System

Abstract

An object of the system according to the embodiment is to provide training for efficiently and inexpensively returning a protected animal to wild.SOLUTION: A system includes a generation unit, a provision unit, a training unit, a monitoring unit, an analysis unit, and an evaluation unit. The generation unit generates a type of a target animal and training contents. The provision unit provides the animal with the training content generated by the generation unit. The training unit trains the animal based on the training content provided by the providing unit. The monitoring unit monitors the movement and reaction of the animal trained by the training unit. The analysis unit analyzes the data collected by the monitoring unit. The evaluation unit evaluates whether or not preparation for wild return has been completed based on the data analyzed by the analysis unit.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] Conventional techniques have had the problem that training rescued animals to be released back into the wild is expensive and it is difficult to provide an appropriate environment for them.

[0005] The system according to the embodiment aims to provide training for releasing rescued animals back into the wild efficiently and at low cost. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a provision unit, a training unit, a monitoring unit, an analysis unit, and an evaluation unit. The generation unit generates a target animal type and training content. The provision unit provides the training content generated by the generation unit to the animal. The training unit trains the animal based on the training content provided by the provision unit. The monitoring unit monitors the movements and reactions of the animal trained by the training unit. The analysis unit analyzes the data collected by the monitoring unit. The evaluation unit evaluates whether the animal is ready for release into the wild based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide training for reintroducing rescued animals into the wild efficiently and at low cost. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention uses a dedicated HMD to enable animals to receive the training necessary for reintroduction into the wild in a VR space. This system uses a large-scale language model (LLM) to generate the type of target animal and the training content required for that animal, and provides this to the animal through the dedicated HMD. The animal wears the HMD and begins training in a VR space. The VR space is designed to allow the animal to receive training in a setting similar to its natural environment. During training, sensors monitor the animal's movements and reactions in real time, collecting data. This data is analyzed by the LLM and used to improve the training content and evaluate the animal's progress. After training, the animal is evaluated for readiness for reintroduction based on the collected data. This enables efficient and effective animal reintroduction, potentially reducing costs and improving the success rate. This system allows efficient and effective training for animals to be reintroduced into the wild. For example, using a dedicated HMD, animals can receive training in a VR space similar to their natural environment. The data collected during training is analyzed by the LLM and used to improve the training content and evaluate the animal's progress. This will enable the release of animals into the wild to be carried out efficiently and effectively, which is expected to reduce costs and improve success rates.

[0029] A wild reintroduction training system according to an embodiment includes a generation unit, a provision unit, a training unit, a monitoring unit, an analysis unit, and an evaluation unit. The generation unit generates a target animal type and training content. For example, the generation unit receives data related to the animal's type and ecology as input and generates training content. The generation unit can generate training content based on the animal's type and ecology using a large-scale language model (LLM). For example, the generation unit can generate flying training for birds and hunting training for mammals. The provision unit provides the training content generated by the generation unit to the animal. The provision unit provides the training content to the animal, for example, through an HMD dedicated to the animal. The provision unit enables the animal to receive training in a VR space using the HMD. The training unit trains the animal based on the training content provided by the provision unit. For example, the training unit provides a VR space designed so that the animal can receive training in a situation similar to a natural environment. The training unit enables the animal to receive training in the VR space. The monitoring unit monitors the movements and reactions of the animal trained by the training unit. The monitoring unit monitors the animal's movements and reactions in real time using, for example, a sensor. The monitoring unit collects the animal's movements and reactions as data. The analysis unit analyzes the data collected by the monitoring unit. The analysis unit analyzes the data collected using, for example, an LLM, and uses the data to improve the training content and evaluate the animal's progress. The analysis unit improves the training content and evaluates the animal's progress based on the collected data. The evaluation unit evaluates whether the animal is ready for release into the wild based on the data analyzed by the analysis unit. The evaluation unit evaluates whether the animal is ready for release into the wild, for example, based on the collected data. The evaluation unit can also conduct additional training as needed. This allows the wild release training system according to the embodiment to efficiently and effectively train animals for release into the wild.

[0030] The generation unit can analyze the animal's past training history and generate appropriate training content. The generation unit analyzes the animal's past training history. The past training history includes, for example, the type of training, frequency, and results. The generation unit generates appropriate training content based on the past training history. For example, the generation unit generates training content that strengthens skills in which the animal excels, based on the past training history. The generation unit can also generate training content that complements skills in which the animal is weak, based on the past training history. The generation unit can also generate training content according to the animal's progress, based on the past training history. This can improve the effectiveness of training by providing optimal training content based on the past training history.

[0031] The generation unit can customize the training content based on the ecology and behavioral patterns of the animal when generating the training content. The generation unit customizes the training content based on the ecology and behavioral patterns of the animal when generating the training content. The ecology of the animal includes, for example, living environment, feeding habits, and behavioral patterns. The generation unit customizes the training content based on the ecology of the animal. For example, in the case of birds, the generation unit can generate training content including flying training. Furthermore, in the case of mammals, the generation unit can generate training content including hunting training. Furthermore, in the case of reptiles, the generation unit can generate training content including how to build hideouts and how to hunt. In this way, by providing training content based on the ecology and behavioral patterns of the animal, the effectiveness of the training can be improved.

[0032] The generation unit can adjust the training intensity based on the animal's health condition when generating the training content. The generation unit adjusts the training intensity taking the animal's health condition into consideration when generating the training content. The animal's health condition includes, for example, weight, body temperature, blood test results, etc. The generation unit adjusts the training intensity based on the animal's health condition. For example, the generation unit provides a normal training intensity when the animal is healthy. Furthermore, the generation unit can provide mild training content when the animal is in a recovery period. Furthermore, the generation unit can provide training content including rehabilitation when the animal is in poor health. In this way, the effectiveness of the training can be improved by providing a training intensity according to the animal's health condition.

[0033] The providing unit can select an appropriate display method based on the visual characteristics of the animal when providing the HMD. The providing unit selects the optimal display method based on the visual characteristics of the animal when providing the HMD. The visual characteristics of animals include, for example, color vision, field of view, and visual acuity. The providing unit selects the display method based on the visual characteristics of the animal. For example, the providing unit can provide images with a wide field of view to birds. The providing unit can also provide stereoscopic images to mammals. The providing unit can also provide images tailored to the color sense of reptiles. This can improve the effectiveness of training by providing a display method tailored to the visual characteristics of the animal.

[0034] The providing unit can update the display content in real time according to the animal's movements when the HMD is provided. The providing unit updates the display content in real time according to the animal's movements when the HMD is provided. Animal movements include, for example, walking patterns, jumping, and rotation. The providing unit updates the display content in real time according to the animal's movements. For example, the providing unit updates the viewpoint in real time when the animal moves. The providing unit can also update the direction of the video in real time when the animal changes direction. The providing unit can also pause the video when the animal stops. This can improve the effectiveness of training by providing display content according to the animal's movements.

[0035] The providing unit can adjust the intensity of the display content based on the health condition of the animal when providing the HMD. The providing unit adjusts the intensity of the display content taking into account the health condition of the animal when providing the HMD. The health condition of the animal includes, for example, weight, body temperature, blood test results, etc. The providing unit adjusts the intensity of the display content based on the health condition of the animal. For example, the providing unit can provide normal image intensity when the animal is healthy. Furthermore, the providing unit can provide mild image intensity when the animal is in a recovery period. Furthermore, the providing unit can provide relaxing image intensity when the animal is in poor health. This can improve the effectiveness of training by providing display content according to the health condition of the animal.

[0036] When providing a VR space, the training department can customize the environment based on the ecology of the animals. When providing a VR space, the training department customizes the environment based on the ecology of the animals. The ecology of animals includes, for example, living environment, feeding habits, and behavior patterns. The training department customizes the environment based on the ecology of the animals. For example, the training department can provide a flying simulation for birds. The training department can also provide a hunting simulation in the forest for mammals. The training department can also provide a desert or wetland simulation for reptiles. In this way, the effectiveness of training can be improved by providing a VR space that is tailored to the ecology of the animals.

[0037] The training department can design training scenarios based on the behavioral patterns of animals when providing the VR space. The training department designs training scenarios based on the behavioral patterns of animals when providing the VR space. Animal behavior patterns include, for example, activity times, movement patterns, and social behavior. The training department designs training scenarios based on the behavioral patterns of animals. For example, the training department can provide scenarios centered on flight training for birds. The training department can also provide scenarios centered on hunting training for mammals. The training department can also provide scenarios centered on how to build hideouts and hunting methods for reptiles. In this way, the effectiveness of training can be improved by providing training scenarios that match the behavioral patterns of animals.

[0038] The training unit can adjust the training intensity based on the animal's health condition when providing the VR space. The training unit adjusts the training intensity taking into account the animal's health condition when providing the VR space. The animal's health condition includes, for example, weight, body temperature, blood test results, etc. The training unit adjusts the training intensity based on the animal's health condition. For example, the training unit can provide a normal training intensity if the animal is healthy. Furthermore, the training unit can provide a mild training intensity if the animal is in a recovery period. Furthermore, the training unit can provide a training intensity that includes rehabilitation if the animal is in poor health. This makes it possible to improve the effectiveness of training by providing training intensity according to the animal's health condition.

[0039] The monitoring unit can appropriately adjust the placement of sensors based on the ecology of the animal during monitoring. The monitoring unit optimizes the placement of sensors based on the ecology of the animal during monitoring. The ecology of the animal includes, for example, living environment, feeding habits, and behavioral patterns. The monitoring unit adjusts the placement of sensors based on the ecology of the animal. For example, the monitoring unit can place sensors in the air for birds to capture their movements in flight. Furthermore, the monitoring unit can place sensors on the ground for mammals to capture their movements on land. Furthermore, the monitoring unit can place sensors in appropriate positions for reptiles to capture their movements on the ground or in water. This allows the effectiveness of monitoring to be improved by providing sensor placement that suits the ecology of the animal.

[0040] The monitoring unit can adjust the timing of data collection based on the behavioral patterns of the animals during monitoring. The monitoring unit adjusts the timing of data collection based on the behavioral patterns of the animals during monitoring. The behavioral patterns of the animals include, for example, activity times, movement patterns, and social behaviors. The monitoring unit adjusts the timing of data collection based on the behavioral patterns of the animals. For example, the monitoring unit activates a sensor when a bird is flying to collect data while it is flying. The monitoring unit can also activate a sensor when a mammal is hunting to collect data while it is hunting. The monitoring unit can also activate a sensor when a reptile is preying to collect data while it is preying. This allows the effectiveness of monitoring to be improved by providing data collection timing according to the behavioral patterns of the animals.

[0041] The monitoring unit can adjust the sensitivity of the sensor based on the health condition of the animal during monitoring. The monitoring unit adjusts the sensitivity of the sensor taking into account the health condition of the animal during monitoring. The health condition of the animal includes, for example, weight, body temperature, blood test results, etc. The monitoring unit adjusts the sensitivity of the sensor based on the health condition of the animal. For example, when the animal is healthy, the monitoring unit performs monitoring with normal sensor sensitivity. Furthermore, when the animal is in a recovery period, the monitoring unit can perform monitoring with a low-sensitivity sensor. Furthermore, when the animal is in poor health, the monitoring unit can collect detailed data with a high-sensitivity sensor. This makes it possible to improve the effectiveness of monitoring by providing sensor sensitivity according to the health condition of the animal.

[0042] The analysis unit can weight the data based on the ecology of the animal during analysis. The analysis unit weights the data based on the ecology of the animal during analysis. The ecology of the animal includes, for example, living environment, feeding habits, and behavior patterns. The analysis unit weights the data based on the ecology of the animal. For example, the analysis unit weights the data by placing emphasis on flight data for birds. The analysis unit can also weight the data by placing emphasis on hunting data for mammals. The analysis unit can also weight the data by placing emphasis on predation data for reptiles. This makes it possible to improve the effectiveness of the analysis by providing data weighting according to the ecology of the animal.

[0043] The analysis unit can filter data based on animal behavior patterns during analysis. The analysis unit can filter data based on animal behavior patterns during analysis. Animal behavior patterns include, for example, activity times, movement patterns, and social behavior. The analysis unit filters data based on animal behavior patterns. For example, the analysis unit can filter flight data based on flight patterns of birds. The analysis unit can also filter hunting data based on hunting patterns of mammals. The analysis unit can also filter predation data based on predation patterns of reptiles. This can improve the effectiveness of the analysis by providing data filtering according to animal behavior patterns.

[0044] The analysis unit can adjust the data analysis method based on the animal's health condition during analysis. The analysis unit adjusts the data analysis method taking into account the animal's health condition during analysis. The animal's health condition includes, for example, weight, body temperature, blood test results, etc. The analysis unit adjusts the data analysis method based on the animal's health condition. For example, the analysis unit uses a normal analysis method when the animal is healthy. Furthermore, the analysis unit can use a mild analysis method when the animal is in recovery. Furthermore, the analysis unit can use a detailed analysis method when the animal is in poor health. This makes it possible to improve the effectiveness of the analysis by providing an analysis method that suits the animal's health condition.

[0045] The evaluation unit can weight the evaluation based on the ecology of the animal during the evaluation. The evaluation unit weights the evaluation based on the ecology of the animal during the evaluation. The ecology of the animal includes, for example, living environment, feeding habits, and behavior patterns. The evaluation unit weights the evaluation based on the ecology of the animal. For example, the evaluation unit weights birds by emphasizing flying ability. Furthermore, the evaluation unit can weight mammals by emphasizing hunting ability. Furthermore, the evaluation unit can weight reptiles by emphasizing predatory ability. In this way, the effectiveness of the evaluation can be improved by providing evaluation weights according to the ecology of the animal.

[0046] The evaluation unit can filter the evaluation based on the behavioral patterns of the animals during the evaluation. The evaluation unit can filter the evaluation based on the behavioral patterns of the animals during the evaluation. The behavioral patterns of the animals include, for example, activity time, movement patterns, and social behavior. The evaluation unit filters the evaluation based on the behavioral patterns of the animals. For example, the evaluation unit can evaluate the flying ability based on the flight patterns of birds. The evaluation unit can also evaluate the hunting ability based on the hunting patterns of mammals. The evaluation unit can also evaluate the predation ability based on the predation patterns of reptiles. This can improve the effectiveness of the evaluation by providing evaluation filtering according to the behavioral patterns of the animals.

[0047] The evaluation unit can adjust the evaluation method based on the health condition of the animal during evaluation. The evaluation unit adjusts the evaluation method taking into account the health condition of the animal during evaluation. The health condition of the animal includes, for example, weight, body temperature, blood test results, etc. The evaluation unit adjusts the evaluation method based on the health condition of the animal. For example, the evaluation unit uses a normal evaluation method when the animal is healthy. Furthermore, the evaluation unit can use a mild evaluation method when the animal is in recovery. Furthermore, the evaluation unit can use a detailed evaluation method when the animal is in poor health. This makes it possible to improve the effectiveness of the evaluation by providing an evaluation method according to the health condition of the animal.

[0048] The evaluation unit can perform the evaluation based on the geographical distribution of the animals during the evaluation. The evaluation unit performs the evaluation taking into account the geographical distribution of the animals during the evaluation. The geographical distribution of the animals includes, for example, habitat, migration range, and regional characteristics. The evaluation unit performs the evaluation based on the geographical distribution of the animals. For example, the evaluation unit can evaluate animals in the Arctic by focusing on their ability to survive in cold regions. Furthermore, the evaluation unit can evaluate animals in tropical rainforests by focusing on their ability to survive in humid environments. Furthermore, the evaluation unit can evaluate animals in desert regions by focusing on their ability to survive in dry regions. This allows the effectiveness of the evaluation to be improved by providing an evaluation that is in accordance with the geographical distribution of the animals.

[0049] The evaluation unit can perform a group evaluation based on the social behavior of the animals during the evaluation. The evaluation unit performs a group evaluation taking into account the social behavior of the animals during the evaluation. The social behavior of animals includes, for example, herd behavior, communication patterns, and social hierarchy. The evaluation unit performs a group evaluation based on the social behavior of the animals. For example, the evaluation unit can evaluate animals that live in groups by focusing on group behavior. Furthermore, the evaluation unit can evaluate animals that often behave solitary by focusing on individual behavior. Furthermore, the evaluation unit can evaluate animals that are highly social by focusing on communicative behavior. This makes it possible to improve the effectiveness of the evaluation by providing an evaluation that is in accordance with the social behavior of the animals.

[0050] The evaluation unit can customize the evaluation criteria based on the animal's past feedback during evaluation. The evaluation unit customizes the evaluation criteria by reflecting the animal's past feedback during evaluation. Past feedback includes, for example, past training results, behavioral data, health data, etc. The evaluation unit customizes the evaluation criteria based on the animal's past feedback. For example, the evaluation unit prioritizes evaluation criteria that the animal prefers based on the past feedback. Furthermore, the evaluation unit can complement evaluation criteria that the animal is not good at based on the past feedback. Furthermore, the evaluation unit can optimize the evaluation criteria based on the past feedback. This can improve the effectiveness of the evaluation by providing evaluation criteria based on the animal's past feedback.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The generation unit can analyze the social behavior of animals and customize the training content. For example, for animals that live in groups, it can generate training content to improve cooperation. For animals that tend to be solitary, it can generate training content to strengthen individual skills. Furthermore, for highly social animals, it can provide training content to improve communication skills. In this way, the effectiveness of training can be improved by providing training content that is tailored to the social behavior of animals.

[0053] The training department can monitor the health of the animals in real time and dynamically adjust the training content. For example, if the animal's body temperature rises, the training department can reduce the intensity of the training. If the animal's heart rate increases, the training department can provide a break. Furthermore, if the animal loses weight, the training department can provide a break for nutritional support. This allows the training content to be tailored to the animal's health, thereby improving the effectiveness of the training.

[0054] The analysis unit can learn the behavioral patterns of the animal and optimize the training content. For example, if the animal is active during a specific time of day, the analysis unit can focus training on that time. Also, if the animal repeats a specific behavior, the analysis unit can provide training content that reinforces that behavior. Furthermore, if the animal feels stressed in a specific environment, the analysis unit can provide training content that helps the animal avoid that environment. In this way, the effectiveness of training can be improved by providing training content that is tailored to the animal's behavioral patterns.

[0055] The generation unit can customize the training content based on the geographical distribution of animals. For example, training content that improves survival ability in cold climates can be provided to animals in the Arctic. Training content that improves survival ability in humid environments can be provided to animals in tropical rainforests. Training content that improves survival ability in dry climates can be provided to animals in desert regions. In this way, the effectiveness of training can be improved by providing training content that is appropriate for the geographical distribution of animals.

[0056] The training unit can optimize the training content based on the animal's past feedback. For example, it can provide training content that strengthens the animal's strong skills based on past training results. It can also provide training content that complements the animal's weak skills based on past feedback. Furthermore, it can adjust the training content based on past feedback and provide training content that suits the animal's progress. In this way, by providing training content based on past feedback, it is possible to improve the effectiveness of training.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The generator generates the target animal species and training content. The generator receives data on the animal species and ecology as input and generates training content using a large-scale language model (LLM). For example, it generates flying training for birds and hunting training for mammals. Step 2: The providing unit provides the training content generated by the generating unit to the animal. The providing unit provides the training content to the animal through an HMD dedicated to the animal, allowing the animal to receive training in the VR space. Step 3: The training department trains the animals based on the training content provided by the provider. The training department provides a VR space designed to allow the animals to receive training in a situation similar to their natural environment, allowing the animals to receive training within the VR space. Step 4: The monitoring unit monitors the movements and reactions of the animal trained by the training unit. The monitoring unit uses sensors to monitor the movements and reactions of the animal in real time and collects them as data. Step 5: The analysis unit analyzes the data collected by the monitoring unit. The analysis unit analyzes the data collected using the LLM and uses it to improve training content and evaluate the animal's progress. Step 6: The evaluation unit evaluates whether the animal is ready for release into the wild based on the data analyzed by the analysis unit. The evaluation unit evaluates whether the animal is ready for release into the wild based on the collected data and can also provide additional training if necessary.

[0059] (Example 2) A system according to an embodiment of the present invention uses a dedicated HMD to enable animals to receive the training necessary for reintroduction into the wild in a VR space. This system uses a large-scale language model (LLM) to generate the type of target animal and the training content required for that animal, and provides this to the animal through the dedicated HMD. The animal wears the HMD and begins training in a VR space. The VR space is designed to allow the animal to receive training in a setting similar to its natural environment. During training, sensors monitor the animal's movements and reactions in real time, collecting data. This data is analyzed by the LLM and used to improve the training content and evaluate the animal's progress. After training, the animal is evaluated for readiness for reintroduction based on the collected data. This enables efficient and effective animal reintroduction, potentially reducing costs and improving the success rate. This system allows efficient and effective training for animals to be reintroduced into the wild. For example, using a dedicated HMD, animals can receive training in a VR space similar to their natural environment. The data collected during training is analyzed by the LLM and used to improve the training content and evaluate the animal's progress. This will enable the release of animals into the wild to be carried out efficiently and effectively, which is expected to reduce costs and improve success rates.

[0060] A wild reintroduction training system according to an embodiment includes a generation unit, a provision unit, a training unit, a monitoring unit, an analysis unit, and an evaluation unit. The generation unit generates a target animal type and training content. For example, the generation unit receives data related to the animal's type and ecology as input and generates training content. The generation unit can generate training content based on the animal's type and ecology using a large-scale language model (LLM). For example, the generation unit can generate flying training for birds and hunting training for mammals. The provision unit provides the training content generated by the generation unit to the animal. The provision unit provides the training content to the animal, for example, through an HMD dedicated to the animal. The provision unit enables the animal to receive training in a VR space using the HMD. The training unit trains the animal based on the training content provided by the provision unit. For example, the training unit provides a VR space designed so that the animal can receive training in a situation similar to a natural environment. The training unit enables the animal to receive training in the VR space. The monitoring unit monitors the movements and reactions of the animal trained by the training unit. The monitoring unit monitors the animal's movements and reactions in real time using, for example, a sensor. The monitoring unit collects the animal's movements and reactions as data. The analysis unit analyzes the data collected by the monitoring unit. The analysis unit analyzes the data collected using, for example, an LLM, and uses the data to improve the training content and evaluate the animal's progress. The analysis unit improves the training content and evaluates the animal's progress based on the collected data. The evaluation unit evaluates whether the animal is ready for release into the wild based on the data analyzed by the analysis unit. The evaluation unit evaluates whether the animal is ready for release into the wild, for example, based on the collected data. The evaluation unit can also conduct additional training as needed. This allows the wild release training system according to the embodiment to efficiently and effectively train animals for release into the wild.

[0061] The generation unit estimates the animal's emotions and adjusts the training content based on the estimated animal emotions. The generation unit estimates the animal's emotions. The animal's emotions are estimated using methods such as facial expression analysis, behavioral analysis, and physiological data analysis. The generation unit adjusts the training content based on the estimated animal emotions. For example, if the animal is feeling stressed, the generation unit reduces the training content and provides a relaxing environment. If the animal is excited, the generation unit can add an activity to release energy. If the animal is feeling anxious, the generation unit can incorporate training content to provide a sense of security. This can improve the effectiveness of the training by providing training content that corresponds to the animal's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0062] The generation unit can analyze the animal's past training history and generate appropriate training content. The generation unit analyzes the animal's past training history. The past training history includes, for example, the type of training, frequency, and results. The generation unit generates appropriate training content based on the past training history. For example, the generation unit generates training content that strengthens skills in which the animal excels, based on the past training history. The generation unit can also generate training content that complements skills in which the animal is weak, based on the past training history. The generation unit can also generate training content according to the animal's progress, based on the past training history. This can improve the effectiveness of training by providing optimal training content based on the past training history.

[0063] The generation unit can customize the training content based on the ecology and behavioral patterns of the animal when generating the training content. The generation unit customizes the training content based on the ecology and behavioral patterns of the animal when generating the training content. The ecology of the animal includes, for example, living environment, feeding habits, and behavioral patterns. The generation unit customizes the training content based on the ecology of the animal. For example, in the case of birds, the generation unit can generate training content including flying training. Furthermore, in the case of mammals, the generation unit can generate training content including hunting training. Furthermore, in the case of reptiles, the generation unit can generate training content including how to build hideouts and how to hunt. In this way, by providing training content based on the ecology and behavioral patterns of the animal, the effectiveness of the training can be improved.

[0064] The generation unit can adjust the training intensity based on the animal's health condition when generating the training content. The generation unit adjusts the training intensity taking the animal's health condition into consideration when generating the training content. The animal's health condition includes, for example, weight, body temperature, blood test results, etc. The generation unit adjusts the training intensity based on the animal's health condition. For example, the generation unit provides a normal training intensity when the animal is healthy. Furthermore, the generation unit can provide mild training content when the animal is in a recovery period. Furthermore, the generation unit can provide training content including rehabilitation when the animal is in poor health. In this way, the effectiveness of the training can be improved by providing a training intensity according to the animal's health condition.

[0065] The providing unit can estimate the emotion of the animal and adjust the display content of the HMD based on the estimated emotion of the animal. The providing unit estimates the emotion of the animal. The emotion of the animal is estimated by, for example, facial expression analysis, behavior analysis, physiological data analysis, or the like. The providing unit adjusts the display content of the HMD based on the estimated emotion of the animal. For example, if the animal is feeling stressed, the providing unit can display images that help the animal relax. If the animal is excited, the providing unit can display images that help the animal release energy. If the animal is feeling anxious, the providing unit can display images that give the animal a sense of security. This can improve the effectiveness of training by providing display content that corresponds to the emotion of the animal. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0066] The providing unit can select an appropriate display method based on the visual characteristics of the animal when providing the HMD. The providing unit selects the optimal display method based on the visual characteristics of the animal when providing the HMD. The visual characteristics of animals include, for example, color vision, field of view, and visual acuity. The providing unit selects the display method based on the visual characteristics of the animal. For example, the providing unit can provide images with a wide field of view to birds. The providing unit can also provide stereoscopic images to mammals. The providing unit can also provide images tailored to the color sense of reptiles. This can improve the effectiveness of training by providing a display method tailored to the visual characteristics of the animal.

[0067] The providing unit can update the display content in real time according to the animal's movements when the HMD is provided. The providing unit updates the display content in real time according to the animal's movements when the HMD is provided. Animal movements include, for example, walking patterns, jumping, and rotation. The providing unit updates the display content in real time according to the animal's movements. For example, the providing unit updates the viewpoint in real time when the animal moves. The providing unit can also update the direction of the video in real time when the animal changes direction. The providing unit can also pause the video when the animal stops. This can improve the effectiveness of training by providing display content according to the animal's movements.

[0068] The providing unit can adjust the intensity of the display content based on the health condition of the animal when providing the HMD. The providing unit adjusts the intensity of the display content taking into account the health condition of the animal when providing the HMD. The health condition of the animal includes, for example, weight, body temperature, blood test results, etc. The providing unit adjusts the intensity of the display content based on the health condition of the animal. For example, the providing unit can provide normal image intensity when the animal is healthy. Furthermore, the providing unit can provide mild image intensity when the animal is in a recovery period. Furthermore, the providing unit can provide relaxing image intensity when the animal is in poor health. This can improve the effectiveness of training by providing display content according to the health condition of the animal.

[0069] The training unit can estimate the animal's emotions and adjust the environment of the VR space based on the estimated animal's emotions. The training unit estimates the animal's emotions. The animal's emotions are estimated using methods such as facial expression analysis, behavioral analysis, and physiological data analysis. The training unit adjusts the environment of the VR space based on the estimated animal's emotions. For example, if the animal is stressed, the training unit can provide an environment where the animal can relax. If the animal is excited, the training unit can provide an environment where the animal can release energy. If the animal is anxious, the training unit can provide an environment where the animal can feel reassured. This can improve the effectiveness of training by providing a VR space that corresponds to the animal's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] When providing a VR space, the training department can customize the environment based on the ecology of the animals. When providing a VR space, the training department customizes the environment based on the ecology of the animals. The ecology of animals includes, for example, living environment, feeding habits, and behavior patterns. The training department customizes the environment based on the ecology of the animals. For example, the training department can provide a flying simulation for birds. The training department can also provide a hunting simulation in the forest for mammals. The training department can also provide a desert or wetland simulation for reptiles. In this way, the effectiveness of training can be improved by providing a VR space that is tailored to the ecology of the animals.

[0071] The training department can design training scenarios based on the behavioral patterns of animals when providing the VR space. The training department designs training scenarios based on the behavioral patterns of animals when providing the VR space. Animal behavior patterns include, for example, activity times, movement patterns, and social behavior. The training department designs training scenarios based on the behavioral patterns of animals. For example, the training department can provide scenarios centered on flight training for birds. The training department can also provide scenarios centered on hunting training for mammals. The training department can also provide scenarios centered on how to build hideouts and hunting methods for reptiles. In this way, the effectiveness of training can be improved by providing training scenarios that match the behavioral patterns of animals.

[0072] The training unit can adjust the training intensity based on the animal's health condition when providing the VR space. The training unit adjusts the training intensity taking into account the animal's health condition when providing the VR space. The animal's health condition includes, for example, weight, body temperature, blood test results, etc. The training unit adjusts the training intensity based on the animal's health condition. For example, the training unit can provide a normal training intensity if the animal is healthy. Furthermore, the training unit can provide a mild training intensity if the animal is in a recovery period. Furthermore, the training unit can provide a training intensity that includes rehabilitation if the animal is in poor health. This makes it possible to improve the effectiveness of training by providing training intensity according to the animal's health condition.

[0073] The monitoring unit can estimate the animal's emotions and adjust the monitoring frequency based on the estimated animal's emotions. The monitoring unit estimates the animal's emotions. The animal's emotions are estimated using methods such as facial expression analysis, behavioral analysis, and physiological data analysis. The monitoring unit adjusts the monitoring frequency based on the estimated animal's emotions. For example, if the animal is stressed, the monitoring unit increases the monitoring frequency to collect detailed data. Also, if the animal is relaxed, the monitoring unit can reduce the monitoring frequency to reduce the animal's burden. Also, if the animal is excited, the monitoring unit can adjust the monitoring frequency to collect appropriate data. This allows for providing a monitoring frequency that corresponds to the animal's emotions, thereby improving the effectiveness of training. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The monitoring unit can appropriately adjust the placement of sensors based on the ecology of the animal during monitoring. The monitoring unit optimizes the placement of sensors based on the ecology of the animal during monitoring. The ecology of the animal includes, for example, living environment, feeding habits, and behavioral patterns. The monitoring unit adjusts the placement of sensors based on the ecology of the animal. For example, the monitoring unit can place sensors in the air for birds to capture their movements in flight. Furthermore, the monitoring unit can place sensors on the ground for mammals to capture their movements on land. Furthermore, the monitoring unit can place sensors in appropriate positions for reptiles to capture their movements on the ground or in water. This allows the effectiveness of monitoring to be improved by providing sensor placement that suits the ecology of the animal.

[0075] The monitoring unit can adjust the timing of data collection based on the behavioral patterns of the animals during monitoring. The monitoring unit adjusts the timing of data collection based on the behavioral patterns of the animals during monitoring. The behavioral patterns of the animals include, for example, activity times, movement patterns, and social behaviors. The monitoring unit adjusts the timing of data collection based on the behavioral patterns of the animals. For example, the monitoring unit activates a sensor when a bird is flying to collect data while it is flying. The monitoring unit can also activate a sensor when a mammal is hunting to collect data while it is hunting. The monitoring unit can also activate a sensor when a reptile is preying to collect data while it is preying. This allows the effectiveness of monitoring to be improved by providing data collection timing according to the behavioral patterns of the animals.

[0076] The monitoring unit can adjust the sensitivity of the sensor based on the health condition of the animal during monitoring. The monitoring unit adjusts the sensitivity of the sensor taking into account the health condition of the animal during monitoring. The health condition of the animal includes, for example, weight, body temperature, blood test results, etc. The monitoring unit adjusts the sensitivity of the sensor based on the health condition of the animal. For example, when the animal is healthy, the monitoring unit performs monitoring with normal sensor sensitivity. Furthermore, when the animal is in a recovery period, the monitoring unit can perform monitoring with a low-sensitivity sensor. Furthermore, when the animal is in poor health, the monitoring unit can collect detailed data with a high-sensitivity sensor. This makes it possible to improve the effectiveness of monitoring by providing sensor sensitivity according to the health condition of the animal.

[0077] The analysis unit can estimate the animal's emotion and adjust the analysis algorithm based on the estimated emotion. The analysis unit estimates the animal's emotion. The animal's emotion is estimated by, for example, facial expression analysis, behavioral analysis, or physiological data analysis. The analysis unit adjusts the analysis algorithm based on the estimated emotion. For example, if the animal is stressed, the analysis unit can use an algorithm that emphasizes stress-related data. If the animal is relaxed, the analysis unit can use an algorithm that emphasizes relaxation-related data. If the animal is excited, the analysis unit can use an algorithm that emphasizes excitement-related data. This provides an analysis algorithm that corresponds to the animal's emotion, thereby improving the effectiveness of the analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The analysis unit can weight the data based on the ecology of the animal during analysis. The analysis unit weights the data based on the ecology of the animal during analysis. The ecology of the animal includes, for example, living environment, feeding habits, and behavior patterns. The analysis unit weights the data based on the ecology of the animal. For example, the analysis unit weights the data by placing emphasis on flight data for birds. The analysis unit can also weight the data by placing emphasis on hunting data for mammals. The analysis unit can also weight the data by placing emphasis on predation data for reptiles. This makes it possible to improve the effectiveness of the analysis by providing data weighting according to the ecology of the animal.

[0079] The analysis unit can filter data based on animal behavior patterns during analysis. The analysis unit can filter data based on animal behavior patterns during analysis. Animal behavior patterns include, for example, activity times, movement patterns, and social behavior. The analysis unit filters data based on animal behavior patterns. For example, the analysis unit can filter flight data based on flight patterns of birds. The analysis unit can also filter hunting data based on hunting patterns of mammals. The analysis unit can also filter predation data based on predation patterns of reptiles. This can improve the effectiveness of the analysis by providing data filtering according to animal behavior patterns.

[0080] The analysis unit can adjust the data analysis method based on the animal's health condition during analysis. The analysis unit adjusts the data analysis method taking into account the animal's health condition during analysis. The animal's health condition includes, for example, weight, body temperature, blood test results, etc. The analysis unit adjusts the data analysis method based on the animal's health condition. For example, the analysis unit uses a normal analysis method when the animal is healthy. Furthermore, the analysis unit can use a mild analysis method when the animal is in recovery. Furthermore, the analysis unit can use a detailed analysis method when the animal is in poor health. This makes it possible to improve the effectiveness of the analysis by providing an analysis method that suits the animal's health condition.

[0081] The evaluation unit can estimate the emotion of the animal and adjust the evaluation criteria based on the estimated emotion of the animal. The evaluation unit estimates the emotion of the animal. The emotion of the animal is estimated by, for example, facial expression analysis, behavior analysis, physiological data analysis, or the like. The evaluation unit adjusts the evaluation criteria based on the estimated emotion of the animal. For example, if the animal is stressed, the evaluation unit can emphasize stress-related evaluation criteria. Also, if the animal is relaxed, the evaluation unit can emphasize relaxation-related evaluation criteria. Also, if the animal is excited, the evaluation unit can emphasize excitement-related evaluation criteria. This can improve the effectiveness of the evaluation by providing evaluation criteria according to the emotion of the animal. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The evaluation unit can weight the evaluation based on the ecology of the animal during the evaluation. The evaluation unit weights the evaluation based on the ecology of the animal during the evaluation. The ecology of the animal includes, for example, living environment, feeding habits, and behavior patterns. The evaluation unit weights the evaluation based on the ecology of the animal. For example, the evaluation unit weights birds by emphasizing flying ability. Furthermore, the evaluation unit can weight mammals by emphasizing hunting ability. Furthermore, the evaluation unit can weight reptiles by emphasizing predatory ability. In this way, the effectiveness of the evaluation can be improved by providing evaluation weights according to the ecology of the animal.

[0083] The evaluation unit can filter the evaluation based on the behavioral patterns of the animals during the evaluation. The evaluation unit can filter the evaluation based on the behavioral patterns of the animals during the evaluation. The behavioral patterns of the animals include, for example, activity time, movement patterns, and social behavior. The evaluation unit filters the evaluation based on the behavioral patterns of the animals. For example, the evaluation unit can evaluate the flying ability based on the flight patterns of birds. The evaluation unit can also evaluate the hunting ability based on the hunting patterns of mammals. The evaluation unit can also evaluate the predation ability based on the predation patterns of reptiles. This can improve the effectiveness of the evaluation by providing evaluation filtering according to the behavioral patterns of the animals.

[0084] The evaluation unit can adjust the evaluation method based on the health condition of the animal during evaluation. The evaluation unit adjusts the evaluation method taking into account the health condition of the animal during evaluation. The health condition of the animal includes, for example, weight, body temperature, blood test results, etc. The evaluation unit adjusts the evaluation method based on the health condition of the animal. For example, the evaluation unit uses a normal evaluation method when the animal is healthy. Furthermore, the evaluation unit can use a mild evaluation method when the animal is in recovery. Furthermore, the evaluation unit can use a detailed evaluation method when the animal is in poor health. This makes it possible to improve the effectiveness of the evaluation by providing an evaluation method according to the health condition of the animal.

[0085] The evaluation unit can estimate the emotion of the animal and prioritize the evaluation results based on the estimated emotion of the animal. The evaluation unit estimates the emotion of the animal. The emotion of the animal is estimated by, for example, facial expression analysis, behavioral analysis, physiological data analysis, or the like. The evaluation unit prioritizes the evaluation results based on the estimated emotion of the animal. For example, if the animal is stressed, the evaluation unit can prioritize stress-related evaluation results. Also, if the animal is relaxed, the evaluation unit can prioritize relaxation-related evaluation results. Also, if the animal is excited, the evaluation unit can prioritize excitement-related evaluation results. This can improve the effectiveness of the evaluation by providing evaluation results according to the emotion of the animal. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The evaluation unit can perform the evaluation based on the geographical distribution of the animals during the evaluation. The evaluation unit performs the evaluation taking into account the geographical distribution of the animals during the evaluation. The geographical distribution of the animals includes, for example, habitat, migration range, and regional characteristics. The evaluation unit performs the evaluation based on the geographical distribution of the animals. For example, the evaluation unit can evaluate animals in the Arctic by focusing on their ability to survive in cold regions. Furthermore, the evaluation unit can evaluate animals in tropical rainforests by focusing on their ability to survive in humid environments. Furthermore, the evaluation unit can evaluate animals in desert regions by focusing on their ability to survive in dry regions. This allows the effectiveness of the evaluation to be improved by providing an evaluation that is in accordance with the geographical distribution of the animals.

[0087] The evaluation unit can perform a group evaluation based on the social behavior of the animals during the evaluation. The evaluation unit performs a group evaluation taking into account the social behavior of the animals during the evaluation. The social behavior of animals includes, for example, herd behavior, communication patterns, and social hierarchy. The evaluation unit performs a group evaluation based on the social behavior of the animals. For example, the evaluation unit can evaluate animals that live in groups by focusing on group behavior. Furthermore, the evaluation unit can evaluate animals that often behave solitary by focusing on individual behavior. Furthermore, the evaluation unit can evaluate animals that are highly social by focusing on communicative behavior. This makes it possible to improve the effectiveness of the evaluation by providing an evaluation that is in accordance with the social behavior of the animals.

[0088] The evaluation unit can customize the evaluation criteria based on the animal's past feedback during evaluation. The evaluation unit customizes the evaluation criteria by reflecting the animal's past feedback during evaluation. Past feedback includes, for example, past training results, behavioral data, health data, etc. The evaluation unit customizes the evaluation criteria based on the animal's past feedback. For example, the evaluation unit prioritizes evaluation criteria that the animal prefers based on the past feedback. Furthermore, the evaluation unit can complement evaluation criteria that the animal is not good at based on the past feedback. Furthermore, the evaluation unit can optimize the evaluation criteria based on the past feedback. This can improve the effectiveness of the evaluation by providing evaluation criteria based on the animal's past feedback. === Hard Collateral 1-1 === For example, each of the multiple elements, including the generation unit, provision unit, training unit, monitoring unit, analysis unit, and evaluation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates training content based on the type and ecology of the animal using the LLM. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the training content to the animal through an HMD dedicated to the animal. The training unit is implemented by the control unit 46A of the smart device 14 and enables the animal to receive training in a VR space. The monitoring unit uses sensors in the smart device 14 to monitor the animal's movements and reactions in real time and collect data. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to improve the training content and evaluate the animal's progress. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates whether the animal is ready for release into the wild based on the collected data. === Hard Collateral 1-2 === For example, each of the multiple elements, including the generation unit, provision unit, training unit, monitoring unit, analysis unit, and evaluation unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates training content based on the type and ecology of the animal using the LLM. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the training content to the animal through an HMD dedicated to the animal. The training unit is implemented by the control unit 46A of the smart glasses 214 and enables the animal to receive training in a VR space. The monitoring unit uses sensors in the smart glasses 214 to monitor the animal's movements and reactions in real time and collect data. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to improve the training content and evaluate the animal's progress. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates whether the animal is ready for release into the wild based on the collected data. === Hard Collateral 1-3 === For example, each of the multiple elements, including the generation unit, provision unit, training unit, monitoring unit, analysis unit, and evaluation unit, is implemented in at least one of the headset-type device 314 and the data processing device 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates training content based on the type and ecology of the animal using the LLM. The provision unit is implemented by the control unit 46A of the headset-type device 314 and provides the training content to the animal through an HMD dedicated to the animal. The training unit is implemented by the control unit 46A of the headset-type device 314 and enables the animal to receive training in a VR space. The monitoring unit uses sensors in the headset-type device 314 to monitor the animal's movements and reactions in real time and collect data. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to improve the training content and evaluate the animal's progress. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates whether the animal is ready for release into the wild based on the collected data. === Hard Collateral 1-4 === For example, each of the multiple elements, including the generation unit, provision unit, training unit, monitoring unit, analysis unit, and evaluation unit, is realized by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates training content based on the type and ecology of the animal using the LLM. The provision unit is realized by the control unit 46A of the robot 414 and provides the training content to the animal through an HMD dedicated to the animal. The training unit is realized by the control unit 46A of the robot 414 and enables the animal to receive training in a VR space. The monitoring unit uses sensors in the robot 414 to monitor the animal's movements and reactions in real time and collect data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to improve the training content and evaluate the animal's progress. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates whether the animal is ready for release into the wild based on the collected data.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The generation unit can analyze the social behavior of animals and customize the training content. For example, for animals that live in groups, it can generate training content to improve cooperation. For animals that tend to be solitary, it can generate training content to strengthen individual skills. Furthermore, for highly social animals, it can provide training content to improve communication skills. In this way, the effectiveness of training can be improved by providing training content that is tailored to the social behavior of animals.

[0091] The providing unit can estimate the emotion of the animal and adjust the timing of training based on the estimated emotion of the animal. For example, if the animal is relaxed, the providing unit can advance the timing to start training. Also, if the animal is feeling stressed, the providing unit can delay the start of training. Furthermore, if the animal is excited, the providing unit can add warm-up exercises to release energy. In this way, the effectiveness of training can be improved by providing training timing that corresponds to the emotion of the animal.

[0092] The training department can monitor the health of the animals in real time and dynamically adjust the training content. For example, if the animal's body temperature rises, the training department can reduce the intensity of the training. If the animal's heart rate increases, the training department can provide a break. Furthermore, if the animal loses weight, the training department can provide a break for nutritional support. This allows the training content to be tailored to the animal's health, thereby improving the effectiveness of the training.

[0093] The monitoring unit can estimate the emotion of the animal and adjust the analysis method of the monitoring data based on the estimated emotion of the animal. For example, if the animal is feeling stressed, the monitoring unit can use an analysis method that emphasizes stress-related data. If the animal is relaxed, the monitoring unit can use an analysis method that emphasizes relaxation-related data. If the animal is excited, the monitoring unit can use an analysis method that emphasizes excitement-related data. This can improve the effectiveness of monitoring by providing an analysis method that corresponds to the emotion of the animal.

[0094] The analysis unit can learn the behavioral patterns of the animal and optimize the training content. For example, if the animal is active during a specific time of day, the analysis unit can focus training on that time. Also, if the animal repeats a specific behavior, the analysis unit can provide training content that reinforces that behavior. Furthermore, if the animal feels stressed in a specific environment, the analysis unit can provide training content that helps the animal avoid that environment. In this way, the effectiveness of training can be improved by providing training content that is tailored to the animal's behavioral patterns.

[0095] The evaluation unit can estimate the emotion of the animal and adjust the method of feeding back the evaluation result based on the estimated emotion of the animal. For example, if the animal is feeling stressed, the evaluation unit can provide feedback to reduce stress. If the animal is relaxed, the evaluation unit can provide feedback to maintain relaxation. Furthermore, if the animal is excited, the evaluation unit can provide feedback to release energy. In this way, the effectiveness of the evaluation can be improved by providing feedback according to the emotion of the animal.

[0096] The generation unit can customize the training content based on the geographical distribution of animals. For example, training content that improves survival ability in cold climates can be provided to animals in the Arctic. Training content that improves survival ability in humid environments can be provided to animals in tropical rainforests. Training content that improves survival ability in dry climates can be provided to animals in desert regions. In this way, the effectiveness of training can be improved by providing training content that is appropriate for the geographical distribution of animals.

[0097] The providing unit can estimate the animal's emotions and adjust the sound settings of the training environment based on the estimated animal's emotions. For example, if the animal is feeling stressed, the providing unit can play relaxing music. If the animal is excited, the providing unit can play lively music to release energy. Furthermore, if the animal is feeling anxious, the providing unit can play natural sounds to provide a sense of security. This can improve the effectiveness of training by providing sound settings that correspond to the animal's emotions.

[0098] The training unit can optimize the training content based on the animal's past feedback. For example, it can provide training content that strengthens the animal's strong skills based on past training results. It can also provide training content that complements the animal's weak skills based on past feedback. Furthermore, it can adjust the training content based on past feedback and provide training content that suits the animal's progress. In this way, by providing training content based on past feedback, it is possible to improve the effectiveness of training.

[0099] The monitoring unit can estimate the emotion of the animal and adjust the display method of the monitoring data based on the estimated emotion of the animal. For example, if the animal is stressed, the monitoring unit can highlight stress-related data. If the animal is relaxed, the monitoring unit can highlight relaxation-related data. If the animal is excited, the monitoring unit can highlight excitement-related data. This can improve the effectiveness of monitoring by providing a data display method that corresponds to the emotion of the animal.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The generator generates the target animal species and training content. The generator receives data on the animal species and ecology as input and generates training content using a large-scale language model (LLM). For example, it generates flying training for birds and hunting training for mammals. Step 2: The providing unit provides the training content generated by the generating unit to the animal. The providing unit provides the training content to the animal through an HMD dedicated to the animal, allowing the animal to receive training in the VR space. Step 3: The training department trains the animals based on the training content provided by the provider. The training department provides a VR space designed to allow the animals to receive training in a situation similar to their natural environment, allowing the animals to receive training within the VR space. Step 4: The monitoring unit monitors the movements and reactions of the animal trained by the training unit. The monitoring unit uses sensors to monitor the movements and reactions of the animal in real time and collects them as data. Step 5: The analysis unit analyzes the data collected by the monitoring unit. The analysis unit analyzes the data collected using the LLM and uses it to improve training content and evaluate the animal's progress. Step 6: The evaluation unit evaluates whether the animal is ready for release into the wild based on the data analyzed by the analysis unit. The evaluation unit evaluates whether the animal is ready for release into the wild based on the collected data and can also provide additional training if necessary.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0109] 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.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0125] 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0141] 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] 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.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive 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.

[0146] 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.

[0147] 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.

[0148] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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 encompasses both emotions 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.

[0157] 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.

[0158] 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).

[0159] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] 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."

[0161] 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.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0167] The hardware resource that executes the specific process 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 process may be a single processor.

[0168] 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.

[0169] 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.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a generation unit that generates a target animal type and training content; a providing unit that provides the training content generated by the generating unit to an animal; a training unit that trains an animal based on the training content provided by the providing unit; a monitoring unit that monitors the movements and reactions of the animal trained by the training unit; an analysis unit that analyzes the data collected by the monitoring unit; an evaluation unit that evaluates whether the animal is ready to be released back into the wild based on the data analyzed by the analysis unit; Equipped with A system characterized by:

2. The generation unit Inferring animal emotions and adjusting training content based on the inferred emotions The system of claim 1 .

3. The generation unit Analyze the animal's past training history and generate appropriate training content The system of claim 1 .

4. The generation unit When generating training content, customize it based on the ecology and behavioral patterns of animals. The system of claim 1 .

5. The generation unit When generating training content, adjust the training intensity based on the animal's health. The system of claim 1 .

6. The providing unit Estimate the animal's emotions and adjust the HMD display content based on the estimated emotions. The system of claim 1 .

7. The providing unit When providing the HMD, select the appropriate display method based on the visual characteristics of the animal. The system of claim 1 .

8. The providing unit When the HMD is provided, the display content is updated in real time according to the animal's movements. The system of claim 1 .

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