for human detection. For example, the Kruger National Park Nature Reserve in South Africa has an approximately 300-kilometer border with Mozambique. In one embodiment, by taking a photograph every 0.2 seconds, the entire length of the boundary is covered with one photograph per minute. Passive IR visuals may not be able to easily see through clouds. active sensing

[0143] In another embodiment, an active sensing system for monitoring protected area boundaries includes one or more LiDAR sensors. Active sensing systems use a higher power payload than passive sensing. Typically, LiDAR sensors illuminate targets with a scattered laser beam and measure distance by return time. The narrow beam maps physical features with high resolution. In one embodiment, one or more LiDAR sensors are used to capture an instant snapshot of approximately 600 meters by 3000 meters of an area in a single pass with a resolution of 30 cm or better. In one embodiment, the frequency is selected to penetrate clouds. The LiDAR sensor can also penetrate foliage (e.g., Lidar Data Filtering and Forestry Study (TIFFS) software detects non-vegetation data such as buildings, power lines, and birds in flight). In one embodiment, a system for monitoring protected area boundaries includes a laser and a telescope detection system coupled to the laser and trained on the area where the boundary intersects. Active noise cancellation

[0144] FIG. 11 illustrates a drone setup 1120 for a simulation of active noise cancellation to determine speaker amplitude and phase, according to one embodiment. A method for active noise cancellation to make drones quieter is described. In one embodiment, the method for active noise cancellation uses the dominant mode or frequency of the drone's noise, but one skilled in the art can extend the method to address harmonics of the dominant mode. The method involves replicating the sound signal produced by each of the drone's engines or rotors. Four propellers, such as propeller 1121, are spaced 2d apart at the corners of a square. The method seeks to minimize the average noise amplitude on a sphere of radius r, only the two-dimensional circular outline of which is shown on the xy plane in FIG. 11. Cartesian coordinates x, y, and z are shown in the upper left of FIG. 11.

[0145] In one embodiment, four speakers are placed at each rotor location. In another embodiment, a single speaker is placed near the four rotor locations, such as in the center of the square indicated by the small circle 1122. As shown by the simulations below, active noise cancellation techniques work when the speaker is closely juxtaposed with the rotor relative to the wavelength of sound λ, i.e., λ>>d.

[0146] In one embodiment, each speaker is collocated with a microphone that records and digitizes an audio signal. The signal is then phase-adjusted to be 180 degrees out of phase with the incident signal or to produce an inverse of the incident signal, and broadcast from the speaker at an adjusted amplitude. The phase adjustment of the broadcast audio signal must take into account any processing delays from the sound entering the microphone, to the digitization of the sound, signal processing, and output of the inverted sound signal from the speaker.

[0147] In other embodiments, the signals generated by each of the engines / rotors at specific frequencies are known in advance, and a speaker or speakers output a predetermined cancellation signal based on frequency and rotor pitch or angle.

[0148] To explain the concept and implement it through simulation, the method assumes that the speakers are located at the center of a square and that each rotor outputs a single tone, or that the dominant tone is an address tone, with no second, third, or higher harmonics present. The following MATLAB® code demonstrates how to effectively cancel rotor noise by finding the optimal amplitude and phase of the tone output by the center speaker. First, the code calculates the signal amplitude and phase to minimize the average noise amplitude over a sphere of radius r. Next, the code compares the amplitude of the noise generated in the xy plane on a square with side length 2r with and without the optimal cancellation signal. Next, the code compares the amplitude of the noise generated in the xz plane on a square with side length 2r with and without the optimal cancellation signal.

[0149] Consider rotors 1-4 each at a position indicated by a three-dimensional vector r_1, 2_2, r_3, r_4. If the speed of sound is c and the frequency of the rotor is f, then the wavelength of sound can be found as λ = c / f. After reading this disclosure, it will be clear to those skilled in the art how this method can be adjusted by determining the speed of sound c at each location by taking into account barometric pressure and humidity, and also by measuring c empirically by transmitting a signal from a speaker and measuring the time the signal is received at a microphone driven by a synchronized clock. Let k = 2π / λ be the scalar wave number of a signal with wavelength λ. If all rotors are spaced apart from each other,

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[0150] This method describes a cancellation signal of amplitude A and phase p, which is assumed to be emitted from a speaker located at the center of a square at the origin of the coordinate system. Next, the compensation signal can be described as follows. [Number] It is assumed to be emitted from a speaker at the center of a square at the origin of the coordinate system. Next, the compensation signal can be described as follows. [Number] Also, the amplitude of the composite signal can be described as follows. [Number] This equation forms the basis of the following MATLAB® simulation, and this method minimizes the average amplitude of the signal on the sphere of radius R by selecting the optimal amplitude A and phase p. However, by considering as follows, the total noise power on the sphere can be optimized as well. [Number]

[0151] As an example, when the drone's rotor is rotating at a speed of about 6000 rpm and the rotors are separated by about 0.3 meters, in order to minimize the average amplitude on a sphere with a radius of about 10 m, using the following MATLAB® code, by this method, for the case where the wavelength λ << d, A = 3.71 is obtained to cancel the cumulative effect of the four rotors, which is close to 4. Also, by this method, for the inversion of the signal, p = 3.14 is also obtained, which is close to π.

[0152] FIG. 12A shows a mesh plot of the amplitude of the signal on the sphere as a function of the spherical coordinate angles phi and theta without a compensation signal 1200, according to one embodiment. FIG. 12A is a mesh plot of log|z(r,t)| on the sphere. FIG. 12B shows a mesh plot of the amplitude of the signal on the sphere as a function of the spherical coordinate angles phi and theta with a near-optimal compensation signal 1210 with A=3.71 and p=3.14, according to one embodiment. FIG. 12B shows a mesh plot of log|z(r,t)| on the sphere. c For the parameters in this example, the average amplitude over the sphere decreases from 0.3712 to 0.0187, a decrease of 20*log10(0.3712 / 0.0187) = 25.96 dB.

[0153] 13A and 13B show mesh plots of sound amplitude in the xy plane on a square with sides of 20 m centered at the origin without (1300) and with (1310) a near-optimal compensation signal, respectively, according to one embodiment. For the parameters in this example, the average amplitude on the square surface was reduced by 13.31 dB. FIG. 13A is a mesh plot of log|z(r,t)| on the xy plane. FIG. 13B is a mesh plot of log|z(r,t)| on the xy plane. c A mesh plot of (r,t)|

[0154] 14A and 14B show mesh plots of sound amplitude in the xz plane on a square with sides of 20 m centered at the origin, with 1400 and without 1410 a near-optimal compensation signal, respectively, according to one embodiment. FIG. 14A is a mesh plot of log|z(r,t)| in the xz plane. FIG. 14B is a mesh plot of log|z(r,t)| in the xz plane. c A mesh plot of |(r,t)|. For the parameters in this example, the average amplitude over the square surface was reduced by 24.42 dB.

[0155] After reading this disclosure, it will be clear to those skilled in the art how this method can be adjusted when different rotors produce sounds of different pitches and amplitudes. It will also be clear after reading this disclosure how this method can be adjusted for rotors moving at different speeds. In one embodiment, the speaker is configured to output individual tones, or individual multi-tone signals, to address second, third, and higher harmonics of each dominant tone to cancel signals coming from each rotor at different angular frequencies. In one embodiment, rather than having different signals at multiple different frequencies output by the speaker, the rotor control law can be designed so that, under some stable flight condition, all rotors rotate at the same frequency. In this case, drone movement can be controlled by uniform frequency changes in pitch or angle and rotors, rather than by different frequencies between rotors. In general, it is desirable to rotate the drone as slowly as possible and to place the speakers as close as possible to each noise source. Many variations of the active noise cancellation approach are possible without changing the basic concepts described herein. Example MATLAB code for simulation

[0156]

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[0157] FIG. 10 is a block diagram of a data processing system 1000, according to one embodiment. Data processing system process 1000 represents any data processing system configured to perform the methods for conserving wildlife and enabling remote wildlife tourism as described herein with respect to FIGS. 1-9 and 11, 12A, 12B, 13A, 13B, 14A, and 14B. In alternative embodiments, data processing system 1000 may be connected (e.g., networked) to other machines over a local area network (LAN), an intranet, an extranet, or the Internet. Data processing system 1000 may operate in the capacity of a server or client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. In at least some embodiments, a drone is coupled to data processing system 1000. In at least some embodiments, a drone includes at least a portion of data processing system 1000. In one example, a drone may communicate with other machines or drones over a network.

[0158] In at least some embodiments, data processing system 1000 includes one or more machine control systems (not shown) (e.g., motors, steering control, brake control, throttle control, etc.) and an airbag system (not shown). In at least some embodiments, system 1000 is configured to execute software instructions to perform various features and functions (e.g., drone driving decisions) and to provide a graphical user interface (GUI) on a display device for a user. In one embodiment, the GUI is a touchscreen with input and output capabilities. In one embodiment, the GUI provides audio (or other) content playback to a user via speaker(s) 1034 and a display system. One or more processors of system 1000 perform various features and functions related to drone operation based at least in part on receiving input from one or more sensors 1032 and cameras 1036. In one embodiment, one or more sensors 1032 include one or more LiDAR sensors, one or more radar sensors, one or more ultrasonic sensors, one or more Global Positioning System (GPS) sensors, additional sensors, or any combination thereof.

[0159] Data processing system 1000 may further include a network interface device. Data processing system 1000 may further include a radio frequency (RF) transceiver that provides frequency shifting, converts received RF signals to baseband, and converts baseband transmit signals to RF. In some descriptions, a wireless transceiver or RF transceiver may be understood to include other signal processing functions, such as modulation / demodulation, encoding / decoding, interleaving / deinterleaving, spreading / despreading, inverse fast Fourier transform (IFFT) / fast Fourier transform (FFT), cyclic prefix attachment / removal, and other signal processing functions.

[0160] In at least some embodiments, data processing system 1000 may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a web appliance, a server, a network router, a switch or bridge, or any machine capable of executing (sequentially or otherwise) a set of instructions that specify operations to be performed by the data processing system. Also, although only a single data processing system is illustrated, the term "data processing system" shall be considered to include any collection of data processing systems that individually or collectively execute a set (or sets) of instructions to perform any one or more of the methodologies described herein.

[0161] Processor 1004 represents one or more general-purpose processing devices, such as, for example, a microprocessor, central processing unit, or other processing device. More specifically, processor 1004 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction width (VLIW) microprocessor, a processor implementing other instruction sets, or a processor implementing a combination of instruction sets. Processor 1004 may also be one or more special-purpose processing devices, such as, for example, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), a network processor, or the like. Processor 1004 is configured to control processing logic to perform the operations described herein with respect to FIGS. 1-9.

[0162] Data processing system 1000 may include multiple components. In one embodiment, these components are attached to one or more motherboards. In an alternative embodiment, these components are fabricated on a single system-on-a-chip (SoC) die rather than a motherboard. Components in data processing system 1000 include, but are not limited to, an integrated circuit die 1002 and at least one communications chip 1008. In some implementations, communications chip 1008 is fabricated as part of integrated circuit die 1002. Integrated circuit die 1002 may include a processor 1004 and on-die memory 1006, often used as cache memory, which may be provided by technologies such as embedded DRAM (eDRAM) or spin-transfer torque memory (STTM or STTM-RAM).

[0163] Data processing system 1000 may include other components that may or may not be physically and electrically coupled to a motherboard or fabricated within an SoC die. These other components include volatile memory 1010 (e.g., DRAM), non-volatile memory 1012 (e.g., ROM or flash memory), a graphics processing unit 1014 (GPU), a digital signal processor 1016, a cryptographic processor 1042 (a dedicated processor that executes cryptographic algorithms in hardware), a chipset 1018, an antenna 1022, a display or touchscreen display 1024, a touchscreen controller 1026, a battery 1020 or other power source, a power amplifier (PA) 1044, a global positioning system (GPS) 1046, a power amplifier (PA) 1048, a power amplifier (PA) 1048, a power supply (PSU ... The one or more sensors 1032 may include, but are not limited to, a GPS (Global Positioning System) device 1028, a compass 1030, one or more sensors 1032 which may include a power sensor to measure power consumption by the system, a motion sensor, a location sensor, or other sensors, one or more speakers 1034, one or more cameras 1036, a user input / output device 1038 (e.g., a keyboard, a mouse, a stylus, touch input, a voice-activated device, a set of speakers, etc.), and a mass storage device 1040 (e.g., a hard disk drive, a compact disc (CD), a digital versatile disc (DVD), etc.). In one embodiment, the one or more sensors 1032 comprise a set of sensors such as those described above with respect to FIGS. 1-9.

[0164] The communications chip 1008 enables wireless communication for data transfer to and from the data processing system 1000. The term “wireless” and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc. that may communicate data through the use of modulated electromagnetic radiation over a non-solid medium. The term does not imply that the associated devices do not include wiring, although in some embodiments they may not. The communications chip 1008 may implement any of a number of wireless standards or protocols, including, but not limited to, Wi-Fi (IEEE 802.11 family), WiMAX (IEEE 802.16 family), IEEE 802.20, Long Term Evolution (LTE), Ev-DO, HSPA+, HSDPA+, HSUPA+, EDGE, GSM, GPRS, CDMA, TDMA, DECT, Bluetooth, its derivatives, and any other wireless protocols designated 3G, 4G, 5G, and beyond. The data processing system 1000 may include multiple communications chips 1008. For example, the first communications chip 1008 may be dedicated to short-range wireless communications such as Wi-Fi and Bluetooth, and the second communications chip 1008 may be dedicated to long-range wireless communications such as GPS, EDGE, GPRS, CDMA, WiMAX, LTE, Ev-DO, and others.

[0165] The term "processor" may refer to any device or part of a device that processes electronic data from registers and / or memory and transforms the electronic data into other electronic data that can be stored in registers and / or memory.

[0166] In various embodiments, data processing system 1000 may be a laptop computer, a netbook computer, a notebook computer, an ultrabook computer, a smartphone, a tablet, a personal digital assistant (PDA), an ultra-mobile PC, a mobile phone, a desktop computer, a server, a printer, a scanner, a monitor, a set-top box, an entertainment control unit, a digital camera, a portable music player, or a digital video recorder. In further implementations, data processing system 1000 may be any other electronic device that processes data.

[0167] The mass storage device 1040 may include a machine-accessible storage medium (more specifically, a computer-readable storage medium) 1045 having stored thereon one or more sets of instructions (e.g., software) that embody any one or more of the methodologies or functions described herein. The software may reside, wholly or at least partially, within memory 1010, memory 1012, memory 1006, and / or within processor 1004 during execution by data processing system 1000, with on-die memory 1006 and processor 1004 also constituting machine-readable storage media. The software may also be transmitted or received via a network interface device.

[0168] Although the machine-accessible storage medium 1045 is shown as a single medium in the exemplary embodiment, the term "machine-readable storage medium" should be considered to include a single medium or multiple media (e.g., centralized or distributed databases, and / or associated caches and servers) that store one or more sets of instructions. The term "machine-readable storage medium" should also be considered to include any medium capable of storing or encoding a set of instructions for execution by a machine, causing a machine to perform any one or more of the methodologies of the present invention. Thus, the term "machine-readable storage medium" should be considered to include, but not be limited to, solid-state memory, and optical and magnetic media.

[0169] The following examples relate to further embodiments.

[0170] A system for remote wildlife viewing may include a set of one or more camera-equipped drones (or airships) or a set of one or more cameras that are remotely controlled via the internet, an application server that enables a web user interface that allows a remote individual or group to log on to the drones and / or cameras, issues commands to control the flight of the drones and / or the direction and zoom of the cameras, and allows the user to visually view what is seen on the cameras and identify various species of animals and plants, and a communications system with wireless components with a range of over 50 meters that connects the drones to the application server.

[0171] A system for remote wildlife viewing may include a set of one or more camera-equipped drones (or airships) or a set of one or more cameras that are remotely controlled via the internet, an application server that enables a web user interface that allows a remote individual or group to log on to the drones and / or cameras, issues commands to control the flight of the drones and / or the direction and zoom of the cameras, and allows the user to visually view what is seen on the cameras and identify various species of animals and plants, and a communications system with wireless components with a range of over 50 meters that connects the drones to the application server, where users reserve and / or pay for a specific allotted time and are then granted control of the drones by the application server for that allotted time.

[0172] A system for remote viewing of wildlife may include a set of one or more camera-equipped drones (or airships) or a set of one or more cameras that are remotely controlled via the internet, an application server that enables a web user interface that allows remote individuals or groups to log on to the drones and / or cameras, issues commands to control the flight of the drones and / or the direction and zoom of the cameras, and allows the user to visually identify various species of animals and plants in what is seen on the cameras, and a communications system with wireless components with a range of over 50 meters that connects the drones to the application server, which enables a game in which individuals are scored based on the number and / or types of species or individual organisms that they identify by photography or other means.

[0173] A system for remote viewing of wildlife may include a set of one or more camera-equipped drones (or airships) or a set of one or more cameras that are remotely controlled via the internet, an application server that enables a web user interface that allows a remote individual or group to log on to the drones and / or cameras and issue commands to control the flight of the drones and / or the direction and zoom of the cameras and allows the user to visually identify various species of animals and plants in the images captured by the cameras, and a communications system with wireless components with a range of over 50 meters that connects the drones to the application server, and automatic image recognition or human reviewers are used to classify the species or animals photographed or videotaped.

[0174] A system for remote viewing of wildlife may include a set of one or more camera-equipped drones (or airships) or a set of one or more cameras that are remotely controlled via the internet, an application server that enables a web user interface that allows a remote individual or group to log on to the drones and / or cameras, issues commands to control the flight of the drones and / or the direction and zoom of the cameras, and allows the user to visually identify various species of animals and plants in images captured by the cameras, and a communications system with wireless components with a range of over 50 meters connecting the drones to the application server, wherein human reviewers are used to classify the species or animals photographed or videotaped, and the reviewed data is used to train a deep learning neural network to automatically classify species or individual animals from the images.

[0175] A system for remote wildlife viewing may include a set of one or more camera-equipped drones (or airships) or a set of one or more cameras that are remotely controlled via the internet, an application server that enables a web user interface that allows a remote individual or group to log on to the drones and / or cameras, issues commands to control the flight of the drones and / or the direction and zoom of the cameras, and allows the user to visually view what is seen on the cameras and identify various species of animals and plants, and a communications system with wireless components with a range of over 50 meters that connects the drones to the application server, wherein the drones include location capabilities and are geofenced so that they cannot leave certain boundaries or go below or above certain altitudes.

[0176] A system for remote wildlife viewing may include a set of one or more camera-equipped drones (or airships) or a set of one or more cameras that are remotely controlled via the internet, an application server that enables a web user interface that allows a remote individual or group to log on to the drones and / or cameras, issues commands to control the flight of the drones and / or the direction and zoom of the cameras, and allows the user to visually view what is seen on the cameras and identify various species of animals and plants, and a communications system with wireless components with a range of over 50 meters that connects the drones to the application server, where the drones are monitored for allotted time or remaining power and returned to a base station for recharging by autonomous or third-party flight.

[0177] A system for remote wildlife viewing may comprise a set of one or more camera-equipped drones (or airships) or a set of one or more cameras that are remotely controlled via the internet, an application server that enables a web user interface that allows a remote individual or group to log on to the drones and / or cameras, issues commands to control the flight of the drones and / or the direction and zoom of the cameras, and allows the user to visually view what is shown on the cameras and identify various species of animals and plants, and a communications system with wireless components that connects the drones to the application server with a range of over 50 meters, and the nature viewing application may involve helium balloons being attached to the drones to effectively reduce weight and extend flight time, for example by drones with larger propellers or propellers that rotate at a different frequency, making the drone quieter so as not to disturb animals, and the drones being able to fly at high speeds and with low noise levels, and the drones are surrounded by a protective material such as lightweight mesh to prevent harm to living creatures; the drones are painted in colors such as green or blue to blend in with their natural environment; the drones include directional microphones that allow the user to hear sounds made by animals and sounds from the direction the microphone is pointed; the drones include a microphone on each drone with audio recognition software that can identify the sound of gunfire, and in conjunction with a standard clock such as a GPS, the microphone allows multiple drones to triangulate the location of gunfire; the drones include image processing and flight control for automatic obstacle avoidance; and / or the drones and / or drone charging stations include equipment that allows the drones to automatically dock for charging.

[0178] A system for remote wildlife viewing may include a set of one or more camera-equipped drones (or airships) or a set of one or more cameras that are remotely controlled via the internet, an application server that enables a web user interface that allows a remote individual or group to log on to the drones and / or cameras, issues commands to control the flight of the drones and / or the direction and zoom of the cameras, and allows users to visually view what is shown on the cameras and identify various species of animals and plants, and a communications system having wireless components with a range of over 50 meters connecting the drones to the application server, wherein the wildlife viewing application may perform functions such as collecting user identification information, e.g., government-issued identification cards, and verifying these identities to prevent use of the system for illegal activities such as poaching, identifying and contacting all users who were viewing cameras within the area of ​​a particular event, e.g., an animal being shot, and allowing users to record sightings of suspicious people or activities and identify illegal activities such as poaching. a system that can send messages to conservation authorities to prevent sightings; a map that allows users to locate themselves and / or mark locations where they have sighted particular species or individual organisms; educational materials for users to learn about species that may be sighted; a process for sharing revenue from users who reserve time on the system with the conservation authorities where the organisms are maintained and managed; a system whereby certain species are awarded different scores to users than other species, e.g., eagles receive 5 points and sparrows receive 1 point; a system for calculating species scores based on how frequently the species is sighted; a system where the application server communicates with conservation authority ground staff regarding the drone's location and / or status for ongoing maintenance; a system that allows users to contribute to animal research, for example by creating movement patterns for particular animals;

[0179] In the foregoing specification, certain exemplary embodiments have been described. It will be apparent that various modifications may be made to these embodiments without departing from the broad spirit and scope of the following claims. The specification and drawings are, therefore, to be regarded in an illustrative rather than a restrictive sense.

Claims

[Claim 1] The invention as described in the description and / or drawings.

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