System

Abstract

An object of a system according to an embodiment is to provide an environment in which an animal can be safely touched.SOLUTION: A system includes a reception part, a generation part, and a reaction part. The reception unit receives selection of an animal from a user. The generation unit digitally generates an animal based on the information received by the reception unit. The reaction unit causes the animal generated by the generation unit to react in real time in accordance with the motion of the user.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 involve risks associated with direct contact with animals, so there is a need for safe methods of interacting with animals.

[0005] The system according to the embodiment aims to provide an environment in which people can interact with animals safely. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a reaction unit. The receiving unit receives an animal selection from a user. The generating unit digitally generates an animal based on the information received by the receiving unit. The reaction unit causes the animal generated by the generating unit to react in real time to the user's movements. [Effects of the Invention]

[0007] The system according to the embodiment can provide an environment in which people can safely interact with animals. [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) An educational system according to an embodiment of the present invention uses a generation AI to digitally generate animals, allowing children to safely interact with them. The educational system simulates interaction by allowing a user to select an animal they want to interact with, and then having the generation AI digitally generate the selected animal, which then responds to the user's movements in real time. For example, the educational system allows a user to select an animal they want to interact with. For example, the user may select an animal such as a lion, elephant, or penguin. This information is input into the generation AI. The educational system then uses the generation AI to digitally generate the selected animal. The generation AI then realistically reproduces the animal's appearance and movements, providing the user with an experience that feels as if they are actually interacting with the animal. For example, the generation AI generates movements such as a lion walking or an elephant wagging its trunk in real time. The educational system then allows the generated animal to respond to the user's movements in real time. For example, when a user reaches out to touch an animal, the animal responds to the user's movement and moves closer. In this way, the user can experience interacting with animals in a safe manner. This allows children to interact with animals without any risk. For example, even dangerous animals such as lions and elephants can be interacted with safely digitally. Children can also interact with animals at home or at school without having to go to specific locations such as zoos or safari parks. This helps children deepen their interest in animals and enhance their learning. This allows the education system to allow children to interact with animals safely. For example, even dangerous animals such as lions and elephants can be interacted with safely digitally. Children can also interact with animals at home or at school without having to go to specific locations such as zoos or safari parks. This helps children deepen their interest in animals and enhance their learning.

[0029] An educational system according to an embodiment includes a reception unit, a generation unit, and a reaction unit. The reception unit receives an animal selection from a user. The user can select an animal such as a lion, an elephant, or a penguin. The reception unit can receive the animal selection using, for example, voice input, text input, or image input. The generation unit uses a generation AI to digitally generate an animal based on the information received by the reception unit. The generation AI realistically reproduces the animal's appearance and behavior using technologies such as deep learning and GAN (generative artificial network). The generation unit generates, in real time, movements such as a lion walking or an elephant wagging its trunk. The reaction unit causes the animal generated by the generation unit to respond in real time to the user's movements. For example, the reaction unit controls the animal to approach in response to the user's movement of reaching out to touch the animal. The reaction unit detects the user's movement using, for example, sensor technology, and causes the animal to respond in real time to the movement. This allows the educational system according to an embodiment to safely interact with animals. For example, even dangerous animals such as lions and elephants can be interacted with safely digitally. Furthermore, children can interact with animals at home or at school without having to go to specific locations such as zoos or safari parks. This will deepen users' interest in animals and enhance their learning.

[0030] The generation unit can realistically reproduce the appearance and behavior of animals using a generation AI. The generation unit realistically reproduces the appearance of animals using, for example, deep learning technology. For example, the generation unit can reproduce the texture of an animal's skin and the movement of its fur in detail. The generation unit can also realistically reproduce the behavior of animals using a generative artificial network (GAN). For example, the generation unit can generate the walking behavior of a lion or the wagging behavior of an elephant in real time. Furthermore, the generation unit can realistically reproduce the facial expressions and voices of animals using a generation AI. For example, the generation unit can realistically reproduce the roar of a lion or the croaking of a penguin. In this way, the appearance and behavior of animals can be realistically reproduced by using the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause a generation AI to generate the appearance and behavior of animals.

[0031] The reaction unit enables the animal to react in real time to the user's movements. The reaction unit, for example, detects the user's movements using sensor technology. For example, the reaction unit uses a camera to track the user's hand movements, and the animal reacts in real time to those movements. The reaction unit can also detect the user's entire body movements using motion capture technology. For example, the reaction unit detects the user's movement of reaching out to touch the animal and controls the animal to approach in response to that movement. Furthermore, the reaction unit can adjust the animal's reaction speed in response to the user's movements. For example, the reaction unit can speed up the animal's reaction speed when the user's movements are fast, and slow down the animal's reaction speed when the user's movements are slow. This allows the animal to react in real time to the user's movements, providing a more realistic interaction experience. Some or all of the above-described processing in the reaction unit may be performed using, for example, AI, or may be performed without AI. For example, the reaction unit can cause AI to detect the user's movements and control the animal's reaction.

[0032] The reception unit can accept information selecting the type and characteristics of an animal the user wants to interact with. The reception unit accepts information selecting the type and characteristics of an animal the user wants to interact with, for example, using voice input. For example, the reception unit can select a lion by the user inputting "lion" by voice. The reception unit can also accept information selecting the type and characteristics of an animal using text input. For example, the reception unit can select an elephant by the user inputting "elephant" by text. The reception unit can also accept information selecting the type and characteristics of an animal using image input. For example, the reception unit can select a lion by the user uploading an image of a lion. In this way, by accepting information selecting the type and characteristics of an animal the user wants to interact with, it is possible to generate an animal according to the user's preference. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can cause AI to process the voice input, text input, and image input.

[0033] The generation unit can generate an animal based on information about the animal selected by the user. The generation unit generates an animal based on information about the animal selected by the user. For example, if the user selects "lion," the generation unit realistically reproduces the appearance and movements of a lion. Furthermore, if the user selects "elephant," the generation unit can also realistically reproduce the appearance and movements of an elephant. Furthermore, the generation unit can generate an animal based on the characteristics of the animal selected by the user. For example, if the user selects "penguin" and further specifies "swimming behavior," the generation unit can realistically reproduce the swimming behavior of a penguin. Thus, by generating an animal based on information about the animal selected by the user, an animal that meets the user's preferences can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information about the animal selected by the user into the generation AI and cause the generation AI to generate the animal.

[0034] The reaction unit can control the animal to approach in response to the user's movement of reaching out to touch the animal. The reaction unit, for example, uses sensor technology to detect the user's movement of reaching out to touch the animal. For example, the reaction unit uses a camera to track the user's hand movement and controls the animal to approach in response to that movement. The reaction unit can also detect the user's whole-body movement using motion capture technology. For example, the reaction unit can detect the user's movement of reaching out to touch the animal and controls the animal to approach in response to that movement. Furthermore, the reaction unit can adjust the animal's reaction speed in response to the user's movement. For example, the reaction unit can speed up the animal's reaction speed when the user's movement is fast and slow down the animal's reaction speed when the user's movement is slow. This allows the animal to approach in response to the user's movement of reaching out to touch the animal, providing a more realistic interaction experience. Some or all of the above-described processing in the reaction unit may be performed using, for example, AI, or may be performed without AI. For example, the reaction unit can cause AI to detect the user's movement and control the animal's reaction.

[0035] The reception unit can analyze the user's past selection history and suggest optimal animal options. The reception unit, for example, can suggest similar animals based on the user's past selection history of animals. For example, the reception unit can analyze the types and characteristics of animals selected by the user in the past and suggest similar animals. The reception unit can also consider the frequency of animals selected by the user in the past and prioritize suggesting the most frequently selected animals. For example, the reception unit can suggest similar animals based on the user's past selection history of animals. The reception unit can also analyze the characteristics of animals selected by the user in the past and suggest similar animals. The reception unit can also consider the frequency of animals selected by the user in the past and prioritize suggesting the most frequently selected animals. In this way, by analyzing the user's past selection history, more appropriate animal options can be suggested. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can cause AI to analyze the user's past selection history and suggest animal options.

[0036] When selecting an animal, the reception unit can customize the options according to the user's age and learning purpose. The reception unit customizes the animal options according to, for example, the user's age. For example, the reception unit suggests animals with high educational value (e.g., elephants and dolphins) for children. The reception unit can also customize the animal options according to the learning purpose. For example, if the learning purpose is ecology, the reception unit suggests animals that are useful for understanding ecosystems (e.g., wolves and sharks). The reception unit can also suggest more complex animals (e.g., lions and bears) for older users. This makes it possible to provide a more appropriate learning experience by providing animal options according to the user's age and learning purpose. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input information such as the user's age and learning purpose into AI and have the AI ​​customize the animal options.

[0037] When selecting an animal, the reception unit can provide an optimal selection means depending on the user's input method. The reception unit presents animal options using, for example, voice input. For example, the reception unit can select a lion by the user's voice input of "lion." The reception unit can also present animal options using text input. For example, the reception unit can select an elephant by the user's text input of "elephant." The reception unit can also present animal options using image input. For example, the reception unit can select a lion by the user's text upload of a lion. This provides an optimal selection means depending on the user's input method, thereby providing a more user-friendly system. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can cause AI to process the voice input, text input, and image input.

[0038] When selecting an animal, the reception unit can prioritize presenting highly relevant animals taking into consideration the user's geographical location information. The reception unit acquires the user's geographical location information using, for example, GPS data. For example, if the user is in a tropical region, the reception unit can prioritize presenting animals that inhabit tropical regions (e.g., lizards and parrots). Furthermore, if the user is in a cold region, the reception unit can prioritize presenting animals that inhabit cold regions (e.g., penguins and polar bears). Furthermore, if the user is in an urban area, the reception unit can prioritize presenting animals that can be found in urban areas (e.g., pigeons and squirrels). This allows the user to select a more relevant animal by providing animal options based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can cause AI to acquire the user's geographical location information and present animal options.

[0039] When selecting an animal, the reception unit can analyze the user's social media activity and suggest related animals. The reception unit, for example, analyzes the user's social media activity. For example, if the user posts about "lions" on social media, the reception unit can preferentially suggest lions. Furthermore, if the user posts about "marine life" on social media, the reception unit can preferentially suggest dolphins and sharks. Furthermore, if the user posts about "pets" on social media, the reception unit can preferentially suggest dogs and cats. This allows for the selection of more relevant animals by providing animal options based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can cause AI to analyze the user's social media activity and suggest animal options.

[0040] When selecting an animal, the reception unit can customize the options by reflecting the user's past feedback. The reception unit, for example, analyzes the user's past feedback. For example, the reception unit prioritizes suggesting animals that the user has previously rated as "fun." The reception unit can also suggest animals that the user has previously rated as "scary." Furthermore, the reception unit can suggest animals that the user has previously rated as "interesting" along with detailed information about them. This allows the user to select a more appropriate animal by providing animal options based on the user's past feedback. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can cause AI to analyze the user's past feedback and customize the animal options.

[0041] The generation unit can reproduce the animal's ecology and behavioral patterns in detail when generating an animal. For example, when generating a lion, the generation unit reproduces hunting behavior and herd behavior in detail. For example, the generation unit realistically reproduces the lion's behavior of chasing prey and herd communication. The generation unit can also reproduce the swimming and nest-building behavior of penguins in detail when generating them. For example, the generation unit realistically reproduces the penguin's swimming behavior in water and nest-building behavior. Furthermore, when generating an elephant, the generation unit can also reproduce the trunk movement and herd movement in detail. For example, the generation unit realistically reproduces the elephant's behavior of lifting an object with its trunk and herd movement. This allows for the detailed reproduction of the animal's ecology and behavioral patterns, thereby providing more realistic animals. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can cause the generation AI to reproduce the animal's ecology and behavioral patterns.

[0042] When generating an animal, the generation unit can customize the animal's behavior scenario according to the user's learning objective. For example, in the case of a learning objective on ecology, the generation unit generates a behavior scenario that emphasizes the animal's role in the ecosystem. For example, the generation unit generates a behavior scenario that emphasizes the lion's role as a predator in the ecosystem. Furthermore, in the case of a learning objective on ethology, the generation unit can also generate a behavior scenario that emphasizes a specific behavior pattern. For example, the generation unit generates a behavior scenario that emphasizes the mating behavior of penguins. Furthermore, in the case of a learning objective on environmental conservation, the generation unit can also generate a behavior scenario that emphasizes the animal's response to changes in its habitat. For example, the generation unit generates a behavior scenario that emphasizes how elephants adapt to changes in their habitat. This allows for a more appropriate learning experience by providing an animal's behavior scenario according to the user's learning objective. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can cause the generation AI to customize the animal's behavior scenario.

[0043] When generating animals, the generation unit can improve the accuracy of the generation by referring to the user's past interaction data. For example, the generation unit generates more realistic animals based on data on animals the user has interacted with in the past. For example, the generation unit optimizes the lion's movements and reactions based on data on lions the user has interacted with in the past. The generation unit can also analyze the user's past interaction data to optimize the animal's movements and reactions. For example, the generation unit improves the accuracy of the generation by reflecting the characteristics of animals the user has previously preferred. Furthermore, the generation unit can customize the animal's movements and reactions based on data on animals the user has interacted with in the past. For example, the generation unit optimizes the penguin's movements and reactions based on data on penguins the user has interacted with in the past. In this way, more realistic animals can be provided by referring to the user's past interaction data. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can cause the generation AI to analyze the user's past interaction data and optimize the animal generation.

[0044] The generation unit can generate a background that recreates the animal's habitat or environment when generating an animal. For example, the generation unit recreates a savanna background when generating a lion. For example, the generation unit realistically recreates the scenery and plants of the savanna where the lion lives. The generation unit can also recreate an Antarctic background when generating a penguin. For example, the generation unit realistically recreates the icebergs and snowy plains of Antarctica where the penguins live. The generation unit can also recreate an African grassland background when generating an elephant. For example, the generation unit realistically recreates the scenery and plants of the African grassland where the elephants live. This provides a background that recreates the animal's habitat or environment, making it possible to provide more realistic animals. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to recreate the animal's habitat or environment.

[0045] The generation unit can realistically reproduce animal cries and sounds when generating animals. For example, the generation unit reproduces a realistic roar when generating a lion. For example, the generation unit reproduces a realistic sound using an audio sample of a lion's roar. The generation unit can also reproduce a realistic roar when generating a penguin. For example, the generation unit reproduces a realistic sound using an audio sample of a penguin's roar. Furthermore, the generation unit can also reproduce a realistic trumpet sound when generating an elephant. For example, the generation unit reproduces a realistic sound using an audio sample of an elephant's trumpet. This realistic reproduction of animal cries and sounds makes it possible to provide more realistic animals. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause a generation AI to reproduce the animal cries and sounds.

[0046] When generating an animal, the generation unit can provide detailed information about the animal according to the user's level of expertise. For example, for beginners, the generation unit provides basic animal information (e.g., name and habitat). For example, the generation unit provides basic information about a lion's name and habitat. For intermediate users, the generation unit can also provide animal behavior patterns and their role in the ecosystem. For example, the generation unit can provide information about penguins' mating behavior and their role in the ecosystem. For advanced users, the generation unit can also provide the animal's genetic information and evolutionary history. For example, the generation unit can provide detailed information about elephants' genetic information and evolutionary history. This allows for a more appropriate learning experience by providing detailed information about animals according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can cause the generation AI to provide detailed information about animals according to the user's level of expertise.

[0047] The reaction unit can learn the user's movement patterns and generate a more natural reaction when the animal reacts. For example, the reaction unit can learn the user's movement of reaching out and generate a reaction that causes the animal to approach naturally. For example, the reaction unit can use a camera to track the user's hand movements and learn the movement patterns. The reaction unit can also learn the user's movement of patting the animal's head and generate a reaction that makes the animal happy. For example, the reaction unit can track the user's hand movements and learn the movement patterns. The reaction unit can also learn the user's movement of feeding the animal and generate a reaction that makes the animal eat the food. For example, the reaction unit can track the user's hand movements and learn the movement patterns. In this way, by learning the user's movement patterns, a more natural reaction can be generated for the animal. Some or all of the above-described processing in the reaction unit may be performed using, for example, AI, or may be performed without AI. For example, the reaction unit can have AI learn the user's movement patterns and generate a reaction for the animal.

[0048] The reaction unit can customize the content of the animal's reaction depending on the user's age and learning goal. For example, the reaction unit customizes the animal to show a playful reaction for children. For example, the reaction unit controls the animal to show a playful behavior toward children. Furthermore, if the learning goal is ecology, the reaction unit can also customize the animal to show a reaction that indicates its role in the ecosystem. For example, the reaction unit controls the animal to show a behavior that indicates its role in the ecosystem. Furthermore, the reaction unit can customize the animal to show more complex behavior for older users. For example, the reaction unit controls the animal to show more complex behavior toward older users. This makes it possible to provide a more appropriate learning experience by providing an animal's reaction according to the user's age and learning goal. Some or all of the above-described processing in the reaction unit may be performed using, or without, AI. For example, the reaction unit can input information about the user's age and learning goal into AI and have the AI ​​customize the animal's reaction.

[0049] The reaction unit can improve the accuracy of the animal's reaction by referring to the user's past interaction data. The reaction unit, for example, optimizes the animal's reaction based on the user's past actions. For example, the reaction unit optimizes the animal's reaction based on the user's past hand actions. The reaction unit can also analyze the user's past interaction data and customize the animal's reaction. For example, the reaction unit analyzes the user's past action patterns and customizes the animal's reaction. The reaction unit can also improve the accuracy of the reaction by reflecting the user's past preferred animal reactions. For example, the reaction unit optimizes the animal's reaction based on the user's past preferred animal reactions. In this way, by referring to the user's past interaction data, it is possible to provide more realistic animal reactions. Some or all of the above-described processing in the reaction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reaction unit can cause AI to analyze the user's past interaction data and optimize the animal's reaction.

[0050] When the animal responds, the reaction unit can customize the content of the reaction by taking into account the user's geographical location information. The reaction unit, for example, acquires the user's geographical location information using GPS data. For example, if the user is in a tropical region, the reaction unit can cause the animal to display a reaction appropriate for a tropical region. Furthermore, if the user is in a cold region, the reaction unit can cause the animal to display a reaction appropriate for a cold region. Furthermore, if the user is in an urban area, the reaction unit can cause the animal to display a reaction appropriate for an urban area. This allows the animal's reaction to be provided based on the user's geographical location information, thereby providing a more relevant reaction. Some or all of the above-described processing in the reaction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reaction unit can cause AI to acquire the user's geographical location information and customize the animal's reaction.

[0051] The reaction unit can analyze the user's social media activity and generate a relevant reaction when an animal reacts. The reaction unit, for example, analyzes the user's social media activity. For example, if a user posts about "lions" on social media, the reaction unit can display a lion's reaction related to the post. Furthermore, if a user posts about "marine life" on social media, the reaction unit can display a dolphin's or shark's reaction related to the post. Furthermore, if a user posts about "pets" on social media, the reaction unit can display a dog's or cat's reaction related to the post. This allows for more relevant reactions by providing animal reactions based on the user's social media activity. Some or all of the above-described processing in the reaction unit can be performed, for example, using AI, or can be performed without AI. For example, the reaction unit can cause AI to analyze the user's social media activity and generate animal reactions.

[0052] The reaction unit can customize the animal's reaction by reflecting the user's past feedback. The reaction unit, for example, analyzes the user's past feedback. For example, the reaction unit prioritizes generating reactions that the user previously rated as "fun." The reaction unit can also generate reactions that the user previously rated as "scary." Furthermore, the reaction unit can generate reactions that the user previously rated as "I want to know more," along with detailed information. This allows the animal's reaction to be provided based on the user's past feedback, thereby providing a more appropriate reaction. Some or all of the above-described processing in the reaction unit may be performed, for example, using AI, or may be performed without AI. For example, the reaction unit can cause AI to analyze the user's past feedback and customize the animal's reaction.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can also monitor the user's health condition and present animal options according to the user's health condition. For example, the reception unit can measure the user's heart rate and blood pressure and suggest calm animals (e.g., rabbits and penguins) if the user needs to relax. Alternatively, the reception unit can suggest active animals (e.g., dogs and dolphins) if the user is not getting enough exercise. Furthermore, the reception unit can adjust the animal's interaction content based on the user's health condition. For example, if the user is feeling stressed, the reception unit controls the animal to behave in a way that has a relaxing effect. This allows the user to have a healthier experience by providing animal options and interaction content according to the user's health condition.

[0055] The generation unit can also simulate the evolutionary process of animals when generating animals. For example, the generation unit can simulate the evolutionary process of lions and visually show the process to the user. The generation unit can also simulate the evolutionary process of penguins and visually show the process to the user. Furthermore, the generation unit can simulate the evolutionary process of elephants and visually show the process to the user. In this way, by simulating the evolutionary process of animals, the user can deepen their understanding of animal evolution. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can cause a generation AI to execute a simulation of the evolutionary process of animals.

[0056] The reception unit can also monitor the user's learning progress and present animal options according to the progress. For example, if the user deepens their knowledge of a particular animal, the reception unit can suggest animals of the next level (e.g., animals in more complex ecosystems). Also, if the user shows interest in a particular theme, the reception unit can suggest animals related to that theme (e.g., marine life, forest life). Furthermore, the reception unit can adjust the interaction content of the animals based on the user's learning progress. For example, if the user is studying ecology, the reception unit can control the animals to behave in a way that indicates their role in the ecosystem. This makes it possible to provide a more effective learning experience by providing animal options and interaction content according to the user's learning progress.

[0057] The generation unit can also simulate the social behavior of animals when generating the animals. For example, the generation unit can simulate the behavior of a pride of lions and visually show the behavior to the user. The generation unit can also simulate the behavior of a herd of penguins and visually show the behavior to the user. Furthermore, the generation unit can simulate the behavior of a herd of elephants and visually show the behavior to the user. In this way, by simulating the social behavior of animals, the user can deepen their understanding of the social behavior of animals. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can cause the generation AI to simulate the social behavior of animals.

[0058] The reception unit can also analyze the user's interests and present related animal options. For example, the reception unit can suggest similar animals based on the user's history of animal selections. Also, if the user shows interest in a particular theme, the reception unit can suggest animals related to that theme (e.g., marine life, forest life). Furthermore, the reception unit can adjust the animal's interaction content based on the user's interests and concerns. For example, if the user shows interest in marine life, the reception unit controls the animal to behave in a way that reflects the ecology of marine life. This makes it possible to provide a more effective learning experience by providing animal options and interaction content that match the user's interests and concerns.

[0059] When generating animals, the generation unit can also emphasize the role of the animals in the ecosystem. For example, the generation unit generates a movement of a lion that emphasizes its role as a predator in the ecosystem. The generation unit can also generate a movement of a penguin that emphasizes its role in the ecosystem. Furthermore, the generation unit can generate a movement of an elephant that emphasizes its role in the ecosystem. By emphasizing the role of animals in the ecosystem, the user can deepen their understanding of the ecosystem. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to emphasize the role of animals in the ecosystem.

[0060] The reception unit can also analyze the user's learning style and present optimal animal options. For example, if the user has a visual learning style, the reception unit can suggest visually appealing animals (e.g., colorful birds, vibrant fish). If the user has an auditory learning style, the reception unit can suggest animals with distinctive sounds or cries (e.g., birds, dolphins). Furthermore, if the user has an experiential learning style, the reception unit can suggest animals with a wide range of interactions (e.g., dogs, cats). This makes it possible to provide a more effective learning experience by providing animal options that match the user's learning style.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives a selection of an animal from the user. The user can select an animal such as a lion, an elephant, or a penguin. The reception unit can receive the selection of an animal using means such as voice input, text input, or image input. Step 2: The generator uses AI to digitally generate animals based on the information received by the receiver. The AI ​​uses deep learning and GAN (generative artificial network) technologies to realistically recreate the animal's appearance and movements. The generator generates movements like a lion walking or an elephant wagging its trunk in real time. Step 3: The reaction unit makes the animal generated by the generation unit react in real time to the user's movements. The reaction unit controls the animal so that it approaches in response to the user's movement of reaching out to touch the animal. The reaction unit detects the user's movements using sensor technology, and the animal reacts in real time to those movements.

[0063] (Example 2) An educational system according to an embodiment of the present invention uses a generation AI to digitally generate animals, allowing children to safely interact with them. The educational system simulates interaction by allowing a user to select an animal they want to interact with, and then having the generation AI digitally generate the selected animal, which then responds to the user's movements in real time. For example, the educational system allows a user to select an animal they want to interact with. For example, the user may select an animal such as a lion, elephant, or penguin. This information is input into the generation AI. The educational system then uses the generation AI to digitally generate the selected animal. The generation AI then realistically reproduces the animal's appearance and movements, providing the user with an experience that feels as if they are actually interacting with the animal. For example, the generation AI generates movements such as a lion walking or an elephant wagging its trunk in real time. The educational system then allows the generated animal to respond to the user's movements in real time. For example, when a user reaches out to touch an animal, the animal responds to the user's movement and moves closer. In this way, the user can experience interacting with animals in a safe manner. This allows children to interact with animals without any risk. For example, even dangerous animals such as lions and elephants can be interacted with safely digitally. Children can also interact with animals at home or at school without having to go to specific locations such as zoos or safari parks. This helps children deepen their interest in animals and enhance their learning. This allows the education system to allow children to interact with animals safely. For example, even dangerous animals such as lions and elephants can be interacted with safely digitally. Children can also interact with animals at home or at school without having to go to specific locations such as zoos or safari parks. This helps children deepen their interest in animals and enhance their learning.

[0064] An educational system according to an embodiment includes a reception unit, a generation unit, and a reaction unit. The reception unit receives an animal selection from a user. The user can select an animal such as a lion, an elephant, or a penguin. The reception unit can receive the animal selection using, for example, voice input, text input, or image input. The generation unit uses a generation AI to digitally generate an animal based on the information received by the reception unit. The generation AI realistically reproduces the animal's appearance and behavior using technologies such as deep learning and GAN (generative artificial network). The generation unit generates, in real time, movements such as a lion walking or an elephant wagging its trunk. The reaction unit causes the animal generated by the generation unit to respond in real time to the user's movements. For example, the reaction unit controls the animal to approach in response to the user's movement of reaching out to touch the animal. The reaction unit detects the user's movement using, for example, sensor technology, and causes the animal to respond in real time to the movement. This allows the educational system according to an embodiment to safely interact with animals. For example, even dangerous animals such as lions and elephants can be interacted with safely digitally. Furthermore, children can interact with animals at home or at school without having to go to specific locations such as zoos or safari parks. This will deepen users' interest in animals and enhance their learning.

[0065] The generation unit can realistically reproduce the appearance and behavior of animals using a generation AI. The generation unit realistically reproduces the appearance of animals using, for example, deep learning technology. For example, the generation unit can reproduce the texture of an animal's skin and the movement of its fur in detail. The generation unit can also realistically reproduce the behavior of animals using a generative artificial network (GAN). For example, the generation unit can generate the walking behavior of a lion or the wagging behavior of an elephant in real time. Furthermore, the generation unit can realistically reproduce the facial expressions and voices of animals using a generation AI. For example, the generation unit can realistically reproduce the roar of a lion or the croaking of a penguin. In this way, the appearance and behavior of animals can be realistically reproduced by using the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause a generation AI to generate the appearance and behavior of animals.

[0066] The reaction unit enables the animal to react in real time to the user's movements. The reaction unit, for example, detects the user's movements using sensor technology. For example, the reaction unit uses a camera to track the user's hand movements, and the animal reacts in real time to those movements. The reaction unit can also detect the user's entire body movements using motion capture technology. For example, the reaction unit detects the user's movement of reaching out to touch the animal and controls the animal to approach in response to that movement. Furthermore, the reaction unit can adjust the animal's reaction speed in response to the user's movements. For example, the reaction unit can speed up the animal's reaction speed when the user's movements are fast, and slow down the animal's reaction speed when the user's movements are slow. This allows the animal to react in real time to the user's movements, providing a more realistic interaction experience. Some or all of the above-described processing in the reaction unit may be performed using, for example, AI, or may be performed without AI. For example, the reaction unit can cause AI to detect the user's movements and control the animal's reaction.

[0067] The reception unit can accept information selecting the type and characteristics of an animal the user wants to interact with. The reception unit accepts information selecting the type and characteristics of an animal the user wants to interact with, for example, using voice input. For example, the reception unit can select a lion by the user inputting "lion" by voice. The reception unit can also accept information selecting the type and characteristics of an animal using text input. For example, the reception unit can select an elephant by the user inputting "elephant" by text. The reception unit can also accept information selecting the type and characteristics of an animal using image input. For example, the reception unit can select a lion by the user uploading an image of a lion. In this way, by accepting information selecting the type and characteristics of an animal the user wants to interact with, it is possible to generate an animal according to the user's preference. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can cause AI to process the voice input, text input, and image input.

[0068] The generation unit can generate an animal based on information about the animal selected by the user. The generation unit generates an animal based on information about the animal selected by the user. For example, if the user selects "lion," the generation unit realistically reproduces the appearance and movements of a lion. Furthermore, if the user selects "elephant," the generation unit can also realistically reproduce the appearance and movements of an elephant. Furthermore, the generation unit can generate an animal based on the characteristics of the animal selected by the user. For example, if the user selects "penguin" and further specifies "swimming behavior," the generation unit can realistically reproduce the swimming behavior of a penguin. Thus, by generating an animal based on information about the animal selected by the user, an animal that meets the user's preferences can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information about the animal selected by the user into the generation AI and cause the generation AI to generate the animal.

[0069] The reaction unit can control the animal to approach in response to the user's movement of reaching out to touch the animal. The reaction unit, for example, uses sensor technology to detect the user's movement of reaching out to touch the animal. For example, the reaction unit uses a camera to track the user's hand movement and controls the animal to approach in response to that movement. The reaction unit can also detect the user's whole-body movement using motion capture technology. For example, the reaction unit can detect the user's movement of reaching out to touch the animal and controls the animal to approach in response to that movement. Furthermore, the reaction unit can adjust the animal's reaction speed in response to the user's movement. For example, the reaction unit can speed up the animal's reaction speed when the user's movement is fast and slow down the animal's reaction speed when the user's movement is slow. This allows the animal to approach in response to the user's movement of reaching out to touch the animal, providing a more realistic interaction experience. Some or all of the above-described processing in the reaction unit may be performed using, for example, AI, or may be performed without AI. For example, the reaction unit can cause AI to detect the user's movement and control the animal's reaction.

[0070] The reception unit can estimate the user's emotions and present animal options based on the estimated user's emotions. The reception unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the reception unit analyzes the user's facial expressions using a camera to estimate whether the user is excited or relaxed. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate whether the user is feeling anxious. The reception unit can also estimate the user's emotions using biometric data. For example, the reception unit analyzes the user's heart rate and electrodermal activity to estimate the user's emotional state. The reception unit presents animal options based on the estimated user's emotions. For example, if the user is excited, active animals (e.g., lions and cheetahs) are preferentially presented as animal options. If the user is relaxed, calm animals (e.g., penguins and rabbits) are preferentially presented as animal options. If the user is anxious, animals that provide a sense of security (e.g., dogs and cats) are preferentially presented as animal options. This allows a more appropriate animal to be selected by presenting animal options according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may cause AI to estimate the user's emotions and present animal options.

[0071] The reception unit can analyze the user's past selection history and suggest optimal animal options. The reception unit, for example, can suggest similar animals based on the user's past selection history of animals. For example, the reception unit can analyze the types and characteristics of animals selected by the user in the past and suggest similar animals. The reception unit can also consider the frequency of animals selected by the user in the past and prioritize suggesting the most frequently selected animals. For example, the reception unit can suggest similar animals based on the user's past selection history of animals. The reception unit can also analyze the characteristics of animals selected by the user in the past and suggest similar animals. The reception unit can also consider the frequency of animals selected by the user in the past and prioritize suggesting the most frequently selected animals. In this way, by analyzing the user's past selection history, more appropriate animal options can be suggested. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can cause AI to analyze the user's past selection history and suggest animal options.

[0072] When selecting an animal, the reception unit can customize the options according to the user's age and learning purpose. The reception unit customizes the animal options according to, for example, the user's age. For example, the reception unit suggests animals with high educational value (e.g., elephants and dolphins) for children. The reception unit can also customize the animal options according to the learning purpose. For example, if the learning purpose is ecology, the reception unit suggests animals that are useful for understanding ecosystems (e.g., wolves and sharks). The reception unit can also suggest more complex animals (e.g., lions and bears) for older users. This makes it possible to provide a more appropriate learning experience by providing animal options according to the user's age and learning purpose. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input information such as the user's age and learning purpose into AI and have the AI ​​customize the animal options.

[0073] When selecting an animal, the reception unit can provide an optimal selection means depending on the user's input method. The reception unit presents animal options using, for example, voice input. For example, the reception unit can select a lion by the user's voice input of "lion." The reception unit can also present animal options using text input. For example, the reception unit can select an elephant by the user's text input of "elephant." The reception unit can also present animal options using image input. For example, the reception unit can select a lion by the user's text upload of a lion. This provides an optimal selection means depending on the user's input method, thereby providing a more user-friendly system. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can cause AI to process the voice input, text input, and image input.

[0074] The reception unit can estimate the user's emotions and adjust the selection order of animals based on the estimated user's emotions. The reception unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the reception unit analyzes the user's facial expressions using a camera to estimate whether the user is excited or relaxed. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate whether the user is feeling anxious. The reception unit can also estimate the user's emotions using biometric data. For example, the reception unit analyzes the user's heart rate and electrodermal activity to estimate the user's emotional state. The reception unit adjusts the selection order of animals based on the estimated user's emotions. For example, if the user is excited, the reception unit randomly presents animal options to attract the user's interest. If the user is relaxed, the reception unit presents animal options in order of calmness. If the user is anxious, the reception unit presents animal options in order of reassurance. In this way, by adjusting the selection order of animals according to the user's emotions, a more appropriate animal can be selected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may cause an AI to estimate the user's emotion and adjust the order of animal selection.

[0075] When selecting an animal, the reception unit can prioritize presenting highly relevant animals taking into consideration the user's geographical location information. The reception unit acquires the user's geographical location information using, for example, GPS data. For example, if the user is in a tropical region, the reception unit can prioritize presenting animals that inhabit tropical regions (e.g., lizards and parrots). Furthermore, if the user is in a cold region, the reception unit can prioritize presenting animals that inhabit cold regions (e.g., penguins and polar bears). Furthermore, if the user is in an urban area, the reception unit can prioritize presenting animals that can be found in urban areas (e.g., pigeons and squirrels). This allows the user to select a more relevant animal by providing animal options based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can cause AI to acquire the user's geographical location information and present animal options.

[0076] When selecting an animal, the reception unit can analyze the user's social media activity and suggest related animals. The reception unit, for example, analyzes the user's social media activity. For example, if the user posts about "lions" on social media, the reception unit can preferentially suggest lions. Furthermore, if the user posts about "marine life" on social media, the reception unit can preferentially suggest dolphins and sharks. Furthermore, if the user posts about "pets" on social media, the reception unit can preferentially suggest dogs and cats. This allows for the selection of more relevant animals by providing animal options based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can cause AI to analyze the user's social media activity and suggest animal options.

[0077] When selecting an animal, the reception unit can customize the options by reflecting the user's past feedback. The reception unit, for example, analyzes the user's past feedback. For example, the reception unit prioritizes suggesting animals that the user has previously rated as "fun." The reception unit can also suggest animals that the user has previously rated as "scary." Furthermore, the reception unit can suggest animals that the user has previously rated as "interesting" along with detailed information about them. This allows the user to select a more appropriate animal by providing animal options based on the user's past feedback. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can cause AI to analyze the user's past feedback and customize the animal options.

[0078] The generation unit can estimate the user's emotions and adjust the animal's appearance and behavior based on the estimated user's emotions. The generation unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the generation unit can analyze the user's facial expressions using a camera to estimate whether the user is excited or relaxed. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate whether the user is feeling anxious. The generation unit can also estimate the user's emotions using biometric data. For example, the generation unit can analyze the user's heart rate and electrodermal activity to estimate the user's emotional state. The generation unit adjusts the animal's appearance and behavior based on the estimated user's emotions. For example, if the user is excited, the animal's movements can be made more active and its colors can be made more vivid. If the user is relaxed, the animal's movements can be made more calming and its colors can be made more subdued. If the user is anxious, the animal's movements can be made slower and its colors can be made more reassuring. This allows the appearance and behavior of the animal to be adjusted according to the user's emotions, thereby providing a more appropriate interaction experience. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may cause AI to estimate the user's emotions and adjust the appearance and behavior of the animal.

[0079] The generation unit can reproduce the animal's ecology and behavioral patterns in detail when generating an animal. For example, when generating a lion, the generation unit reproduces hunting behavior and herd behavior in detail. For example, the generation unit realistically reproduces the lion's behavior of chasing prey and herd communication. The generation unit can also reproduce the swimming and nest-building behavior of penguins in detail when generating them. For example, the generation unit realistically reproduces the penguin's swimming behavior in water and nest-building behavior. Furthermore, when generating an elephant, the generation unit can also reproduce the trunk movement and herd movement in detail. For example, the generation unit realistically reproduces the elephant's behavior of lifting an object with its trunk and herd movement. This allows for the detailed reproduction of the animal's ecology and behavioral patterns, thereby providing more realistic animals. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can cause the generation AI to reproduce the animal's ecology and behavioral patterns.

[0080] When generating an animal, the generation unit can customize the animal's behavior scenario according to the user's learning objective. For example, in the case of a learning objective on ecology, the generation unit generates a behavior scenario that emphasizes the animal's role in the ecosystem. For example, the generation unit generates a behavior scenario that emphasizes the lion's role as a predator in the ecosystem. Furthermore, in the case of a learning objective on ethology, the generation unit can also generate a behavior scenario that emphasizes a specific behavior pattern. For example, the generation unit generates a behavior scenario that emphasizes the mating behavior of penguins. Furthermore, in the case of a learning objective on environmental conservation, the generation unit can also generate a behavior scenario that emphasizes the animal's response to changes in its habitat. For example, the generation unit generates a behavior scenario that emphasizes how elephants adapt to changes in their habitat. This allows for a more appropriate learning experience by providing an animal's behavior scenario according to the user's learning objective. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can cause the generation AI to customize the animal's behavior scenario.

[0081] When generating animals, the generation unit can improve the accuracy of the generation by referring to the user's past interaction data. For example, the generation unit generates more realistic animals based on data on animals the user has interacted with in the past. For example, the generation unit optimizes the lion's movements and reactions based on data on lions the user has interacted with in the past. The generation unit can also analyze the user's past interaction data to optimize the animal's movements and reactions. For example, the generation unit improves the accuracy of the generation by reflecting the characteristics of animals the user has previously preferred. Furthermore, the generation unit can customize the animal's movements and reactions based on data on animals the user has interacted with in the past. For example, the generation unit optimizes the penguin's movements and reactions based on data on penguins the user has interacted with in the past. In this way, more realistic animals can be provided by referring to the user's past interaction data. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can cause the generation AI to analyze the user's past interaction data and optimize the animal generation.

[0082] The generation unit can estimate the user's emotions and adjust the animal generation speed based on the estimated user's emotions. The generation unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the generation unit can analyze the user's facial expressions using a camera to estimate whether the user is excited or relaxed. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate whether the user is feeling anxious. The generation unit can also estimate the user's emotions using biometric data. For example, the generation unit can analyze the user's heart rate and electrodermal activity to estimate the user's emotional state. The generation unit adjusts the animal generation speed based on the estimated user's emotions. For example, if the user is excited, the animal generation speed can be increased to allow the user to interact with the animal immediately. If the user is relaxed, the animal generation speed can be decreased to provide a relaxing experience. If the user is anxious, the animal generation speed can be adjusted to provide a sense of security. This allows the user to enjoy a more appropriate interaction experience by providing an animal generation speed that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may cause AI to estimate the user's emotion and adjust the generation speed of the animals.

[0083] The generation unit can generate a background that recreates the animal's habitat or environment when generating an animal. For example, the generation unit recreates a savanna background when generating a lion. For example, the generation unit realistically recreates the scenery and plants of the savanna where the lion lives. The generation unit can also recreate an Antarctic background when generating a penguin. For example, the generation unit realistically recreates the icebergs and snowy plains of Antarctica where the penguins live. The generation unit can also recreate an African grassland background when generating an elephant. For example, the generation unit realistically recreates the scenery and plants of the African grassland where the elephants live. This provides a background that recreates the animal's habitat or environment, making it possible to provide more realistic animals. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to recreate the animal's habitat or environment.

[0084] The generation unit can realistically reproduce animal cries and sounds when generating animals. For example, the generation unit reproduces a realistic roar when generating a lion. For example, the generation unit reproduces a realistic sound using an audio sample of a lion's roar. The generation unit can also reproduce a realistic roar when generating a penguin. For example, the generation unit reproduces a realistic sound using an audio sample of a penguin's roar. Furthermore, the generation unit can also reproduce a realistic trumpet sound when generating an elephant. For example, the generation unit reproduces a realistic sound using an audio sample of an elephant's trumpet. This realistic reproduction of animal cries and sounds makes it possible to provide more realistic animals. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause a generation AI to reproduce the animal cries and sounds.

[0085] When generating an animal, the generation unit can provide detailed information about the animal according to the user's level of expertise. For example, for beginners, the generation unit provides basic animal information (e.g., name and habitat). For example, the generation unit provides basic information about a lion's name and habitat. For intermediate users, the generation unit can also provide animal behavior patterns and their role in the ecosystem. For example, the generation unit can provide information about penguins' mating behavior and their role in the ecosystem. For advanced users, the generation unit can also provide the animal's genetic information and evolutionary history. For example, the generation unit can provide detailed information about elephants' genetic information and evolutionary history. This allows for a more appropriate learning experience by providing detailed information about animals according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can cause the generation AI to provide detailed information about animals according to the user's level of expertise.

[0086] The reaction unit can estimate the user's emotions and adjust the animal's reaction speed based on the estimated user's emotions. The reaction unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the reaction unit can analyze the user's facial expressions using a camera to estimate whether the user is excited or relaxed. The reaction unit can also estimate the user's emotions using voice analysis technology. For example, the reaction unit can analyze the tone and speed of the user's voice to estimate whether the user is feeling anxious. The reaction unit can also estimate the user's emotions using biometric data. For example, the reaction unit can analyze the user's heart rate or electrodermal activity to estimate the user's emotional state. The reaction unit adjusts the animal's reaction speed based on the estimated user's emotions. For example, if the user is excited, the animal's reaction speed can be increased to respond immediately. If the user is relaxed, the animal's reaction speed can be decreased to provide a relaxing experience. If the user is anxious, the animal's reaction speed can be adjusted to provide a sense of security. This allows the animal's reaction speed to be adjusted in accordance with the user's emotions, thereby providing a more appropriate interaction experience. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reaction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reaction unit may cause an AI to estimate the user's emotion and adjust the animal's reaction speed.

[0087] The reaction unit can learn the user's movement patterns and generate a more natural reaction when the animal reacts. For example, the reaction unit can learn the user's movement of reaching out and generate a reaction that causes the animal to approach naturally. For example, the reaction unit can use a camera to track the user's hand movements and learn the movement patterns. The reaction unit can also learn the user's movement of patting the animal's head and generate a reaction that makes the animal happy. For example, the reaction unit can track the user's hand movements and learn the movement patterns. The reaction unit can also learn the user's movement of feeding the animal and generate a reaction that makes the animal eat the food. For example, the reaction unit can track the user's hand movements and learn the movement patterns. In this way, by learning the user's movement patterns, a more natural reaction can be generated for the animal. Some or all of the above-described processing in the reaction unit may be performed using, for example, AI, or may be performed without AI. For example, the reaction unit can have AI learn the user's movement patterns and generate a reaction for the animal.

[0088] The reaction unit can customize the content of the animal's reaction depending on the user's age and learning goal. For example, the reaction unit customizes the animal to show a playful reaction for children. For example, the reaction unit controls the animal to show a playful behavior toward children. Furthermore, if the learning goal is ecology, the reaction unit can also customize the animal to show a reaction that indicates its role in the ecosystem. For example, the reaction unit controls the animal to show a behavior that indicates its role in the ecosystem. Furthermore, the reaction unit can customize the animal to show more complex behavior for older users. For example, the reaction unit controls the animal to show more complex behavior toward older users. This makes it possible to provide a more appropriate learning experience by providing an animal's reaction according to the user's age and learning goal. Some or all of the above-described processing in the reaction unit may be performed using, or without, AI. For example, the reaction unit can input information about the user's age and learning goal into AI and have the AI ​​customize the animal's reaction.

[0089] The reaction unit can improve the accuracy of the animal's reaction by referring to the user's past interaction data. The reaction unit, for example, optimizes the animal's reaction based on the user's past actions. For example, the reaction unit optimizes the animal's reaction based on the user's past hand actions. The reaction unit can also analyze the user's past interaction data and customize the animal's reaction. For example, the reaction unit analyzes the user's past action patterns and customizes the animal's reaction. The reaction unit can also improve the accuracy of the reaction by reflecting the user's past preferred animal reactions. For example, the reaction unit optimizes the animal's reaction based on the user's past preferred animal reactions. In this way, by referring to the user's past interaction data, it is possible to provide more realistic animal reactions. Some or all of the above-described processing in the reaction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reaction unit can cause AI to analyze the user's past interaction data and optimize the animal's reaction.

[0090] The reaction unit can estimate the user's emotions and adjust the animal's reaction content based on the estimated user's emotions. The reaction unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the reaction unit can analyze the user's facial expressions using a camera to estimate whether the user is excited or relaxed. The reaction unit can also estimate the user's emotions using voice analysis technology. For example, the reaction unit can analyze the tone and speed of the user's voice to estimate whether the user is feeling anxious. The reaction unit can also estimate the user's emotions using biometric data. For example, the reaction unit can analyze the user's heart rate and electrodermal activity to estimate the user's emotional state. The reaction unit adjusts the animal's reaction content based on the estimated user's emotions. For example, if the user is excited, the animal reacts actively. If the user is relaxed, the animal reacts calmly. If the user is anxious, the animal reacts in a way that provides a sense of security. This allows the animal's reaction content to be provided in accordance with the user's emotions, thereby providing a more appropriate interaction experience. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reaction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reaction unit may cause an AI to estimate the user's emotion and adjust the animal's reaction content.

[0091] When the animal responds, the reaction unit can customize the content of the reaction by taking into account the user's geographical location information. The reaction unit, for example, acquires the user's geographical location information using GPS data. For example, if the user is in a tropical region, the reaction unit can cause the animal to display a reaction appropriate for a tropical region. Furthermore, if the user is in a cold region, the reaction unit can cause the animal to display a reaction appropriate for a cold region. Furthermore, if the user is in an urban area, the reaction unit can cause the animal to display a reaction appropriate for an urban area. This allows the animal's reaction to be provided based on the user's geographical location information, thereby providing a more relevant reaction. Some or all of the above-described processing in the reaction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reaction unit can cause AI to acquire the user's geographical location information and customize the animal's reaction.

[0092] The reaction unit can analyze the user's social media activity and generate a relevant reaction when an animal reacts. The reaction unit, for example, analyzes the user's social media activity. For example, if a user posts about "lions" on social media, the reaction unit can display a lion's reaction related to the post. Furthermore, if a user posts about "marine life" on social media, the reaction unit can display a dolphin's or shark's reaction related to the post. Furthermore, if a user posts about "pets" on social media, the reaction unit can display a dog's or cat's reaction related to the post. This allows for more relevant reactions by providing animal reactions based on the user's social media activity. Some or all of the above-described processing in the reaction unit can be performed, for example, using AI, or can be performed without AI. For example, the reaction unit can cause AI to analyze the user's social media activity and generate animal reactions.

[0093] The reaction unit can customize the animal's reaction by reflecting the user's past feedback. The reaction unit, for example, analyzes the user's past feedback. For example, the reaction unit prioritizes generating reactions that the user previously rated as "fun." The reaction unit can also generate reactions that the user previously rated as "scary." Furthermore, the reaction unit can generate reactions that the user previously rated as "I want to know more," along with detailed information. This allows the animal's reaction to be provided based on the user's past feedback, thereby providing a more appropriate reaction. Some or all of the above-described processing in the reaction unit may be performed, for example, using AI, or may be performed without AI. For example, the reaction unit can cause AI to analyze the user's past feedback and customize the animal's reaction. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, generation unit, and reaction unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and the user can input voice or text. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and digitally generates an animal using a generation AI. The reaction unit is realized, for example, by the control unit 46A of the smart device 14, and the animal reacts in real time to the user's movements. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, and reaction unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, allowing the user to input voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and digitally generates an animal using a generation AI. The reaction unit is realized, for example, by the control unit 46A of the smart glasses 214, and the animal reacts in real time to the user's movements. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and reaction unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, allowing the user to input voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and digitally generates an animal using a generation AI. The reaction unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and the animal reacts in real time to the user's movements. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and reaction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, allowing the user to input voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and digitally generates an animal using a generation AI. The reaction unit is realized, for example, by the control unit 46A of the robot 414, and the animal reacts in real time to the user's movements.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The reception unit can also monitor the user's health condition and present animal options according to the user's health condition. For example, the reception unit can measure the user's heart rate and blood pressure and suggest calm animals (e.g., rabbits and penguins) if the user needs to relax. Alternatively, the reception unit can suggest active animals (e.g., dogs and dolphins) if the user is not getting enough exercise. Furthermore, the reception unit can adjust the animal's interaction content based on the user's health condition. For example, if the user is feeling stressed, the reception unit controls the animal to behave in a way that has a relaxing effect. This allows the user to have a healthier experience by providing animal options and interaction content according to the user's health condition.

[0096] The generation unit can also simulate the evolutionary process of animals when generating animals. For example, the generation unit can simulate the evolutionary process of lions and visually show the process to the user. The generation unit can also simulate the evolutionary process of penguins and visually show the process to the user. Furthermore, the generation unit can simulate the evolutionary process of elephants and visually show the process to the user. In this way, by simulating the evolutionary process of animals, the user can deepen their understanding of animal evolution. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can cause a generation AI to execute a simulation of the evolutionary process of animals.

[0097] The reaction unit can also estimate the user's emotions and adjust the animal's interaction content based on the estimated user's emotions. For example, if the user is excited, the animal responds actively. Alternatively, if the user is relaxed, the animal responds calmly. Furthermore, if the user is anxious, the animal responds in a reassuring manner. This allows the animal's interaction content to be tailored to the user's emotions, providing a more appropriate interaction experience. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reaction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reaction unit can have an AI perform the estimation of the user's emotions and the adjustment of the animal's interaction content.

[0098] The reception unit can also monitor the user's learning progress and present animal options according to the progress. For example, if the user deepens their knowledge of a particular animal, the reception unit can suggest animals of the next level (e.g., animals in more complex ecosystems). Also, if the user shows interest in a particular theme, the reception unit can suggest animals related to that theme (e.g., marine life, forest life). Furthermore, the reception unit can adjust the interaction content of the animals based on the user's learning progress. For example, if the user is studying ecology, the reception unit can control the animals to behave in a way that indicates their role in the ecosystem. This makes it possible to provide a more effective learning experience by providing animal options and interaction content according to the user's learning progress.

[0099] The generation unit can also simulate the social behavior of animals when generating the animals. For example, the generation unit can simulate the behavior of a pride of lions and visually show the behavior to the user. The generation unit can also simulate the behavior of a herd of penguins and visually show the behavior to the user. Furthermore, the generation unit can simulate the behavior of a herd of elephants and visually show the behavior to the user. In this way, by simulating the social behavior of animals, the user can deepen their understanding of the social behavior of animals. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can cause the generation AI to simulate the social behavior of animals.

[0100] The reaction unit can also estimate the user's emotions and adjust the animal's interaction content based on the estimated user's emotions. For example, if the user is excited, the animal responds actively. Alternatively, if the user is relaxed, the animal responds calmly. Furthermore, if the user is anxious, the animal responds in a reassuring manner. This allows the animal's interaction content to be tailored to the user's emotions, providing a more appropriate interaction experience. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reaction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reaction unit can have an AI perform the estimation of the user's emotions and the adjustment of the animal's interaction content.

[0101] The reception unit can also analyze the user's interests and present related animal options. For example, the reception unit can suggest similar animals based on the user's history of animal selections. Also, if the user shows interest in a particular theme, the reception unit can suggest animals related to that theme (e.g., marine life, forest life). Furthermore, the reception unit can adjust the animal's interaction content based on the user's interests and concerns. For example, if the user shows interest in marine life, the reception unit controls the animal to behave in a way that reflects the ecology of marine life. This makes it possible to provide a more effective learning experience by providing animal options and interaction content that match the user's interests and concerns.

[0102] When generating animals, the generation unit can also emphasize the role of the animals in the ecosystem. For example, the generation unit generates a movement of a lion that emphasizes its role as a predator in the ecosystem. The generation unit can also generate a movement of a penguin that emphasizes its role in the ecosystem. Furthermore, the generation unit can generate a movement of an elephant that emphasizes its role in the ecosystem. By emphasizing the role of animals in the ecosystem, the user can deepen their understanding of the ecosystem. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to emphasize the role of animals in the ecosystem.

[0103] The reaction unit can also estimate the user's emotions and adjust the animal's interaction content based on the estimated user's emotions. For example, if the user is excited, the animal responds actively. Alternatively, if the user is relaxed, the animal responds calmly. Furthermore, if the user is anxious, the animal responds in a reassuring manner. This allows the animal's interaction content to be tailored to the user's emotions, providing a more appropriate interaction experience. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reaction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reaction unit can have an AI perform the estimation of the user's emotions and the adjustment of the animal's interaction content.

[0104] The reception unit can also analyze the user's learning style and present optimal animal options. For example, if the user has a visual learning style, the reception unit can suggest visually appealing animals (e.g., colorful birds, vibrant fish). If the user has an auditory learning style, the reception unit can suggest animals with distinctive sounds or cries (e.g., birds, dolphins). Furthermore, if the user has an experiential learning style, the reception unit can suggest animals with a wide range of interactions (e.g., dogs, cats). This makes it possible to provide a more effective learning experience by providing animal options that match the user's learning style.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The reception unit receives a selection of an animal from the user. The user can select an animal such as a lion, an elephant, or a penguin. The reception unit can receive the selection of an animal using means such as voice input, text input, or image input. Step 2: The generator uses AI to digitally generate animals based on the information received by the receiver. The AI ​​uses deep learning and GAN (generative artificial network) technologies to realistically recreate the animal's appearance and movements. The generator generates movements like a lion walking or an elephant wagging its trunk in real time. Step 3: The reaction unit makes the animal generated by the generation unit react in real time to the user's movements. The reaction unit controls the animal so that it approaches in response to the user's movement of reaching out to touch the animal. The reaction unit detects the user's movements using sensor technology, and the animal reacts in real time to those movements.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 AI 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 AI 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 AI 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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 reception unit that receives an animal selection from a user; a generation unit that digitally generates an animal based on the information received by the reception unit; a reaction unit that causes the animal generated by the generation unit to react in real time to the user's movements; Equipped with A system characterized by:

2. The generation unit Generative AI recreates the appearance and behavior of animals realistically 2. The system of claim 1.

3. The reaction section is The animals react in real time to the user's movements.

2. The system of claim 1.

4. The reception unit Accepts information to select the type and characteristics of the animal the user wants to interact with 2. The system of claim 1.

5. The generation unit Generate an animal based on the information of the animal selected by the user 2. The system of claim 1.

6. The reaction section is The animal is controlled so that it approaches in response to the user's movement to reach out and touch the animal.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and present animal options based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past selection history and suggests the best animal options 2. The system of claim 1.

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