System

Abstract

An object of the system according to the embodiment is to reproduce and interact with a virtual creature in real time on the basis of a user's preference and behavior.SOLUTION: A system according to an embodiment includes a learning unit, a reproduction unit, and an interaction analysis unit. The learning unit learns preferences and actions of the user. The reproducing section reproduces the phantom creature in real time based on the preference and the behavior learned by the learning section. The dialogue analysis unit analyzes a dialogue when a user has a dialogue with a living thing through a smartphone or AR glasses.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 technologies have had the problem of making it difficult to recreate and interact with mythical creatures in real time based on a user's preferences and behavior.

[0005] The system according to the embodiment aims to recreate and interact with fantastical creatures in real time based on the user's preferences and behavior. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, a reproduction unit, and a dialogue analysis unit. The learning unit learns a user's preferences and behavior. The reproduction unit reproduces a mythical creature in real time based on the preferences and behavior learned by the learning unit. The dialogue analysis unit analyzes the dialogue when the user interacts with the creature through a smartphone or AR glasses. [Effects of the Invention]

[0007] The system according to the embodiment can recreate and interact with fantastical creatures in real time based on the user's preferences and behavior. [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 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 augmented reality (AR) pet cafe experience system according to an embodiment of the present invention is a system that can invite extinct animals and mythical creatures to provide a new experience to users. As a result, the augmented reality (AR) pet cafe experience system can provide users with interactive interactions with extinct animals and mythical creatures.

[0029] An augmented reality (AR) pet cafe experience system according to an embodiment includes a learning unit, a reproduction unit, and a dialogue analysis unit. The learning unit learns user preferences and behavior. For example, the learning unit analyzes a user's browsing history and purchase history to learn the user's preferences. The learning unit can also learn user behavior based on survey results. The learning unit can also predict future behavior based on the user's past behavior data. The reproduction unit recreates mythical creatures in real time based on the preferences and behavior learned by the learning unit. For example, the reproduction unit recreates dinosaurs and dragons around the user using AR technology. The reproduction unit can also recreate mammoths and unicorns using VR technology. The reproduction unit can also recreate phoenixes using 3D modeling technology. The dialogue analysis unit analyzes the dialogue when the user interacts with the creatures through a smartphone or AR glasses. For example, the dialogue analysis unit analyzes the user's voice commands using voice recognition technology. The dialogue analysis unit can also analyze the user's dialogue using natural language processing technology. The dialogue analysis unit can also analyze the user's emotions using emotion analysis technology, allowing the augmented reality (AR) pet cafe experience system according to the embodiment to provide a new experience to the user by recreating mythical creatures in real time based on the user's preferences and behavior and analyzing the dialogue.

[0030] The learning unit can predict future behavior based on the user's past behavioral data and generate corresponding creature behavior. The learning unit, for example, analyzes the user's past behavioral data and develops an algorithm for predicting future behavior. For example, it predicts which creature to suggest next based on what creatures the user has played with in the past. The learning unit can also predict the user's future behavior using a machine learning algorithm. For example, it predicts what action the user will take next based on what actions the user has taken in the past. The learning unit can also predict the user's future behavior using a statistical model. For example, it statistically predicts future behavior based on the user's past behavioral data. This makes it possible to predict the user's future behavior and generate corresponding creature behavior.

[0031] When learning a user's preferences, the learning unit can compare them with data from other users and suggest creatures that share a common interest. The learning unit, for example, collects data from other users and develops an algorithm to suggest creatures that share a common interest. For example, creatures that share a common interest can be suggested based on data from users who are interested in the same creatures. The learning unit can also collect social media data to learn a user's preferences. For example, it can analyze what creatures a user has expressed interest in on social media and suggest creatures that share a common interest. The learning unit can also compare the results of a survey with data from other users and suggest creatures that share a common interest. For example, it can analyze survey results and suggest creatures that share a common interest. In this way, it can suggest creatures that share a common interest by comparing them with data from other users.

[0032] The learning unit can utilize multimodal data such as voice or gestures when learning a user's preferences. The learning unit, for example, analyzes the user's voice data to build a system that learns the user's preferences. For example, it analyzes the content and tone of what the user says to suggest a favorite creature. The learning unit can also analyze the user's gesture data to learn the user's preferences. For example, it can analyze the user's hand movements and posture to suggest a favorite creature. The learning unit can also analyze gaze data to learn the user's preferences. For example, it can analyze which creature the user is looking at to suggest a favorite creature. In this way, by utilizing multimodal data such as voice and gestures, the user's preferences can be learned more accurately.

[0033] The learning unit can incorporate data from different cultural spheres or regions to learn user preferences and make suggestions from a global perspective. The learning unit, for example, collects data from different cultural spheres or regions and builds a system that learns user preferences. For example, it selects creatures to suggest to users based on popular creatures in each region. The learning unit can also collect consumer behavior data for each region and learn user preferences. For example, it analyzes consumer behavior data for each region and selects creatures to suggest to users. The learning unit can also collect cultural background data and learn user preferences. For example, it analyzes cultural background data for each region and selects creatures to suggest to users. In this way, by incorporating data from different cultural spheres and regions, it becomes possible to make suggestions from a global perspective.

[0034] The reproduction unit can perfectly synchronize the movements of the creature with the movements of the user, providing a realistic experience. The reproduction unit, for example, analyzes the user's movements in real time and builds a system that perfectly synchronizes the movements of the creature. For example, when the user waves their hand, the creature makes the same movement. The reproduction unit can also use motion capture technology to analyze the user's movements and synchronize the movements of the creature. For example, the user's body movements can be captured and reflected in the movements of the creature. The reproduction unit can also use real-time tracking technology to analyze the user's movements and synchronize the movements of the creature. For example, the user's movements can be tracked in real time and reflected in the movements of the creature. This allows the movements of the creature to be perfectly synchronized with the user's movements, providing a more realistic experience.

[0035] The reproduction unit can automatically adapt the creature's appearance or behavior according to the user's environment. For example, the reproduction unit analyzes the user's environmental data and builds a system that automatically adapts the creature's appearance and behavior. For example, when the user is outdoors, the creature's behavior becomes more active. The reproduction unit can also change the creature's appearance according to the indoor environment. For example, when the user is indoors, the reproduction unit can change the creature's color or pattern. The reproduction unit can also adapt the creature's behavior based on weather information. For example, on rainy days, the creature's behavior is reduced. This allows the creature's appearance and behavior to automatically adapt according to the user's environment, providing a more realistic experience.

[0036] The reproduction unit can add audio or haptic feedback to the reproduction of a creature to provide a multisensory experience. For example, the reproduction unit can add audio feedback to the reproduction of a creature, creating a system in which the creature responds when the user speaks to it. For example, when a user speaks to a dinosaur, the dinosaur responds with a roar. The reproduction unit can also add haptic feedback to the creature's movements using a haptic device. For example, when a user touches a dragon, the haptic device vibrates to provide haptic feedback. The reproduction unit can also generate the creature's voice using speech synthesis technology. For example, when a user speaks to a unicorn, the unicorn responds using speech synthesis technology. In this way, adding audio or haptic feedback can provide a more realistic experience.

[0037] The reproduction unit can reproduce interactions between different creatures and provide scenarios in which multiple creatures appear simultaneously. For example, the reproduction unit builds a system that reproduces interactions between different creatures. For example, it can provide a scenario in which a dinosaur and a dragon play together. The reproduction unit can also reproduce interactions between creatures using motion simulation technology. For example, it can provide a scenario in which a mammoth and a unicorn cooperate to complete a task. The reproduction unit can also reproduce interactions between creatures based on behavioral pattern settings. For example, it can provide a scenario in which a phoenix and a dragon compete. This makes it possible to provide a more diverse experience by reproducing interactions between different creatures.

[0038] The dialogue analysis unit analyzes the user's voice commands and uses natural language processing to enable the creatures to have natural conversations. The dialogue analysis unit, for example, analyzes the user's voice commands and uses natural language processing technology to build a system in which the creatures can have natural conversations. For example, when a user speaks to a dinosaur, the dinosaur responds in natural language. The dialogue analysis unit can also analyze the user's voice commands using voice recognition technology. For example, it analyzes the user's voice and generates an appropriate response. The dialogue analysis unit can also analyze the user's emotions using emotion analysis technology and reflect them in the dialogue. For example, if the user is happy, it generates a dialogue that reflects the emotion of joy. In this way, by analyzing the user's voice commands and using natural language processing, the creatures can have more natural conversations.

[0039] The dialogue analysis unit can analyze the user's movements in real time and cause the creature to react in accordance with those movements. The dialogue analysis unit, for example, builds a system that analyzes the user's movements in real time and causes the creature to react in accordance with those movements. For example, when the user waves their hand, the creature waves back. The dialogue analysis unit can also analyze the user's movements using motion capture technology. For example, the user's body movements can be captured and reflected in the creature's movements. The dialogue analysis unit can also analyze the user's movements using real-time tracking technology. For example, the user's movements can be tracked in real time and reflected in the creature's movements. This allows the creature to react in accordance with the user's movements, providing a more interactive experience.

[0040] The dialogue analysis unit can add a multi-user function that allows multiple users to participate simultaneously in dialogue via smartphones or AR glasses. The dialogue analysis unit builds a system that adds a multi-user function that allows multiple users to participate simultaneously in dialogue via smartphones or AR glasses. For example, multiple users interact with a dinosaur at the same time. The dialogue analysis unit can also use technology to achieve simultaneous connection of multiple users. For example, multiple users interact with a creature at the same time using real-time communication technology. The dialogue analysis unit can also use technology to achieve synchronization of interactions. For example, when multiple users play with a creature at the same time, the interactions are synchronized. This allows the multi-user function that allows multiple users to participate simultaneously to provide a more interactive experience.

[0041] The dialogue analysis unit can customize a dialogue or play scenario based on the user's past behavioral data. The dialogue analysis unit, for example, builds a system that customizes dialogue or play scenarios based on the user's past behavioral data. For example, the dialogue analysis unit generates the next scenario based on what creatures the user has played with in the past. The dialogue analysis unit can also analyze the user's behavior based on location information and customize a scenario. For example, a scenario is generated based on places the user has visited in the past. The dialogue analysis unit can also analyze the user's behavior based on app usage history and customize a scenario. For example, a scenario is generated based on data from apps the user has used in the past. In this way, a more personalized experience can be provided by customizing dialogue or play scenarios based on the user's past behavioral data.

[0042] The system can monitor the health or growth of a living creature in real time based on the user's behavioral data. For example, the system is constructed to monitor the health or growth of a living creature in real time based on the user's behavioral data. For example, the system may analyze how often the user feeds the unicorn to monitor its health. The system may also use biometrics technology to monitor the living creature's health. For example, the system may measure the living creature's heart rate and body temperature to monitor its health. The system may also use behavior analysis technology to monitor the living creature's growth. For example, the system may analyze the living creature's movements and behavior patterns to monitor its growth. This allows for more appropriate care by monitoring the living creature's health and growth in real time based on the user's behavioral data.

[0043] The system can automatically customize the appearance or behavior of a creature according to a user's preferences. For example, the system is constructed to automatically customize the appearance and behavior of a creature according to a user's preferences. For example, if a user prefers a particular color or pattern, the system changes the creature's appearance based on that information. The system can also customize the creature's behavior according to a user's preferences. For example, if a user prefers a particular behavior, the system sets the creature's behavior based on that information. The system can also customize the creature's appearance and behavior based on a user's emotions. For example, if the user is happy, the system suggests a brightly colored creature. This makes it possible to provide a more personalized experience by automatically customizing the creature's appearance and behavior according to the user's preferences.

[0044] The system can add a function for caring for creatures in cooperation with other users. For example, a system can be constructed that adds a function for caring for creatures in cooperation with other users. For example, multiple users can feed unicorns at the same time. The system can also set collaborative tasks so that users can cooperate in caring for creatures. For example, users can cooperate in cleaning a dragon's nest. The system can also use a real-time communication function to allow users to communicate with each other while caring for creatures. For example, users can cooperate in caring for creatures through chat or video calls. This allows users to cooperate with other users in caring for creatures, providing a more interactive experience.

[0045] The system can provide a function for customizing a creature that allows a user to reflect an appearance or behavior that the user has designed himself / herself. For example, the system is constructed to provide a system that provides a function for a user to reflect an appearance or behavior that the user has designed himself / herself in a creature. For example, the appearance of the creature can be changed based on a picture drawn by the user. The system can also provide a customization tool, allowing the user to design the appearance or behavior of the creature. For example, the user can use the customization tool to change the color or pattern of the creature. The system can also provide design templates, allowing the user to customize the appearance or behavior of the creature based on the template. For example, the user can select a template and change the appearance of the creature based on the template. This can provide a more personalized experience by reflecting the appearance and behavior that the user has designed himself / herself.

[0046] The system can automatically edit photos or videos taken by users to generate optimal content for sharing. For example, a system can be constructed that automatically edits photos and videos taken by users to generate optimal content for sharing. For example, a system can automatically edit a photo of a dinosaur taken by a user and post it to a social networking site. The system can also automatically edit photos using an image processing algorithm. For example, the system can adjust the brightness and contrast of a photo to generate an optimal image. The system can also automatically edit videos using video editing software. For example, the system can cut out unnecessary parts of a video to generate an optimal video. This allows the system to automatically edit photos and videos taken by users and generate optimal content for sharing, enabling more effective sharing.

[0047] The system can analyze other users' reactions to shared content and make improvement suggestions based on the feedback. For example, a system can be constructed that analyzes other users' reactions to shared content and makes improvement suggestions based on the feedback. For example, the system analyzes reactions to a unicorn photo posted by a user and makes improvement suggestions. The system can also analyze other users' reactions using comment analysis technology. For example, the system can analyze the content of comments and make suggestions for improvement. The system can also analyze other users' reactions using emotion analysis technology. For example, the system can analyze the emotions in comments and provide feedback. The system can also collect other users' opinions and make improvement suggestions using a feedback collection function. For example, the system can collect feedback through a survey and make suggestions for improvement. This makes it possible to generate more effective shared content by analyzing other users' reactions and making improvement suggestions based on the feedback.

[0048] The system can add a function that allows users to interact with other users in real time regarding shared content. For example, a system is constructed that adds a function that allows users to interact with other users in real time regarding shared content. For example, other users comment in real time on a photo of a dinosaur posted by a user. The system can also use a chat function to interact with other users in real time. For example, a user communicates with other users through chat. The system can also use a video call function to interact with other users in real time. For example, a user communicates with other users through video calls. This allows for a more interactive shared experience thanks to the function that allows users to interact with other users in real time.

[0049] The system may provide a function for automatically optimizing and posting shared content to different platforms or social networking sites. For example, a system may be constructed that provides a function for automatically optimizing and posting shared content to different platforms or social networking sites. For example, a system may automatically optimize a photo of a dinosaur taken by a user and post it to a social networking site. The system may also use format conversion technology to optimize shared content for different platforms. For example, the system may convert the format of a photo or video and post it in the optimal format. The system may also use a posting scheduling function to automatically post shared content. For example, the system may automatically post content at a time set by the user. This function for automatically optimizing and posting content to different platforms or social networking sites enables more effective sharing.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The system can monitor the health of the creature based on the user's past behavioral data and suggest appropriate care. For example, it can analyze how often the user feeds the creature and suggest the appropriate amount of food. It can also analyze how much time the user spends playing with the creature and suggest the appropriate amount of exercise. It can also analyze how often the user checks the creature's health and suggest regular health checks. This allows the system to monitor the creature's health based on the user's past behavioral data and suggest appropriate care, making it possible to raise healthier creatures.

[0052] The system can automatically customize the creature's appearance and behavior based on the user's preferences. For example, if the user prefers a particular color or pattern, the system can change the creature's appearance based on that information. Also, if the user prefers a particular behavior, the system can set the creature's behavior based on that information. Furthermore, if the user prefers a particular voice, the system can set the creature's voice based on that information. This allows the system to provide a more personalized experience by automatically customizing the creature's appearance and behavior based on the user's preferences.

[0053] The system can provide a function that allows a user to reflect the creature's appearance and behavior that the user has designed. For example, the user can change the creature's appearance based on a drawing drawn by the user. The user can also change the color and pattern of the creature using a customization tool. Furthermore, the user can select a template and customize the creature's appearance and behavior based on that template. This allows the user to reflect the creature's designed appearance and behavior, providing a more personalized experience.

[0054] The system can monitor the growth of a creature in real time based on the user's behavioral data and propose an appropriate growth plan. For example, it can analyze the amount of time the user spends playing with the creature and propose an appropriate growth plan. It can also analyze the type and amount of food the user gives the creature and propose an appropriate nutritional plan. It can also analyze how often the user checks the creature's health and propose regular health checks. This allows the system to monitor the growth of a creature in real time based on the user's behavioral data and propose an appropriate growth plan, making it possible to raise healthier creatures.

[0055] The system can automatically edit photos and videos taken by users to generate optimal content for sharing. For example, it can automatically edit a photo of a dinosaur taken by a user and post it to a social networking site. The system can also automatically edit photos using image processing algorithms. For example, it can adjust the brightness and contrast of a photo to generate an optimal image. Furthermore, the system can automatically edit videos using video editing software. For example, it can cut out unnecessary parts of a video to generate an optimal video. This allows the system to automatically edit photos and videos taken by users and generate optimal content for sharing, enabling more effective sharing.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The learning unit learns the user's preferences and behavior. For example, the learning unit analyzes the user's browsing history and purchase history to learn the user's preferences. It can also learn user behavior based on survey results. Furthermore, the learning unit can predict future behavior based on the user's past behavior data. Step 2: The re-creator recreates the fantastical creatures in real time based on the preferences and behaviors learned by the learner. For example, the re-creator can recreate dinosaurs and dragons around the user using AR technology, mammoths and unicorns using VR technology, and even a phoenix using 3D modeling technology. Step 3: The dialogue analysis unit analyzes the dialogue when the user interacts with the creature through the smartphone or AR glasses. For example, the dialogue analysis unit analyzes the user's voice commands using voice recognition technology. It can also analyze the user's dialogue using natural language processing technology. It can also analyze the user's emotions using emotion analysis technology.

[0058] (Example 2) An augmented reality (AR) pet cafe experience system according to an embodiment of the present invention is a system that can invite extinct animals and mythical creatures to provide a new experience to users. As a result, the augmented reality (AR) pet cafe experience system can provide users with interactive interactions with extinct animals and mythical creatures.

[0059] An augmented reality (AR) pet cafe experience system according to an embodiment includes a learning unit, a reproduction unit, and a dialogue analysis unit. The learning unit learns user preferences and behavior. For example, the learning unit analyzes a user's browsing history and purchase history to learn the user's preferences. The learning unit can also learn user behavior based on survey results. The learning unit can also predict future behavior based on the user's past behavior data. The reproduction unit recreates mythical creatures in real time based on the preferences and behavior learned by the learning unit. For example, the reproduction unit recreates dinosaurs and dragons around the user using AR technology. The reproduction unit can also recreate mammoths and unicorns using VR technology. The reproduction unit can also recreate phoenixes using 3D modeling technology. The dialogue analysis unit analyzes the dialogue when the user interacts with the creatures through a smartphone or AR glasses. For example, the dialogue analysis unit analyzes the user's voice commands using voice recognition technology. The dialogue analysis unit can also analyze the user's dialogue using natural language processing technology. The dialogue analysis unit can also analyze the user's emotions using emotion analysis technology, allowing the augmented reality (AR) pet cafe experience system according to the embodiment to provide a new experience to the user by recreating mythical creatures in real time based on the user's preferences and behavior and analyzing the dialogue.

[0060] The learning unit can analyze the user's emotional state in real time and suggest creatures that correspond to the emotion. The learning unit, for example, analyzes the user's facial expressions and vocal tone to estimate the emotional state in real time. For example, the learning unit analyzes the user's emotions using a camera or microphone, and if the user's emotions are strong, suggests creatures that the user likes. The learning unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on heart rate fluctuations and suggests creatures that correspond to the emotion. This makes it possible to suggest creatures that correspond to the user's emotional state.

[0061] The learning unit can predict future behavior based on the user's past behavioral data and generate corresponding creature behavior. The learning unit, for example, analyzes the user's past behavioral data and develops an algorithm for predicting future behavior. For example, it predicts which creature to suggest next based on what creatures the user has played with in the past. The learning unit can also predict the user's future behavior using a machine learning algorithm. For example, it predicts what action the user will take next based on what actions the user has taken in the past. The learning unit can also predict the user's future behavior using a statistical model. For example, it statistically predicts future behavior based on the user's past behavioral data. This makes it possible to predict the user's future behavior and generate corresponding creature behavior.

[0062] When learning a user's preferences, the learning unit can compare them with data from other users and suggest creatures that share a common interest. The learning unit, for example, collects data from other users and develops an algorithm to suggest creatures that share a common interest. For example, creatures that share a common interest can be suggested based on data from users who are interested in the same creatures. The learning unit can also collect social media data to learn a user's preferences. For example, it can analyze what creatures a user has expressed interest in on social media and suggest creatures that share a common interest. The learning unit can also compare the results of a survey with data from other users and suggest creatures that share a common interest. For example, it can analyze survey results and suggest creatures that share a common interest. In this way, it can suggest creatures that share a common interest by comparing them with data from other users.

[0063] The learning unit can utilize multimodal data such as voice or gestures when learning a user's preferences. The learning unit, for example, analyzes the user's voice data to build a system that learns the user's preferences. For example, it analyzes the content and tone of what the user says to suggest a favorite creature. The learning unit can also analyze the user's gesture data to learn the user's preferences. For example, it can analyze the user's hand movements and posture to suggest a favorite creature. The learning unit can also analyze gaze data to learn the user's preferences. For example, it can analyze which creature the user is looking at to suggest a favorite creature. In this way, by utilizing multimodal data such as voice and gestures, the user's preferences can be learned more accurately.

[0064] The learning unit can incorporate data from different cultural spheres or regions to learn user preferences and make suggestions from a global perspective. The learning unit, for example, collects data from different cultural spheres or regions and builds a system that learns user preferences. For example, it selects creatures to suggest to users based on popular creatures in each region. The learning unit can also collect consumer behavior data for each region and learn user preferences. For example, it analyzes consumer behavior data for each region and selects creatures to suggest to users. The learning unit can also collect cultural background data and learn user preferences. For example, it analyzes cultural background data for each region and selects creatures to suggest to users. In this way, by incorporating data from different cultural spheres and regions, it becomes possible to make suggestions from a global perspective.

[0065] The learning unit can use the emotion estimation function to track changes in preferences based on the user's emotions in real time and suggest the most suitable creature. The learning unit, for example, uses the emotion estimation function to build a system that tracks changes in preferences based on the user's emotions in real time. For example, it can suggest a favorite creature based on the user's emotion score. The learning unit can also use facial expression recognition technology to analyze the user's emotions and track changes in preferences. For example, it can analyze changes in the user's facial expressions and suggest a favorite creature. The learning unit can also use voice analysis technology to analyze the user's emotions and track changes in preferences. For example, it can analyze the tone and speed of the user's voice and suggest a favorite creature. This makes it possible to track changes in preferences based on the user's emotions in real time and suggest the most suitable creature.

[0066] The reproduction unit can perfectly synchronize the movements of the creature with the movements of the user, providing a realistic experience. The reproduction unit, for example, analyzes the user's movements in real time and builds a system that perfectly synchronizes the movements of the creature. For example, when the user waves their hand, the creature makes the same movement. The reproduction unit can also use motion capture technology to analyze the user's movements and synchronize the movements of the creature. For example, the user's body movements can be captured and reflected in the movements of the creature. The reproduction unit can also use real-time tracking technology to analyze the user's movements and synchronize the movements of the creature. For example, the user's movements can be tracked in real time and reflected in the movements of the creature. This allows the movements of the creature to be perfectly synchronized with the user's movements, providing a more realistic experience.

[0067] The reproduction unit can automatically adapt the creature's appearance or behavior according to the user's environment. For example, the reproduction unit analyzes the user's environmental data and builds a system that automatically adapts the creature's appearance and behavior. For example, when the user is outdoors, the creature's behavior becomes more active. The reproduction unit can also change the creature's appearance according to the indoor environment. For example, when the user is indoors, the reproduction unit can change the creature's color or pattern. The reproduction unit can also adapt the creature's behavior based on weather information. For example, on rainy days, the creature's behavior is reduced. This allows the creature's appearance and behavior to automatically adapt according to the user's environment, providing a more realistic experience.

[0068] The reproduction unit can reflect the user's emotional state when recreating the creature and generate behavior that corresponds to the emotion. The reproduction unit, for example, builds a system that analyzes the user's emotional state and generates behavior for the creature accordingly. For example, if the user is happy, the creature will also perform a happy behavior. The reproduction unit can also analyze the user's emotions using an emotion recognition algorithm and generate behavior for the creature. For example, it can analyze the user's facial expressions and voice and generate behavior that corresponds to the emotion. The reproduction unit can also generate behavior for the creature using emotion-based behavior generation technology. For example, the creature's behavior is set based on the user's emotion score. This allows the creature's behavior to reflect the user's emotional state, providing a more personalized experience.

[0069] The reproduction unit can add audio or haptic feedback to the reproduction of a creature to provide a multisensory experience. For example, the reproduction unit can add audio feedback to the reproduction of a creature, creating a system in which the creature responds when the user speaks to it. For example, when a user speaks to a dinosaur, the dinosaur responds with a roar. The reproduction unit can also add haptic feedback to the creature's movements using a haptic device. For example, when a user touches a dragon, the haptic device vibrates to provide haptic feedback. The reproduction unit can also generate the creature's voice using speech synthesis technology. For example, when a user speaks to a unicorn, the unicorn responds using speech synthesis technology. In this way, adding audio or haptic feedback can provide a more realistic experience.

[0070] The reproduction unit can reproduce interactions between different creatures and provide scenarios in which multiple creatures appear simultaneously. For example, the reproduction unit builds a system that reproduces interactions between different creatures. For example, it can provide a scenario in which a dinosaur and a dragon play together. The reproduction unit can also reproduce interactions between creatures using motion simulation technology. For example, it can provide a scenario in which a mammoth and a unicorn cooperate to complete a task. The reproduction unit can also reproduce interactions between creatures based on behavioral pattern settings. For example, it can provide a scenario in which a phoenix and a dragon compete. This makes it possible to provide a more diverse experience by reproducing interactions between different creatures.

[0071] The reproduction unit can use the emotion estimation function to customize the creature's behavior based on the user's emotions, providing a personalized experience. For example, the reproduction unit uses the emotion estimation function to build a system that customizes the creature's behavior based on the user's emotions. For example, if the user is happy, the creature will also perform a happy motion. The reproduction unit can also analyze the user's emotions using an emotion recognition algorithm and customize the creature's behavior. For example, it can analyze the user's facial expressions and voice and generate a motion according to the emotion. The reproduction unit can also customize the creature's behavior using emotion-based motion generation technology. For example, it can set the creature's behavior based on the user's emotion score. This makes it possible to provide a more personalized experience by customizing the creature's behavior based on the user's emotions.

[0072] The dialogue analysis unit analyzes the user's voice commands and uses natural language processing to enable the creatures to have natural conversations. The dialogue analysis unit, for example, analyzes the user's voice commands and uses natural language processing technology to build a system in which the creatures can have natural conversations. For example, when a user speaks to a dinosaur, the dinosaur responds in natural language. The dialogue analysis unit can also analyze the user's voice commands using voice recognition technology. For example, it analyzes the user's voice and generates an appropriate response. The dialogue analysis unit can also analyze the user's emotions using emotion analysis technology and reflect them in the dialogue. For example, if the user is happy, it generates a dialogue that reflects the emotion of joy. In this way, by analyzing the user's voice commands and using natural language processing, the creatures can have more natural conversations.

[0073] The dialogue analysis unit can analyze the user's movements in real time and cause the creature to react in accordance with those movements. The dialogue analysis unit, for example, builds a system that analyzes the user's movements in real time and causes the creature to react in accordance with those movements. For example, when the user waves their hand, the creature waves back. The dialogue analysis unit can also analyze the user's movements using motion capture technology. For example, the user's body movements can be captured and reflected in the creature's movements. The dialogue analysis unit can also analyze the user's movements using real-time tracking technology. For example, the user's movements can be tracked in real time and reflected in the creature's movements. This allows the creature to react in accordance with the user's movements, providing a more interactive experience.

[0074] The dialogue analysis unit can analyze the emotional state of a user and generate dialogue and play scenarios according to the emotions. The dialogue analysis unit, for example, builds a system that analyzes the emotional state of a user and generates dialogue and play scenarios according to the emotions. For example, if the user is happy, it provides a fun dialogue and play scenario. The dialogue analysis unit can also use facial expression recognition technology to analyze the user's emotions and reflect them in the scenario. For example, it can analyze changes in the user's facial expressions and generate a scenario according to the emotions. The dialogue analysis unit can also use voice analysis technology to analyze the user's emotions and reflect them in the scenario. For example, it can analyze the tone and speed of the user's voice and generate a scenario according to the emotions. This makes it possible to provide a more personalized experience by generating dialogue and play scenarios according to the user's emotional state.

[0075] The dialogue analysis unit can add a multi-user function that allows multiple users to participate simultaneously in dialogue via smartphones or AR glasses. The dialogue analysis unit builds a system that adds a multi-user function that allows multiple users to participate simultaneously in dialogue via smartphones or AR glasses. For example, multiple users interact with a dinosaur at the same time. The dialogue analysis unit can also use technology to achieve simultaneous connection of multiple users. For example, multiple users interact with a creature at the same time using real-time communication technology. The dialogue analysis unit can also use technology to achieve synchronization of interactions. For example, when multiple users play with a creature at the same time, the interactions are synchronized. This allows the multi-user function that allows multiple users to participate simultaneously to provide a more interactive experience.

[0076] The dialogue analysis unit can customize a dialogue or play scenario based on the user's past behavioral data. The dialogue analysis unit, for example, builds a system that customizes dialogue or play scenarios based on the user's past behavioral data. For example, the dialogue analysis unit generates the next scenario based on what creatures the user has played with in the past. The dialogue analysis unit can also analyze the user's behavior based on location information and customize a scenario. For example, a scenario is generated based on places the user has visited in the past. The dialogue analysis unit can also analyze the user's behavior based on app usage history and customize a scenario. For example, a scenario is generated based on data from apps the user has used in the past. In this way, a more personalized experience can be provided by customizing dialogue or play scenarios based on the user's past behavioral data.

[0077] The dialogue analysis unit can use the emotion estimation function to generate dialogue or play scenarios based on the user's emotions in real time. The dialogue analysis unit, for example, uses the emotion estimation function to build a system that generates dialogue or play scenarios based on the user's emotions in real time. For example, if the user is happy, it provides an enjoyable scenario. The dialogue analysis unit can also use facial expression recognition technology to analyze the user's emotions and reflect them in the scenario. For example, it can analyze changes in the user's facial expressions and generate a scenario based on the emotions. The dialogue analysis unit can also use voice analysis technology to analyze the user's emotions and reflect them in the scenario. For example, it can analyze the tone and speed of the user's voice and generate a scenario based on the emotions. This makes it possible to provide a more personalized experience by generating dialogue or play scenarios based on the user's emotions in real time.

[0078] The system can monitor the health or growth of a living creature in real time based on the user's behavioral data. For example, the system is constructed to monitor the health or growth of a living creature in real time based on the user's behavioral data. For example, the system may analyze how often the user feeds the unicorn to monitor its health. The system may also use biometrics technology to monitor the living creature's health. For example, the system may measure the living creature's heart rate and body temperature to monitor its health. The system may also use behavior analysis technology to monitor the living creature's growth. For example, the system may analyze the living creature's movements and behavior patterns to monitor its growth. This allows for more appropriate care by monitoring the living creature's health and growth in real time based on the user's behavioral data.

[0079] The system can automatically customize the appearance or behavior of a creature according to a user's preferences. For example, the system is constructed to automatically customize the appearance and behavior of a creature according to a user's preferences. For example, if a user prefers a particular color or pattern, the system changes the creature's appearance based on that information. The system can also customize the creature's behavior according to a user's preferences. For example, if a user prefers a particular behavior, the system sets the creature's behavior based on that information. The system can also customize the creature's appearance and behavior based on a user's emotions. For example, if the user is happy, the system suggests a brightly colored creature. This makes it possible to provide a more personalized experience by automatically customizing the creature's appearance and behavior according to the user's preferences.

[0080] The system can analyze the user's emotional state and make customization suggestions for the creature based on the emotion. For example, the system can be constructed to analyze the user's emotional state and make customization suggestions for the creature based on the emotion. For example, if the user is happy, a brightly colored creature is suggested. The system can also use facial expression recognition technology to analyze the user's emotions and make customization suggestions. For example, the system can analyze changes in the user's facial expression and suggest the creature's appearance and behavior based on the emotion. The system can also use voice analysis technology to analyze the user's emotions and make customization suggestions. For example, the system can analyze the tone and speed of the user's voice and suggest the creature's appearance and behavior based on the emotion. This allows for a more personalized experience by making customization suggestions for the creature based on the user's emotional state.

[0081] The system can add a function for caring for creatures in cooperation with other users. For example, a system can be constructed that adds a function for caring for creatures in cooperation with other users. For example, multiple users can feed unicorns at the same time. The system can also set collaborative tasks so that users can cooperate in caring for creatures. For example, users can cooperate in cleaning a dragon's nest. The system can also use a real-time communication function to allow users to communicate with each other while caring for creatures. For example, users can cooperate in caring for creatures through chat or video calls. This allows users to cooperate with other users in caring for creatures, providing a more interactive experience.

[0082] The system can provide a function for customizing a creature that allows a user to reflect an appearance or behavior that the user has designed himself / herself. For example, the system is constructed to provide a system that provides a function for a user to reflect an appearance or behavior that the user has designed himself / herself in a creature. For example, the appearance of the creature can be changed based on a picture drawn by the user. The system can also provide a customization tool, allowing the user to design the appearance or behavior of the creature. For example, the user can use the customization tool to change the color or pattern of the creature. The system can also provide design templates, allowing the user to customize the appearance or behavior of the creature based on the template. For example, the user can select a template and change the appearance of the creature based on the template. This can provide a more personalized experience by reflecting the appearance and behavior that the user has designed himself / herself.

[0083] The system can use an emotion estimation function to make customization suggestions for creatures based on the user's emotions in real time. For example, the system can use the emotion estimation function to build a system that makes customization suggestions for creatures based on the user's emotions in real time. For example, if the user is happy, a brightly colored creature is suggested. The system can also use facial expression recognition technology to analyze the user's emotions and make customization suggestions. For example, the system can analyze changes in the user's facial expression and suggest the creature's appearance and behavior according to the emotion. The system can also use voice analysis technology to analyze the user's emotions and make customization suggestions. For example, the system can analyze the tone and speed of the user's voice and suggest the creature's appearance and behavior according to the emotion. This makes it possible to provide a more personalized experience by making customization suggestions for creatures based on the user's emotions in real time.

[0084] The system can automatically edit photos or videos taken by users to generate optimal content for sharing. For example, a system can be constructed that automatically edits photos and videos taken by users to generate optimal content for sharing. For example, a system can automatically edit a photo of a dinosaur taken by a user and post it to a social networking site. The system can also automatically edit photos using an image processing algorithm. For example, the system can adjust the brightness and contrast of a photo to generate an optimal image. The system can also automatically edit videos using video editing software. For example, the system can cut out unnecessary parts of a video to generate an optimal video. This allows the system to automatically edit photos and videos taken by users and generate optimal content for sharing, enabling more effective sharing.

[0085] The system can analyze other users' reactions to shared content and make improvement suggestions based on the feedback. For example, a system can be constructed that analyzes other users' reactions to shared content and makes improvement suggestions based on the feedback. For example, the system analyzes reactions to a unicorn photo posted by a user and makes improvement suggestions. The system can also analyze other users' reactions using comment analysis technology. For example, the system can analyze the content of comments and make suggestions for improvement. The system can also analyze other users' reactions using emotion analysis technology. For example, the system can analyze the emotions in comments and provide feedback. The system can also collect other users' opinions and make improvement suggestions using a feedback collection function. For example, the system can collect feedback through a survey and make suggestions for improvement. This makes it possible to generate more effective shared content by analyzing other users' reactions and making improvement suggestions based on the feedback.

[0086] The system can analyze a user's emotional state and suggest content to be shared according to the emotion. For example, a system can be constructed that analyzes a user's emotional state and suggests content to be shared according to the emotion. For example, if the user is happy, the system can suggest sharing fun photos and videos. The system can also use facial expression recognition technology to analyze a user's emotions and suggest content to be shared. For example, the system can analyze changes in the user's facial expression and suggest content according to the emotion. The system can also use voice analysis technology to analyze a user's emotions and suggest content to be shared. For example, the system can analyze the tone and speed of the user's voice and suggest content according to the emotion. This enables more effective sharing by suggesting content to be shared according to the user's emotional state.

[0087] The system can add a function that allows users to interact with other users in real time regarding shared content. For example, a system is constructed that adds a function that allows users to interact with other users in real time regarding shared content. For example, other users comment in real time on a photo of a dinosaur posted by a user. The system can also use a chat function to interact with other users in real time. For example, a user communicates with other users through chat. The system can also use a video call function to interact with other users in real time. For example, a user communicates with other users through video calls. This allows for a more interactive shared experience thanks to the function that allows users to interact with other users in real time.

[0088] The system may provide a function for automatically optimizing and posting shared content to different platforms or social networking sites. For example, a system may be constructed that provides a function for automatically optimizing and posting shared content to different platforms or social networking sites. For example, a system may automatically optimize a photo of a dinosaur taken by a user and post it to a social networking site. The system may also use format conversion technology to optimize shared content for different platforms. For example, the system may convert the format of a photo or video and post it in the optimal format. The system may also use a posting scheduling function to automatically post shared content. For example, the system may automatically post content at a time set by the user. This function for automatically optimizing and posting content to different platforms or social networking sites enables more effective sharing.

[0089] The system can use an emotion estimation function to suggest content to be shared in real time based on a user's emotions. For example, the system can be constructed using the emotion estimation function to suggest content to be shared in real time based on a user's emotions. For example, if a user is happy, the system can suggest sharing fun photos and videos. The system can also use facial expression recognition technology to analyze a user's emotions and suggest content to be shared. For example, the system can analyze changes in a user's facial expression and suggest content that corresponds to the emotion. The system can also use voice analysis technology to analyze a user's emotions and suggest content to be shared. For example, the system can analyze the tone and speed of a user's voice and suggest content that corresponds to the emotion. This allows for more effective sharing by suggesting content to be shared in real time based on a user's emotions.

[0090] The system can use an emotion estimation function to suggest content to be shared in real time based on a user's emotions. For example, the system can be constructed using the emotion estimation function to suggest content to be shared in real time based on a user's emotions. For example, if a user is happy, the system can suggest sharing fun photos and videos. The system can also use facial expression recognition technology to analyze a user's emotions and suggest content to be shared. For example, the system can analyze changes in a user's facial expression and suggest content that corresponds to the emotion. The system can also use voice analysis technology to analyze a user's emotions and suggest content to be shared. For example, the system can analyze the tone and speed of a user's voice and suggest content that corresponds to the emotion. This allows for more effective sharing by suggesting content to be shared in real time based on a user's emotions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The system can estimate the user's emotions and customize the creature's behavior to suit the user's preferences based on the estimated emotions. For example, if the user is relaxed, the creature can perform calm movements. If the user is excited, the creature can perform active movements. Furthermore, if the user is sad, the creature can perform comforting movements. This allows the creature's behavior to be customized according to the user's emotions, providing a more personalized experience.

[0093] The system can estimate the user's emotions and suggest creatures that are suitable for the user based on the estimated emotions. For example, if the user is feeling stressed, the system can suggest creatures that have a relaxing effect. If the user is happy, the system can suggest creatures that will further enhance the user's joyful mood. Furthermore, if the user is feeling lonely, the system can suggest friendly creatures. This allows the system to provide a more personalized experience by suggesting creatures that correspond to the user's emotions.

[0094] The system can estimate the user's emotions and customize the creature's appearance based on the estimated emotions. For example, if the user is depressed, a brightly colored creature can be suggested. If the user is excited, a vividly colored creature can be suggested. Furthermore, if the user is relaxed, a calmly colored creature can be suggested. This allows the system to customize the creature's appearance according to the user's emotions, providing a more personalized experience.

[0095] The system can estimate the user's emotions and customize the voice of the creature based on the estimated emotions. For example, if the user is relaxed, a creature with a calm voice can be suggested. If the user is excited, a creature with a lively voice can be suggested. Furthermore, if the user is sad, a creature with a gentle voice can be suggested. This allows the system to provide a more personalized experience by customizing the voice of the creature according to the user's emotions.

[0096] The system can estimate the user's emotions and customize the creature's behavior based on the estimated emotions. For example, if the user is relaxed, the creature will behave calmly. If the user is excited, the creature will behave lively. Furthermore, if the user is sad, the creature can behave in a comforting manner. This allows the creature's behavior to be customized according to the user's emotions, providing a more personalized experience.

[0097] The system can monitor the health of the creature based on the user's past behavioral data and suggest appropriate care. For example, it can analyze how often the user feeds the creature and suggest the appropriate amount of food. It can also analyze how much time the user spends playing with the creature and suggest the appropriate amount of exercise. It can also analyze how often the user checks the creature's health and suggest regular health checks. This allows the system to monitor the creature's health based on the user's past behavioral data and suggest appropriate care, making it possible to raise healthier creatures.

[0098] The system can automatically customize the creature's appearance and behavior based on the user's preferences. For example, if the user prefers a particular color or pattern, the system can change the creature's appearance based on that information. Also, if the user prefers a particular behavior, the system can set the creature's behavior based on that information. Furthermore, if the user prefers a particular voice, the system can set the creature's voice based on that information. This allows the system to provide a more personalized experience by automatically customizing the creature's appearance and behavior based on the user's preferences.

[0099] The system can provide a function that allows a user to reflect the creature's appearance and behavior that the user has designed. For example, the user can change the creature's appearance based on a drawing drawn by the user. The user can also change the color and pattern of the creature using a customization tool. Furthermore, the user can select a template and customize the creature's appearance and behavior based on that template. This allows the user to reflect the creature's designed appearance and behavior, providing a more personalized experience.

[0100] The system can monitor the growth of a creature in real time based on the user's behavioral data and propose an appropriate growth plan. For example, it can analyze the amount of time the user spends playing with the creature and propose an appropriate growth plan. It can also analyze the type and amount of food the user gives the creature and propose an appropriate nutritional plan. It can also analyze how often the user checks the creature's health and propose regular health checks. This allows the system to monitor the growth of a creature in real time based on the user's behavioral data and propose an appropriate growth plan, making it possible to raise healthier creatures.

[0101] The system can automatically edit photos and videos taken by users to generate optimal content for sharing. For example, it can automatically edit a photo of a dinosaur taken by a user and post it to a social networking site. The system can also automatically edit photos using image processing algorithms. For example, it can adjust the brightness and contrast of a photo to generate an optimal image. Furthermore, the system can automatically edit videos using video editing software. For example, it can cut out unnecessary parts of a video to generate an optimal video. This allows the system to automatically edit photos and videos taken by users and generate optimal content for sharing, enabling more effective sharing.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The learning unit learns the user's preferences and behavior. For example, the learning unit analyzes the user's browsing history and purchase history to learn the user's preferences. It can also learn user behavior based on survey results. Furthermore, the learning unit can predict future behavior based on the user's past behavior data. Step 2: The re-creator recreates the fantastical creatures in real time based on the preferences and behaviors learned by the learner. For example, the re-creator can recreate dinosaurs and dragons around the user using AR technology, mammoths and unicorns using VR technology, and even a phoenix using 3D modeling technology. Step 3: The dialogue analysis unit analyzes the dialogue when the user interacts with the creature through the smartphone or AR glasses. For example, the dialogue analysis unit analyzes the user's voice commands using voice recognition technology. It can also analyze the user's dialogue using natural language processing technology. It can also analyze the user's emotions using emotion analysis technology.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

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

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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. [Explanation of symbols]

[0171] 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 learning unit that learns user preferences and behaviors; a reproduction unit that reproduces a fantasy creature in real time based on the preferences and behaviors learned by the learning unit; and a dialogue analysis unit that analyzes the dialogue when the user interacts with a living creature through the smartphone or the AR glasses. A system characterized by:

2. The learning unit Analyzing the emotional state of the user in real time and suggesting the creature according to the emotion 2. The system of claim 1.

3. The learning unit Leveraging multimodal voice or gesture data in learning the user's preferences 2. The system of claim 1.

4. The reproducing section The creature's movements are perfectly synchronized with the user's movements, providing a realistic experience 2. The system of claim 1.

5. The dialogue analysis unit Analyzing the user's voice commands and using natural language processing to have the creature engage in natural dialogue 2. The system of claim 1.

6. The system comprises: The health or growth of the living creature is monitored in real time based on the behavioral data of the user.

2. The system of claim 1.

7. The system comprises: Automatically edits photos or videos taken by the user to generate optimal sharing content 2. The system of claim 1.

8. The dialogue analysis unit Using an emotion estimation function, a dialogue or play scenario is generated in real time based on the user's emotions.

2. The system of claim 1.

Images