system

Abstract

The system according to the embodiment aims to enable individuals to easily grow high-quality coffee at home. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, a monitoring unit, and a predicting unit. The receiving unit receives information from a user. The generating unit sets environmental conditions based on the information received by the receiving unit. The monitoring unit monitors the environmental conditions set by the generating unit. The predicting unit predicts the progress of cultivation or the harvest time based on the information monitored by the monitoring unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for individuals to easily grow high-quality coffee at home.

[0005] The system according to the embodiment aims to enable individuals to easily grow high-quality coffee at home. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, a monitoring unit, and a predicting unit. The receiving unit receives information from a user. The generating unit sets environmental conditions based on the information received by the receiving unit. The monitoring unit monitors the environmental conditions set by the generating unit. The predicting unit predicts the progress of cultivation or the harvest time based on the information monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable individuals to easily grow high-quality coffee at home. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An automated coffee garden system according to an embodiment of the present invention utilizes AI to easily cultivate high-quality coffee at home. This automated coffee garden system allows users to start coffee cultivation through an app, and AI automatically controls optimal environmental conditions and supports tasks such as watering and temperature management. For example, when users input information about the type of coffee being cultivated and the cultivation environment, the AI ​​analyzes the information and automatically adjusts the appropriate temperature, humidity, and light levels. The system also features a soil humidity sensor to monitor the soil humidity and automatically provide water as needed, as well as a temperature sensor to maintain an appropriate temperature. Furthermore, the app provides cultivation progress and harvest forecasts, allowing users to easily monitor the progress of their cultivation. For example, it displays information such as the coffee's growth stage and the number of days remaining until harvest. The app also provides information about the coffee being cultivated and advice on how to cultivate it, including the characteristics of each coffee variety and optimal cultivation methods. This allows busy coffee lovers to easily enjoy authentic coffee at home. The automated coffee garden system allows users to cultivate and harvest high-quality coffee without any effort.

[0029] An automatic coffee garden system according to an embodiment includes a reception unit, a generation unit, a monitoring unit, and a prediction unit. The reception unit receives information from a user. Examples of information from the user include, but are not limited to, cultivation conditions, environmental settings, and personal preferences. The reception unit receives information such as the coffee variety and the climate conditions of the cultivation location input by the user through an app. The generation unit sets environmental conditions based on the information received by the reception unit. The generation unit automatically adjusts, for example, appropriate temperature, humidity, and amount of light. The generation unit can analyze the user's information and set optimal environmental conditions using a generation AI. For example, the generation AI sets appropriate temperature and humidity based on the information input by the user. The generation unit can also automatically adjust the amount of light using the generation AI. The monitoring unit monitors the environmental conditions set by the generation unit. For example, the monitoring unit monitors soil humidity using a sensor and automatically supplies water as needed. The monitoring unit can also maintain an appropriate temperature using a temperature sensor. The monitoring unit can monitor environmental conditions using the generation AI. For example, the monitoring unit inputs data from a soil humidity sensor into the generation AI, and the generation AI determines the appropriate timing for watering. The prediction unit predicts the cultivation progress or harvest time based on the information monitored by the monitoring unit. The prediction unit predicts, for example, the coffee growth stage or the number of days remaining until harvest. The prediction unit can predict the cultivation progress using the generation AI. For example, the prediction unit predicts the coffee growth stage based on data from the monitoring unit. This allows the automatic coffee garden system according to the embodiment to enable users to easily cultivate and harvest high-quality coffee. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs user information into the generation AI, and the generation AI can set optimal environmental conditions. Some or all of the above-described processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.For example, the monitoring unit can input data from a soil moisture sensor to the generation AI, which can then determine the appropriate timing for watering. Some or all of the above-described processing in the prediction unit can be performed using, or without, the generation AI. For example, the prediction unit can input data from the monitoring unit to the generation AI, which can then predict the progress of cultivation.

[0030] The monitoring unit can monitor the soil humidity using a sensor and automatically supply water as needed. The monitoring unit, for example, monitors the soil humidity using a sensor and automatically supplies water as needed. For example, the monitoring unit measures the soil humidity in real time using a soil humidity sensor. The monitoring unit can also automatically supply water when the soil humidity falls below a certain threshold. For example, the monitoring unit can automatically activate an irrigation system to supply water when the soil humidity drops. In this way, appropriate watering is achieved by automatically managing the soil humidity. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input data from a soil humidity sensor into the generation AI, which can then determine the appropriate timing for watering.

[0031] The monitoring unit can maintain the temperature using a temperature sensor. The monitoring unit maintains the temperature using, for example, a temperature sensor. For example, the monitoring unit uses a temperature sensor to measure the temperature of the cultivation environment in real time. The monitoring unit can also automatically activate a temperature adjustment system to maintain an appropriate temperature when the temperature is outside a set range. For example, the monitoring unit activates a cooling system when the temperature is too high, and activates a heating system when the temperature is too low. In this way, appropriate temperature management is achieved by using a temperature sensor. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input data from a temperature sensor into the generation AI, which can then determine appropriate temperature adjustment.

[0032] The generation unit can automatically adjust the temperature, humidity, and amount of light. The generation unit automatically adjusts, for example, the temperature, humidity, and amount of light. For example, the generation unit measures each condition of the cultivation environment in real time using a temperature sensor, a humidity sensor, and a light sensor. The generation unit can also automatically adjust the appropriate temperature, humidity, and amount of light based on the measured data. For example, the generation unit activates a cooling system when the temperature is too high and a heating system when the temperature is too low. The generation unit also activates a humidification system when the humidity is too low and a dehumidification system when the humidity is too high. Furthermore, the generation unit activates a lighting system when the amount of light is insufficient and a shading system when the amount of light is excessive. In this way, the generation unit automatically adjusts the environmental conditions to provide an optimal cultivation environment. Some or all of the above-mentioned processes in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data from sensors into the generation AI, which then sets appropriate environmental conditions.

[0033] The prediction unit can predict the growth stage of coffee and the number of days remaining until harvest. The prediction unit predicts, for example, the growth stage of coffee and the number of days remaining until harvest. For example, the prediction unit predicts the growth stage of coffee based on data from the monitoring unit. The prediction unit can also predict the number of days remaining until harvest based on the growth stage. For example, the prediction unit calculates the number of days remaining until harvest taking into account the growth rate of coffee and environmental conditions. In this way, the prediction unit predicts the growth stage and harvest time, making it easier for the user to grasp the progress of cultivation. Some or all of the above-mentioned processing in the prediction unit may be performed using, or without, a generation AI, for example. For example, the prediction unit can input data from the monitoring unit into the generation AI, which can then predict the progress of cultivation.

[0034] The providing unit can provide information regarding the characteristics and optimal cultivation methods for each coffee variety. The providing unit provides, for example, information regarding the characteristics and optimal cultivation methods for each coffee variety. For example, the providing unit suggests a specific cultivation method for Arabica coffee and a different cultivation method for Robusta coffee. The providing unit can also provide information such as the optimal temperature, humidity, and amount of light based on the characteristics of each variety. For example, the providing unit suggests setting a specific temperature and humidity for Arabica coffee and suggesting a different temperature and humidity for Robusta coffee. In this way, the providing unit provides the characteristics and cultivation methods for each variety, allowing the user to cultivate coffee effectively. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input user information into the generation AI, which then suggests the optimal cultivation method.

[0035] The reception unit can analyze the user's past cultivation history and select the optimal information input method. The reception unit, for example, analyzes the user's past cultivation history and selects the optimal information input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit can also simplify the information input for a specific variety based on the user's past cultivation history. Furthermore, the reception unit can analyze the user's past cultivation history and suggest the most efficient input method. In this way, by analyzing the past cultivation history, the optimal information input method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past cultivation data into the generation AI, which then selects the optimal information input method.

[0036] The reception unit can filter information based on the user's current living situation and areas of interest when inputting information. For example, the reception unit can filter information based on the user's current living situation and areas of interest when inputting information. For example, if the user is busy, the reception unit requests the user to input the minimum amount of information necessary. The reception unit can also prioritize input of information related to areas in which the user is interested. Furthermore, the reception unit can suggest an appropriate information input method depending on the user's living situation. This enables efficient information input by filtering information based on the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's living situation data into a generation AI, which can then suggest an appropriate information input method.

[0037] The reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information when inputting information. For example, the reception unit prioritizes inputting highly relevant information in consideration of the user's geographical location information when inputting information. For example, when the user is in a specific area, the reception unit prioritizes inputting information related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize inputting information related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize inputting information related to the user's home. In this way, highly relevant information can be prioritized by considering the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location data to the generation AI, which can then prioritize inputting highly relevant information.

[0038] The reception unit can analyze the user's social media activity and input relevant information when inputting information. For example, the reception unit can analyze the user's social media activity and input relevant information when inputting information. For example, the reception unit can input relevant information based on information shared by the user on social media. The reception unit can also input information related to topics in which the user has shown interest on social media. Furthermore, the reception unit can analyze the user's social media activity and input the most relevant information. In this way, highly relevant information can be input by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media data to the generation AI, which can then input the relevant information.

[0039] The generation unit can customize the settings based on the characteristics of each coffee variety when setting the environmental conditions. For example, the generation unit customizes the settings based on the characteristics of each coffee variety when setting the environmental conditions. For example, the generation unit sets a specific temperature and humidity for Arabica coffee. The generation unit can also set a different temperature and humidity for Robusta coffee. Furthermore, the generation unit can set the optimal amount of light based on the characteristics of each variety. This makes it possible to provide an optimal cultivation environment by customizing the environmental conditions based on the characteristics of each coffee variety. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input coffee variety data into the generation AI, which then sets the optimal environmental conditions.

[0040] The generation unit can set optimal environmental conditions by referring to past cultivation data when setting the environmental conditions. The generation unit can set optimal environmental conditions by referring to past cultivation data, for example. For example, the generation unit sets optimal temperature and humidity based on past cultivation data. The generation unit can also set optimal light intensity based on past cultivation data. Furthermore, the generation unit can set optimal watering frequency based on past cultivation data. In this way, optimal environmental conditions can be set by referring to past cultivation data. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input past cultivation data into the generation AI, which can then set optimal environmental conditions.

[0041] The generation unit can optimally set environmental conditions by taking into account the user's geographical location information when setting the environmental conditions. For example, the generation unit can optimally set environmental conditions by taking into account the user's geographical location information when setting the environmental conditions. For example, if the user is in a hot and humid region, the generation unit can set environmental conditions appropriate for that region. Also, if the user is in a cold region, the generation unit can set environmental conditions appropriate for that region. Furthermore, the generation unit can set an optimal amount of light based on the user's geographical location information. In this way, optimal environmental conditions can be set by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's geographical location data into the generation AI, which can then set optimal environmental conditions.

[0042] The generation unit can analyze the user's social media activity and set relevant settings when setting environmental conditions. For example, the generation unit can analyze the user's social media activity and set relevant settings when setting environmental conditions. For example, the generation unit can set relevant environmental conditions based on information shared by the user on social media. The generation unit can also set environmental conditions related to topics in which the user has shown interest on social media. Furthermore, the generation unit can analyze the user's social media activity and set the most relevant environmental conditions. In this way, highly relevant environmental conditions can be set by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's social media data into the generation AI, which can then set relevant environmental conditions.

[0043] The monitoring unit can analyze the components of the soil during monitoring and automatically supply fertilizer as needed. The monitoring unit, for example, can analyze the components of the soil during monitoring and automatically supply fertilizer as needed. For example, the monitoring unit can analyze the components of the soil and automatically supply necessary nutrients. The monitoring unit can also determine the optimal type and amount of fertilizer based on the analysis of the soil components. Furthermore, the monitoring unit can also analyze the components of the soil and optimize the nutritional balance. In this way, necessary nutrients can be automatically supplied by analyzing the components of the soil. Some or all of the above-mentioned processing in the monitoring unit can be performed using, or without, the generation AI, for example. For example, the monitoring unit can input soil component data into the generation AI, which can determine the necessary nutrients and automatically supply fertilizer.

[0044] The monitoring unit can measure the light intensity and quality with a sensor during monitoring and maintain an optimal lighting environment. For example, the monitoring unit can measure the light intensity and quality with a sensor during monitoring and maintain an optimal lighting environment. For example, the monitoring unit can measure the light intensity with a sensor and maintain an optimal lighting environment. The monitoring unit can also measure the light quality with a sensor and provide an optimal lighting environment for growth. Furthermore, the monitoring unit can measure the light intensity and quality with a sensor and maintain a balanced lighting environment. In this way, an optimal lighting environment can be maintained by measuring the light intensity and quality. Some or all of the above-described processing in the monitoring unit can be performed using, or without, the generation AI, for example. For example, the monitoring unit can input data on the light intensity and quality into the generation AI, which can then set an optimal lighting environment.

[0045] The monitoring unit can select the optimal monitoring method during monitoring by taking into account the user's geographical location information. For example, the monitoring unit selects the optimal monitoring method by taking into account the user's geographical location information during monitoring. For example, if the user is in a hot and humid region, the monitoring unit selects a monitoring method appropriate for that region. Also, if the user is in a cold region, the monitoring unit can select a monitoring method appropriate for that region. Furthermore, the monitoring unit can select the optimal monitoring method based on the user's geographical location information. In this way, the optimal monitoring method can be selected by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input the user's geographical location data into the generation AI, which can then select the optimal monitoring method.

[0046] The monitoring unit can analyze the user's social media activities during monitoring and provide relevant monitoring data. For example, the monitoring unit can analyze the user's social media activities during monitoring and provide relevant monitoring data. For example, the monitoring unit can provide relevant monitoring data based on information shared by the user on social media. The monitoring unit can also provide monitoring data related to topics in which the user has shown interest on social media. Furthermore, the monitoring unit can analyze the user's social media activities and provide the most relevant monitoring data. In this way, by analyzing the user's social media activities, highly relevant monitoring data can be provided. Some or all of the above-described processing in the monitoring unit can be performed using, or without, a generation AI. For example, the monitoring unit can input the user's social media data into a generation AI, which can provide the relevant monitoring data.

[0047] The prediction unit can optimize the prediction algorithm by referring to past cultivation data when making predictions. The prediction unit can optimize the prediction algorithm by referring to past cultivation data when making predictions, for example. For example, the prediction unit sets an optimal prediction algorithm based on past cultivation data. The prediction unit can also set an algorithm that improves prediction accuracy based on past cultivation data. Furthermore, the prediction unit can select the most efficient prediction algorithm based on past cultivation data. In this way, the prediction algorithm can be optimized by referring to past cultivation data. Some or all of the above-mentioned processing in the prediction unit may be performed using, or without, the generation AI, for example. For example, the prediction unit can input past cultivation data into the generation AI, which can then set an optimal prediction algorithm.

[0048] The prediction unit can improve prediction accuracy by taking into account the growth characteristics of each coffee variety during prediction. The prediction unit, for example, improves prediction accuracy by taking into account the growth characteristics of each coffee variety during prediction. For example, the prediction unit improves prediction accuracy by taking into account the growth characteristics of Arabica. The prediction unit can also improve prediction accuracy by taking into account the growth characteristics of Robusta. Furthermore, the prediction unit can set an optimal prediction algorithm based on the growth characteristics of each variety. In this way, prediction accuracy is improved by taking into account the growth characteristics of each coffee variety. Some or all of the above-mentioned processing in the prediction unit may be performed using, or without, a generation AI, for example. For example, the prediction unit can input coffee variety data into the generation AI, which can then set an optimal prediction algorithm.

[0049] The prediction unit can make an optimal prediction by taking into account the user's geographical location information when making a prediction. The prediction unit can make an optimal prediction by taking into account the user's geographical location information when making a prediction, for example. For example, if the user is in a hot and humid area, the prediction unit can make a prediction appropriate for that area. Also, if the user is in a cold area, the prediction unit can make a prediction appropriate for that area. Furthermore, the prediction unit can make an optimal prediction based on the user's geographical location information. In this way, an optimal prediction can be made by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the prediction unit can input the user's geographical location data into the generation AI, which can make an optimal prediction.

[0050] The prediction unit may analyze the user's social media activity and provide relevant predicted data during prediction. For example, the prediction unit may analyze the user's social media activity and provide relevant predicted data during prediction. For example, the prediction unit may provide relevant predicted data based on information shared by the user on social media. The prediction unit may also provide predicted data related to topics in which the user has shown interest on social media. Furthermore, the prediction unit may analyze the user's social media activity and provide the most relevant predicted data. In this way, highly relevant predicted data can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the prediction unit may be performed using, or without, a generation AI. For example, the prediction unit may input the user's social media data into the generation AI, which may provide relevant predicted data.

[0051] The providing unit can provide optimal information by referring to the user's past cultivation history when providing information. For example, the providing unit can provide optimal information by referring to the user's past cultivation history when providing information. For example, the providing unit can suggest an optimal cultivation method based on the user's past cultivation history. The providing unit can also suggest an optimal type and amount of fertilizer based on the user's past cultivation history. Furthermore, the providing unit can also suggest an optimal watering frequency based on the user's past cultivation history. In this way, optimal information can be provided by referring to the user's past cultivation history. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI, for example. For example, the providing unit can input the user's past cultivation data into the generation AI, which can then provide optimal information.

[0052] The providing unit can provide advice customized based on the characteristics of each coffee variety when providing information. The providing unit, for example, provides advice customized based on the characteristics of each coffee variety when providing information. For example, the providing unit can suggest a specific cultivation method for Arabica coffee. The providing unit can also suggest a different cultivation method for Robusta coffee. Furthermore, the providing unit can also suggest the optimal cultivation method based on the characteristics of each variety. In this way, by providing advice customized based on the characteristics of each coffee variety, the optimal cultivation method can be suggested. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input coffee variety data into the generation AI, which can then suggest the optimal cultivation method.

[0053] The providing unit can provide optimal information by taking into account the user's geographical location information when providing information. For example, the providing unit can provide optimal information by taking into account the user's geographical location information when providing information. For example, if the user is in a hot and humid region, the providing unit can provide information appropriate for that region. Also, if the user is in a cold region, the providing unit can provide information appropriate for that region. Furthermore, the providing unit can provide optimal information based on the user's geographical location information. In this way, optimal information can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's geographical location data into the generation AI, which can then provide optimal information.

[0054] The providing unit can analyze the user's social media activity and provide relevant information when providing information. For example, the providing unit can analyze the user's social media activity and provide relevant information when providing information. For example, the providing unit can provide relevant information based on information shared by the user on social media. The providing unit can also provide information related to topics in which the user has shown interest on social media. Furthermore, the providing unit can analyze the user's social media activity and provide the most relevant information. In this way, highly relevant information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's social media data into the generation AI, which can then provide the relevant information.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can analyze the user's cultivation history and suggest the optimal cultivation method based on past successes and failures. For example, the reception unit can analyze the varieties of coffee grown by the user in the past and the environmental conditions, and suggest the optimal cultivation method under similar conditions. The reception unit can also take past failures into consideration and provide advice on how to avoid making the same mistakes. Furthermore, the reception unit can suggest the optimal cultivation schedule based on the user's cultivation history. This allows the user to utilize their past experience to cultivate coffee more effectively.

[0057] The monitoring unit can analyze soil components and automatically supply fertilizer as needed. For example, the monitoring unit can analyze soil components in real time and automatically supply necessary nutrients. The monitoring unit can also select and automatically supply appropriate fertilizer when soil components are lacking. Furthermore, the monitoring unit can periodically analyze soil components and optimize the nutrient balance. This allows for proper management of soil components to support coffee growth.

[0058] The monitoring unit not only maintains the temperature using a temperature sensor, but also analyzes temperature fluctuation patterns and proposes optimal temperature control methods. For example, the monitoring unit analyzes past temperature data to understand temperature fluctuation patterns by season and time of day. The monitoring unit can also propose appropriate temperature control methods when there are large temperature fluctuations. Furthermore, the monitoring unit can also propose energy-efficient temperature control methods when there are small temperature fluctuations. This makes it possible to support coffee growth by optimizing temperature control.

[0059] The generation unit not only automatically adjusts the temperature, humidity, and amount of light, but also customizes the environmental conditions based on the user's preferences. For example, the generation unit sets optimal environmental conditions based on the user's preferred coffee flavor and aroma. If the user prefers a specific cultivation method, the generation unit can also adjust the environmental conditions to suit that method. Furthermore, the generation unit can fine-tune the environmental conditions according to the user's preferences. This allows coffee to be cultivated according to the user's preferences.

[0060] The prediction unit not only predicts the coffee's growth stage and the number of days remaining until harvest, but also improves prediction accuracy by taking into account the user's cultivation history. For example, the prediction unit analyzes growth data of coffee grown by the user in the past and predicts growth patterns under similar conditions. The prediction unit can also improve prediction accuracy of the harvest time based on the past cultivation history. Furthermore, the prediction unit can also suggest the optimal harvest time by taking into account the user's cultivation history. This allows the user to grow coffee based on more accurate predictions.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives information from the user. This information includes cultivation conditions, environmental settings, and personal preferences. For example, the application receives information such as the coffee variety and the climate conditions of the cultivation location entered by the user through the application. Step 2: The generator sets the environmental conditions based on the information received by the receiver. The generator automatically adjusts the appropriate temperature, humidity, amount of light, etc. The generator uses AI to analyze the user's information and set the optimal environmental conditions. Step 3: The monitoring unit monitors the environmental conditions set by the generation unit. The monitoring unit monitors the soil moisture with a sensor and automatically supplies water as needed. A temperature sensor can also be used to maintain an appropriate temperature. The generation AI can be used to monitor the environmental conditions. Step 4: The prediction unit predicts the progress of cultivation or the harvest time based on the information monitored by the monitoring unit. The prediction unit predicts the coffee's growth stage and the number of days remaining until harvest. The generation AI can be used to predict the progress of cultivation.

[0063] (Example 2) An automated coffee garden system according to an embodiment of the present invention utilizes AI to easily cultivate high-quality coffee at home. This automated coffee garden system allows users to start coffee cultivation through an app, and AI automatically controls optimal environmental conditions and supports tasks such as watering and temperature management. For example, when users input information about the type of coffee being cultivated and the cultivation environment, the AI ​​analyzes the information and automatically adjusts the appropriate temperature, humidity, and light levels. The system also features a soil humidity sensor to monitor the soil humidity and automatically provide water as needed, as well as a temperature sensor to maintain an appropriate temperature. Furthermore, the app provides cultivation progress and harvest forecasts, allowing users to easily monitor the progress of their cultivation. For example, it displays information such as the coffee's growth stage and the number of days remaining until harvest. The app also provides information about the coffee being cultivated and advice on how to cultivate it, including the characteristics of each coffee variety and optimal cultivation methods. This allows busy coffee lovers to easily enjoy authentic coffee at home. The automated coffee garden system allows users to cultivate and harvest high-quality coffee without any effort.

[0064] An automatic coffee garden system according to an embodiment includes a reception unit, a generation unit, a monitoring unit, and a prediction unit. The reception unit receives information from a user. Examples of information from the user include, but are not limited to, cultivation conditions, environmental settings, and personal preferences. The reception unit receives information such as the coffee variety and the climate conditions of the cultivation location input by the user through an app. The generation unit sets environmental conditions based on the information received by the reception unit. The generation unit automatically adjusts, for example, appropriate temperature, humidity, and amount of light. The generation unit can analyze the user's information and set optimal environmental conditions using a generation AI. For example, the generation AI sets appropriate temperature and humidity based on the information input by the user. The generation unit can also automatically adjust the amount of light using the generation AI. The monitoring unit monitors the environmental conditions set by the generation unit. For example, the monitoring unit monitors soil humidity using a sensor and automatically supplies water as needed. The monitoring unit can also maintain an appropriate temperature using a temperature sensor. The monitoring unit can monitor environmental conditions using the generation AI. For example, the monitoring unit inputs data from a soil humidity sensor into the generation AI, and the generation AI determines the appropriate timing for watering. The prediction unit predicts the cultivation progress or harvest time based on the information monitored by the monitoring unit. The prediction unit predicts, for example, the coffee growth stage or the number of days remaining until harvest. The prediction unit can predict the cultivation progress using the generation AI. For example, the prediction unit predicts the coffee growth stage based on data from the monitoring unit. This allows the automatic coffee garden system according to the embodiment to enable users to easily cultivate and harvest high-quality coffee. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs user information into the generation AI, and the generation AI can set optimal environmental conditions. Some or all of the above-described processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.For example, the monitoring unit can input data from a soil moisture sensor to the generation AI, which can then determine the appropriate timing for watering. Some or all of the above-described processing in the prediction unit can be performed using, or without, the generation AI. For example, the prediction unit can input data from the monitoring unit to the generation AI, which can then predict the progress of cultivation.

[0065] The monitoring unit can monitor the soil humidity using a sensor and automatically supply water as needed. The monitoring unit, for example, monitors the soil humidity using a sensor and automatically supplies water as needed. For example, the monitoring unit measures the soil humidity in real time using a soil humidity sensor. The monitoring unit can also automatically supply water when the soil humidity falls below a certain threshold. For example, the monitoring unit can automatically activate an irrigation system to supply water when the soil humidity drops. In this way, appropriate watering is achieved by automatically managing the soil humidity. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input data from a soil humidity sensor into the generation AI, which can then determine the appropriate timing for watering.

[0066] The monitoring unit can maintain the temperature using a temperature sensor. The monitoring unit maintains the temperature using, for example, a temperature sensor. For example, the monitoring unit uses a temperature sensor to measure the temperature of the cultivation environment in real time. The monitoring unit can also automatically activate a temperature adjustment system to maintain an appropriate temperature when the temperature is outside a set range. For example, the monitoring unit activates a cooling system when the temperature is too high, and activates a heating system when the temperature is too low. In this way, appropriate temperature management is achieved by using a temperature sensor. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input data from a temperature sensor into the generation AI, which can then determine appropriate temperature adjustment.

[0067] The generation unit can automatically adjust the temperature, humidity, and amount of light. The generation unit automatically adjusts, for example, the temperature, humidity, and amount of light. For example, the generation unit measures each condition of the cultivation environment in real time using a temperature sensor, a humidity sensor, and a light sensor. The generation unit can also automatically adjust the appropriate temperature, humidity, and amount of light based on the measured data. For example, the generation unit activates a cooling system when the temperature is too high and a heating system when the temperature is too low. The generation unit also activates a humidification system when the humidity is too low and a dehumidification system when the humidity is too high. Furthermore, the generation unit activates a lighting system when the amount of light is insufficient and a shading system when the amount of light is excessive. In this way, the generation unit automatically adjusts the environmental conditions to provide an optimal cultivation environment. Some or all of the above-mentioned processes in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data from sensors into the generation AI, which then sets appropriate environmental conditions.

[0068] The prediction unit can predict the growth stage of coffee and the number of days remaining until harvest. The prediction unit predicts, for example, the growth stage of coffee and the number of days remaining until harvest. For example, the prediction unit predicts the growth stage of coffee based on data from the monitoring unit. The prediction unit can also predict the number of days remaining until harvest based on the growth stage. For example, the prediction unit calculates the number of days remaining until harvest taking into account the growth rate of coffee and environmental conditions. In this way, the prediction unit predicts the growth stage and harvest time, making it easier for the user to grasp the progress of cultivation. Some or all of the above-mentioned processing in the prediction unit may be performed using, or without, a generation AI, for example. For example, the prediction unit can input data from the monitoring unit into the generation AI, which can then predict the progress of cultivation.

[0069] The providing unit can provide information regarding the characteristics and optimal cultivation methods for each coffee variety. The providing unit provides, for example, information regarding the characteristics and optimal cultivation methods for each coffee variety. For example, the providing unit suggests a specific cultivation method for Arabica coffee and a different cultivation method for Robusta coffee. The providing unit can also provide information such as the optimal temperature, humidity, and amount of light based on the characteristics of each variety. For example, the providing unit suggests setting a specific temperature and humidity for Arabica coffee and suggesting a different temperature and humidity for Robusta coffee. In this way, the providing unit provides the characteristics and cultivation methods for each variety, allowing the user to cultivate coffee effectively. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input user information into the generation AI, which then suggests the optimal cultivation method.

[0070] The reception unit can estimate the user's emotion and adjust the timing of information input based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of information input based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit delays the timing of information input so that the user can input in a relaxed state. Furthermore, if the user is relaxed, the reception unit can also advance the timing of information input so that the input proceeds smoothly. Furthermore, if the user is in a hurry, the reception unit can optimize the timing of information input so that the input can be completed quickly. This allows the user's stress to be reduced by adjusting the timing of information input according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can then estimate the user's emotions and adjust the timing of information input.

[0071] The reception unit can analyze the user's past cultivation history and select the optimal information input method. The reception unit, for example, analyzes the user's past cultivation history and selects the optimal information input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit can also simplify the information input for a specific variety based on the user's past cultivation history. Furthermore, the reception unit can analyze the user's past cultivation history and suggest the most efficient input method. In this way, by analyzing the past cultivation history, the optimal information input method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past cultivation data into the generation AI, which then selects the optimal information input method.

[0072] The reception unit can filter information based on the user's current living situation and areas of interest when inputting information. For example, the reception unit can filter information based on the user's current living situation and areas of interest when inputting information. For example, if the user is busy, the reception unit requests the user to input the minimum amount of information necessary. The reception unit can also prioritize input of information related to areas in which the user is interested. Furthermore, the reception unit can suggest an appropriate information input method depending on the user's living situation. This enables efficient information input by filtering information based on the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's living situation data into a generation AI, which can then suggest an appropriate information input method.

[0073] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of information to be input based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize input of important information. Furthermore, when the user is relaxed, the reception unit can prioritize input of detailed information. Furthermore, when the user is in a hurry, the reception unit can prioritize input of the minimum necessary information. In this way, by determining the priority of information according to the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can then estimate the user's emotions and determine the priority of information.

[0074] The reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information when inputting information. For example, the reception unit prioritizes inputting highly relevant information in consideration of the user's geographical location information when inputting information. For example, when the user is in a specific area, the reception unit prioritizes inputting information related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize inputting information related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize inputting information related to the user's home. In this way, highly relevant information can be prioritized by considering the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location data to the generation AI, which can then prioritize inputting highly relevant information.

[0075] The reception unit can analyze the user's social media activity and input relevant information when inputting information. For example, the reception unit can analyze the user's social media activity and input relevant information when inputting information. For example, the reception unit can input relevant information based on information shared by the user on social media. The reception unit can also input information related to topics in which the user has shown interest on social media. Furthermore, the reception unit can analyze the user's social media activity and input the most relevant information. In this way, highly relevant information can be input by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media data to the generation AI, which can then input the relevant information.

[0076] The generation unit can estimate the user's emotion and adjust the method for setting environmental conditions based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the method for setting environmental conditions based on the estimated user emotion. For example, the generation unit can set relaxed environmental conditions when the user is relaxed. The generation unit can also quickly complete the setting when the user is in a hurry. Furthermore, the generation unit can set stimulating environmental conditions when the user is excited. This allows for providing more appropriate environmental conditions by adjusting the method for setting environmental conditions according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's facial expression data into the generation AI, which can then estimate the user's emotion and adjust the method for setting environmental conditions.

[0077] The generation unit can customize the settings based on the characteristics of each coffee variety when setting the environmental conditions. For example, the generation unit customizes the settings based on the characteristics of each coffee variety when setting the environmental conditions. For example, the generation unit sets a specific temperature and humidity for Arabica coffee. The generation unit can also set a different temperature and humidity for Robusta coffee. Furthermore, the generation unit can set the optimal amount of light based on the characteristics of each variety. This makes it possible to provide an optimal cultivation environment by customizing the environmental conditions based on the characteristics of each coffee variety. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input coffee variety data into the generation AI, which then sets the optimal environmental conditions.

[0078] The generation unit can set optimal environmental conditions by referring to past cultivation data when setting the environmental conditions. The generation unit can set optimal environmental conditions by referring to past cultivation data, for example. For example, the generation unit sets optimal temperature and humidity based on past cultivation data. The generation unit can also set optimal light intensity based on past cultivation data. Furthermore, the generation unit can set optimal watering frequency based on past cultivation data. In this way, optimal environmental conditions can be set by referring to past cultivation data. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input past cultivation data into the generation AI, which can then set optimal environmental conditions.

[0079] The generation unit can estimate the user's emotions and determine the priority of environmental conditions based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and determines the priority of environmental conditions based on the estimated user emotions. For example, if the user is stressed, the generation unit can prioritize relaxing environmental conditions. Furthermore, if the user is relaxed, the generation unit can prioritize environmental conditions that are optimal for growth. Furthermore, if the user is in a hurry, the generation unit can prioritize environmental conditions that allow quick setup. This allows for more appropriate environmental conditions to be provided by determining the priority of environmental conditions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's facial expression data into the generation AI, which can then estimate the user's emotions and determine the priority of environmental conditions.

[0080] The generation unit can optimally set environmental conditions by taking into account the user's geographical location information when setting the environmental conditions. For example, the generation unit can optimally set environmental conditions by taking into account the user's geographical location information when setting the environmental conditions. For example, if the user is in a hot and humid region, the generation unit can set environmental conditions appropriate for that region. Also, if the user is in a cold region, the generation unit can set environmental conditions appropriate for that region. Furthermore, the generation unit can set an optimal amount of light based on the user's geographical location information. In this way, optimal environmental conditions can be set by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's geographical location data into the generation AI, which can then set optimal environmental conditions.

[0081] The generation unit can analyze the user's social media activity and set relevant settings when setting environmental conditions. For example, the generation unit can analyze the user's social media activity and set relevant settings when setting environmental conditions. For example, the generation unit can set relevant environmental conditions based on information shared by the user on social media. The generation unit can also set environmental conditions related to topics in which the user has shown interest on social media. Furthermore, the generation unit can analyze the user's social media activity and set the most relevant environmental conditions. In this way, highly relevant environmental conditions can be set by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's social media data into the generation AI, which can then set relevant environmental conditions.

[0082] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, the monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can reduce the monitoring frequency to allow the user to relax. Furthermore, if the user is relaxed, the monitoring unit can increase the monitoring frequency and provide detailed information. Furthermore, if the user is in a hurry, the monitoring unit can optimize the monitoring frequency to provide information quickly. This allows for more appropriate monitoring by adjusting the monitoring frequency according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit can be performed using, for example, the generation AI, or without the generation AI. For example, the monitoring unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the monitoring frequency.

[0083] The monitoring unit can analyze the components of the soil during monitoring and automatically supply fertilizer as needed. The monitoring unit, for example, can analyze the components of the soil during monitoring and automatically supply fertilizer as needed. For example, the monitoring unit can analyze the components of the soil and automatically supply necessary nutrients. The monitoring unit can also determine the optimal type and amount of fertilizer based on the analysis of the soil components. Furthermore, the monitoring unit can also analyze the components of the soil and optimize the nutritional balance. In this way, necessary nutrients can be automatically supplied by analyzing the components of the soil. Some or all of the above-mentioned processing in the monitoring unit can be performed using, or without, the generation AI, for example. For example, the monitoring unit can input soil component data into the generation AI, which can determine the necessary nutrients and automatically supply fertilizer.

[0084] The monitoring unit can measure the light intensity and quality with a sensor during monitoring and maintain an optimal lighting environment. For example, the monitoring unit can measure the light intensity and quality with a sensor during monitoring and maintain an optimal lighting environment. For example, the monitoring unit can measure the light intensity with a sensor and maintain an optimal lighting environment. The monitoring unit can also measure the light quality with a sensor and provide an optimal lighting environment for growth. Furthermore, the monitoring unit can measure the light intensity and quality with a sensor and maintain a balanced lighting environment. In this way, an optimal lighting environment can be maintained by measuring the light intensity and quality. Some or all of the above-described processing in the monitoring unit can be performed using, or without, the generation AI, for example. For example, the monitoring unit can input data on the light intensity and quality into the generation AI, which can then set an optimal lighting environment.

[0085] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, the monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is nervous, the monitoring unit can provide a simple, highly visible display method. If the user is relaxed, the monitoring unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the monitoring unit can also provide a display method that focuses on the main points. This allows for more appropriate information provision by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the monitoring unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the monitoring unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the display method of the monitoring results.

[0086] The monitoring unit can select the optimal monitoring method during monitoring by taking into account the user's geographical location information. For example, the monitoring unit selects the optimal monitoring method by taking into account the user's geographical location information during monitoring. For example, if the user is in a hot and humid region, the monitoring unit selects a monitoring method appropriate for that region. Also, if the user is in a cold region, the monitoring unit can select a monitoring method appropriate for that region. Furthermore, the monitoring unit can select the optimal monitoring method based on the user's geographical location information. In this way, the optimal monitoring method can be selected by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input the user's geographical location data into the generation AI, which can then select the optimal monitoring method.

[0087] The monitoring unit can analyze the user's social media activities during monitoring and provide relevant monitoring data. For example, the monitoring unit can analyze the user's social media activities during monitoring and provide relevant monitoring data. For example, the monitoring unit can provide relevant monitoring data based on information shared by the user on social media. The monitoring unit can also provide monitoring data related to topics in which the user has shown interest on social media. Furthermore, the monitoring unit can analyze the user's social media activities and provide the most relevant monitoring data. In this way, by analyzing the user's social media activities, highly relevant monitoring data can be provided. Some or all of the above-described processing in the monitoring unit can be performed using, or without, a generation AI. For example, the monitoring unit can input the user's social media data into a generation AI, which can provide the relevant monitoring data.

[0088] The prediction unit can estimate the user's emotion and adjust the display method of the prediction result based on the estimated user's emotion. For example, the prediction unit can estimate the user's emotion and adjust the display method of the prediction result based on the estimated user's emotion. For example, if the user is nervous, the prediction unit can provide a simple, highly visible display method. If the user is relaxed, the prediction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can also provide a display method that focuses on the main points. This allows for adjusting the display method according to the user's emotion to provide a more appropriate prediction result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the prediction unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the prediction unit can input the user's facial expression data into the generation AI, which can estimate the user's emotion and adjust the display method of the prediction result.

[0089] The prediction unit can optimize the prediction algorithm by referring to past cultivation data when making predictions. The prediction unit can optimize the prediction algorithm by referring to past cultivation data when making predictions, for example. For example, the prediction unit sets an optimal prediction algorithm based on past cultivation data. The prediction unit can also set an algorithm that improves prediction accuracy based on past cultivation data. Furthermore, the prediction unit can select the most efficient prediction algorithm based on past cultivation data. In this way, the prediction algorithm can be optimized by referring to past cultivation data. Some or all of the above-mentioned processing in the prediction unit may be performed using, or without, the generation AI, for example. For example, the prediction unit can input past cultivation data into the generation AI, which can then set an optimal prediction algorithm.

[0090] The prediction unit can improve prediction accuracy by taking into account the growth characteristics of each coffee variety during prediction. The prediction unit, for example, improves prediction accuracy by taking into account the growth characteristics of each coffee variety during prediction. For example, the prediction unit improves prediction accuracy by taking into account the growth characteristics of Arabica. The prediction unit can also improve prediction accuracy by taking into account the growth characteristics of Robusta. Furthermore, the prediction unit can set an optimal prediction algorithm based on the growth characteristics of each variety. In this way, prediction accuracy is improved by taking into account the growth characteristics of each coffee variety. Some or all of the above-mentioned processing in the prediction unit may be performed using, or without, a generation AI, for example. For example, the prediction unit can input coffee variety data into the generation AI, which can then set an optimal prediction algorithm.

[0091] The prediction unit can estimate the user's emotions and prioritize the prediction results based on the estimated user emotions. The prediction unit, for example, estimates the user's emotions and prioritizes the prediction results based on the estimated user emotions. For example, the prediction unit can prioritize displaying important prediction results when the user is stressed. The prediction unit can also prioritize displaying detailed prediction results when the user is relaxed. Furthermore, the prediction unit can prioritize displaying the minimum necessary prediction results when the user is in a hurry. This allows important prediction results to be prioritized by prioritizing the prediction results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the prediction unit can be performed using, for example, the generation AI. For example, the prediction unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and prioritize the prediction results.

[0092] The prediction unit can make an optimal prediction by taking into account the user's geographical location information when making a prediction. The prediction unit can make an optimal prediction by taking into account the user's geographical location information when making a prediction, for example. For example, if the user is in a hot and humid area, the prediction unit can make a prediction appropriate for that area. Also, if the user is in a cold area, the prediction unit can make a prediction appropriate for that area. Furthermore, the prediction unit can make an optimal prediction based on the user's geographical location information. In this way, an optimal prediction can be made by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the prediction unit can input the user's geographical location data into the generation AI, which can make an optimal prediction.

[0093] The prediction unit may analyze the user's social media activity and provide relevant predicted data during prediction. For example, the prediction unit may analyze the user's social media activity and provide relevant predicted data during prediction. For example, the prediction unit may provide relevant predicted data based on information shared by the user on social media. The prediction unit may also provide predicted data related to topics in which the user has shown interest on social media. Furthermore, the prediction unit may analyze the user's social media activity and provide the most relevant predicted data. In this way, highly relevant predicted data can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the prediction unit may be performed using, or without, a generation AI. For example, the prediction unit may input the user's social media data into the generation AI, which may provide relevant predicted data.

[0094] The providing unit can estimate the user's emotions and adjust the information provision method based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the information provision method based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible information provision method. Furthermore, if the user is relaxed, the providing unit can provide an information provision method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can provide an information provision method that focuses on the main points. This allows for more appropriate information provision by adjusting the information provision method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the information provision method.

[0095] The providing unit can provide optimal information by referring to the user's past cultivation history when providing information. For example, the providing unit can provide optimal information by referring to the user's past cultivation history when providing information. For example, the providing unit can suggest an optimal cultivation method based on the user's past cultivation history. The providing unit can also suggest an optimal type and amount of fertilizer based on the user's past cultivation history. Furthermore, the providing unit can also suggest an optimal watering frequency based on the user's past cultivation history. In this way, optimal information can be provided by referring to the user's past cultivation history. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI, for example. For example, the providing unit can input the user's past cultivation data into the generation AI, which can then provide optimal information.

[0096] The providing unit can provide advice customized based on the characteristics of each coffee variety when providing information. The providing unit, for example, provides advice customized based on the characteristics of each coffee variety when providing information. For example, the providing unit can suggest a specific cultivation method for Arabica coffee. The providing unit can also suggest a different cultivation method for Robusta coffee. Furthermore, the providing unit can also suggest the optimal cultivation method based on the characteristics of each variety. In this way, by providing advice customized based on the characteristics of each coffee variety, the optimal cultivation method can be suggested. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input coffee variety data into the generation AI, which can then suggest the optimal cultivation method.

[0097] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of information provision based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit can prioritize providing important information. Furthermore, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the providing unit can prioritize providing the minimum necessary information. In this way, by determining the priority of information provision according to the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and determine the priority of information provision.

[0098] The providing unit can provide optimal information by taking into account the user's geographical location information when providing information. For example, the providing unit can provide optimal information by taking into account the user's geographical location information when providing information. For example, if the user is in a hot and humid region, the providing unit can provide information appropriate for that region. Also, if the user is in a cold region, the providing unit can provide information appropriate for that region. Furthermore, the providing unit can provide optimal information based on the user's geographical location information. In this way, optimal information can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's geographical location data into the generation AI, which can then provide optimal information.

[0099] The providing unit can analyze the user's social media activity and provide relevant information when providing information. For example, the providing unit can analyze the user's social media activity and provide relevant information when providing information. For example, the providing unit can provide relevant information based on information shared by the user on social media. The providing unit can also provide information related to topics in which the user has shown interest on social media. Furthermore, the providing unit can analyze the user's social media activity and provide the most relevant information. In this way, highly relevant information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's social media data into the generation AI, which can then provide the relevant information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, monitoring unit, and prediction unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives information input by a user through an app. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets optimal environmental conditions using a generation AI. The monitoring unit monitors environmental conditions using a sensor of the smart device 14 and automatically supplies water as needed. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts the progress of cultivation and the harvest time using a generation AI. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, monitoring unit, and prediction unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives information input by a user through an app. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets optimal environmental conditions using a generation AI. The monitoring unit monitors environmental conditions using a sensor of the smart glasses 214 and automatically supplies water as needed. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts the progress of cultivation and the harvest time using a generation AI. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, monitoring unit, and prediction unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives information input by a user through an app. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets optimal environmental conditions using a generation AI. The monitoring unit monitors environmental conditions using a sensor of the headset-type terminal 314 and automatically supplies water as needed. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts the progress of cultivation and the harvest time using a generation AI. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, monitoring unit, and prediction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives information input by a user through an app. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets optimal environmental conditions using a generation AI. The monitoring unit monitors environmental conditions using a sensor of the robot 414 and automatically supplies water as needed. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts the progress of cultivation and the harvest time using a generation AI.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The reception unit can analyze the user's cultivation history and suggest the optimal cultivation method based on past successes and failures. For example, the reception unit can analyze the varieties of coffee grown by the user in the past and the environmental conditions, and suggest the optimal cultivation method under similar conditions. The reception unit can also take past failures into consideration and provide advice on how to avoid making the same mistakes. Furthermore, the reception unit can suggest the optimal cultivation schedule based on the user's cultivation history. This allows the user to utilize their past experience to cultivate coffee more effectively.

[0102] The monitoring unit can analyze soil components and automatically supply fertilizer as needed. For example, the monitoring unit can analyze soil components in real time and automatically supply necessary nutrients. The monitoring unit can also select and automatically supply appropriate fertilizer when soil components are lacking. Furthermore, the monitoring unit can periodically analyze soil components and optimize the nutrient balance. This allows for proper management of soil components to support coffee growth.

[0103] The monitoring unit not only maintains the temperature using a temperature sensor, but also analyzes temperature fluctuation patterns and proposes optimal temperature control methods. For example, the monitoring unit analyzes past temperature data to understand temperature fluctuation patterns by season and time of day. The monitoring unit can also propose appropriate temperature control methods when there are large temperature fluctuations. Furthermore, the monitoring unit can also propose energy-efficient temperature control methods when there are small temperature fluctuations. This makes it possible to support coffee growth by optimizing temperature control.

[0104] The generation unit not only automatically adjusts the temperature, humidity, and amount of light, but also customizes the environmental conditions based on the user's preferences. For example, the generation unit sets optimal environmental conditions based on the user's preferred coffee flavor and aroma. If the user prefers a specific cultivation method, the generation unit can also adjust the environmental conditions to suit that method. Furthermore, the generation unit can fine-tune the environmental conditions according to the user's preferences. This allows coffee to be cultivated according to the user's preferences.

[0105] The prediction unit not only predicts the coffee's growth stage and the number of days remaining until harvest, but also improves prediction accuracy by taking into account the user's cultivation history. For example, the prediction unit analyzes growth data of coffee grown by the user in the past and predicts growth patterns under similar conditions. The prediction unit can also improve prediction accuracy of the harvest time based on the past cultivation history. Furthermore, the prediction unit can also suggest the optimal harvest time by taking into account the user's cultivation history. This allows the user to grow coffee based on more accurate predictions.

[0106] The providing unit can not only provide information on the characteristics of each coffee variety and the optimal cultivation method, but also estimate the user's emotions and adjust the information provision method based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible information provision method. If the user is relaxed, the providing unit can also provide an information provision method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide an information provision method that focuses on the main points. In this way, by adjusting the information provision method according to the user's emotions, more appropriate information can be provided.

[0107] The reception unit can estimate the user's emotions and adjust the timing of information input based on the estimated emotions, as well as suggest an input method according to the user's emotions. For example, if the user is feeling stressed, the reception unit can suggest voice input, allowing the user to input in a relaxed state. Alternatively, if the user is relaxed, the reception unit can suggest text input, allowing the user to input detailed information. Furthermore, if the user is in a hurry, the reception unit can suggest a simplified input method, allowing the user to complete input quickly. In this way, the efficiency of information input can be improved by suggesting the optimal input method according to the user's emotions.

[0108] The generation unit can estimate the user's emotions and not only adjust the method for setting environmental conditions based on the estimated emotions, but also suggest changes to the environmental conditions according to the user's emotions. For example, if the user is relaxed, the generation unit can suggest changing the environmental conditions to more relaxed ones. Also, if the user is in a hurry, the generation unit can suggest environmental conditions that allow for quick setting. Furthermore, if the user is excited, the generation unit can suggest stimulating environmental conditions. In this way, by suggesting changes to the environmental conditions according to the user's emotions, more appropriate environmental conditions can be provided.

[0109] The monitoring unit can estimate the user's emotions and adjust the frequency of monitoring based on the estimated emotions, as well as adjust the notification method of the monitoring results according to the user's emotions. For example, if the user is feeling stressed, the monitoring unit can provide a simple and highly visible notification method. If the user is relaxed, the monitoring unit can also provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can also provide a notification method that focuses on the main points. In this way, by adjusting the notification method of the monitoring results according to the user's emotions, more appropriate information can be provided.

[0110] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated emotions, as well as adjust the level of detail of the prediction results according to the user's emotions. For example, if the user is nervous, the prediction unit can provide a simple, highly visible prediction result. If the user is relaxed, the prediction unit can also provide a detailed prediction result. Furthermore, if the user is in a hurry, the prediction unit can also provide a prediction result that focuses on the main points. In this way, by adjusting the level of detail of the prediction results according to the user's emotions, more appropriate information can be provided.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit receives information from the user. This information includes cultivation conditions, environmental settings, and personal preferences. For example, the application receives information such as the coffee variety and the climate conditions of the cultivation location entered by the user through the application. Step 2: The generator sets the environmental conditions based on the information received by the receiver. The generator automatically adjusts the appropriate temperature, humidity, amount of light, etc. The generator uses AI to analyze the user's information and set the optimal environmental conditions. Step 3: The monitoring unit monitors the environmental conditions set by the generation unit. The monitoring unit monitors the soil moisture with a sensor and automatically supplies water as needed. A temperature sensor can also be used to maintain an appropriate temperature. The generation AI can be used to monitor the environmental conditions. Step 4: The prediction unit predicts the progress of cultivation or the harvest time based on the information monitored by the monitoring unit. The prediction unit predicts the coffee's growth stage and the number of days remaining until harvest. The generation AI can be used to predict the progress of cultivation.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information from a user; a generating unit that sets environmental conditions based on the information received by the receiving unit; a monitoring unit that monitors the environmental conditions set by the generating unit; a prediction unit that predicts the progress of cultivation or the harvest time based on the information monitored by the monitoring unit; Equipped with A system characterized by:

2. The monitoring unit Sensors monitor soil moisture and automatically supply water as needed.

2. The system of claim 1.

3. The monitoring unit Use a temperature sensor to maintain temperature 2. The system of claim 1.

4. The generation unit Automatically adjusts temperature, humidity, and light levels 2. The system of claim 1.

5. The prediction unit Predicting coffee growth stages and days remaining until harvest 2. The system of claim 1.

6. The company will have a section that provides information on the characteristics of each coffee variety and the best cultivation methods.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of information input based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past cultivation history and select the optimal information input method 2. The system of claim 1.

9. The reception unit As you enter information, it filters it based on your current life situation and interests.

2. The system of claim 1.

Images