System

Abstract

A system is provided.SOLUTION: A system for assisting a protected animal to return to wild, comprising: means for selecting a type of animal to be trained; means for generating a virtual space based on training content; means for setting an animal-specific display device for displaying the generated virtual space; means for detecting a stress level of the animal; and means for controlling continuation or suspension of training based on the stress level.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In today's world, there is a need to properly train rescued animals before returning them to the wild. However, current training methods are expensive and do not provide a simulation of the animals' natural environment. Furthermore, there is a lack of means to provide an appropriate training environment while monitoring the animals' stress levels in real time. The present invention aims to solve these problems by providing a system for low-cost and efficient wild reintroduction training. [Means for solving the problem]

[0005] The present invention provides a system for supporting the release of rescued animals into the wild, including a means for selecting the type of animal to be trained, a means for generating a virtual space based on the training content, a means for configuring a display device dedicated to the animal to display the generated virtual space, a means for detecting the stress level of the animal, and a means for controlling the continuation or interruption of training based on the stress level. In particular, the virtual space is generated using artificial intelligence to provide optimized training content. Furthermore, the use of a dedicated head-mounted display allows animals to train in a virtual environment that is immersive and similar to their natural environment. This allows for the provision of real-time monitoring and an appropriate training environment at low cost, thereby enabling efficient training for release into the wild.

[0006] "Training animals" refers to specific species or individuals that are rescued and then trained with the aim of being released back into the wild.

[0007] "Training content" refers to specific activities and scenarios that teach animals the skills and behaviors they will need to reintegrate into the wild.

[0008] A "virtual space" is a simulated environment digitally created using generative artificial intelligence to provide animals with an experience similar to that of their wild environment.

[0009] A "display device dedicated to animals" is a device that can be worn by an animal and is used to visually present a virtual space to the animal.

[0010] A "head-mounted display" is a display device that is attached to an animal's head, allowing the animal to visually experience a virtual space.

[0011] "Stress level" is an index that indicates the physiological state of an animal and indicates the degree of stress that the animal experiences during training.

[0012] "Generative artificial intelligence" refers to algorithms and systems that use machine learning and deep learning to automatically generate virtual spaces and training scenarios.

[0013] "Means for controlling the continuation or discontinuation of training" refers to a method or system for managing and regulating the progress of training so that animals are trained at an appropriate stress level. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] An embodiment of this invention is a system for supporting the return of rescued animals to the wild, which provides a training environment tailored to the specific animal being trained, and monitors and controls the training status in real time.

[0036] First, the user uses the server to select the type of animal to be trained, for example, a fox. Next, the user operates the server to determine the training content required for that animal, for example, basic survival skills.

[0037] The server uses a generative AI to generate a virtual space based on the selected animal type and training content. This virtual space simulates an environment for training the skills and behaviors that animals need in the wild. The server names this virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[0038] The device then acquires data from an EEG sensor attached to the animal to detect its stress level. If the device determines that the animal's stress level is "low" based on this data, it uses a head-mounted display to display the generated virtual space to the animal. The animal undergoes training through this virtual space, acquiring the skills it will need in the wild.

[0039] During training, the device constantly monitors the animal's stress level and biological data. If the stress level becomes too high, the device will automatically stop the training or adjust the environment.

[0040] As a concrete example, when applying this system to Fox No. 1, the user uses the server to select "fox" and set "basic survival skills" as the training content. The server then uses a generation AI to generate a "VR space for training basic survival skills for foxes."

[0041] Next, the device checks Fox 1's stress level using an EEG sensor and determines that it is "low." After this, the device displays a "VR space for basic survival skills training for foxes" on Fox 1's head-mounted display, and Fox 1 begins training in this virtual space.

[0042] During training, the terminal constantly monitors Fox 1's stress level and other vital data, adjusting the training environment or halting training as necessary. In this way, this system provides a concrete method for efficiently training animals for reintroduction into the wild at low cost.

[0043] The processing flow will be explained below.

[0044] Step 1:

[0045] The user operates the server to select the type of animal to be trained, for example, "fox."

[0046] Step 2:

[0047] The user operates the server to determine the training content, for example, "basic survival skills."

[0048] Step 3:

[0049] The server uses generative AI to generate a virtual space based on the selected animal species and training content, providing a scenario optimized for training the skills the animals will need in the wild.

[0050] Step 4:

[0051] The server names the generated virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[0052] Step 5:

[0053] The device uses an EEG sensor attached to the animal to detect the animal's stress level, and based on this data, evaluates the stress level and determines it as "low."

[0054] Step 6:

[0055] If the device determines that the stress level is low, it will set up a virtual space on the animal's dedicated display device (head-mounted display). For example, Fox 1's head-mounted display will display a "VR space for basic survival skills training for foxes."

[0056] Step 7:

[0057] Fox 1 will begin training in a virtual environment where the animals will learn the necessary skills and prepare for release back into the wild.

[0058] Step 8:

[0059] During training, the device constantly monitors Fox 1's stress levels and biological data in real time. If the stress level becomes too high, the device will automatically halt the training or adjust the environment.

[0060] Step 9:

[0061] After the training is completed, the terminal will evaluate Fox 1's progress and acquired skills, and if necessary, create a next training plan. Based on these results, it will provide further optimized training content.

[0062] Example 1

[0063] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0064] In modern society, the release of rescued animals into the wild has become an important issue. Conventional training methods have been insufficient in managing stress for animals, making it difficult to provide an efficient training environment. It has also been difficult to consistently provide different training content for each animal. Therefore, there is a need for a system that can provide an appropriate training environment in real time according to the type and situation of the animal, and monitor and adjust the stress level of the animal.

[0065] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0066] In this invention, the server includes a means for selecting the type of animal to be trained, a means for generating a virtual space based on the training content, and a means for creating and transmitting prompts using artificial intelligence when generating the virtual space, thereby making it possible to provide an appropriate virtual training environment according to the type of animal and the training content.

[0067] The terminal includes a means for setting a display device dedicated to the animal that displays the generated virtual space, a means for detecting the stress level of the animal, a means for controlling the continuation or interruption of training based on the stress level, and a means for monitoring the animal's biological data in real time, thereby making it possible to monitor the animal's stress level and biological data in real time and adjust the training environment as needed.

[0068] By combining these methods, we provide a system that provides optimal training tailored to the individual circumstances of animals and enables efficient, low-stress training for reintroduction into the wild.

[0069] The "means for selecting the type of animal to train" is a system component that provides an interface through which a user can select a particular type of animal to assist in the release of rescued animals back into the wild.

[0070] The "means for generating a virtual space based on training content" is a system component that automatically generates a virtual space based on the selected animal type and training content appropriate for that animal.

[0071] The "means for setting up a display device dedicated to the animal that displays the generated virtual space" is a system component that prepares and sets up a device that enables the animal to visually recognize the generated virtual space.

[0072] A "means for detecting stress level of an animal" is a system component that analyzes data from sensors attached to the animal and monitors the stress level of the animal in real time.

[0073] The "means for controlling continuation or interruption of training based on the stress level" is a system component that automatically decides whether to continue or interrupt training depending on the detected stress level.

[0074] "Means for creating and sending prompt sentences using generative AI" refers to a system component that uses a generative AI model to generate prompt sentences that are suited to the type of animal and the training content, and sends the prompt sentences to the AI.

[0075] "Means for monitoring biological data of an animal in real time" refers to a system component that acquires and analyzes biological data, such as heart rate and body temperature, of an animal in real time.

[0076] An embodiment of this invention is a method for providing a training environment tailored to a specific animal as a training target, and for monitoring and controlling the training status in real time, in a system for supporting the return of rescued animals to the wild.

[0077] First, the user uses the server to select the type of animal to be trained, for example, a fox. Next, the user operates the server to determine the training content required for that animal, for example, basic survival skills.

[0078] The server generates a virtual space using a generative AI model (e.g., OpenAI's GPT-3) based on the selected animal type and training content. This virtual space simulates an environment for training the skills and behaviors that animals need in the wild. The server names this virtual space "VR space for basic survival skill training for foxes" and sets it as the training environment.

[0079] Next, the device acquires data from an EEG sensor attached to the animal to detect the animal's stress level. If the animal's stress level is determined to be "low" based on this data, the device uses a display device dedicated to the animal (e.g., Oculus Rift) to display the generated virtual space to the animal. The animal undergoes training through this virtual space, thereby acquiring the skills necessary for the wild.

[0080] During training, the device constantly monitors the animal's stress level and vital data (e.g., heart rate, body temperature, etc.). If the stress level becomes too high, the device will automatically halt the training or adjust the environment.

[0081] As a concrete example, when applying this system to Fox No. 1, the user uses the server to select "fox" and set "basic survival skills" as the training content. The server then uses the generative AI model to generate a "VR space for training basic survival skills for foxes."

[0082] Next, the device checks Fox 1's stress level using an EEG sensor and determines that it is low. After this, the device displays a "VR space for basic survival skills training for foxes" on Fox 1's head-mounted display (e.g., HTC Vive), and Fox 1 begins training in this virtual space.

[0083] During training, the terminal constantly monitors Fox 1's stress levels and other vital signs, adjusting the training environment or halting training as necessary.

[0084] As described above, this system provides a specific method for efficiently training animals for reintroduction into the wild at low cost.

[0085] Example prompt sentence:

[0086] "Create a VR space for training basic survival skills for foxes."

[0087] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0088] Step 1:

[0089] The user selects an animal type. The user logs in to the server and selects an animal type from the system's main menu. For example, to select a "fox," the user clicks "fox" in the drop-down menu in the UI. The selected animal type is the input, and the animal type data is obtained as the output.

[0090] Step 2:

[0091] The user decides the training content. After selecting the type of animal, the user operates a menu to decide the training content. For example, the user may set "basic survival skills" as the training content. The user clicks on the appropriate item from the list of training content. The selected training content is the input, and training content data is obtained as the output.

[0092] Step 3:

[0093] The server generates a virtual space. Based on the selected animal type and training content, the server uses a generative AI model (e.g., OpenAI's GPT-3) to generate the virtual space. Specifically, the server combines a pre-prepared prompt sentence with the animal type and training content and sends it to the generative AI. This prompt sentence is the input, and the generated virtual space data is obtained as the output.

[0094] Step 4:

[0095] The device monitors the animal's stress level. The device acquires data in real time from an EEG sensor attached to the animal and analyzes the stress level. The acquired EEG data is the input, and the stress level data is the output.

[0096] Step 5:

[0097] The device displays the virtual space to the animal. If the stress level is determined to be "low," the device displays the virtual space created on a display device dedicated to the animal (e.g., Oculus Rift). Stress level data is the input, and the virtual space is displayed as the output.

[0098] Step 6:

[0099] The terminal monitors and adjusts the animal during training. During training, the terminal constantly monitors the animal's stress level and biometric data (e.g., heart rate, body temperature, etc.). If the stress level is determined to be "high," the terminal temporarily suspends training and adjusts the environment in the virtual space. Stress level and biometric data are inputs, and the output is the action taken to continue or suspend training.

[0100] Specific actions may include sending electronic alerts or notifications to users or administrators.

[0101] (Application example 1)

[0102] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0103] The current system for protecting and supporting the release of wild animals has problems such as limited training environments and inadequate stress management and real-time monitoring of biological data, which makes it difficult to provide effective training for the safe release of animals into the wild and reduces the efficiency of conservation efforts.

[0104] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0105] In this invention, the server includes means for selecting the type of animal to be trained, means for generating a virtual space based on the training content, means for setting up a display device dedicated to the animal that displays the generated virtual space, means for detecting the stress level of the animal, means for controlling the continuation or interruption of training based on the stress level, means for simulating a safe environment and conducting training using the virtual space generated based on the type of animal to be protected and the training content, and means for monitoring and analyzing the animal's biological data in real time, which enables stress management of the animal and real-time monitoring of the biological data, thereby providing a safe and effective training environment.

[0106] The "means for selecting the type of animal to be trained" refers to a method and device for identifying the type of protected wild animal and selecting appropriate training content based on that type.

[0107] The "means for generating a virtual space based on training content" refers to a method and apparatus for creating a virtual environment according to the selected training content.

[0108] The "means for setting up a display device dedicated to an animal to display the generated virtual space" refers to a method and apparatus for setting up a specific device to visually present the training virtual space to an animal.

[0109] The "means for detecting the stress level of an animal" refers to a method and device for analyzing the biological data of an animal and measuring the degree of stress based on the results.

[0110] The "means for controlling the continuation or discontinuation of training based on the stress level" refers to a method and device for determining and controlling whether to continue or discontinue training in accordance with fluctuations in the stress level of an animal.

[0111] "Means for simulating a safe environment and conducting training using a virtual space created based on the type of animal to be protected and the training content" refers to a method and device for conducting training that enables the animal to behave safely, using a virtual environment that corresponds to the type of animal to be protected and the necessary training content.

[0112] "Means for monitoring and analyzing animal vital data in real time" refers to methods and devices for instantly acquiring and analyzing vital data obtained from sensors attached to animals.

[0113] This invention relates to a system that supports the return of protected wild animals to the wild, and specifically, it provides a training environment tailored to the animal being trained, and can monitor and control the training status in real time.

[0114] First, the user accesses the server and selects the type of animal to be trained. This allows the user to specify the animal to be protected (e.g., "deer") and set the training content required for that animal (e.g., "patrol route in the habitat").

[0115] Based on the selected animal species and training, the server uses a generative AI model to generate a virtual space designed to simulate the skills and behaviors required in the animal's natural environment.

[0116] Next, the device acquires data from an EEG sensor attached to the animal and detects the animal's stress level in real time. If the animal's stress level is determined to be "low" based on this data, the device displays the virtual space created using a display device dedicated to the animal (e.g., a head-mounted display). This allows the animal to begin training in the virtual space.

[0117] During training, the device constantly monitors the animals' stress levels and other vital data, automatically adjusting the training environment or halting training as necessary, enabling real-time monitoring of animal stress and vital data, providing a safe and effective training environment.

[0118] For example, the following prompts can be fed into a generative AI model to generate the desired virtual space:

[0119] "The target animal is deer, and we're tasked with creating a virtual environment to train patrol routes in their habitat. This environment includes natural forests, rivers, hills, etc., designed to allow deer to move safely."

[0120] The hardware used includes head-mounted displays such as Oculus Quest, animal biometric sensors, and high-performance servers such as AWS EC2 instances, while the software includes generative AI systems such as OpenAI GPT-3 and real-time monitoring and data analysis platforms such as Apache Kafka and Apache Spark.

[0121] This invention is expected to make it possible to train animals for reintroduction into the wild more efficiently and with less stress, further promoting conservation efforts.

[0122] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0123] Step 1:

[0124] A user accesses the server and selects the type of animal to protect.

[0125] Input: Data about the type of animal (e.g. "deer")

[0126] Output: Selection data about animal species

[0127] Specific behavior: The user operates an interface to select a type of animal, and the server records this selection.

[0128] Step 2:

[0129] The user accesses the server and sets the training content.

[0130] Input: Training data (e.g., "patrol route in habitat area")

[0131] Output: Setting data regarding training content

[0132] Specific operation: The user operates the interface to select the training content, and the server records this setting.

[0133] Step 3:

[0134] The server inputs prompt sentences into the generative AI model and generates a virtual space.

[0135] Input: Data about the animal type and training

[0136] Output: Generated virtual space

[0137] Specific operation: The server inputs the following prompt to the generative AI model: "The target animal is a deer. Please create a virtual environment to train patrol routes in its habitat. This environment includes natural forests, rivers, hills, etc., and is designed to allow deer to move safely."

[0138] Step 4:

[0139] The device collects data from an EEG sensor attached to the animal to detect the animal's stress level.

[0140] Input: Biometric data obtained from EEG sensors

[0141] Output: Animal's stress level (e.g. "low")

[0142] How it works: The brainwave sensor collects the animal's biological data, which the device analyzes to determine its stress level.

[0143] Step 5:

[0144] The terminal sets up a display device dedicated to the animal and displays the virtual space.

[0145] Input: Generated virtual space, animal stress level (only if low)

[0146] Output: Display of a virtual space that animals can visually recognize

[0147] Specific operation: The device displays the generated virtual space on a head-mounted display dedicated to the animal.

[0148] Step 6:

[0149] The device monitors the animal's vital data in real time during training and adjusts or halts the training as necessary.

[0150] Input: Real-time biometric data acquired during training

[0151] Output: Instructions to continue, adjust, or stop training

[0152] Specific operation: The device constantly monitors the animal's biological data and automatically adjusts the training content and environment if there are changes in stress levels or other important indicators.

[0153] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0154] A mode for implementing this invention is to provide a training environment tailored to a specific animal as a training target in a system for supporting the return of rescued animals to the wild, and in addition to monitoring and controlling the training status in real time, to recognize the emotions of the user to provide an even more optimal training environment.

[0155] First, the user operates the server to select the type of animal to be trained, for example, "fox." Next, the user operates the server to determine the training content required for that animal, for example, "basic survival skills."

[0156] The server uses a generative AI to generate a virtual space based on the selected animal type and training content. This virtual space simulates an environment for training the skills and behaviors that animals need in the wild. The server names this virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[0157] The device then acquires data from an EEG sensor attached to the animal to detect its stress level. If the device determines that the animal's stress level is "low" based on this data, it uses a head-mounted display to display the generated virtual space to the animal. The animal undergoes training through this virtual space, acquiring the skills it will need in the wild.

[0158] During training, the device constantly monitors the animal's stress level and biological data. If the stress level becomes too high, the device will automatically stop the training or adjust the environment.

[0159] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotional data and adjusts the training scenario in the virtual space based on the analysis results. For example, if the user is feeling stressed or anxious, the emotion engine can use that data to adjust the training environment to be gentler. This not only makes training the animal more effective, but also reduces the psychological burden on the user.

[0160] As a concrete example, when applying this system to Fox No. 1, the user uses the server to select "fox" and set "basic survival skills" as the training content. The server then uses a generation AI to generate a "VR space for training basic survival skills for foxes."

[0161] Next, the device checks Fox 1's stress level using an EEG sensor and determines that it is "low." After this, the device displays a "VR space for basic survival skills training for foxes" on Fox 1's head-mounted display, and Fox 1 begins training in this virtual space.

[0162] During training, the device constantly monitors Fox 1's stress level and other biometric data, adjusting the training environment or halting training as necessary. Furthermore, the emotion engine adjusts the training environment appropriately based on the user's emotional data, ensuring more effective training for the animal. This provides a concrete method for efficiently training animals for release into the wild at low cost, reducing the burden on both the user and the animal.

[0163] The processing flow will be explained below.

[0164] Step 1:

[0165] The user operates the server to select the type of animal to be trained, for example, "fox."

[0166] Step 2:

[0167] The user operates the server to determine the training content, for example, "basic survival skills."

[0168] Step 3:

[0169] The server uses generative AI to generate a virtual space based on the selected animal species and training content, simulating an environment for training the skills and behaviors the animal would need in the wild.

[0170] Step 4:

[0171] The server names the generated virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[0172] Step 5:

[0173] The device uses an EEG sensor attached to the animal to capture data on the animal's stress level, which can then be used to assess the animal's readiness for training.

[0174] Step 6:

[0175] The terminal evaluates the animal's stress level based on the acquired data, and if it determines that it is "low," it proceeds to the next step.

[0176] Step 7:

[0177] The device is set up with a display device (head-mounted display) specifically for animals, and the generated virtual space is displayed to the animal. In the case of Fox 1, the head-mounted display displays a "VR space for training basic survival skills for foxes."

[0178] Step 8:

[0179] Fox 1 will begin training in a virtual environment where animals will learn the necessary skills and prepare for release back into the wild.

[0180] Step 9:

[0181] During training, the terminal constantly monitors Fox 1's stress levels and biological data in real time. If the stress level becomes too high, the terminal will halt the training or adjust the environment.

[0182] Step 10:

[0183] The system's built-in emotion engine analyzes the user's emotional data and adjusts the virtual training scenario based on the data if the user is feeling stressed or anxious.

[0184] Step 11:

[0185] The training environment adjusted by the emotion engine is provided to the animal again, which not only makes training for the animal more effective but also reduces the psychological burden on the user.

[0186] Step 12:

[0187] After completing the training, the terminal will evaluate Fox 1's progress and the skills it has acquired. If necessary, it will create a training plan for the next training session. Based on the results, further training content will be provided that is optimal for the new training.

[0188] Example 2

[0189] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0190] Today's training for reintroducing rescued animals into the wild requires providing an optimal training environment for each animal and taking into account the animal's stress level and the user's emotional state. Current methods lack a system that comprehensively manages these factors, limiting the effectiveness of training. It is also necessary to simultaneously reduce the user's stress and anxiety. To achieve this, we aim to develop a system that improves the efficiency of animal training while also reducing the user's psychological burden.

[0191] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0192] In this invention, the server includes means for selecting the type of animal to be trained, means for generating a virtual space based on the training content, means for setting up a display device dedicated to the animal to display the generated virtual space, means for detecting the stress level of the animal, means for controlling the continuation or interruption of training based on the stress level, and means for recognizing the user's emotions and adjusting the training environment, thereby improving the efficiency of wild release training for animals and simultaneously reducing the psychological burden on the user.

[0193] The "means for selecting the type of animal to be trained" refers to an interface that allows the user to specify the type of animal to be trained on the system.

[0194] "Means for generating a virtual space based on training content" refers to a mechanism that uses artificial intelligence to create a virtual space for animals to receive training in accordance with specified training content.

[0195] "Means for setting up a display device dedicated to the animal that displays the generated virtual space" refers to a mechanism for attaching and setting up a display device dedicated to the animal so that the animal can see the generated virtual space.

[0196] "Means for detecting stress levels in animals" refers to a mechanism for analyzing data obtained from sensors attached to animals and assessing their stress levels.

[0197] "Means for controlling the continuation or discontinuation of training based on the stress level" refers to a control mechanism for continuing or discontinuing training or adjusting the training content based on the evaluation results of the animal's stress level.

[0198] "Means for recognizing the user's emotions and adjusting the training environment" refers to a mechanism for analyzing the user's emotional state and adjusting the virtual training space and environmental settings based on the results.

[0199] The embodiment of the present invention is a system for supporting the release of rescued animals back into the wild. This system operates in cooperation with users, a server, and terminals.

[0200] First, the user accesses the server's interface from their device. They select the type of animal to be trained and set the appropriate training content. A general web application or mobile application is used as the user interface. For example, the user selects a "fox" and specifies "basic survival skills" as the training content.

[0201] The server then uses a generative AI model (e.g., Azure OpenAI) to generate a virtual space based on the selected animal and training content. This virtual space simulates the environment for training the skills and behaviors the animal would need in its natural environment. The generated virtual space is named "VR space for basic survival skills training for foxes" and saved on the server.

[0202] Next, the device acquires data from an EEG sensor (e.g., NeuroSky) attached to the animal to detect the animal's stress level. If the stress level is determined to be "low" based on this data, the device uses a display device (e.g., Oculus Rift) dedicated to the animal to display the generated virtual space to the animal. The animal can then receive training through this virtual space.

[0203] During training, the device constantly monitors the animal's stress level and other biometric data and adjusts the training environment as needed, including the ability to stop training or adjust the difficulty of the environment if stress levels become too high.

[0204] Furthermore, the server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine (e.g., Affectiva) analyzes the user's emotional data and adjusts the virtual training scenario based on the analysis results. This allows the training environment to be more calming if the user is feeling stressed or anxious.

[0205] As a specific example, if the training subject is "Fox No. 1," the user selects "Fox" through the server and sets "basic survival skills." The server uses a generative AI model to generate an appropriate virtual space. After the device uses an EEG sensor to determine that Fox No. 1's stress level is "low," the device displays the training VR space on Fox No. 1's head-mounted display and begins training. During training, the device continues to monitor the stress level and adjusts the environment as necessary. The server's emotion engine analyzes the user's emotional data and adjusts the training environment as appropriate.

[0206] Example prompt: "Create an appropriate VR environment for training Fox 1, the fox, in basic survival skills. Fox 1 has a low stress level, so we will train him in this virtual environment. Please also take into account the user's emotional data and adjust the training environment accordingly."

[0207] As described above, this system effectively trains animals for reintroduction into the wild, while at the same time reducing the psychological burden on users.

[0208] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0209] Step 1:

[0210] Selection of animal species and training content

[0211] Input: The user accesses the system and selects the type of animal to be trained and the training content.

[0212] How it works: The user operates the device and selects the type of animal, such as "fox," and the training content, such as "basic survival skills," from the interface on the server.

[0213] Output: The selected animal type and training details are sent to the server and saved.

[0214] Step 2:

[0215] Virtual space generation

[0216] Input: The server receives information about the animal type and training details sent by the user.

[0217] How it works: The server calls a generative AI model (e.g., Azure OpenAI) and sends a prompt to the model to generate a virtual space based on the selected animal type and training content. For example, the prompt "Generate a VR space for training basic survival skills for foxes" is input to the model.

[0218] Output: The server acquires the virtual space data output from the generative AI model and saves it as a "VR space for training basic survival skills for foxes."

[0219] Step 3:

[0220] Stress Level Detection

[0221] Input: Stress level data is sent to the terminal from an EEG sensor attached to the animal.

[0222] How it works: The device periodically collects data from an EEG sensor (e.g., NeuroSky), analyzes the data, and evaluates the stress level. If the stress level is determined to be "low," it moves on to the next step.

[0223] Output: The stress level assessment results are saved on the device and the conditions for the next processing step are set.

[0224] Step 4:

[0225] Virtual space display

[0226] Input: Evaluation results of animals rated as having a "low" stress level and virtual space data stored on the server.

[0227] Operation: The terminal displays the virtual space obtained from the server to the animal through a display device dedicated to the animal (e.g., Oculus Rift). Specifically, the terminal attaches a head-mounted display to the animal and displays the generated VR space.

[0228] Output: The animal begins training in the virtual space.

[0229] Step 5:

[0230] Monitoring during training

[0231] Input: Stress levels and other biometric data sent periodically by the animal during training.

[0232] How it works: The device monitors the animal's stress level and other biometric data in real time during training. If the stress level becomes too high, the device will automatically halt the training or adjust the difficulty of the training environment. Specifically, the device selects and executes appropriate actions based on the data obtained.

[0233] Output: The training environment is adjusted or training is interrupted, reducing the animal's stress level.

[0234] Step 6:

[0235] Analyzing user emotion data and adjusting the environment

[0236] Input: The user's emotional data is sent to the emotion recognition engine.

[0237] How it works: An emotion engine (e.g., Affectiva) built into the server analyzes the user's emotional data and adjusts the virtual training scenario based on the results. Specifically, it automatically changes the virtual environment settings and training difficulty according to the user's emotional state.

[0238] Output: A tailored training environment is provided for the animal, reducing the psychological burden on the user.

[0239] Through these processing steps, the system can provide an optimal training environment for both the animal and the user.

[0240] (Application example 2)

[0241] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0242] Training rescued animals for release into the wild requires a high level of expertise and a great deal of effort, making it difficult to do so efficiently. Furthermore, conventional methods cannot take into account the animal's stress or the user's emotions in real time, which can reduce the effectiveness of the training. Therefore, there is a need for effective and efficient training for reintroducing animals into the wild. Furthermore, police dog training requires an environment that efficiently trains specific skills (e.g., explosive detection), but it is difficult to adaptively change that environment in real time.

[0243] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0244] In this invention, the server includes a means for selecting the type of animal to be trained to support the release of rescued animals into the wild, a means for generating a virtual space based on the training content, a means for configuring a display device dedicated to the animal to display the generated virtual space, a means for detecting the stress level of the animal, a means for controlling the continuation or interruption of training based on the stress level, and a means for recognizing the user's emotions and adjusting the training environment in the virtual space. This enables efficient release-into-the-wild training while minimizing stress on the animal. Furthermore, in training specific skills for police dogs, an appropriate virtual environment can be provided while taking the user's emotions into consideration, achieving high training effectiveness.

[0245] A "trainable animal" is an animal that is to be trained to acquire a particular skill or behavior.

[0246] "Training content" refers to the specific skills and behaviors that the animal must master.

[0247] A "virtual space" is a simulated training environment created by generative AI.

[0248] An "animal-specific display device" is a device that animals can wear to visually experience a virtual space.

[0249] "Stress level" is an indicator of the degree of mental and physical strain an animal experiences during training.

[0250] "Generative AI" is an artificial intelligence technology that analyzes large amounts of data and automatically generates new information and spaces.

[0251] "User's emotions" refers to the psychological state of the user who manages and supervises the training.

[0252] The "means for recognizing emotions and adjusting the training environment in the virtual space" is a device or system that has the function of analyzing the user's emotional state and adaptively changing the training environment based on the results.

[0253] The system for implementing this invention is designed to efficiently release rescued animals back into the wild and train them in specific skills. The roles of the server, terminal, and user are clearly defined, and each step is explained in detail.

[0254] Hardware and Software Use

[0255] server:

[0256] The server is used as a means to select the animal species and training content, and is equipped with a generative artificial intelligence (AI) model that can generate a virtual space based on user instructions.

[0257] Examples of devices used: high-performance servers (e.g., Dell PowerEdge), generative AI models (e.g., OpenAI GPT).

[0258] Device:

[0259] The device collects the animal's biological data and monitors its stress level based on that data. It also displays a virtual world to the animal wearing a head-mounted display.

[0260] Examples of devices used: EEG sensors (e.g., NeuroSky), head-mounted displays (e.g., Oculus Rift).

[0261] User:

[0262] The user operates the server to select the animal and training content, and the emotion data is analyzed by the emotion analysis engine, which adaptively adjusts the training environment.

[0263] Examples of devices used: emotion analysis software (e.g., Affectiva).

[0264] Specific examples of system use

[0265] 1. Animal and training selection:

[0266] Users connect to the server using a smartphone app or PC and select the animal to be trained (e.g., German Shepherd) and the training content (e.g., explosive detection training).

[0267] 2. Creating a virtual training environment:

[0268] Based on the information selected by the user, the server inputs prompt statements into the generative AI model to generate a virtual space.

[0269] Example prompt sentence:

[0270] Create a VR environment for explosive detection training for German Shepherds.

[0271] 3. Monitoring Animal Stress Levels and Initiating Training:

[0272] The device uses an EEG sensor to monitor the animal's stress level, and if the stress level is low, a virtual space is displayed on the head-mounted display, and the animal begins training through the virtual space.

[0273] 4. Adaptive adjustment of the training environment:

[0274] The system analyzes the user's emotional data using an emotion analysis engine and dynamically adjusts the training environment based on the results. For example, if the user is feeling stressed, the training environment will be adjusted to be more gentle.

[0275] Specific example details

[0276] For example, if a police station uses a German shepherd for explosive detection training, the user selects the animal and the training content using the server. Then, a prompt is input into the generative AI model to generate a "VR environment for German shepherd explosive detection training." The system then monitors the animal's stress level using an EEG sensor, initiates training, and adaptively adjusts the environment in real time.

[0277] In this way, the present invention makes it possible to effectively and efficiently train animals for reintroduction into the wild or for specific skills.

[0278] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0279] Step 1:

[0280] The user connects to the server and selects the animal to be trained and the training content. As input, the user provides information on the type of animal and the training content. Based on this input data, the server creates a prompt sentence to be passed to the generative AI model. Data on the selected animal's characteristics and training content is used to create this prompt sentence.

[0281] The server stores the input information in a database and prepares it for the next processing step.

[0282] The output is a prompt and training selection information.

[0283] Step 2:

[0284] The server inputs a prompt sentence into the generative AI model to generate a virtual training environment. The prompt sentence is passed to the generative AI model as input. The server uses the generative AI model to generate an appropriate virtual space based on this prompt sentence.

[0285] This generated virtual space contains scenarios and environmental elements that are optimal for the training content and are suitable for training specific skills of animals.

[0286] The output is virtual training environment data.

[0287] Step 3:

[0288] The device collects the animal's biological data and monitors its stress level. As input, the device receives biological data from an EEG sensor. The device analyzes this biological data and evaluates the animal's stress level.

[0289] The device determines the stress level as "low" or "high" based on the criteria and passes the result on to the next step.

[0290] The output is stress level data.

[0291] Step 4:

[0292] When the stress level is low, the device displays the virtual space on a display device dedicated to the animal. Stress level data and virtual training environment data are passed to the device as input. The device uses these data to display the virtual training environment on a head-mounted display.

[0293] The animals begin training in the virtual space through this display.

[0294] The output is a representation of the virtual space.

[0295] Step 5:

[0296] The server acquires the user's emotional data and analyzes it with an emotion analysis engine. As input, the emotion data from the emotion analysis software is sent to the server. The server analyzes this data and identifies the user's mental state.

[0297] Based on the analysis results, the server generates setting information for adaptively changing the virtual training environment.

[0298] The output is the configuration information of the virtual training environment.

[0299] Step 6:

[0300] The server sends the setting information of the virtual training environment to the terminal and adjusts the environment. The setting information of the virtual training environment is provided as input. The terminal dynamically adjusts the environmental elements of the virtual space based on this information.

[0301] Add or remove specific situations or stimuli to create an environment that allows the animal to receive optimal training.

[0302] The output is a tailored virtual training environment.

[0303] Step 7:

[0304] The device monitors the progress of the training and continues or stops it as needed. It uses inputs such as the animal's stress level, biometric data, and environmental adjustments. The device analyzes this data in real time to evaluate the effectiveness of the training.

[0305] The device ensures effective training continues and, if necessary, interrupts training to reduce strain on the animal.

[0306] The output is an instruction to continue or discontinue training.

[0307] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0308] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0309] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0310] [Second embodiment]

[0311] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0312] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0313] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0314] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0315] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0316] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0317] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0318] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0319] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0320] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0321] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0322] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0323] An embodiment of this invention is a system for supporting the return of rescued animals to the wild, which provides a training environment tailored to the specific animal being trained, and monitors and controls the training status in real time.

[0324] First, the user uses the server to select the type of animal to be trained, for example, a fox. Next, the user operates the server to determine the training content required for that animal, for example, basic survival skills.

[0325] The server uses a generative AI to generate a virtual space based on the selected animal type and training content. This virtual space simulates an environment for training the skills and behaviors that animals need in the wild. The server names this virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[0326] The device then acquires data from an EEG sensor attached to the animal to detect its stress level. If the device determines that the animal's stress level is "low" based on this data, it uses a head-mounted display to display the generated virtual space to the animal. The animal undergoes training through this virtual space, acquiring the skills it will need in the wild.

[0327] During training, the device constantly monitors the animal's stress level and biological data. If the stress level becomes too high, the device will automatically stop the training or adjust the environment.

[0328] As a concrete example, when applying this system to Fox No. 1, the user uses the server to select "fox" and set "basic survival skills" as the training content. The server then uses a generation AI to generate a "VR space for training basic survival skills for foxes."

[0329] Next, the device checks Fox 1's stress level using an EEG sensor and determines that it is "low." After this, the device displays a "VR space for basic survival skills training for foxes" on Fox 1's head-mounted display, and Fox 1 begins training in this virtual space.

[0330] During training, the terminal constantly monitors Fox 1's stress level and other vital data, adjusting the training environment or halting training as necessary. In this way, this system provides a concrete method for efficiently training animals for reintroduction into the wild at low cost.

[0331] The processing flow will be explained below.

[0332] Step 1:

[0333] The user operates the server to select the type of animal to be trained, for example, "fox."

[0334] Step 2:

[0335] The user operates the server to determine the training content, for example, "basic survival skills."

[0336] Step 3:

[0337] The server uses generative AI to generate a virtual space based on the selected animal species and training content, providing a scenario optimized for training the skills the animals will need in the wild.

[0338] Step 4:

[0339] The server names the generated virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[0340] Step 5:

[0341] The device uses an EEG sensor attached to the animal to detect the animal's stress level, and based on this data, evaluates the stress level and determines it as "low."

[0342] Step 6:

[0343] If the device determines that the stress level is low, it will set up a virtual space on the animal's dedicated display device (head-mounted display). For example, Fox 1's head-mounted display will display a "VR space for basic survival skills training for foxes."

[0344] Step 7:

[0345] Fox 1 will begin training in a virtual environment where the animals will learn the necessary skills and prepare for release back into the wild.

[0346] Step 8:

[0347] During training, the device constantly monitors Fox 1's stress levels and biological data in real time. If the stress level becomes too high, the device will automatically halt the training or adjust the environment.

[0348] Step 9:

[0349] After the training is completed, the terminal will evaluate Fox 1's progress and acquired skills, and if necessary, create a next training plan. Based on these results, it will provide further optimized training content.

[0350] Example 1

[0351] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0352] In modern society, the release of rescued animals into the wild has become an important issue. Conventional training methods have been insufficient in managing stress for animals, making it difficult to provide an efficient training environment. It has also been difficult to consistently provide different training content for each animal. Therefore, there is a need for a system that can provide an appropriate training environment in real time according to the type and situation of the animal, and monitor and adjust the stress level of the animal.

[0353] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0354] In this invention, the server includes a means for selecting the type of animal to be trained, a means for generating a virtual space based on the training content, and a means for creating and transmitting prompts using artificial intelligence when generating the virtual space, thereby making it possible to provide an appropriate virtual training environment according to the type of animal and the training content.

[0355] The terminal includes a means for setting a display device dedicated to the animal that displays the generated virtual space, a means for detecting the stress level of the animal, a means for controlling the continuation or interruption of training based on the stress level, and a means for monitoring the animal's biological data in real time, thereby making it possible to monitor the animal's stress level and biological data in real time and adjust the training environment as needed.

[0356] By combining these methods, we provide a system that provides optimal training tailored to the individual circumstances of animals and enables efficient, low-stress training for reintroduction into the wild.

[0357] The "means for selecting the type of animal to train" is a system component that provides an interface through which a user can select a particular type of animal to assist in the release of rescued animals back into the wild.

[0358] The "means for generating a virtual space based on training content" is a system component that automatically generates a virtual space based on the selected animal type and training content appropriate for that animal.

[0359] The "means for setting up a display device dedicated to the animal that displays the generated virtual space" is a system component that prepares and sets up a device that enables the animal to visually recognize the generated virtual space.

[0360] A "means for detecting stress level of an animal" is a system component that analyzes data from sensors attached to the animal and monitors the stress level of the animal in real time.

[0361] The "means for controlling continuation or interruption of training based on the stress level" is a system component that automatically decides whether to continue or interrupt training depending on the detected stress level.

[0362] "Means for creating and sending prompt sentences using generative AI" refers to a system component that uses a generative AI model to generate prompt sentences that are suited to the type of animal and the training content, and sends the prompt sentences to the AI.

[0363] "Means for monitoring biological data of an animal in real time" refers to a system component that acquires and analyzes biological data, such as heart rate and body temperature, of an animal in real time.

[0364] An embodiment of this invention is a method for providing a training environment tailored to a specific animal as a training target, and for monitoring and controlling the training status in real time, in a system for supporting the return of rescued animals to the wild.

[0365] First, the user uses the server to select the type of animal to be trained, for example, a fox. Next, the user operates the server to determine the training content required for that animal, for example, basic survival skills.

[0366] The server generates a virtual space using a generative AI model (e.g., OpenAI's GPT-3) based on the selected animal type and training content. This virtual space simulates an environment for training the skills and behaviors that animals need in the wild. The server names this virtual space "VR space for basic survival skill training for foxes" and sets it as the training environment.

[0367] Next, the device acquires data from an EEG sensor attached to the animal to detect the animal's stress level. If the animal's stress level is determined to be "low" based on this data, the device uses a display device dedicated to the animal (e.g., Oculus Rift) to display the generated virtual space to the animal. The animal undergoes training through this virtual space, thereby acquiring the skills necessary for the wild.

[0368] During training, the device constantly monitors the animal's stress level and vital data (e.g., heart rate, body temperature, etc.). If the stress level becomes too high, the device will automatically halt the training or adjust the environment.

[0369] As a concrete example, when applying this system to Fox No. 1, the user uses the server to select "fox" and set "basic survival skills" as the training content. The server then uses the generative AI model to generate a "VR space for training basic survival skills for foxes."

[0370] Next, the device checks Fox 1's stress level using an EEG sensor and determines that it is low. After this, the device displays a "VR space for basic survival skills training for foxes" on Fox 1's head-mounted display (e.g., HTC Vive), and Fox 1 begins training in this virtual space.

[0371] During training, the terminal constantly monitors Fox 1's stress levels and other vital signs, adjusting the training environment or halting training as necessary.

[0372] As described above, this system provides a specific method for efficiently training animals for reintroduction into the wild at low cost.

[0373] Example prompt sentence:

[0374] "Create a VR space for training basic survival skills for foxes."

[0375] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0376] Step 1:

[0377] The user selects an animal type. The user logs in to the server and selects an animal type from the system's main menu. For example, to select a "fox," the user clicks "fox" in the drop-down menu in the UI. The selected animal type is the input, and the animal type data is obtained as the output.

[0378] Step 2:

[0379] The user decides the training content. After selecting the type of animal, the user operates a menu to decide the training content. For example, the user may set "basic survival skills" as the training content. The user clicks on the appropriate item from the list of training content. The selected training content is the input, and training content data is obtained as the output.

[0380] Step 3:

[0381] The server generates a virtual space. Based on the selected animal type and training content, the server uses a generative AI model (e.g., OpenAI's GPT-3) to generate the virtual space. Specifically, the server combines a pre-prepared prompt sentence with the animal type and training content and sends it to the generative AI. This prompt sentence is the input, and the generated virtual space data is obtained as the output.

[0382] Step 4:

[0383] The device monitors the animal's stress level. The device acquires data in real time from an EEG sensor attached to the animal and analyzes the stress level. The acquired EEG data is the input, and the stress level data is the output.

[0384] Step 5:

[0385] The device displays the virtual space to the animal. If the stress level is determined to be "low," the device displays the virtual space created on a display device dedicated to the animal (e.g., Oculus Rift). Stress level data is the input, and the virtual space is displayed as the output.

[0386] Step 6:

[0387] The terminal monitors and adjusts the animal during training. During training, the terminal constantly monitors the animal's stress level and biometric data (e.g., heart rate, body temperature, etc.). If the stress level is determined to be "high," the terminal temporarily suspends training and adjusts the environment in the virtual space. Stress level and biometric data are inputs, and the output is the action taken to continue or suspend training.

[0388] Specific actions may include sending electronic alerts or notifications to users or administrators.

[0389] (Application example 1)

[0390] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0391] The current system for protecting and supporting the release of wild animals has problems such as limited training environments and inadequate stress management and real-time monitoring of biological data, which makes it difficult to provide effective training for the safe release of animals into the wild and reduces the efficiency of conservation efforts.

[0392] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0393] In this invention, the server includes means for selecting the type of animal to be trained, means for generating a virtual space based on the training content, means for setting up a display device dedicated to the animal that displays the generated virtual space, means for detecting the stress level of the animal, means for controlling the continuation or interruption of training based on the stress level, means for simulating a safe environment and conducting training using the virtual space generated based on the type of animal to be protected and the training content, and means for monitoring and analyzing the animal's biological data in real time, which enables stress management of the animal and real-time monitoring of the biological data, thereby providing a safe and effective training environment.

[0394] The "means for selecting the type of animal to be trained" refers to a method and device for identifying the type of protected wild animal and selecting appropriate training content based on that type.

[0395] The "means for generating a virtual space based on training content" refers to a method and apparatus for creating a virtual environment according to the selected training content.

[0396] The "means for setting up a display device dedicated to an animal to display the generated virtual space" refers to a method and apparatus for setting up a specific device to visually present the training virtual space to an animal.

[0397] The "means for detecting the stress level of an animal" refers to a method and device for analyzing the biological data of an animal and measuring the degree of stress based on the results.

[0398] The "means for controlling the continuation or discontinuation of training based on the stress level" refers to a method and device for determining and controlling whether to continue or discontinue training in accordance with fluctuations in the stress level of an animal.

[0399] "Means for simulating a safe environment and conducting training using a virtual space created based on the type of animal to be protected and the training content" refers to a method and device for conducting training that enables the animal to behave safely, using a virtual environment that corresponds to the type of animal to be protected and the necessary training content.

[0400] "Means for monitoring and analyzing animal vital data in real time" refers to methods and devices for instantly acquiring and analyzing vital data obtained from sensors attached to animals.

[0401] This invention relates to a system that supports the return of protected wild animals to the wild, and specifically, it provides a training environment tailored to the animal being trained, and can monitor and control the training status in real time.

[0402] First, the user accesses the server and selects the type of animal to be trained. This allows the user to specify the animal to be protected (e.g., "deer") and set the training content required for that animal (e.g., "patrol route in the habitat").

[0403] Based on the selected animal species and training, the server uses a generative AI model to generate a virtual space designed to simulate the skills and behaviors required in the animal's natural environment.

[0404] Next, the device acquires data from an EEG sensor attached to the animal and detects the animal's stress level in real time. If the animal's stress level is determined to be "low" based on this data, the device displays the virtual space created using a display device dedicated to the animal (e.g., a head-mounted display). This allows the animal to begin training in the virtual space.

[0405] During training, the device constantly monitors the animals' stress levels and other vital data, automatically adjusting the training environment or halting training as necessary, enabling real-time monitoring of animal stress and vital data, providing a safe and effective training environment.

[0406] For example, the following prompts can be fed into a generative AI model to generate the desired virtual space:

[0407] "The target animal is deer, and we're tasked with creating a virtual environment to train patrol routes in their habitat. This environment includes natural forests, rivers, hills, etc., designed to allow deer to move safely."

[0408] The hardware used includes head-mounted displays such as Oculus Quest, animal biometric sensors, and high-performance servers such as AWS EC2 instances, while the software includes generative AI systems such as OpenAI GPT-3 and real-time monitoring and data analysis platforms such as Apache Kafka and Apache Spark.

[0409] This invention is expected to make it possible to train animals for reintroduction into the wild more efficiently and with less stress, further promoting conservation efforts.

[0410] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0411] Step 1:

[0412] A user accesses the server and selects the type of animal to protect.

[0413] Input: Data about the type of animal (e.g. "deer")

[0414] Output: Selection data about animal species

[0415] Specific behavior: The user operates an interface to select a type of animal, and the server records this selection.

[0416] Step 2:

[0417] The user accesses the server and sets the training content.

[0418] Input: Training data (e.g., "patrol route in habitat area")

[0419] Output: Setting data regarding training content

[0420] Specific operation: The user operates the interface to select the training content, and the server records this setting.

[0421] Step 3:

[0422] The server inputs prompt sentences into the generative AI model and generates a virtual space.

[0423] Input: Data about the animal type and training

[0424] Output: Generated virtual space

[0425] Specific operation: The server inputs the following prompt to the generative AI model: "The target animal is a deer. Please create a virtual environment to train patrol routes in its habitat. This environment includes natural forests, rivers, hills, etc., and is designed to allow deer to move safely."

[0426] Step 4:

[0427] The device collects data from an EEG sensor attached to the animal to detect the animal's stress level.

[0428] Input: Biometric data obtained from EEG sensors

[0429] Output: Animal's stress level (e.g. "low")

[0430] How it works: The brainwave sensor collects the animal's biological data, which the device analyzes to determine its stress level.

[0431] Step 5:

[0432] The terminal sets up a display device dedicated to the animal and displays the virtual space.

[0433] Input: Generated virtual space, animal stress level (only if low)

[0434] Output: Display of a virtual space that animals can visually recognize

[0435] Specific operation: The device displays the generated virtual space on a head-mounted display dedicated to the animal.

[0436] Step 6:

[0437] The device monitors the animal's vital data in real time during training and adjusts or halts the training as necessary.

[0438] Input: Real-time biometric data acquired during training

[0439] Output: Instructions to continue, adjust, or stop training

[0440] Specific operation: The device constantly monitors the animal's biological data and automatically adjusts the training content and environment if there are changes in stress levels or other important indicators.

[0441] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0442] A mode for implementing this invention is to provide a training environment tailored to a specific animal as a training target in a system for supporting the return of rescued animals to the wild, and in addition to monitoring and controlling the training status in real time, to recognize the emotions of the user to provide an even more optimal training environment.

[0443] First, the user operates the server to select the type of animal to be trained, for example, "fox." Next, the user operates the server to determine the training content required for that animal, for example, "basic survival skills."

[0444] The server uses a generative AI to generate a virtual space based on the selected animal type and training content. This virtual space simulates an environment for training the skills and behaviors that animals need in the wild. The server names this virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[0445] The device then acquires data from an EEG sensor attached to the animal to detect its stress level. If the device determines that the animal's stress level is "low" based on this data, it uses a head-mounted display to display the generated virtual space to the animal. The animal undergoes training through this virtual space, acquiring the skills it will need in the wild.

[0446] During training, the device constantly monitors the animal's stress level and biological data. If the stress level becomes too high, the device will automatically stop the training or adjust the environment.

[0447] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotional data and adjusts the training scenario in the virtual space based on the analysis results. For example, if the user is feeling stressed or anxious, the emotion engine can use that data to adjust the training environment to be gentler. This not only makes training the animal more effective, but also reduces the psychological burden on the user.

[0448] As a concrete example, when applying this system to Fox No. 1, the user uses the server to select "fox" and set "basic survival skills" as the training content. The server then uses a generation AI to generate a "VR space for training basic survival skills for foxes."

[0449] Next, the device checks Fox 1's stress level using an EEG sensor and determines that it is "low." After this, the device displays a "VR space for basic survival skills training for foxes" on Fox 1's head-mounted display, and Fox 1 begins training in this virtual space.

[0450] During training, the device constantly monitors Fox 1's stress level and other biometric data, adjusting the training environment or halting training as necessary. Furthermore, the emotion engine adjusts the training environment appropriately based on the user's emotional data, ensuring more effective training for the animal. This provides a concrete method for efficiently training animals for release into the wild at low cost, reducing the burden on both the user and the animal.

[0451] The processing flow will be explained below.

[0452] Step 1:

[0453] The user operates the server to select the type of animal to be trained, for example, "fox."

[0454] Step 2:

[0455] The user operates the server to determine the training content, for example, "basic survival skills."

[0456] Step 3:

[0457] The server uses generative AI to generate a virtual space based on the selected animal species and training content, simulating an environment for training the skills and behaviors the animal would need in the wild.

[0458] Step 4:

[0459] The server names the generated virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[0460] Step 5:

[0461] The device uses an EEG sensor attached to the animal to capture data on the animal's stress level, which can then be used to assess the animal's readiness for training.

[0462] Step 6:

[0463] The terminal evaluates the animal's stress level based on the acquired data, and if it determines that it is "low," it proceeds to the next step.

[0464] Step 7:

[0465] The device is set up with a display device (head-mounted display) specifically for animals, and the generated virtual space is displayed to the animal. In the case of Fox 1, the head-mounted display displays a "VR space for training basic survival skills for foxes."

[0466] Step 8:

[0467] Fox 1 will begin training in a virtual environment where animals will learn the necessary skills and prepare for release back into the wild.

[0468] Step 9:

[0469] During training, the terminal constantly monitors Fox 1's stress levels and biological data in real time. If the stress level becomes too high, the terminal will halt the training or adjust the environment.

[0470] Step 10:

[0471] The system's built-in emotion engine analyzes the user's emotional data and adjusts the virtual training scenario based on the data if the user is feeling stressed or anxious.

[0472] Step 11:

[0473] The training environment adjusted by the emotion engine is provided to the animal again, which not only makes training for the animal more effective but also reduces the psychological burden on the user.

[0474] Step 12:

[0475] After completing the training, the terminal will evaluate Fox 1's progress and the skills it has acquired. If necessary, it will create a training plan for the next training session. Based on the results, further training content will be provided that is optimal for the new training.

[0476] Example 2

[0477] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0478] Today's training for reintroducing rescued animals into the wild requires providing an optimal training environment for each animal and taking into account the animal's stress level and the user's emotional state. Current methods lack a system that comprehensively manages these factors, limiting the effectiveness of training. It is also necessary to simultaneously reduce the user's stress and anxiety. To achieve this, we aim to develop a system that improves the efficiency of animal training while also reducing the user's psychological burden.

[0479] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0480] In this invention, the server includes means for selecting the type of animal to be trained, means for generating a virtual space based on the training content, means for setting up a display device dedicated to the animal to display the generated virtual space, means for detecting the stress level of the animal, means for controlling the continuation or interruption of training based on the stress level, and means for recognizing the user's emotions and adjusting the training environment, thereby improving the efficiency of wild release training for animals and simultaneously reducing the psychological burden on the user.

[0481] The "means for selecting the type of animal to be trained" refers to an interface that allows the user to specify the type of animal to be trained on the system.

[0482] "Means for generating a virtual space based on training content" refers to a mechanism that uses artificial intelligence to create a virtual space for animals to receive training in accordance with specified training content.

[0483] "Means for setting up a display device dedicated to the animal that displays the generated virtual space" refers to a mechanism for attaching and setting up a display device dedicated to the animal so that the animal can see the generated virtual space.

[0484] "Means for detecting stress levels in animals" refers to a mechanism for analyzing data obtained from sensors attached to animals and assessing their stress levels.

[0485] "Means for controlling the continuation or discontinuation of training based on the stress level" refers to a control mechanism for continuing or discontinuing training or adjusting the training content based on the evaluation results of the animal's stress level.

[0486] "Means for recognizing the user's emotions and adjusting the training environment" refers to a mechanism for analyzing the user's emotional state and adjusting the virtual training space and environmental settings based on the results.

[0487] The embodiment of the present invention is a system for supporting the release of rescued animals back into the wild. This system operates in cooperation with users, a server, and terminals.

[0488] First, the user accesses the server's interface from their device. They select the type of animal to be trained and set the appropriate training content. A general web application or mobile application is used as the user interface. For example, the user selects a "fox" and specifies "basic survival skills" as the training content.

[0489] The server then uses a generative AI model (e.g., Azure OpenAI) to generate a virtual space based on the selected animal and training content. This virtual space simulates the environment for training the skills and behaviors the animal would need in its natural environment. The generated virtual space is named "VR space for basic survival skills training for foxes" and saved on the server.

[0490] Next, the device acquires data from an EEG sensor (e.g., NeuroSky) attached to the animal to detect the animal's stress level. If the stress level is determined to be "low" based on this data, the device uses a display device (e.g., Oculus Rift) dedicated to the animal to display the generated virtual space to the animal. The animal can then receive training through this virtual space.

[0491] During training, the device constantly monitors the animal's stress level and other biometric data and adjusts the training environment as needed, including the ability to stop training or adjust the difficulty of the environment if stress levels become too high.

[0492] Furthermore, the server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine (e.g., Affectiva) analyzes the user's emotional data and adjusts the virtual training scenario based on the analysis results. This allows the training environment to be more calming if the user is feeling stressed or anxious.

[0493] As a specific example, if the training subject is "Fox No. 1," the user selects "Fox" through the server and sets "basic survival skills." The server uses a generative AI model to generate an appropriate virtual space. After the device uses an EEG sensor to determine that Fox No. 1's stress level is "low," the device displays the training VR space on Fox No. 1's head-mounted display and begins training. During training, the device continues to monitor the stress level and adjusts the environment as necessary. The server's emotion engine analyzes the user's emotional data and adjusts the training environment as appropriate.

[0494] Example prompt: "Create an appropriate VR environment for training Fox 1, the fox, in basic survival skills. Fox 1 has a low stress level, so we will train him in this virtual environment. Please also take into account the user's emotional data and adjust the training environment accordingly."

[0495] As described above, this system effectively trains animals for reintroduction into the wild, while at the same time reducing the psychological burden on users.

[0496] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0497] Step 1:

[0498] Selection of animal species and training content

[0499] Input: The user accesses the system and selects the type of animal to be trained and the training content.

[0500] How it works: The user operates the device and selects the type of animal, such as "fox," and the training content, such as "basic survival skills," from the interface on the server.

[0501] Output: The selected animal type and training details are sent to the server and saved.

[0502] Step 2:

[0503] Virtual space generation

[0504] Input: The server receives information about the animal type and training details sent by the user.

[0505] How it works: The server calls a generative AI model (e.g., Azure OpenAI) and sends a prompt to the model to generate a virtual space based on the selected animal type and training content. For example, the prompt "Generate a VR space for training basic survival skills for foxes" is input to the model.

[0506] Output: The server acquires the virtual space data output from the generative AI model and saves it as a "VR space for training basic survival skills for foxes."

[0507] Step 3:

[0508] Stress Level Detection

[0509] Input: Stress level data is sent to the terminal from an EEG sensor attached to the animal.

[0510] How it works: The device periodically collects data from an EEG sensor (e.g., NeuroSky), analyzes the data, and evaluates the stress level. If the stress level is determined to be "low," it moves on to the next step.

[0511] Output: The stress level assessment results are saved on the device and the conditions for the next processing step are set.

[0512] Step 4:

[0513] Virtual space display

[0514] Input: Evaluation results of animals rated as having a "low" stress level and virtual space data stored on the server.

[0515] Operation: The terminal displays the virtual space obtained from the server to the animal through a display device dedicated to the animal (e.g., Oculus Rift). Specifically, the terminal attaches a head-mounted display to the animal and displays the generated VR space.

[0516] Output: The animal begins training in the virtual space.

[0517] Step 5:

[0518] Monitoring during training

[0519] Input: Stress levels and other biometric data sent periodically by the animal during training.

[0520] How it works: The device monitors the animal's stress level and other biometric data in real time during training. If the stress level becomes too high, the device will automatically halt the training or adjust the difficulty of the training environment. Specifically, the device selects and executes appropriate actions based on the data obtained.

[0521] Output: The training environment is adjusted or training is interrupted, reducing the animal's stress level.

[0522] Step 6:

[0523] Analyzing user emotion data and adjusting the environment

[0524] Input: The user's emotional data is sent to the emotion recognition engine.

[0525] How it works: An emotion engine (e.g., Affectiva) built into the server analyzes the user's emotional data and adjusts the virtual training scenario based on the results. Specifically, it automatically changes the virtual environment settings and training difficulty according to the user's emotional state.

[0526] Output: A tailored training environment is provided for the animal, reducing the psychological burden on the user.

[0527] Through these processing steps, the system can provide an optimal training environment for both the animal and the user.

[0528] (Application example 2)

[0529] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0530] Training rescued animals for release into the wild requires a high level of expertise and a great deal of effort, making it difficult to do so efficiently. Furthermore, conventional methods cannot take into account the animal's stress or the user's emotions in real time, which can reduce the effectiveness of the training. Therefore, there is a need for effective and efficient training for reintroducing animals into the wild. Furthermore, police dog training requires an environment that efficiently trains specific skills (e.g., explosive detection), but it is difficult to adaptively change that environment in real time.

[0531] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0532] In this invention, the server includes a means for selecting the type of animal to be trained to support the release of rescued animals into the wild, a means for generating a virtual space based on the training content, a means for configuring a display device dedicated to the animal to display the generated virtual space, a means for detecting the stress level of the animal, a means for controlling the continuation or interruption of training based on the stress level, and a means for recognizing the user's emotions and adjusting the training environment in the virtual space. This enables efficient release-into-the-wild training while minimizing stress on the animal. Furthermore, in training specific skills for police dogs, an appropriate virtual environment can be provided while taking the user's emotions into consideration, achieving high training effectiveness.

[0533] A "trainable animal" is an animal that is to be trained to acquire a particular skill or behavior.

[0534] "Training content" refers to the specific skills and behaviors that the animal must master.

[0535] A "virtual space" is a simulated training environment created by generative AI.

[0536] An "animal-specific display device" is a device that animals can wear to visually experience a virtual space.

[0537] "Stress level" is an indicator of the degree of mental and physical strain an animal experiences during training.

[0538] "Generative AI" is an artificial intelligence technology that analyzes large amounts of data and automatically generates new information and spaces.

[0539] "User's emotions" refers to the psychological state of the user who manages and supervises the training.

[0540] The "means for recognizing emotions and adjusting the training environment in the virtual space" is a device or system that has the function of analyzing the user's emotional state and adaptively changing the training environment based on the results.

[0541] The system for implementing this invention is designed to efficiently release rescued animals back into the wild and train them in specific skills. The roles of the server, terminal, and user are clearly defined, and each step is explained in detail.

[0542] Hardware and Software Use

[0543] server:

[0544] The server is used as a means to select the animal species and training content, and is equipped with a generative artificial intelligence (AI) model that can generate a virtual space based on user instructions.

[0545] Examples of devices used: high-performance servers (e.g., Dell PowerEdge), generative AI models (e.g., OpenAI GPT).

[0546] Device:

[0547] The device collects the animal's biological data and monitors its stress level based on that data. It also displays a virtual world to the animal wearing a head-mounted display.

[0548] Examples of devices used: EEG sensors (e.g., NeuroSky), head-mounted displays (e.g., Oculus Rift).

[0549] User:

[0550] The user operates the server to select the animal and training content, and the emotion data is analyzed by the emotion analysis engine, which adaptively adjusts the training environment.

[0551] Examples of devices used: emotion analysis software (e.g., Affectiva).

[0552] Specific examples of system use

[0553] 1. Animal and training selection:

[0554] Users connect to the server using a smartphone app or PC and select the animal to be trained (e.g., German Shepherd) and the training content (e.g., explosive detection training).

[0555] 2. Creating a virtual training environment:

[0556] Based on the information selected by the user, the server inputs prompt statements into the generative AI model to generate a virtual space.

[0557] Example prompt sentence:

[0558] Create a VR environment for explosive detection training for German Shepherds.

[0559] 3. Monitoring Animal Stress Levels and Initiating Training:

[0560] The device uses an EEG sensor to monitor the animal's stress level, and if the stress level is low, a virtual space is displayed on the head-mounted display, and the animal begins training through the virtual space.

[0561] 4. Adaptive adjustment of the training environment:

[0562] The system analyzes the user's emotional data using an emotion analysis engine and dynamically adjusts the training environment based on the results. For example, if the user is feeling stressed, the training environment will be adjusted to be more gentle.

[0563] Specific example details

[0564] For example, if a police station uses a German shepherd for explosive detection training, the user selects the animal and the training content using the server. Then, a prompt is input into the generative AI model to generate a "VR environment for German shepherd explosive detection training." The system then monitors the animal's stress level using an EEG sensor, initiates training, and adaptively adjusts the environment in real time.

[0565] In this way, the present invention makes it possible to effectively and efficiently train animals for reintroduction into the wild or for specific skills.

[0566] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0567] Step 1:

[0568] The user connects to the server and selects the animal to be trained and the training content. As input, the user provides information on the type of animal and the training content. Based on this input data, the server creates a prompt sentence to be passed to the generative AI model. Data on the selected animal's characteristics and training content is used to create this prompt sentence.

[0569] The server stores the input information in a database and prepares it for the next processing step.

[0570] The output is a prompt and training selection information.

[0571] Step 2:

[0572] The server inputs a prompt sentence into the generative AI model to generate a virtual training environment. The prompt sentence is passed to the generative AI model as input. The server uses the generative AI model to generate an appropriate virtual space based on this prompt sentence.

[0573] This generated virtual space contains scenarios and environmental elements that are optimal for the training content and are suitable for training specific skills of animals.

[0574] The output is virtual training environment data.

[0575] Step 3:

[0576] The device collects the animal's biological data and monitors its stress level. As input, the device receives biological data from an EEG sensor. The device analyzes this biological data and evaluates the animal's stress level.

[0577] The device determines the stress level as "low" or "high" based on the criteria and passes the result on to the next step.

[0578] The output is stress level data.

[0579] Step 4:

[0580] When the stress level is low, the device displays the virtual space on a display device dedicated to the animal. Stress level data and virtual training environment data are passed to the device as input. The device uses these data to display the virtual training environment on a head-mounted display.

[0581] The animals begin training in the virtual space through this display.

[0582] The output is a representation of the virtual space.

[0583] Step 5:

[0584] The server acquires the user's emotional data and analyzes it with an emotion analysis engine. As input, the emotion data from the emotion analysis software is sent to the server. The server analyzes this data and identifies the user's mental state.

[0585] Based on the analysis results, the server generates setting information for adaptively changing the virtual training environment.

[0586] The output is the configuration information of the virtual training environment.

[0587] Step 6:

[0588] The server sends the setting information of the virtual training environment to the terminal and adjusts the environment. The setting information of the virtual training environment is provided as input. The terminal dynamically adjusts the environmental elements of the virtual space based on this information.

[0589] Add or remove specific situations or stimuli to create an environment that allows the animal to receive optimal training.

[0590] The output is a tailored virtual training environment.

[0591] Step 7:

[0592] The device monitors the progress of the training and continues or stops it as needed. It uses inputs such as the animal's stress level, biometric data, and environmental adjustments. The device analyzes this data in real time to evaluate the effectiveness of the training.

[0593] The device ensures effective training continues and, if necessary, interrupts training to reduce strain on the animal.

[0594] The output is an instruction to continue or discontinue training.

[0595] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0596] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0597] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0598] [Third embodiment]

[0599] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0600] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0601] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0602] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0603] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0604] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0605] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0606] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0607] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0608] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0609] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0610] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0611] An embodiment of this invention is a system for supporting the return of rescued animals to the wild, which provides a training environment tailored to the specific animal being trained, and monitors and controls the training status in real time.

[0612] First, the user uses the server to select the type of animal to be trained, for example, a fox. Next, the user operates the server to determine the training content required for that animal, for example, basic survival skills.

[0613] The server uses a generative AI to generate a virtual space based on the selected animal type and training content. This virtual space simulates an environment for training the skills and behaviors that animals need in the wild. The server names this virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[0614] The device then acquires data from an EEG sensor attached to the animal to detect its stress level. If the device determines that the animal's stress level is "low" based on this data, it uses a head-mounted display to display the generated virtual space to the animal. The animal undergoes training through this virtual space, acquiring the skills it will need in the wild.

[0615] During training, the device constantly monitors the animal's stress level and biological data. If the stress level becomes too high, the device will automatically stop the training or adjust the environment.

[0616] As a concrete example, when applying this system to Fox No. 1, the user uses the server to select "fox" and set "basic survival skills" as the training content. The server then uses a generation AI to generate a "VR space for training basic survival skills for foxes."

[0617] Next, the device checks Fox 1's stress level using an EEG sensor and determines that it is "low." After this, the device displays a "VR space for basic survival skills training for foxes" on Fox 1's head-mounted display, and Fox 1 begins training in this virtual space.

[0618] During training, the terminal constantly monitors Fox 1's stress level and other vital data, adjusting the training environment or halting training as necessary. In this way, this system provides a concrete method for efficiently training animals for reintroduction into the wild at low cost.

[0619] The processing flow will be explained below.

[0620] Step 1:

[0621] The user operates the server to select the type of animal to be trained, for example, "fox."

[0622] Step 2:

[0623] The user operates the server to determine the training content, for example, "basic survival skills."

[0624] Step 3:

[0625] The server uses generative AI to generate a virtual space based on the selected animal species and training content, providing a scenario optimized for training the skills the animals will need in the wild.

[0626] Step 4:

[0627] The server names the generated virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[0628] Step 5:

[0629] The device uses an EEG sensor attached to the animal to detect the animal's stress level, and based on this data, evaluates the stress level and determines it as "low."

[0630] Step 6:

[0631] If the device determines that the stress level is low, it will set up a virtual space on the animal's dedicated display device (head-mounted display). For example, Fox 1's head-mounted display will display a "VR space for basic survival skills training for foxes."

[0632] Step 7:

[0633] Fox 1 will begin training in a virtual environment where the animals will learn the necessary skills and prepare for release back into the wild.

[0634] Step 8:

[0635] During training, the device constantly monitors Fox 1's stress levels and biological data in real time. If the stress level becomes too high, the device will automatically halt the training or adjust the environment.

[0636] Step 9:

[0637] After the training is completed, the terminal will evaluate Fox 1's progress and acquired skills, and if necessary, create a next training plan. Based on these results, it will provide further optimized training content.

[0638] Example 1

[0639] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0640] In modern society, the release of rescued animals into the wild has become an important issue. Conventional training methods have been insufficient in managing stress for animals, making it difficult to provide an efficient training environment. It has also been difficult to consistently provide different training content for each animal. Therefore, there is a need for a system that can provide an appropriate training environment in real time according to the type and situation of the animal, and monitor and adjust the stress level of the animal.

[0641] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0642] In this invention, the server includes a means for selecting the type of animal to be trained, a means for generating a virtual space based on the training content, and a means for creating and transmitting prompts using artificial intelligence when generating the virtual space, thereby making it possible to provide an appropriate virtual training environment according to the type of animal and the training content.

[0643] The terminal includes a means for setting a display device dedicated to the animal that displays the generated virtual space, a means for detecting the stress level of the animal, a means for controlling the continuation or interruption of training based on the stress level, and a means for monitoring the animal's biological data in real time, thereby making it possible to monitor the animal's stress level and biological data in real time and adjust the training environment as needed.

[0644] By combining these methods, we provide a system that provides optimal training tailored to the individual circumstances of animals and enables efficient, low-stress training for reintroduction into the wild.

[0645] The "means for selecting the type of animal to train" is a system component that provides an interface through which a user can select a particular type of animal to assist in the release of rescued animals back into the wild.

[0646] The "means for generating a virtual space based on training content" is a system component that automatically generates a virtual space based on the selected animal type and training content appropriate for that animal.

[0647] The "means for setting up a display device dedicated to the animal that displays the generated virtual space" is a system component that prepares and sets up a device that enables the animal to visually recognize the generated virtual space.

[0648] A "means for detecting stress level of an animal" is a system component that analyzes data from sensors attached to the animal and monitors the stress level of the animal in real time.

[0649] The "means for controlling continuation or interruption of training based on the stress level" is a system component that automatically decides whether to continue or interrupt training depending on the detected stress level.

[0650] "Means for creating and sending prompt sentences using generative AI" refers to a system component that uses a generative AI model to generate prompt sentences that are suited to the type of animal and the training content, and sends the prompt sentences to the AI.

[0651] "Means for monitoring biological data of an animal in real time" refers to a system component that acquires and analyzes biological data, such as heart rate and body temperature, of an animal in real time.

[0652] An embodiment of this invention is a method for providing a training environment tailored to a specific animal as a training target, and for monitoring and controlling the training status in real time, in a system for supporting the return of rescued animals to the wild.

[0653] First, the user uses the server to select the type of animal to be trained, for example, a fox. Next, the user operates the server to determine the training content required for that animal, for example, basic survival skills.

[0654] The server generates a virtual space using a generative AI model (e.g., OpenAI's GPT-3) based on the selected animal type and training content. This virtual space simulates an environment for training the skills and behaviors that animals need in the wild. The server names this virtual space "VR space for basic survival skill training for foxes" and sets it as the training environment.

[0655] Next, the device acquires data from an EEG sensor attached to the animal to detect the animal's stress level. If the animal's stress level is determined to be "low" based on this data, the device uses a display device dedicated to the animal (e.g., Oculus Rift) to display the generated virtual space to the animal. The animal undergoes training through this virtual space, thereby acquiring the skills necessary for the wild.

[0656] During training, the device constantly monitors the animal's stress level and vital data (e.g., heart rate, body temperature, etc.). If the stress level becomes too high, the device will automatically halt the training or adjust the environment.

[0657] As a concrete example, when applying this system to Fox No. 1, the user uses the server to select "fox" and set "basic survival skills" as the training content. The server then uses the generative AI model to generate a "VR space for training basic survival skills for foxes."

[0658] Next, the device checks Fox 1's stress level using an EEG sensor and determines that it is low. After this, the device displays a "VR space for basic survival skills training for foxes" on Fox 1's head-mounted display (e.g., HTC Vive), and Fox 1 begins training in this virtual space.

[0659] During training, the terminal constantly monitors Fox 1's stress levels and other vital signs, adjusting the training environment or halting training as necessary.

[0660] As described above, this system provides a specific method for efficiently training animals for reintroduction into the wild at low cost.

[0661] Example prompt sentence:

[0662] "Create a VR space for training basic survival skills for foxes."

[0663] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0664] Step 1:

[0665] The user selects an animal type. The user logs in to the server and selects an animal type from the system's main menu. For example, to select a "fox," the user clicks "fox" in the drop-down menu in the UI. The selected animal type is the input, and the animal type data is obtained as the output.

[0666] Step 2:

[0667] The user decides the training content. After selecting the type of animal, the user operates a menu to decide the training content. For example, the user may set "basic survival skills" as the training content. The user clicks on the appropriate item from the list of training content. The selected training content is the input, and training content data is obtained as the output.

[0668] Step 3:

[0669] The server generates a virtual space. Based on the selected animal type and training content, the server uses a generative AI model (e.g., OpenAI's GPT-3) to generate the virtual space. Specifically, the server combines a pre-prepared prompt sentence with the animal type and training content and sends it to the generative AI. This prompt sentence is the input, and the generated virtual space data is obtained as the output.

[0670] Step 4:

[0671] The device monitors the animal's stress level. The device acquires data in real time from an EEG sensor attached to the animal and analyzes the stress level. The acquired EEG data is the input, and the stress level data is the output.

[0672] Step 5:

[0673] The device displays the virtual space to the animal. If the stress level is determined to be "low," the device displays the virtual space created on a display device dedicated to the animal (e.g., Oculus Rift). Stress level data is the input, and the virtual space is displayed as the output.

[0674] Step 6:

[0675] The terminal monitors and adjusts the animal during training. During training, the terminal constantly monitors the animal's stress level and biometric data (e.g., heart rate, body temperature, etc.). If the stress level is determined to be "high," the terminal temporarily suspends training and adjusts the environment in the virtual space. Stress level and biometric data are inputs, and the output is the action taken to continue or suspend training.

[0676] Specific actions may include sending electronic alerts or notifications to users or administrators.

[0677] (Application example 1)

[0678] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0679] The current system for protecting and supporting the release of wild animals has problems such as limited training environments and inadequate stress management and real-time monitoring of biological data, which makes it difficult to provide effective training for the safe release of animals into the wild and reduces the efficiency of conservation efforts.

[0680] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0681] In this invention, the server includes means for selecting the type of animal to be trained, means for generating a virtual space based on the training content, means for setting up a display device dedicated to the animal that displays the generated virtual space, means for detecting the stress level of the animal, means for controlling the continuation or interruption of training based on the stress level, means for simulating a safe environment and conducting training using the virtual space generated based on the type of animal to be protected and the training content, and means for monitoring and analyzing the animal's biological data in real time, which enables stress management of the animal and real-time monitoring of the biological data, thereby providing a safe and effective training environment.

[0682] The "means for selecting the type of animal to be trained" refers to a method and device for identifying the type of protected wild animal and selecting appropriate training content based on that type.

[0683] The "means for generating a virtual space based on training content" refers to a method and apparatus for creating a virtual environment according to the selected training content.

[0684] The "means for setting up a display device dedicated to an animal to display the generated virtual space" refers to a method and apparatus for setting up a specific device to visually present the training virtual space to an animal.

[0685] The "means for detecting the stress level of an animal" refers to a method and device for analyzing the biological data of an animal and measuring the degree of stress based on the results.

[0686] The "means for controlling the continuation or discontinuation of training based on the stress level" refers to a method and device for determining and controlling whether to continue or discontinue training in accordance with fluctuations in the stress level of an animal.

[0687] "Means for simulating a safe environment and conducting training using a virtual space created based on the type of animal to be protected and the training content" refers to a method and device for conducting training that enables the animal to behave safely, using a virtual environment that corresponds to the type of animal to be protected and the necessary training content.

[0688] "Means for monitoring and analyzing animal vital data in real time" refers to methods and devices for instantly acquiring and analyzing vital data obtained from sensors attached to animals.

[0689] This invention relates to a system that supports the return of protected wild animals to the wild, and specifically, it provides a training environment tailored to the animal being trained, and can monitor and control the training status in real time.

[0690] First, the user accesses the server and selects the type of animal to be trained. This allows the user to specify the animal to be protected (e.g., "deer") and set the training content required for that animal (e.g., "patrol route in the habitat").

[0691] Based on the selected animal species and training, the server uses a generative AI model to generate a virtual space designed to simulate the skills and behaviors required in the animal's natural environment.

[0692] Next, the device acquires data from an EEG sensor attached to the animal and detects the animal's stress level in real time. If the animal's stress level is determined to be "low" based on this data, the device displays the virtual space created using a display device dedicated to the animal (e.g., a head-mounted display). This allows the animal to begin training in the virtual space.

[0693] During training, the device constantly monitors the animals' stress levels and other vital data, automatically adjusting the training environment or halting training as necessary, enabling real-time monitoring of animal stress and vital data, providing a safe and effective training environment.

[0694] For example, the following prompts can be fed into a generative AI model to generate the desired virtual space:

[0695] "The target animal is deer, and we're tasked with creating a virtual environment to train patrol routes in their habitat. This environment includes natural forests, rivers, hills, etc., designed to allow deer to move safely."

[0696] The hardware used includes head-mounted displays such as Oculus Quest, animal biometric sensors, and high-performance servers such as AWS EC2 instances, while the software includes generative AI systems such as OpenAI GPT-3 and real-time monitoring and data analysis platforms such as Apache Kafka and Apache Spark.

[0697] This invention is expected to make it possible to train animals for reintroduction into the wild more efficiently and with less stress, further promoting conservation efforts.

[0698] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0699] Step 1:

[0700] A user accesses the server and selects the type of animal to protect.

[0701] Input: Data about the type of animal (e.g. "deer")

[0702] Output: Selection data about animal species

[0703] Specific behavior: The user operates an interface to select a type of animal, and the server records this selection.

[0704] Step 2:

[0705] The user accesses the server and sets the training content.

[0706] Input: Training data (e.g., "patrol route in habitat area")

[0707] Output: Setting data regarding training content

[0708] Specific operation: The user operates the interface to select the training content, and the server records this setting.

[0709] Step 3:

[0710] The server inputs prompt sentences into the generative AI model and generates a virtual space.

[0711] Input: Data about the animal type and training

[0712] Output: Generated virtual space

[0713] Specific operation: The server inputs the following prompt to the generative AI model: "The target animal is a deer. Please create a virtual environment to train patrol routes in its habitat. This environment includes natural forests, rivers, hills, etc., and is designed to allow deer to move safely."

[0714] Step 4:

[0715] The device collects data from an EEG sensor attached to the animal to detect the animal's stress level.

[0716] Input: Biometric data obtained from EEG sensors

[0717] Output: Animal's stress level (e.g. "low")

[0718] How it works: The brainwave sensor collects the animal's biological data, which the device analyzes to determine its stress level.

[0719] Step 5:

[0720] The terminal sets up a display device dedicated to the animal and displays the virtual space.

[0721] Input: Generated virtual space, animal stress level (only if low)

[0722] Output: Display of a virtual space that animals can visually recognize

[0723] Specific operation: The device displays the generated virtual space on a head-mounted display dedicated to the animal.

[0724] Step 6:

[0725] The device monitors the animal's vital data in real time during training and adjusts or halts the training as necessary.

[0726] Input: Real-time biometric data acquired during training

[0727] Output: Instructions to continue, adjust, or stop training

[0728] Specific operation: The device constantly monitors the animal's biological data and automatically adjusts the training content and environment if there are changes in stress levels or other important indicators.

[0729] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0730] A mode for implementing this invention is to provide a training environment tailored to a specific animal as a training target in a system for supporting the return of rescued animals to the wild, and in addition to monitoring and controlling the training status in real time, to recognize the emotions of the user to provide an even more optimal training environment.

[0731] First, the user operates the server to select the type of animal to be trained, for example, "fox." Next, the user operates the server to determine the training content required for that animal, for example, "basic survival skills."

[0732] The server uses a generative AI to generate a virtual space based on the selected animal type and training content. This virtual space simulates an environment for training the skills and behaviors that animals need in the wild. The server names this virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[0733] The device then acquires data from an EEG sensor attached to the animal to detect its stress level. If the device determines that the animal's stress level is "low" based on this data, it uses a head-mounted display to display the generated virtual space to the animal. The animal undergoes training through this virtual space, acquiring the skills it will need in the wild.

[0734] During training, the device constantly monitors the animal's stress level and biological data. If the stress level becomes too high, the device will automatically stop the training or adjust the environment.

[0735] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotional data and adjusts the training scenario in the virtual space based on the analysis results. For example, if the user is feeling stressed or anxious, the emotion engine can use that data to adjust the training environment to be gentler. This not only makes training the animal more effective, but also reduces the psychological burden on the user.

[0736] As a concrete example, when applying this system to Fox No. 1, the user uses the server to select "fox" and set "basic survival skills" as the training content. The server then uses a generation AI to generate a "VR space for training basic survival skills for foxes."

[0737] Next, the device checks Fox 1's stress level using an EEG sensor and determines that it is "low." After this, the device displays a "VR space for basic survival skills training for foxes" on Fox 1's head-mounted display, and Fox 1 begins training in this virtual space.

[0738] During training, the device constantly monitors Fox 1's stress level and other biometric data, adjusting the training environment or halting training as necessary. Furthermore, the emotion engine adjusts the training environment appropriately based on the user's emotional data, ensuring more effective training for the animal. This provides a concrete method for efficiently training animals for release into the wild at low cost, reducing the burden on both the user and the animal.

[0739] The processing flow will be explained below.

[0740] Step 1:

[0741] The user operates the server to select the type of animal to be trained, for example, "fox."

[0742] Step 2:

[0743] The user operates the server to determine the training content, for example, "basic survival skills."

[0744] Step 3:

[0745] The server uses generative AI to generate a virtual space based on the selected animal species and training content, simulating an environment for training the skills and behaviors the animal would need in the wild.

[0746] Step 4:

[0747] The server names the generated virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[0748] Step 5:

[0749] The device uses an EEG sensor attached to the animal to capture data on the animal's stress level, which can then be used to assess the animal's readiness for training.

[0750] Step 6:

[0751] The terminal evaluates the animal's stress level based on the acquired data, and if it determines that it is "low," it proceeds to the next step.

[0752] Step 7:

[0753] The device is set up with a display device (head-mounted display) specifically for animals, and the generated virtual space is displayed to the animal. In the case of Fox 1, the head-mounted display displays a "VR space for training basic survival skills for foxes."

[0754] Step 8:

[0755] Fox 1 will begin training in a virtual environment where animals will learn the necessary skills and prepare for release back into the wild.

[0756] Step 9:

[0757] During training, the terminal constantly monitors Fox 1's stress levels and biological data in real time. If the stress level becomes too high, the terminal will halt the training or adjust the environment.

[0758] Step 10:

[0759] The system's built-in emotion engine analyzes the user's emotional data and adjusts the virtual training scenario based on the data if the user is feeling stressed or anxious.

[0760] Step 11:

[0761] The training environment adjusted by the emotion engine is provided to the animal again, which not only makes training for the animal more effective but also reduces the psychological burden on the user.

[0762] Step 12:

[0763] After completing the training, the terminal will evaluate Fox 1's progress and the skills it has acquired. If necessary, it will create a training plan for the next training session. Based on the results, further training content will be provided that is optimal for the new training.

[0764] Example 2

[0765] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0766] Today's training for reintroducing rescued animals into the wild requires providing an optimal training environment for each animal and taking into account the animal's stress level and the user's emotional state. Current methods lack a system that comprehensively manages these factors, limiting the effectiveness of training. It is also necessary to simultaneously reduce the user's stress and anxiety. To achieve this, we aim to develop a system that improves the efficiency of animal training while also reducing the user's psychological burden.

[0767] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0768] In this invention, the server includes means for selecting the type of animal to be trained, means for generating a virtual space based on the training content, means for setting up a display device dedicated to the animal to display the generated virtual space, means for detecting the stress level of the animal, means for controlling the continuation or interruption of training based on the stress level, and means for recognizing the user's emotions and adjusting the training environment, thereby improving the efficiency of wild release training for animals and simultaneously reducing the psychological burden on the user.

[0769] The "means for selecting the type of animal to be trained" refers to an interface that allows the user to specify the type of animal to be trained on the system.

[0770] "Means for generating a virtual space based on training content" refers to a mechanism that uses artificial intelligence to create a virtual space for animals to receive training in accordance with specified training content.

[0771] "Means for setting up a display device dedicated to the animal that displays the generated virtual space" refers to a mechanism for attaching and setting up a display device dedicated to the animal so that the animal can see the generated virtual space.

[0772] "Means for detecting stress levels in animals" refers to a mechanism for analyzing data obtained from sensors attached to animals and assessing their stress levels.

[0773] "Means for controlling the continuation or discontinuation of training based on the stress level" refers to a control mechanism for continuing or discontinuing training or adjusting the training content based on the evaluation results of the animal's stress level.

[0774] "Means for recognizing the user's emotions and adjusting the training environment" refers to a mechanism for analyzing the user's emotional state and adjusting the virtual training space and environmental settings based on the results.

[0775] The embodiment of the present invention is a system for supporting the release of rescued animals back into the wild. This system operates in cooperation with users, a server, and terminals.

[0776] First, the user accesses the server's interface from their device. They select the type of animal to be trained and set the appropriate training content. A general web application or mobile application is used as the user interface. For example, the user selects a "fox" and specifies "basic survival skills" as the training content.

[0777] The server then uses a generative AI model (e.g., Azure OpenAI) to generate a virtual space based on the selected animal and training content. This virtual space simulates the environment for training the skills and behaviors the animal would need in its natural environment. The generated virtual space is named "VR space for basic survival skills training for foxes" and saved on the server.

[0778] Next, the device acquires data from an EEG sensor (e.g., NeuroSky) attached to the animal to detect the animal's stress level. If the stress level is determined to be "low" based on this data, the device uses a display device (e.g., Oculus Rift) dedicated to the animal to display the generated virtual space to the animal. The animal can then receive training through this virtual space.

[0779] During training, the device constantly monitors the animal's stress level and other biometric data and adjusts the training environment as needed, including the ability to stop training or adjust the difficulty of the environment if stress levels become too high.

[0780] Furthermore, the server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine (e.g., Affectiva) analyzes the user's emotional data and adjusts the virtual training scenario based on the analysis results. This allows the training environment to be more calming if the user is feeling stressed or anxious.

[0781] As a specific example, if the training subject is "Fox No. 1," the user selects "Fox" through the server and sets "basic survival skills." The server uses a generative AI model to generate an appropriate virtual space. After the device uses an EEG sensor to determine that Fox No. 1's stress level is "low," the device displays the training VR space on Fox No. 1's head-mounted display and begins training. During training, the device continues to monitor the stress level and adjusts the environment as necessary. The server's emotion engine analyzes the user's emotional data and adjusts the training environment as appropriate.

[0782] Example prompt: "Create an appropriate VR environment for training Fox 1, the fox, in basic survival skills. Fox 1 has a low stress level, so we will train him in this virtual environment. Please also take into account the user's emotional data and adjust the training environment accordingly."

[0783] As described above, this system effectively trains animals for reintroduction into the wild, while at the same time reducing the psychological burden on users.

[0784] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0785] Step 1:

[0786] Selection of animal species and training content

[0787] Input: The user accesses the system and selects the type of animal to be trained and the training content.

[0788] How it works: The user operates the device and selects the type of animal, such as "fox," and the training content, such as "basic survival skills," from the interface on the server.

[0789] Output: The selected animal type and training details are sent to the server and saved.

[0790] Step 2:

[0791] Virtual space generation

[0792] Input: The server receives information about the animal type and training details sent by the user.

[0793] How it works: The server calls a generative AI model (e.g., Azure OpenAI) and sends a prompt to the model to generate a virtual space based on the selected animal type and training content. For example, the prompt "Generate a VR space for training basic survival skills for foxes" is input to the model.

[0794] Output: The server acquires the virtual space data output from the generative AI model and saves it as a "VR space for training basic survival skills for foxes."

[0795] Step 3:

[0796] Stress Level Detection

[0797] Input: Stress level data is sent to the terminal from an EEG sensor attached to the animal.

[0798] How it works: The device periodically collects data from an EEG sensor (e.g., NeuroSky), analyzes the data, and evaluates the stress level. If the stress level is determined to be "low," it moves on to the next step.

[0799] Output: The stress level assessment results are saved on the device and the conditions for the next processing step are set.

[0800] Step 4:

[0801] Virtual space display

[0802] Input: Evaluation results of animals rated as having a "low" stress level and virtual space data stored on the server.

[0803] Operation: The terminal displays the virtual space obtained from the server to the animal through a display device dedicated to the animal (e.g., Oculus Rift). Specifically, the terminal attaches a head-mounted display to the animal and displays the generated VR space.

[0804] Output: The animal begins training in the virtual space.

[0805] Step 5:

[0806] Monitoring during training

[0807] Input: Stress levels and other biometric data sent periodically by the animal during training.

[0808] How it works: The device monitors the animal's stress level and other biometric data in real time during training. If the stress level becomes too high, the device will automatically halt the training or adjust the difficulty of the training environment. Specifically, the device selects and executes appropriate actions based on the data obtained.

[0809] Output: The training environment is adjusted or training is interrupted, reducing the animal's stress level.

[0810] Step 6:

[0811] Analyzing user emotion data and adjusting the environment

[0812] Input: The user's emotional data is sent to the emotion recognition engine.

[0813] How it works: An emotion engine (e.g., Affectiva) built into the server analyzes the user's emotional data and adjusts the virtual training scenario based on the results. Specifically, it automatically changes the virtual environment settings and training difficulty according to the user's emotional state.

[0814] Output: A tailored training environment is provided for the animal, reducing the psychological burden on the user.

[0815] Through these processing steps, the system can provide an optimal training environment for both the animal and the user.

[0816] (Application example 2)

[0817] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0818] Training rescued animals for release into the wild requires a high level of expertise and a great deal of effort, making it difficult to do so efficiently. Furthermore, conventional methods cannot take into account the animal's stress or the user's emotions in real time, which can reduce the effectiveness of the training. Therefore, there is a need for effective and efficient training for reintroducing animals into the wild. Furthermore, police dog training requires an environment that efficiently trains specific skills (e.g., explosive detection), but it is difficult to adaptively change that environment in real time.

[0819] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0820] In this invention, the server includes a means for selecting the type of animal to be trained to support the release of rescued animals into the wild, a means for generating a virtual space based on the training content, a means for configuring a display device dedicated to the animal to display the generated virtual space, a means for detecting the stress level of the animal, a means for controlling the continuation or interruption of training based on the stress level, and a means for recognizing the user's emotions and adjusting the training environment in the virtual space. This enables efficient release-into-the-wild training while minimizing stress on the animal. Furthermore, in training specific skills for police dogs, an appropriate virtual environment can be provided while taking the user's emotions into consideration, achieving high training effectiveness.

[0821] A "trainable animal" is an animal that is to be trained to acquire a particular skill or behavior.

[0822] "Training content" refers to the specific skills and behaviors that the animal must master.

[0823] A "virtual space" is a simulated training environment created by generative AI.

[0824] An "animal-specific display device" is a device that animals can wear to visually experience a virtual space.

[0825] "Stress level" is an indicator of the degree of mental and physical strain an animal experiences during training.

[0826] "Generative AI" is an artificial intelligence technology that analyzes large amounts of data and automatically generates new information and spaces.

[0827] "User's emotions" refers to the psychological state of the user who manages and supervises the training.

[0828] The "means for recognizing emotions and adjusting the training environment in the virtual space" is a device or system that has the function of analyzing the user's emotional state and adaptively changing the training environment based on the results.

[0829] The system for implementing this invention is designed to efficiently release rescued animals back into the wild and train them in specific skills. The roles of the server, terminal, and user are clearly defined, and each step is explained in detail.

[0830] Hardware and Software Use

[0831] server:

[0832] The server is used as a means to select the animal species and training content, and is equipped with a generative artificial intelligence (AI) model that can generate a virtual space based on user instructions.

[0833] Examples of devices used: high-performance servers (e.g., Dell PowerEdge), generative AI models (e.g., OpenAI GPT).

[0834] Device:

[0835] The device collects the animal's biological data and monitors its stress level based on that data. It also displays a virtual world to the animal wearing a head-mounted display.

[0836] Examples of devices used: EEG sensors (e.g., NeuroSky), head-mounted displays (e.g., Oculus Rift).

[0837] User:

[0838] The user operates the server to select the animal and training content, and the emotion data is analyzed by the emotion analysis engine, which adaptively adjusts the training environment.

[0839] Examples of devices used: emotion analysis software (e.g., Affectiva).

[0840] Specific examples of system use

[0841] 1. Animal and training selection:

[0842] Users connect to the server using a smartphone app or PC and select the animal to be trained (e.g., German Shepherd) and the training content (e.g., explosive detection training).

[0843] 2. Creating a virtual training environment:

[0844] Based on the information selected by the user, the server inputs prompt statements into the generative AI model to generate a virtual space.

[0845] Example prompt sentence:

[0846] Create a VR environment for explosive detection training for German Shepherds.

[0847] 3. Monitoring Animal Stress Levels and Initiating Training:

[0848] The device uses an EEG sensor to monitor the animal's stress level, and if the stress level is low, a virtual space is displayed on the head-mounted display, and the animal begins training through the virtual space.

[0849] 4. Adaptive adjustment of the training environment:

[0850] The system analyzes the user's emotional data using an emotion analysis engine and dynamically adjusts the training environment based on the results. For example, if the user is feeling stressed, the training environment will be adjusted to be more gentle.

[0851] Specific example details

[0852] For example, if a police station uses a German shepherd for explosive detection training, the user selects the animal and the training content using the server. Then, a prompt is input into the generative AI model to generate a "VR environment for German shepherd explosive detection training." The system then monitors the animal's stress level using an EEG sensor, initiates training, and adaptively adjusts the environment in real time.

[0853] In this way, the present invention makes it possible to effectively and efficiently train animals for reintroduction into the wild or for specific skills.

[0854] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0855] Step 1:

[0856] The user connects to the server and selects the animal to be trained and the training content. As input, the user provides information on the type of animal and the training content. Based on this input data, the server creates a prompt sentence to be passed to the generative AI model. Data on the selected animal's characteristics and training content is used to create this prompt sentence.

[0857] The server stores the input information in a database and prepares it for the next processing step.

[0858] The output is a prompt and training selection information.

[0859] Step 2:

[0860] The server inputs a prompt sentence into the generative AI model to generate a virtual training environment. The prompt sentence is passed to the generative AI model as input. The server uses the generative AI model to generate an appropriate virtual space based on this prompt sentence.

[0861] This generated virtual space contains scenarios and environmental elements that are optimal for the training content and are suitable for training specific skills of animals.

[0862] The output is virtual training environment data.

[0863] Step 3:

[0864] The device collects the animal's biological data and monitors its stress level. As input, the device receives biological data from an EEG sensor. The device analyzes this biological data and evaluates the animal's stress level.

[0865] The device determines the stress level as "low" or "high" based on the criteria and passes the result on to the next step.

[0866] The output is stress level data.

[0867] Step 4:

[0868] When the stress level is low, the device displays the virtual space on a display device dedicated to the animal. Stress level data and virtual training environment data are passed to the device as input. The device uses these data to display the virtual training environment on a head-mounted display.

[0869] The animals begin training in the virtual space through this display.

[0870] The output is a representation of the virtual space.

[0871] Step 5:

[0872] The server acquires the user's emotional data and analyzes it with an emotion analysis engine. As input, the emotion data from the emotion analysis software is sent to the server. The server analyzes this data and identifies the user's mental state.

[0873] Based on the analysis results, the server generates setting information for adaptively changing the virtual training environment.

[0874] The output is the configuration information of the virtual training environment.

[0875] Step 6:

[0876] The server sends the setting information of the virtual training environment to the terminal and adjusts the environment. The setting information of the virtual training environment is provided as input. The terminal dynamically adjusts the environmental elements of the virtual space based on this information.

[0877] Add or remove specific situations or stimuli to create an environment that allows the animal to receive optimal training.

[0878] The output is a tailored virtual training environment.

[0879] Step 7:

[0880] The device monitors the progress of the training and continues or stops it as needed. It uses inputs such as the animal's stress level, biometric data, and environmental adjustments. The device analyzes this data in real time to evaluate the effectiveness of the training.

[0881] The device ensures effective training continues and, if necessary, interrupts training to reduce strain on the animal.

[0882] The output is an instruction to continue or discontinue training.

[0883] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0884] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0885] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0886] [Fourth embodiment]

[0887] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0888] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0889] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0890] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0891] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0892] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0893] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0894] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0895] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0896] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0897] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0898] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0899] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0900] An embodiment of this invention is a system for supporting the return of rescued animals to the wild, which provides a training environment tailored to the specific animal being trained, and monitors and controls the training status in real time.

[0901] First, the user uses the server to select the type of animal to be trained, for example, a fox. Next, the user operates the server to determine the training content required for that animal, for example, basic survival skills.

[0902] The server uses a generative AI to generate a virtual space based on the selected animal type and training content. This virtual space simulates an environment for training the skills and behaviors that animals need in the wild. The server names this virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[0903] The device then acquires data from an EEG sensor attached to the animal to detect its stress level. If the device determines that the animal's stress level is "low" based on this data, it uses a head-mounted display to display the generated virtual space to the animal. The animal undergoes training through this virtual space, acquiring the skills it will need in the wild.

[0904] During training, the device constantly monitors the animal's stress level and biological data. If the stress level becomes too high, the device will automatically stop the training or adjust the environment.

[0905] As a concrete example, when applying this system to Fox No. 1, the user uses the server to select "fox" and set "basic survival skills" as the training content. The server then uses a generation AI to generate a "VR space for training basic survival skills for foxes."

[0906] Next, the device checks Fox 1's stress level using an EEG sensor and determines that it is "low." After this, the device displays a "VR space for basic survival skills training for foxes" on Fox 1's head-mounted display, and Fox 1 begins training in this virtual space.

[0907] During training, the terminal constantly monitors Fox 1's stress level and other vital data, adjusting the training environment or halting training as necessary. In this way, this system provides a concrete method for efficiently training animals for reintroduction into the wild at low cost.

[0908] The processing flow will be explained below.

[0909] Step 1:

[0910] The user operates the server to select the type of animal to be trained, for example, "fox."

[0911] Step 2:

[0912] The user operates the server to determine the training content, for example, "basic survival skills."

[0913] Step 3:

[0914] The server uses generative AI to generate a virtual space based on the selected animal species and training content, providing a scenario optimized for training the skills the animals will need in the wild.

[0915] Step 4:

[0916] The server names the generated virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[0917] Step 5:

[0918] The device uses an EEG sensor attached to the animal to detect the animal's stress level, and based on this data, evaluates the stress level and determines it as "low."

[0919] Step 6:

[0920] If the device determines that the stress level is low, it will set up a virtual space on the animal's dedicated display device (head-mounted display). For example, Fox 1's head-mounted display will display a "VR space for basic survival skills training for foxes."

[0921] Step 7:

[0922] Fox 1 will begin training in a virtual environment where the animals will learn the necessary skills and prepare for release back into the wild.

[0923] Step 8:

[0924] During training, the device constantly monitors Fox 1's stress levels and biological data in real time. If the stress level becomes too high, the device will automatically halt the training or adjust the environment.

[0925] Step 9:

[0926] After the training is completed, the terminal will evaluate Fox 1's progress and acquired skills, and if necessary, create a next training plan. Based on these results, it will provide further optimized training content.

[0927] Example 1

[0928] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0929] In modern society, the release of rescued animals into the wild has become an important issue. Conventional training methods have been insufficient in managing stress for animals, making it difficult to provide an efficient training environment. It has also been difficult to consistently provide different training content for each animal. Therefore, there is a need for a system that can provide an appropriate training environment in real time according to the type and situation of the animal, and monitor and adjust the stress level of the animal.

[0930] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0931] In this invention, the server includes a means for selecting the type of animal to be trained, a means for generating a virtual space based on the training content, and a means for creating and transmitting prompts using artificial intelligence when generating the virtual space, thereby making it possible to provide an appropriate virtual training environment according to the type of animal and the training content.

[0932] The terminal includes a means for setting a display device dedicated to the animal that displays the generated virtual space, a means for detecting the stress level of the animal, a means for controlling the continuation or interruption of training based on the stress level, and a means for monitoring the animal's biological data in real time, thereby making it possible to monitor the animal's stress level and biological data in real time and adjust the training environment as needed.

[0933] By combining these methods, we provide a system that provides optimal training tailored to the individual circumstances of animals and enables efficient, low-stress training for reintroduction into the wild.

[0934] The "means for selecting the type of animal to train" is a system component that provides an interface through which a user can select a particular type of animal to assist in the release of rescued animals back into the wild.

[0935] The "means for generating a virtual space based on training content" is a system component that automatically generates a virtual space based on the selected animal type and training content appropriate for that animal.

[0936] The "means for setting up a display device dedicated to the animal that displays the generated virtual space" is a system component that prepares and sets up a device that enables the animal to visually recognize the generated virtual space.

[0937] A "means for detecting stress level of an animal" is a system component that analyzes data from sensors attached to the animal and monitors the stress level of the animal in real time.

[0938] The "means for controlling continuation or interruption of training based on the stress level" is a system component that automatically decides whether to continue or interrupt training depending on the detected stress level.

[0939] "Means for creating and sending prompt sentences using generative AI" refers to a system component that uses a generative AI model to generate prompt sentences that are suited to the type of animal and the training content, and sends the prompt sentences to the AI.

[0940] "Means for monitoring biological data of an animal in real time" refers to a system component that acquires and analyzes biological data, such as heart rate and body temperature, of an animal in real time.

[0941] An embodiment of this invention is a method for providing a training environment tailored to a specific animal as a training target, and for monitoring and controlling the training status in real time, in a system for supporting the return of rescued animals to the wild.

[0942] First, the user uses the server to select the type of animal to be trained, for example, a fox. Next, the user operates the server to determine the training content required for that animal, for example, basic survival skills.

[0943] The server generates a virtual space using a generative AI model (e.g., OpenAI's GPT-3) based on the selected animal type and training content. This virtual space simulates an environment for training the skills and behaviors that animals need in the wild. The server names this virtual space "VR space for basic survival skill training for foxes" and sets it as the training environment.

[0944] Next, the device acquires data from an EEG sensor attached to the animal to detect the animal's stress level. If the animal's stress level is determined to be "low" based on this data, the device uses a display device dedicated to the animal (e.g., Oculus Rift) to display the generated virtual space to the animal. The animal undergoes training through this virtual space, thereby acquiring the skills necessary for the wild.

[0945] During training, the device constantly monitors the animal's stress level and vital data (e.g., heart rate, body temperature, etc.). If the stress level becomes too high, the device will automatically halt the training or adjust the environment.

[0946] As a concrete example, when applying this system to Fox No. 1, the user uses the server to select "fox" and set "basic survival skills" as the training content. The server then uses the generative AI model to generate a "VR space for training basic survival skills for foxes."

[0947] Next, the device checks Fox 1's stress level using an EEG sensor and determines that it is low. After this, the device displays a "VR space for basic survival skills training for foxes" on Fox 1's head-mounted display (e.g., HTC Vive), and Fox 1 begins training in this virtual space.

[0948] During training, the terminal constantly monitors Fox 1's stress levels and other vital signs, adjusting the training environment or halting training as necessary.

[0949] As described above, this system provides a specific method for efficiently training animals for reintroduction into the wild at low cost.

[0950] Example prompt sentence:

[0951] "Create a VR space for training basic survival skills for foxes."

[0952] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0953] Step 1:

[0954] The user selects an animal type. The user logs in to the server and selects an animal type from the system's main menu. For example, to select a "fox," the user clicks "fox" in the drop-down menu in the UI. The selected animal type is the input, and the animal type data is obtained as the output.

[0955] Step 2:

[0956] The user decides the training content. After selecting the type of animal, the user operates a menu to decide the training content. For example, the user may set "basic survival skills" as the training content. The user clicks on the appropriate item from the list of training content. The selected training content is the input, and training content data is obtained as the output.

[0957] Step 3:

[0958] The server generates a virtual space. Based on the selected animal type and training content, the server uses a generative AI model (e.g., OpenAI's GPT-3) to generate the virtual space. Specifically, the server combines a pre-prepared prompt sentence with the animal type and training content and sends it to the generative AI. This prompt sentence is the input, and the generated virtual space data is obtained as the output.

[0959] Step 4:

[0960] The device monitors the animal's stress level. The device acquires data in real time from an EEG sensor attached to the animal and analyzes the stress level. The acquired EEG data is the input, and the stress level data is the output.

[0961] Step 5:

[0962] The device displays the virtual space to the animal. If the stress level is determined to be "low," the device displays the virtual space created on a display device dedicated to the animal (e.g., Oculus Rift). Stress level data is the input, and the virtual space is displayed as the output.

[0963] Step 6:

[0964] The terminal monitors and adjusts the animal during training. During training, the terminal constantly monitors the animal's stress level and biometric data (e.g., heart rate, body temperature, etc.). If the stress level is determined to be "high," the terminal temporarily suspends training and adjusts the environment in the virtual space. Stress level and biometric data are inputs, and the output is the action taken to continue or suspend training.

[0965] Specific actions may include sending electronic alerts or notifications to users or administrators.

[0966] (Application example 1)

[0967] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0968] The current system for protecting and supporting the release of wild animals has problems such as limited training environments and inadequate stress management and real-time monitoring of biological data, which makes it difficult to provide effective training for the safe release of animals into the wild and reduces the efficiency of conservation efforts.

[0969] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0970] In this invention, the server includes means for selecting the type of animal to be trained, means for generating a virtual space based on the training content, means for setting up a display device dedicated to the animal that displays the generated virtual space, means for detecting the stress level of the animal, means for controlling the continuation or interruption of training based on the stress level, means for simulating a safe environment and conducting training using the virtual space generated based on the type of animal to be protected and the training content, and means for monitoring and analyzing the animal's biological data in real time, which enables stress management of the animal and real-time monitoring of the biological data, thereby providing a safe and effective training environment.

[0971] The "means for selecting the type of animal to be trained" refers to a method and device for identifying the type of protected wild animal and selecting appropriate training content based on that type.

[0972] The "means for generating a virtual space based on training content" refers to a method and apparatus for creating a virtual environment according to the selected training content.

[0973] The "means for setting up a display device dedicated to an animal to display the generated virtual space" refers to a method and apparatus for setting up a specific device to visually present the training virtual space to an animal.

[0974] The "means for detecting the stress level of an animal" refers to a method and device for analyzing the biological data of an animal and measuring the degree of stress based on the results.

[0975] The "means for controlling the continuation or discontinuation of training based on the stress level" refers to a method and device for determining and controlling whether to continue or discontinue training in accordance with fluctuations in the stress level of an animal.

[0976] "Means for simulating a safe environment and conducting training using a virtual space created based on the type of animal to be protected and the training content" refers to a method and device for conducting training that enables the animal to behave safely, using a virtual environment that corresponds to the type of animal to be protected and the necessary training content.

[0977] "Means for monitoring and analyzing animal vital data in real time" refers to methods and devices for instantly acquiring and analyzing vital data obtained from sensors attached to animals.

[0978] This invention relates to a system that supports the return of protected wild animals to the wild, and specifically, it provides a training environment tailored to the animal being trained, and can monitor and control the training status in real time.

[0979] First, the user accesses the server and selects the type of animal to be trained. This allows the user to specify the animal to be protected (e.g., "deer") and set the training content required for that animal (e.g., "patrol route in the habitat").

[0980] Based on the selected animal species and training, the server uses a generative AI model to generate a virtual space designed to simulate the skills and behaviors required in the animal's natural environment.

[0981] Next, the device acquires data from an EEG sensor attached to the animal and detects the animal's stress level in real time. If the animal's stress level is determined to be "low" based on this data, the device displays the virtual space created using a display device dedicated to the animal (e.g., a head-mounted display). This allows the animal to begin training in the virtual space.

[0982] During training, the device constantly monitors the animals' stress levels and other vital data, automatically adjusting the training environment or halting training as necessary, enabling real-time monitoring of animal stress and vital data, providing a safe and effective training environment.

[0983] For example, the following prompts can be fed into a generative AI model to generate the desired virtual space:

[0984] "The target animal is deer, and we're tasked with creating a virtual environment to train patrol routes in their habitat. This environment includes natural forests, rivers, hills, etc., designed to allow deer to move safely."

[0985] The hardware used includes head-mounted displays such as Oculus Quest, animal biometric sensors, and high-performance servers such as AWS EC2 instances, while the software includes generative AI systems such as OpenAI GPT-3 and real-time monitoring and data analysis platforms such as Apache Kafka and Apache Spark.

[0986] This invention is expected to make it possible to train animals for reintroduction into the wild more efficiently and with less stress, further promoting conservation efforts.

[0987] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0988] Step 1:

[0989] A user accesses the server and selects the type of animal to protect.

[0990] Input: Data about the type of animal (e.g. "deer")

[0991] Output: Selection data about animal species

[0992] Specific behavior: The user operates an interface to select a type of animal, and the server records this selection.

[0993] Step 2:

[0994] The user accesses the server and sets the training content.

[0995] Input: Training data (e.g., "patrol route in habitat area")

[0996] Output: Setting data regarding training content

[0997] Specific operation: The user operates the interface to select the training content, and the server records this setting.

[0998] Step 3:

[0999] The server inputs prompt sentences into the generative AI model and generates a virtual space.

[1000] Input: Data about the animal type and training

[1001] Output: Generated virtual space

[1002] Specific operation: The server inputs the following prompt to the generative AI model: "The target animal is a deer. Please create a virtual environment to train patrol routes in its habitat. This environment includes natural forests, rivers, hills, etc., and is designed to allow deer to move safely."

[1003] Step 4:

[1004] The device collects data from an EEG sensor attached to the animal to detect the animal's stress level.

[1005] Input: Biometric data obtained from EEG sensors

[1006] Output: Animal's stress level (e.g. "low")

[1007] How it works: The brainwave sensor collects the animal's biological data, which the device analyzes to determine its stress level.

[1008] Step 5:

[1009] The terminal sets up a display device dedicated to the animal and displays the virtual space.

[1010] Input: Generated virtual space, animal stress level (only if low)

[1011] Output: Display of a virtual space that animals can visually recognize

[1012] Specific operation: The device displays the generated virtual space on a head-mounted display dedicated to the animal.

[1013] Step 6:

[1014] The device monitors the animal's vital data in real time during training and adjusts or halts the training as necessary.

[1015] Input: Real-time biometric data acquired during training

[1016] Output: Instructions to continue, adjust, or stop training

[1017] Specific operation: The device constantly monitors the animal's biological data and automatically adjusts the training content and environment if there are changes in stress levels or other important indicators.

[1018] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1019] A mode for implementing this invention is to provide a training environment tailored to a specific animal as a training target in a system for supporting the return of rescued animals to the wild, and in addition to monitoring and controlling the training status in real time, to recognize the emotions of the user to provide an even more optimal training environment.

[1020] First, the user operates the server to select the type of animal to be trained, for example, "fox." Next, the user operates the server to determine the training content required for that animal, for example, "basic survival skills."

[1021] The server uses a generative AI to generate a virtual space based on the selected animal type and training content. This virtual space simulates an environment for training the skills and behaviors that animals need in the wild. The server names this virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[1022] The device then acquires data from an EEG sensor attached to the animal to detect its stress level. If the device determines that the animal's stress level is "low" based on this data, it uses a head-mounted display to display the generated virtual space to the animal. The animal undergoes training through this virtual space, acquiring the skills it will need in the wild.

[1023] During training, the device constantly monitors the animal's stress level and biological data. If the stress level becomes too high, the device will automatically stop the training or adjust the environment.

[1024] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotional data and adjusts the training scenario in the virtual space based on the analysis results. For example, if the user is feeling stressed or anxious, the emotion engine can use that data to adjust the training environment to be gentler. This not only makes training the animal more effective, but also reduces the psychological burden on the user.

[1025] As a concrete example, when applying this system to Fox No. 1, the user uses the server to select "fox" and set "basic survival skills" as the training content. The server then uses a generation AI to generate a "VR space for training basic survival skills for foxes."

[1026] Next, the device checks Fox 1's stress level using an EEG sensor and determines that it is "low." After this, the device displays a "VR space for basic survival skills training for foxes" on Fox 1's head-mounted display, and Fox 1 begins training in this virtual space.

[1027] During training, the device constantly monitors Fox 1's stress level and other biometric data, adjusting the training environment or halting training as necessary. Furthermore, the emotion engine adjusts the training environment appropriately based on the user's emotional data, ensuring more effective training for the animal. This provides a concrete method for efficiently training animals for release into the wild at low cost, reducing the burden on both the user and the animal.

[1028] The processing flow will be explained below.

[1029] Step 1:

[1030] The user operates the server to select the type of animal to be trained, for example, "fox."

[1031] Step 2:

[1032] The user operates the server to determine the training content, for example, "basic survival skills."

[1033] Step 3:

[1034] The server uses generative AI to generate a virtual space based on the selected animal species and training content, simulating an environment for training the skills and behaviors the animal would need in the wild.

[1035] Step 4:

[1036] The server names the generated virtual space "VR space for training basic survival skills for foxes" and sets it as the training environment.

[1037] Step 5:

[1038] The device uses an EEG sensor attached to the animal to capture data on the animal's stress level, which can then be used to assess the animal's readiness for training.

[1039] Step 6:

[1040] The terminal evaluates the animal's stress level based on the acquired data, and if it determines that it is "low," it proceeds to the next step.

[1041] Step 7:

[1042] The device is set up with a display device (head-mounted display) specifically for animals, and the generated virtual space is displayed to the animal. In the case of Fox 1, the head-mounted display displays a "VR space for training basic survival skills for foxes."

[1043] Step 8:

[1044] Fox 1 will begin training in a virtual environment where animals will learn the necessary skills and prepare for release back into the wild.

[1045] Step 9:

[1046] During training, the terminal constantly monitors Fox 1's stress levels and biological data in real time. If the stress level becomes too high, the terminal will halt the training or adjust the environment.

[1047] Step 10:

[1048] The system's built-in emotion engine analyzes the user's emotional data and adjusts the virtual training scenario based on the data if the user is feeling stressed or anxious.

[1049] Step 11:

[1050] The training environment adjusted by the emotion engine is provided to the animal again, which not only makes training for the animal more effective but also reduces the psychological burden on the user.

[1051] Step 12:

[1052] After completing the training, the terminal will evaluate Fox 1's progress and the skills it has acquired. If necessary, it will create a training plan for the next training session. Based on the results, further training content will be provided that is optimal for the new training.

[1053] Example 2

[1054] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1055] Today's training for reintroducing rescued animals into the wild requires providing an optimal training environment for each animal and taking into account the animal's stress level and the user's emotional state. Current methods lack a system that comprehensively manages these factors, limiting the effectiveness of training. It is also necessary to simultaneously reduce the user's stress and anxiety. To achieve this, we aim to develop a system that improves the efficiency of animal training while also reducing the user's psychological burden.

[1056] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1057] In this invention, the server includes means for selecting the type of animal to be trained, means for generating a virtual space based on the training content, means for setting up a display device dedicated to the animal to display the generated virtual space, means for detecting the stress level of the animal, means for controlling the continuation or interruption of training based on the stress level, and means for recognizing the user's emotions and adjusting the training environment, thereby improving the efficiency of wild release training for animals and simultaneously reducing the psychological burden on the user.

[1058] The "means for selecting the type of animal to be trained" refers to an interface that allows the user to specify the type of animal to be trained on the system.

[1059] "Means for generating a virtual space based on training content" refers to a mechanism that uses artificial intelligence to create a virtual space for animals to receive training in accordance with specified training content.

[1060] "Means for setting up a display device dedicated to the animal that displays the generated virtual space" refers to a mechanism for attaching and setting up a display device dedicated to the animal so that the animal can see the generated virtual space.

[1061] "Means for detecting stress levels in animals" refers to a mechanism for analyzing data obtained from sensors attached to animals and assessing their stress levels.

[1062] "Means for controlling the continuation or discontinuation of training based on the stress level" refers to a control mechanism for continuing or discontinuing training or adjusting the training content based on the evaluation results of the animal's stress level.

[1063] "Means for recognizing the user's emotions and adjusting the training environment" refers to a mechanism for analyzing the user's emotional state and adjusting the virtual training space and environmental settings based on the results.

[1064] The embodiment of the present invention is a system for supporting the release of rescued animals back into the wild. This system operates in cooperation with users, a server, and terminals.

[1065] First, the user accesses the server's interface from their device. They select the type of animal to be trained and set the appropriate training content. A general web application or mobile application is used as the user interface. For example, the user selects a "fox" and specifies "basic survival skills" as the training content.

[1066] The server then uses a generative AI model (e.g., Azure OpenAI) to generate a virtual space based on the selected animal and training content. This virtual space simulates the environment for training the skills and behaviors the animal would need in its natural environment. The generated virtual space is named "VR space for basic survival skills training for foxes" and saved on the server.

[1067] Next, the device acquires data from an EEG sensor (e.g., NeuroSky) attached to the animal to detect the animal's stress level. If the stress level is determined to be "low" based on this data, the device uses a display device (e.g., Oculus Rift) dedicated to the animal to display the generated virtual space to the animal. The animal can then receive training through this virtual space.

[1068] During training, the device constantly monitors the animal's stress level and other biometric data and adjusts the training environment as needed, including the ability to stop training or adjust the difficulty of the environment if stress levels become too high.

[1069] Furthermore, the server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine (e.g., Affectiva) analyzes the user's emotional data and adjusts the virtual training scenario based on the analysis results. This allows the training environment to be more calming if the user is feeling stressed or anxious.

[1070] As a specific example, if the training subject is "Fox No. 1," the user selects "Fox" through the server and sets "basic survival skills." The server uses a generative AI model to generate an appropriate virtual space. After the device uses an EEG sensor to determine that Fox No. 1's stress level is "low," the device displays the training VR space on Fox No. 1's head-mounted display and begins training. During training, the device continues to monitor the stress level and adjusts the environment as necessary. The server's emotion engine analyzes the user's emotional data and adjusts the training environment as appropriate.

[1071] Example prompt: "Create an appropriate VR environment for training Fox 1, the fox, in basic survival skills. Fox 1 has a low stress level, so we will train him in this virtual environment. Please also take into account the user's emotional data and adjust the training environment accordingly."

[1072] As described above, this system effectively trains animals for reintroduction into the wild, while at the same time reducing the psychological burden on users.

[1073] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1074] Step 1:

[1075] Selection of animal species and training content

[1076] Input: The user accesses the system and selects the type of animal to be trained and the training content.

[1077] How it works: The user operates the device and selects the type of animal, such as "fox," and the training content, such as "basic survival skills," from the interface on the server.

[1078] Output: The selected animal type and training details are sent to the server and saved.

[1079] Step 2:

[1080] Virtual space generation

[1081] Input: The server receives information about the animal type and training details sent by the user.

[1082] How it works: The server calls a generative AI model (e.g., Azure OpenAI) and sends a prompt to the model to generate a virtual space based on the selected animal type and training content. For example, the prompt "Generate a VR space for training basic survival skills for foxes" is input to the model.

[1083] Output: The server acquires the virtual space data output from the generative AI model and saves it as a "VR space for training basic survival skills for foxes."

[1084] Step 3:

[1085] Stress Level Detection

[1086] Input: Stress level data is sent to the terminal from an EEG sensor attached to the animal.

[1087] How it works: The device periodically collects data from an EEG sensor (e.g., NeuroSky), analyzes the data, and evaluates the stress level. If the stress level is determined to be "low," it moves on to the next step.

[1088] Output: The stress level assessment results are saved on the device and the conditions for the next processing step are set.

[1089] Step 4:

[1090] Virtual space display

[1091] Input: Evaluation results of animals rated as having a "low" stress level and virtual space data stored on the server.

[1092] Operation: The terminal displays the virtual space obtained from the server to the animal through a display device dedicated to the animal (e.g., Oculus Rift). Specifically, the terminal attaches a head-mounted display to the animal and displays the generated VR space.

[1093] Output: The animal begins training in the virtual space.

[1094] Step 5:

[1095] Monitoring during training

[1096] Input: Stress levels and other biometric data sent periodically by the animal during training.

[1097] How it works: The device monitors the animal's stress level and other biometric data in real time during training. If the stress level becomes too high, the device will automatically halt the training or adjust the difficulty of the training environment. Specifically, the device selects and executes appropriate actions based on the data obtained.

[1098] Output: The training environment is adjusted or training is interrupted, reducing the animal's stress level.

[1099] Step 6:

[1100] Analyzing user emotion data and adjusting the environment

[1101] Input: The user's emotional data is sent to the emotion recognition engine.

[1102] How it works: An emotion engine (e.g., Affectiva) built into the server analyzes the user's emotional data and adjusts the virtual training scenario based on the results. Specifically, it automatically changes the virtual environment settings and training difficulty according to the user's emotional state.

[1103] Output: A tailored training environment is provided for the animal, reducing the psychological burden on the user.

[1104] Through these processing steps, the system can provide an optimal training environment for both the animal and the user.

[1105] (Application example 2)

[1106] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1107] Training rescued animals for release into the wild requires a high level of expertise and a great deal of effort, making it difficult to do so efficiently. Furthermore, conventional methods cannot take into account the animal's stress or the user's emotions in real time, which can reduce the effectiveness of the training. Therefore, there is a need for effective and efficient training for reintroducing animals into the wild. Furthermore, police dog training requires an environment that efficiently trains specific skills (e.g., explosive detection), but it is difficult to adaptively change that environment in real time.

[1108] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1109] In this invention, the server includes a means for selecting the type of animal to be trained to support the release of rescued animals into the wild, a means for generating a virtual space based on the training content, a means for configuring a display device dedicated to the animal to display the generated virtual space, a means for detecting the stress level of the animal, a means for controlling the continuation or interruption of training based on the stress level, and a means for recognizing the user's emotions and adjusting the training environment in the virtual space. This enables efficient release-into-the-wild training while minimizing stress on the animal. Furthermore, in training specific skills for police dogs, an appropriate virtual environment can be provided while taking the user's emotions into consideration, achieving high training effectiveness.

[1110] A "trainable animal" is an animal that is to be trained to acquire a particular skill or behavior.

[1111] "Training content" refers to the specific skills and behaviors that the animal must master.

[1112] A "virtual space" is a simulated training environment created by generative AI.

[1113] An "animal-specific display device" is a device that animals can wear to visually experience a virtual space.

[1114] "Stress level" is an indicator of the degree of mental and physical strain an animal experiences during training.

[1115] "Generative AI" is an artificial intelligence technology that analyzes large amounts of data and automatically generates new information and spaces.

[1116] "User's emotions" refers to the psychological state of the user who manages and supervises the training.

[1117] The "means for recognizing emotions and adjusting the training environment in the virtual space" is a device or system that has the function of analyzing the user's emotional state and adaptively changing the training environment based on the results.

[1118] The system for implementing this invention is designed to efficiently release rescued animals back into the wild and train them in specific skills. The roles of the server, terminal, and user are clearly defined, and each step is explained in detail.

[1119] Hardware and Software Use

[1120] server:

[1121] The server is used as a means to select the animal species and training content, and is equipped with a generative artificial intelligence (AI) model that can generate a virtual space based on user instructions.

[1122] Examples of devices used: high-performance servers (e.g., Dell PowerEdge), generative AI models (e.g., OpenAI GPT).

[1123] Device:

[1124] The device collects the animal's biological data and monitors its stress level based on that data. It also displays a virtual world to the animal wearing a head-mounted display.

[1125] Examples of devices used: EEG sensors (e.g., NeuroSky), head-mounted displays (e.g., Oculus Rift).

[1126] User:

[1127] The user operates the server to select the animal and training content, and the emotion data is analyzed by the emotion analysis engine, which adaptively adjusts the training environment.

[1128] Examples of devices used: emotion analysis software (e.g., Affectiva).

[1129] Specific examples of system use

[1130] 1. Animal and training selection:

[1131] Users connect to the server using a smartphone app or PC and select the animal to be trained (e.g., German Shepherd) and the training content (e.g., explosive detection training).

[1132] 2. Creating a virtual training environment:

[1133] Based on the information selected by the user, the server inputs prompt statements into the generative AI model to generate a virtual space.

[1134] Example prompt sentence:

[1135] Create a VR environment for explosive detection training for German Shepherds.

[1136] 3. Monitoring Animal Stress Levels and Initiating Training:

[1137] The device uses an EEG sensor to monitor the animal's stress level, and if the stress level is low, a virtual space is displayed on the head-mounted display, and the animal begins training through the virtual space.

[1138] 4. Adaptive adjustment of the training environment:

[1139] The system analyzes the user's emotional data using an emotion analysis engine and dynamically adjusts the training environment based on the results. For example, if the user is feeling stressed, the training environment will be adjusted to be more gentle.

[1140] Specific example details

[1141] For example, if a police station uses a German shepherd for explosive detection training, the user selects the animal and the training content using the server. Then, a prompt is input into the generative AI model to generate a "VR environment for German shepherd explosive detection training." The system then monitors the animal's stress level using an EEG sensor, initiates training, and adaptively adjusts the environment in real time.

[1142] In this way, the present invention makes it possible to effectively and efficiently train animals for reintroduction into the wild or for specific skills.

[1143] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1144] Step 1:

[1145] The user connects to the server and selects the animal to be trained and the training content. As input, the user provides information on the type of animal and the training content. Based on this input data, the server creates a prompt sentence to be passed to the generative AI model. Data on the selected animal's characteristics and training content is used to create this prompt sentence.

[1146] The server stores the input information in a database and prepares it for the next processing step.

[1147] The output is a prompt and training selection information.

[1148] Step 2:

[1149] The server inputs a prompt sentence into the generative AI model to generate a virtual training environment. The prompt sentence is passed to the generative AI model as input. The server uses the generative AI model to generate an appropriate virtual space based on this prompt sentence.

[1150] This generated virtual space contains scenarios and environmental elements that are optimal for the training content and are suitable for training specific skills of animals.

[1151] The output is virtual training environment data.

[1152] Step 3:

[1153] The device collects the animal's biological data and monitors its stress level. As input, the device receives biological data from an EEG sensor. The device analyzes this biological data and evaluates the animal's stress level.

[1154] The device determines the stress level as "low" or "high" based on the criteria and passes the result on to the next step.

[1155] The output is stress level data.

[1156] Step 4:

[1157] When the stress level is low, the device displays the virtual space on a display device dedicated to the animal. Stress level data and virtual training environment data are passed to the device as input. The device uses these data to display the virtual training environment on a head-mounted display.

[1158] The animals begin training in the virtual space through this display.

[1159] The output is a representation of the virtual space.

[1160] Step 5:

[1161] The server acquires the user's emotional data and analyzes it with an emotion analysis engine. As input, the emotion data from the emotion analysis software is sent to the server. The server analyzes this data and identifies the user's mental state.

[1162] Based on the analysis results, the server generates setting information for adaptively changing the virtual training environment.

[1163] The output is the configuration information of the virtual training environment.

[1164] Step 6:

[1165] The server sends the setting information of the virtual training environment to the terminal and adjusts the environment. The setting information of the virtual training environment is provided as input. The terminal dynamically adjusts the environmental elements of the virtual space based on this information.

[1166] Add or remove specific situations or stimuli to create an environment that allows the animal to receive optimal training.

[1167] The output is a tailored virtual training environment.

[1168] Step 7:

[1169] The device monitors the progress of the training and continues or stops it as needed. It uses inputs such as the animal's stress level, biometric data, and environmental adjustments. The device analyzes this data in real time to evaluate the effectiveness of the training.

[1170] The device ensures effective training continues and, if necessary, interrupts training to reduce strain on the animal.

[1171] The output is an instruction to continue or discontinue training.

[1172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1173] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1174] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1175] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1176] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1177] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1178] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1179] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1180] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1182] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1183] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1186] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1187] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1188] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1191] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1192] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1193] The following is further disclosed regarding the above embodiment.

[1194] (Claim 1)

[1195] A means for selecting species of animals to be trained to assist in the reintroduction of rescued animals into the wild;

[1196] A means for generating a virtual space based on training content;

[1197] a means for setting a display device dedicated to animals that displays the generated virtual space;

[1198] a means for detecting a stress level of an animal;

[1199] a means for controlling continuation or interruption of training based on said stress level;

[1200] A system including:

[1201] (Claim 2)

[1202] The system according to claim 1, wherein the virtual space is generated by a generative artificial intelligence.

[1203] (Claim 3)

[1204] 2. The system of claim 1, wherein the animal-specific display device is a head-mounted display.

[1205] "Example 1"

[1206] (Claim 1)

[1207] A means for selecting species of animals to be trained to assist in the reintroduction of rescued animals into the wild;

[1208] A means for generating a virtual space based on training content;

[1209] a means for setting a display device dedicated to animals that displays the generated virtual space;

[1210] a means for detecting a stress level of an animal;

[1211] a means for controlling continuation or interruption of training based on said stress level;

[1212] A means for creating and sending prompt sentences using artificial intelligence to generate the virtual space;

[1213] a means for monitoring the animal's vital data in real time;

[1214] A system including:

[1215] (Claim 2)

[1216] The system according to claim 1, wherein the virtual space is generated by a generative artificial intelligence.

[1217] (Claim 3)

[1218] 2. The system of claim 1, wherein the animal-specific display device is a head-mounted display.

[1219] "Application Example 1"

[1220] (Claim 1)

[1221] A means for selecting species of animals to be trained to assist in the reintroduction of rescued animals into the wild;

[1222] A means for generating a virtual space based on training content;

[1223] a means for setting a display device dedicated to animals that displays the generated virtual space;

[1224] a means for detecting a stress level of an animal;

[1225] a means for controlling continuation or interruption of training based on said stress level;

[1226] A means for simulating a safe environment and conducting training using a virtual space generated based on the type of animal to be protected and the training content;

[1227] A means of monitoring and analyzing the animal's vital data in real time;

[1228] A system including:

[1229] (Claim 2)

[1230] The system according to claim 1, wherein the virtual space is generated by a generative artificial intelligence.

[1231] (Claim 3)

[1232] 2. The system of claim 1, wherein the animal-specific display device is a head-mounted display.

[1233] "Example 2: Combining Emotion Engines"

[1234] (Claim 1)

[1235] a means for selecting the type of animal to be trained;

[1236] A means for generating a virtual space based on training content;

[1237] a means for setting a display device dedicated to animals that displays the generated virtual space;

[1238] a means for detecting a stress level of an animal;

[1239] a means for controlling continuation or interruption of training based on said stress level;

[1240] a means for recognizing a user's emotions and adjusting the training environment;

[1241] A system including:

[1242] (Claim 2)

[1243] The system according to claim 1, wherein the virtual space is generated by a generative artificial intelligence.

[1244] (Claim 3)

[1245] 2. The system of claim 1, wherein the animal-specific display device is a head-mounted display.

[1246] "Application example 2 when combining emotion engines"

[1247] (Claim 1)

[1248] A means for selecting species of animals to be trained to assist in the reintroduction of rescued animals into the wild;

[1249] A means for generating a virtual space based on training content;

[1250] a means for setting a display device dedicated to animals that displays the generated virtual space;

[1251] a means for detecting a stress level of an animal;

[1252] a means for controlling continuation or interruption of training based on said stress level;

[1253] a means for recognizing a user's emotion and adjusting the virtual training environment;

[1254] A system including:

[1255] (Claim 2)

[1256] The system according to claim 1, wherein the virtual space is generated by a generative artificial intelligence.

[1257] (Claim 3)

[1258] 2. The system of claim 1, wherein the animal-specific display device is a head-mounted display. [Explanation of symbols]

[1259] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for selecting species of animals to be trained to assist in the reintroduction of rescued animals into the wild; A means for generating a virtual space based on training content; a means for setting a display device dedicated to animals that displays the generated virtual space; a means for detecting a stress level of an animal; a means for controlling continuation or interruption of training based on said stress level; A system including:

2. The system according to claim 1 , wherein the virtual space is generated by a generative artificial intelligence.

3. The system of claim 1 , wherein the animal-specific display device is a head-mounted display.

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