system

Abstract

The system according to this embodiment aims to utilize data from fish caught in real life in a virtual space to raise a virtual fish that is exclusive to the user. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a management unit, and a provision unit. The collection unit collects data on fish caught by the user in real life. The generation unit analyzes the data collected by the collection unit and generates fish in a virtual space. The management unit manages and raises the virtual fish generated by the generation unit. The provision unit provides ecological information on the virtual fish managed by the management unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the data of real caught fish has not been fully utilized in the virtual space, and there is room for improvement.

[0005] The system according to the embodiment aims to utilize the data of real caught fish in the virtual space and grow virtual fish dedicated to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a management unit, and a provision unit. The collection unit collects data on fish caught by the user in real life. The generation unit analyzes the data collected by the collection unit and generates fish in a virtual space. The management unit manages and raises the virtual fish generated by the generation unit. The provision unit provides ecological information on the virtual fish managed by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can utilize data from fish caught in real life in a virtual space to raise a virtual fish that is exclusive to the user. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The virtual fishing system according to an embodiment of the present invention is a system in which an AI collects data from the user's own real fishing and raises "a virtual fish just for you." This virtual fishing system collects data on fish caught by the user in real life (fish species, weight, size, color pattern, location, etc.), and the AI ​​analyzes this data to generate a fish in a virtual space. The generated virtual fish can be managed and raised like a digital pet, and by adding elements that allow users to learn about the ecology of the fish they caught, the system fuses the real and virtual fishing experiences. For example, the user inputs data on a fish they caught in real life. For example, they input information such as fish species, weight, size, color pattern, and location where it was caught. This information is collected by the AI. Next, the AI ​​analyzes the collected data and generates a fish in a virtual space. The generated virtual fish can be managed and raised by the user like a digital pet. For example, the user can feed the virtual fish or adjust the environment of the aquarium. Furthermore, the AI ​​provides content that allows users to learn about the ecology of the fish they caught. For example, it provides content that allows users to learn about the ecology of the fish they caught and fishing techniques. This allows users to deepen their knowledge of fishing. Furthermore, users can engage in interactive experiences such as "fishing contests" and "trading" with other users in the virtual space. For example, users can hold fishing contests with each other to compete to see who can catch the biggest fish. They can also trade the fish they catch with other users. This system allows users to recreate and further enjoy their real-world fishing experience in a virtual space. For example, they can recreate the fish they catch in the virtual space and manage them in a digital aquarium. AI can also provide content that teaches users about fish ecology and fishing techniques, and can even predict the next fish they will catch in real time. This can double the enjoyment of fishing for users. In short, the virtual fishing system allows users to recreate and further enjoy their real-world fishing experience in a virtual space.

[0029] The virtual fishing system according to this embodiment comprises a collection unit, a generation unit, a management unit, and a provision unit. The collection unit collects data on fish caught by the user in real life. This data may include, but is not limited to, the species, weight, size, color, pattern, and location where the fish was caught. The collection unit can, for example, manually input the data on fish caught by the user. The collection unit can also automatically collect data on fish caught using a device such as a smartphone or tablet. For example, the collection unit can take a picture of a fish using a smartphone camera and automatically recognize the species and size from the picture. Furthermore, the collection unit can automatically acquire information on the fishing location using GPS functionality. The generation unit analyzes the data collected by the collection unit and generates fish in a virtual space. The generation unit can, for example, use AI to analyze the collected data and generate fish in a virtual space. The generation unit generates realistic fish in a virtual space based on data such as species, size, and color. The generation unit can also simulate the movement and behavior patterns of fish based on the collected data. For example, the generation unit uses AI to learn the movements and behavioral patterns of fish and generates realistic fish in a virtual space. The management unit manages and raises the virtual fish generated by the generation unit. The management unit allows users to, for example, feed the virtual fish or adjust the aquarium environment. The management unit can also monitor the health of the virtual fish and suggest appropriate management methods. For example, the management unit uses AI to analyze the health of the virtual fish and suggest appropriate food and aquarium environment. The provision unit provides ecological information of the virtual fish managed by the management unit. The provision unit provides, for example, content that allows users to learn about the ecology of the fish they have caught and fishing techniques. The provision unit can, for example, use AI to analyze ecological information of the fish they have caught and provide it to the user. For example, the provision unit provides information about the ecology of the fish they have caught and fishing techniques in the form of text, images, videos, etc. As a result, the virtual fishing system according to this embodiment can reproduce the user's real fishing experience in a virtual space and make it even more enjoyable.Some or all of the above-described processes in the collection, generation, management, and provision units may be performed using AI, for example, or without AI. For example, the collection unit can manually input data on fish caught by the user. The generation unit generates realistic fish in a virtual space based on the collected data. The management unit monitors the health status of the virtual fish and suggests appropriate management methods. The provision unit provides information about the ecology of the caught fish and fishing techniques.

[0030] The data collection unit collects data on fish caught by users in real life. This data includes, but is not limited to, the species, weight, size, color, pattern, and location where the fish was caught. The data collection unit can, for example, manually input data on fish caught by users. It can also automatically collect data on fish caught using devices such as smartphones and tablets. For example, the data collection unit can take a picture of a fish using a smartphone camera and automatically recognize the species and size from the picture. Furthermore, the data collection unit can automatically acquire information about the fishing location using GPS functionality. Specifically, the image of the fish taken with the smartphone camera is identified by image recognition technology to determine the species and size. The image recognition technology utilizes a deep learning model to analyze the characteristics of the fish with high accuracy. For example, it analyzes the pattern, color, and shape of the fish's scales and identifies the species by comparing it with a database. The size of the fish is measured by comparing it with a reference object in the image. Using GPS functionality, the latitude and longitude information of the fishing location is automatically acquired and stored in the database. This allows the data collection unit to efficiently collect detailed data on fish caught by users and provide the information necessary for their recreation in the virtual space. Furthermore, the data collection unit saves the user's fish catch data to a cloud server, making it accessible from other devices. This allows users to view and edit their fish catch data from their home PC or tablet. To maintain data consistency, the data collection unit also includes checking functions to prevent duplicate data and incorrect entries. For example, to prevent the same fish from being entered multiple times, it uses image recognition technology to compare fish characteristics and detect duplicate data. In addition, when users manually enter data, guidelines are displayed on the input form to prevent errors. As a result, the data collection unit collects accurate and reliable data, enabling realistic recreation in the virtual space.

[0031] The generation unit analyzes the data collected by the collection unit and generates fish in a virtual space. The generation unit can, for example, use AI to analyze the collected data and generate fish in a virtual space. Specifically, it generates realistic fish models using 3D modeling technology based on collected data such as fish species, size, and color patterns. 3D modeling technology analyzes images taken from multiple viewpoints to construct a three-dimensional model in order to reproduce the shape and texture of the fish in detail. Furthermore, the generation unit uses AI to learn the ecology of fish in order to simulate the movement and behavior patterns of fish. For example, it simulates how fish swim, how they hunt for food, and their interactions with other fish to reproduce realistic movements in the virtual space. The AI ​​learns the movement and behavior patterns of fish based on the collected data and generates realistic movements in the virtual space. As a result, the generation unit can realistically reproduce the fish that the user has caught in the virtual space, providing the user with a new experience. Furthermore, the generation unit can also simulate the growth and changes of fish within the virtual space. For example, it simulates the growth process of fish, seasonal changes in color and pattern, and breeding behavior, recreating a realistic ecosystem in a virtual space. This allows users to observe the growth and changes of fish in the virtual space and gain a deeper understanding. The generation unit can recreate the ecology of fish in the virtual space in detail based on data of fish caught by the user, providing users with a new experience.

[0032] The management unit manages and raises the virtual fish generated by the generation unit. For example, the management unit allows users to feed the virtual fish and adjust the aquarium environment. Specifically, users can select and feed appropriate food to the fish in the virtual space. The type and amount of food vary depending on the fish species and growth stage, and providing appropriate food helps maintain the fish's health. The management unit also provides functions for adjusting the aquarium environment. For example, it can adjust water temperature, water quality, and oxygen concentration to maintain a comfortable environment for the fish. The management unit uses AI to analyze the health of the virtual fish and propose appropriate management methods. For example, the AI ​​monitors the fish's movements and behavioral patterns, and issues warnings to the user if abnormalities are detected. It also suggests appropriate food and aquarium environment based on the fish's health. This allows users to efficiently manage the health of their fish in the virtual space. Furthermore, the management unit records the growth and changes of the virtual fish and provides this information to the user. For example, it records the fish's growth process, changes in color and pattern, and breeding behavior so that users can review it later. This allows users to observe the growth and changes of fish in the virtual space, gaining a deeper understanding. The management team can monitor the health of the virtual fish and suggest appropriate management methods, providing users with a new experience.

[0033] The service provider provides ecological information on virtual fish managed by the management provider. For example, the service provider offers content that allows users to learn about the ecology of caught fish and fishing techniques. Specifically, the service provider can use AI to analyze ecological information on caught fish and provide it to users. For example, it can provide information about the ecology of caught fish and fishing techniques in the form of text, images, and videos. Based on the collected data, the AI ​​analyzes the ecology and behavioral patterns of fish and provides information to users in an easy-to-understand format. For example, it can provide information on the fish's habitat, diet, and reproductive behavior, which users can use to improve their fishing skills. The service provider also provides a function to share information with other users based on the data of fish caught by the user. For example, users can share photos and data of fish they have caught and interact with other users. This allows users to share their fishing experiences and receive feedback from other users. Furthermore, the service provider also has a function to provide the latest information and news related to fishing. For example, it provides information on new fishing spots, fishing events, and the latest fishing techniques, ensuring that users always have access to the latest information. This allows the service provider to offer users comprehensive information about fishing, further expanding the enjoyment of fishing.

[0034] The interactive unit can provide interactive experiences with other users in a virtual space. For example, the interactive unit can host a fishing contest where users compete to see who can catch the biggest fish. The interactive unit can also exchange the fish that users have caught with other users. For example, the interactive unit can host a fishing contest in a virtual space where users can compete with each other. The interactive unit can also exchange the fish that users have caught with other users. This allows users to enjoy interactive experiences with other users in a virtual space. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can use AI to provide interactive experiences between users.

[0035] The data collection unit can analyze the user's past fishing history and select the optimal data collection method. For example, the data collection unit can prioritize data collection at the same location based on data of fish the user has caught in the past. The data collection unit can also focus on collecting data related to specific fish species from the user's past fishing history. Furthermore, the data collection unit can analyze the user's fishing history and collect data based on the fishing method with the highest success rate. This enables efficient data collection by selecting the optimal data collection method based on the user's past fishing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past fishing history data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can filter data based on the user's current fishing environment and weather conditions during data collection. For example, the data collection unit can prioritize collecting data for appropriate fish species, taking into account the current weather conditions. The data collection unit can also filter relevant data based on the user's fishing environment (freshwater, saltwater, etc.). Furthermore, the data collection unit can collect data for specific fish species depending on the time of day of fishing. This allows for the collection of more relevant data by filtering the data based on the user's current fishing environment and weather conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current weather data into a generating AI and have the generating AI perform filtering for appropriate fish species.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is fishing in a specific area, the data collection unit will prioritize the collection of data related to that area. The data collection unit can also collect data on fishing spots near the user's current location. Furthermore, the data collection unit can collect new data by comparing it with past data based on the user's geographical location information. This allows for the collection of more useful data by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0038] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on fishing information shared by the user on social media. The data collection unit can also analyze the content of the user's social media posts and collect data on fish species of interest. Furthermore, the data collection unit can collect data based on fishing information shared by the user's social media followers. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0039] The generation unit can adjust the accuracy of the virtual fish based on the level of detail of the collected data during generation. For example, if detailed data is collected, the generation unit will generate a realistic virtual fish. Furthermore, if the data is incomplete, the generation unit can generate a virtual fish using estimates. In addition, the generation unit can adjust the appearance and movement of the virtual fish according to the level of detail of the data. This allows for the generation of more realistic virtual fish by adjusting the accuracy based on the level of detail of the collected data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the collected data into a generation AI and have the generation AI adjust the accuracy of the virtual fish.

[0040] The generation unit can apply different generation algorithms to each fish species during generation. For example, the generation unit can generate virtual fish with different growth patterns for each fish species. It can also generate virtual fish with different colors and patterns for each fish species. Furthermore, it can generate virtual fish with different movements and behavioral patterns for each fish species. By applying different generation algorithms to each fish species, a wider variety of virtual fish can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data for each fish species into a generation AI and have the generation AI execute the application of different generation algorithms.

[0041] The generation unit can adjust the growth rate of virtual fish based on the time of year when generating them. For example, the generation unit can generate virtual fish with a fast growth rate for fish caught in the spring. It can also generate virtual fish with a slow growth rate for fish caught in the winter. Furthermore, the generation unit can adjust the growth pattern of the virtual fish according to the time of year. By adjusting the growth rate of virtual fish based on the time of year, a more realistic growth pattern can be reproduced. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data about the time of year of fishing into a generation AI and have the generation AI perform the adjustment of the growth rate of virtual fish.

[0042] The generation unit can adjust the ecology of virtual fish based on the fishing location during generation. For example, if a fish is caught in freshwater, the generation unit will generate a virtual fish suited to a freshwater environment. The generation unit can also generate a virtual fish suited to a saltwater environment if a fish is caught in saltwater. Furthermore, the generation unit can adjust the behavioral patterns of the virtual fish according to the fishing location. By adjusting the ecology of virtual fish based on the fishing location, it is possible to generate more realistic virtual fish. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data about the fishing location into a generation AI and have the generation AI perform the adjustment of the virtual fish's ecology.

[0043] The management department can select the optimal management method by referring to the user's past management history during management. For example, the management department can propose the optimal management method based on the management methods the user has used in the past. The management department can also select a management method with a high success rate from the user's past management history. Furthermore, the management department can analyze the user's management history and propose the most efficient management method. This enables efficient management by selecting the optimal management method based on the user's past management history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's past management history data into a generating AI and have the generating AI select the optimal management method.

[0044] The management unit can apply different management methods depending on the growth stage of the virtual fish during management. For example, if the virtual fish is a juvenile, the management unit can apply a management method that promotes growth. If the virtual fish is an adult, the management unit can also apply a management method that maintains health. Furthermore, if the virtual fish is an old fish, the management unit can also apply a management method that extends its lifespan. This allows for more appropriate management by applying different management methods according to the growth stage of the virtual fish. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input data on the growth stage of the virtual fish into a generating AI and have the generating AI execute the application of different management methods.

[0045] The management department can select the optimal management method during management, taking into account the user's geographical location information. For example, if the user is in a specific region, the management department can propose a management method suitable for that region. The management department can also utilize management resources close to the user's current location. Furthermore, the management department can select the optimal management method by comparing the user's geographical location information with past data. This enables efficient management by selecting the optimal management method while considering the user's geographical location information. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0046] The management department can analyze users' social media activity and propose management methods during the management process. For example, the management department can propose the optimal management method based on management methods shared by users on social media. Furthermore, the management department can analyze the content of users' social media posts and propose management methods of interest. In addition, the management department can propose management methods based on management methods shared by users' social media followers. This allows for the efficient proposal of relevant management methods by analyzing users' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input user social media activity data into a generating AI and have the generating AI propose the optimal management method.

[0047] The content provider can provide optimal content by referring to the user's past learning history at the time of delivery. For example, the provider can provide relevant ecological information based on what the user has learned in the past. The provider can also prioritize providing information on fish species of interest based on the user's learning history. Furthermore, the provider can analyze the user's learning history and suggest the most effective learning method. This enables efficient learning by providing optimal content based on the user's past learning history. Some or all of the above processing in the content provider may be performed using AI, for example, or without AI. For example, the provider can input the user's past learning history data into a generating AI and have the generating AI perform the task of providing optimal content.

[0048] The information provider can provide different ecological information for each fish species at the time of provision. For example, the information provider can provide content that explains the different habitats and behavioral patterns for each fish species. It can also provide content that explains the different diets and reproductive methods for each fish species. Furthermore, it can provide content that explains different fishing tips and techniques for each fish species. By providing different ecological information for each fish species, it becomes possible to provide more detailed information. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input ecological information for each fish species into a generating AI and have the generating AI perform the provision of different ecological information.

[0049] The information provider can provide optimal ecological information by considering the user's geographical location at the time of provision. For example, if the user is fishing in a specific area, the information provider can provide ecological information related to that area. The information provider can also provide ecological information for fishing spots close to the user's current location. Furthermore, the information provider can provide new ecological information by comparing it with past data based on the user's geographical location. This enables efficient information provision by providing optimal ecological information by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location into a generating AI and have the generating AI perform the task of providing optimal ecological information.

[0050] The service provider can analyze the user's social media activity and provide relevant ecological information at the time of delivery. For example, the service provider can provide relevant ecological information based on fishing information shared by the user on social media. The service provider can also analyze the content of the user's social media posts and provide ecological information on fish species of interest. Furthermore, the service provider can provide ecological information based on fishing information shared by the user's social media followers. In this way, relevant ecological information can be efficiently provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant ecological information.

[0051] The interactive unit can provide the optimal experience during an interactive experience by referring to the user's past participation history. For example, the interactive unit can provide relevant experiences based on the user's past interactive experiences. Furthermore, the interactive unit can prioritize providing experiences of interest based on the user's past participation history. In addition, the interactive unit can analyze the user's participation history and suggest the most effective experience. This enables an efficient experience by providing the optimal experience based on the user's past participation history. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input the user's past participation history data into a generating AI and have the generating AI perform the task of providing the optimal experience.

[0052] The interactive unit can provide an optimal experience by considering the user's geographical location during an interactive experience. For example, if the user is fishing in a specific area, the interactive unit can provide an interactive experience related to that area. It can also provide an interactive experience of fishing spots near the user's current location. Furthermore, the interactive unit can provide a new interactive experience by comparing the user's geographical location with past data. This enables an efficient experience by providing an optimal experience by considering the user's geographical location. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing an optimal experience.

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

[0054] The virtual fishing system may also include a "notification unit." The notification unit can provide real-time advice and information to the user while they are fishing. For example, the notification unit can notify the user in real time of changes in weather conditions and water temperature at their fishing location. It can also advise the user on the best fishing techniques and bait selection for the time of day they are fishing. Furthermore, the notification unit can receive messages and advice from other users in real time while the user is fishing. This allows the user to receive necessary information and advice in real time while fishing, increasing their chances of success. Some or all of the above processing in the notification unit may be performed using AI, for example, or not. For example, the notification unit can input weather data and water temperature data into a generating AI and have the generating AI provide optimal advice.

[0055] The virtual fishing system may also include a "guide unit." The guide unit provides support as a virtual guide while the user is fishing. For example, the guide unit can provide advice on selecting a fishing spot and preparing fishing equipment before the user starts fishing. The guide unit can also provide real-time explanations of fishing techniques and fish behavior during fishing. Furthermore, the guide unit can provide analysis of the catch and advice for the next fishing trip after the user has finished fishing. This allows the user to enjoy fishing more effectively with the support of a virtual guide. Some or all of the above processes in the guide unit may be performed using AI, for example, or not using AI. For example, the guide unit can input the user's fishing data into a generating AI and have the generating AI provide optimal advice.

[0056] The virtual fishing system may also include an "entertainment section." The entertainment section provides additional content to enhance the user's fishing experience. For example, the entertainment section could offer mini-games or quizzes during fishing, providing entertainment for users in between fishing sessions. It could also provide trivia and interesting anecdotes about the fish caught. Furthermore, the entertainment section could play music or nature sounds while the user is fishing, creating a relaxing atmosphere. This allows users to enjoy fishing in a more fun and relaxed way. Some or all of the above-mentioned processes in the entertainment section may be performed using AI, for example, or without AI. For example, the entertainment section could input the user's fishing data into a generating AI and have the generating AI provide the most suitable content.

[0057] The virtual fishing system may also include a "communication unit." The communication unit provides functions that allow users to communicate with each other in real time. For example, the communication unit allows users to chat with other users while they are fishing. The communication unit can also allow users to share photos and videos of the fish they have caught with other users. Furthermore, the communication unit can allow users to exchange fishing advice and information with each other. This allows users to enjoy fishing more while communicating with other users. Some or all of the above processing in the communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can input user chat data into a generating AI and have the generating AI perform the task of providing optimal communication.

[0058] The virtual fishing system may also include a "feedback unit." This unit provides feedback to the user after they finish fishing, regarding their catch and fishing technique. For example, it can analyze data on the fish the user caught and evaluate their fishing success rate and areas for improvement. It can also suggest areas for improvement and new techniques for the user's next fishing trip. Furthermore, it can provide advice and motivation to help the user enjoy fishing. This allows the user to improve their fishing skills while preparing for their next trip. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For instance, the feedback unit could input the user's fishing data into a generating AI and have the AI ​​provide optimal feedback.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects data on fish caught by the user in real time. The collected data includes fish species, weight, size, color pattern, and fishing location. The data collection unit can accept manual data input from the user, or it can automatically collect data using devices such as smartphones and tablets. For example, a smartphone camera can be used to take a picture of a fish, and the system can automatically recognize the fish species and size from that picture. It can also automatically acquire fishing location information using GPS functionality. Step 2: The generation unit analyzes the data collected by the collection unit and generates fish in a virtual space. The generation unit uses AI to analyze the collected data and generates realistic fish in a virtual space based on data such as fish species, size, and color patterns. The generation unit can also simulate the movement and behavior patterns of fish based on the collected data. Step 3: The management unit manages and raises the virtual fish generated by the generation unit. The management unit allows users to feed the virtual fish and adjust the aquarium environment. The management unit can also monitor the health of the virtual fish and suggest appropriate management methods. For example, it can use AI to analyze the health of the virtual fish and suggest appropriate food and aquarium environment. Step 4: The service provider provides ecological information about virtual fish managed by the management provider. The service provider provides content that allows users to learn about the ecology of the fish they have caught and fishing techniques. For example, it can use AI to analyze the ecological information of the fish that have been caught and provide it to the user. The service provider provides information about the ecology of the fish that have been caught and fishing techniques in the form of text, images, videos, etc.

[0061] (Example of form 2) The virtual fishing system according to an embodiment of the present invention is a system in which an AI collects data from the user's own real fishing and raises "a virtual fish just for you." This virtual fishing system collects data on fish caught by the user in real life (fish species, weight, size, color pattern, location, etc.), and the AI ​​analyzes this data to generate a fish in a virtual space. The generated virtual fish can be managed and raised like a digital pet, and by adding elements that allow users to learn about the ecology of the fish they caught, the system fuses the real and virtual fishing experiences. For example, the user inputs data on a fish they caught in real life. For example, they input information such as fish species, weight, size, color pattern, and location where it was caught. This information is collected by the AI. Next, the AI ​​analyzes the collected data and generates a fish in a virtual space. The generated virtual fish can be managed and raised by the user like a digital pet. For example, the user can feed the virtual fish or adjust the environment of the aquarium. Furthermore, the AI ​​provides content that allows users to learn about the ecology of the fish they caught. For example, it provides content that allows users to learn about the ecology of the fish they caught and fishing techniques. This allows users to deepen their knowledge of fishing. Furthermore, users can engage in interactive experiences such as "fishing contests" and "trading" with other users in the virtual space. For example, users can hold fishing contests with each other to compete to see who can catch the biggest fish. They can also trade the fish they catch with other users. This system allows users to recreate and further enjoy their real-world fishing experience in a virtual space. For example, they can recreate the fish they catch in the virtual space and manage them in a digital aquarium. AI can also provide content that teaches users about fish ecology and fishing techniques, and can even predict the next fish they will catch in real time. This can double the enjoyment of fishing for users. In short, the virtual fishing system allows users to recreate and further enjoy their real-world fishing experience in a virtual space.

[0062] The virtual fishing system according to this embodiment comprises a collection unit, a generation unit, a management unit, and a provision unit. The collection unit collects data on fish caught by the user in real life. This data may include, but is not limited to, the species, weight, size, color, pattern, and location where the fish was caught. The collection unit can, for example, manually input the data on fish caught by the user. The collection unit can also automatically collect data on fish caught using a device such as a smartphone or tablet. For example, the collection unit can take a picture of a fish using a smartphone camera and automatically recognize the species and size from the picture. Furthermore, the collection unit can automatically acquire information on the fishing location using GPS functionality. The generation unit analyzes the data collected by the collection unit and generates fish in a virtual space. The generation unit can, for example, use AI to analyze the collected data and generate fish in a virtual space. The generation unit generates realistic fish in a virtual space based on data such as species, size, and color. The generation unit can also simulate the movement and behavior patterns of fish based on the collected data. For example, the generation unit uses AI to learn the movements and behavioral patterns of fish and generates realistic fish in a virtual space. The management unit manages and raises the virtual fish generated by the generation unit. The management unit allows users to, for example, feed the virtual fish or adjust the aquarium environment. The management unit can also monitor the health of the virtual fish and suggest appropriate management methods. For example, the management unit uses AI to analyze the health of the virtual fish and suggest appropriate food and aquarium environment. The provision unit provides ecological information of the virtual fish managed by the management unit. The provision unit provides, for example, content that allows users to learn about the ecology of the fish they have caught and fishing techniques. The provision unit can, for example, use AI to analyze ecological information of the fish they have caught and provide it to the user. For example, the provision unit provides information about the ecology of the fish they have caught and fishing techniques in the form of text, images, videos, etc. As a result, the virtual fishing system according to this embodiment can reproduce the user's real fishing experience in a virtual space and make it even more enjoyable.Some or all of the above-described processes in the collection, generation, management, and provision units may be performed using AI, for example, or without AI. For example, the collection unit can manually input data on fish caught by the user. The generation unit generates realistic fish in a virtual space based on the collected data. The management unit monitors the health status of the virtual fish and suggests appropriate management methods. The provision unit provides information about the ecology of the caught fish and fishing techniques.

[0063] The data collection unit collects data on fish caught by users in real life. This data includes, but is not limited to, the species, weight, size, color, pattern, and location where the fish was caught. The data collection unit can, for example, manually input data on fish caught by users. It can also automatically collect data on fish caught using devices such as smartphones and tablets. For example, the data collection unit can take a picture of a fish using a smartphone camera and automatically recognize the species and size from the picture. Furthermore, the data collection unit can automatically acquire information about the fishing location using GPS functionality. Specifically, the image of the fish taken with the smartphone camera is identified by image recognition technology to determine the species and size. The image recognition technology utilizes a deep learning model to analyze the characteristics of the fish with high accuracy. For example, it analyzes the pattern, color, and shape of the fish's scales and identifies the species by comparing it with a database. The size of the fish is measured by comparing it with a reference object in the image. Using GPS functionality, the latitude and longitude information of the fishing location is automatically acquired and stored in the database. This allows the data collection unit to efficiently collect detailed data on fish caught by users and provide the information necessary for their recreation in the virtual space. Furthermore, the data collection unit saves the user's fish catch data to a cloud server, making it accessible from other devices. This allows users to view and edit their fish catch data from their home PC or tablet. To maintain data consistency, the data collection unit also includes checking functions to prevent duplicate data and incorrect entries. For example, to prevent the same fish from being entered multiple times, it uses image recognition technology to compare fish characteristics and detect duplicate data. In addition, when users manually enter data, guidelines are displayed on the input form to prevent errors. As a result, the data collection unit collects accurate and reliable data, enabling realistic recreation in the virtual space.

[0064] The generation unit analyzes the data collected by the collection unit and generates fish in a virtual space. The generation unit can, for example, use AI to analyze the collected data and generate fish in a virtual space. Specifically, it generates realistic fish models using 3D modeling technology based on collected data such as fish species, size, and color patterns. 3D modeling technology analyzes images taken from multiple viewpoints to construct a three-dimensional model in order to reproduce the shape and texture of the fish in detail. Furthermore, the generation unit uses AI to learn the ecology of fish in order to simulate the movement and behavior patterns of fish. For example, it simulates how fish swim, how they hunt for food, and their interactions with other fish to reproduce realistic movements in the virtual space. The AI ​​learns the movement and behavior patterns of fish based on the collected data and generates realistic movements in the virtual space. As a result, the generation unit can realistically reproduce the fish that the user has caught in the virtual space, providing the user with a new experience. Furthermore, the generation unit can also simulate the growth and changes of fish within the virtual space. For example, it simulates the growth process of fish, seasonal changes in color and pattern, and breeding behavior, recreating a realistic ecosystem in a virtual space. This allows users to observe the growth and changes of fish in the virtual space and gain a deeper understanding. The generation unit can recreate the ecology of fish in the virtual space in detail based on data of fish caught by the user, providing users with a new experience.

[0065] The management unit manages and raises the virtual fish generated by the generation unit. For example, the management unit allows users to feed the virtual fish and adjust the aquarium environment. Specifically, users can select and feed appropriate food to the fish in the virtual space. The type and amount of food vary depending on the fish species and growth stage, and providing appropriate food helps maintain the fish's health. The management unit also provides functions for adjusting the aquarium environment. For example, it can adjust water temperature, water quality, and oxygen concentration to maintain a comfortable environment for the fish. The management unit uses AI to analyze the health of the virtual fish and propose appropriate management methods. For example, the AI ​​monitors the fish's movements and behavioral patterns, and issues warnings to the user if abnormalities are detected. It also suggests appropriate food and aquarium environment based on the fish's health. This allows users to efficiently manage the health of their fish in the virtual space. Furthermore, the management unit records the growth and changes of the virtual fish and provides this information to the user. For example, it records the fish's growth process, changes in color and pattern, and breeding behavior so that users can review it later. This allows users to observe the growth and changes of fish in the virtual space, gaining a deeper understanding. The management team can monitor the health of the virtual fish and suggest appropriate management methods, providing users with a new experience.

[0066] The service provider provides ecological information on virtual fish managed by the management provider. For example, the service provider offers content that allows users to learn about the ecology of caught fish and fishing techniques. Specifically, the service provider can use AI to analyze ecological information on caught fish and provide it to users. For example, it can provide information about the ecology of caught fish and fishing techniques in the form of text, images, and videos. Based on the collected data, the AI ​​analyzes the ecology and behavioral patterns of fish and provides information to users in an easy-to-understand format. For example, it can provide information on the fish's habitat, diet, and reproductive behavior, which users can use to improve their fishing skills. The service provider also provides a function to share information with other users based on the data of fish caught by the user. For example, users can share photos and data of fish they have caught and interact with other users. This allows users to share their fishing experiences and receive feedback from other users. Furthermore, the service provider also has a function to provide the latest information and news related to fishing. For example, it provides information on new fishing spots, fishing events, and the latest fishing techniques, ensuring that users always have access to the latest information. This allows the service provider to offer users comprehensive information about fishing, further expanding the enjoyment of fishing.

[0067] The interactive unit can provide interactive experiences with other users in a virtual space. For example, the interactive unit can host a fishing contest where users compete to see who can catch the biggest fish. The interactive unit can also exchange the fish that users have caught with other users. For example, the interactive unit can host a fishing contest in a virtual space where users can compete with each other. The interactive unit can also exchange the fish that users have caught with other users. This allows users to enjoy interactive experiences with other users in a virtual space. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can use AI to provide interactive experiences between users.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is excited, the data collection unit can immediately start collecting data and obtain information in real time. If the user is relaxed, the data collection unit can collect data at regular intervals, matching the user's pace. Furthermore, if the user is stressed, the data collection unit can temporarily stop collecting data and wait until the user calms down. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0069] The data collection unit can analyze the user's past fishing history and select the optimal data collection method. For example, the data collection unit can prioritize data collection at the same location based on data of fish the user has caught in the past. The data collection unit can also focus on collecting data related to specific fish species from the user's past fishing history. Furthermore, the data collection unit can analyze the user's fishing history and collect data based on the fishing method with the highest success rate. This enables efficient data collection by selecting the optimal data collection method based on the user's past fishing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past fishing history data into a generating AI and have the generating AI select the optimal data collection method.

[0070] The data collection unit can filter data based on the user's current fishing environment and weather conditions during data collection. For example, the data collection unit can prioritize collecting data for appropriate fish species, taking into account the current weather conditions. The data collection unit can also filter relevant data based on the user's fishing environment (freshwater, saltwater, etc.). Furthermore, the data collection unit can collect data for specific fish species depending on the time of day of fishing. This allows for the collection of more relevant data by filtering the data based on the user's current fishing environment and weather conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current weather data into a generating AI and have the generating AI perform filtering for appropriate fish species.

[0071] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit can prioritize collecting important data in real time. If the user is relaxed, the data collection unit can also collect detailed data for later analysis. Furthermore, if the user is stressed, the data collection unit can prioritize collecting simple data to reduce the user's burden. This allows for the collection of more important data by prioritizing data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0072] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is fishing in a specific area, the data collection unit will prioritize the collection of data related to that area. The data collection unit can also collect data on fishing spots near the user's current location. Furthermore, the data collection unit can collect new data by comparing it with past data based on the user's geographical location information. This allows for the collection of more useful data by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0073] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on fishing information shared by the user on social media. The data collection unit can also analyze the content of the user's social media posts and collect data on fish species of interest. Furthermore, the data collection unit can collect data based on fishing information shared by the user's social media followers. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0074] The generation unit can estimate the user's emotions and adjust the method of generating virtual fish based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate virtual fish with slow movements. If the user is excited, the generation unit can also generate virtual fish with lively movements. Furthermore, if the user is stressed, the generation unit can generate virtual fish with a calming effect. By adjusting the method of generating virtual fish according to the user's emotions, more appropriate virtual fish can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0075] The generation unit can adjust the accuracy of the virtual fish based on the level of detail of the collected data during generation. For example, if detailed data is collected, the generation unit will generate a realistic virtual fish. Furthermore, if the data is incomplete, the generation unit can generate a virtual fish using estimates. In addition, the generation unit can adjust the appearance and movement of the virtual fish according to the level of detail of the data. This allows for the generation of more realistic virtual fish by adjusting the accuracy based on the level of detail of the collected data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the collected data into a generation AI and have the generation AI adjust the accuracy of the virtual fish.

[0076] The generation unit can apply different generation algorithms to each fish species during generation. For example, the generation unit can generate virtual fish with different growth patterns for each fish species. It can also generate virtual fish with different colors and patterns for each fish species. Furthermore, it can generate virtual fish with different movements and behavioral patterns for each fish species. By applying different generation algorithms to each fish species, a wider variety of virtual fish can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data for each fish species into a generation AI and have the generation AI execute the application of different generation algorithms.

[0077] The generation unit can estimate the user's emotions and adjust the appearance of the virtual fish based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a virtual fish with calm colors. It can also generate a virtual fish with vibrant colors if the user is excited. Furthermore, if the user is stressed, the generation unit can generate a virtual fish with calming colors. This allows for the creation of more appealing virtual fish by adjusting their appearance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the user's emotion estimation.

[0078] The generation unit can adjust the growth rate of virtual fish based on the time of year when generating them. For example, the generation unit can generate virtual fish with a fast growth rate for fish caught in the spring. It can also generate virtual fish with a slow growth rate for fish caught in the winter. Furthermore, the generation unit can adjust the growth pattern of the virtual fish according to the time of year. By adjusting the growth rate of virtual fish based on the time of year, a more realistic growth pattern can be reproduced. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data about the time of year of fishing into a generation AI and have the generation AI perform the adjustment of the growth rate of virtual fish.

[0079] The generation unit can adjust the ecology of virtual fish based on the fishing location during generation. For example, if a fish is caught in freshwater, the generation unit will generate a virtual fish suited to a freshwater environment. The generation unit can also generate a virtual fish suited to a saltwater environment if a fish is caught in saltwater. Furthermore, the generation unit can adjust the behavioral patterns of the virtual fish according to the fishing location. By adjusting the ecology of virtual fish based on the fishing location, it is possible to generate more realistic virtual fish. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data about the fishing location into a generation AI and have the generation AI perform the adjustment of the virtual fish's ecology.

[0080] The management unit can estimate the user's emotions and adjust the virtual fish management method based on the estimated emotions. For example, if the user is relaxed, the management unit can provide a relaxed management method. If the user is excited, the management unit can also provide an active management method. Furthermore, if the user is stressed, the management unit can provide a simple and hassle-free management method. This allows for more appropriate management by adjusting the virtual fish management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, or not using AI. For example, the management unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0081] The management department can select the optimal management method by referring to the user's past management history during management. For example, the management department can propose the optimal management method based on the management methods the user has used in the past. The management department can also select a management method with a high success rate from the user's past management history. Furthermore, the management department can analyze the user's management history and propose the most efficient management method. This enables efficient management by selecting the optimal management method based on the user's past management history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's past management history data into a generating AI and have the generating AI select the optimal management method.

[0082] The management unit can apply different management methods depending on the growth stage of the virtual fish during management. For example, if the virtual fish is a juvenile, the management unit can apply a management method that promotes growth. If the virtual fish is an adult, the management unit can also apply a management method that maintains health. Furthermore, if the virtual fish is an old fish, the management unit can also apply a management method that extends its lifespan. This allows for more appropriate management by applying different management methods according to the growth stage of the virtual fish. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input data on the growth stage of the virtual fish into a generating AI and have the generating AI execute the application of different management methods.

[0083] The management unit can estimate the user's emotions and determine management priorities based on the estimated emotions. For example, if the user is excited, the management unit can prioritize important management tasks. If the user is relaxed, the management unit can also perform more detailed management tasks. Furthermore, if the user is stressed, the management unit can prioritize simpler management tasks. This allows for prioritizing more important management tasks based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the management unit may be performed using AI or not. For example, the management unit can input user facial expression data into a generative AI and have the generative AI estimate the user's emotions.

[0084] The management department can select the optimal management method during management, taking into account the user's geographical location information. For example, if the user is in a specific region, the management department can propose a management method suitable for that region. The management department can also utilize management resources close to the user's current location. Furthermore, the management department can select the optimal management method by comparing the user's geographical location information with past data. This enables efficient management by selecting the optimal management method while considering the user's geographical location information. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0085] The management department can analyze users' social media activity and propose management methods during the management process. For example, the management department can propose the optimal management method based on management methods shared by users on social media. Furthermore, the management department can analyze the content of users' social media posts and propose management methods of interest. In addition, the management department can propose management methods based on management methods shared by users' social media followers. This allows for the efficient proposal of relevant management methods by analyzing users' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input user social media activity data into a generating AI and have the generating AI propose the optimal management method.

[0086] The service provider can estimate the user's emotions and adjust the method of providing ecological information based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed ecological information. If the user is excited, the service provider can also provide visually appealing ecological information. Furthermore, if the user is stressed, the service provider can provide concise and to-the-point ecological information. By adjusting the method of providing ecological information according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0087] The content provider can provide optimal content by referring to the user's past learning history at the time of delivery. For example, the provider can provide relevant ecological information based on what the user has learned in the past. The provider can also prioritize providing information on fish species of interest based on the user's learning history. Furthermore, the provider can analyze the user's learning history and suggest the most effective learning method. This enables efficient learning by providing optimal content based on the user's past learning history. Some or all of the above processing in the content provider may be performed using AI, for example, or without AI. For example, the provider can input the user's past learning history data into a generating AI and have the generating AI perform the task of providing optimal content.

[0088] The information provider can provide different ecological information for each fish species at the time of provision. For example, the information provider can provide content that explains the different habitats and behavioral patterns for each fish species. It can also provide content that explains the different diets and reproductive methods for each fish species. Furthermore, it can provide content that explains different fishing tips and techniques for each fish species. By providing different ecological information for each fish species, it becomes possible to provide more detailed information. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input ecological information for each fish species into a generating AI and have the generating AI perform the provision of different ecological information.

[0089] The service provider can estimate the user's emotions and prioritize ecological information based on the estimated emotions. For example, if the user is excited, the service provider will prioritize providing important ecological information. It can also provide detailed ecological information if the user is relaxed. Furthermore, if the user is stressed, the service provider can provide concise and to-the-point ecological information. This allows for the prioritization of more important information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0090] The information provider can provide optimal ecological information by considering the user's geographical location at the time of provision. For example, if the user is fishing in a specific area, the information provider can provide ecological information related to that area. The information provider can also provide ecological information for fishing spots close to the user's current location. Furthermore, the information provider can provide new ecological information by comparing it with past data based on the user's geographical location. This enables efficient information provision by providing optimal ecological information by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location into a generating AI and have the generating AI perform the task of providing optimal ecological information.

[0091] The service provider can analyze the user's social media activity and provide relevant ecological information at the time of delivery. For example, the service provider can provide relevant ecological information based on fishing information shared by the user on social media. The service provider can also analyze the content of the user's social media posts and provide ecological information on fish species of interest. Furthermore, the service provider can provide ecological information based on fishing information shared by the user's social media followers. In this way, relevant ecological information can be efficiently provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant ecological information.

[0092] The interactive unit can estimate the user's emotions and adjust how the interactive experience is delivered based on the estimated emotions. For example, if the user is relaxed, the interactive unit can provide a relaxed interactive experience. If the user is excited, the interactive unit can also provide an active interactive experience. Furthermore, if the user is stressed, the interactive unit can provide a simple and hassle-free interactive experience. In this way, a more appropriate experience can be provided by adjusting how the interactive experience is delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0093] The interactive unit can provide the optimal experience during an interactive experience by referring to the user's past participation history. For example, the interactive unit can provide relevant experiences based on the user's past interactive experiences. Furthermore, the interactive unit can prioritize providing experiences of interest based on the user's past participation history. In addition, the interactive unit can analyze the user's participation history and suggest the most effective experience. This enables an efficient experience by providing the optimal experience based on the user's past participation history. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input the user's past participation history data into a generating AI and have the generating AI perform the task of providing the optimal experience.

[0094] The interactive unit can estimate the user's emotions and prioritize interactive experiences based on those emotions. For example, if the user is excited, the interactive unit will prioritize providing important interactive experiences. It can also provide detailed interactive experiences if the user is relaxed. Furthermore, if the user is stressed, the interactive unit can provide concise and to-the-point interactive experiences. This allows for prioritizing more important experiences based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interactive unit may be performed using AI or not. For example, the interactive unit can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0095] The interactive unit can provide an optimal experience by considering the user's geographical location during an interactive experience. For example, if the user is fishing in a specific area, the interactive unit can provide an interactive experience related to that area. It can also provide an interactive experience of fishing spots near the user's current location. Furthermore, the interactive unit can provide a new interactive experience by comparing the user's geographical location with past data. This enables an efficient experience by providing an optimal experience by considering the user's geographical location. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing an optimal experience.

[0096] The interactive unit can estimate the user's emotions and adjust how the interactive experience is delivered based on the estimated emotions. For example, if the user is relaxed, the interactive unit can provide a relaxed interactive experience. If the user is excited, the interactive unit can also provide an active interactive experience. Furthermore, if the user is stressed, the interactive unit can provide a simple and hassle-free interactive experience. In this way, a more appropriate experience can be provided by adjusting how the interactive experience is delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The virtual fishing system may also include a "notification unit." The notification unit can provide real-time advice and information to the user while they are fishing. For example, the notification unit can notify the user in real time of changes in weather conditions and water temperature at their fishing location. It can also advise the user on the best fishing techniques and bait selection for the time of day they are fishing. Furthermore, the notification unit can receive messages and advice from other users in real time while the user is fishing. This allows the user to receive necessary information and advice in real time while fishing, increasing their chances of success. Some or all of the above processing in the notification unit may be performed using AI, for example, or not. For example, the notification unit can input weather data and water temperature data into a generating AI and have the generating AI provide optimal advice.

[0099] The virtual fishing system may also include a "guide unit." The guide unit provides support as a virtual guide while the user is fishing. For example, the guide unit can provide advice on selecting a fishing spot and preparing fishing equipment before the user starts fishing. The guide unit can also provide real-time explanations of fishing techniques and fish behavior during fishing. Furthermore, the guide unit can provide analysis of the catch and advice for the next fishing trip after the user has finished fishing. This allows the user to enjoy fishing more effectively with the support of a virtual guide. Some or all of the above processes in the guide unit may be performed using AI, for example, or not using AI. For example, the guide unit can input the user's fishing data into a generating AI and have the generating AI provide optimal advice.

[0100] The virtual fishing system may also include an "entertainment section." The entertainment section provides additional content to enhance the user's fishing experience. For example, the entertainment section could offer mini-games or quizzes during fishing, providing entertainment for users in between fishing sessions. It could also provide trivia and interesting anecdotes about the fish caught. Furthermore, the entertainment section could play music or nature sounds while the user is fishing, creating a relaxing atmosphere. This allows users to enjoy fishing in a more fun and relaxed way. Some or all of the above-mentioned processes in the entertainment section may be performed using AI, for example, or without AI. For example, the entertainment section could input the user's fishing data into a generating AI and have the generating AI provide the most suitable content.

[0101] The virtual fishing system may also include a "communication unit." The communication unit provides functions that allow users to communicate with each other in real time. For example, the communication unit allows users to chat with other users while they are fishing. The communication unit can also allow users to share photos and videos of the fish they have caught with other users. Furthermore, the communication unit can allow users to exchange fishing advice and information with each other. This allows users to enjoy fishing more while communicating with other users. Some or all of the above processing in the communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can input user chat data into a generating AI and have the generating AI perform the task of providing optimal communication.

[0102] The virtual fishing system may also include a "feedback unit." This unit provides feedback to the user after they finish fishing, regarding their catch and fishing technique. For example, it can analyze data on the fish the user caught and evaluate their fishing success rate and areas for improvement. It can also suggest areas for improvement and new techniques for the user's next fishing trip. Furthermore, it can provide advice and motivation to help the user enjoy fishing. This allows the user to improve their fishing skills while preparing for their next trip. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For instance, the feedback unit could input the user's fishing data into a generating AI and have the AI ​​provide optimal feedback.

[0103] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is excited, the data collection unit can immediately start collecting data and obtain information in real time. If the user is relaxed, the data collection unit can collect data at regular intervals, matching the user's pace. Furthermore, if the user is stressed, the data collection unit can temporarily stop collecting data and wait until the user calms down. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0104] The generation unit can estimate the user's emotions and adjust the method of generating virtual fish based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate virtual fish with slow movements. If the user is excited, the generation unit can also generate virtual fish with lively movements. Furthermore, if the user is stressed, the generation unit can generate virtual fish with a calming effect. By adjusting the method of generating virtual fish according to the user's emotions, more appropriate virtual fish can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0105] The management unit can estimate the user's emotions and adjust the virtual fish management method based on the estimated emotions. For example, if the user is relaxed, the management unit can provide a relaxed management method. If the user is excited, the management unit can also provide an active management method. Furthermore, if the user is stressed, the management unit can provide a simple and hassle-free management method. This allows for more appropriate management by adjusting the virtual fish management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, or not using AI. For example, the management unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0106] The service provider can estimate the user's emotions and adjust the method of providing ecological information based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed ecological information. If the user is excited, the service provider can also provide visually appealing ecological information. Furthermore, if the user is stressed, the service provider can provide concise and to-the-point ecological information. By adjusting the method of providing ecological information according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0107] The interactive unit can estimate the user's emotions and adjust how the interactive experience is delivered based on the estimated emotions. For example, if the user is relaxed, the interactive unit can provide a relaxed interactive experience. If the user is excited, the interactive unit can also provide an active interactive experience. Furthermore, if the user is stressed, the interactive unit can provide a simple and hassle-free interactive experience. In this way, a more appropriate experience can be provided by adjusting how the interactive experience is delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The data collection unit collects data on fish caught by the user in real time. The collected data includes fish species, weight, size, color pattern, and fishing location. The data collection unit can accept manual data input from the user, or it can automatically collect data using devices such as smartphones and tablets. For example, a smartphone camera can be used to take a picture of a fish, and the system can automatically recognize the fish species and size from that picture. It can also automatically acquire fishing location information using GPS functionality. Step 2: The generation unit analyzes the data collected by the collection unit and generates fish in a virtual space. The generation unit uses AI to analyze the collected data and generates realistic fish in a virtual space based on data such as fish species, size, and color patterns. The generation unit can also simulate the movement and behavior patterns of fish based on the collected data. Step 3: The management unit manages and raises the virtual fish generated by the generation unit. The management unit allows users to feed the virtual fish and adjust the aquarium environment. The management unit can also monitor the health of the virtual fish and suggest appropriate management methods. For example, it can use AI to analyze the health of the virtual fish and suggest appropriate food and aquarium environment. Step 4: The service provider provides ecological information about virtual fish managed by the management provider. The service provider provides content that allows users to learn about the ecology of the fish they have caught and fishing techniques. For example, it can use AI to analyze the ecological information of the fish that have been caught and provide it to the user. The service provider provides information about the ecology of the fish that have been caught and fishing techniques in the form of text, images, videos, etc.

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the collection unit, generation unit, management unit, provision unit, and interactive unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects fish data using the camera 42 and GPS function of the smart device 14. The generation unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and generates fish in a virtual space. The management unit manages and raises the virtual fish using the control unit 46A of the smart device 14. The provision unit analyzes the ecological information of the caught fish using the specific processing unit 290 of the data processing unit 12 and provides it to the user. The interactive unit enables fishing contests and fish exchanges in a virtual space using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the collection unit, generation unit, management unit, provision unit, and interactive unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects fish data using the camera 42 and GPS function of the smart glasses 214. The generation unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and generates fish in a virtual space. The management unit manages and raises the virtual fish using the control unit 46A of the smart glasses 214. The provision unit analyzes the ecological information of the caught fish using the specific processing unit 290 of the data processing unit 12 and provides it to the user. The interactive unit enables fishing contests and fish exchanges in a virtual space using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the collection unit, generation unit, management unit, provision unit, and interactive unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects fish data using the camera 42 and GPS function of the headset terminal 314. The generation unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and generates fish in a virtual space. The management unit manages and raises the virtual fish using the control unit 46A of the headset terminal 314. The provision unit analyzes the ecological information of the caught fish using the specific processing unit 290 of the data processing unit 12 and provides it to the user. The interactive unit enables fishing contests and fish exchanges in a virtual space using the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, generation unit, management unit, provision unit, and interactive unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects fish data using the camera 42 and GPS function of the robot 414. The generation unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and generates fish in a virtual space. The management unit manages and raises the virtual fish using the control unit 46A of the robot 414. The provision unit analyzes the ecological information of the caught fish using the specific processing unit 290 of the data processing unit 12 and provides it to the user. The interactive unit enables fishing contests and fish exchanges in a virtual space using the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A data collection unit that collects data on fish caught by users in real life, A generation unit analyzes the data collected by the aforementioned collection unit and generates fish in a virtual space, A management unit manages and raises the virtual fish generated by the generation unit, The system comprises a provisioning unit that provides ecological information of virtual fish managed by the aforementioned management unit. A system characterized by the following features. (Note 2) It features an interactive section that provides an interactive experience with other users in a virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Analyze the user's past fishing history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is During data collection, filtering is performed based on the user's current fishing environment and weather conditions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is The system estimates the user's emotions and adjusts the virtual fish generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is During generation, the accuracy of the virtual fish is adjusted based on the level of detail of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is During generation, a different generation algorithm is applied for each fish species. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the appearance of the virtual fish based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, adjust the growth rate of the virtual fish based on the time of year when fishing takes place. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the ecology of the virtual fish is adjusted based on the fishing location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned management department, It estimates the user's emotions and adjusts how virtual fish are managed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned management department, During management, the system selects the optimal management method by referring to the user's past management history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned management department, During management, different management methods are applied depending on the growth stage of the virtual fish. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned management department, It estimates user sentiment and determines management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, It estimates user sentiment and determines management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, During management, the optimal management method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, During management, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the method of providing biometric information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing content, the system will refer to the user's past learning history to deliver the most suitable content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the information, we will provide ecological information that differs for each fish species. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and prioritizes ecological information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, we will provide optimal ecological information considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant biometric information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned interactive unit is It estimates the user's emotions and adjusts how the interactive experience is delivered based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned interactive unit is During interactive experiences, we refer to the user's past participation history to provide the optimal experience. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned interactive unit is It estimates the user's emotions and prioritizes the interactive experience based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned interactive unit is When providing interactive experiences, we take the user's geographical location into consideration to deliver the optimal experience. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on fish caught by users in real life, A generation unit analyzes the data collected by the aforementioned collection unit and generates fish in a virtual space, A management unit manages and raises the virtual fish generated by the generation unit, The system comprises a provisioning unit that provides ecological information of virtual fish managed by the aforementioned management unit. A system characterized by the following features.

2. It features an interactive section that provides an interactive experience with other users in a virtual space. The system according to feature 1.

3. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

4. The aforementioned collection unit is Analyze the user's past fishing history and select the optimal data collection method. The system according to feature 1.

5. The aforementioned collection unit is During data collection, filtering is performed based on the user's current fishing environment and weather conditions. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system according to feature 1.

9. The generating unit is It estimates the user's emotions and adjusts the method of generating virtual fish based on the estimated user emotions. The system according to feature 1.

10. The generating unit is During generation, the accuracy of the virtual fish is adjusted based on the level of detail of the collected data. The system according to feature 1.

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