system

Abstract

The system according to the embodiment aims to prevent people from buying pets carelessly and to solve the problem of euthanasia. [Solution] A system according to an embodiment includes a simulation unit, a modeling unit, an experience unit, and an awareness-raising unit. The simulation unit allows a user to select a virtual pet and conducts a simulation before adopting the pet. The modeling unit models the behavioral patterns and reactions of the pet simulated by the simulation unit and creates a personality through machine learning. The experience unit allows the user to experience realistic pet behavior based on the personality created by the modeling unit. The awareness-raising unit uses generative AI to create educational content and storytelling related to pet care.

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 not providing sufficient effective means to prevent people from buying pets carelessly and to solve the problem of euthanasia.

[0005] The system according to the embodiment aims to prevent people from buying pets carelessly and to solve the problem of euthanasia. [Means for solving the problem]

[0006] The system according to the embodiment includes a simulation unit, a modeling unit, an experience unit, and an awareness-raising unit. The simulation unit allows a user to select a virtual pet and conducts a simulation before adopting the pet. The modeling unit models the behavioral patterns and reactions of the pet simulated by the simulation unit and creates a personality through machine learning. The experience unit allows the user to experience realistic pet behavior based on the personality created by the modeling unit. The awareness-raising unit uses a generation AI to provide educational content and storytelling related to pet care. [Effects of the Invention]

[0007] The system according to the embodiment can prevent people from buying pets carelessly and solve the problem of pet euthanasia. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A virtual pet system according to an embodiment of the present invention aims to prevent the careless purchase of dogs and cats in order to solve the problem of euthanasia. This virtual pet system is an application that allows users to simulate and test a pet before adopting it. Specifically, users can choose a virtual pet and simulate it before actually owning it. Virtual pets require training and respond to the owner's actions. For example, users can observe the pet's reactions by feeding it or taking it for walks. Furthermore, a generative AI is used to model the pet's behavioral patterns and reactions, and a personality is created through machine learning. This allows users to experience realistic pet behavior. Generative AI can also be used to create educational content and storytelling about pet care and disseminate information. For example, it provides stories about the precautions to take when caring for a pet and the responsibilities of owning a pet. Through this application, users can understand the responsibilities of owning a pet before adopting one and prevent careless purchases. Experiencing the pet's behavior and personality in advance also helps users choose the right pet. This allows the virtual pet system to run a simulation before the user gets a pet, allowing them to experience the pet's behavior and personality, and providing educational content on pet care, thereby discouraging users from making hasty purchases of pets.

[0029] A virtual pet system according to an embodiment includes a simulation unit, a modeling unit, an experience unit, and an education unit. The simulation unit allows a user to select a virtual pet and conduct a simulation before adopting the pet. The user can select a virtual pet such as a dog, cat, or bird. The simulation unit allows the user to check the pet's reactions by feeding the selected virtual pet and taking it for walks. For example, when the user feeds the virtual pet, the virtual pet eats and becomes more satisfied. When the user takes the virtual pet for walks, the virtual pet exercises and becomes healthier. The modeling unit uses a generation AI to model the pet's behavioral patterns and reactions and create a personality through machine learning. For example, the generation AI collects behavioral data of the pet and analyzes its behavioral patterns. Next, the generation AI creates the pet's personality based on the analysis results. For example, if the pet has an active personality, it will exhibit behaviors that favor play. On the other hand, if the pet has a calm personality, it will exhibit behaviors that favor quiet time. The experience unit allows the user to experience realistic pet behavior based on the personality formed by the modeling unit. For example, when a user plays with a virtual pet, the pet responds to the play and enjoys interacting with the user. Furthermore, when a user trains a virtual pet, the pet responds to the training and learns. The education unit uses a generating AI to provide educational content and storytelling related to pet care. For example, the education unit provides stories about precautions to take when raising a pet and the responsibilities of owning a pet. This allows users to understand the responsibilities before owning a pet and discourages careless purchases. Thus, the virtual pet system according to the embodiment allows users to simulate a pet before adopting it, experience its behavior and personality, and provide educational content related to care, thereby discouraging careless purchases.

[0030] The simulation unit allows a user to select a virtual pet, feed it, and take it for a walk, allowing the user to check the pet's reaction. For example, when a user selects a virtual pet and feeds it, the virtual pet eats and its satisfaction level increases. For example, when a user selects a virtual dog pet and feeds it dog food, the dog eats and its satisfaction level increases. When a user selects a virtual cat pet and feeds it cat food, the cat eats and its satisfaction level increases. When a user selects a virtual bird pet and feeds it bird food, the bird eats and its satisfaction level increases. Furthermore, when a user takes a virtual dog pet for a walk, the pet exercises and its health level improves. For example, when a user takes a virtual dog pet for a walk, the dog exercises and its health level improves. When a user takes a virtual cat pet for a walk, the cat exercises and its health level improves. When a user takes a virtual bird pet for a walk, the bird exercises and its health level improves. This allows the user to easily understand their pet's behavior by checking their reaction.

[0031] The modeling unit can model the behavioral patterns and reactions of a pet and form a personality through machine learning. The modeling unit uses a generation AI to model the behavioral patterns and reactions of a pet and form a personality through machine learning. For example, the generation AI collects behavioral data of a pet and analyzes its behavioral patterns. Next, the generation AI forms a personality of a pet based on the analysis results. For example, if a pet has an active personality, it will exhibit behaviors that favor play. On the other hand, if a pet has a calm personality, it will exhibit behaviors that favor quiet time. In this way, by modeling the behavioral patterns and reactions of a pet and forming a personality through machine learning, it is possible to reproduce realistic pet behavior.

[0032] The experience unit allows the user to experience realistic pet behavior. The experience unit allows the user to experience realistic pet behavior based on the personality formed by the modeling unit. For example, when a user plays with a virtual pet, the pet responds to the play and enjoys interacting with the user. Also, when a user trains a virtual pet, the pet responds to the training and learns. This allows the user to experience realistic pet behavior, making it easier for the user to understand the responsibilities of owning a pet before owning one.

[0033] The education department can provide information on precautions to take when raising a pet and stories about the responsibilities of owning a pet. The education department uses generative AI to create educational content and storytelling about pet ownership. For example, the education department can provide information on precautions to take when raising a pet and stories about the responsibilities of owning a pet. This helps users understand the responsibilities of owning a pet before owning one, preventing them from making a careless purchase.

[0034] The simulation unit can analyze the user's past pet-raising experience and select an appropriate simulation scenario. The simulation unit uses a generation AI to analyze the user's past pet-raising experience and select an appropriate simulation scenario. For example, if the user has previously owned a dog, a dog simulation scenario is provided preferentially. If the user has previously owned a cat, a cat simulation scenario is provided preferentially. If the user has never owned a pet before, a simulation scenario for beginners is provided. This allows the user to have a more realistic experience by providing the optimal simulation scenario based on the user's past pet-raising experience.

[0035] The simulation unit can adjust the behavioral patterns of the pet based on the user's lifestyle during the simulation. The simulation unit uses the generation AI to adjust the behavioral patterns of the pet based on the user's lifestyle during the simulation. For example, if the user is a morning person, the simulation unit sets the pet's behavioral patterns to be more active in the morning. If the user is a night owl, the simulation unit sets the pet's behavioral patterns to be more active in the evening. If the user has an irregular lifestyle, the simulation unit sets the pet's behavioral patterns randomly. This allows for a more realistic simulation by adjusting the pet's behavioral patterns based on the user's lifestyle.

[0036] The simulation unit can customize the behavior of the pet based on the user's geographic location information during the simulation. The simulation unit uses the generation AI to customize the behavior of the pet based on the user's geographic location information during the simulation. For example, if the user lives in an urban area, the simulation unit adapts the behavior of the pet to the urban environment. Also, if the user lives in a suburban area, the simulation unit adapts the behavior of the pet to the suburban environment. Also, if the user lives in a rural area, the simulation unit adapts the behavior of the pet to the rural environment. This allows for a more realistic simulation by customizing the behavior of the pet based on the user's geographic location information.

[0037] The simulation unit can analyze the user's social media activity during the simulation and provide a relevant simulation scenario. The simulation unit uses the generation AI to analyze the user's social media activity during the simulation and provide a relevant simulation scenario. For example, if the user posts a lot about pets, a pet simulation scenario is provided. Also, if the user does a lot of outdoor activities, a simulation scenario set in an outdoor environment is provided. Also, if the user does a lot of indoor activities, a simulation scenario set in an indoor environment is provided. In this way, by providing simulation scenarios based on the user's social media activity, the user can experience scenarios that are more interesting to them.

[0038] The modeling unit can improve the accuracy of personality formation based on the pet's past behavioral data during modeling. The modeling unit uses the generation AI to improve the accuracy of personality formation based on the pet's past behavioral data during modeling. For example, the modeling unit adjusts the personality formation algorithm based on the pet's past behavioral data. Also, the pet's past behavioral data is analyzed to extract personality formation patterns. Also, the pet's past behavioral data is referenced to improve the accuracy of personality formation. As a result, the accuracy of personality formation is improved by referencing the pet's past behavioral data.

[0039] The modeling unit can apply different personality formation algorithms to each type of pet during modeling. The modeling unit uses the generation AI to apply different personality formation algorithms to each type of pet during modeling. For example, a dog personality formation algorithm is applied to realistically reproduce a dog's personality. A cat personality formation algorithm is applied to realistically reproduce a cat's personality. A personality formation algorithm for other pets is also applied to realistically reproduce the personality of each pet. In this way, by applying different personality formation algorithms to each type of pet, the personality of each pet can be realistically reproduced.

[0040] The modeling unit can form a personality taking into account the health condition of the pet when modeling. The modeling unit uses a generation AI to form a personality taking into account the health condition of the pet when modeling. For example, if the pet is healthy, the personality is set to be active. If the pet is sick, the personality is set to be calm. If the pet is elderly, the personality is set to be calm. In this way, by forming a personality based on the health condition of the pet, a more realistic pet personality can be reproduced.

[0041] The modeling unit can improve the accuracy of personality formation by referring to pet-related literature during modeling. The modeling unit uses the generation AI to improve the accuracy of personality formation by referring to pet-related literature during modeling. For example, the modeling unit adjusts the personality formation algorithm based on pet-related literature. Also, pet-related literature is analyzed to extract personality formation patterns. Also, pet-related literature is referred to improve the accuracy of personality formation. In this way, by referring to pet-related literature, the accuracy of personality formation is improved.

[0042] The experience unit can provide an optimal experience scenario by referring to the user's past pet-keeping history during the experience. The experience unit uses the generation AI to provide an optimal experience scenario by referring to the user's past pet-keeping history during the experience. For example, if the user has previously owned a dog, a dog experience scenario is provided preferentially. Also, if the user has previously owned a cat, a cat experience scenario is provided preferentially. Also, if the user has never owned a pet before, an experience scenario for beginners is provided. In this way, by providing an optimal experience scenario based on the user's past pet-keeping history, the user can have a more realistic experience.

[0043] The experience unit can customize the pet's behavior based on the user's living environment during the experience. The experience unit uses the generation AI to customize the pet's behavior based on the user's living environment during the experience. For example, if the user lives in an urban area, the pet's behavior is adapted to the urban environment. Also, if the user lives in a suburban area, the pet's behavior is adapted to the suburban environment. Also, if the user lives in a rural area, the pet's behavior is adapted to the rural environment. This allows for a more realistic experience by customizing the pet's behavior based on the user's living environment.

[0044] The experience unit can customize the pet's behavior during the experience, taking into account the user's geographic location information. The experience unit uses the generation AI to customize the pet's behavior during the experience, taking into account the user's geographic location information. For example, if the user lives in an urban area, the pet's behavior is adapted to an urban environment. Also, if the user lives in a suburban area, the pet's behavior is adapted to a suburban environment. Also, if the user lives in a rural area, the pet's behavior is adapted to a rural environment. This allows for a more realistic experience by customizing the pet's behavior based on the user's geographic location information.

[0045] The experience unit can analyze the user's social media activity during the experience and provide related experience scenarios. The experience unit uses the generation AI to analyze the user's social media activity during the experience and provide related experience scenarios. For example, if the user posts a lot about pets, an experience scenario related to pets is provided. Also, if the user does a lot of outdoor activities, an experience scenario in an outdoor environment is provided. Also, if the user does a lot of indoor activities, an experience scenario in an indoor environment is provided. In this way, by providing experience scenarios based on the user's social media activity, the user can experience scenarios that are more interesting to them.

[0046] The awareness department can improve the accuracy of the content by referring to past awareness data during awareness generation. The awareness department uses the generative AI to improve the accuracy of the content by referring to past awareness data during awareness generation. For example, the content of the content is adjusted based on past awareness data. Also, past awareness data is analyzed to extract effective ways of expression. Also, the accuracy of the content is improved by referring to past awareness data. In this way, the accuracy of the content is improved by referring to past awareness data.

[0047] The awareness section can provide different awareness content for each type of pet during awareness raising. The awareness section uses the generation AI to provide different awareness content for each type of pet during awareness raising. For example, awareness content about dogs is provided, explaining precautions for raising dogs. Also, awareness content about cats is provided, explaining precautions for raising cats. Also, awareness content about other pets is provided, explaining precautions for raising each pet. In this way, by providing different awareness content for each type of pet, it is possible to provide information appropriate for each pet.

[0048] The awareness section can provide optimal awareness content by taking into account the user's geographical location information during awareness generation. The awareness section uses the generation AI to provide optimal awareness content by taking into account the user's geographical location information during awareness generation. For example, if the user lives in an urban area, awareness content suitable for urban environments is provided. Also, if the user lives in the suburbs, awareness content suitable for suburban environments is provided. Also, if the user lives in a rural area, awareness content suitable for rural environments is provided. In this way, by providing optimal awareness content based on the user's geographical location information, it is possible to provide content that is easier for the user to understand.

[0049] The awareness department can analyze the user's social media activity during awareness generation and provide relevant awareness content. The awareness department uses the generation AI to analyze the user's social media activity during awareness generation and provide relevant awareness content. For example, if the user posts a lot about pets, awareness content about pet care is provided. Also, if the user does a lot of outdoor activities, awareness content about pet care in outdoor environments is provided. Also, if the user does a lot of indoor activities, awareness content about pet care in indoor environments is provided. In this way, by providing awareness content based on the user's social media activity, it is possible to provide content that is more interesting to the user.

[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 virtual pet system may further include a pet health management unit. The health management unit monitors the pet's health condition and suggests appropriate care. For example, it records the pet's weight and food intake and provides advice to prevent overweight and malnutrition. It can also track the pet's exercise volume and suggest an appropriate exercise plan. Furthermore, it can send reminders for regular health checks based on the pet's health condition. This makes it easier for users to maintain their pet's health.

[0052] The simulation unit can customize the behavior of the pet taking into account the user's family structure. For example, if the user has children, the pet's behavior can be set to be gentle toward the children. Also, if the user lives with elderly people, the pet's behavior can be set to be gentle. Furthermore, if the user lives alone, the pet's behavior can be set to be more interactive. This allows for a more realistic experience by simulating the pet's behavior according to the user's family structure.

[0053] The modeling unit can change the behavioral patterns of pets according to the seasons. For example, pets may prefer cool places in summer and warm places in winter. Also, pets may prefer playing outside in spring and spending time indoors in autumn. Furthermore, advice on health care for pets according to the seasons can be provided. By recreating pet behavior according to the seasons, users can have a more realistic experience.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The simulation unit allows the user to select a virtual pet and conducts a simulation before welcoming the pet. The user can select a virtual pet such as a dog, cat, or bird. The simulation unit can check the pet's reaction by feeding the virtual pet selected by the user and taking it for walks. For example, when the user feeds the virtual pet, the virtual pet eats and becomes happier. Also, when the user takes the virtual pet for walks, the virtual pet exercises and becomes healthier. Step 2: The modeling unit uses the generation AI to model the pet's behavioral patterns and reactions, and forms a personality through machine learning. For example, the generation AI collects data on the pet's behavior and analyzes its behavioral patterns. Next, the generation AI forms the pet's personality based on the analysis results. For example, if the pet has an active personality, it will exhibit behaviors that favor play. On the other hand, if the pet has a calm personality, it will exhibit behaviors that favor quiet time. Step 3: The experience unit allows the user to experience realistic pet behavior based on the personality formed by the modeling unit. For example, when the user plays with the virtual pet, the pet responds to the play and enjoys interacting with the user. Also, when the user trains the virtual pet, the pet responds to the training and learns. Step 4: The education department uses generative AI to create educational content and storytelling about pet ownership. For example, the education department provides stories about the precautions to take when raising a pet and the responsibilities of owning a pet. This helps users understand the responsibilities before owning a pet and discourages them from making a careless purchase.

[0056] (Example 2) A virtual pet system according to an embodiment of the present invention aims to prevent the careless purchase of dogs and cats in order to solve the problem of euthanasia. This virtual pet system is an application that allows users to simulate and test a pet before adopting it. Specifically, users can choose a virtual pet and simulate it before actually owning it. Virtual pets require training and respond to the owner's actions. For example, users can observe the pet's reactions by feeding it or taking it for walks. Furthermore, a generative AI is used to model the pet's behavioral patterns and reactions, and a personality is created through machine learning. This allows users to experience realistic pet behavior. Generative AI can also be used to create educational content and storytelling about pet care and disseminate information. For example, it provides stories about the precautions to take when caring for a pet and the responsibilities of owning a pet. Through this application, users can understand the responsibilities of owning a pet before adopting one and prevent careless purchases. Experiencing the pet's behavior and personality in advance also helps users choose the right pet. This allows the virtual pet system to run a simulation before the user gets a pet, allowing them to experience the pet's behavior and personality, and providing educational content on pet care, thereby discouraging users from making hasty purchases of pets.

[0057] A virtual pet system according to an embodiment includes a simulation unit, a modeling unit, an experience unit, and an education unit. The simulation unit allows a user to select a virtual pet and conduct a simulation before adopting the pet. The user can select a virtual pet such as a dog, cat, or bird. The simulation unit allows the user to check the pet's reactions by feeding the selected virtual pet and taking it for walks. For example, when the user feeds the virtual pet, the virtual pet eats and becomes more satisfied. When the user takes the virtual pet for walks, the virtual pet exercises and becomes healthier. The modeling unit uses a generation AI to model the pet's behavioral patterns and reactions and create a personality through machine learning. For example, the generation AI collects behavioral data of the pet and analyzes its behavioral patterns. Next, the generation AI creates the pet's personality based on the analysis results. For example, if the pet has an active personality, it will exhibit behaviors that favor play. On the other hand, if the pet has a calm personality, it will exhibit behaviors that favor quiet time. The experience unit allows the user to experience realistic pet behavior based on the personality formed by the modeling unit. For example, when a user plays with a virtual pet, the pet responds to the play and enjoys interacting with the user. Furthermore, when a user trains a virtual pet, the pet responds to the training and learns. The education unit uses a generating AI to provide educational content and storytelling related to pet care. For example, the education unit provides stories about precautions to take when raising a pet and the responsibilities of owning a pet. This allows users to understand the responsibilities before owning a pet and discourages careless purchases. Thus, the virtual pet system according to the embodiment allows users to simulate a pet before adopting it, experience its behavior and personality, and provide educational content related to care, thereby discouraging careless purchases.

[0058] The simulation unit allows a user to select a virtual pet, feed it, and take it for a walk, allowing the user to check the pet's reaction. For example, when a user selects a virtual pet and feeds it, the virtual pet eats and its satisfaction level increases. For example, when a user selects a virtual dog pet and feeds it dog food, the dog eats and its satisfaction level increases. When a user selects a virtual cat pet and feeds it cat food, the cat eats and its satisfaction level increases. When a user selects a virtual bird pet and feeds it bird food, the bird eats and its satisfaction level increases. Furthermore, when a user takes a virtual dog pet for a walk, the pet exercises and its health level improves. For example, when a user takes a virtual dog pet for a walk, the dog exercises and its health level improves. When a user takes a virtual cat pet for a walk, the cat exercises and its health level improves. When a user takes a virtual bird pet for a walk, the bird exercises and its health level improves. This allows the user to easily understand their pet's behavior by checking their reaction.

[0059] The modeling unit can model the behavioral patterns and reactions of a pet and form a personality through machine learning. The modeling unit uses a generation AI to model the behavioral patterns and reactions of a pet and form a personality through machine learning. For example, the generation AI collects behavioral data of a pet and analyzes its behavioral patterns. Next, the generation AI forms a personality of a pet based on the analysis results. For example, if a pet has an active personality, it will exhibit behaviors that favor play. On the other hand, if a pet has a calm personality, it will exhibit behaviors that favor quiet time. In this way, by modeling the behavioral patterns and reactions of a pet and forming a personality through machine learning, it is possible to reproduce realistic pet behavior.

[0060] The experience unit allows the user to experience realistic pet behavior. The experience unit allows the user to experience realistic pet behavior based on the personality formed by the modeling unit. For example, when a user plays with a virtual pet, the pet responds to the play and enjoys interacting with the user. Also, when a user trains a virtual pet, the pet responds to the training and learns. This allows the user to experience realistic pet behavior, making it easier for the user to understand the responsibilities of owning a pet before owning one.

[0061] The education department can provide information on precautions to take when raising a pet and stories about the responsibilities of owning a pet. The education department uses generative AI to create educational content and storytelling about pet ownership. For example, the education department can provide information on precautions to take when raising a pet and stories about the responsibilities of owning a pet. This helps users understand the responsibilities of owning a pet before owning one, preventing them from making a careless purchase.

[0062] The simulation unit can estimate the user's emotions and adjust the difficulty of the simulation based on the estimated user's emotions. The simulation unit uses the generation AI to estimate the user's emotions and adjust the difficulty of the simulation based on the estimated user's emotions. For example, if the user is feeling stressed, the simulation difficulty is set low and an easy task is provided. If the user is relaxed, the simulation difficulty is set high and a complex task is provided. If the user is excited, the simulation difficulty is set medium and a balanced task is provided. In this way, by adjusting the difficulty of the simulation according to the user's emotions, the user can perform the simulation without feeling stressed.

[0063] The simulation unit can analyze the user's past pet-raising experience and select an appropriate simulation scenario. The simulation unit uses a generation AI to analyze the user's past pet-raising experience and select an appropriate simulation scenario. For example, if the user has previously owned a dog, a dog simulation scenario is provided preferentially. If the user has previously owned a cat, a cat simulation scenario is provided preferentially. If the user has never owned a pet before, a simulation scenario for beginners is provided. This allows the user to have a more realistic experience by providing the optimal simulation scenario based on the user's past pet-raising experience.

[0064] The simulation unit can adjust the behavioral patterns of the pet based on the user's lifestyle during the simulation. The simulation unit uses the generation AI to adjust the behavioral patterns of the pet based on the user's lifestyle during the simulation. For example, if the user is a morning person, the simulation unit sets the pet's behavioral patterns to be more active in the morning. If the user is a night owl, the simulation unit sets the pet's behavioral patterns to be more active in the evening. If the user has an irregular lifestyle, the simulation unit sets the pet's behavioral patterns randomly. This allows for a more realistic simulation by adjusting the pet's behavioral patterns based on the user's lifestyle.

[0065] The simulation unit can estimate the user's emotions and adjust the feedback of the simulation based on the estimated user's emotions. The simulation unit uses the generation AI to estimate the user's emotions and adjust the feedback of the simulation based on the estimated user's emotions. For example, if the user is feeling stressed, it provides a lot of positive feedback. If the user is relaxed, it provides balanced feedback. If the user is excited, it provides quick feedback. In this way, by adjusting the feedback according to the user's emotions, the user can receive more appropriate feedback.

[0066] The simulation unit can customize the behavior of the pet based on the user's geographic location information during the simulation. The simulation unit uses the generation AI to customize the behavior of the pet based on the user's geographic location information during the simulation. For example, if the user lives in an urban area, the simulation unit adapts the behavior of the pet to the urban environment. Also, if the user lives in a suburban area, the simulation unit adapts the behavior of the pet to the suburban environment. Also, if the user lives in a rural area, the simulation unit adapts the behavior of the pet to the rural environment. This allows for a more realistic simulation by customizing the behavior of the pet based on the user's geographic location information.

[0067] The simulation unit can analyze the user's social media activity during the simulation and provide a relevant simulation scenario. The simulation unit uses the generation AI to analyze the user's social media activity during the simulation and provide a relevant simulation scenario. For example, if the user posts a lot about pets, a pet simulation scenario is provided. Also, if the user does a lot of outdoor activities, a simulation scenario set in an outdoor environment is provided. Also, if the user does a lot of indoor activities, a simulation scenario set in an indoor environment is provided. In this way, by providing simulation scenarios based on the user's social media activity, the user can experience scenarios that are more interesting to them.

[0068] The modeling unit can estimate the user's emotions and adjust the pet's personality based on the estimated user's emotions. The modeling unit uses a generation AI to estimate the user's emotions and adjust the pet's personality based on the estimated user's emotions. For example, if the user is relaxed, the pet's personality is set to be calm. If the user is stressed, the pet's personality is set to be soothing. If the user is excited, the pet's personality is set to be active. In this way, by adjusting the pet's personality based on the user's emotions, a more realistic pet personality can be reproduced.

[0069] The modeling unit can improve the accuracy of personality formation based on the pet's past behavioral data during modeling. The modeling unit uses the generation AI to improve the accuracy of personality formation based on the pet's past behavioral data during modeling. For example, the modeling unit adjusts the personality formation algorithm based on the pet's past behavioral data. Also, the pet's past behavioral data is analyzed to extract personality formation patterns. Also, the pet's past behavioral data is referenced to improve the accuracy of personality formation. As a result, the accuracy of personality formation is improved by referencing the pet's past behavioral data.

[0070] The modeling unit can apply different personality formation algorithms to each type of pet during modeling. The modeling unit uses the generation AI to apply different personality formation algorithms to each type of pet during modeling. For example, a dog personality formation algorithm is applied to realistically reproduce a dog's personality. A cat personality formation algorithm is applied to realistically reproduce a cat's personality. A personality formation algorithm for other pets is also applied to realistically reproduce the personality of each pet. In this way, by applying different personality formation algorithms to each type of pet, the personality of each pet can be realistically reproduced.

[0071] The modeling unit can estimate the user's emotions and adjust the pet's behavior pattern based on the estimated user's emotions. The modeling unit uses a generation AI to estimate the user's emotions and adjust the pet's behavior pattern based on the estimated user's emotions. For example, if the user is relaxed, the pet's behavior pattern is set to be calm. If the user is stressed, the pet's behavior pattern is set to be soothing. If the user is excited, the pet's behavior pattern is set to be active. In this way, by adjusting the pet's behavior pattern according to the user's emotions, more realistic pet behavior can be reproduced.

[0072] The modeling unit can form a personality taking into account the health condition of the pet when modeling. The modeling unit uses a generation AI to form a personality taking into account the health condition of the pet when modeling. For example, if the pet is healthy, the personality is set to be active. If the pet is sick, the personality is set to be calm. If the pet is elderly, the personality is set to be calm. In this way, by forming a personality based on the health condition of the pet, a more realistic pet personality can be reproduced.

[0073] The modeling unit can improve the accuracy of personality formation by referring to pet-related literature during modeling. The modeling unit uses the generation AI to improve the accuracy of personality formation by referring to pet-related literature during modeling. For example, the modeling unit adjusts the personality formation algorithm based on pet-related literature. Also, pet-related literature is analyzed to extract personality formation patterns. Also, pet-related literature is referred to improve the accuracy of personality formation. In this way, by referring to pet-related literature, the accuracy of personality formation is improved.

[0074] The experience unit can estimate the user's emotions and adjust the reality of the experience based on the estimated user emotions. The experience unit uses the generation AI to estimate the user's emotions and adjust the reality of the experience based on the estimated user emotions. For example, if the user is relaxed, the reality of the experience is set high. If the user is feeling stressed, the reality of the experience is set low. If the user is excited, the reality of the experience is set to medium. In this way, the reality of the experience can be adjusted according to the user's emotions, allowing the user to have a more appropriate experience.

[0075] The experience unit can provide an optimal experience scenario by referring to the user's past pet-keeping history during the experience. The experience unit uses the generation AI to provide an optimal experience scenario by referring to the user's past pet-keeping history during the experience. For example, if the user has previously owned a dog, a dog experience scenario is provided preferentially. Also, if the user has previously owned a cat, a cat experience scenario is provided preferentially. Also, if the user has never owned a pet before, an experience scenario for beginners is provided. In this way, by providing an optimal experience scenario based on the user's past pet-keeping history, the user can have a more realistic experience.

[0076] The experience unit can customize the pet's behavior based on the user's living environment during the experience. The experience unit uses the generation AI to customize the pet's behavior based on the user's living environment during the experience. For example, if the user lives in an urban area, the pet's behavior is adapted to the urban environment. Also, if the user lives in a suburban area, the pet's behavior is adapted to the suburban environment. Also, if the user lives in a rural area, the pet's behavior is adapted to the rural environment. This allows for a more realistic experience by customizing the pet's behavior based on the user's living environment.

[0077] The experience unit can estimate the user's emotions and adjust the experience feedback based on the estimated user emotions. The experience unit uses a generation AI to estimate the user's emotions and adjust the experience feedback based on the estimated user emotions. For example, if the user is feeling stressed, it provides a lot of positive feedback. If the user is relaxed, it provides balanced feedback. If the user is excited, it provides quick feedback. In this way, by adjusting the feedback according to the user's emotions, the user can receive more appropriate feedback.

[0078] The experience unit can customize the pet's behavior during the experience, taking into account the user's geographic location information. The experience unit uses the generation AI to customize the pet's behavior during the experience, taking into account the user's geographic location information. For example, if the user lives in an urban area, the pet's behavior is adapted to an urban environment. Also, if the user lives in a suburban area, the pet's behavior is adapted to a suburban environment. Also, if the user lives in a rural area, the pet's behavior is adapted to a rural environment. This allows for a more realistic experience by customizing the pet's behavior based on the user's geographic location information.

[0079] The experience unit can analyze the user's social media activity during the experience and provide related experience scenarios. The experience unit uses the generation AI to analyze the user's social media activity during the experience and provide related experience scenarios. For example, if the user posts a lot about pets, an experience scenario related to pets is provided. Also, if the user does a lot of outdoor activities, an experience scenario in an outdoor environment is provided. Also, if the user does a lot of indoor activities, an experience scenario in an indoor environment is provided. In this way, by providing experience scenarios based on the user's social media activity, the user can experience scenarios that are more interesting to them.

[0080] The awareness unit can estimate the user's emotions and adjust the way the awareness content is presented based on the estimated user emotions. The awareness unit uses the generation AI to estimate the user's emotions and adjust the way the awareness content is presented based on the estimated user emotions. For example, if the user is relaxed, the awareness content is provided using a calm expression method. If the user is feeling stressed, the awareness content is provided using a concise and easy-to-understand expression method. If the user is excited, the awareness content is provided using a visually stimulating expression method. In this way, by adjusting the way the awareness content is presented according to the user's emotions, it is possible to provide content that is easier for the user to understand.

[0081] The awareness department can improve the accuracy of the content by referring to past awareness data during awareness generation. The awareness department uses the generative AI to improve the accuracy of the content by referring to past awareness data during awareness generation. For example, the content of the content is adjusted based on past awareness data. Also, past awareness data is analyzed to extract effective ways of expression. Also, the accuracy of the content is improved by referring to past awareness data. In this way, the accuracy of the content is improved by referring to past awareness data.

[0082] The awareness section can provide different awareness content for each type of pet during awareness raising. The awareness section uses the generation AI to provide different awareness content for each type of pet during awareness raising. For example, awareness content about dogs is provided, explaining precautions for raising dogs. Also, awareness content about cats is provided, explaining precautions for raising cats. Also, awareness content about other pets is provided, explaining precautions for raising each pet. In this way, by providing different awareness content for each type of pet, it is possible to provide information appropriate for each pet.

[0083] The awareness unit can estimate the user's emotions and adjust the length of the awareness content based on the estimated user emotions. The awareness unit uses the generation AI to estimate the user's emotions and adjust the length of the awareness content based on the estimated user emotions. For example, if the user is relaxed, longer awareness content is provided. If the user is stressed, short and to the point awareness content is provided. If the user is excited, medium-length awareness content is provided. In this way, by adjusting the length of the awareness content according to the user's emotions, content that is easier for the user to understand can be provided.

[0084] The awareness section can provide optimal awareness content by taking into account the user's geographical location information during awareness generation. The awareness section uses the generation AI to provide optimal awareness content by taking into account the user's geographical location information during awareness generation. For example, if the user lives in an urban area, awareness content suitable for urban environments is provided. Also, if the user lives in the suburbs, awareness content suitable for suburban environments is provided. Also, if the user lives in a rural area, awareness content suitable for rural environments is provided. In this way, by providing optimal awareness content based on the user's geographical location information, it is possible to provide content that is easier for the user to understand.

[0085] The awareness department can analyze the user's social media activity during awareness generation and provide relevant awareness content. The awareness department uses the generation AI to analyze the user's social media activity during awareness generation and provide relevant awareness content. For example, if the user posts a lot about pets, awareness content about pet care is provided. Also, if the user does a lot of outdoor activities, awareness content about pet care in outdoor environments is provided. Also, if the user does a lot of indoor activities, awareness content about pet care in indoor environments is provided. In this way, by providing awareness content based on the user's social media activity, it is possible to provide content that is more interesting to the user. === Hard Collateral 1-1 === Each of the multiple elements, including the simulation unit, modeling unit, experience unit, and education unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the simulation unit is realized by the control unit 46A of the smart device 14 and simulates a user selecting a virtual pet and feeding and taking the pet for walks. The modeling unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses generative AI to model the pet's behavioral patterns and reactions and create a personality. The experience unit is realized, for example, by the control unit 46A of the smart device 14 and allows the user to experience realistic pet behavior. The education unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides educational content and storytelling related to pet care. === Hard Collateral 1-2 === Each of the multiple elements, including the simulation unit, modeling unit, experience unit, and education unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the simulation unit is realized by the control unit 46A of the smart glasses 214 and simulates a user selecting a virtual pet and feeding and taking the pet for walks. The modeling unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses generative AI to model the pet's behavioral patterns and reactions and create a personality. The experience unit is realized, for example, by the control unit 46A of the smart glasses 214 and allows the user to experience realistic pet behavior. The education unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides educational content and storytelling related to pet care. === Hard Collateral 1-3 === Each of the multiple elements, including the simulation unit, modeling unit, experience unit, and education unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the simulation unit is realized by the control unit 46A of the headset-type terminal 314 and simulates a user selecting a virtual pet and feeding and taking the pet for walks. The modeling unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses a generative AI to model the pet's behavioral patterns and reactions and create a personality. The experience unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and allows the user to experience realistic pet behavior. The education unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides educational content and storytelling related to pet care. === Hard Collateral 1-4 === Each of the multiple elements, including the simulation unit, modeling unit, experience unit, and education unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the simulation unit is realized by the control unit 46A of the robot 414 and simulates a user selecting a virtual pet and feeding and taking the pet for walks. The modeling unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses generative AI to model the pet's behavioral patterns and reactions and create a personality. The experience unit is realized, for example, by the control unit 46A of the robot 414 and allows the user to experience realistic pet behavior. The education unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides educational content and storytelling related to pet care.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The virtual pet system may further include a pet health management unit. The health management unit monitors the pet's health condition and suggests appropriate care. For example, it records the pet's weight and food intake and provides advice to prevent overweight and malnutrition. It can also track the pet's exercise volume and suggest an appropriate exercise plan. Furthermore, it can send reminders for regular health checks based on the pet's health condition. This makes it easier for users to maintain their pet's health.

[0088] The simulation unit can customize the behavior of the pet taking into account the user's family structure. For example, if the user has children, the pet's behavior can be set to be gentle toward the children. Also, if the user lives with elderly people, the pet's behavior can be set to be gentle. Furthermore, if the user lives alone, the pet's behavior can be set to be more interactive. This allows for a more realistic experience by simulating the pet's behavior according to the user's family structure.

[0089] The modeling unit can change the behavioral patterns of pets according to the seasons. For example, pets may prefer cool places in summer and warm places in winter. Also, pets may prefer playing outside in spring and spending time indoors in autumn. Furthermore, advice on health care for pets according to the seasons can be provided. By recreating pet behavior according to the seasons, users can have a more realistic experience.

[0090] The experience unit can estimate the user's feelings toward the pet and adjust the pet's behavior based on the estimated feelings. For example, if the user feels affection toward the pet, the pet may exhibit more affectionate behavior. If the user feels anxious toward the pet, the pet may exhibit calm behavior. Furthermore, if the user is excited toward the pet, the pet may exhibit active behavior. This allows the user to experience the pet's behavior according to the user's emotions, enabling more realistic interactions.

[0091] The awareness unit can estimate the user's feelings toward the pet and adjust the tone of the awareness content based on the estimated feelings. For example, if the user feels affection toward the pet, the awareness content can be provided in an inspiring tone. If the user feels anxious about the pet, the awareness content can be provided in a reassuring tone. Furthermore, if the user is excited about the pet, the awareness content can be provided in an energetic tone. This allows for more effective awareness by providing awareness content that matches the user's feelings.

[0092] The simulation unit can estimate the user's feelings toward the pet and adjust the simulation feedback based on the estimated feelings. For example, if the user feels affection toward the pet, it can provide a lot of positive feedback. If the user feels anxious toward the pet, it can provide feedback that gives a sense of security. Furthermore, if the user is excited toward the pet, it can also provide quick feedback. This allows for a more appropriate simulation experience by providing feedback according to the user's feelings.

[0093] The simulation unit can estimate the user's feelings toward the pet and adjust the difficulty of the simulation based on the estimated feelings. For example, if the user feels affection toward the pet, the simulation difficulty can be set low and an easy task can be provided. Alternatively, if the user feels anxious toward the pet, the simulation difficulty can be set medium and a balanced task can be provided. Furthermore, if the user is excited toward the pet, the simulation difficulty can be set high and a complex task can be provided. This allows for a more appropriate simulation experience by adjusting the difficulty of the simulation according to the user's feelings.

[0094] The simulation unit can estimate the user's feelings toward their pet and select a simulation scenario based on the estimated feelings. For example, if the user feels affection toward their pet, a scenario involving playing with the pet or relaxing can be provided. If the user feels anxious about their pet, a scenario regarding pet care and training can be provided. Furthermore, if the user is excited about their pet, an active scenario can be provided. This allows for a more appropriate simulation experience by providing a simulation scenario that matches the user's feelings.

[0095] The simulation unit can estimate the user's feelings toward the pet and adjust the simulation feedback based on the estimated feelings. For example, if the user feels affection toward the pet, it can provide a lot of positive feedback. If the user feels anxious toward the pet, it can provide feedback that gives a sense of security. Furthermore, if the user is excited toward the pet, it can also provide quick feedback. This allows for a more appropriate simulation experience by providing feedback according to the user's feelings.

[0096] The simulation unit can estimate the user's feelings toward the pet and adjust the difficulty of the simulation based on the estimated feelings. For example, if the user feels affection toward the pet, the simulation difficulty can be set low and an easy task can be provided. Alternatively, if the user feels anxious toward the pet, the simulation difficulty can be set medium and a balanced task can be provided. Furthermore, if the user is excited toward the pet, the simulation difficulty can be set high and a complex task can be provided. This allows for a more appropriate simulation experience by adjusting the difficulty of the simulation according to the user's feelings.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The simulation unit allows the user to select a virtual pet and conducts a simulation before welcoming the pet. The user can select a virtual pet such as a dog, cat, or bird. The simulation unit can check the pet's reaction by feeding the virtual pet selected by the user and taking it for walks. For example, when the user feeds the virtual pet, the virtual pet eats and becomes happier. Also, when the user takes the virtual pet for walks, the virtual pet exercises and becomes healthier. Step 2: The modeling unit uses the generation AI to model the pet's behavioral patterns and reactions, and forms a personality through machine learning. For example, the generation AI collects data on the pet's behavior and analyzes its behavioral patterns. Next, the generation AI forms the pet's personality based on the analysis results. For example, if the pet has an active personality, it will exhibit behaviors that favor play. On the other hand, if the pet has a calm personality, it will exhibit behaviors that favor quiet time. Step 3: The experience unit allows the user to experience realistic pet behavior based on the personality formed by the modeling unit. For example, when the user plays with the virtual pet, the pet responds to the play and enjoys interacting with the user. Also, when the user trains the virtual pet, the pet responds to the training and learns. Step 4: The education department uses generative AI to create educational content and storytelling about pet ownership. For example, the education department provides stories about the precautions to take when raising a pet and the responsibilities of owning a pet. This helps users understand the responsibilities before owning a pet and discourages them from making a careless purchase.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0101] 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.

[0102] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0147] 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.

[0148] 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.

[0149] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] [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 simulation unit that allows a user to select a virtual pet and performs a simulation before welcoming the pet; a modeling unit that models the behavioral patterns and reactions of the pet simulated by the simulation unit and forms a personality by machine learning; an experience unit that allows a user to experience realistic pet behavior based on the personality formed by the modeling unit; An educational unit that uses generation AI to create educational content and storytelling related to pet care. A system characterized by:

2. The simulation unit Users can choose a virtual pet, feed it, take it for walks and see how it responds. The system of claim 1 .

3. The modeling unit Model your pet's behavioral patterns and reactions, and use machine learning to create a personality The system of claim 1 .

4. The experience section includes: Allowing users to experience realistic pet behavior The system of claim 1 .

5. The awareness department: Providing stories about the precautions and responsibilities of owning a pet The system of claim 1 .

6. The simulation unit Estimate the user's emotions and adjust the difficulty of the simulation based on the estimated user emotions. The system of claim 1 .

7. The simulation unit Analyze the user's past pet-keeping experience and select an appropriate simulation scenario The system of claim 1 .

8. The simulation unit During the simulation, the pet's behavior patterns are adjusted based on the user's daily routine. The system of claim 1 .

9. The simulation unit Estimate the user's emotions and adjust the simulation feedback based on the estimated user emotions. The system of claim 1 .

10. The simulation unit Customize pet behavior based on the user's geographic location during simulation The system of claim 1 .

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