system

Abstract

The system according to this embodiment aims to optimize the mushroom cultivation environment and enable growth prediction and early detection of diseases. [Solution] The system according to the embodiment comprises an analysis unit, an advice unit, a confirmation unit, and a prediction unit. The analysis unit analyzes the growing environment. The advice unit provides advice on optimal temperature and humidity control and harvest time based on the growing environment analyzed by the analysis unit. The confirmation unit checks the growth status in real time. The prediction unit predicts mushroom growth and detects diseases early.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 prior art, it cannot be said that the cultivation environment of mushrooms has been sufficiently optimized to perform growth prediction and early detection of diseases, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize the cultivation environment of mushrooms and perform growth prediction and early detection of diseases.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, an advice unit, a confirmation unit, and a prediction unit. The analysis unit analyzes the growing environment. The advice unit provides advice on optimal temperature and humidity control and harvest time based on the growing environment analyzed by the analysis unit. The confirmation unit checks the growth status in real time. The prediction unit predicts mushroom growth and detects diseases early. [Effects of the Invention]

[0007] The system according to this embodiment can optimize the mushroom cultivation environment and enable growth prediction and early detection of diseases. [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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 system according to an embodiment of the present invention is an application system that provides enjoyable support for cultivating edible mushrooms. This application system allows users to begin cultivating mushrooms through the application. The application system utilizes AI technology to individually optimize the mushroom cultivation process. The cultivated mushrooms are transformed into characters using AR technology, providing a fusion of real and virtual experiences. Furthermore, the application system allows users to interact with others through community functions and share cultivation information and recipes. In addition, the application system not only maximizes the deliciousness of the mushrooms but also provides health advice. This allows users to easily take the first step towards a self-sufficient lifestyle through mushroom cultivation. For example, a user downloads the application and begins cultivating mushrooms. The application system analyzes the user's cultivation environment and advises on optimal temperature and humidity control and harvest time. Real-time growth status can be checked within the application, supporting mushroom growth prediction and early disease detection. For example, the application system monitors the mushroom's growth and provides advice on watering and temperature adjustment at the appropriate time. Next, the cultivated mushrooms are transformed into characters using AR technology. Users can take photos of the mushrooms they cultivate and transform them into AR characters within the application. This allows them to enjoy an AR experience where mushrooms appear in the real world. For example, when a user holds up their smartphone, a mushroom character will appear on the screen, and they can observe it moving around. Furthermore, users can interact with others through community features. Users can share cultivation information and recipes, and consult with other users about cultivation problems. For example, users can post photos of the mushrooms they have cultivated to the community and receive advice from other users. They can also share mushroom recipes and get advice on health aspects. In this way, users can easily take the first step towards a self-sufficient lifestyle through mushroom cultivation. Users can enjoy cultivating mushrooms while achieving a healthy diet. They can also interact with others through the community and share the joy of cultivation.This will make mushroom cultivation more accessible and an attractive activity for many people. The application system will allow users to easily take the first step towards a self-sufficient lifestyle through mushroom cultivation.

[0029] The application system according to this embodiment comprises an analysis unit, an advice unit, a confirmation unit, and a prediction unit. The analysis unit analyzes the growing environment. The growing environment includes, but is not limited to, temperature, humidity, light intensity, and soil type. The analysis unit collects data on the growing environment using sensors, for example, and analyzes it using AI technology. The advice unit advises on optimal temperature and humidity management and harvest timing based on the growing environment analyzed by the analysis unit. The advice unit analyzes data on the growing environment using AI technology, for example, and proposes the optimal management method. The advice unit provides, for example, specific numerical ranges for temperature and humidity and advises the user on appropriate management methods. The advice unit provides, for example, criteria for determining the harvest time and advises the user on the optimal harvest timing. The confirmation unit checks the growth status in real time. The confirmation unit collects real-time data using sensors, for example, and displays it within the application. The confirmation unit displays the growth status of mushrooms in graphs and charts, for example, providing the user with a visually easy-to-understand presentation. The confirmation unit sets the data update frequency and provides the latest information in real time. The prediction unit performs mushroom growth prediction and early disease detection. The prediction unit uses, for example, AI technology to predict growth and provides the user with the prediction results. The prediction unit sets, for example, the algorithm to be used to improve the accuracy of the prediction. The prediction unit sets, for example, criteria for detecting disease symptoms and diagnosing them early. As a result, the application system according to the embodiment enables analysis of the growing environment, optimal advice, real-time monitoring, growth prediction, and early disease detection.

[0030] The analysis department analyzes the growing environment. This includes, but is not limited to, factors such as temperature, humidity, light intensity, and soil type. For example, the analysis department collects data on the growing environment using sensors and analyzes it using AI technology. Specifically, temperature sensors measure the temperature of the growing environment in real time, humidity sensors detect humidity in the air, light sensors measure light intensity, and soil sensors detect soil type, moisture content, pH value, etc. The data obtained from these sensors is transmitted to a central database and analyzed using AI technology. The AI ​​technology uses machine learning algorithms to analyze the data and detect patterns and anomalies in the growing environment. For example, it analyzes temperature and humidity fluctuation patterns to identify the optimal growing environment. It also analyzes data such as soil type, moisture content, and pH value to identify areas for improvement in the growing environment. This allows the analysis department to collect detailed data on the growing environment and provide the optimal growing environment by analyzing it using AI technology. Furthermore, the analysis department centrally manages the collected data and can collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the advice and prediction departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the analysis unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The advisory department provides advice on optimal temperature and humidity management and harvest timing based on the growing environment analyzed by the analysis department. For example, the advisory department uses AI technology to analyze growing environment data and propose optimal management methods. Specifically, the AI ​​technology uses machine learning algorithms to analyze growing environment data and identify optimal temperature and humidity ranges. For instance, it determines whether the temperature is within an appropriate range and provides advice on temperature adjustments as needed. It also determines whether the humidity is within an appropriate range and provides advice on humidity adjustments as needed. Furthermore, the advisory department provides criteria for determining harvest timing and advises users on the optimal harvest time. For example, it analyzes the growth status of the mushrooms and identifies the optimal harvest time. This allows the advisory department to analyze growing environment data and provide advice on optimal management methods and harvest timing. Additionally, the advisory department can collect user feedback and continuously improve the accuracy and effectiveness of its advice. For example, it reviews and improves advice based on user feedback. The advisory department can also reliably transmit information using multiple communication methods. For example, it uses not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. This allows the advisory unit to provide users with quick and reliable advice, helping to optimize the training environment.

[0032] The monitoring unit checks the growth status in real time. For example, the monitoring unit collects real-time data using sensors and displays it within the application. Specifically, it collects data from temperature sensors, humidity sensors, light sensors, soil sensors, etc., in real time and displays it as graphs and charts within the application. This allows users to visually check the growth status of the mushrooms in an easy-to-understand way. Furthermore, the monitoring unit can set the data update frequency to provide the latest information in real time. For example, the data update frequency can be set to every hour, and the latest data can be displayed in real time. The monitoring unit also has an anomaly detection function and can notify the user if abnormal data is detected. For example, it can issue an alert if the temperature or humidity exceeds the appropriate range to draw the user's attention. This allows the monitoring unit to check the growth status in real time and detect anomalies early. Furthermore, the monitoring unit can centrally manage the collected data and cooperate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the advisory and prediction units. This allows the monitoring unit to collect data efficiently and effectively and improve the overall system performance.

[0033] The prediction unit predicts mushroom growth and detects diseases early. For example, it uses AI technology to predict growth and provides the user with the prediction results. Specifically, the AI ​​technology analyzes past data using machine learning algorithms to predict mushroom growth patterns. For instance, it predicts mushroom growth rate and harvest time based on past temperature, humidity, light intensity, and soil data. The prediction unit also detects disease symptoms and sets criteria for early diagnosis. For example, it uses an algorithm that analyzes mushroom growth and detects signs of disease to enable early disease detection. This allows the prediction unit to predict mushroom growth, detect diseases early, and provide the user with the prediction results. Furthermore, the prediction unit can continuously revise its prediction results based on real-time updated data to respond to the latest conditions. For example, if temperature or humidity changes rapidly, the prediction unit immediately incorporates new data and updates the prediction results. The prediction unit can also perform more accurate risk assessments by considering regional characteristics and past disaster history. This allows the prediction unit to always provide highly accurate risk predictions based on the latest information, supporting quick and appropriate responses.

[0034] The transformation unit transforms cultivated mushrooms into characters using AR technology. For example, the transformation unit takes a photo of a mushroom cultivated by the user and transforms it into an AR character within the application. The transformation unit provides an experience in which mushrooms appear in the real world using AR technology. For example, when the user holds up their smartphone, the transformation unit displays the mushroom character on the screen, and the user can observe it moving around. In this way, by transforming cultivated mushrooms into characters using AR technology, a fusion experience of the real and virtual worlds is provided. AR technology uses devices such as smartphones and tablets. Some or all of the above processing in the transformation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the transformation unit can input a photo of a mushroom into a generative AI and have the generative AI generate the character.

[0035] The Community section provides community functions. For example, the Community section provides a platform for users to share cultivation information and recipes and interact with other users. For example, the Community section provides a chat function that allows users to communicate in real time. For example, the Community section provides a forum function that allows users to share information on a topic-by-topic basis. In this way, by providing community functions, users can interact with like-minded individuals and share cultivation information and recipes. Some or all of the above processes in the Community section may be performed using AI, for example, or not using AI. For example, the Community section can input user posts into AI and have the AI ​​analyze the relevant information.

[0036] The sharing section allows users to share cultivation information and recipes. For example, it provides features for users to share photos of mushrooms they have grown and their cultivation procedures. The sharing section provides a platform for users to post mushroom recipes and share them with other users. The sharing section allows users to share cultivation tips and tricks and exchange information with other users. This enables information exchange among users by sharing cultivation information and recipes. Some or all of the above-described processes in the sharing section may be performed using AI, or not. For example, the sharing section can input user posts into AI and have the AI ​​analyze the relevant information.

[0037] The Health Department provides health advice. For example, the Health Department provides advice on the nutritional information and health benefits of mushrooms. For example, the Health Department provides advice on diet and exercise tailored to the user's health condition. For example, the Health Department provides information to help users lead a healthy life. In this way, by providing health advice, it supports users in leading a healthy life. Some or all of the above processes in the Health Department may be performed using AI, for example, or not using AI. For example, the Health Department can input the user's health data into AI and have the AI ​​generate health advice.

[0038] The verification unit can check the real-time growth status within the application. The verification unit can, for example, collect real-time data using sensors and display it within the application. The verification unit can, for example, display the mushroom growth status in graphs or charts, providing the user with a visually easy-to-understand presentation. The verification unit can, for example, set the data update frequency to provide the latest information in real time. This allows users to constantly monitor the mushroom's growth by checking the real-time growth status. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input data acquired from sensors into AI and have the AI ​​analyze the real-time growth status.

[0039] The prediction unit performs mushroom growth prediction and early disease detection. The prediction unit uses, for example, AI technology to predict growth and provides the user with the prediction results. The prediction unit sets the algorithm to be used to improve the accuracy of the prediction. The prediction unit sets criteria for detecting disease symptoms and diagnosing them early. This enables appropriate countermeasures by predicting mushroom growth and detecting diseases early. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input data acquired from sensors into the AI ​​and have the AI ​​perform growth prediction and early disease detection.

[0040] The analysis unit optimizes its analysis algorithm by referring to past cultivation data during analysis. For example, the analysis unit extracts optimal temperature and humidity patterns based on past cultivation data and applies them to the current cultivation environment. For example, the analysis unit analyzes past cultivation data to identify disease occurrence patterns and propose preventive measures. For example, the analysis unit predicts the optimal timing for harvest by referring to past cultivation data. This improves the accuracy of the analysis algorithm by referring to past cultivation data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past cultivation data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0041] The analysis unit applies different analytical methods to each type of mushroom during analysis. For example, the analysis unit sets the optimal temperature and humidity for each type of mushroom and adjusts the growing environment. For example, the analysis unit analyzes the risk of different diseases for each type of mushroom and proposes preventive measures. For example, the analysis unit analyzes the different growth patterns for each type of mushroom and predicts the harvest time. By applying different analytical methods to each type of mushroom, more accurate analytical results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for each type of mushroom into a generating AI and have the generating AI execute the application of analytical methods.

[0042] The analysis unit analyzes the growing environment while considering the user's geographical location information. For example, the analysis unit analyzes the growing environment by referring to regional climate data based on the user's geographical location information. For example, the analysis unit analyzes the growing environment by referring to regional soil data based on the user's geographical location information. For example, the analysis unit analyzes regional pest and disease risks based on the user's geographical location information and proposes preventive measures. This makes it possible to analyze a growing environment suitable for the region by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis of the growing environment.

[0043] The analysis unit analyzes users' social media activity and obtains relevant development environment data during the analysis. For example, the analysis unit extracts hints about the development environment from users' social media posts and incorporates them into the analysis. For example, the analysis unit optimizes the development environment by referring to the success stories of other users from users' social media activity. For example, the analysis unit analyzes trends related to the development environment from users' social media activity and provides the latest advice. In this way, relevant development environment data can be obtained by analyzing users' social media activity. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input users' social media data into a generating AI and have the generating AI perform the acquisition of development environment data.

[0044] The advice unit updates its advice in real time in response to changes in the growing environment. For example, if the temperature of the growing environment changes, the advice unit provides real-time advice on appropriate temperature adjustment. For example, if the humidity of the growing environment changes, the advice unit provides real-time advice on appropriate humidity adjustment. For example, if signs of disease are observed in the growing environment, the advice unit provides real-time advice on early detection and countermeasures. In this way, appropriate advice can be provided by updating the advice content in real time in response to changes in the growing environment. Some or all of the above processing in the advice unit may be performed using AI, for example, or without using AI. For example, the advice unit can input growing environment data into a generating AI and have the generating AI perform the update of the advice content.

[0045] The advice unit provides different advice depending on the stage of mushroom growth. For example, during the germination stage, the advice unit advises on appropriate temperature and humidity control methods. During the growth stage, the advice unit provides advice on appropriate nutrient supply and disease prevention. During the harvest stage, the advice unit advises on the optimal harvesting timing and method. By providing different advice according to the stage of mushroom growth, it is possible to propose appropriate cultivation methods. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input mushroom growth data into a generating AI and have the generating AI execute advice according to the growth stage.

[0046] The advice unit provides optimal advice by referring to the user's past cultivation history. For example, the advice unit advises on optimal temperature and humidity settings based on the user's past cultivation history. For example, the advice unit analyzes the user's past cultivation history and advises on disease prevention measures. For example, the advice unit refers to the user's past cultivation history and advises on the optimal timing for harvesting. In this way, optimal advice can be provided by referring to the user's past cultivation history. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's past cultivation data into a generating AI and have the generating AI execute optimal advice.

[0047] The advice unit adjusts the timing of advice based on the user's lifestyle. For example, if the user is a morning person, the advice unit will provide advice in the morning. For example, if the user is a night owl, the advice unit will provide advice in the evening. The advice unit provides advice at an appropriate time, according to the user's lifestyle. By adjusting the timing of advice based on the user's lifestyle, advice can be provided at an appropriate time. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's lifestyle data into a generating AI and have the generating AI execute the timing of the advice.

[0048] The verification unit optimizes the displayed content by referring to past growth data during verification. For example, the verification unit displays the optimal temperature and humidity pattern based on past growth data. For example, the verification unit analyzes past growth data and displays disease occurrence patterns. For example, the verification unit refers to past growth data and displays the optimal timing for harvesting. This improves the accuracy of the displayed content by referring to past growth data. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input past growth data into a generating AI and have the generating AI perform the optimization of the displayed content.

[0049] The verification unit applies different display methods to each type of mushroom during verification. For example, the verification unit displays the optimal temperature and humidity settings for each type of mushroom. For example, the verification unit displays different disease risks for each type of mushroom. For example, the verification unit displays different growth patterns for each type of mushroom. By applying different display methods to each type of mushroom, more accurate information can be provided. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input data for each type of mushroom into a generating AI and have the generating AI execute the application of the display method.

[0050] The verification unit displays the growth status while considering the user's geographical location information during verification. For example, the verification unit displays the growth status by referring to regional climate data based on the user's geographical location information. For example, the verification unit displays the growth status by referring to regional soil data based on the user's geographical location information. For example, the verification unit displays the regional pest and disease risk based on the user's geographical location information. This makes it possible to display growth status appropriate for the region by considering the user's geographical location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the user's geographical location information into a generating AI and have the generating AI execute the display of the growth status.

[0051] The verification unit analyzes the user's social media activity during verification and displays relevant growth status data. For example, the verification unit extracts hints about the growing environment from the user's social media posts and reflects them in the growth status. For example, the verification unit references the success stories of other users from the user's social media activity and optimizes the growth status. For example, the verification unit analyzes trends related to the growing environment from the user's social media activity and displays the latest growth status. In this way, relevant growth status data can be displayed by analyzing the user's social media activity. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's social media data into a generating AI and have the generating AI execute the display of growth status data.

[0052] The prediction unit optimizes its prediction algorithm by referring to past growth data during the prediction process. For example, the prediction unit extracts the optimal growth pattern based on past growth data and applies it to the current growing environment. For example, the prediction unit analyzes past growth data to identify disease occurrence patterns and proposes preventive measures. For example, the prediction unit predicts the optimal timing for harvest by referring to past growth data. This improves the accuracy of the prediction algorithm by referring to past growth data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past growth data into a generating AI and have the generating AI perform the optimization of the prediction algorithm.

[0053] The prediction unit applies different prediction methods to each type of mushroom during the prediction process. For example, the prediction unit analyzes the optimal growth pattern for each type of mushroom and adjusts the prediction algorithm. For example, the prediction unit predicts the risk of different diseases for each type of mushroom and proposes preventive measures. For example, the prediction unit predicts different harvest times for each type of mushroom and proposes the optimal timing. By applying different prediction methods to each type of mushroom, more accurate prediction results can be provided. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data for each type of mushroom into a generating AI and have the generating AI perform the application of prediction methods.

[0054] The prediction unit performs growth predictions while considering the user's geographical location information. For example, the prediction unit performs growth predictions by referencing regional climate data based on the user's geographical location information. For example, the prediction unit performs growth predictions by referencing regional soil data based on the user's geographical location information. For example, the prediction unit predicts regional pest and disease risks based on the user's geographical location information and proposes preventive measures. This makes it possible to perform growth predictions that are appropriate for the region by considering the user's geographical location information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the user's geographical location information into a generating AI and have the generating AI perform the growth prediction.

[0055] The prediction unit analyzes the user's social media activity and obtains relevant growth prediction data during the prediction process. For example, the prediction unit extracts hints about the nurturing environment from the user's social media posts and reflects them in the growth prediction. For example, the prediction unit optimizes the growth prediction by referencing the success stories of other users from the user's social media activity. For example, the prediction unit analyzes trends related to the nurturing environment from the user's social media activity and provides the latest growth prediction. This allows relevant growth prediction data to be obtained by analyzing the user's social media activity. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's social media data into a generating AI and have the generating AI perform the acquisition of growth prediction data.

[0056] The conversion unit generates different characters during the conversion process, depending on the growth stage of the mushroom. For example, during the germination stage, the conversion unit generates a young character. During the growth stage, the conversion unit generates a grown character. During the harvest stage, the conversion unit generates a mature character. This allows users to visually enjoy the growth process by generating different characters according to the growth stage of the mushroom. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input mushroom growth data into a generation AI and cause the generation AI to generate characters according to the growth stage.

[0057] The transformation unit customizes the character's movements according to the user's preferences during the transformation process. For example, the transformation unit sets the user's preferred movements and reflects them in the character. For example, the transformation unit adjusts the speed and pattern of the character's movements according to the user's preferences. For example, the transformation unit adds specific actions to the character based on the user's preferences. This allows for a more personalized experience by customizing the character's movements according to the user's preferences. Some or all of the above processes in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can input user preference data into a generating AI and have the generating AI perform the customization of the character's movements.

[0058] The conversion unit generates characters while considering the user's geographical location information during the conversion process. For example, the conversion unit generates characters that reflect regional characteristics based on the user's geographical location information. For example, the conversion unit generates characters that reflect regional culture and customs based on the user's geographical location information. For example, the conversion unit generates characters that reflect regional climate and environment based on the user's geographical location information. In this way, by considering the user's geographical location information, it is possible to generate characters that are appropriate for the region. Some or all of the above-described processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the user's geographical location information into a generation AI and have the generation AI perform character generation.

[0059] The transformation unit analyzes the user's social media activity during the transformation process and generates relevant characters. For example, the transformation unit generates characters that reflect the user's preferences and interests from the user's social media posts. For example, the transformation unit generates characters that reflect commonalities with other users from the user's social media activity. For example, the transformation unit generates characters that reflect trends from the user's social media activity. In this way, relevant characters can be generated by analyzing the user's social media activity. Some or all of the above processing in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can input the user's social media data into a generation AI and have the generation AI perform character generation.

[0060] The community department analyzes the interaction history within the community and proposes the optimal method of interaction. For example, the community department proposes the optimal method of interaction based on the user's past interaction history. For example, the community department analyzes the user's past interaction history and finds common ground with other users to propose. For example, the community department refers to the user's past interaction history and proposes the optimal timing for interaction. In this way, the optimal method of interaction can be proposed by analyzing the interaction history within the community. Some or all of the above processes in the community department may be performed using AI, for example, or without AI. For example, the community department can input the user's interaction history data into a generating AI and have the generating AI execute suggestions for interaction methods.

[0061] The community department applies different display methods to each topic within the community. For example, the community department sets the optimal display method for each topic and provides it to the user. For example, the community department sets different information priorities for each topic and adjusts the display method. For example, the community department applies different visual designs for each topic and adjusts the display method. By applying different display methods to each topic, more appropriate information can be provided. Some or all of the above processes in the community department may be performed using AI, for example, or not using AI. For example, the community department can input topic-specific data into a generating AI and have the generating AI perform the application of display methods.

[0062] The Community Department proposes the most suitable interaction method within the community, taking into account the user's geographical location. For example, the Community Department proposes interaction methods that reflect regional characteristics based on the user's geographical location. For example, the Community Department proposes interaction methods that reflect regional culture and customs based on the user's geographical location. For example, the Community Department proposes interaction methods that reflect regional climate and environment based on the user's geographical location. In this way, by considering the user's geographical location, it is possible to propose interaction methods that are appropriate for the region. Some or all of the above processing in the Community Department may be performed using AI, for example, or without AI. For example, the Community Department can input the user's geographical location information into a generating AI and have the generating AI execute suggestions for interaction methods.

[0063] The Community Department analyzes users' social media activity during interactions within the community and proposes relevant interaction methods. For example, the Community Department proposes interaction methods that reflect users' preferences and interests based on their social media posts. For example, the Community Department proposes interaction methods that reflect commonalities with other users based on users' social media activity. For example, the Community Department proposes interaction methods that reflect trends based on users' social media activity. In this way, relevant interaction methods can be proposed by analyzing users' social media activity. Some or all of the above processing in the Community Department may be performed using AI, for example, or without AI. For example, the Community Department can input users' social media data into a generating AI and have the generating AI execute suggestions for interaction methods.

[0064] The sharing unit, when sharing, refers to past sharing history to suggest the optimal sharing method. For example, the sharing unit suggests the optimal sharing method based on the user's past sharing history. For example, the sharing unit analyzes the user's past sharing history, finds commonalities with other users, and makes suggestions. For example, the sharing unit refers to the user's past sharing history to suggest sharing at the optimal time. In this way, the optimal sharing method can be suggested by referring to past sharing history. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input past sharing history data into a generating AI and have the generating AI execute the suggestion of a sharing method.

[0065] The sharing unit applies different sharing methods depending on the type of information when sharing. For example, when sharing cultivation information, the sharing unit applies a sharing method that includes detailed data and graphs. When sharing recipe information, for example, the sharing unit applies a sharing method that includes photos and videos. When sharing health advice, for example, the sharing unit applies a concise and easy-to-understand sharing method. By applying different sharing methods depending on the type of information, more appropriate information can be provided. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input data for each type of information into a generating AI and have the generating AI execute the application of the sharing method.

[0066] The sharing unit proposes the optimal sharing method when sharing, taking into account the user's geographical location information. For example, the sharing unit proposes a sharing method that reflects regional characteristics based on the user's geographical location information. For example, the sharing unit proposes a sharing method that reflects regional culture and customs based on the user's geographical location information. For example, the sharing unit proposes a sharing method that reflects regional climate and environment based on the user's geographical location information. In this way, by considering the user's geographical location information, it is possible to propose a sharing method that is appropriate for the region. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the user's geographical location information into a generating AI and have the generating AI execute the proposal of a sharing method.

[0067] The sharing unit analyzes the user's social media activity when sharing and suggests relevant sharing methods. For example, the sharing unit suggests sharing methods that reflect the user's preferences and interests from their social media posts. For example, the sharing unit suggests sharing methods that reflect commonalities with other users from the user's social media activity. For example, the sharing unit suggests sharing methods that reflect trends from the user's social media activity. In this way, relevant sharing methods can be suggested by analyzing the user's social media activity. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the user's social media data into a generating AI and have the generating AI perform the task of suggesting sharing methods.

[0068] The health department provides optimal advice by referring to past health data when giving health advice. For example, the health department provides optimal diet and exercise advice based on the user's past health data. For example, the health department analyzes the user's past health data and advises on disease prevention measures. For example, the health department provides optimal advice for maintaining health by referring to the user's past health data. In this way, optimal advice can be provided by referring to past health data. Some or all of the above processes in the health department may be performed using AI, for example, or without using AI. For example, the health department can input past health data into a generating AI and have the generating AI execute optimal advice.

[0069] The health department provides different health advice depending on the user's lifestyle. For example, if the user is a morning person, the health department will provide health advice suitable for the morning hours. For example, if the user is a night owl, the health department will provide health advice suitable for the evening hours. For example, the health department will provide health advice at the appropriate time according to the user's lifestyle. By providing different advice according to the user's lifestyle, more appropriate information can be provided. Some or all of the above processing in the health department may be performed using AI, for example, or without AI. For example, the health department can input the user's lifestyle data into a generating AI and have the generating AI perform the provision of advice.

[0070] The Health Department provides optimal health advice by considering the user's geographical location. For example, the Health Department provides health advice that is appropriate for the local climate and environment based on the user's geographical location. For example, the Health Department provides health advice that utilizes local ingredients based on the user's geographical location. For example, the Health Department provides health advice that is appropriate for the local exercise environment based on the user's geographical location. In this way, by considering the user's geographical location, it is possible to provide advice that is appropriate for the region. Some or all of the above processing in the Health Department may be performed using AI, for example, or without AI. For example, the Health Department can input the user's geographical location into a generating AI and have the generating AI perform the provision of advice.

[0071] The Health Department analyzes the user's social media activity when providing health advice and offers relevant advice. For example, the Health Department provides advice that reflects the user's interests and concerns regarding health based on their social media posts. For example, the Health Department provides health advice that reflects commonalities with other users based on the user's social media activity. For example, the Health Department provides health advice that reflects trends based on the user's social media activity. In this way, relevant advice can be provided by analyzing the user's social media activity. Some or all of the above processing in the Health Department may be performed using AI, for example, or without AI. For example, the Health Department can input the user's social media data into a generating AI and have the generating AI perform the provision of advice.

[0072] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0073] The application system also includes a rewards section. This section can reward users with virtual items or points when they successfully cultivate mushrooms. For example, if a user successfully harvests mushrooms, the rewards section can provide a special item usable within the application. The rewards section can also award points to users who share information with other users or actively participate in the community. Furthermore, the rewards section can award special badges or titles to users when they achieve certain cultivation goals. This increases user motivation for cultivation activities and encourages more active use of the application.

[0074] The application system also includes a notification unit. This unit can notify users of important information and advice in real time. For example, if the temperature or humidity of the growing environment is not appropriate, the notification unit can immediately notify the user and prompt them to take appropriate action. It can also notify users of the harvest time and watering timing according to the mushroom's growth stage. Furthermore, the notification unit can also notify users of new posts and comments within the community. This allows users to take action at the right time without missing important information.

[0075] The application system also includes a learning section. This section can provide learning content to deepen users' knowledge of mushroom cultivation. For example, it can offer video tutorials on mushroom cultivation methods and disease prevention. The learning section can also provide features that allow users to take quizzes and tests related to cultivation. Furthermore, the learning section can provide a forum for users to share knowledge and engage in discussions with other users. This allows users to deepen their knowledge of mushroom cultivation and cultivate more effectively.

[0076] The application system also includes a customization section. This section allows users to customize the application's interface and functions to their liking. For example, users can change the application's theme color and background image. Furthermore, the customization section allows users to select the functions they use and the information they display, creating an interface that is easy for them to use. In addition, the customization section allows users to set the frequency and content of notifications. This allows users to customize the application to their preferences and use it more comfortably.

[0077] The application system also includes a feedback section. This section allows users to provide feedback on the application's usability and areas for improvement. For example, users can submit opinions and requests regarding the application's features and interface. The feedback section can also collect user feedback and use it to improve the application. Furthermore, the feedback section can provide a function that allows users to comment on and rate other users' feedback. This allows the application to reflect user opinions and evolve into a more user-friendly experience.

[0078] The following briefly describes the processing flow for example form 1.

[0079] Step 1: The analysis department analyzes the growing environment. This includes factors such as temperature, humidity, light intensity, and soil type. The analysis department collects data on the growing environment using sensors and analyzes it using AI technology. Step 2: The advice unit provides advice on optimal temperature and humidity control and harvest timing based on the growing environment analyzed by the analysis unit. The advice unit uses AI technology to analyze the growing environment data and proposes optimal management methods. For example, it provides specific numerical ranges for temperature and humidity and advises the user on appropriate management methods. It also provides criteria for determining the harvest time and advises the user on the optimal harvest timing. Step 3: The monitoring unit checks the growth status in real time. The monitoring unit collects real-time data using sensors and displays it within the application. For example, it displays the mushroom growth status in graphs and charts, providing the user with a visually easy-to-understand explanation. It also sets the data update frequency to provide the latest information in real time. Step 4: The prediction unit predicts mushroom growth and detects diseases early. The prediction unit uses AI technology to predict growth and provides the user with the prediction results. For example, it sets the algorithm to be used to improve the accuracy of the prediction. It also sets criteria for detecting disease symptoms and diagnosing them early.

[0080] (Example of form 2) The system according to an embodiment of the present invention is an application system that provides enjoyable support for cultivating edible mushrooms. This application system allows users to begin cultivating mushrooms through the application. The application system utilizes AI technology to individually optimize the mushroom cultivation process. The cultivated mushrooms are transformed into characters using AR technology, providing a fusion of real and virtual experiences. Furthermore, the application system allows users to interact with others through community functions and share cultivation information and recipes. In addition, the application system not only maximizes the deliciousness of the mushrooms but also provides health advice. This allows users to easily take the first step towards a self-sufficient lifestyle through mushroom cultivation. For example, a user downloads the application and begins cultivating mushrooms. The application system analyzes the user's cultivation environment and advises on optimal temperature and humidity control and harvest time. Real-time growth status can be checked within the application, supporting mushroom growth prediction and early disease detection. For example, the application system monitors the mushroom's growth and provides advice on watering and temperature adjustment at the appropriate time. Next, the cultivated mushrooms are transformed into characters using AR technology. Users can take photos of the mushrooms they cultivate and transform them into AR characters within the application. This allows them to enjoy an AR experience where mushrooms appear in the real world. For example, when a user holds up their smartphone, a mushroom character will appear on the screen, and they can observe it moving around. Furthermore, users can interact with others through community features. Users can share cultivation information and recipes, and consult with other users about cultivation problems. For example, users can post photos of the mushrooms they have cultivated to the community and receive advice from other users. They can also share mushroom recipes and get advice on health aspects. In this way, users can easily take the first step towards a self-sufficient lifestyle through mushroom cultivation. Users can enjoy cultivating mushrooms while achieving a healthy diet. They can also interact with others through the community and share the joy of cultivation.This will make mushroom cultivation more accessible and an attractive activity for many people. The application system will allow users to easily take the first step towards a self-sufficient lifestyle through mushroom cultivation.

[0081] The application system according to this embodiment comprises an analysis unit, an advice unit, a confirmation unit, and a prediction unit. The analysis unit analyzes the growing environment. The growing environment includes, but is not limited to, temperature, humidity, light intensity, and soil type. The analysis unit collects data on the growing environment using sensors, for example, and analyzes it using AI technology. The advice unit advises on optimal temperature and humidity management and harvest timing based on the growing environment analyzed by the analysis unit. The advice unit analyzes data on the growing environment using AI technology, for example, and proposes the optimal management method. The advice unit provides, for example, specific numerical ranges for temperature and humidity and advises the user on appropriate management methods. The advice unit provides, for example, criteria for determining the harvest time and advises the user on the optimal harvest timing. The confirmation unit checks the growth status in real time. The confirmation unit collects real-time data using sensors, for example, and displays it within the application. The confirmation unit displays the growth status of mushrooms in graphs and charts, for example, providing the user with a visually easy-to-understand presentation. The confirmation unit sets the data update frequency and provides the latest information in real time. The prediction unit performs mushroom growth prediction and early disease detection. The prediction unit uses, for example, AI technology to predict growth and provides the user with the prediction results. The prediction unit sets, for example, the algorithm to be used to improve the accuracy of the prediction. The prediction unit sets, for example, criteria for detecting disease symptoms and diagnosing them early. As a result, the application system according to the embodiment enables analysis of the growing environment, optimal advice, real-time monitoring, growth prediction, and early disease detection.

[0082] The analysis department analyzes the growing environment. This includes, but is not limited to, factors such as temperature, humidity, light intensity, and soil type. For example, the analysis department collects data on the growing environment using sensors and analyzes it using AI technology. Specifically, temperature sensors measure the temperature of the growing environment in real time, humidity sensors detect humidity in the air, light sensors measure light intensity, and soil sensors detect soil type, moisture content, pH value, etc. The data obtained from these sensors is transmitted to a central database and analyzed using AI technology. The AI ​​technology uses machine learning algorithms to analyze the data and detect patterns and anomalies in the growing environment. For example, it analyzes temperature and humidity fluctuation patterns to identify the optimal growing environment. It also analyzes data such as soil type, moisture content, and pH value to identify areas for improvement in the growing environment. This allows the analysis department to collect detailed data on the growing environment and provide the optimal growing environment by analyzing it using AI technology. Furthermore, the analysis department centrally manages the collected data and can collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the advice and prediction departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the analysis unit to collect data efficiently and effectively, improving the overall system performance.

[0083] The advisory department provides advice on optimal temperature and humidity management and harvest timing based on the growing environment analyzed by the analysis department. For example, the advisory department uses AI technology to analyze growing environment data and propose optimal management methods. Specifically, the AI ​​technology uses machine learning algorithms to analyze growing environment data and identify optimal temperature and humidity ranges. For instance, it determines whether the temperature is within an appropriate range and provides advice on temperature adjustments as needed. It also determines whether the humidity is within an appropriate range and provides advice on humidity adjustments as needed. Furthermore, the advisory department provides criteria for determining harvest timing and advises users on the optimal harvest time. For example, it analyzes the growth status of the mushrooms and identifies the optimal harvest time. This allows the advisory department to analyze growing environment data and provide advice on optimal management methods and harvest timing. Additionally, the advisory department can collect user feedback and continuously improve the accuracy and effectiveness of its advice. For example, it reviews and improves advice based on user feedback. The advisory department can also reliably transmit information using multiple communication methods. For example, it uses not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. This allows the advisory unit to provide users with quick and reliable advice, helping to optimize the training environment.

[0084] The monitoring unit checks the growth status in real time. For example, the monitoring unit collects real-time data using sensors and displays it within the application. Specifically, it collects data from temperature sensors, humidity sensors, light sensors, soil sensors, etc., in real time and displays it as graphs and charts within the application. This allows users to visually check the growth status of the mushrooms in an easy-to-understand way. Furthermore, the monitoring unit can set the data update frequency to provide the latest information in real time. For example, the data update frequency can be set to every hour, and the latest data can be displayed in real time. The monitoring unit also has an anomaly detection function and can notify the user if abnormal data is detected. For example, it can issue an alert if the temperature or humidity exceeds the appropriate range to draw the user's attention. This allows the monitoring unit to check the growth status in real time and detect anomalies early. Furthermore, the monitoring unit can centrally manage the collected data and cooperate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the advisory and prediction units. This allows the monitoring unit to collect data efficiently and effectively and improve the overall system performance.

[0085] The prediction unit predicts mushroom growth and detects diseases early. For example, it uses AI technology to predict growth and provides the user with the prediction results. Specifically, the AI ​​technology analyzes past data using machine learning algorithms to predict mushroom growth patterns. For instance, it predicts mushroom growth rate and harvest time based on past temperature, humidity, light intensity, and soil data. The prediction unit also detects disease symptoms and sets criteria for early diagnosis. For example, it uses an algorithm that analyzes mushroom growth and detects signs of disease to enable early disease detection. This allows the prediction unit to predict mushroom growth, detect diseases early, and provide the user with the prediction results. Furthermore, the prediction unit can continuously revise its prediction results based on real-time updated data to respond to the latest conditions. For example, if temperature or humidity changes rapidly, the prediction unit immediately incorporates new data and updates the prediction results. The prediction unit can also perform more accurate risk assessments by considering regional characteristics and past disaster history. This allows the prediction unit to always provide highly accurate risk predictions based on the latest information, supporting quick and appropriate responses.

[0086] The transformation unit transforms cultivated mushrooms into characters using AR technology. For example, the transformation unit takes a photo of a mushroom cultivated by the user and transforms it into an AR character within the application. The transformation unit provides an experience in which mushrooms appear in the real world using AR technology. For example, when the user holds up their smartphone, the transformation unit displays the mushroom character on the screen, and the user can observe it moving around. In this way, by transforming cultivated mushrooms into characters using AR technology, a fusion experience of the real and virtual worlds is provided. AR technology uses devices such as smartphones and tablets. Some or all of the above processing in the transformation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the transformation unit can input a photo of a mushroom into a generative AI and have the generative AI generate the character.

[0087] The Community section provides community functions. For example, the Community section provides a platform for users to share cultivation information and recipes and interact with other users. For example, the Community section provides a chat function that allows users to communicate in real time. For example, the Community section provides a forum function that allows users to share information on a topic-by-topic basis. In this way, by providing community functions, users can interact with like-minded individuals and share cultivation information and recipes. Some or all of the above processes in the Community section may be performed using AI, for example, or not using AI. For example, the Community section can input user posts into AI and have the AI ​​analyze the relevant information.

[0088] The sharing section allows users to share cultivation information and recipes. For example, it provides features for users to share photos of mushrooms they have grown and their cultivation procedures. The sharing section provides a platform for users to post mushroom recipes and share them with other users. The sharing section allows users to share cultivation tips and tricks and exchange information with other users. This enables information exchange among users by sharing cultivation information and recipes. Some or all of the above-described processes in the sharing section may be performed using AI, or not. For example, the sharing section can input user posts into AI and have the AI ​​analyze the relevant information.

[0089] The Health Department provides health advice. For example, the Health Department provides advice on the nutritional information and health benefits of mushrooms. For example, the Health Department provides advice on diet and exercise tailored to the user's health condition. For example, the Health Department provides information to help users lead a healthy life. In this way, by providing health advice, it supports users in leading a healthy life. Some or all of the above processes in the Health Department may be performed using AI, for example, or not using AI. For example, the Health Department can input the user's health data into AI and have the AI ​​generate health advice.

[0090] The verification unit can check the real-time growth status within the application. The verification unit can, for example, collect real-time data using sensors and display it within the application. The verification unit can, for example, display the mushroom growth status in graphs or charts, providing the user with a visually easy-to-understand presentation. The verification unit can, for example, set the data update frequency to provide the latest information in real time. This allows users to constantly monitor the mushroom's growth by checking the real-time growth status. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input data acquired from sensors into AI and have the AI ​​analyze the real-time growth status.

[0091] The prediction unit performs mushroom growth prediction and early disease detection. The prediction unit uses, for example, AI technology to predict growth and provides the user with the prediction results. The prediction unit sets the algorithm to be used to improve the accuracy of the prediction. The prediction unit sets criteria for detecting disease symptoms and diagnosing them early. This enables appropriate countermeasures by predicting mushroom growth and detecting diseases early. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input data acquired from sensors into the AI ​​and have the AI ​​perform growth prediction and early disease detection.

[0092] The analysis unit estimates the user's emotions and adjusts the analysis method of the training environment based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple analysis result and omits detailed information. For example, if the user is relaxed, the analysis unit provides a detailed analysis result and suggests fine-tuning of the training environment. For example, if the user is in a hurry, the analysis unit provides a rapid analysis result and immediately actionable advice. This allows for more appropriate analysis results by adjusting the analysis method of the training environment 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 analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The analysis unit optimizes its analysis algorithm by referring to past cultivation data during analysis. For example, the analysis unit extracts optimal temperature and humidity patterns based on past cultivation data and applies them to the current cultivation environment. For example, the analysis unit analyzes past cultivation data to identify disease occurrence patterns and propose preventive measures. For example, the analysis unit predicts the optimal timing for harvest by referring to past cultivation data. This improves the accuracy of the analysis algorithm by referring to past cultivation data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past cultivation data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0094] The analysis unit applies different analytical methods to each type of mushroom during analysis. For example, the analysis unit sets the optimal temperature and humidity for each type of mushroom and adjusts the growing environment. For example, the analysis unit analyzes the risk of different diseases for each type of mushroom and proposes preventive measures. For example, the analysis unit analyzes the different growth patterns for each type of mushroom and predicts the harvest time. By applying different analytical methods to each type of mushroom, more accurate analytical results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for each type of mushroom into a generating AI and have the generating AI execute the application of analytical methods.

[0095] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The analysis unit analyzes the growing environment while considering the user's geographical location information. For example, the analysis unit analyzes the growing environment by referring to regional climate data based on the user's geographical location information. For example, the analysis unit analyzes the growing environment by referring to regional soil data based on the user's geographical location information. For example, the analysis unit analyzes regional pest and disease risks based on the user's geographical location information and proposes preventive measures. This makes it possible to analyze a growing environment suitable for the region by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis of the growing environment.

[0097] The analysis unit analyzes users' social media activity and obtains relevant development environment data during the analysis. For example, the analysis unit extracts hints about the development environment from users' social media posts and incorporates them into the analysis. For example, the analysis unit optimizes the development environment by referring to the success stories of other users from users' social media activity. For example, the analysis unit analyzes trends related to the development environment from users' social media activity and provides the latest advice. In this way, relevant development environment data can be obtained by analyzing users' social media activity. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input users' social media data into a generating AI and have the generating AI perform the acquisition of development environment data.

[0098] The advice unit estimates the user's emotions and adjusts the way it expresses advice based on the estimated emotions. For example, if the user is stressed, the advice unit provides simple and easy-to-understand advice. If the user is relaxed, the advice unit provides detailed advice and suggests fine-tuning the training environment. If the user is in a hurry, the advice unit provides advice that can be acted upon quickly. By adjusting the way advice is expressed according to the user's emotions, more appropriate advice can be provided. 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 advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The advice unit updates its advice in real time in response to changes in the growing environment. For example, if the temperature of the growing environment changes, the advice unit provides real-time advice on appropriate temperature adjustment. For example, if the humidity of the growing environment changes, the advice unit provides real-time advice on appropriate humidity adjustment. For example, if signs of disease are observed in the growing environment, the advice unit provides real-time advice on early detection and countermeasures. In this way, appropriate advice can be provided by updating the advice content in real time in response to changes in the growing environment. Some or all of the above processing in the advice unit may be performed using AI, for example, or without using AI. For example, the advice unit can input growing environment data into a generating AI and have the generating AI perform the update of the advice content.

[0100] The advice unit provides different advice depending on the stage of mushroom growth. For example, during the germination stage, the advice unit advises on appropriate temperature and humidity control methods. During the growth stage, the advice unit provides advice on appropriate nutrient supply and disease prevention. During the harvest stage, the advice unit advises on the optimal harvesting timing and method. By providing different advice according to the stage of mushroom growth, it is possible to propose appropriate cultivation methods. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input mushroom growth data into a generating AI and have the generating AI execute advice according to the growth stage.

[0101] The advice unit estimates the user's emotions and prioritizes advice based on the estimated emotions. For example, if the user is stressed, the advice unit prioritizes providing the most important advice. For example, if the user is relaxed, the advice unit prioritizes providing detailed advice. For example, if the user is in a hurry, the advice unit prioritizes providing advice that can be acted on quickly. This ensures that important advice is prioritized by determining the priority of advice 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 advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The advice unit provides optimal advice by referring to the user's past cultivation history. For example, the advice unit advises on optimal temperature and humidity settings based on the user's past cultivation history. For example, the advice unit analyzes the user's past cultivation history and advises on disease prevention measures. For example, the advice unit refers to the user's past cultivation history and advises on the optimal timing for harvesting. In this way, optimal advice can be provided by referring to the user's past cultivation history. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's past cultivation data into a generating AI and have the generating AI execute optimal advice.

[0103] The advice unit adjusts the timing of advice based on the user's lifestyle. For example, if the user is a morning person, the advice unit will provide advice in the morning. For example, if the user is a night owl, the advice unit will provide advice in the evening. The advice unit provides advice at an appropriate time, according to the user's lifestyle. By adjusting the timing of advice based on the user's lifestyle, advice can be provided at an appropriate time. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's lifestyle data into a generating AI and have the generating AI execute the timing of the advice.

[0104] The confirmation unit estimates the user's emotions and adjusts the display method of the growth status based on the estimated user's emotions. For example, if the user is nervous, the confirmation unit provides a simple and highly visible display method. For example, if the user is relaxed, the confirmation unit provides a display method that includes detailed information. For example, if the user is in a hurry, the confirmation unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the growth status according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without using AI. For example, the confirmation unit can input the user's emotion data into the generative AI and have the generative AI perform emotion estimation.

[0105] The verification unit optimizes the displayed content by referring to past growth data during verification. For example, the verification unit displays the optimal temperature and humidity pattern based on past growth data. For example, the verification unit analyzes past growth data and displays disease occurrence patterns. For example, the verification unit refers to past growth data and displays the optimal timing for harvesting. This improves the accuracy of the displayed content by referring to past growth data. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input past growth data into a generating AI and have the generating AI perform the optimization of the displayed content.

[0106] The verification unit applies different display methods to each type of mushroom during verification. For example, the verification unit displays the optimal temperature and humidity settings for each type of mushroom. For example, the verification unit displays different disease risks for each type of mushroom. For example, the verification unit displays different growth patterns for each type of mushroom. By applying different display methods to each type of mushroom, more accurate information can be provided. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input data for each type of mushroom into a generating AI and have the generating AI execute the application of the display method.

[0107] The verification unit estimates the user's emotions and determines the priority of growth statuses based on the estimated user emotions. For example, if the user is stressed, the verification unit prioritizes displaying the most important growth statuses. For example, if the user is relaxed, the verification unit prioritizes displaying detailed growth statuses. For example, if the user is in a hurry, the verification unit prioritizes displaying growth statuses that can be quickly checked. In this way, by prioritizing growth statuses according to the user's emotions, important information can be provided preferentially. 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 verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The verification unit displays the growth status while considering the user's geographical location information during verification. For example, the verification unit displays the growth status by referring to regional climate data based on the user's geographical location information. For example, the verification unit displays the growth status by referring to regional soil data based on the user's geographical location information. For example, the verification unit displays the regional pest and disease risk based on the user's geographical location information. This makes it possible to display growth status appropriate for the region by considering the user's geographical location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the user's geographical location information into a generating AI and have the generating AI execute the display of the growth status.

[0109] The verification unit analyzes the user's social media activity during verification and displays relevant growth status data. For example, the verification unit extracts hints about the growing environment from the user's social media posts and reflects them in the growth status. For example, the verification unit references the success stories of other users from the user's social media activity and optimizes the growth status. For example, the verification unit analyzes trends related to the growing environment from the user's social media activity and displays the latest growth status. In this way, relevant growth status data can be displayed by analyzing the user's social media activity. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's social media data into a generating AI and have the generating AI execute the display of growth status data.

[0110] The prediction unit estimates the user's emotions and adjusts the growth prediction method based on the estimated user emotions. For example, if the user is stressed, the prediction unit provides a simple and easy-to-understand growth prediction. For example, if the user is relaxed, the prediction unit provides a detailed growth prediction and suggests fine-tuning of the growth environment. For example, if the user is in a hurry, the prediction unit provides a growth prediction that can be quickly implemented. By adjusting the growth prediction method according to the user's emotions, more appropriate prediction results can be provided. 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 prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0111] The prediction unit optimizes its prediction algorithm by referring to past growth data during the prediction process. For example, the prediction unit extracts the optimal growth pattern based on past growth data and applies it to the current growing environment. For example, the prediction unit analyzes past growth data to identify disease occurrence patterns and proposes preventive measures. For example, the prediction unit predicts the optimal timing for harvest by referring to past growth data. This improves the accuracy of the prediction algorithm by referring to past growth data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past growth data into a generating AI and have the generating AI perform the optimization of the prediction algorithm.

[0112] The prediction unit applies different prediction methods to each type of mushroom during the prediction process. For example, the prediction unit analyzes the optimal growth pattern for each type of mushroom and adjusts the prediction algorithm. For example, the prediction unit predicts the risk of different diseases for each type of mushroom and proposes preventive measures. For example, the prediction unit predicts different harvest times for each type of mushroom and proposes the optimal timing. By applying different prediction methods to each type of mushroom, more accurate prediction results can be provided. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data for each type of mushroom into a generating AI and have the generating AI perform the application of prediction methods.

[0113] The prediction unit estimates the user's emotions and adjusts the display method of the prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction unit provides a simple and highly visible display method. For example, if the user is relaxed, the prediction unit provides a display method that includes detailed information. For example, if the user is in a hurry, the prediction unit provides a display method that gets straight to the point. By adjusting the display method of the prediction results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0114] The prediction unit performs growth predictions while considering the user's geographical location information. For example, the prediction unit performs growth predictions by referencing regional climate data based on the user's geographical location information. For example, the prediction unit performs growth predictions by referencing regional soil data based on the user's geographical location information. For example, the prediction unit predicts regional pest and disease risks based on the user's geographical location information and proposes preventive measures. This makes it possible to perform growth predictions that are appropriate for the region by considering the user's geographical location information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the user's geographical location information into a generating AI and have the generating AI perform the growth prediction.

[0115] The prediction unit analyzes the user's social media activity and obtains relevant growth prediction data during the prediction process. For example, the prediction unit extracts hints about the nurturing environment from the user's social media posts and reflects them in the growth prediction. For example, the prediction unit optimizes the growth prediction by referencing the success stories of other users from the user's social media activity. For example, the prediction unit analyzes trends related to the nurturing environment from the user's social media activity and provides the latest growth prediction. This allows relevant growth prediction data to be obtained by analyzing the user's social media activity. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's social media data into a generating AI and have the generating AI perform the acquisition of growth prediction data.

[0116] The transformation unit estimates the user's emotions and adjusts the character design based on the estimated emotions. For example, if the user is relaxed, the transformation unit generates a character with a calm design. For example, if the user is excited, the transformation unit generates a character with a lively and colorful design. For example, if the user is stressed, the transformation unit generates a character with a calming design. By adjusting the character design according to the user's emotions, a more appropriate character can be generated. 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 transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The conversion unit generates different characters during the conversion process, depending on the growth stage of the mushroom. For example, during the germination stage, the conversion unit generates a young character. During the growth stage, the conversion unit generates a grown character. During the harvest stage, the conversion unit generates a mature character. This allows users to visually enjoy the growth process by generating different characters according to the growth stage of the mushroom. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input mushroom growth data into a generation AI and cause the generation AI to generate characters according to the growth stage.

[0118] The transformation unit customizes the character's movements according to the user's preferences during the transformation process. For example, the transformation unit sets the user's preferred movements and reflects them in the character. For example, the transformation unit adjusts the speed and pattern of the character's movements according to the user's preferences. For example, the transformation unit adds specific actions to the character based on the user's preferences. This allows for a more personalized experience by customizing the character's movements according to the user's preferences. Some or all of the above processes in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can input user preference data into a generating AI and have the generating AI perform the customization of the character's movements.

[0119] The transformation unit estimates the user's emotions and adjusts the character's display method based on the estimated emotions. For example, if the user is nervous, the transformation unit provides a simple and highly visible display method. For example, if the user is relaxed, the transformation unit provides a display method that includes detailed information. For example, if the user is in a hurry, the transformation unit provides a display method that gets straight to the point. By adjusting the character's display method according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0120] The conversion unit generates characters while considering the user's geographical location information during the conversion process. For example, the conversion unit generates characters that reflect regional characteristics based on the user's geographical location information. For example, the conversion unit generates characters that reflect regional culture and customs based on the user's geographical location information. For example, the conversion unit generates characters that reflect regional climate and environment based on the user's geographical location information. In this way, by considering the user's geographical location information, it is possible to generate characters that are appropriate for the region. Some or all of the above-described processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the user's geographical location information into a generation AI and have the generation AI perform character generation.

[0121] The transformation unit analyzes the user's social media activity during the transformation process and generates relevant characters. For example, the transformation unit generates characters that reflect the user's preferences and interests from the user's social media posts. For example, the transformation unit generates characters that reflect commonalities with other users from the user's social media activity. For example, the transformation unit generates characters that reflect trends from the user's social media activity. In this way, relevant characters can be generated by analyzing the user's social media activity. Some or all of the above processing in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can input the user's social media data into a generation AI and have the generation AI perform character generation.

[0122] The community section estimates the user's emotions and adjusts how the community is displayed based on the estimated emotions. For example, if the user is stressed, the community section provides a simple and highly visible display. If the user is relaxed, the community section provides a display that includes detailed information. If the user is in a hurry, the community section provides a display that gets straight to the point. By adjusting how the community is displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 community section may be performed using AI or not using AI. For example, the community section can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0123] The community department analyzes the interaction history within the community and proposes the optimal method of interaction. For example, the community department proposes the optimal method of interaction based on the user's past interaction history. For example, the community department analyzes the user's past interaction history and finds common ground with other users to propose. For example, the community department refers to the user's past interaction history and proposes the optimal timing for interaction. In this way, the optimal method of interaction can be proposed by analyzing the interaction history within the community. Some or all of the above processes in the community department may be performed using AI, for example, or without AI. For example, the community department can input the user's interaction history data into a generating AI and have the generating AI execute suggestions for interaction methods.

[0124] The community department applies different display methods to each topic within the community. For example, the community department sets the optimal display method for each topic and provides it to the user. For example, the community department sets different information priorities for each topic and adjusts the display method. For example, the community department applies different visual designs for each topic and adjusts the display method. By applying different display methods to each topic, more appropriate information can be provided. Some or all of the above processes in the community department may be performed using AI, for example, or not using AI. For example, the community department can input topic-specific data into a generating AI and have the generating AI perform the application of display methods.

[0125] The community section estimates the user's emotions and prioritizes communities based on those emotions. For example, if the user is stressed, the community section prioritizes displaying the most important community information. For example, if the user is relaxed, the community section prioritizes displaying detailed community information. For example, if the user is in a hurry, the community section prioritizes displaying community information that can be quickly accessed. This allows for the priority provision of important information by prioritizing communities 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 community section may be performed using AI or not. For example, the community section can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0126] The Community Department proposes the most suitable interaction method within the community, taking into account the user's geographical location. For example, the Community Department proposes interaction methods that reflect regional characteristics based on the user's geographical location. For example, the Community Department proposes interaction methods that reflect regional culture and customs based on the user's geographical location. For example, the Community Department proposes interaction methods that reflect regional climate and environment based on the user's geographical location. In this way, by considering the user's geographical location, it is possible to propose interaction methods that are appropriate for the region. Some or all of the above processing in the Community Department may be performed using AI, for example, or without AI. For example, the Community Department can input the user's geographical location information into a generating AI and have the generating AI execute suggestions for interaction methods.

[0127] The Community Department analyzes users' social media activity during interactions within the community and proposes relevant interaction methods. For example, the Community Department proposes interaction methods that reflect users' preferences and interests based on their social media posts. For example, the Community Department proposes interaction methods that reflect commonalities with other users based on users' social media activity. For example, the Community Department proposes interaction methods that reflect trends based on users' social media activity. In this way, relevant interaction methods can be proposed by analyzing users' social media activity. Some or all of the above processing in the Community Department may be performed using AI, for example, or without AI. For example, the Community Department can input users' social media data into a generating AI and have the generating AI execute suggestions for interaction methods.

[0128] The sharing section estimates the user's emotions and adjusts how shared information is displayed based on the estimated emotions. For example, if the user is nervous, the sharing section provides a simple and highly visible display method. If the user is relaxed, the sharing section provides a display method that includes detailed information. If the user is in a hurry, the sharing section provides a display method that gets straight to the point. By adjusting how shared information is displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 sharing section may be performed using AI, for example, or without AI. For example, the sharing section can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0129] The sharing unit, when sharing, refers to past sharing history to suggest the optimal sharing method. For example, the sharing unit suggests the optimal sharing method based on the user's past sharing history. For example, the sharing unit analyzes the user's past sharing history, finds commonalities with other users, and makes suggestions. For example, the sharing unit refers to the user's past sharing history to suggest sharing at the optimal time. In this way, the optimal sharing method can be suggested by referring to past sharing history. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input past sharing history data into a generating AI and have the generating AI execute the suggestion of a sharing method.

[0130] The sharing unit applies different sharing methods depending on the type of information when sharing. For example, when sharing cultivation information, the sharing unit applies a sharing method that includes detailed data and graphs. When sharing recipe information, for example, the sharing unit applies a sharing method that includes photos and videos. When sharing health advice, for example, the sharing unit applies a concise and easy-to-understand sharing method. By applying different sharing methods depending on the type of information, more appropriate information can be provided. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input data for each type of information into a generating AI and have the generating AI execute the application of the sharing method.

[0131] The sharing section estimates the user's emotions and prioritizes shared information based on the estimated emotions. For example, if the user is stressed, the sharing section prioritizes displaying the most important shared information. For example, if the user is relaxed, the sharing section prioritizes displaying detailed shared information. For example, if the user is in a hurry, the sharing section prioritizes displaying shared information that can be quickly viewed. This allows important information to be provided preferentially by prioritizing shared information 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 sharing section may be performed using AI or not using AI. For example, the sharing section can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0132] The sharing unit proposes the optimal sharing method when sharing, taking into account the user's geographical location information. For example, the sharing unit proposes a sharing method that reflects regional characteristics based on the user's geographical location information. For example, the sharing unit proposes a sharing method that reflects regional culture and customs based on the user's geographical location information. For example, the sharing unit proposes a sharing method that reflects regional climate and environment based on the user's geographical location information. In this way, by considering the user's geographical location information, it is possible to propose a sharing method that is appropriate for the region. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the user's geographical location information into a generating AI and have the generating AI execute the proposal of a sharing method.

[0133] The sharing unit analyzes the user's social media activity when sharing and suggests relevant sharing methods. For example, the sharing unit suggests sharing methods that reflect the user's preferences and interests from their social media posts. For example, the sharing unit suggests sharing methods that reflect commonalities with other users from the user's social media activity. For example, the sharing unit suggests sharing methods that reflect trends from the user's social media activity. In this way, relevant sharing methods can be suggested by analyzing the user's social media activity. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the user's social media data into a generating AI and have the generating AI perform the task of suggesting sharing methods.

[0134] The health department estimates the user's emotions and adjusts the display method of health advice based on the estimated emotions. For example, if the user is stressed, the health department provides a simple and highly visible display method. For example, if the user is relaxed, the health department provides a display method that includes detailed information. For example, if the user is in a hurry, the health department provides a display method that gets straight to the point. By adjusting the display method of health advice according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 health department may be performed using AI or not using AI. For example, the health department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0135] The health department provides optimal advice by referring to past health data when giving health advice. For example, the health department provides optimal diet and exercise advice based on the user's past health data. For example, the health department analyzes the user's past health data and advises on disease prevention measures. For example, the health department provides optimal advice for maintaining health by referring to the user's past health data. In this way, optimal advice can be provided by referring to past health data. Some or all of the above processes in the health department may be performed using AI, for example, or without using AI. For example, the health department can input past health data into a generating AI and have the generating AI execute optimal advice.

[0136] The health department provides different health advice depending on the user's lifestyle. For example, if the user is a morning person, the health department will provide health advice suitable for the morning hours. For example, if the user is a night owl, the health department will provide health advice suitable for the evening hours. For example, the health department will provide health advice at the appropriate time according to the user's lifestyle. By providing different advice according to the user's lifestyle, more appropriate information can be provided. Some or all of the above processing in the health department may be performed using AI, for example, or without AI. For example, the health department can input the user's lifestyle data into a generating AI and have the generating AI perform the provision of advice.

[0137] The health department estimates the user's emotions and prioritizes health advice based on the estimated emotions. For example, if the user is stressed, the health department prioritizes providing the most important health advice. For example, if the user is relaxed, the health department prioritizes providing detailed health advice. For example, if the user is in a hurry, the health department prioritizes providing health advice that can be acted on quickly. This allows important information to be provided preferentially by prioritizing health advice 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 health department may be performed using AI or not using AI. For example, the health department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0138] The Health Department provides optimal health advice by considering the user's geographical location. For example, the Health Department provides health advice that is appropriate for the local climate and environment based on the user's geographical location. For example, the Health Department provides health advice that utilizes local ingredients based on the user's geographical location. For example, the Health Department provides health advice that is appropriate for the local exercise environment based on the user's geographical location. In this way, by considering the user's geographical location, it is possible to provide advice that is appropriate for the region. Some or all of the above processing in the Health Department may be performed using AI, for example, or without AI. For example, the Health Department can input the user's geographical location into a generating AI and have the generating AI perform the provision of advice.

[0139] The Health Department analyzes the user's social media activity when providing health advice and offers relevant advice. For example, the Health Department provides advice that reflects the user's interests and concerns regarding health based on their social media posts. For example, the Health Department provides health advice that reflects commonalities with other users based on the user's social media activity. For example, the Health Department provides health advice that reflects trends based on the user's social media activity. In this way, relevant advice can be provided by analyzing the user's social media activity. Some or all of the above processing in the Health Department may be performed using AI, for example, or without AI. For example, the Health Department can input the user's social media data into a generating AI and have the generating AI perform the provision of advice.

[0140] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0141] The application system also includes a rewards section. This section can reward users with virtual items or points when they successfully cultivate mushrooms. For example, if a user successfully harvests mushrooms, the rewards section can provide a special item usable within the application. The rewards section can also award points to users who share information with other users or actively participate in the community. Furthermore, the rewards section can award special badges or titles to users when they achieve certain cultivation goals. This increases user motivation for cultivation activities and encourages more active use of the application.

[0142] The application system also includes a notification unit. This unit can notify users of important information and advice in real time. For example, if the temperature or humidity of the growing environment is not appropriate, the notification unit can immediately notify the user and prompt them to take appropriate action. It can also notify users of the harvest time and watering timing according to the mushroom's growth stage. Furthermore, the notification unit can also notify users of new posts and comments within the community. This allows users to take action at the right time without missing important information.

[0143] The application system also includes a learning section. This section can provide learning content to deepen users' knowledge of mushroom cultivation. For example, it can offer video tutorials on mushroom cultivation methods and disease prevention. The learning section can also provide features that allow users to take quizzes and tests related to cultivation. Furthermore, the learning section can provide a forum for users to share knowledge and engage in discussions with other users. This allows users to deepen their knowledge of mushroom cultivation and cultivate more effectively.

[0144] The application system also includes a customization section. This section allows users to customize the application's interface and functions to their liking. For example, users can change the application's theme color and background image. Furthermore, the customization section allows users to select the functions they use and the information they display, creating an interface that is easy for them to use. In addition, the customization section allows users to set the frequency and content of notifications. This allows users to customize the application to their preferences and use it more comfortably.

[0145] The application system also includes a feedback section. This section allows users to provide feedback on the application's usability and areas for improvement. For example, users can submit opinions and requests regarding the application's features and interface. The feedback section can also collect user feedback and use it to improve the application. Furthermore, the feedback section can provide a function that allows users to comment on and rate other users' feedback. This allows the application to reflect user opinions and evolve into a more user-friendly experience.

[0146] The application system further includes an emotion estimation unit. This unit estimates the user's emotions and can adjust the application's interface and functions based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can provide a simple and highly visible interface. If the user is relaxed, the emotion estimation unit can provide an interface with more detailed information. Furthermore, if the user is in a hurry, the emotion estimation unit can prioritize providing functions that can be executed quickly. This allows the application to provide more appropriate information by adjusting its interface and functions according to the user's emotions.

[0147] The application system also includes an emotion estimation unit. This unit estimates the user's emotions and can adjust the content and expression of advice based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can provide simple and easy-to-understand advice. If the user is relaxed, the unit can provide detailed advice and suggest fine-tuning of the learning environment. Furthermore, if the user is in a hurry, the emotion estimation unit can provide quickly actionable advice. This allows for more appropriate advice to be provided by adjusting the content and expression of advice according to the user's emotions.

[0148] The application system further includes an emotion estimation unit. This unit estimates the user's emotions and can adjust the way they interact within the community based on the estimated emotions. For example, if the user is feeling tense, the emotion estimation unit can provide a simple and easily understandable way of interacting. If the user is relaxed, the unit can provide an interaction that includes more detailed information. Furthermore, if the user is in a hurry, the unit can provide an interaction that can be quickly reviewed. This allows for the provision of more appropriate information by adjusting the way users interact within the community according to their emotions.

[0149] The application system further includes an emotion estimation unit. This unit estimates the user's emotions and can adjust the content and display method of health advice based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can provide simple and easy-to-understand health advice. If the user is relaxed, the emotion estimation unit can provide health advice with more detailed information. Furthermore, if the user is in a hurry, the emotion estimation unit can provide quickly actionable health advice. This allows for the provision of more appropriate information by adjusting the content and display method of health advice according to the user's emotions.

[0150] The application system also includes an emotion estimation unit. This unit estimates the user's emotions and can adjust the design and movements of the AR character based on the estimated emotions. For example, if the user is relaxed, the emotion estimation unit can generate a character with a calm design. If the user is excited, the emotion estimation unit can generate a character with a lively and colorful design. Furthermore, if the user is stressed, the emotion estimation unit can generate a character with a soothing design. This allows for the generation of a more appropriate character by adjusting the design and movements of the AR character according to the user's emotions.

[0151] The following briefly describes the processing flow for example form 2.

[0152] Step 1: The analysis department analyzes the growing environment. This includes factors such as temperature, humidity, light intensity, and soil type. The analysis department collects data on the growing environment using sensors and analyzes it using AI technology. Step 2: The advice unit provides advice on optimal temperature and humidity control and harvest timing based on the growing environment analyzed by the analysis unit. The advice unit uses AI technology to analyze the growing environment data and proposes optimal management methods. For example, it provides specific numerical ranges for temperature and humidity and advises the user on appropriate management methods. It also provides criteria for determining the harvest time and advises the user on the optimal harvest timing. Step 3: The monitoring unit checks the growth status in real time. The monitoring unit collects real-time data using sensors and displays it within the application. For example, it displays the mushroom growth status in graphs and charts, providing the user with a visually easy-to-understand explanation. It also sets the data update frequency to provide the latest information in real time. Step 4: The prediction unit predicts mushroom growth and detects diseases early. The prediction unit uses AI technology to predict growth and provides the user with the prediction results. For example, it sets the algorithm to be used to improve the accuracy of the prediction. It also sets criteria for detecting disease symptoms and diagnosing them early.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the analysis unit, advice unit, confirmation unit, prediction unit, transformation unit, community unit, sharing unit, and health unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit collects data on the growing environment using the sensors of the smart device 14 and analyzes it using AI technology by the specific processing unit 290 of the data processing unit 12. The advice unit, for example, analyzes the data on the growing environment using the specific processing unit 290 of the data processing unit 12 and proposes the optimal management method. The confirmation unit, for example, collects real-time data using the sensors of the smart device 14 and displays it in the application. The prediction unit, for example, performs growth prediction and early detection of diseases using the specific processing unit 290 of the data processing unit 12. The transformation unit, for example, takes a picture of a mushroom using the camera of the smart device 14 and transforms it into an AR character using the specific processing unit 290 of the data processing unit 12. The community unit, for example, provides chat and forum functions within the application of the smart device 14. The sharing unit, for example, shares cultivation information and recipes within the application of the smart device 14. The health unit, for example, generates health advice using the specific processing unit 290 of the data processing device 12 and provides it within the application of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the analysis unit, advice unit, verification unit, prediction unit, transformation unit, community unit, sharing unit, and health unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit collects data on the growing environment using the sensors of the smart glasses 214 and analyzes it using AI technology by the specific processing unit 290 of the data processing unit 12. The advice unit, for example, analyzes the data on the growing environment using the specific processing unit 290 of the data processing unit 12 and proposes the optimal management method. The verification unit, for example, collects real-time data using the sensors of the smart glasses 214 and displays it in the application. The prediction unit, for example, performs growth prediction and early detection of diseases using the specific processing unit 290 of the data processing unit 12. The transformation unit, for example, takes a picture of a mushroom using the camera of the smart glasses 214 and transforms it into an AR character using the specific processing unit 290 of the data processing unit 12. The community unit, for example, provides chat and forum functions within the application of the smart glasses 214. The shared section, for example, shares cultivation information and recipes within the application of the smart glasses 214. The health section, for example, generates health advice using the specific processing unit 290 of the data processing device 12 and provides it within the application of the smart glasses 214. The correspondence between each section and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0173] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Each of the multiple elements described above, including the analysis unit, advice unit, verification unit, prediction unit, transformation unit, community unit, sharing unit, and health unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit collects data on the growing environment using the sensors of the headset terminal 314 and analyzes it using AI technology by the specific processing unit 290 of the data processing unit 12. The advice unit, for example, analyzes the data on the growing environment using the specific processing unit 290 of the data processing unit 12 and proposes the optimal management method. The verification unit, for example, collects real-time data using the sensors of the headset terminal 314 and displays it in the application. The prediction unit, for example, performs growth prediction and early detection of diseases using the specific processing unit 290 of the data processing unit 12. The transformation unit, for example, takes a picture of a mushroom using the camera of the headset terminal 314 and transforms it into an AR character using the specific processing unit 290 of the data processing unit 12. The community unit, for example, provides chat and forum functions within the application of the headset terminal 314. The sharing unit, for example, shares cultivation information and recipes within the application of the headset terminal 314. The health unit, for example, generates health advice using the specific processing unit 290 of the data processing device 12 and provides it within the application of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0189] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.).

[0202] 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.

[0203] 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.

[0204] 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.

[0205] Each of the multiple elements described above, including the analysis unit, advice unit, verification unit, prediction unit, transformation unit, community unit, sharing unit, and health unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit collects data on the growing environment using the sensors of the robot 414 and analyzes it using AI technology by the specific processing unit 290 of the data processing unit 12. The advice unit analyzes the data on the growing environment using the specific processing unit 290 of the data processing unit 12 and proposes the optimal management method. The verification unit collects real-time data using the sensors of the robot 414 and displays it in the application. The prediction unit performs growth prediction and early detection of diseases using the specific processing unit 290 of the data processing unit 12. The transformation unit takes a picture of a mushroom using the camera of the robot 414 and transforms it into an AR character using the specific processing unit 290 of the data processing unit 12. The community unit provides chat and forum functions within the application of the robot 414. The sharing unit shares cultivation information and recipes within the application of the robot 414. The health unit, for example, generates health advice using the specific processing unit 290 of the data processing unit 12 and provides it within the application of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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."

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] (Note 1) The analysis department analyzes the training environment, Based on the growing environment analyzed by the aforementioned analysis unit, the advice unit provides advice on optimal temperature and humidity control and harvest time. A confirmation unit for checking the growth status in real time, It includes a prediction unit that predicts mushroom growth and detects diseases early. A system characterized by the following features. (Note 2) It features a transformation unit that uses AR technology to turn cultivated mushrooms into characters. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a community section that provides community functions. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a shared area for sharing cultivation information and recipes. The system described in Appendix 1, characterized by the features described herein. (Note 5) We have a health department that provides health advice. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned verification unit is Check real-time growth status within the app. The system described in Appendix 1, characterized by the features described herein. (Note 7) The prediction unit, Predicting mushroom growth and early detection of diseases. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method of the training environment based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During analysis, the analysis algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During analysis, different analytical methods are applied to each type of mushroom. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During analysis, the user's geographical location information is taken into consideration when analyzing the training environment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During the analysis, we analyze users' social media activity and obtain relevant developmental environment data. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned advice section, When providing advice, the advice content will be updated in real time in response to changes in the training environment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice section, When providing advice, offer different advice depending on the stage of mushroom growth. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, When providing advice, we refer to the user's past training history to provide the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, When providing advice, the timing of the advice will be adjusted based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned verification unit is The system estimates the user's emotions and adjusts the display method for growth status based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned verification unit is During verification, the display content is optimized by referring to past growth data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned verification unit is During verification, different display methods will be applied for each type of mushroom. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned verification unit is It estimates the user's emotions and determines the priority of growth stages based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned verification unit is When checking, the growth status will be displayed taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned verification unit is During verification, the system analyzes the user's social media activity and displays relevant growth data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, We estimate user sentiment and adjust the growth forecasting method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, When making predictions, the prediction algorithm is optimized by referring to historical growth data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The prediction unit, When making predictions, different prediction methods are applied for each type of mushroom. The system described in Appendix 1, characterized by the features described herein. (Note 29) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The prediction unit, When making predictions, growth forecasts are made taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The prediction unit, During the forecasting process, we analyze users' social media activity and obtain relevant growth forecast data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The conversion unit is It estimates the user's emotions and adjusts the character design based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The conversion unit is During the transformation, different characters are generated depending on the mushroom's growth stage. The system described in Appendix 2, characterized by the features described herein. (Note 34) The conversion unit is During conversion, the character's movements are customized according to the user's preferences. The system described in Appendix 2, characterized by the features described herein. (Note 35) The conversion unit is It estimates the user's emotions and adjusts how characters are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The conversion unit is During conversion, the user's geographical location information is taken into consideration when generating the character. The system described in Appendix 2, characterized by the features described herein. (Note 37) The conversion unit is During the conversion process, the system analyzes the user's social media activity and generates relevant characters. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned community department, It estimates user sentiment and adjusts how communities are displayed based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned community department, We analyze the interaction history within the community and propose the most suitable method of interaction. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned community department, Apply different display methods to each topic within the community. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned community department, It estimates user sentiment and determines community priorities based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned community department, When users interact within the community, we suggest the most suitable interaction method by taking their geographical location into consideration. The system described in Appendix 3, characterized by the features described herein. (Supplementary Note 43) The community section analyzes the user's social media activities during communication within the community and proposes relevant communication methods The system according to Supplementary Note 3, characterized by the above. (Supplementary Note 44) The sharing section estimates the user's emotion and adjusts the display method of shared information based on the estimated user emotion The system according to Supplementary Note 4, characterized by the above. (Supplementary Note 45) The sharing section refers to the past sharing history during sharing and proposes an optimal sharing method The system according to Supplementary Note 4, characterized by the above. (Supplementary Note 46) The sharing section applies different sharing methods for different types of information during sharing The system according to Supplementary Note 4, characterized by the above. (Supplementary Note 47) The sharing section estimates the user's emotion and determines the priority of shared information based on the estimated user emotion The system according to Supplementary Note 4, characterized by the above. (Supplementary Note 48) The sharing section considers the user's geographical location information during sharing and proposes an optimal sharing method The system according to Supplementary Note 4, characterized by the above. (Supplementary Note 49) The sharing section analyzes the user's social media activities during sharing and proposes relevant sharing methods The system according to Supplementary Note 4, characterized by the above. (Supplementary Note 50) The health section estimates the user's emotion and adjusts the display method of health advice based on the estimated user emotion The system according to Supplementary Note 5, characterized by the above. (Supplementary Note 51) The aforementioned health department, When providing health advice, we refer to past health data to provide the most appropriate advice. The system described in Appendix 5, characterized by the features described herein. (Note 52) The aforementioned health department, When providing health advice, we offer different advice depending on the user's lifestyle. The system described in Appendix 5, characterized by the features described herein. (Note 53) The aforementioned health department, It estimates the user's emotions and prioritizes health advice based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 54) The aforementioned health department, When providing health advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 5, characterized by the features described herein. (Note 55) The aforementioned health department, When providing health advice, we analyze the user's social media activity and offer relevant advice. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0225] 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 analysis department analyzes the training environment, Based on the growing environment analyzed by the aforementioned analysis unit, an advice unit provides advice on optimal temperature and humidity control and harvest time. A confirmation unit for checking the growth status in real time, It includes a prediction unit that predicts mushroom growth and detects diseases early. A system characterized by the following features.

2. It features a transformation unit that uses AR technology to turn cultivated mushrooms into characters. The system according to feature 1.

3. It has a community section that provides community functions. The system according to feature 1.

4. It features a shared area for sharing cultivation information and recipes. The system according to feature 1.

5. We have a health department that provides health advice. The system according to feature 1.

6. The aforementioned verification unit is Check real-time growth status within the app. The system according to feature 1.

7. The prediction unit, Predicting mushroom growth and early detection of diseases. The system according to feature 1.

8. The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method of the training environment based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit is During analysis, the analysis algorithm is optimized by referring to past training data. The system according to feature 1.

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