system

Abstract

A system in accordance with an embodiment is directed to generating and displaying a digital model of a pet in a VR space.SOLUTION: A system includes a data collection part, a digitalization part, a generation part, and a display part. The data collection unit collects moving image data of a pet or sensing data of a living body of the pet. The digitizing section digitizes the data collected by the data collecting section. The generation unit generates a digital model of the pet in a digital space based on the data digitized by the digitization unit. The display part displays the digital pet generated by the generation part in the VR space.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately generate digital models of pets and display them in VR spaces, and there is room for improvement.

[0005] The system according to the embodiment aims to generate a digital model of a pet and display it in a VR space. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a digitization unit, a generation unit, and a display unit. The data collection unit collects video data or biological sensing data of the pet. The digitization unit digitizes the data collected by the data collection unit. The generation unit generates a digital model of the pet in a digital space based on the data digitized by the digitization unit. The display unit displays the digital pet generated by the generation unit in a VR space. [Effects of the Invention]

[0007] The system according to the embodiment can generate a digital model of a pet and display it in a VR space. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The digital pet system according to an embodiment of the present invention is a system that digitizes video data and biological sensing data of pets and recreates the target pet in a digital space. This allows users to observe the growth process of their pet and spend time with their digital pet using VR.

[0029] The digital pet system according to the embodiment includes a data collection unit, a digitization unit, a generation unit, and a display unit. The data collection unit collects video data or biological sensing data of the pet. For example, the data collection unit may capture video data of the pet with a camera and collect the data. The data collection unit may also measure the pet's weight and size and collect the data. The data collection unit may also collect vital data such as the pet's heart rate and body temperature. The digitization unit digitizes the data collected by the data collection unit. For example, the digitization unit may convert the collected video data into a digital format. The digitization unit may also convert the collected biological data into a digital format. The digitization unit may also convert the collected vital data into a digital format. The generation unit generates a digital model of the pet in a digital space based on the data digitized by the digitization unit. For example, the generation unit may analyze data such as the pet's size, weight, and shape to create a realistic digital pet. The generation unit may also use growth curve data to reproduce the pet's growth process. The generation unit can also use an emotion estimation function to reproduce the emotional state of the pet. The display unit displays the digital pet generated by the generation unit in a VR space. For example, the display unit displays the digital pet using a VR headset. The display unit can also provide interactions for the user to spend time with the digital pet. The display unit can also provide scenarios for the user to play with or take walks with the digital pet. This allows the digital pet system according to the embodiment to allow the user to observe the growth process of the pet and spend time with the digital pet using VR. For example, the user can record the growth of the pet and generate a digital pet based on that data, allowing the user to look back on the pet's growth. Furthermore, by playing with the digital pet using VR, the user can enjoy an experience similar to that of a real pet.

[0030] The data collection unit can collect and digitize a pet's behavioral patterns and dietary history. For example, to collect a pet's behavioral patterns, the data collection unit uses a wearable device attached to the pet to record its daily movements and activity level. This allows the pet's exercise volume and rest time to be saved as digital data. To collect a pet's dietary history, the data collection unit uses a dedicated dietary record app to record the pet's daily dietary content and intake. This allows the pet's nutritional status and dietary patterns to be saved as digital data. The data collection unit also integrates the pet's behavioral patterns and dietary history to build a system for comprehensively evaluating the pet's health. For example, the system can analyze the relationship between exercise volume and dietary content to help with health management. This allows the pet's health to be comprehensively evaluated by digitizing the pet's behavioral patterns and dietary history.

[0031] The data collection unit can collect and digitize vital data such as a pet's heart rate and body temperature. For example, the data collection unit uses a wearable device attached to the pet to monitor the heart rate and body temperature in real time. This allows the pet's health condition to be constantly monitored. In addition, the data collection unit conducts regular health checks to collect vital data and saves the results as digital data. For example, it digitizes the results of a veterinarian's examination. The data collection unit also analyzes heart rate and body temperature data and builds a system that issues an alert if an abnormality is detected. For example, it sends a notification if the pet's body temperature suddenly rises. In this way, by digitizing the pet's vital data, the pet's health condition can be constantly monitored.

[0032] The data collection unit can collect and digitize pet voice data and scent data. For example, the data collection unit uses a dedicated microphone to collect pet meows and sounds and digitizes the voice data. This makes it possible to analyze pet communication patterns. The data collection unit also uses an odor sensor to collect scent data and saves the pet's body odor and environmental scents as digital data. This makes it possible to monitor the pet's health condition and changes in the environment. The data collection unit also integrates the voice data and scent data to build a system that comprehensively evaluates the pet's behavior and emotional state. For example, it analyzes how the pet reacts to specific scents. This makes it possible to comprehensively evaluate the pet's behavior and emotional state by digitizing the pet's voice data and scent data.

[0033] The data collection unit can collect and digitize data on different types of pets. The data collection unit collects data on different types of pets, such as dogs, cats, and birds, and builds a digital database according to their respective characteristics. For example, it collects data on dog behavior patterns and cat meows. The data collection unit also develops a system that integrates data on different types of pets and manages it on a common digital platform. This allows for centralized management of data on multiple pets. The data collection unit also analyzes data on different types of pets and builds a system that compares their respective characteristics and behavior patterns. For example, it analyzes differences in exercise levels and eating patterns between dogs and cats. This allows for centralized management of data on multiple pets by digitizing data on different types of pets.

[0034] The generation unit can learn the pet's movements and behaviors in real time and reflect them in the digital model. For example, the generation unit analyzes video data of the pet and extracts movement patterns to allow the generation AI to learn the pet's movements and behaviors in real time. This allows the digital model to reproduce realistic movements. The generation unit also uses a machine learning algorithm to learn the pet's movements and behaviors and uses the pet's behavior data as training data. This allows the generation AI to accurately reproduce the pet's movements. The generation unit also uses sensors attached to the pet to collect movement data to learn the pet's movements in real time. This allows the digital model to always reflect the latest movements. This allows the pet's movements and behaviors to be learned in real time and reflected in the digital model, making it possible to provide a more realistic digital pet.

[0035] The generation unit can use high-resolution 3D scan data to reproduce the pet's fur and texture in detail. For example, the generation unit uses a high-resolution 3D scanner to scan the pet's appearance to reproduce the pet's fur and texture in detail. This gives the digital model a realistic texture. The generation unit also develops an algorithm to reproduce the pet's fur and texture based on the 3D scan data. For example, it reproduces the length, color, and light reflection of the fur in detail. The generation unit also uses the high-resolution 3D scan data to build a system that updates the pet's appearance in real time. This ensures that the digital model always reflects the latest appearance. This allows the pet's fur and texture to be reproduced in detail, making it possible to provide a more realistic digital pet.

[0036] The generation unit can add the pet's voice and cries to the digital model. For example, the generation unit collects the pet's cries and sounds and adds that data to the digital model. This allows the digital pet to have a realistic voice. The generation unit also analyzes the pet's voice and cries and develops an algorithm to integrate the audio data into the digital model. For example, it reproduces the cries that the pet makes in specific situations. The generation unit also builds a system that collects the pet's voice and cries in real time and reflects them in the digital model. This allows the digital pet to always have the latest voice. As a result, adding the pet's voice and cries to the digital model can provide a more realistic experience.

[0037] The generation unit can simulate the behavior of a pet in different environments (indoors, outdoors, etc.) and reflect the results in the digital model. For example, the generation unit collects environmental data to simulate the behavior of a pet in different environments and reflects the data in the digital model. For example, the generation unit reproduces the pet's movements indoors and outdoors. The generation unit also analyzes the pet's behavior patterns based on the environmental data and develops an algorithm to simulate movements in different environments. This allows the digital model to move in realistic environments. The generation unit also builds a system that simulates the pet's behavior in different environments in real time and reflects the results in the digital model. This allows the digital pet to always adapt to the latest environment. This allows the pet's behavior in different environments to be simulated, providing a more realistic digital pet.

[0038] The generation unit can use the growth curve data to reproduce the pet's growth process in detail. The generation unit, for example, collects the pet's growth curve data and develops an algorithm that reproduces the pet's growth process in detail based on that data. For example, it displays changes in weight and size over time. The generation unit also simulates the pet's growth process based on the growth curve data and generates digital data according to the age selected by the user. This realistically reproduces the pet's growth. The generation unit also analyzes the pet's growth curve data and reflects important events in the growth process (for example, the first walk or vaccination) in the digital data. In this way, the growth curve data can be used to reproduce the pet's growth process in detail.

[0039] The generation unit can reproduce changes in the pet's health condition and behavioral patterns according to age. The generation unit, for example, collects data on the pet's health condition and behavioral patterns and develops an algorithm that reproduces changes according to age. For example, the generation unit reflects changes in exercise volume and eating patterns that occur with age in the digital data. The generation unit also simulates the pet's health condition according to age and generates digital data corresponding to the age selected by the user. This realistically reproduces changes in the pet's health condition. The generation unit also analyzes data on the pet's behavioral patterns and builds a system that reflects changes according to age in the digital model. For example, it reproduces the active movements of a young pet and the calm movements of an older pet. This allows the pet's growth to be realistically reproduced by reproducing changes in the pet's health condition and behavioral patterns according to age.

[0040] The generation unit adds a function to compare pets of different ages, allowing users to visually understand differences in growth. The generation unit, for example, builds a system that visually displays the growth process to compare pets of different ages. For example, it displays graphs showing changes in a pet's size and weight at age 1 and age 5. The generation unit also adds a function to display data on pets of different ages side by side, allowing users to visually understand differences in growth. This allows users to understand their pet's growth at a glance. The generation unit also develops a system that visually displays changes in a pet's health condition and behavioral patterns based on comparative data on the growth process. For example, it shows differences in exercise volume and eating patterns by age. This allows users to visually understand differences in growth by comparing pets of different ages.

[0041] The generation unit can provide health advice and care methods according to the age of the pet. For example, the generation unit builds a system that provides health advice according to the age of the pet. For example, it suggests appropriate diet and exercise amounts for a specific age. The generation unit also generates digital data based on advice from veterinarians and information from specialist books to provide age-appropriate care methods. This allows users to properly manage their pet's health. The generation unit also develops a system that provides health advice and care methods according to the age of the pet in real time. For example, it displays alerts and reminders according to the pet's health condition. This allows users to properly manage their pet's health by providing health advice and care methods according to the pet's age.

[0042] The display unit can diversify interactions when spending time with a pet in a VR space and add scenarios such as play and training. For example, the display unit adds play and training scenarios to diversify interactions when spending time with a pet in a VR space. For example, it simulates ball play and sitting training. The display unit also adds a function that allows a user to customize the scenario when spending time with a pet in a VR space. This allows the user to have an experience that suits their preferences. The display unit also builds a system that learns the pet's movements and reactions in real time and reflects them in the scenario, in order to diversify interactions in the VR space. This provides a more realistic experience. This diversifies interactions when spending time with a pet in a VR space, making it possible to provide a more realistic experience.

[0043] The display unit can learn the pet's movements and reactions in real time and change them according to the user's actions. For example, the display unit uses a machine learning algorithm to learn the pet's movements and reactions in real time and uses the user's behavioral data as training data. As a result, the pet's movements change according to the user's actions. The display unit also collects user behavioral data in real time and builds a system that learns the pet's movements and reactions. For example, it analyzes the pet's reaction when the user talks to the pet. The display unit also develops an algorithm that learns the pet's movements and reactions in real time and changes them according to the user's actions. As a result, the pet responds naturally to the user's actions. As a result, the pet can learn the pet's movements and reactions in real time and change them according to the user's actions, providing a more realistic experience.

[0044] The display unit can add a function that allows the user to select different environments (such as a park or a beach) when spending time with a pet in a VR space. For example, the display unit adds a function that allows the user to select different environments when spending time with a pet in a VR space. For example, environments such as a park, a beach, or a mountain can be simulated. The display unit also adds a function that allows the user to customize the environment in which the user spends time with their pet in the VR space. This allows the user to spend time with their pet in an environment that suits their preferences. The display unit also builds a system that simulates the movements and reactions of the pet in different environments and realistically reproduces them in the VR space. This allows the user to experience spending time with their pet in a variety of environments. This allows the user to select different environments when spending time with their pet in a VR space, providing a more diverse experience.

[0045] The display unit can add a multi-user function that allows multiple users to simultaneously interact with pets in a VR space. The display unit adds a multi-user function that allows multiple users to simultaneously interact with pets in a VR space. For example, family and friends can play with pets together. To realize the multi-user function, the display unit also builds a system that supports communication between users in the VR space. For example, voice chat and gesture recognition are introduced. The display unit also develops a system that synchronizes data in real time when multiple users simultaneously interact with pets, providing a smooth experience. This allows for natural interaction between users. This allows multiple users to simultaneously interact with pets in a VR space, providing a more diverse experience.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data collection unit can collect and digitize a pet's behavioral patterns and dietary history. For example, to collect a pet's behavioral patterns, a wearable device attached to the pet is used to record its daily movements and activity level. This allows the pet's exercise volume and rest time to be saved as digital data. To collect a pet's dietary history, the data collection unit uses a dedicated diet record app to record the pet's daily dietary content and intake. This allows the pet's nutritional status and dietary patterns to be saved as digital data. The data collection unit also integrates the pet's behavioral patterns and dietary history to build a system for comprehensively evaluating the pet's health. For example, the relationship between exercise volume and dietary content can be analyzed to help with health management. This allows the pet's health to be comprehensively evaluated by digitizing the pet's behavioral patterns and dietary history.

[0048] The data collection unit can collect and digitize vital data such as a pet's heart rate and body temperature. For example, a wearable device attached to the pet can be used to monitor the heart rate and body temperature in real time. This allows the pet's health to be constantly monitored. The data collection unit also conducts regular health checks to collect vital data and stores the results as digital data. For example, it digitizes the results of a veterinarian's examination. The data collection unit also analyzes heart rate and body temperature data and creates a system that issues an alert if an abnormality is detected. For example, it sends a notification if the pet's body temperature suddenly rises. In this way, by digitizing the pet's vital data, the pet's health can be constantly monitored.

[0049] The data collection unit can collect and digitize pet voice data and scent data. For example, a dedicated microphone is used to collect pet meows and sounds, and the sound data is digitized. This makes it possible to analyze pet communication patterns. The data collection unit also uses an odor sensor to collect scent data, and stores the pet's body odor and environmental scents as digital data. This makes it possible to monitor the pet's health and environmental changes. The data collection unit also integrates the voice data and scent data to build a system that comprehensively evaluates the pet's behavior and emotional state. For example, it analyzes how the pet reacts to specific scents. This makes it possible to comprehensively evaluate the pet's behavior and emotional state by digitizing the pet's voice data and scent data.

[0050] The data collection unit can collect and digitize data on different types of pets. For example, it can collect data on different types of pets, such as dogs, cats, and birds, and build a digital database based on their respective characteristics. For example, it can collect data on dog behavior patterns and cat meows. The data collection unit can also develop a system that integrates data on different types of pets and manages it on a common digital platform. This allows for centralized management of data on multiple pets. The data collection unit can also analyze data on different types of pets and build a system that compares their respective characteristics and behavior patterns. For example, it can analyze differences in exercise levels and eating patterns between dogs and cats. This allows for centralized management of data on multiple pets by digitizing the data on different types of pets.

[0051] The generation unit can learn the pet's movements and behaviors in real time and reflect them in the digital model. For example, to have the generation AI learn the pet's movements and behaviors in real time, it analyzes video data of the pet and extracts movement patterns. This allows the digital model to reproduce realistic movements. The generation unit also uses a machine learning algorithm to learn the pet's movements and behaviors and uses the pet's behavior data as training data. This allows the generation AI to accurately reproduce the pet's movements. The generation unit also uses sensors attached to the pet to collect movement data to learn the pet's movements in real time. This allows the digital model to always reflect the latest movements. This allows the pet's movements and behaviors to be learned in real time and reflected in the digital model, making it possible to provide a more realistic digital pet.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The data collection unit collects video data or biological sensing data of the pet. For example, the data collection unit may take a video of the pet with a camera and collect the data. The data collection unit may also measure the pet's weight and size and collect the data. The data collection unit may also collect vital data such as the pet's heart rate and body temperature. Step 2: The digitizing unit digitizes the data collected by the data collecting unit. For example, the digitizing unit converts collected video data into a digital format. The digitizing unit can also convert collected biometric data and vital data into a digital format. Step 3: The generation unit generates a digital model of the pet in a digital space based on the data digitized by the digitization unit. For example, the generation unit analyzes data such as the pet's size, weight, and shape to create a realistic digital pet. The generation unit can also use growth curve data and emotion estimation functions to recreate the pet's growth process. Step 4: The display unit displays the digital pet generated by the generation unit in the VR space. For example, the display unit displays the digital pet using a VR headset. The display unit may also provide interactions and scenarios for the user to spend time with the digital pet.

[0054] (Example 2) The digital pet system according to an embodiment of the present invention is a system that digitizes video data and biological sensing data of pets and recreates the target pet in a digital space. This allows users to observe the growth process of their pet and spend time with their digital pet using VR.

[0055] The digital pet system according to the embodiment includes a data collection unit, a digitization unit, a generation unit, and a display unit. The data collection unit collects video data or biological sensing data of the pet. For example, the data collection unit may capture video data of the pet with a camera and collect the data. The data collection unit may also measure the pet's weight and size and collect the data. The data collection unit may also collect vital data such as the pet's heart rate and body temperature. The digitization unit digitizes the data collected by the data collection unit. For example, the digitization unit may convert the collected video data into a digital format. The digitization unit may also convert the collected biological data into a digital format. The digitization unit may also convert the collected vital data into a digital format. The generation unit generates a digital model of the pet in a digital space based on the data digitized by the digitization unit. For example, the generation unit may analyze data such as the pet's size, weight, and shape to create a realistic digital pet. The generation unit may also use growth curve data to reproduce the pet's growth process. The generation unit can also use an emotion estimation function to reproduce the emotional state of the pet. The display unit displays the digital pet generated by the generation unit in a VR space. For example, the display unit displays the digital pet using a VR headset. The display unit can also provide interactions for the user to spend time with the digital pet. The display unit can also provide scenarios for the user to play with or take walks with the digital pet. This allows the digital pet system according to the embodiment to allow the user to observe the growth process of the pet and spend time with the digital pet using VR. For example, the user can record the growth of the pet and generate a digital pet based on that data, allowing the user to look back on the pet's growth. Furthermore, by playing with the digital pet using VR, the user can enjoy an experience similar to that of a real pet.

[0056] The data collection unit can collect and digitize a pet's behavioral patterns and dietary history. For example, to collect a pet's behavioral patterns, the data collection unit uses a wearable device attached to the pet to record its daily movements and activity level. This allows the pet's exercise volume and rest time to be saved as digital data. To collect a pet's dietary history, the data collection unit uses a dedicated dietary record app to record the pet's daily dietary content and intake. This allows the pet's nutritional status and dietary patterns to be saved as digital data. The data collection unit also integrates the pet's behavioral patterns and dietary history to build a system for comprehensively evaluating the pet's health. For example, the system can analyze the relationship between exercise volume and dietary content to help with health management. This allows the pet's health to be comprehensively evaluated by digitizing the pet's behavioral patterns and dietary history.

[0057] The data collection unit can collect and digitize vital data such as a pet's heart rate and body temperature. For example, the data collection unit uses a wearable device attached to the pet to monitor the heart rate and body temperature in real time. This allows the pet's health condition to be constantly monitored. In addition, the data collection unit conducts regular health checks to collect vital data and saves the results as digital data. For example, it digitizes the results of a veterinarian's examination. The data collection unit also analyzes heart rate and body temperature data and builds a system that issues an alert if an abnormality is detected. For example, it sends a notification if the pet's body temperature suddenly rises. In this way, by digitizing the pet's vital data, the pet's health condition can be constantly monitored.

[0058] The data collection unit can use the emotion estimation function to estimate the emotional state of a pet and digitize the data. The data collection unit, for example, analyzes the pet's facial expressions and behavior to develop an algorithm for estimating the emotional state. For example, it collects data on the pet's characteristics when it is happy or anxious. The data collection unit also uses a camera equipped with emotion estimation function to analyze the pet's facial expressions and movements in real time. This allows the pet's emotional state to be constantly monitored. The data collection unit also digitizes the pet's emotional data and builds a system that records emotional changes over time. For example, it stores data on the emotions the pet feels in specific situations. This allows the pet's emotional state to be digitized, allowing emotional changes to be recorded over time.

[0059] The data collection unit can collect and digitize pet voice data and scent data. For example, the data collection unit uses a dedicated microphone to collect pet meows and sounds and digitizes the voice data. This makes it possible to analyze pet communication patterns. The data collection unit also uses an odor sensor to collect scent data and saves the pet's body odor and environmental scents as digital data. This makes it possible to monitor the pet's health condition and changes in the environment. The data collection unit also integrates the voice data and scent data to build a system that comprehensively evaluates the pet's behavior and emotional state. For example, it analyzes how the pet reacts to specific scents. This makes it possible to comprehensively evaluate the pet's behavior and emotional state by digitizing the pet's voice data and scent data.

[0060] The data collection unit can collect and digitize data on different types of pets. The data collection unit collects data on different types of pets, such as dogs, cats, and birds, and builds a digital database according to their respective characteristics. For example, it collects data on dog behavior patterns and cat meows. The data collection unit also develops a system that integrates data on different types of pets and manages it on a common digital platform. This allows for centralized management of data on multiple pets. The data collection unit also analyzes data on different types of pets and builds a system that compares their respective characteristics and behavior patterns. For example, it analyzes differences in exercise levels and eating patterns between dogs and cats. This allows for centralized management of data on multiple pets by digitizing data on different types of pets.

[0061] The data collection unit can collect and digitize emotional data when a user spends time with a pet. The data collection unit, for example, analyzes the user's facial expressions and behavior when spending time with a pet to build a system that collects emotional data. For example, it digitizes the user's smile and tone of voice when playing with a pet. The data collection unit also uses a camera and microphone equipped with an emotion estimation function to monitor the user's emotional state in real time. This allows the user's emotional data to be constantly collected. The data collection unit also integrates the user's emotional data with pet data to develop a system that comprehensively evaluates the relationship between the user and the pet. For example, it analyzes the changes in emotions over the time the user spends with the pet. This allows the relationship between the user and the pet to be comprehensively evaluated by digitizing the emotional data when the user spends time with the pet.

[0062] The generation unit can learn the pet's movements and behaviors in real time and reflect them in the digital model. For example, the generation unit analyzes video data of the pet and extracts movement patterns to allow the generation AI to learn the pet's movements and behaviors in real time. This allows the digital model to reproduce realistic movements. The generation unit also uses a machine learning algorithm to learn the pet's movements and behaviors and uses the pet's behavior data as training data. This allows the generation AI to accurately reproduce the pet's movements. The generation unit also uses sensors attached to the pet to collect movement data to learn the pet's movements in real time. This allows the digital model to always reflect the latest movements. This allows the pet's movements and behaviors to be learned in real time and reflected in the digital model, making it possible to provide a more realistic digital pet.

[0063] The generation unit can use high-resolution 3D scan data to reproduce the pet's fur and texture in detail. For example, the generation unit uses a high-resolution 3D scanner to scan the pet's appearance to reproduce the pet's fur and texture in detail. This gives the digital model a realistic texture. The generation unit also develops an algorithm to reproduce the pet's fur and texture based on the 3D scan data. For example, it reproduces the length, color, and light reflection of the fur in detail. The generation unit also uses the high-resolution 3D scan data to build a system that updates the pet's appearance in real time. This ensures that the digital model always reflects the latest appearance. This allows the pet's fur and texture to be reproduced in detail, making it possible to provide a more realistic digital pet.

[0064] The generation unit can reflect the emotional state of the pet in the digital model using the emotion estimation function. For example, the generation unit uses the emotion estimation function to analyze the emotional state of the pet in real time and reflect the results in the digital model. For example, the generation unit reproduces the facial expressions and movements of a happy pet. The generation unit also collects emotional data of the pet and develops an algorithm that reflects movements and expressions corresponding to the emotional state in the digital model. This allows the digital model to have realistic emotions. The generation unit also uses a camera equipped with an emotion estimation function to analyze the facial expressions and movements of the pet in real time. This allows the digital model to always reflect the latest emotional state. By reflecting the emotional state of the pet in the digital model, a digital pet with more realistic emotions can be provided.

[0065] The generation unit can add the pet's voice and cries to the digital model. For example, the generation unit collects the pet's cries and sounds and adds that data to the digital model. This allows the digital pet to have a realistic voice. The generation unit also analyzes the pet's voice and cries and develops an algorithm to integrate the audio data into the digital model. For example, it reproduces the cries that the pet makes in specific situations. The generation unit also builds a system that collects the pet's voice and cries in real time and reflects them in the digital model. This allows the digital pet to always have the latest voice. As a result, adding the pet's voice and cries to the digital model can provide a more realistic experience.

[0066] The generation unit can simulate the behavior of a pet in different environments (indoors, outdoors, etc.) and reflect the results in the digital model. For example, the generation unit collects environmental data to simulate the behavior of a pet in different environments and reflects the data in the digital model. For example, the generation unit reproduces the pet's movements indoors and outdoors. The generation unit also analyzes the pet's behavior patterns based on the environmental data and develops an algorithm to simulate movements in different environments. This allows the digital model to move in realistic environments. The generation unit also builds a system that simulates the pet's behavior in different environments in real time and reflects the results in the digital model. This allows the digital pet to always adapt to the latest environment. This allows the pet's behavior in different environments to be simulated, providing a more realistic digital pet.

[0067] The generation unit can use the emotion estimation function to reflect emotional data when the user interacts with the pet in the digital model. For example, the generation unit uses the emotion estimation function to collect emotional data when the user interacts with the pet and reflects the results in the digital model. For example, the generation unit reproduces the pet's reaction when the user is happy. The generation unit also develops an algorithm that reflects the pet's movements and facial expressions in the digital model based on the user's emotional data. This allows the digital pet to react in accordance with the user's emotions. The generation unit also uses a camera and microphone equipped with the emotion estimation function to analyze the user's emotional state in real time. This allows the digital model to always reflect the latest emotional state. This allows the emotional data when the user interacts with the pet to be reflected in the digital model, providing a more realistic experience.

[0068] The generation unit can use the growth curve data to reproduce the pet's growth process in detail. The generation unit, for example, collects the pet's growth curve data and develops an algorithm that reproduces the pet's growth process in detail based on that data. For example, it displays changes in weight and size over time. The generation unit also simulates the pet's growth process based on the growth curve data and generates digital data according to the age selected by the user. This realistically reproduces the pet's growth. The generation unit also analyzes the pet's growth curve data and reflects important events in the growth process (for example, the first walk or vaccination) in the digital data. In this way, the growth curve data can be used to reproduce the pet's growth process in detail.

[0069] The generation unit can reproduce changes in the pet's health condition and behavioral patterns according to age. The generation unit, for example, collects data on the pet's health condition and behavioral patterns and develops an algorithm that reproduces changes according to age. For example, the generation unit reflects changes in exercise volume and eating patterns that occur with age in the digital data. The generation unit also simulates the pet's health condition according to age and generates digital data corresponding to the age selected by the user. This realistically reproduces changes in the pet's health condition. The generation unit also analyzes data on the pet's behavioral patterns and builds a system that reflects changes according to age in the digital model. For example, it reproduces the active movements of a young pet and the calm movements of an older pet. This allows the pet's growth to be realistically reproduced by reproducing changes in the pet's health condition and behavioral patterns according to age.

[0070] The generation unit can use the emotion estimation function to reproduce the emotional state of the pet according to its age. For example, the generation unit uses the emotion estimation function to analyze the emotional state of the pet according to its age and reflects that data in the digital model. For example, it can reproduce the excited state of a young pet and the calm state of an older pet. The generation unit also collects emotional data of the pet according to its age and develops an algorithm that reflects changes in the emotional state in the digital model. This allows for realistic reproduction of changes in the pet's emotions. The generation unit also uses a camera and microphone equipped with the emotion estimation function to analyze the emotional state of the pet according to its age in real time. This allows the digital model to always reflect the latest emotional state. This allows for realistic reproduction of the pet's growth by using the emotion estimation function to reproduce the emotional state of the pet according to its age.

[0071] The generation unit adds a function to compare pets of different ages, allowing users to visually understand differences in growth. The generation unit, for example, builds a system that visually displays the growth process to compare pets of different ages. For example, it displays graphs showing changes in a pet's size and weight at age 1 and age 5. The generation unit also adds a function to display data on pets of different ages side by side, allowing users to visually understand differences in growth. This allows users to understand their pet's growth at a glance. The generation unit also develops a system that visually displays changes in a pet's health condition and behavioral patterns based on comparative data on the growth process. For example, it shows differences in exercise volume and eating patterns by age. This allows users to visually understand differences in growth by comparing pets of different ages.

[0072] The generation unit can provide health advice and care methods according to the age of the pet. For example, the generation unit builds a system that provides health advice according to the age of the pet. For example, it suggests appropriate diet and exercise amounts for a specific age. The generation unit also generates digital data based on advice from veterinarians and information from specialist books to provide age-appropriate care methods. This allows users to properly manage their pet's health. The generation unit also develops a system that provides health advice and care methods according to the age of the pet in real time. For example, it displays alerts and reminders according to the pet's health condition. This allows users to properly manage their pet's health by providing health advice and care methods according to the pet's age.

[0073] The generation unit can use the emotion estimation function to collect emotional data when a user interacts with pets of different ages and provide feedback. The generation unit, for example, uses the emotion estimation function to collect emotional data when a user interacts with pets of different ages and builds a system that provides feedback based on the results. For example, the generation unit analyzes emotional changes when a user spends time with pets of a particular age. The generation unit also develops an algorithm that evaluates the quality of interactions with pets of different ages based on the user's emotional data and suggests areas for improvement, thereby providing a better experience for the user. The generation unit also uses a camera or microphone equipped with the emotion estimation function to analyze the user's emotional state in real time. This constantly optimizes the quality of interactions with pets of different ages. This allows a better experience for the user by collecting emotional data when a user interacts with pets of different ages and providing feedback.

[0074] The display unit can diversify interactions when spending time with a pet in a VR space and add scenarios such as play and training. For example, the display unit adds play and training scenarios to diversify interactions when spending time with a pet in a VR space. For example, it simulates ball play and sitting training. The display unit also adds a function that allows a user to customize the scenario when spending time with a pet in a VR space. This allows the user to have an experience that suits their preferences. The display unit also builds a system that learns the pet's movements and reactions in real time and reflects them in the scenario, in order to diversify interactions in the VR space. This provides a more realistic experience. This diversifies interactions when spending time with a pet in a VR space, making it possible to provide a more realistic experience.

[0075] The display unit can learn the pet's movements and reactions in real time and change them according to the user's actions. For example, the display unit uses a machine learning algorithm to learn the pet's movements and reactions in real time and uses the user's behavioral data as training data. As a result, the pet's movements change according to the user's actions. The display unit also collects user behavioral data in real time and builds a system that learns the pet's movements and reactions. For example, it analyzes the pet's reaction when the user talks to the pet. The display unit also develops an algorithm that learns the pet's movements and reactions in real time and changes them according to the user's actions. As a result, the pet responds naturally to the user's actions. As a result, the pet can learn the pet's movements and reactions in real time and change them according to the user's actions, providing a more realistic experience.

[0076] The display unit can use the emotion estimation function to generate a pet's reaction according to the user's emotional state. For example, the display unit uses the emotion estimation function to analyze the user's emotional state in real time and build a system that generates a pet's reaction based on the results. For example, when the user is happy, the pet shows a happy reaction along with the user. The display unit also develops an algorithm that changes the pet's behavior and facial expression based on the user's emotional data. This allows the pet to show a natural reaction according to the user's emotions. The display unit also uses a camera and microphone equipped with the emotion estimation function to analyze the user's emotional state in real time. This allows the pet's reaction to always reflect the user's latest emotional state. This allows the emotion estimation function to generate a pet's reaction according to the user's emotional state, providing a more realistic experience.

[0077] The display unit can add a function that allows the user to select different environments (such as a park or a beach) when spending time with a pet in a VR space. For example, the display unit adds a function that allows the user to select different environments when spending time with a pet in a VR space. For example, environments such as a park, a beach, or a mountain can be simulated. The display unit also adds a function that allows the user to customize the environment in which the user spends time with their pet in the VR space. This allows the user to spend time with their pet in an environment that suits their preferences. The display unit also builds a system that simulates the movements and reactions of the pet in different environments and realistically reproduces them in the VR space. This allows the user to experience spending time with their pet in a variety of environments. This allows the user to select different environments when spending time with their pet in a VR space, providing a more diverse experience.

[0078] The display unit can add a multi-user function that allows multiple users to simultaneously interact with pets in a VR space. The display unit adds a multi-user function that allows multiple users to simultaneously interact with pets in a VR space. For example, family and friends can play with pets together. To realize the multi-user function, the display unit also builds a system that supports communication between users in the VR space. For example, voice chat and gesture recognition are introduced. The display unit also develops a system that synchronizes data in real time when multiple users simultaneously interact with pets, providing a smooth experience. This allows for natural interaction between users. This allows multiple users to simultaneously interact with pets in a VR space, providing a more diverse experience.

[0079] The display unit uses the emotion estimation function to collect emotional data when a user spends time with a pet in a VR space, thereby improving the quality of the experience. For example, the display unit uses the emotion estimation function to collect emotional data when a user spends time with a pet in a VR space, and builds a system to improve the quality of the experience based on the results. For example, the display unit enhances the pet's reactions when the user is having fun. The display unit also develops an algorithm that adjusts the pet's behavior and environment in the VR space based on the user's emotional data, thereby providing a better experience for the user. The display unit also uses a camera and microphone equipped with the emotion estimation function to analyze the user's emotional state in real time. This constantly optimizes the experience in the VR space. The emotion estimation function can be used to collect emotional data when a user spends time with a pet in a VR space, thereby improving the quality of the experience, thereby providing a better experience.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The data collection unit can collect and digitize a pet's behavioral patterns and dietary history. For example, to collect a pet's behavioral patterns, a wearable device attached to the pet is used to record its daily movements and activity level. This allows the pet's exercise volume and rest time to be saved as digital data. To collect a pet's dietary history, the data collection unit uses a dedicated diet record app to record the pet's daily dietary content and intake. This allows the pet's nutritional status and dietary patterns to be saved as digital data. The data collection unit also integrates the pet's behavioral patterns and dietary history to build a system for comprehensively evaluating the pet's health. For example, the relationship between exercise volume and dietary content can be analyzed to help with health management. This allows the pet's health to be comprehensively evaluated by digitizing the pet's behavioral patterns and dietary history.

[0082] The data collection unit can collect and digitize vital data such as a pet's heart rate and body temperature. For example, a wearable device attached to the pet can be used to monitor the heart rate and body temperature in real time. This allows the pet's health to be constantly monitored. The data collection unit also conducts regular health checks to collect vital data and stores the results as digital data. For example, it digitizes the results of a veterinarian's examination. The data collection unit also analyzes heart rate and body temperature data and creates a system that issues an alert if an abnormality is detected. For example, it sends a notification if the pet's body temperature suddenly rises. In this way, by digitizing the pet's vital data, the pet's health can be constantly monitored.

[0083] The data collection unit can collect and digitize pet voice data and scent data. For example, a dedicated microphone is used to collect pet meows and sounds, and the sound data is digitized. This makes it possible to analyze pet communication patterns. The data collection unit also uses an odor sensor to collect scent data, and stores the pet's body odor and environmental scents as digital data. This makes it possible to monitor the pet's health and environmental changes. The data collection unit also integrates the voice data and scent data to build a system that comprehensively evaluates the pet's behavior and emotional state. For example, it analyzes how the pet reacts to specific scents. This makes it possible to comprehensively evaluate the pet's behavior and emotional state by digitizing the pet's voice data and scent data.

[0084] The data collection unit can collect and digitize data on different types of pets. For example, it can collect data on different types of pets, such as dogs, cats, and birds, and build a digital database based on their respective characteristics. For example, it can collect data on dog behavior patterns and cat meows. The data collection unit can also develop a system that integrates data on different types of pets and manages it on a common digital platform. This allows for centralized management of data on multiple pets. The data collection unit can also analyze data on different types of pets and build a system that compares their respective characteristics and behavior patterns. For example, it can analyze differences in exercise levels and eating patterns between dogs and cats. This allows for centralized management of data on multiple pets by digitizing the data on different types of pets.

[0085] The generation unit can learn the pet's movements and behaviors in real time and reflect them in the digital model. For example, to have the generation AI learn the pet's movements and behaviors in real time, it analyzes video data of the pet and extracts movement patterns. This allows the digital model to reproduce realistic movements. The generation unit also uses a machine learning algorithm to learn the pet's movements and behaviors and uses the pet's behavior data as training data. This allows the generation AI to accurately reproduce the pet's movements. The generation unit also uses sensors attached to the pet to collect movement data to learn the pet's movements in real time. This allows the digital model to always reflect the latest movements. This allows the pet's movements and behaviors to be learned in real time and reflected in the digital model, making it possible to provide a more realistic digital pet.

[0086] The data collection unit can use the emotion estimation function to estimate a pet's emotional state and digitize that data. For example, it can analyze a pet's facial expressions and behavior to develop an algorithm that estimates its emotional state. For example, it can collect data on pet characteristics when the pet is happy or anxious. The data collection unit also uses a camera equipped with emotion estimation function to analyze the pet's facial expressions and movements in real time. This allows for constant monitoring of the pet's emotional state. The data collection unit also digitizes the pet's emotional data and builds a system that records emotional changes over time. For example, it can save data on the emotions a pet feels in specific situations. This allows for digitizing a pet's emotional state to record emotional changes over time.

[0087] The generation unit can use the emotion estimation function to reflect the emotional state of the pet in the digital model. For example, the emotion estimation function can be used to analyze the emotional state of the pet in real time and reflect the results in the digital model. For example, the pet's facial expressions and movements when it is happy can be reproduced. The generation unit also collects emotional data from the pet and develops an algorithm that reflects movements and expressions corresponding to the emotional state in the digital model. This allows the digital model to have realistic emotions. The generation unit also uses a camera equipped with an emotion estimation function to analyze the facial expressions and movements of the pet in real time. This allows the digital model to always reflect the latest emotional state. By reflecting the emotional state of the pet in the digital model, it is possible to provide a digital pet with more realistic emotions.

[0088] The generation unit can use the emotion estimation function to reflect emotional data when the user interacts with the pet in the digital model. For example, the emotion estimation function can be used to collect emotional data when the user interacts with the pet and reflect the results in the digital model. For example, the pet's reaction when the user is happy can be reproduced. The generation unit also develops an algorithm based on the user's emotional data to reflect the pet's movements and facial expressions in the digital model. This allows the digital pet to react in accordance with the user's emotions. The generation unit also uses a camera and microphone equipped with the emotion estimation function to analyze the user's emotional state in real time. This allows the digital model to always reflect the latest emotional state. This allows the emotional data when the user interacts with the pet to be reflected in the digital model, providing a more realistic experience.

[0089] The display unit can use the emotion estimation function to generate a pet's reaction according to the user's emotional state. For example, a system can be constructed that uses the emotion estimation function to analyze the user's emotional state in real time and generate a pet's reaction based on the results. For example, when the user is happy, the pet shows a happy reaction along with the user. The display unit also develops an algorithm that changes the pet's behavior and facial expression based on the user's emotional data. This allows the pet to show a natural reaction according to the user's emotions. The display unit also uses a camera and microphone equipped with the emotion estimation function to analyze the user's emotional state in real time. This allows the pet's reaction to always reflect the user's latest emotional state. This allows the emotion estimation function to generate a pet's reaction according to the user's emotional state, providing a more realistic experience.

[0090] The display unit uses the emotion estimation function to collect emotional data when a user spends time with a pet in a VR space, thereby improving the quality of the experience. For example, the emotion estimation function is used to collect emotional data when a user spends time with a pet in a VR space, and a system is constructed to improve the quality of the experience based on the results. For example, the pet's reactions are enhanced when the user is having fun. The display unit also develops an algorithm that adjusts the pet's behavior and environment in the VR space based on the user's emotional data. This allows the user to have a better experience. The display unit also uses a camera and microphone equipped with the emotion estimation function to analyze the user's emotional state in real time. This constantly optimizes the experience in the VR space. This allows the user to provide a better experience by using the emotion estimation function to collect emotional data when a user spends time with a pet in a VR space, thereby improving the quality of the experience.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The data collection unit collects video data or biological sensing data of the pet. For example, the data collection unit may take a video of the pet with a camera and collect the data. The data collection unit may also measure the pet's weight and size and collect the data. The data collection unit may also collect vital data such as the pet's heart rate and body temperature. Step 2: The digitizing unit digitizes the data collected by the data collecting unit. For example, the digitizing unit converts collected video data into a digital format. The digitizing unit can also convert collected biometric data and vital data into a digital format. Step 3: The generation unit generates a digital model of the pet in a digital space based on the data digitized by the digitization unit. For example, the generation unit analyzes data such as the pet's size, weight, and shape to create a realistic digital pet. The generation unit can also use growth curve data and emotion estimation functions to recreate the pet's growth process. Step 4: The display unit displays the digital pet generated by the generation unit in the VR space. For example, the display unit displays the digital pet using a VR headset. The display unit may also provide interactions and scenarios for the user to spend time with the digital pet.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 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 data collection unit that collects video data or biological sensing data of the pet; a digitizing unit that digitizes the data collected by the data collecting unit; a generation unit that generates a digital model of the pet in a digital space based on the data digitized by the digitization unit; a display unit that displays the digital pet generated by the generation unit in a VR space. A system characterized by:

2. The data collection unit Estimate the emotional state of the pet and digitize the data.

2. The system of claim 1.

3. The generation unit Reflecting your pet's emotional state in a digital model 2. The system of claim 1.

4. The generation unit Recreate your pet's emotional state according to its age 2. The system of claim 1.

5. The display unit Generating a reaction of the pet according to the emotional state of the user 2. The system of claim 1.

Images