system

Abstract

The system according to this embodiment aims to connect users with each other through virtual and real-world agricultural experiences, and to facilitate information exchange and advice exchange. [Solution] The system according to this embodiment comprises an experience unit, an SNS unit, an advice unit, and a donation unit. The experience unit allows users to experience rice farming virtually and in real life. The SNS unit connects users with other users based on the experience provided by the experience unit. The advice unit provides optimal advice to users connected through the SNS unit. The donation unit accepts donations.

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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that users are not sufficiently connected through virtual and real agricultural experiences to exchange information and receive advice.

[0005] The system according to the embodiment aims to connect users through virtual and real agricultural experiences to exchange information and receive advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an experience unit, an SNS unit, an advice unit, and a donation unit. The experience unit allows users to experience rice farming virtually and in real life. The SNS unit connects users with other users based on the experience provided by the experience unit. The advice unit provides optimal advice to users connected through the SNS unit. The donation unit accepts donations. [Effects of the Invention]

[0007] The system according to this embodiment allows users to connect with each other through virtual and real-world agricultural experiences, and to exchange information and receive advice. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 rice farming experience system according to an embodiment of the present invention is a system that allows users to experience rice farming virtually and in real life. This rice farming experience system allows users to experience rice farming virtually and in real life, connect with each other through SNS functions and real events, and promote food education. Furthermore, the rice farming experience system utilizes AI to provide users with optimal advice and is targeted at a wide range of people, from adults to children, who are interested in food. For example, users can experience rice farming virtually or in real life. In the virtual experience, users learn the rice farming process through an app, and in the real experience, they can actually grow rice. Users can receive harvested rice by making a donation. Next, they can connect with other users through SNS functions, give each other advice, and share their rice and menu recipes. This allows users to share information and form a community. In addition, real events are held where users can actually experience rice farming. This allows a wide range of people, from adults to children, who are interested in food, to participate and promote food education. This service utilizes AI to provide users with optimal advice. For example, it automatically generates specific advice based on local climate and weather information and the user's cultivation progress. Furthermore, it suggests an optimal schedule tailored to the user, including planting, watering, and harvesting timing. For children, it generates content with easy-to-understand and fun illustrations and stories. Thus, the rice farming experience system allows users to experience rice farming both virtually and in real life, connecting with each other through social media functions and real-world events, and promoting food education. It also utilizes AI to provide optimal advice to users, targeting a wide range of people, from adults to children, who are interested in food. In this way, the rice farming experience system allows users to experience rice farming virtually and in real life, connect with other users, receive optimal advice, and make donations.

[0029] The rice farming experience system according to this embodiment comprises an experience section, an SNS section, an advice section, and a donation section. The experience section allows users to experience rice farming virtually and in real life. For example, the experience section allows users to learn the rice farming process through an app. The experience section also allows users to actually grow rice. For example, the experience section can grow rice in partnership with the JA Group's bucket rice cultivation program. Furthermore, the experience section allows users to receive harvested rice by making a donation. The SNS section allows users to connect with other users based on the experience provided by the experience section. For example, the SNS section allows users to give each other advice, share their rice harvests and recipes. This enables the SNS section to allow users to share information and form communities. The advice section can provide optimal advice to users connected through the SNS section. For example, the advice section automatically generates specific advice based on local climate and weather information, and the user's cultivation progress. The advice section can also propose an optimal schedule tailored to the user, including planting, watering, and harvesting timing. The donation section can accept donations. For example, the donation section can receive harvested rice when a user makes a donation. Thus, the rice farming experience system according to this embodiment allows users to experience rice farming virtually and in real life, connect with other users, receive optimal advice, and make donations.

[0030] The Experience Department allows users to experience rice farming both virtually and in real life. Specifically, users can learn each step of rice farming through a dedicated application. This application simulates the entire process from sowing to harvesting, providing users with an interactive experience. For example, users can manage a virtual rice paddy within the app, sowing seeds at the appropriate time, watering, and fertilizing. This allows users to acquire basic knowledge and skills in rice farming. The Experience Department also allows users to actually grow rice. For example, in partnership with the JA Group's bucket rice cultivation program, users can grow rice at home using buckets. In this case, users can record the progress of cultivation through the app and receive necessary advice. Furthermore, the Experience Department provides a system where users can receive harvested rice in exchange for making a donation. Users can support local farmers through donations and receive harvested rice in return. In this way, the Experience Department not only allows users to experience rice farming both virtually and in real life, but also deepens connections with the local community.

[0031] The SNS section allows users to connect with each other based on experiences provided by the Experience section. Specifically, users can use the in-app SNS function to share information and communicate with other users. For example, users can post photos and videos of their cultivation progress and receive comments and advice from other users. The SNS section also allows users to give each other advice and share their rice harvest and menu recipes. This enables users to share knowledge and experience about rice cultivation and form a community. Furthermore, the SNS section provides group functions based on specific themes and events, allowing users to connect with like-minded individuals who share common interests and goals. For example, there could be groups to share cultivation methods suited to specific regions or climate conditions, or groups to plan events such as harvest festivals or cooking contests. In this way, the SNS section can promote interaction among users and provide a richer experience.

[0032] The Advice Department can provide optimal advice to users connected through the Social Networking Service (SNS) Department. Specifically, the Advice Department automatically generates specific advice based on local climate and weather information, as well as the user's cultivation progress. For example, it uses AI to analyze data entered by users and information shared on the SNS Department to propose the optimal cultivation methods and timings. By providing users with optimal schedules tailored to their needs, such as sowing, watering, and harvesting timing, users can grow rice efficiently. The Advice Department can also create long-term cultivation plans by utilizing past data and statistical information. For example, it can propose optimal cultivation methods for specific regions and times based on past weather data and yield data. Furthermore, the Advice Department can provide countermeasures for unexpected situations such as extreme weather or pest outbreaks. In this way, the Advice Department can always provide users with the latest and most optimal advice, supporting their success in rice cultivation.

[0033] The donation department can accept donations. Specifically, it provides a system where users can receive harvested rice by making donations through the app. For example, users can select the donation amount within the app and make the donation using payment methods such as credit cards or electronic money. In addition to receiving the harvested rice, users who make a donation will also be provided with information about the farmer and region to which they donated. This allows users to know how their donation is being used and to feel the significance of their donation. Furthermore, the donation department can also offer benefits and rewards to users who make donations. For example, users who make donations may be invited to special events or given exclusive products. In addition, the donation department manages donation history and achievements, providing a function that allows users to reflect on their donation activities. This allows the donation department to communicate the significance of donations to users and encourage continued donation activities.

[0034] The Events Department can host real-world events. For example, the Events Department can host real-world events such as workshops and field trips, allowing users to experience rice farming through these events. Some or all of the above-mentioned processes in the Events Department may be performed using AI, or not. For example, the Events Department can input the content of a real-world event into a generating AI and have the generating AI execute the detailed schedule and content of the event.

[0035] The children's section can provide content for children. For example, the children's section can provide content for children such as educational games and animations. This allows children to learn while having fun. Some or all of the above-mentioned processes in the children's section may be performed using AI, for example, or not using AI. For example, the children's section can input content for children into a generating AI and have the generating AI create easy-to-understand and fun illustrations and stories.

[0036] The supply unit can provide the harvested rice. The supply unit can, for example, receive the rice harvested by the user. The supply unit can, for example, clarify the delivery method and the type of rice to be provided. This allows the user to receive the harvested rice. Some or all of the above processing in the supply unit may be performed using AI, for example, or not using AI. For example, the supply unit can input the delivery schedule for the harvested rice into a generating AI and have the generating AI execute the optimal delivery method.

[0037] The advice unit can automatically generate specific advice based on local climate and weather information, as well as the user's cultivation progress. For example, the advice unit can provide the user with optimal advice based on local climate and weather information. The advice unit can clarify, for example, the source of weather data and the frequency of data updates. Furthermore, the advice unit can provide specific advice based on the user's cultivation progress. For example, the advice unit can provide specific advice to the user based on growth stage checklists and photographic records. This allows the user to receive specific advice based on local climate and weather information. Some or all of the above processing in the advice unit is performed using, for example, a generation AI. For example, the advice unit can input local climate and weather information into the generation AI and have the generation AI execute specific advice.

[0038] The advice unit can suggest an optimal schedule tailored to the user, including planting, watering, and harvesting timing. For example, the advice unit can suggest the optimal schedule based on a cultivation calendar and reminder function. This enables the user to cultivate efficiently. Some or all of the above processes in the advice unit are performed using, for example, a generative AI. For instance, the advice unit can input the user's cultivation progress into the generative AI and have the AI ​​execute the optimal schedule.

[0039] The children's section can generate content with easy-to-understand and fun illustrations and stories. For example, the children's section can generate easy-to-understand and fun illustrations and stories based on character settings and storyboards. This allows children to learn while having fun. Some or all of the above-mentioned processes in the children's section may be performed using, for example, a generation AI, or not using a generation AI. For example, the children's section can input character settings and storyboards into a generation AI and have the generation AI produce easy-to-understand and fun illustrations and stories.

[0040] The experience unit can analyze a user's past experience history and select the optimal experience method. For example, the experience unit can suggest similar experiences based on experiences the user has enjoyed in the past. It can also suggest different experiences based on experiences the user has avoided in the past. Furthermore, the experience unit can suggest the optimal sequence of experiences based on the user's past experience history. In this way, it can provide the optimal experience method based on the user's past experience history. Some or all of the above processing in the experience unit may be performed using AI, for example, or without AI. For example, the experience unit can input the user's past experience history data into a generating AI and have the generating AI select the optimal experience method.

[0041] The experience unit can filter experiences based on the user's current lifestyle and areas of interest. For example, if the user is busy, the experience unit can suggest experiences that can be completed in a short time. Furthermore, if the user has a specific area of ​​interest, the experience unit can suggest experiences related to that area. In addition, the experience unit can filter and provide appropriate experiences according to the user's lifestyle. This allows for the provision of experiences tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the experience unit may be performed using AI, for example, or without AI. For example, the experience unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0042] The experience unit can prioritize providing highly relevant experiences by considering the user's geographical location during the experience. For example, if the user is in a specific region, the experience unit can provide experiences related to that region. Furthermore, if the user is traveling, the experience unit can provide experiences related to their travel destination. In addition, the experience unit can suggest the most suitable experiences based on the user's geographical location. This allows for the provision of experiences tailored to the user's geographical location. Some or all of the above processing in the experience unit may be performed using AI, for example, or without AI. For example, the experience unit can input the user's geographical location into a generating AI and have the generating AI provide highly relevant experiences.

[0043] The experience unit can analyze the user's social media activity during the experience and provide relevant experiences. For example, the experience unit can suggest experiences based on what the user has shown interest in on social media. It can also provide experiences that are of high interest to the user based on their social media activity. Furthermore, the experience unit can analyze the user's social media activity and suggest the most suitable experience. This allows the experience unit to provide experiences based on the user's social media activity. Some or all of the above processing in the experience unit may be performed using AI, for example, or without AI. For example, the experience unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant experiences.

[0044] The SNS (Social Networking Service) unit can analyze a user's past posting history when they use social networking services (SNS) and select the optimal display method. For example, the SNS unit can prioritize displaying similar posts based on posts the user has liked in the past. It can also prioritize displaying different posts based on posts the user has avoided in the past. Furthermore, the SNS unit can suggest the optimal display method based on the user's past posting history. This allows the SNS unit to provide the optimal display method based on the user's past posting history. Some or all of the above processing in the SNS unit may be performed using AI, for example, or without AI. For example, the SNS unit can input the user's past posting history data into a generating AI and have the generating AI select the optimal display method.

[0045] The SNS (Social Networking Service) unit can filter posts based on the user's areas of interest when they use the SNS. For example, if a user has a specific area of ​​interest, the SNS unit can prioritize displaying posts related to that area. The SNS unit can also filter and display relevant posts based on the user's areas of interest. Furthermore, the SNS unit can suggest the most suitable posts based on the user's areas of interest. This allows the SNS unit to provide posts that are tailored to the user's areas of interest. Some or all of the above processing in the SNS unit may be performed using AI, for example, or without AI. For example, the SNS unit can input user area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0046] The SNS (Social Networking Service) section can prioritize displaying highly relevant posts when a user is using social networking services, taking into account the user's geographical location. For example, if a user is in a specific region, the SNS section can prioritize displaying posts related to that region. Furthermore, if a user is traveling, the SNS section can prioritize displaying posts related to their travel destination. In addition, the SNS section can suggest optimal posts based on the user's geographical location. This allows the SNS section to provide posts tailored to the user's geographical location. Some or all of the above processing in the SNS section may be performed using AI, or without AI. For example, the SNS section can input the user's geographical location into a generating AI and have the generating AI provide highly relevant posts.

[0047] The SNS (Social Networking Service) unit can analyze a user's social media activity when they use social media and display relevant posts. For example, the SNS unit can suggest posts based on what the user has shown interest in on social media. Furthermore, the SNS unit can prioritize displaying posts of high interest based on the user's social media activity. In addition, the SNS unit can analyze the user's social media activity and suggest the most suitable posts. This allows the SNS unit to provide posts based on the user's social media activity. Some or all of the above processing in the SNS unit may be performed using AI, for example, or without AI. For example, the SNS unit can input the user's social media activity data into a generating AI and have the generating AI provide relevant posts.

[0048] The advice unit can determine the priority of advice based on the user's cultivation progress when providing advice. For example, if the user is a beginner, the advice unit can prioritize providing basic advice. If the user is an intermediate user, the advice unit can prioritize providing detailed advice. Furthermore, if the user is an advanced user, the advice unit can prioritize providing expert advice. This allows the advice unit to provide advice priorities according to the user's cultivation progress. Some or all of the above processing in the advice unit is performed using, for example, a generating AI. For example, the advice unit can input the user's cultivation progress data into the generating AI and have the generating AI determine the priority of advice.

[0049] The advice unit can adjust the order of advice by referring to the user's cultivation-related information when providing advice. For example, if the user is cultivating rice, the advice unit can provide advice according to each step of rice cultivation. If the user is cultivating vegetables, the advice unit can provide advice according to each step of vegetable cultivation. Furthermore, if the user is cultivating fruit trees, the advice unit can provide advice according to each step of fruit tree cultivation. This allows the advice unit to provide an order of advice based on the user's cultivation-related information. Some or all of the above processing in the advice unit is performed using, for example, a generating AI. For example, the advice unit can input the user's cultivation-related information into the generating AI and have the generating AI adjust the order of advice.

[0050] The donation unit can analyze a user's past donation history to select the most suitable donation method when proposing a donation. For example, the donation unit can suggest an appropriate donation amount based on the amount the user has donated in the past. It can also suggest an appropriate donation timing based on the frequency of the user's past donations. Furthermore, the donation unit can prioritize suggesting specific donation recipients based on the user's past donation history. This allows the unit to provide the most suitable donation method based on the user's past donation history. Some or all of the above processing in the donation unit may be performed using AI, for example, or without AI. For example, the donation unit can input the user's past donation history data into a generating AI and have the generating AI select the most suitable donation method.

[0051] The donation unit can filter donation proposals based on the user's current living situation and areas of interest. For example, if the user has a specific area of ​​interest, the donation unit can suggest donation recipients related to that area. The donation unit can also filter and provide appropriate donation recipients according to the user's living situation. Furthermore, the donation unit can suggest the most suitable donation recipient based on the user's areas of interest. This allows the donation unit to provide donation proposals based on the user's living situation and areas of interest. Some or all of the above processing in the donation unit may be performed using AI, for example, or not using AI. For example, the donation unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0052] The donation unit can prioritize highly relevant donation suggestions by considering the user's geographical location when a donation is proposed. For example, if the user is in a specific region, the donation unit can suggest donation destinations relevant to that region. Furthermore, if the user is traveling, the donation unit can suggest donation destinations relevant to their travel destination. In addition, the donation unit can suggest the most suitable donation destination based on the user's geographical location. This allows the donation unit to provide donation suggestions based on the user's geographical location. Some or all of the above processing in the donation unit may be performed using AI, for example, or without AI. For example, the donation unit can input the user's geographical location into a generating AI and have the generating AI provide highly relevant donation suggestions.

[0053] The Events Department can analyze a user's past participation history to select the most suitable event content when an event is being held. For example, the Events Department can suggest similar events based on events the user has previously attended. It can also suggest different events based on events the user has previously avoided. Furthermore, the Events Department can suggest the optimal order of events based on the user's past participation history. This allows the Events Department to provide the most suitable event content based on the user's past participation history. Some or all of the above processing in the Events Department may be performed using AI, for example, or without AI. For example, the Events Department can input the user's past participation history data into a generating AI and have the generating AI select the most suitable event content.

[0054] The event department can filter events based on the user's current lifestyle and areas of interest when an event is being held. For example, if a user has a specific area of ​​interest, the event department can suggest events related to that area. The event department can also filter and provide appropriate events according to the user's lifestyle. Furthermore, the event department can suggest the most suitable events based on the user's areas of interest. This allows the event department to provide events tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the event department may be performed using AI, for example, or without AI. For example, the event department can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0055] The Events Department can prioritize events that are highly relevant to the user, taking into account the user's geographical location when an event is being held. For example, if the user is in a specific region, the Events Department can suggest events related to that region. Also, if the user is traveling, the Events Department can suggest events related to their travel destination. Furthermore, the Events Department can suggest the most suitable events based on the user's geographical location. This allows the Events Department to provide events tailored to the user's geographical location. Some or all of the above processing in the Events Department may be performed using AI, for example, or without AI. For example, the Events Department can input the user's geographical location into a generating AI and have the generating AI provide highly relevant events.

[0056] The children's content department can analyze a user's past usage history to select the most suitable content when providing children's content. For example, the children's content department can suggest similar content based on content the user has previously enjoyed. It can also suggest different content based on content the user has previously avoided. Furthermore, the children's content department can suggest the optimal order of content based on the user's past usage history. This allows for the provision of optimal content based on the user's past usage history. Some or all of the above processes in the children's content department may be performed using AI, for example, or without AI. For example, the children's content department can input the user's past usage history data into a generating AI and have the generating AI select the optimal content.

[0057] The children's section can filter children's content based on the user's current lifestyle and areas of interest. For example, if a user has a specific area of ​​interest, the children's section can suggest content related to that area. Furthermore, the children's section can filter and provide appropriate content according to the user's lifestyle. In addition, the children's section can suggest optimal content based on the user's areas of interest. This allows for the provision of children's content tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the children's section may be performed using AI, or without AI. For example, the children's section can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0058] The children's section can prioritize providing highly relevant content by considering the user's geographical location when delivering children's content. For example, if the user is in a specific region, the children's section can suggest content related to that region. Furthermore, if the user is traveling, the children's section can suggest content related to their travel destination. In addition, the children's section can suggest optimal content based on the user's geographical location. This allows for the provision of children's content tailored to the user's geographical location. Some or all of the above processing in the children's section may be performed using AI, or without AI. For example, the children's section can input the user's geographical location into a generating AI and have the generating AI provide highly relevant content.

[0059] The children's department can analyze users' social media activity when providing children's content and provide relevant content. For example, the children's department can suggest content based on what users have shown interest in on social media. Furthermore, the children's department can prioritize providing content of high interest based on users' social media activity. In addition, the children's department can analyze users' social media activity and suggest optimal content. This allows for the provision of children's content based on users' social media activity. Some or all of the above processing in the children's department may be performed using AI, for example, or without AI. For example, the children's department can input user social media activity data into a generating AI and have the generating AI provide relevant content.

[0060] The delivery unit can analyze the user's past receiving history to select the optimal delivery method when providing rice. For example, the delivery unit can suggest an appropriate type of rice based on the types of rice the user has received in the past. It can also suggest an appropriate delivery timing based on the frequency with which the user has received rice in the past. Furthermore, the delivery unit can prioritize suggesting a specific delivery method based on the user's past receiving history. This allows the delivery unit to provide the optimal delivery method based on the user's past receiving history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past receiving history data into a generating AI and have the generating AI select the optimal delivery method.

[0061] The supply unit can filter rice based on the user's current lifestyle and areas of interest when providing it. For example, if the user has a specific area of ​​interest, the supply unit can suggest rice related to that area. The supply unit can also filter and provide appropriate rice according to the user's lifestyle. Furthermore, the supply unit can suggest the most suitable rice based on the user's areas of interest. This allows for a method of providing rice that is tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0062] The service provider can prioritize the most relevant delivery method when providing rice, taking into account the user's geographical location. For example, if the user is in a specific region, the service provider can suggest rice related to that region. Furthermore, if the user is traveling, the service provider can suggest rice related to their travel destination. In addition, the service provider can suggest the most suitable rice based on the user's geographical location. This allows for the provision of rice delivery methods tailored to the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input the user's geographical location into a generating AI and have the generating AI provide the most relevant delivery method.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The experience unit can analyze a user's past experience history and select the optimal experience method. For example, it can suggest similar experiences based on experiences the user has enjoyed in the past. It can also suggest different experiences based on experiences the user has avoided in the past. Furthermore, it can suggest the optimal sequence of experiences based on the user's past experience history. In this way, it can provide the optimal experience method based on the user's past experience history. Some or all of the above processing in the experience unit may be performed using AI, for example, or without AI. For example, the experience unit can input the user's past experience history data into a generating AI and have the generating AI select the optimal experience method.

[0065] The SNS (Social Networking Service) unit can analyze a user's past posting history when they use social networking services (SNS) and select the optimal display method. For example, it can prioritize displaying similar posts based on posts the user has liked in the past. It can also prioritize displaying different posts based on posts the user has avoided in the past. Furthermore, it can suggest the optimal display method based on the user's past posting history. This allows the SNS unit to provide the optimal display method based on the user's past posting history. Some or all of the above processing in the SNS unit may be performed using AI, for example, or without AI. For example, the SNS unit can input the user's past posting history data into a generating AI and have the generating AI select the optimal display method.

[0066] The advice unit can prioritize advice based on the user's cultivation progress when providing it. For example, if the user is a beginner, basic advice can be prioritized. If the user is an intermediate user, detailed advice can be prioritized. Furthermore, if the user is an advanced user, expert advice can be prioritized. This allows for prioritizing advice according to the user's cultivation progress. Some or all of the above processing in the advice unit is performed using, for example, a generating AI. For example, the advice unit can input the user's cultivation progress data into the generating AI and have the generating AI determine the priority of the advice.

[0067] The event department can analyze a user's past participation history to select the most suitable event content when an event is being held. For example, it can suggest similar events based on events the user has previously attended. It can also suggest different events based on events the user has previously avoided. Furthermore, it can suggest the optimal order of events based on the user's past participation history. This allows the event department to provide the most suitable event content based on the user's past participation history. Some or all of the above processing in the event department may be performed using AI, for example, or without AI. For example, the event department can input the user's past participation history data into a generating AI and have the generating AI select the most suitable event content.

[0068] The delivery unit can analyze the user's past receipt history to select the optimal delivery method when providing rice. For example, it can suggest an appropriate type of rice based on the types of rice the user has received in the past. It can also suggest an appropriate delivery timing based on the frequency of the user's past receipts. Furthermore, it can prioritize suggesting specific delivery methods based on the user's past receipt history. This allows the delivery unit to provide the optimal delivery method based on the user's past receipt history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past receipt history data into a generating AI and have the generating AI select the optimal delivery method.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The experience section allows users to experience rice farming both virtually and in real life. Through the app, users can learn the rice farming process and actually grow rice. For example, they can grow rice in real life in partnership with the JA Group's bucket rice farming program. Users can also receive harvested rice by making a donation. Step 2: The SNS section allows users to connect with each other based on the experiences provided by the Experience section. Users can give each other advice, share their favorite rice dishes and menu recipes, share information, and form communities. Step 3: The advice section provides optimal advice to users connected through the social networking section. It automatically generates specific advice based on local climate and weather information, as well as the user's cultivation progress, and can propose the optimal schedule tailored to the user, including planting, watering, and harvesting timing. Step 4: The donation department can accept donations. Users can receive harvested rice by making a donation.

[0071] (Example of form 2) The rice farming experience system according to an embodiment of the present invention is a system that allows users to experience rice farming virtually and in real life. This rice farming experience system allows users to experience rice farming virtually and in real life, connect with each other through SNS functions and real events, and promote food education. Furthermore, the rice farming experience system utilizes AI to provide users with optimal advice and is targeted at a wide range of people, from adults to children, who are interested in food. For example, users can experience rice farming virtually or in real life. In the virtual experience, users learn the rice farming process through an app, and in the real experience, they can actually grow rice. Users can receive harvested rice by making a donation. Next, they can connect with other users through SNS functions, give each other advice, and share their rice and menu recipes. This allows users to share information and form a community. In addition, real events are held where users can actually experience rice farming. This allows a wide range of people, from adults to children, who are interested in food, to participate and promote food education. This service utilizes AI to provide users with optimal advice. For example, it automatically generates specific advice based on local climate and weather information and the user's cultivation progress. Furthermore, it suggests an optimal schedule tailored to the user, including planting, watering, and harvesting timing. For children, it generates content with easy-to-understand and fun illustrations and stories. Thus, the rice farming experience system allows users to experience rice farming both virtually and in real life, connecting with each other through social media functions and real-world events, and promoting food education. It also utilizes AI to provide optimal advice to users, targeting a wide range of people, from adults to children, who are interested in food. In this way, the rice farming experience system allows users to experience rice farming virtually and in real life, connect with other users, receive optimal advice, and make donations.

[0072] The rice farming experience system according to this embodiment comprises an experience section, an SNS section, an advice section, and a donation section. The experience section allows users to experience rice farming virtually and in real life. For example, the experience section allows users to learn the rice farming process through an app. The experience section also allows users to actually grow rice. For example, the experience section can grow rice in partnership with the JA Group's bucket rice cultivation program. Furthermore, the experience section allows users to receive harvested rice by making a donation. The SNS section allows users to connect with other users based on the experience provided by the experience section. For example, the SNS section allows users to give each other advice, share their rice harvests and recipes. This enables the SNS section to allow users to share information and form communities. The advice section can provide optimal advice to users connected through the SNS section. For example, the advice section automatically generates specific advice based on local climate and weather information, and the user's cultivation progress. The advice section can also propose an optimal schedule tailored to the user, including planting, watering, and harvesting timing. The donation section can accept donations. For example, the donation section can receive harvested rice when a user makes a donation. Thus, the rice farming experience system according to this embodiment allows users to experience rice farming virtually and in real life, connect with other users, receive optimal advice, and make donations.

[0073] The Experience Department allows users to experience rice farming both virtually and in real life. Specifically, users can learn each step of rice farming through a dedicated application. This application simulates the entire process from sowing to harvesting, providing users with an interactive experience. For example, users can manage a virtual rice paddy within the app, sowing seeds at the appropriate time, watering, and fertilizing. This allows users to acquire basic knowledge and skills in rice farming. The Experience Department also allows users to actually grow rice. For example, in partnership with the JA Group's bucket rice cultivation program, users can grow rice at home using buckets. In this case, users can record the progress of cultivation through the app and receive necessary advice. Furthermore, the Experience Department provides a system where users can receive harvested rice in exchange for making a donation. Users can support local farmers through donations and receive harvested rice in return. In this way, the Experience Department not only allows users to experience rice farming both virtually and in real life, but also deepens connections with the local community.

[0074] The SNS section allows users to connect with each other based on experiences provided by the Experience section. Specifically, users can use the in-app SNS function to share information and communicate with other users. For example, users can post photos and videos of their cultivation progress and receive comments and advice from other users. The SNS section also allows users to give each other advice and share their rice harvest and menu recipes. This enables users to share knowledge and experience about rice cultivation and form a community. Furthermore, the SNS section provides group functions based on specific themes and events, allowing users to connect with like-minded individuals who share common interests and goals. For example, there could be groups to share cultivation methods suited to specific regions or climate conditions, or groups to plan events such as harvest festivals or cooking contests. In this way, the SNS section can promote interaction among users and provide a richer experience.

[0075] The Advice Department can provide optimal advice to users connected through the Social Networking Service (SNS) Department. Specifically, the Advice Department automatically generates specific advice based on local climate and weather information, as well as the user's cultivation progress. For example, it uses AI to analyze data entered by users and information shared on the SNS Department to propose the optimal cultivation methods and timings. By providing users with optimal schedules tailored to their needs, such as sowing, watering, and harvesting timing, users can grow rice efficiently. The Advice Department can also create long-term cultivation plans by utilizing past data and statistical information. For example, it can propose optimal cultivation methods for specific regions and times based on past weather data and yield data. Furthermore, the Advice Department can provide countermeasures for unexpected situations such as extreme weather or pest outbreaks. In this way, the Advice Department can always provide users with the latest and most optimal advice, supporting their success in rice cultivation.

[0076] The donation department can accept donations. Specifically, it provides a system where users can receive harvested rice by making donations through the app. For example, users can select the donation amount within the app and make the donation using payment methods such as credit cards or electronic money. In addition to receiving the harvested rice, users who make a donation will also be provided with information about the farmer and region to which they donated. This allows users to know how their donation is being used and to feel the significance of their donation. Furthermore, the donation department can also offer benefits and rewards to users who make donations. For example, users who make donations may be invited to special events or given exclusive products. In addition, the donation department manages donation history and achievements, providing a function that allows users to reflect on their donation activities. This allows the donation department to communicate the significance of donations to users and encourage continued donation activities.

[0077] The Events Department can host real-world events. For example, the Events Department can host real-world events such as workshops and field trips, allowing users to experience rice farming through these events. Some or all of the above-mentioned processes in the Events Department may be performed using AI, or not. For example, the Events Department can input the content of a real-world event into a generating AI and have the generating AI execute the detailed schedule and content of the event.

[0078] The children's section can provide content for children. For example, the children's section can provide content for children such as educational games and animations. This allows children to learn while having fun. Some or all of the above-mentioned processes in the children's section may be performed using AI, for example, or not using AI. For example, the children's section can input content for children into a generating AI and have the generating AI create easy-to-understand and fun illustrations and stories.

[0079] The supply unit can provide the harvested rice. The supply unit can, for example, receive the rice harvested by the user. The supply unit can, for example, clarify the delivery method and the type of rice to be provided. This allows the user to receive the harvested rice. Some or all of the above processing in the supply unit may be performed using AI, for example, or not using AI. For example, the supply unit can input the delivery schedule for the harvested rice into a generating AI and have the generating AI execute the optimal delivery method.

[0080] The advice unit can automatically generate specific advice based on local climate and weather information, as well as the user's cultivation progress. For example, the advice unit can provide the user with optimal advice based on local climate and weather information. The advice unit can clarify, for example, the source of weather data and the frequency of data updates. Furthermore, the advice unit can provide specific advice based on the user's cultivation progress. For example, the advice unit can provide specific advice to the user based on growth stage checklists and photographic records. This allows the user to receive specific advice based on local climate and weather information. Some or all of the above processing in the advice unit is performed using, for example, a generation AI. For example, the advice unit can input local climate and weather information into the generation AI and have the generation AI execute specific advice.

[0081] The advice unit can suggest an optimal schedule tailored to the user, including planting, watering, and harvesting timing. For example, the advice unit can suggest the optimal schedule based on a cultivation calendar and reminder function. This enables the user to cultivate efficiently. Some or all of the above processes in the advice unit are performed using, for example, a generative AI. For instance, the advice unit can input the user's cultivation progress into the generative AI and have the AI ​​execute the optimal schedule.

[0082] The children's section can generate content with easy-to-understand and fun illustrations and stories. For example, the children's section can generate easy-to-understand and fun illustrations and stories based on character settings and storyboards. This allows children to learn while having fun. Some or all of the above-mentioned processes in the children's section may be performed using, for example, a generation AI, or not using a generation AI. For example, the children's section can input character settings and storyboards into a generation AI and have the generation AI produce easy-to-understand and fun illustrations and stories.

[0083] The experience unit can estimate the user's emotions and customize the experience based on those emotions. For example, if the user is stressed, the experience unit can provide a relaxing virtual experience. If the user is excited, the experience unit can provide an experience that includes challenging tasks. Furthermore, if the user is tired, the experience unit can provide a simple and relaxing experience. This allows the experience to be tailored 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 experience unit may be performed using AI, or not. For example, the experience unit can input user emotion data into a generative AI and have the generative AI customize the experience.

[0084] The experience unit can analyze a user's past experience history and select the optimal experience method. For example, the experience unit can suggest similar experiences based on experiences the user has enjoyed in the past. It can also suggest different experiences based on experiences the user has avoided in the past. Furthermore, the experience unit can suggest the optimal sequence of experiences based on the user's past experience history. In this way, it can provide the optimal experience method based on the user's past experience history. Some or all of the above processing in the experience unit may be performed using AI, for example, or without AI. For example, the experience unit can input the user's past experience history data into a generating AI and have the generating AI select the optimal experience method.

[0085] The experience unit can filter experiences based on the user's current lifestyle and areas of interest. For example, if the user is busy, the experience unit can suggest experiences that can be completed in a short time. Furthermore, if the user has a specific area of ​​interest, the experience unit can suggest experiences related to that area. In addition, the experience unit can filter and provide appropriate experiences according to the user's lifestyle. This allows for the provision of experiences tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the experience unit may be performed using AI, for example, or without AI. For example, the experience unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0086] The experience unit can estimate the user's emotions and determine the priority of experiences based on those emotions. For example, if the user is relaxed, the experience unit can prioritize relaxing experiences. If the user is excited, the experience unit can prioritize challenging experiences. Furthermore, if the user is tired, the experience unit can prioritize easy and relaxing experiences. This allows for prioritizing experiences 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 experience unit may be performed using AI, or not using AI. For example, the experience unit can input user emotion data into a generative AI and have the generative AI determine the priority of experiences.

[0087] The experience unit can prioritize providing highly relevant experiences by considering the user's geographical location during the experience. For example, if the user is in a specific region, the experience unit can provide experiences related to that region. Furthermore, if the user is traveling, the experience unit can provide experiences related to their travel destination. In addition, the experience unit can suggest the most suitable experiences based on the user's geographical location. This allows for the provision of experiences tailored to the user's geographical location. Some or all of the above processing in the experience unit may be performed using AI, for example, or without AI. For example, the experience unit can input the user's geographical location into a generating AI and have the generating AI provide highly relevant experiences.

[0088] The experience unit can analyze the user's social media activity during the experience and provide relevant experiences. For example, the experience unit can suggest experiences based on what the user has shown interest in on social media. It can also provide experiences that are of high interest to the user based on their social media activity. Furthermore, the experience unit can analyze the user's social media activity and suggest the most suitable experience. This allows the experience unit to provide experiences based on the user's social media activity. Some or all of the above processing in the experience unit may be performed using AI, for example, or without AI. For example, the experience unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant experiences.

[0089] The SNS (Social Networking Service) unit can estimate the user's emotions and adjust the way SNS is displayed based on the estimated emotions. For example, if the user is relaxed, the SNS unit can provide an interface with calming colors. If the user is excited, the SNS unit can provide an interface with bright colors. Furthermore, if the user is tired, the SNS unit can provide a simple and highly visible interface. This allows for the display of SNS in a way that corresponds to the user's emotions. 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 SNS unit may be performed using AI, or not using AI. For example, the SNS unit can input user emotion data into a generative AI and have the generative AI adjust the way SNS is displayed.

[0090] The SNS (Social Networking Service) unit can analyze a user's past posting history when they use social networking services (SNS) and select the optimal display method. For example, the SNS unit can prioritize displaying similar posts based on posts the user has liked in the past. It can also prioritize displaying different posts based on posts the user has avoided in the past. Furthermore, the SNS unit can suggest the optimal display method based on the user's past posting history. This allows the SNS unit to provide the optimal display method based on the user's past posting history. Some or all of the above processing in the SNS unit may be performed using AI, for example, or without AI. For example, the SNS unit can input the user's past posting history data into a generating AI and have the generating AI select the optimal display method.

[0091] The SNS (Social Networking Service) unit can filter posts based on the user's areas of interest when they use the SNS. For example, if a user has a specific area of ​​interest, the SNS unit can prioritize displaying posts related to that area. The SNS unit can also filter and display relevant posts based on the user's areas of interest. Furthermore, the SNS unit can suggest the most suitable posts based on the user's areas of interest. This allows the SNS unit to provide posts that are tailored to the user's areas of interest. Some or all of the above processing in the SNS unit may be performed using AI, for example, or without AI. For example, the SNS unit can input user area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0092] The SNS (Social Networking Service) section can estimate the user's emotions and determine the priority of social media posts based on those emotions. For example, if the user is relaxed, the SNS section can prioritize displaying relaxing posts. If the user is excited, the SNS section can prioritize displaying challenging posts. Furthermore, if the user is tired, the SNS section can prioritize displaying easy and relaxing posts. This allows for the provision of social media priorities that correspond 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 SNS section may be performed using AI, or not using AI. For example, the SNS section can input user emotion data into a generative AI and have the generative AI determine the priority of social media posts.

[0093] The SNS (Social Networking Service) section can prioritize displaying highly relevant posts when a user is using social networking services, taking into account the user's geographical location. For example, if a user is in a specific region, the SNS section can prioritize displaying posts related to that region. Furthermore, if a user is traveling, the SNS section can prioritize displaying posts related to their travel destination. In addition, the SNS section can suggest optimal posts based on the user's geographical location. This allows the SNS section to provide posts tailored to the user's geographical location. Some or all of the above processing in the SNS section may be performed using AI, or without AI. For example, the SNS section can input the user's geographical location into a generating AI and have the generating AI provide highly relevant posts.

[0094] The SNS (Social Networking Service) unit can analyze a user's social media activity when they use social media and display relevant posts. For example, the SNS unit can suggest posts based on what the user has shown interest in on social media. Furthermore, the SNS unit can prioritize displaying posts of high interest based on the user's social media activity. In addition, the SNS unit can analyze the user's social media activity and suggest the most suitable posts. This allows the SNS unit to provide posts based on the user's social media activity. Some or all of the above processing in the SNS unit may be performed using AI, for example, or without AI. For example, the SNS unit can input the user's social media activity data into a generating AI and have the generating AI provide relevant posts.

[0095] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is relaxed, the advice unit can provide advice in a calm tone. If the user is excited, the advice unit can provide advice in an energetic tone. Furthermore, if the user is tired, the advice unit can provide advice in a concise and easy-to-understand tone. This allows the advice unit to provide advice that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processes in the advice unit may be performed using AI, or not using AI. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the way it expresses the advice.

[0096] The advice unit can determine the priority of advice based on the user's cultivation progress when providing advice. For example, if the user is a beginner, the advice unit can prioritize providing basic advice. If the user is an intermediate user, the advice unit can prioritize providing detailed advice. Furthermore, if the user is an advanced user, the advice unit can prioritize providing expert advice. This allows the advice unit to provide advice priorities according to the user's cultivation progress. Some or all of the above processing in the advice unit is performed using, for example, a generating AI. For example, the advice unit can input the user's cultivation progress data into the generating AI and have the generating AI determine the priority of advice.

[0097] The advice unit can adjust the order of advice by referring to the user's cultivation-related information when providing advice. For example, if the user is cultivating rice, the advice unit can provide advice according to each step of rice cultivation. If the user is cultivating vegetables, the advice unit can provide advice according to each step of vegetable cultivation. Furthermore, if the user is cultivating fruit trees, the advice unit can provide advice according to each step of fruit tree cultivation. This allows the advice unit to provide an order of advice based on the user's cultivation-related information. Some or all of the above processing in the advice unit is performed using, for example, a generating AI. For example, the advice unit can input the user's cultivation-related information into the generating AI and have the generating AI adjust the order of advice.

[0098] The donation unit can estimate the user's emotions and adjust its donation suggestion method based on the estimated emotions. For example, if the user is relaxed, the donation unit can suggest a donation using gentle language. If the user is excited, the donation unit can suggest a donation using energetic language. Furthermore, if the user is tired, the donation unit can suggest a donation using concise and easy-to-understand language. This allows the system to provide donation suggestions tailored 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-described processing in the donation unit may be performed using AI or not. For example, the donation unit can input user emotion data into a generative AI and have the generative AI adjust the donation suggestion method.

[0099] The donation unit can analyze a user's past donation history to select the most suitable donation method when proposing a donation. For example, the donation unit can suggest an appropriate donation amount based on the amount the user has donated in the past. It can also suggest an appropriate donation timing based on the frequency of the user's past donations. Furthermore, the donation unit can prioritize suggesting specific donation recipients based on the user's past donation history. This allows the unit to provide the most suitable donation method based on the user's past donation history. Some or all of the above processing in the donation unit may be performed using AI, for example, or without AI. For example, the donation unit can input the user's past donation history data into a generating AI and have the generating AI select the most suitable donation method.

[0100] The donation unit can filter donation proposals based on the user's current living situation and areas of interest. For example, if the user has a specific area of ​​interest, the donation unit can suggest donation recipients related to that area. The donation unit can also filter and provide appropriate donation recipients according to the user's living situation. Furthermore, the donation unit can suggest the most suitable donation recipient based on the user's areas of interest. This allows the donation unit to provide donation proposals based on the user's living situation and areas of interest. Some or all of the above processing in the donation unit may be performed using AI, for example, or not using AI. For example, the donation unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0101] The donation unit can estimate the user's emotions and determine donation priorities based on those emotions. For example, if the user is relaxed, the donation unit can prioritize suggesting donations to relaxing organizations. If the user is excited, the donation unit can prioritize suggesting challenging donations. Furthermore, if the user is tired, the donation unit can prioritize suggesting easy and relaxing donations. This allows for donation priorities tailored 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 donation unit may be performed using AI or not. For example, the donation unit can input user emotion data into a generative AI and have the generative AI determine donation priorities.

[0102] The donation unit can prioritize highly relevant donation suggestions by considering the user's geographical location when a donation is proposed. For example, if the user is in a specific region, the donation unit can suggest donation destinations relevant to that region. Furthermore, if the user is traveling, the donation unit can suggest donation destinations relevant to their travel destination. In addition, the donation unit can suggest the most suitable donation destination based on the user's geographical location. This allows the donation unit to provide donation suggestions based on the user's geographical location. Some or all of the above processing in the donation unit may be performed using AI, for example, or without AI. For example, the donation unit can input the user's geographical location into a generating AI and have the generating AI provide highly relevant donation suggestions.

[0103] The event unit can estimate the user's emotions and adjust the event content based on those emotions. For example, if the user is relaxed, the event unit can provide a relaxing event. If the user is excited, the event unit can provide a challenging event. Furthermore, if the user is tired, the event unit can provide a simple and relaxing event. This allows the event content to be tailored 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 event unit may be performed using AI, or not. For example, the event unit can input user emotion data into a generative AI and have the generative AI adjust the event content.

[0104] The Events Department can analyze a user's past participation history to select the most suitable event content when an event is being held. For example, the Events Department can suggest similar events based on events the user has previously attended. It can also suggest different events based on events the user has previously avoided. Furthermore, the Events Department can suggest the optimal order of events based on the user's past participation history. This allows the Events Department to provide the most suitable event content based on the user's past participation history. Some or all of the above processing in the Events Department may be performed using AI, for example, or without AI. For example, the Events Department can input the user's past participation history data into a generating AI and have the generating AI select the most suitable event content.

[0105] The event department can filter events based on the user's current lifestyle and areas of interest when an event is being held. For example, if a user has a specific area of ​​interest, the event department can suggest events related to that area. The event department can also filter and provide appropriate events according to the user's lifestyle. Furthermore, the event department can suggest the most suitable events based on the user's areas of interest. This allows the event department to provide events tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the event department may be performed using AI, for example, or without AI. For example, the event department can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0106] The event unit can estimate the user's emotions and determine event priorities based on those emotions. For example, if the user is relaxed, the event unit can prioritize relaxing events. If the user is excited, the event unit can prioritize challenging events. Furthermore, if the user is tired, the event unit can prioritize easy and relaxing events. This allows for event prioritization 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 event unit may be performed using AI or not. For example, the event unit can input user emotion data into a generative AI and have the generative AI determine event priorities.

[0107] The Events Department can prioritize events that are highly relevant to the user, taking into account the user's geographical location when an event is being held. For example, if the user is in a specific region, the Events Department can suggest events related to that region. Also, if the user is traveling, the Events Department can suggest events related to their travel destination. Furthermore, the Events Department can suggest the most suitable events based on the user's geographical location. This allows the Events Department to provide events tailored to the user's geographical location. Some or all of the above processing in the Events Department may be performed using AI, for example, or without AI. For example, the Events Department can input the user's geographical location into a generating AI and have the generating AI provide highly relevant events.

[0108] The children's section can estimate the user's emotions and adjust the presentation of children's content based on those emotions. For example, if the user is relaxed, the children's section can provide content in a calm manner. If the user is excited, the children's section can provide content in an energetic manner. Furthermore, if the user is tired, the children's section can provide content in a concise and easy-to-understand manner. This allows for the provision of children's content presentation methods that correspond 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 children's section may be performed using AI, for example, or not using AI. For example, the children's section can input user emotion data into a generative AI and have the generative AI adjust the presentation of children's content.

[0109] The children's content department can analyze a user's past usage history to select the most suitable content when providing children's content. For example, the children's content department can suggest similar content based on content the user has previously enjoyed. It can also suggest different content based on content the user has previously avoided. Furthermore, the children's content department can suggest the optimal order of content based on the user's past usage history. This allows for the provision of optimal content based on the user's past usage history. Some or all of the above processes in the children's content department may be performed using AI, for example, or without AI. For example, the children's content department can input the user's past usage history data into a generating AI and have the generating AI select the optimal content.

[0110] The children's section can filter children's content based on the user's current lifestyle and areas of interest. For example, if a user has a specific area of ​​interest, the children's section can suggest content related to that area. Furthermore, the children's section can filter and provide appropriate content according to the user's lifestyle. In addition, the children's section can suggest optimal content based on the user's areas of interest. This allows for the provision of children's content tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the children's section may be performed using AI, or without AI. For example, the children's section can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0111] The children's section can estimate the user's emotions and prioritize children's content based on those emotions. For example, if the user is relaxed, the children's section can prioritize relaxing content. If the user is excited, the children's section can prioritize challenging content. Furthermore, if the user is tired, the children's section can prioritize easy and relaxing content. This allows for prioritizing children's content 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 children's section may be performed using AI or not. For example, the children's section can input user emotion data into a generative AI and have the generative AI determine the priority of children's content.

[0112] The children's section can prioritize providing highly relevant content by considering the user's geographical location when delivering children's content. For example, if the user is in a specific region, the children's section can suggest content related to that region. Furthermore, if the user is traveling, the children's section can suggest content related to their travel destination. In addition, the children's section can suggest optimal content based on the user's geographical location. This allows for the provision of children's content tailored to the user's geographical location. Some or all of the above processing in the children's section may be performed using AI, or without AI. For example, the children's section can input the user's geographical location into a generating AI and have the generating AI provide highly relevant content.

[0113] The children's department can analyze users' social media activity when providing children's content and provide relevant content. For example, the children's department can suggest content based on what users have shown interest in on social media. Furthermore, the children's department can prioritize providing content of high interest based on users' social media activity. In addition, the children's department can analyze users' social media activity and suggest optimal content. This allows for the provision of children's content based on users' social media activity. Some or all of the above processing in the children's department may be performed using AI, for example, or without AI. For example, the children's department can input user social media activity data into a generating AI and have the generating AI provide relevant content.

[0114] The serving unit can estimate the user's emotions and adjust the way the rice is served based on the estimated emotions. For example, if the user is relaxed, the serving unit can serve the rice using gentle language. If the user is excited, the serving unit can serve the rice using energetic language. Furthermore, if the user is tired, the serving unit can serve the rice using concise and easy-to-understand language. This allows the serving unit to provide a method of serving rice that corresponds to the user's emotions. 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 serving unit may be performed using AI, for example, or not using AI. For example, the serving unit can input user emotion data into a generative AI and have the generative AI adjust the way the rice is served.

[0115] The delivery unit can analyze the user's past receiving history to select the optimal delivery method when providing rice. For example, the delivery unit can suggest an appropriate type of rice based on the types of rice the user has received in the past. It can also suggest an appropriate delivery timing based on the frequency with which the user has received rice in the past. Furthermore, the delivery unit can prioritize suggesting a specific delivery method based on the user's past receiving history. This allows the delivery unit to provide the optimal delivery method based on the user's past receiving history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past receiving history data into a generating AI and have the generating AI select the optimal delivery method.

[0116] The supply unit can filter rice based on the user's current lifestyle and areas of interest when providing it. For example, if the user has a specific area of ​​interest, the supply unit can suggest rice related to that area. The supply unit can also filter and provide appropriate rice according to the user's lifestyle. Furthermore, the supply unit can suggest the most suitable rice based on the user's areas of interest. This allows for a method of providing rice that is tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0117] The serving unit can estimate the user's emotions and determine the priority of rice distribution based on the estimated emotions. For example, if the user is relaxed, the serving unit can prioritize providing relaxing rice. If the user is excited, the serving unit can prioritize providing challenging rice. Furthermore, if the user is tired, the serving unit can prioritize providing easy and relaxing rice. This allows for providing a rice distribution priority that corresponds 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 serving unit may be performed using AI, for example, or not using AI. For example, the serving unit can input user emotion data into a generative AI and have the generative AI determine the priority of rice distribution.

[0118] The service provider can prioritize the most relevant delivery method when providing rice, taking into account the user's geographical location. For example, if the user is in a specific region, the service provider can suggest rice related to that region. Furthermore, if the user is traveling, the service provider can suggest rice related to their travel destination. In addition, the service provider can suggest the most suitable rice based on the user's geographical location. This allows for the provision of rice delivery methods tailored to the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input the user's geographical location into a generating AI and have the generating AI provide the most relevant delivery method.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] The experience unit can estimate the user's emotions and customize the experience based on those emotions. For example, if the user is stressed, it can provide a relaxing virtual experience. If the user is excited, it can provide an experience that includes challenging tasks. Furthermore, if the user is tired, it can provide a simple and relaxing experience. This allows the experience to be tailored 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 experience unit may be performed using AI, or not. For example, the experience unit can input user emotion data into a generative AI and have the generative AI customize the experience.

[0121] The SNS unit can estimate the user's emotions and adjust the way SNS is displayed based on the estimated emotions. For example, if the user is relaxed, it can provide an interface with calm colors. If the user is excited, it can provide an interface with bright colors. Furthermore, if the user is tired, it can provide a simple and highly visible interface. This allows for the provision of SNS display methods that correspond 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 SNS unit may be performed using AI, for example, or without AI. For example, the SNS unit can input user emotion data into a generative AI and have the generative AI adjust the way SNS is displayed.

[0122] The advice unit can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is relaxed, advice can be provided in a calm manner. If the user is excited, advice can be provided in an energetic manner. Furthermore, if the user is tired, advice can be provided in a concise and easy-to-understand manner. This allows for the provision of advice expressions that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The 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 advice unit may be performed using AI, or not using AI. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the way advice is expressed.

[0123] The donation unit can estimate the user's emotions and adjust the donation suggestion method based on the estimated emotions. For example, if the user is relaxed, the donation suggestion can be made in a gentle manner. If the user is excited, the donation suggestion can be made in an energetic manner. Furthermore, if the user is tired, the donation suggestion can be made in a concise and easy-to-understand manner. This allows for the provision of donation suggestions that are tailored 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 donation unit may be performed using AI or not. For example, the donation unit can input user emotion data into a generative AI and have the generative AI adjust the donation suggestion method.

[0124] The event unit can estimate the user's emotions and adjust the event content based on those emotions. For example, if the user is relaxed, it can provide a relaxing event. If the user is excited, it can provide a challenging event. Furthermore, if the user is tired, it can provide a simple and relaxing event. This allows the event content to be tailored 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 event unit may be performed using AI, or not. For example, the event unit can input user emotion data into a generative AI and have the generative AI adjust the event content.

[0125] The experience unit can analyze a user's past experience history and select the optimal experience method. For example, it can suggest similar experiences based on experiences the user has enjoyed in the past. It can also suggest different experiences based on experiences the user has avoided in the past. Furthermore, it can suggest the optimal sequence of experiences based on the user's past experience history. In this way, it can provide the optimal experience method based on the user's past experience history. Some or all of the above processing in the experience unit may be performed using AI, for example, or without AI. For example, the experience unit can input the user's past experience history data into a generating AI and have the generating AI select the optimal experience method.

[0126] The SNS (Social Networking Service) unit can analyze a user's past posting history when they use social networking services (SNS) and select the optimal display method. For example, it can prioritize displaying similar posts based on posts the user has liked in the past. It can also prioritize displaying different posts based on posts the user has avoided in the past. Furthermore, it can suggest the optimal display method based on the user's past posting history. This allows the SNS unit to provide the optimal display method based on the user's past posting history. Some or all of the above processing in the SNS unit may be performed using AI, for example, or without AI. For example, the SNS unit can input the user's past posting history data into a generating AI and have the generating AI select the optimal display method.

[0127] The advice unit can prioritize advice based on the user's cultivation progress when providing it. For example, if the user is a beginner, basic advice can be prioritized. If the user is an intermediate user, detailed advice can be prioritized. Furthermore, if the user is an advanced user, expert advice can be prioritized. This allows for prioritizing advice according to the user's cultivation progress. Some or all of the above processing in the advice unit is performed using, for example, a generating AI. For example, the advice unit can input the user's cultivation progress data into the generating AI and have the generating AI determine the priority of the advice.

[0128] The event department can analyze a user's past participation history to select the most suitable event content when an event is being held. For example, it can suggest similar events based on events the user has previously attended. It can also suggest different events based on events the user has previously avoided. Furthermore, it can suggest the optimal order of events based on the user's past participation history. This allows the event department to provide the most suitable event content based on the user's past participation history. Some or all of the above processing in the event department may be performed using AI, for example, or without AI. For example, the event department can input the user's past participation history data into a generating AI and have the generating AI select the most suitable event content.

[0129] The delivery unit can analyze the user's past receipt history to select the optimal delivery method when providing rice. For example, it can suggest an appropriate type of rice based on the types of rice the user has received in the past. It can also suggest an appropriate delivery timing based on the frequency of the user's past receipts. Furthermore, it can prioritize suggesting specific delivery methods based on the user's past receipt history. This allows the delivery unit to provide the optimal delivery method based on the user's past receipt history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past receipt history data into a generating AI and have the generating AI select the optimal delivery method.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The experience section allows users to experience rice farming both virtually and in real life. Through the app, users can learn the rice farming process and actually grow rice. For example, they can grow rice in real life in partnership with the JA Group's bucket rice farming program. Users can also receive harvested rice by making a donation. Step 2: The SNS section allows users to connect with each other based on the experiences provided by the Experience section. Users can give each other advice, share their favorite rice dishes and menu recipes, share information, and form communities. Step 3: The advice section provides optimal advice to users connected through the social networking section. It automatically generates specific advice based on local climate and weather information, as well as the user's cultivation progress, and can propose the optimal schedule tailored to the user, including planting, watering, and harvesting timing. Step 4: The donation department can accept donations. Users can receive harvested rice by making a donation.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the experience section, SNS section, advice section, donation section, event section, children's section, and provision section, is implemented by at least one of the smart device 14 and the data processing device 12. For example, the experience section allows users to learn about the rice cultivation process through an app on the smart device 14, and the specific processing unit 290 of the data processing device 12 provides a virtual experience. The SNS section enables connections between users through the control unit 46A of the smart device 14, and the advice section provides optimal advice through the specific processing unit 290 of the data processing device 12. The donation section accepts donations through the control unit 46A of the smart device 14, and the event section generates a schedule for real-world events through the specific processing unit 290 of the data processing device 12. The children's section provides educational content through an app on the smart device 14, and the provision section manages the delivery schedule for harvested rice through the specific processing unit 290 of the data processing device 12. The correspondence between each section and the devices and control units is not limited to the example described above, and various changes are possible.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the experience section, SNS section, advice section, donation section, event section, children's section, and provision section, is implemented by at least one of the smart glasses 214 and the data processing device 12. For example, the experience section allows users to learn about the rice cultivation process through an app on the smart glasses 214, and the specific processing unit 290 of the data processing device 12 provides a virtual experience. The SNS section enables connections between users through the control unit 46A of the smart glasses 214, and the advice section provides optimal advice through the specific processing unit 290 of the data processing device 12. The donation section accepts donations through the control unit 46A of the smart glasses 214, and the event section generates a schedule for real-world events through the specific processing unit 290 of the data processing device 12. The children's section provides educational content through an app on the smart glasses 214, and the provision section manages the delivery schedule for harvested rice through the specific processing unit 290 of the data processing device 12. The correspondence between each section and the devices and control units is not limited to the example described above, and various changes are possible.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the experience section, SNS section, advice section, donation section, event section, children's section, and provision section, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the experience section allows users to learn about the rice cultivation process through an app on the headset terminal 314, and the specific processing unit 290 of the data processing unit 12 provides a virtual experience. The SNS section enables connections between users through the control unit 46A of the headset terminal 314, and the advice section provides optimal advice through the specific processing unit 290 of the data processing unit 12. The donation section accepts donations through the control unit 46A of the headset terminal 314, and the event section generates a schedule for real-world events through the specific processing unit 290 of the data processing unit 12. The children's section provides educational content through an app on the headset terminal 314, and the provision section manages the delivery schedule for harvested rice through the specific processing unit 290 of the data processing unit 12. The correspondence between each section and the devices and control units is not limited to the example described above, and various changes are possible.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] Each of the multiple elements described above, including the experience section, SNS section, advice section, donation section, event section, children's section, and provision section, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the experience section allows users to learn about the rice cultivation process through the robot 414's app, and the specific processing unit 290 of the data processing unit 12 provides a virtual experience. The SNS section enables connections between users through the control unit 46A of the robot 414, and the advice section provides optimal advice through the specific processing unit 290 of the data processing unit 12. The donation section accepts donations through the control unit 46A of the robot 414, and the event section generates a schedule for real-world events through the specific processing unit 290 of the data processing unit 12. The children's section provides educational content through the robot 414's app, and the provision section manages the delivery schedule for harvested rice through the specific processing unit 290 of the data processing unit 12. The correspondence between each section and the devices and control units is not limited to the example described above, and various changes are possible.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] (Note 1) The experience section offers virtual and real-world experiences of rice farming, Based on the experience provided by the aforementioned experience section, there is an SNS section where users can connect with other users, The Advice Unit provides optimal advice to users connected through the aforementioned SNS Unit, It includes a donation department that accepts donations. A system characterized by the following features. (Note 2) We have an events department that organizes real-world events. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a children's section that provides content for children. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a serving section that provides the harvested rice. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice section, It automatically generates specific advice based on local climate and weather information, as well as the user's cultivation progress. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, We propose the optimal schedule tailored to the user, including planting, watering, and harvesting timing. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned children's section is, We create content with easy-to-understand and fun illustrations and stories. The system described in Appendix 3, characterized by the features described herein. (Note 8) The aforementioned experience section is, It estimates the user's emotions and customizes the experience based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned experience section is, Analyze the user's past experience history and select the optimal experience method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned experience section is, During the experience, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned experience section is, It estimates the user's emotions and determines the priority of the experience based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned experience section is, During the user experience, the system prioritizes providing relevant experiences by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned experience section is, During the user experience, we analyze the user's social media activity and provide relevant experiences. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned SNS section, It estimates user sentiment and adjusts how social media content is displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned SNS section, When using social media, the system analyzes the user's past posting history to select the optimal display method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned SNS section, When using social media, filtering is performed based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned SNS section, It estimates user sentiment and determines social media priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned SNS section, When using social media, the system prioritizes displaying posts that are highly relevant to the user, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned SNS section, When using social media, the system analyzes the user's social media activity and displays relevant posts. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, When providing advice, the system prioritizes the advice based on the user's cultivation progress. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, When providing advice, the system adjusts the order of advice by referring to the user's cultivation-related information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned donation department, It estimates the user's emotions and adjusts the donation suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned donation department, When proposing a donation, the system analyzes the user's past donation history to select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned donation department, When submitting a donation proposal, filtering is performed based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned donation department, It estimates the user's emotions and determines the priority of donations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned donation department, When submitting a donation proposal, the system prioritizes highly relevant donation proposals by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned event section, It estimates the user's emotions and adjusts the event content based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned event section, When organizing an event, the system analyzes users' past participation history to select the most suitable event content. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned event section, When an event is held, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned event section, It estimates the user's emotions and determines the priority of events based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned event section, When organizing events, the system prioritizes events that are highly relevant to the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned children's section is, We estimate user emotions and adjust the presentation of children's content based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned children's section is, When providing content for children, we analyze the user's past usage history to select the most suitable content. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned children's section is, When providing content for children, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned children's section is, It estimates user sentiment and prioritizes child-friendly content based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned children's section is, When providing content for children, we prioritize delivering relevant content by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned children's section is, When providing content for children, we analyze users' social media activity and provide relevant content. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way rice is served based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned supply unit is, When providing rice, the system analyzes the user's past receipt history to select the optimal delivery method. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned supply unit is, When providing rice, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of rice distribution based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned supply unit is, When providing rice, we prioritize the most relevant delivery method by taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0204] 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. The experience section offers virtual and real-world experiences of rice farming, Based on the experience provided by the aforementioned experience section, there is an SNS section where users can connect with other users, The Advice Unit provides optimal advice to users connected through the aforementioned SNS Unit, It includes a donation department that accepts donations. A system characterized by the following features.

2. We have an events department that organizes real-world events. The system according to feature 1.

3. It has a children's section that provides content for children. The system according to feature 1.

4. It has a serving section that provides the harvested rice. The system according to feature 1.

5. The aforementioned advice section, It automatically generates specific advice based on local climate and weather information, as well as the user's cultivation progress. The system according to feature 1.

6. The aforementioned advice section, We propose the optimal schedule tailored to the user, including planting, watering, and harvesting timing. The system according to feature 1.

7. The aforementioned children's section is, We create content with easy-to-understand and fun illustrations and stories. The system according to claim 3.

8. The aforementioned experience section is, It estimates the user's emotions and customizes the experience based on those estimated emotions. The system according to feature 1.

9. The aforementioned experience section is, Analyze the user's past experience history and select the optimal experience method. The system according to feature 1.

10. The aforementioned experience section is, During the experience, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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