system

Abstract

The system according to the embodiment aims to monitor the growth status of agricultural crops in real time, propose appropriate cultivation methods, and automatically carry out agricultural work. [Solution] A system according to an embodiment includes a monitoring unit, a proposal unit, a working unit, and a harvesting unit. The monitoring unit monitors the growth status of agricultural crops in real time. The proposal unit proposes an appropriate cultivation method based on data collected by the monitoring unit. The working unit automatically performs agricultural work based on the cultivation method proposed by the proposal unit. The harvesting unit performs harvesting work based on the results of the agricultural work performed by the working 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 technologies do not adequately monitor the growth status of agricultural crops in real time, suggest appropriate cultivation methods, and automatically carry out agricultural work, so there is room for improvement.

[0005] The system according to the embodiment aims to monitor the growth status of agricultural crops in real time, propose appropriate cultivation methods, and automatically carry out agricultural work. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a proposal unit, a working unit, and a harvesting unit. The monitoring unit monitors the growth status of agricultural crops in real time. The proposal unit proposes an appropriate cultivation method based on data collected by the monitoring unit. The working unit automatically performs agricultural work based on the cultivation method proposed by the proposal unit. The harvesting unit performs harvesting work based on the results of the agricultural work performed by the working unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the growth status of agricultural crops in real time, propose appropriate cultivation methods, and automatically carry out agricultural work. [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) The smart agriculture system according to an embodiment of the present invention is a next-generation smart agriculture solution that utilizes generative AI to automate and optimize agricultural processes, improving agricultural productivity and promoting a sustainable food supply. This smart agriculture system streamlines and optimizes agricultural processes by monitoring crop growth in real time, proposing optimal cultivation methods, and automatically performing agricultural and harvesting tasks. For example, generative AI uses sensors to monitor crop growth in real time and propose optimal cultivation methods. Robots then automatically perform agricultural tasks based on the proposed cultivation methods. For example, robots automatically water and fertilize crops to support their growth. Furthermore, when harvest time arrives, the robots automatically harvest and properly store the harvested crops. This system streamlines agricultural processes and improves agricultural productivity. Furthermore, detailed monitoring of crop growth by generative AI improves crop quality and realizes a sustainable food supply. For example, generative AI can detect pest infestations early and propose appropriate countermeasures to minimize damage to crops. Furthermore, farms with robots are an effective means of resolving labor shortages. By having robots automate agricultural tasks, it can resolve the labor shortage problem and reduce the burden on farmers. For example, by having robots take over heavy labor and simple tasks, farmers can focus on more advanced tasks. In this way, robot-powered farms are a next-generation smart agriculture solution that utilizes technologies such as generative AI to automate and optimize agricultural processes, improving agricultural productivity and promoting a sustainable food supply. This allows smart agriculture systems to streamline agricultural processes and improve agricultural productivity. It also helps achieve a sustainable food supply and reduces the burden on farmers.

[0029] A smart agriculture system according to an embodiment includes a monitoring unit, a proposal unit, a working unit, and a harvesting unit. The monitoring unit monitors the growth status of agricultural crops in real time. For example, the monitoring unit collects information on the growth status of agricultural crops in real time using sensors. The sensors include a temperature sensor, a humidity sensor, and a light sensor. For example, the temperature sensor measures the temperature around the agricultural crops and collects data. The humidity sensor measures the humidity around the agricultural crops and collects data. The light sensor measures the amount of light hitting the agricultural crops and collects data. The proposal unit proposes an optimal cultivation method based on the data collected by the monitoring unit. For example, the proposal unit analyzes the collected data and proposes an optimal cultivation method using a generation AI. The generation AI analyzes the data and proposes an optimal cultivation method using a text generation AI (e.g., LLM) or a multimodal generation AI. The working unit automatically performs agricultural work based on the cultivation method proposed by the proposal unit. For example, the working unit uses a robot to water and fertilize crops. The robot automatically waters and fertilizes crops based on the proposed cultivation method. For example, a robot adjusts the timing and amount of watering based on data from sensors. Similarly, the timing and amount of fertilization are adjusted based on data from sensors. The harvesting unit performs harvesting work based on the results of the agricultural work performed by the working unit. For example, the harvesting unit uses a robot to perform harvesting work when it is time to harvest. The robot stores the harvested products appropriately. For example, the robot is equipped with equipment for storing the harvested products at appropriate temperatures and humidity. As a result, the smart agriculture system according to the embodiment can monitor the growth status of agricultural products in real time, propose optimal cultivation methods, automatically perform agricultural work, and perform harvesting work, thereby making it possible to improve the efficiency and optimize the agricultural process.

[0030] The monitoring unit can collect information on the growth status of agricultural crops in real time using sensors. Examples of sensors include temperature sensors, humidity sensors, and light sensors. The monitoring unit, for example, uses a temperature sensor to measure the temperature around the agricultural crops and collect data. For example, the temperature sensor measures the temperature around the agricultural crops in real time and transmits the data to the monitoring unit. The monitoring unit can also use a humidity sensor to measure the humidity around the agricultural crops and collect data. For example, the humidity sensor measures the humidity around the agricultural crops in real time and transmits the data to the monitoring unit. The monitoring unit can also use a light sensor to measure the amount of light hitting the agricultural crops and collect data. For example, the light sensor measures the amount of light hitting the agricultural crops in real time and transmits the data to the monitoring unit. In this way, by using sensors, the growth status of agricultural crops can be grasped in detail in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input data from the sensor into a generation AI, which analyzes the data to grasp the growth status.

[0031] The suggestion unit can suggest an appropriate cultivation method based on the collected data. The suggestion unit, for example, uses a generation AI to analyze the collected data and suggest an optimal cultivation method. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI, and suggests an optimal cultivation method. The generation AI suggests an optimal cultivation method based on, for example, collected temperature data, humidity data, light data, etc. For example, the generation AI analyzes temperature data and suggests an appropriate temperature control method. Analyzes humidity data and suggests an appropriate humidity control method. Analyzes light data and suggests an appropriate light control method. In this way, by suggesting an optimal cultivation method based on the collected data, it is possible to optimize the growth of agricultural crops. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input collected data to the generation AI, which then analyzes the data and suggests an optimal cultivation method.

[0032] The working unit can water and fertilize based on the proposed cultivation method. The working unit, for example, uses a robot to water and fertilize. For example, the working unit can automatically water based on the proposed cultivation method using a robot. For example, the robot can adjust the timing and amount of watering based on data from sensors. For example, the robot can optimize the timing and amount of watering based on data from temperature sensors and humidity sensors. The working unit can also automatically fertilize based on the proposed cultivation method using a robot. For example, the robot can adjust the timing and amount of fertilization based on data from sensors. For example, the robot can optimize the timing and amount of fertilization based on data from soil sensors. This can support the growth of crops by watering and fertilizing based on the proposed cultivation method. Some or all of the above-described processing in the working unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the working unit can input data from a sensor into a generation AI, which can analyze the data and optimize the timing and amount of watering and fertilization.

[0033] The harvesting unit can perform harvesting work when it is time to harvest and store the harvested products appropriately. The harvesting unit can automatically perform harvesting work when it is time to harvest, for example, using a robot. For example, the harvesting unit can properly store the harvested products using a robot. For example, the robot is equipped with equipment for storing the harvested products at appropriate temperatures and humidity. For example, the robot stores the harvested products in a refrigerator or freezer. The harvesting unit can also properly classify and store the harvested products. For example, the robot classifies the harvested products by type and stores them in an appropriate storage location. This allows the quality of the harvested products to be maintained by performing harvesting work when it is time to harvest and storing the harvested products appropriately. Some or all of the above-described processing in the harvesting unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the harvesting unit can input data on the harvested products into a generation AI, which can analyze the data and optimize the storage method for the harvested products.

[0034] Furthermore, the smart agriculture system includes a detection unit that detects the occurrence of pests and diseases at an early stage. The detection unit detects the occurrence of pests and diseases at an early stage, for example, using a generation AI. For example, the detection unit uses sensors to monitor the occurrence of pests and diseases in real time. For example, the detection unit uses temperature sensors and humidity sensors to grasp the environment for pest and disease occurrence in detail. For example, the temperature sensor detects the temperature suitable for pest and disease occurrence and transmits the data to the generation AI. The humidity sensor detects the humidity suitable for pest and disease occurrence and transmits the data to the generation AI. The generation AI analyzes this data and detects the occurrence of pest and disease at an early stage. For example, the generation AI can evaluate the risk of pest and disease occurrence based on the temperature data and humidity data and detect it at an early stage. This allows for early detection of pest and disease occurrence, thereby minimizing damage to crops. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit inputs data from sensors into the generation AI, which analyzes the data and detects the occurrence of pest and disease at an early stage.

[0035] Furthermore, the smart agriculture system includes a allocation unit that allocates tasks to resolve the labor shortage. The allocation unit allocates tasks to resolve the labor shortage, for example, using AI. For example, the allocation unit allocates agricultural tasks to multiple robots. For example, the allocation unit allocates watering to robot A, fertilizing to robot B, and harvesting to robot C. In this way, by allocating tasks to resolve the labor shortage, the burden on farmers can be reduced. Some or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI. For example, the allocation unit can use AI to optimally allocate the tasks of each robot.

[0036] The monitoring unit can combine different sensors to grasp the growth status from multiple angles. For example, the monitoring unit uses a generation AI to combine different sensors to grasp the growth status from multiple angles. For example, the monitoring unit combines a temperature sensor and a humidity sensor to grasp the growth environment of the crops in detail. For example, the temperature sensor measures the temperature around the crops and sends the data to the generation AI. The humidity sensor measures the humidity around the crops and sends the data to the generation AI. The generation AI analyzes this data to grasp the growth environment of the crops in detail. The monitoring unit can also combine a light sensor and a soil sensor to simultaneously monitor the amount of light and the nutritional status of the soil. For example, the light sensor measures the amount of light hitting the crops and sends the data to the generation AI. The soil sensor measures the nutritional status of the soil and sends the data to the generation AI. The generation AI analyzes this data to grasp the growth status of the crops from multiple angles. Furthermore, the monitoring unit can combine a camera and a weather sensor to evaluate the growth status by integrating visual data and weather data. For example, a camera takes images of crops and sends the data to the generation AI. A weather sensor measures weather data and sends the data to the generation AI. The generation AI analyzes this data and evaluates the growth status of the crops from various angles. This allows the growth status to be understood in detail by combining different sensors. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, the generation AI. For example, the monitoring unit inputs data from a sensor into the generation AI, and the generation AI analyzes the data to understand the growth status from various angles.

[0037] The monitoring unit can compare the monitoring data with past data to detect abnormalities early. The monitoring unit can compare the monitoring data with past data using, for example, a generation AI to detect abnormalities early. For example, the monitoring unit can compare current temperature data with past data to detect abnormal temperature changes. For example, the generation AI can compare current temperature data with past temperature data to detect abnormal temperature changes. The monitoring unit can also compare current humidity data with past data to detect abnormal humidity changes. For example, the generation AI can compare current humidity data with past humidity data to detect abnormal humidity changes. The monitoring unit can also compare current light intensity data with past data to detect abnormal light intensity changes. For example, the generation AI can compare current light intensity data with past light intensity data to detect abnormal light intensity changes. This allows for early detection of abnormalities by comparing with past data. Some or all of the above-described 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 current data and past data into the generation AI, which can analyze the data to detect abnormalities early.

[0038] The monitoring unit can predict the growth status by combining weather data. The monitoring unit, for example, uses a generation AI to combine weather data to predict the growth status. For example, the monitoring unit predicts future growth status based on current weather data. For example, the generation AI predicts future growth status based on current temperature data, precipitation data, wind speed data, etc. The monitoring unit can also predict future growth status by comparing past weather data with current data. For example, the generation AI predicts future growth status based on past temperature data, precipitation data, wind speed data, etc. The monitoring unit can also suggest optimal cultivation methods based on weather forecast data. For example, the generation AI suggests optimal cultivation methods based on weather forecast data. In this way, the growth status can be predicted by combining weather data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input weather data into the generation AI, which can analyze the data to predict the growth status.

[0039] The monitoring unit can store the monitoring data in the cloud and share the data with other farms. The monitoring unit can store the monitoring data in the cloud and share the data with other farms, for example, using a generation AI. For example, the monitoring unit can automatically upload the monitoring data to the cloud using the generation AI. For example, the generation AI can store the monitoring data in the cloud and share the data with other farms. The monitoring unit can also share the data with other farms and jointly analyze the growth status. For example, the generation AI can share the data with other farms and jointly analyze the growth status. Furthermore, the monitoring unit can evaluate the growth status of an entire region based on the data on the cloud. For example, the generation AI can evaluate the growth status of an entire region based on the data on the cloud. In this way, by storing the data in the cloud, it is possible to share the data with other farms and jointly analyze the growth status. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the monitoring unit can input the monitoring data into the generation AI, which can analyze the data, store it in the cloud, and share the data with other farms.

[0040] The suggestion unit can select the optimal cultivation method by referring to past success cases. The suggestion unit selects the optimal cultivation method by referring to past success cases, for example, using a generation AI. For example, the suggestion unit proposes the optimal cultivation method based on past success cases. For example, the generation AI analyzes past data and selects a cultivation method with a high success rate. The suggestion unit can also suggest the optimal cultivation method under similar conditions by referring to past cases. For example, the generation AI proposes the optimal cultivation method under similar climatic and soil conditions based on past cases. In this way, the optimal cultivation method can be selected by referring to past success cases. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input data on past success cases into the generation AI, which can then analyze the data and select the optimal cultivation method.

[0041] The suggestion unit can simulate different cultivation methods and select an appropriate method. The suggestion unit, for example, uses a generation AI to simulate different cultivation methods and select an optimal method. For example, the suggestion unit can use a generation AI to simulate different cultivation methods and select an optimal method. For example, the generation AI can simulate different cultivation methods and propose the most efficient cultivation method based on the results. The suggestion unit can also compare multiple cultivation methods and select an optimal method. For example, the generation AI can simulate multiple cultivation methods and select an optimal method based on the results. In this way, the optimal method can be selected by simulating different cultivation methods. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input data on different cultivation methods into the generation AI, which can analyze the data and select an optimal method.

[0042] The proposal unit can propose an optimal cultivation method by referring to regional agricultural data. The proposal unit, for example, uses a generation AI to propose the optimal cultivation method by referring to regional agricultural data. For example, the proposal unit can use a generation AI to propose the optimal cultivation method based on regional weather data. For example, the generation AI can analyze regional weather data and propose a cultivation method suitable for the region. The proposal unit can also propose the optimal cultivation method based on regional soil data. For example, the generation AI can analyze regional soil data and propose a cultivation method suitable for the region. The proposal unit can also propose the optimal cultivation method based on regional agricultural production data. For example, the generation AI can analyze regional agricultural production data and propose a cultivation method suitable for the region. In this way, by referring to regional agricultural data, the optimal cultivation method suitable for the region can be proposed. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input regional agricultural data into the generation AI, which can analyze the data and propose the optimal cultivation method.

[0043] The proposal unit can display the proposal content in multiple languages ​​to promote international use. The proposal unit can, for example, use a generation AI to display the proposal content in multiple languages. For example, the proposal unit can use a generation AI to display the proposal content in English and Japanese. For example, the generation AI can display the proposal content in English and Japanese to promote international use. The proposal unit can also use a generation AI to display the proposal content in Chinese and Spanish. For example, the generation AI can display the proposal content in Chinese and Spanish to promote international use. The proposal unit can also use a generation AI to display the proposal content in French and German. For example, the generation AI can display the proposal content in French and German to promote international use. In this way, displaying the proposal content in multiple languages ​​can promote international use. Some or all of the above-mentioned processing in the proposal unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the proposal unit can input the proposal content into a generation AI, which can analyze the data and display it in multiple languages.

[0044] The working unit can maximize efficiency by selecting different working modes. The working unit maximizes efficiency by selecting different working modes, for example, using a generating AI. For example, the working unit performs work by switching between a normal mode and a high-speed mode. For example, the generating AI maximizes work efficiency by switching between the normal mode and the high-speed mode. The working unit can also perform work by switching between an energy-saving mode and a power mode. For example, the generating AI maximizes work efficiency by switching between the energy-saving mode and the power mode. The working unit can also perform work by switching between an automatic mode and a manual mode. For example, the generating AI maximizes work efficiency by switching between the automatic mode and the manual mode. In this way, work efficiency can be maximized by selecting different working modes. Some or all of the above-mentioned processing in the working unit may be performed using, for example, the generating AI, or may be performed without using the generating AI. For example, the working unit can input work mode data into the generating AI, which analyzes the data and selects the optimal work mode.

[0045] The work unit can provide feedback on the work results in real time and reflect them in the next work. The work unit, for example, uses a generation AI to provide feedback on the work results in real time and reflect them in the next work. For example, the work unit can use a generation AI to analyze the work results in real time and reflect them in the next work. For example, the generation AI can analyze the work results in real time and optimize the next work based on the results. The work unit can also upload the work results to the cloud and reflect them in the next work. For example, the generation AI can upload the work results to the cloud and optimize the next work based on the data. The work unit can also share the work results with other robots and reflect them in the next work. For example, the generation AI can share the work results with other robots and optimize the next work based on the data. In this way, the work results can be fed back in real time and reflected in the next work, improving the accuracy of the work. Some or all of the above-mentioned processing in the work unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the work unit can input data on the work results to the generation AI, which can analyze the data and reflect it in the next work.

[0046] The working unit can share work in cooperation with other robots. The working unit, for example, uses a generating AI to share work in cooperation with other robots. For example, the working unit uses a generating AI to share watering work in cooperation with other robots. For example, the generating AI collaborates with other robots to efficiently share watering work. The working unit can also use a generating AI to share fertilizing work in cooperation with other robots. For example, the generating AI collaborates with other robots to efficiently share fertilizing work. The working unit can also use a generating AI to share harvesting work in cooperation with other robots. For example, the generating AI collaborates with other robots to efficiently share harvesting work. In this way, work efficiency can be improved by sharing work in cooperation with other robots. Some or all of the above-mentioned processing in the working unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the working unit can input collaboration data with other robots into the generating AI, which analyzes the data and efficiently shares work.

[0047] The work unit can store work data in the cloud and share the data with other farms. The work unit, for example, uses a generation AI to store the work data in the cloud and share the data with other farms. For example, the work unit automatically uploads the work data to the cloud using the generation AI. For example, the generation AI stores the work data in the cloud and shares the data with other farms. The work unit can also share data with other farms and jointly optimize work. For example, the generation AI shares data with other farms and jointly optimize work. Furthermore, the work unit can evaluate the work efficiency of the entire region based on the data on the cloud. For example, the generation AI evaluates the work efficiency of the entire region based on the data on the cloud. In this way, by storing the work data in the cloud, the data can be shared with other farms and jointly optimize work. Some or all of the above-mentioned processing in the work unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the work unit can input the work data into the generation AI, which analyzes the data, stores it in the cloud, and shares the data with other farms.

[0048] The harvesting unit can maximize efficiency by selecting different harvesting methods. The harvesting unit maximizes efficiency by selecting different harvesting methods, for example, using a generation AI. For example, the harvesting unit switches between manual harvesting and automatic harvesting. For example, the generation AI switches between manual harvesting and automatic harvesting to maximize harvesting efficiency. The harvesting unit can also switch between bulk harvesting and partial harvesting to maximize harvesting efficiency. For example, the generation AI switches between bulk harvesting and partial harvesting to maximize harvesting efficiency. The harvesting unit can also switch between daytime harvesting and nighttime harvesting to maximize harvesting efficiency. For example, the generation AI switches between daytime harvesting and nighttime harvesting to maximize harvesting efficiency. In this way, harvesting efficiency can be maximized by selecting different harvesting methods. Some or all of the above-described processing in the harvesting unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the harvesting unit can input harvesting method data into the generation AI, which analyzes the data and selects the optimal harvesting method.

[0049] The harvesting unit can provide feedback on the harvest results in real time and reflect them in the next harvest. The harvesting unit, for example, uses a generation AI to provide feedback on the harvest results in real time and reflect them in the next harvest. For example, the harvesting unit can use a generation AI to analyze the harvest results in real time and reflect them in the next harvest. For example, the generation AI can analyze the harvest results in real time and optimize the next harvest based on the results. The harvesting unit can also upload the harvest results to the cloud and reflect them in the next harvest. For example, the generation AI can upload the harvest results to the cloud and optimize the next harvest based on the data. The harvesting unit can also share the harvest results with other robots and reflect them in the next harvest. For example, the generation AI can share the harvest results with other robots and optimize the next harvest based on the data. This allows the harvest results to be fed back in real time and reflected in the next harvest, improving harvesting accuracy. Some or all of the above-described processing in the harvesting unit may be performed using, or without, the generation AI. For example, the harvesting unit can input data on the harvest results to the generation AI, which can analyze the data and reflect it in the next harvest.

[0050] The harvesting unit can cooperate with other robots to share the harvesting work. The harvesting unit, for example, uses a generation AI to cooperate with other robots to share the harvesting work. For example, the harvesting unit uses a generation AI to cooperate with other robots to share the fruit harvesting work. For example, the generation AI cooperates with other robots to efficiently share the fruit harvesting work. The harvesting unit can also use a generation AI to cooperate with other robots to share the vegetable harvesting work. For example, the generation AI cooperates with other robots to efficiently share the vegetable harvesting work. Furthermore, the harvesting unit can also use a generation AI to cooperate with other robots to share the grain harvesting work. For example, the generation AI cooperates with other robots to efficiently share the grain harvesting work. In this way, by cooperating with other robots to share the harvesting work, the efficiency of the harvest can be improved. Some or all of the above-mentioned processing in the harvesting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the harvesting department can input collaboration data with other robots into the generation AI, which can then analyze the data to efficiently share the harvesting work.

[0051] The harvesting unit can store harvest data in the cloud and share the data with other farms. The harvesting unit can store harvest data in the cloud and share the data with other farms, for example, using a generation AI. For example, the harvesting unit can automatically upload harvest data to the cloud using the generation AI. For example, the generation AI can store harvest data in the cloud and share the data with other farms. The harvesting unit can also share data with other farms and jointly optimize harvesting operations. For example, the generation AI can share data with other farms and jointly optimize harvesting operations. Furthermore, the harvesting unit can evaluate harvest efficiency for an entire region based on the data on the cloud. For example, the generation AI evaluates harvest efficiency for an entire region based on the data on the cloud. In this way, by storing harvest data in the cloud, data can be shared with other farms and jointly optimize harvesting operations. Some or all of the above-mentioned processing in the harvesting unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the harvesting unit can input harvest data into the generation AI, which can analyze the data and store it in the cloud, and share the data with other farms.

[0052] The detection unit can combine different sensors to grasp the occurrence of pests and diseases from multiple angles. The detection unit, for example, uses a generation AI to combine different sensors to grasp the occurrence of pests and diseases from multiple angles. For example, the detection unit combines a temperature sensor and a humidity sensor to grasp the environment for pest and disease occurrence in detail. For example, the temperature sensor detects the temperature suitable for pest and disease occurrence and sends the data to the generation AI. The humidity sensor detects the humidity suitable for pest and disease occurrence and sends the data to the generation AI. The generation AI analyzes this data and grasps the occurrence of pests and diseases from multiple angles. The detection unit can also combine a light sensor and a soil sensor to simultaneously monitor the risk of pest and disease occurrence. For example, the light sensor detects the amount of light suitable for pest and disease occurrence and sends the data to the generation AI. The soil sensor detects the nutrient state of the soil suitable for pest and disease occurrence and sends the data to the generation AI. The generation AI analyzes this data and grasps the occurrence of pest and disease from multiple angles. Furthermore, the detection unit can combine a camera and a weather sensor to integrate visual data and weather data to evaluate the occurrence of pests and diseases. For example, the camera visually detects the occurrence of pests and diseases and sends the data to the generation AI. The weather sensor detects weather conditions suitable for the occurrence of pests and diseases and sends the data to the generation AI. The generation AI analyzes this data and evaluates the occurrence of pests and diseases from multiple perspectives. In this way, by combining different sensors, it is possible to grasp the occurrence of pests and diseases in detail. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit inputs data from the sensor into the generation AI, and the generation AI analyzes the data to grasp the occurrence of pests and diseases from multiple perspectives.

[0053] The detection unit can compare the detection data with past data to detect abnormalities early. The detection unit can compare the detection data with past data using, for example, a generation AI to detect abnormalities early. For example, the detection unit can compare current temperature data with past data to detect abnormal temperature changes. For example, the generation AI can compare current temperature data with past temperature data to detect abnormal temperature changes. The detection unit can also compare current humidity data with past data to detect abnormal humidity changes. For example, the generation AI can compare current humidity data with past humidity data to detect abnormal humidity changes. The detection unit can also compare current light intensity data with past data to detect abnormal light intensity changes. For example, the generation AI can compare current light intensity data with past light intensity data to detect abnormal light intensity changes. This allows for early detection of abnormalities by comparing with past data. Some or all of the above-described processing in the detection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the detection unit can input current data and past data into the generation AI, which can analyze the data to detect abnormalities early.

[0054] The detection unit can predict the occurrence of pests and diseases by combining weather data. The detection unit, for example, uses a generation AI to combine weather data and predict the occurrence of pests and diseases. For example, the detection unit predicts future pest and disease occurrence based on current weather data. For example, the generation AI predicts future pest and disease occurrence based on current temperature data, precipitation data, wind speed data, etc. The detection unit can also predict future pest and disease occurrence by comparing past weather data with current data. For example, the generation AI predicts future pest and disease occurrence based on past temperature data, precipitation data, wind speed data, etc. The detection unit can also propose optimal countermeasures based on weather forecast data. For example, the generation AI proposes optimal countermeasures based on weather forecast data. In this way, the occurrence of pests and diseases can be predicted by combining weather data. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit can input weather data into the generation AI, which analyzes the data and predicts the occurrence of pests and diseases.

[0055] The detection unit can store the detection data in the cloud and share the data with other farms. The detection unit can store the detection data in the cloud and share the data with other farms using, for example, a generation AI. For example, the detection unit can automatically upload the detection data to the cloud using the generation AI. For example, the generation AI can store the detection data in the cloud and share the data with other farms. The detection unit can also share data with other farms and jointly analyze pest and disease outbreaks. For example, the generation AI can share data with other farms and jointly analyze pest and disease outbreaks. Furthermore, the detection unit can evaluate the risk of pest and disease outbreaks in an entire region based on the data in the cloud. For example, the generation AI evaluates the risk of pest and disease outbreaks in an entire region based on the data in the cloud. In this way, by storing the data in the cloud, it is possible to share the data with other farms and jointly analyze pest and disease outbreaks. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit can input the detection data to the generation AI, which can analyze the data, store it in the cloud, and share the data with other farms.

[0056] The allocation unit can optimally allocate tasks by taking into account the capabilities of each robot. The allocation unit, for example, uses a generation AI to optimally allocate tasks by taking into account the capabilities of each robot. For example, the allocation unit allocates tasks by taking into account the speed and accuracy of each robot. For example, the generation AI analyzes the speed and accuracy of each robot and optimally allocates tasks based on that data. The allocation unit can also allocate tasks by taking into account the remaining battery level of each robot. For example, the generation AI analyzes the remaining battery level of each robot and optimally allocates tasks based on that data. Furthermore, the allocation unit can also allocate tasks by taking into account the expertise of each robot. For example, the generation AI analyzes the expertise of each robot and optimally allocates tasks based on that data. In this way, optimal work allocation can be achieved by taking into account the capabilities of each robot. Some or all of the above-described processing in the allocation unit may be performed using, or without, the generation AI. For example, the allocation unit can input capability data of each robot into the generation AI, which can analyze the data and optimally allocate tasks.

[0057] The allocation unit can feed back the allocation results in real time and reflect them in the next allocation. The allocation unit, for example, uses a generation AI to feed back the allocation results in real time and reflect them in the next allocation. For example, the allocation unit uses a generation AI to analyze the allocation results in real time and reflect them in the next allocation. For example, the generation AI analyzes the allocation results in real time and optimizes the next allocation based on the results. The allocation unit can also upload the allocation results to the cloud and reflect them in the next allocation. For example, the generation AI uploads the allocation results to the cloud and optimizes the next allocation based on the data. The allocation unit can also share the allocation results with other robots and reflect them in the next allocation. For example, the generation AI shares the allocation results with other robots and optimizes the next allocation based on the data. In this way, the allocation results can be fed back in real time and reflected in the next allocation, improving the accuracy of the work. Some or all of the above-mentioned processing in the allocation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the allocation department can input the allocation result data into the generation AI, which can then analyze the data and reflect it in the next allocation.

[0058] The allocation unit can share work in cooperation with other farms. The allocation unit, for example, uses a generating AI to share work in cooperation with other farms. For example, the allocation unit uses a generating AI to share watering work in cooperation with other farms. For example, the generating AI collaborates with other farms to efficiently share watering work. The allocation unit can also use a generating AI to share fertilizing work in cooperation with other farms. For example, the generating AI collaborates with other farms to efficiently share fertilizing work. The allocation unit can also use a generating AI to share harvesting work in cooperation with other farms. For example, the generating AI collaborates with other farms to efficiently share harvesting work. In this way, by sharing work in cooperation with other farms, the efficiency of work can be improved. Some or all of the above-mentioned processing in the allocation unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the division of labor can input data on collaboration with other farms into the generation AI, which can then analyze the data and efficiently divide up the work.

[0059] The allocating unit can store the allocated data in the cloud and share the data with other farms. The allocating unit can store the allocated data in the cloud and share the data with other farms, for example, using a generation AI. For example, the allocating unit can automatically upload the allocated data to the cloud using the generation AI. For example, the generation AI can store the allocated data in the cloud and share the data with other farms. The allocating unit can also share data with other farms and jointly optimize operations. For example, the generation AI can share data with other farms and jointly optimize operations. Furthermore, the allocating unit can evaluate the work efficiency of the entire region based on the data on the cloud. For example, the generation AI evaluates the work efficiency of the entire region based on the data on the cloud. In this way, by storing the allocated data in the cloud, it is possible to share the data with other farms and jointly optimize operations. Some or all of the above-mentioned processing in the allocating unit may be performed using, or without, the generation AI. For example, the allocating unit can input the allocated data into the generation AI, which can analyze the data, store it in the cloud, and share the data with other farms.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The monitoring unit not only monitors the growth status of crops in real time, but can also monitor the nutritional status and microbial activity of the soil. For example, a soil sensor is used to measure the nutrient level in the soil and send the data to the generation AI. The generation AI analyzes this data and evaluates the nutritional status of the soil. It also uses a microbial sensor to monitor the activity of microorganisms in the soil and send the data to the generation AI. The generation AI analyzes this data and evaluates the microbial activity status. This allows for a more detailed understanding of the crop growth environment and the proposal of optimal cultivation methods.

[0062] The monitoring unit not only collects information on the growth status of agricultural crops in real time, but also combines it with weather data to make growth predictions. For example, weather sensors are used to collect data such as temperature, humidity, and precipitation, and this data is sent to the generation AI. The generation AI analyzes this data and predicts future weather conditions. It can also compare past weather data with current data to predict future growth conditions. This makes it possible to propose optimal cultivation methods that take weather conditions into account.

[0063] The working unit not only waters and fertilizes based on the proposed cultivation method, but also provides real-time feedback on the results of the work and reflects them in the next work. For example, it can use generative AI to analyze the work results in real time and optimize the next work based on the results. It can also upload the work results to the cloud and share them with other robots, improving the accuracy of the work.

[0064] When it's time to harvest, the harvesting department not only harvests the produce and stores it properly, but also provides real-time feedback on the harvest results, which can be reflected in the next harvest. For example, generative AI can be used to analyze the harvest results in real time and optimize the next harvest based on the results. Harvesting results can also be uploaded to the cloud and shared with other robots, improving harvesting accuracy.

[0065] The allocation unit not only allocates tasks to resolve labor shortages, but also optimally allocates tasks by taking into account the capabilities of each robot. For example, it allocates tasks by taking into account the speed and accuracy of each robot. The generation AI analyzes the speed and accuracy of each robot and optimally allocates tasks based on that data. It can also allocate tasks by taking into account the remaining battery life of each robot. This allows the capabilities of each robot to be utilized to the fullest.

[0066] The proposal unit not only proposes appropriate cultivation methods based on collected data, but can also select the optimal cultivation method by referring to past success stories. For example, it can use generative AI to analyze past data and select cultivation methods with a high success rate. It can also propose the optimal cultivation method under similar conditions based on past success stories. This makes it possible to select the optimal cultivation method by utilizing past success stories.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The monitoring unit monitors the growth status of the crops in real time. For example, the monitoring unit collects information on the growth status of the crops in real time using a temperature sensor, humidity sensor, light sensor, etc. The temperature sensor measures the temperature around the crops, the humidity sensor measures the surrounding humidity, and the light sensor measures the amount of light hitting the crops. Step 2: The proposal unit proposes the optimal cultivation method based on the data collected by the monitoring unit. For example, the proposal unit analyzes the data using a generative AI and proposes the optimal cultivation method. Generative AI includes text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The working unit automatically performs farm work based on the cultivation method proposed by the proposing unit. For example, the working unit uses a robot to water and fertilize. The robot adjusts the timing and amount of watering and fertilization based on data from sensors. Step 4: The harvesting department performs harvesting work based on the results of the farming work performed by the working department. For example, when it is time to harvest, the harvesting department uses a robot to harvest and stores the harvested produce at the appropriate temperature and humidity.

[0069] (Example 2) The smart agriculture system according to an embodiment of the present invention is a next-generation smart agriculture solution that utilizes generative AI to automate and optimize agricultural processes, improving agricultural productivity and promoting a sustainable food supply. This smart agriculture system streamlines and optimizes agricultural processes by monitoring crop growth in real time, proposing optimal cultivation methods, and automatically performing agricultural and harvesting tasks. For example, generative AI uses sensors to monitor crop growth in real time and propose optimal cultivation methods. Robots then automatically perform agricultural tasks based on the proposed cultivation methods. For example, robots automatically water and fertilize crops to support their growth. Furthermore, when harvest time arrives, the robots automatically harvest and properly store the harvested crops. This system streamlines agricultural processes and improves agricultural productivity. Furthermore, detailed monitoring of crop growth by generative AI improves crop quality and realizes a sustainable food supply. For example, generative AI can detect pest infestations early and propose appropriate countermeasures to minimize damage to crops. Furthermore, farms with robots are an effective means of resolving labor shortages. By having robots automate agricultural tasks, it can resolve the labor shortage problem and reduce the burden on farmers. For example, by having robots take over heavy labor and simple tasks, farmers can focus on more advanced tasks. In this way, robot-powered farms are a next-generation smart agriculture solution that utilizes technologies such as generative AI to automate and optimize agricultural processes, improving agricultural productivity and promoting a sustainable food supply. This allows smart agriculture systems to streamline agricultural processes and improve agricultural productivity. It also helps achieve a sustainable food supply and reduces the burden on farmers.

[0070] A smart agriculture system according to an embodiment includes a monitoring unit, a proposal unit, a working unit, and a harvesting unit. The monitoring unit monitors the growth status of agricultural crops in real time. For example, the monitoring unit collects information on the growth status of agricultural crops in real time using sensors. The sensors include a temperature sensor, a humidity sensor, and a light sensor. For example, the temperature sensor measures the temperature around the agricultural crops and collects data. The humidity sensor measures the humidity around the agricultural crops and collects data. The light sensor measures the amount of light hitting the agricultural crops and collects data. The proposal unit proposes an optimal cultivation method based on the data collected by the monitoring unit. For example, the proposal unit analyzes the collected data and proposes an optimal cultivation method using a generation AI. The generation AI analyzes the data and proposes an optimal cultivation method using a text generation AI (e.g., LLM) or a multimodal generation AI. The working unit automatically performs agricultural work based on the cultivation method proposed by the proposal unit. For example, the working unit uses a robot to water and fertilize crops. The robot automatically waters and fertilizes crops based on the proposed cultivation method. For example, a robot adjusts the timing and amount of watering based on data from sensors. Similarly, the timing and amount of fertilization are adjusted based on data from sensors. The harvesting unit performs harvesting work based on the results of the agricultural work performed by the working unit. For example, the harvesting unit uses a robot to perform harvesting work when it is time to harvest. The robot stores the harvested products appropriately. For example, the robot is equipped with equipment for storing the harvested products at appropriate temperatures and humidity. As a result, the smart agriculture system according to the embodiment can monitor the growth status of agricultural products in real time, propose optimal cultivation methods, automatically perform agricultural work, and perform harvesting work, thereby making it possible to improve the efficiency and optimize the agricultural process.

[0071] The monitoring unit can collect information on the growth status of agricultural crops in real time using sensors. Examples of sensors include temperature sensors, humidity sensors, and light sensors. The monitoring unit, for example, uses a temperature sensor to measure the temperature around the agricultural crops and collect data. For example, the temperature sensor measures the temperature around the agricultural crops in real time and transmits the data to the monitoring unit. The monitoring unit can also use a humidity sensor to measure the humidity around the agricultural crops and collect data. For example, the humidity sensor measures the humidity around the agricultural crops in real time and transmits the data to the monitoring unit. The monitoring unit can also use a light sensor to measure the amount of light hitting the agricultural crops and collect data. For example, the light sensor measures the amount of light hitting the agricultural crops in real time and transmits the data to the monitoring unit. In this way, by using sensors, the growth status of agricultural crops can be grasped in detail in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input data from the sensor into a generation AI, which analyzes the data to grasp the growth status.

[0072] The suggestion unit can suggest an appropriate cultivation method based on the collected data. The suggestion unit, for example, uses a generation AI to analyze the collected data and suggest an optimal cultivation method. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI, and suggests an optimal cultivation method. The generation AI suggests an optimal cultivation method based on, for example, collected temperature data, humidity data, light data, etc. For example, the generation AI analyzes temperature data and suggests an appropriate temperature control method. Analyzes humidity data and suggests an appropriate humidity control method. Analyzes light data and suggests an appropriate light control method. In this way, by suggesting an optimal cultivation method based on the collected data, it is possible to optimize the growth of agricultural crops. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input collected data to the generation AI, which then analyzes the data and suggests an optimal cultivation method.

[0073] The working unit can water and fertilize based on the proposed cultivation method. The working unit, for example, uses a robot to water and fertilize. For example, the working unit can automatically water based on the proposed cultivation method using a robot. For example, the robot can adjust the timing and amount of watering based on data from sensors. For example, the robot can optimize the timing and amount of watering based on data from temperature sensors and humidity sensors. The working unit can also automatically fertilize based on the proposed cultivation method using a robot. For example, the robot can adjust the timing and amount of fertilization based on data from sensors. For example, the robot can optimize the timing and amount of fertilization based on data from soil sensors. This can support the growth of crops by watering and fertilizing based on the proposed cultivation method. Some or all of the above-described processing in the working unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the working unit can input data from a sensor into a generation AI, which can analyze the data and optimize the timing and amount of watering and fertilization.

[0074] The harvesting unit can perform harvesting work when it is time to harvest and store the harvested products appropriately. The harvesting unit can automatically perform harvesting work when it is time to harvest, for example, using a robot. For example, the harvesting unit can properly store the harvested products using a robot. For example, the robot is equipped with equipment for storing the harvested products at appropriate temperatures and humidity. For example, the robot stores the harvested products in a refrigerator or freezer. The harvesting unit can also properly classify and store the harvested products. For example, the robot classifies the harvested products by type and stores them in an appropriate storage location. This allows the quality of the harvested products to be maintained by performing harvesting work when it is time to harvest and storing the harvested products appropriately. Some or all of the above-described processing in the harvesting unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the harvesting unit can input data on the harvested products into a generation AI, which can analyze the data and optimize the storage method for the harvested products.

[0075] Furthermore, the smart agriculture system includes a detection unit that detects the occurrence of pests and diseases at an early stage. The detection unit detects the occurrence of pests and diseases at an early stage, for example, using a generation AI. For example, the detection unit uses sensors to monitor the occurrence of pests and diseases in real time. For example, the detection unit uses temperature sensors and humidity sensors to grasp the environment for pest and disease occurrence in detail. For example, the temperature sensor detects the temperature suitable for pest and disease occurrence and transmits the data to the generation AI. The humidity sensor detects the humidity suitable for pest and disease occurrence and transmits the data to the generation AI. The generation AI analyzes this data and detects the occurrence of pest and disease at an early stage. For example, the generation AI can evaluate the risk of pest and disease occurrence based on the temperature data and humidity data and detect it at an early stage. This allows for early detection of pest and disease occurrence, thereby minimizing damage to crops. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit inputs data from sensors into the generation AI, which analyzes the data and detects the occurrence of pest and disease at an early stage.

[0076] Furthermore, the smart agriculture system includes a distribution unit that distributes tasks to resolve the labor shortage. The distribution unit distributes tasks to resolve the labor shortage, for example, using AI. For example, the distribution unit distributes agricultural tasks to multiple robots. For example, the distribution unit distributes watering tasks to robot A, fertilizing tasks to robot B, and harvesting tasks to robot C. In this way, by dividing tasks to resolve the labor shortage, the burden on farmers can be reduced. Some or all of the above-mentioned processing in the distribution unit may be performed, for example, using AI, or may be performed without using AI. For example, the distribution unit can use AI to optimally distribute the tasks of each robot.

[0077] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user's emotions. The monitoring unit, for example, uses a generation AI to estimate the user's emotions. For example, the monitoring unit can estimate the user's emotions using facial expression recognition technology. For example, a camera can be used to capture the user's facial expressions, and the generation AI can analyze the facial expression data to estimate the emotions. The monitoring unit can also estimate the user's emotions using voice analysis technology. For example, a microphone can be used to record the user's voice, and the generation AI can analyze the voice data to estimate the emotions. The monitoring unit can also estimate the user's emotions using survey results. For example, a survey can be conducted on the user, and the generation AI can analyze the results to estimate the emotions. This allows the monitoring frequency to be adjusted according to the user's emotions, thereby reducing the burden on the user. 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 emotional data into the generation AI, which can then analyze the data and adjust the monitoring frequency.

[0078] The monitoring unit can combine different sensors to grasp the growth status from multiple angles. For example, the monitoring unit uses a generation AI to combine different sensors to grasp the growth status from multiple angles. For example, the monitoring unit combines a temperature sensor and a humidity sensor to grasp the growth environment of the crops in detail. For example, the temperature sensor measures the temperature around the crops and sends the data to the generation AI. The humidity sensor measures the humidity around the crops and sends the data to the generation AI. The generation AI analyzes this data to grasp the growth environment of the crops in detail. The monitoring unit can also combine a light sensor and a soil sensor to simultaneously monitor the amount of light and the nutritional status of the soil. For example, the light sensor measures the amount of light hitting the crops and sends the data to the generation AI. The soil sensor measures the nutritional status of the soil and sends the data to the generation AI. The generation AI analyzes this data to grasp the growth status of the crops from multiple angles. Furthermore, the monitoring unit can combine a camera and a weather sensor to evaluate the growth status by integrating visual data and weather data. For example, a camera takes images of crops and sends the data to the generation AI. A weather sensor measures weather data and sends the data to the generation AI. The generation AI analyzes this data and evaluates the growth status of the crops from various angles. This allows the growth status to be understood in detail by combining different sensors. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, the generation AI. For example, the monitoring unit inputs data from a sensor into the generation AI, and the generation AI analyzes the data to understand the growth status from various angles.

[0079] The monitoring unit can compare the monitoring data with past data to detect abnormalities early. The monitoring unit can compare the monitoring data with past data using, for example, a generation AI to detect abnormalities early. For example, the monitoring unit can compare current temperature data with past data to detect abnormal temperature changes. For example, the generation AI can compare current temperature data with past temperature data to detect abnormal temperature changes. The monitoring unit can also compare current humidity data with past data to detect abnormal humidity changes. For example, the generation AI can compare current humidity data with past humidity data to detect abnormal humidity changes. The monitoring unit can also compare current light intensity data with past data to detect abnormal light intensity changes. For example, the generation AI can compare current light intensity data with past light intensity data to detect abnormal light intensity changes. This allows for early detection of abnormalities by comparing with past data. Some or all of the above-described 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 current data and past data into the generation AI, which can analyze the data to detect abnormalities early.

[0080] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. The monitoring unit, for example, uses a generation AI to estimate the user's emotions. For example, the monitoring unit can estimate the user's emotions using facial expression recognition technology. For example, a camera can capture the user's facial expressions, and the generation AI can analyze the facial expression data to estimate the user's emotions. The monitoring unit can also estimate the user's emotions using voice analysis technology. For example, a microphone can record the user's voice, and the generation AI can analyze the voice data to estimate the user's emotions. The monitoring unit can also estimate the user's emotions using survey results. For example, a user can be surveyed, and the generation AI can analyze the results to estimate the user's emotions. This allows the user to better understand the user's emotions by adjusting the display method of the monitoring results according to their emotions. For example, if the user is feeling stressed, the generation AI can display the monitoring results in a simple graph. If the user is relaxed, the generation AI can display the monitoring results with detailed data. Furthermore, if the user is in a hurry, the generation AI can display monitoring results that highlight only the key points. 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 may input user emotion data into the generation AI, which may analyze the data and adjust the display method of the monitoring results.

[0081] The monitoring unit can predict the growth status by combining weather data. The monitoring unit, for example, uses a generation AI to combine weather data to predict the growth status. For example, the monitoring unit predicts future growth status based on current weather data. For example, the generation AI predicts future growth status based on current temperature data, precipitation data, wind speed data, etc. The monitoring unit can also predict future growth status by comparing past weather data with current data. For example, the generation AI predicts future growth status based on past temperature data, precipitation data, wind speed data, etc. The monitoring unit can also suggest optimal cultivation methods based on weather forecast data. For example, the generation AI suggests optimal cultivation methods based on weather forecast data. In this way, the growth status can be predicted by combining weather data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input weather data into the generation AI, which can analyze the data to predict the growth status.

[0082] The monitoring unit can store the monitoring data in the cloud and share the data with other farms. The monitoring unit can store the monitoring data in the cloud and share the data with other farms, for example, using a generation AI. For example, the monitoring unit can automatically upload the monitoring data to the cloud using the generation AI. For example, the generation AI can store the monitoring data in the cloud and share the data with other farms. The monitoring unit can also share the data with other farms and jointly analyze the growth status. For example, the generation AI can share the data with other farms and jointly analyze the growth status. Furthermore, the monitoring unit can evaluate the growth status of an entire region based on the data on the cloud. For example, the generation AI can evaluate the growth status of an entire region based on the data on the cloud. In this way, by storing the data in the cloud, it is possible to share the data with other farms and jointly analyze the growth status. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the monitoring unit can input the monitoring data into the generation AI, which can analyze the data, store it in the cloud, and share the data with other farms.

[0083] The suggestion unit can estimate the user's emotions and adjust the content of suggestions based on the estimated user's emotions. The suggestion unit, for example, uses a generation AI to estimate the user's emotions. For example, the suggestion unit can estimate the user's emotions using facial expression recognition technology. For example, the suggestion unit can capture the user's facial expressions using a camera, and the generation AI can analyze the facial expression data to estimate the emotion. The suggestion unit can also estimate the user's emotions using voice analysis technology. For example, the suggestion unit can record the user's voice using a microphone, and the generation AI can analyze the voice data to estimate the emotion. The suggestion unit can also estimate the user's emotions using survey results. For example, the suggestion unit can conduct a survey of the user, and the generation AI can analyze the results to estimate the emotion. This allows the suggestion content to be adjusted according to the user's emotions, thereby making the most suitable suggestion for the user. For example, if the user is feeling stressed, the generation AI can make simple and easy-to-implement suggestions. Also, if the user is relaxed, the generation AI can make suggestions with detailed explanations. Furthermore, if the user is in a hurry, the generation AI can make suggestions that can be implemented quickly. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input user emotion data into the generation AI, which may analyze the data and adjust the suggestion content.

[0084] The suggestion unit can select the optimal cultivation method by referring to past success cases. The suggestion unit selects the optimal cultivation method by referring to past success cases, for example, using a generation AI. For example, the suggestion unit proposes the optimal cultivation method based on past success cases. For example, the generation AI analyzes past data and selects a cultivation method with a high success rate. The suggestion unit can also suggest the optimal cultivation method under similar conditions by referring to past cases. For example, the generation AI proposes the optimal cultivation method under similar climatic and soil conditions based on past cases. In this way, the optimal cultivation method can be selected by referring to past success cases. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input data on past success cases into the generation AI, which can then analyze the data and select the optimal cultivation method.

[0085] The suggestion unit can simulate different cultivation methods and select an appropriate method. The suggestion unit, for example, uses a generation AI to simulate different cultivation methods and select an optimal method. For example, the suggestion unit can use a generation AI to simulate different cultivation methods and select an optimal method. For example, the generation AI can simulate different cultivation methods and propose the most efficient cultivation method based on the results. The suggestion unit can also compare multiple cultivation methods and select an optimal method. For example, the generation AI can simulate multiple cultivation methods and select an optimal method based on the results. In this way, the optimal method can be selected by simulating different cultivation methods. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input data on different cultivation methods into the generation AI, which can analyze the data and select an optimal method.

[0086] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user's emotions. The suggestion unit, for example, uses a generation AI to estimate the user's emotions. For example, the suggestion unit can estimate the user's emotions using facial expression recognition technology. For example, the suggestion unit can capture the user's facial expressions using a camera, and the generation AI can analyze the facial expression data to estimate the emotion. The suggestion unit can also estimate the user's emotions using voice analysis technology. For example, the suggestion unit can record the user's voice using a microphone, and the generation AI can analyze the voice data to estimate the emotion. The suggestion unit can also estimate the user's emotions using survey results. For example, the suggestion unit can conduct a survey of the user, and the generation AI can analyze the results to estimate the emotion. This allows the suggestion unit to prioritize suggestions that are important to the user by prioritizing them according to the user's emotions. For example, if the user is feeling stressed, the generation AI can prioritize important suggestions. Also, if the user is relaxed, the generation AI can prioritize detailed suggestions. Furthermore, if the user is in a hurry, the generation AI can prioritize suggestions that can be implemented quickly. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input user emotion data into the generation AI, which may analyze the data and determine the priority of suggestions.

[0087] The proposal unit can propose an optimal cultivation method by referring to regional agricultural data. The proposal unit, for example, uses a generation AI to propose the optimal cultivation method by referring to regional agricultural data. For example, the proposal unit can use a generation AI to propose the optimal cultivation method based on regional weather data. For example, the generation AI can analyze regional weather data and propose a cultivation method suitable for the region. The proposal unit can also propose the optimal cultivation method based on regional soil data. For example, the generation AI can analyze regional soil data and propose a cultivation method suitable for the region. The proposal unit can also propose the optimal cultivation method based on regional agricultural production data. For example, the generation AI can analyze regional agricultural production data and propose a cultivation method suitable for the region. In this way, by referring to regional agricultural data, the optimal cultivation method suitable for the region can be proposed. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input regional agricultural data into the generation AI, which can analyze the data and propose the optimal cultivation method.

[0088] The proposal unit can display the proposal content in multiple languages ​​to promote international use. The proposal unit can, for example, use a generation AI to display the proposal content in multiple languages. For example, the proposal unit can use a generation AI to display the proposal content in English and Japanese. For example, the generation AI can display the proposal content in English and Japanese to promote international use. The proposal unit can also use a generation AI to display the proposal content in Chinese and Spanish. For example, the generation AI can display the proposal content in Chinese and Spanish to promote international use. The proposal unit can also use a generation AI to display the proposal content in French and German. For example, the generation AI can display the proposal content in French and German to promote international use. In this way, displaying the proposal content in multiple languages ​​can promote international use. Some or all of the above-mentioned processing in the proposal unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the proposal unit can input the proposal content into a generation AI, which can analyze the data and display it in multiple languages.

[0089] The work unit can estimate the user's emotions and adjust the timing of tasks based on the estimated user emotions. The work unit, for example, uses a generation AI to estimate the user's emotions. For example, the work unit can estimate the user's emotions using facial expression recognition technology. For example, a camera can be used to capture the user's facial expression, and the generation AI can analyze the facial expression data to estimate the emotion. The work unit can also estimate the user's emotions using voice analysis technology. For example, a microphone can be used to record the user's voice, and the generation AI can analyze the voice data to estimate the emotion. Furthermore, the work unit can estimate the user's emotions using survey results. For example, a survey can be conducted on the user, and the generation AI can analyze the results to estimate the emotion. This allows the user's burden to be reduced by adjusting the timing of tasks according to the user's emotions. For example, if the user is feeling stressed, the generation AI can delay the timing of tasks. Also, if the user is relaxed, the generation AI can speed up the timing of tasks. Furthermore, if the user is in a hurry, the generation AI can optimize the timing of tasks. Some or all of the above-described processing in the working unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the working unit may input user emotion data into the generation AI, which may analyze the data and adjust the timing of the work.

[0090] The working unit can maximize efficiency by selecting different working modes. The working unit maximizes efficiency by selecting different working modes, for example, using a generating AI. For example, the working unit performs work by switching between a normal mode and a high-speed mode. For example, the generating AI maximizes work efficiency by switching between the normal mode and the high-speed mode. The working unit can also perform work by switching between an energy-saving mode and a power mode. For example, the generating AI maximizes work efficiency by switching between the energy-saving mode and the power mode. The working unit can also perform work by switching between an automatic mode and a manual mode. For example, the generating AI maximizes work efficiency by switching between the automatic mode and the manual mode. In this way, work efficiency can be maximized by selecting different working modes. Some or all of the above-mentioned processing in the working unit may be performed using, for example, the generating AI, or may be performed without using the generating AI. For example, the working unit can input work mode data into the generating AI, which analyzes the data and selects the optimal work mode.

[0091] The work unit can provide feedback on the work results in real time and reflect them in the next work. The work unit, for example, uses a generation AI to provide feedback on the work results in real time and reflect them in the next work. For example, the work unit can use a generation AI to analyze the work results in real time and reflect them in the next work. For example, the generation AI can analyze the work results in real time and optimize the next work based on the results. The work unit can also upload the work results to the cloud and reflect them in the next work. For example, the generation AI can upload the work results to the cloud and optimize the next work based on the data. The work unit can also share the work results with other robots and reflect them in the next work. For example, the generation AI can share the work results with other robots and optimize the next work based on the data. In this way, the work results can be fed back in real time and reflected in the next work, improving the accuracy of the work. Some or all of the above-mentioned processing in the work unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the work unit can input data on the work results to the generation AI, which can analyze the data and reflect it in the next work.

[0092] The work unit can estimate the user's emotions and determine the priority of tasks based on the estimated user's emotions. The work unit, for example, uses a generation AI to estimate the user's emotions. For example, the work unit can estimate the user's emotions using facial expression recognition technology. For example, the work unit can capture the user's facial expressions using a camera, and the generation AI can analyze the facial expression data to estimate the emotion. The work unit can also estimate the user's emotions using voice analysis technology. For example, the work unit can record the user's voice using a microphone, and the generation AI can analyze the voice data to estimate the emotion. Furthermore, the work unit can estimate the user's emotions using survey results. For example, the work unit can conduct a survey of the user, and the generation AI can analyze the results to estimate the emotion. This allows the work priority to be determined according to the user's emotions, so that important tasks can be performed first. For example, if the user is feeling stressed, the generation AI can prioritize important tasks. Also, if the user is relaxed, the generation AI can prioritize detailed tasks. Furthermore, if the user is in a hurry, the generation AI can prioritize tasks that can be completed quickly. Some or all of the above-described processing in the working unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the working unit may input user emotion data into the generation AI, which may analyze the data and determine the priority of tasks.

[0093] The working unit can share work in cooperation with other robots. The working unit, for example, uses a generating AI to share work in cooperation with other robots. For example, the working unit uses a generating AI to share watering work in cooperation with other robots. For example, the generating AI collaborates with other robots to efficiently share watering work. The working unit can also use a generating AI to share fertilizing work in cooperation with other robots. For example, the generating AI collaborates with other robots to efficiently share fertilizing work. The working unit can also use a generating AI to share harvesting work in cooperation with other robots. For example, the generating AI collaborates with other robots to efficiently share harvesting work. In this way, work efficiency can be improved by sharing work in cooperation with other robots. Some or all of the above-mentioned processing in the working unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the working unit can input collaboration data with other robots into the generating AI, which analyzes the data and efficiently shares work.

[0094] The work unit can store work data in the cloud and share the data with other farms. The work unit, for example, uses a generation AI to store the work data in the cloud and share the data with other farms. For example, the work unit automatically uploads the work data to the cloud using the generation AI. For example, the generation AI stores the work data in the cloud and shares the data with other farms. The work unit can also share data with other farms and jointly optimize work. For example, the generation AI shares data with other farms and jointly optimize work. Furthermore, the work unit can evaluate the work efficiency of the entire region based on the data on the cloud. For example, the generation AI evaluates the work efficiency of the entire region based on the data on the cloud. In this way, by storing the work data in the cloud, the data can be shared with other farms and jointly optimize work. Some or all of the above-mentioned processing in the work unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the work unit can input the work data into the generation AI, which analyzes the data, stores it in the cloud, and shares the data with other farms.

[0095] The harvesting unit can estimate the user's emotions and adjust the timing of harvesting based on the estimated user's emotions. The harvesting unit, for example, uses a generation AI to estimate the user's emotions. For example, the harvesting unit can estimate the user's emotions using facial expression recognition technology. For example, a camera can be used to capture the user's facial expression, and the generation AI can analyze the facial expression data to estimate the emotion. The harvesting unit can also estimate the user's emotions using voice analysis technology. For example, a microphone can be used to record the user's voice, and the generation AI can analyze the voice data to estimate the emotion. Furthermore, the harvesting unit can estimate the user's emotions using survey results. For example, a survey can be conducted on the user, and the generation AI can analyze the results to estimate the emotion. This allows the user's burden to be reduced by adjusting the timing of harvesting according to the user's emotions. For example, if the user is feeling stressed, the generation AI can delay the timing of harvesting. Also, if the user is relaxed, the generation AI can advance the timing of harvesting. Furthermore, if the user is in a hurry, the generation AI can optimize the timing of harvesting. Some or all of the above-described processing in the harvesting unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the harvesting unit may input user emotion data into the generation AI, which may analyze the data and adjust the timing of harvesting.

[0096] The harvesting unit can maximize efficiency by selecting different harvesting methods. The harvesting unit maximizes efficiency by selecting different harvesting methods, for example, using a generation AI. For example, the harvesting unit switches between manual harvesting and automatic harvesting. For example, the generation AI switches between manual harvesting and automatic harvesting to maximize harvesting efficiency. The harvesting unit can also switch between bulk harvesting and partial harvesting to maximize harvesting efficiency. For example, the generation AI switches between bulk harvesting and partial harvesting to maximize harvesting efficiency. The harvesting unit can also switch between daytime harvesting and nighttime harvesting to maximize harvesting efficiency. For example, the generation AI switches between daytime harvesting and nighttime harvesting to maximize harvesting efficiency. In this way, harvesting efficiency can be maximized by selecting different harvesting methods. Some or all of the above-described processing in the harvesting unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the harvesting unit can input harvesting method data into the generation AI, which analyzes the data and selects the optimal harvesting method.

[0097] The harvesting unit can provide feedback on the harvest results in real time and reflect them in the next harvest. The harvesting unit, for example, uses a generation AI to provide feedback on the harvest results in real time and reflect them in the next harvest. For example, the harvesting unit can use a generation AI to analyze the harvest results in real time and reflect them in the next harvest. For example, the generation AI can analyze the harvest results in real time and optimize the next harvest based on the results. The harvesting unit can also upload the harvest results to the cloud and reflect them in the next harvest. For example, the generation AI can upload the harvest results to the cloud and optimize the next harvest based on the data. The harvesting unit can also share the harvest results with other robots and reflect them in the next harvest. For example, the generation AI can share the harvest results with other robots and optimize the next harvest based on the data. This allows the harvest results to be fed back in real time and reflected in the next harvest, improving harvesting accuracy. Some or all of the above-described processing in the harvesting unit may be performed using, or without, the generation AI. For example, the harvesting unit can input data on the harvest results to the generation AI, which can analyze the data and reflect it in the next harvest.

[0098] The harvesting unit can estimate the user's emotions and determine harvesting priorities based on the estimated user's emotions. The harvesting unit, for example, uses a generation AI to estimate the user's emotions. For example, the harvesting unit can estimate the user's emotions using facial expression recognition technology. For example, a camera can be used to capture the user's facial expressions, and the generation AI can analyze the facial expression data to estimate the emotions. The harvesting unit can also estimate the user's emotions using voice analysis technology. For example, a microphone can be used to record the user's voice, and the generation AI can analyze the voice data to estimate the emotions. The harvesting unit can also estimate the user's emotions using survey results. For example, a survey can be conducted on the user, and the generation AI can analyze the results to estimate the emotions. This allows the harvesting priority to be determined according to the user's emotions, allowing important harvesting to be prioritized. For example, if the user is stressed, the generation AI can prioritize important harvesting. Also, if the user is relaxed, the generation AI can prioritize detailed harvesting. Furthermore, if the user is in a hurry, the generation AI can prioritize harvesting that can be completed quickly. Some or all of the above-described processing in the harvesting unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the harvesting unit may input user emotion data into the generation AI, which may analyze the data and determine harvesting priorities.

[0099] The harvesting unit can cooperate with other robots to share the harvesting work. The harvesting unit, for example, uses a generation AI to cooperate with other robots to share the harvesting work. For example, the harvesting unit uses a generation AI to cooperate with other robots to share the fruit harvesting work. For example, the generation AI cooperates with other robots to efficiently share the fruit harvesting work. The harvesting unit can also use a generation AI to cooperate with other robots to share the vegetable harvesting work. For example, the generation AI cooperates with other robots to efficiently share the vegetable harvesting work. Furthermore, the harvesting unit can also use a generation AI to cooperate with other robots to share the grain harvesting work. For example, the generation AI cooperates with other robots to efficiently share the grain harvesting work. In this way, by cooperating with other robots to share the harvesting work, the efficiency of the harvest can be improved. Some or all of the above-mentioned processing in the harvesting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the harvesting department can input collaboration data with other robots into the generation AI, which can then analyze the data to efficiently share the harvesting work.

[0100] The harvesting unit can store harvest data in the cloud and share the data with other farms. The harvesting unit can store harvest data in the cloud and share the data with other farms, for example, using a generation AI. For example, the harvesting unit can automatically upload harvest data to the cloud using the generation AI. For example, the generation AI can store harvest data in the cloud and share the data with other farms. The harvesting unit can also share data with other farms and jointly optimize harvesting operations. For example, the generation AI can share data with other farms and jointly optimize harvesting operations. Furthermore, the harvesting unit can evaluate harvest efficiency for an entire region based on the data on the cloud. For example, the generation AI evaluates harvest efficiency for an entire region based on the data on the cloud. In this way, by storing harvest data in the cloud, data can be shared with other farms and jointly optimize harvesting operations. Some or all of the above-mentioned processing in the harvesting unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the harvesting unit can input harvest data into the generation AI, which can analyze the data and store it in the cloud, and share the data with other farms.

[0101] The detection unit can estimate the user's emotions and adjust the detection frequency based on the estimated user emotions. The detection unit, for example, uses a generation AI to estimate the user's emotions. For example, the detection unit can estimate the user's emotions using facial expression recognition technology. For example, a camera can capture the user's facial expressions, and the generation AI can analyze the facial expression data to estimate the user's emotions. The detection unit can also estimate the user's emotions using voice analysis technology. For example, a microphone can record the user's voice, and the generation AI can analyze the voice data to estimate the user's emotions. The detection unit can also estimate the user's emotions using survey results. For example, a user can be surveyed, and the generation AI can analyze the results to estimate the user's emotions. This reduces the burden on the user by adjusting the detection frequency according to the user's emotions. For example, if the user is stressed, the generation AI can reduce the detection frequency and lessen notifications. Also, if the user is relaxed, the generation AI can increase the detection frequency and provide more detailed data. Furthermore, if the user is in a hurry, the generation AI can minimize the detection frequency and notify only important information. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the detection unit may input user emotion data into the generation AI, which may analyze the data and adjust the detection frequency.

[0102] The detection unit can combine different sensors to grasp the occurrence of pests and diseases from multiple angles. The detection unit, for example, uses a generation AI to combine different sensors to grasp the occurrence of pests and diseases from multiple angles. For example, the detection unit combines a temperature sensor and a humidity sensor to grasp the environment for pest and disease occurrence in detail. For example, the temperature sensor detects the temperature suitable for pest and disease occurrence and sends the data to the generation AI. The humidity sensor detects the humidity suitable for pest and disease occurrence and sends the data to the generation AI. The generation AI analyzes this data and grasps the occurrence of pests and diseases from multiple angles. The detection unit can also combine a light sensor and a soil sensor to simultaneously monitor the risk of pest and disease occurrence. For example, the light sensor detects the amount of light suitable for pest and disease occurrence and sends the data to the generation AI. The soil sensor detects the nutrient state of the soil suitable for pest and disease occurrence and sends the data to the generation AI. The generation AI analyzes this data and grasps the occurrence of pest and disease from multiple angles. Furthermore, the detection unit can combine a camera and a weather sensor to integrate visual data and weather data to evaluate the occurrence of pests and diseases. For example, the camera visually detects the occurrence of pests and diseases and sends the data to the generation AI. The weather sensor detects weather conditions suitable for the occurrence of pests and diseases and sends the data to the generation AI. The generation AI analyzes this data and evaluates the occurrence of pests and diseases from multiple perspectives. In this way, by combining different sensors, it is possible to grasp the occurrence of pests and diseases in detail. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit inputs data from the sensor into the generation AI, and the generation AI analyzes the data to grasp the occurrence of pests and diseases from multiple perspectives.

[0103] The detection unit can compare the detection data with past data to detect abnormalities early. The detection unit can compare the detection data with past data using, for example, a generation AI to detect abnormalities early. For example, the detection unit can compare current temperature data with past data to detect abnormal temperature changes. For example, the generation AI can compare current temperature data with past temperature data to detect abnormal temperature changes. The detection unit can also compare current humidity data with past data to detect abnormal humidity changes. For example, the generation AI can compare current humidity data with past humidity data to detect abnormal humidity changes. The detection unit can also compare current light intensity data with past data to detect abnormal light intensity changes. For example, the generation AI can compare current light intensity data with past light intensity data to detect abnormal light intensity changes. This allows for early detection of abnormalities by comparing with past data. Some or all of the above-described processing in the detection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the detection unit can input current data and past data into the generation AI, which can analyze the data to detect abnormalities early.

[0104] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the estimated user emotions. The detection unit, for example, uses a generation AI to estimate the user's emotions. For example, the detection unit can estimate the user's emotions using facial expression recognition technology. For example, a camera can capture the user's facial expressions, and the generation AI can analyze the facial expression data to estimate the emotion. The detection unit can also estimate the user's emotions using voice analysis technology. For example, a microphone can record the user's voice, and the generation AI can analyze the voice data to estimate the emotion. The detection unit can also estimate the user's emotions using survey results. For example, a user can be surveyed, and the generation AI can analyze the results to estimate the emotion. This allows the user to understand the user's emotions better by adjusting the display method of the detection results according to their emotions. For example, if the user is feeling stressed, the generation AI can display the detection results in a simple graph. If the user is relaxed, the generation AI can display the detection results with detailed data. Furthermore, if the user is in a hurry, the generation AI can display the detection results with only the main points emphasized. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the detection unit may input user emotion data into the generation AI, which may analyze the data and adjust the display method of the detection result.

[0105] The detection unit can predict the occurrence of pests and diseases by combining weather data. The detection unit, for example, uses a generation AI to combine weather data and predict the occurrence of pests and diseases. For example, the detection unit predicts future pest and disease occurrence based on current weather data. For example, the generation AI predicts future pest and disease occurrence based on current temperature data, precipitation data, wind speed data, etc. The detection unit can also predict future pest and disease occurrence by comparing past weather data with current data. For example, the generation AI predicts future pest and disease occurrence based on past temperature data, precipitation data, wind speed data, etc. The detection unit can also propose optimal countermeasures based on weather forecast data. For example, the generation AI proposes optimal countermeasures based on weather forecast data. In this way, the occurrence of pests and diseases can be predicted by combining weather data. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit can input weather data into the generation AI, which analyzes the data and predicts the occurrence of pests and diseases.

[0106] The detection unit can store the detection data in the cloud and share the data with other farms. The detection unit can store the detection data in the cloud and share the data with other farms using, for example, a generation AI. For example, the detection unit can automatically upload the detection data to the cloud using the generation AI. For example, the generation AI can store the detection data in the cloud and share the data with other farms. The detection unit can also share data with other farms and jointly analyze pest and disease outbreaks. For example, the generation AI can share data with other farms and jointly analyze pest and disease outbreaks. Furthermore, the detection unit can evaluate the risk of pest and disease outbreaks in an entire region based on the data in the cloud. For example, the generation AI evaluates the risk of pest and disease outbreaks in an entire region based on the data in the cloud. In this way, by storing the data in the cloud, it is possible to share the data with other farms and jointly analyze pest and disease outbreaks. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit can input the detection data to the generation AI, which can analyze the data, store it in the cloud, and share the data with other farms.

[0107] The allocation unit can estimate the user's emotions and adjust the method of task allocation based on the estimated user's emotions. The allocation unit, for example, uses a generation AI to estimate the user's emotions. For example, the allocation unit can estimate the user's emotions using facial expression recognition technology. For example, a camera can be used to capture the user's facial expression, and the generation AI can analyze the facial expression data to estimate the emotion. The allocation unit can also estimate the user's emotions using voice analysis technology. For example, a microphone can be used to record the user's voice, and the generation AI can analyze the voice data to estimate the emotion. The allocation unit can also estimate the user's emotions using survey results. For example, a user can be surveyed, and the generation AI can analyze the results to estimate the emotion. This allows the user's burden to be reduced by adjusting the method of task allocation according to the user's emotions. For example, if the user is stressed, the generation AI can prioritize simple tasks. Also, if the user is relaxed, the generation AI can prioritize complex tasks. Furthermore, if the user is in a hurry, the generation AI can prioritize tasks that can be completed quickly. Some or all of the above-described processing in the allocation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the allocation unit may input user emotion data into the generation AI, which may analyze the data and adjust the method of work allocation.

[0108] The allocation unit can optimally allocate tasks by taking into account the capabilities of each robot. The allocation unit, for example, uses a generation AI to optimally allocate tasks by taking into account the capabilities of each robot. For example, the allocation unit allocates tasks by taking into account the speed and accuracy of each robot. For example, the generation AI analyzes the speed and accuracy of each robot and optimally allocates tasks based on that data. The allocation unit can also allocate tasks by taking into account the remaining battery level of each robot. For example, the generation AI analyzes the remaining battery level of each robot and optimally allocates tasks based on that data. Furthermore, the allocation unit can also allocate tasks by taking into account the expertise of each robot. For example, the generation AI analyzes the expertise of each robot and optimally allocates tasks based on that data. In this way, optimal work allocation can be achieved by taking into account the capabilities of each robot. Some or all of the above-described processing in the allocation unit may be performed using, or without, the generation AI. For example, the allocation unit can input capability data of each robot into the generation AI, which can analyze the data and optimally allocate tasks.

[0109] The allocation unit can feed back the allocation results in real time and reflect them in the next allocation. The allocation unit, for example, uses a generation AI to feed back the allocation results in real time and reflect them in the next allocation. For example, the allocation unit uses a generation AI to analyze the allocation results in real time and reflect them in the next allocation. For example, the generation AI analyzes the allocation results in real time and optimizes the next allocation based on the results. The allocation unit can also upload the allocation results to the cloud and reflect them in the next allocation. For example, the generation AI uploads the allocation results to the cloud and optimizes the next allocation based on the data. The allocation unit can also share the allocation results with other robots and reflect them in the next allocation. For example, the generation AI shares the allocation results with other robots and optimizes the next allocation based on the data. In this way, the allocation results can be fed back in real time and reflected in the next allocation, improving the accuracy of the work. Some or all of the above-mentioned processing in the allocation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the allocation department can input the allocation result data into the generation AI, which can then analyze the data and reflect it in the next allocation.

[0110] The allocation unit can estimate the user's emotions and determine the priority of work allocation based on the estimated user's emotions. The allocation unit estimates the user's emotions, for example, using a generation AI. For example, the allocation unit estimates the user's emotions using facial expression recognition technology. For example, a camera is used to capture the user's facial expression, and the generation AI analyzes the facial expression data to estimate the emotion. The allocation unit can also estimate the user's emotions using voice analysis technology. For example, a microphone is used to record the user's voice, and the generation AI analyzes the voice data to estimate the emotion. Furthermore, the allocation unit can estimate the user's emotions using survey results. For example, a survey is conducted on the user, and the generation AI analyzes the results to estimate the emotion. This allows the priority of work allocation to be determined according to the user's emotions, thereby prioritizing important tasks. For example, if the user is feeling stressed, the generation AI can prioritize important tasks. Also, if the user is relaxed, the generation AI can prioritize detailed tasks. Furthermore, if the user is in a hurry, the generation AI can prioritize tasks that can be completed quickly. Some or all of the above-described processing in the allocation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the allocation unit may input user emotion data into the generation AI, which may analyze the data and determine the priority of work allocation.

[0111] The allocation unit can share work in cooperation with other farms. The allocation unit, for example, uses a generating AI to share work in cooperation with other farms. For example, the allocation unit uses a generating AI to share watering work in cooperation with other farms. For example, the generating AI collaborates with other farms to efficiently share watering work. The allocation unit can also use a generating AI to share fertilizing work in cooperation with other farms. For example, the generating AI collaborates with other farms to efficiently share fertilizing work. The allocation unit can also use a generating AI to share harvesting work in cooperation with other farms. For example, the generating AI collaborates with other farms to efficiently share harvesting work. In this way, by sharing work in cooperation with other farms, the efficiency of work can be improved. Some or all of the above-mentioned processing in the allocation unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the division of labor can input data on collaboration with other farms into the generation AI, which can then analyze the data and efficiently divide up the work.

[0112] The allocating unit can store the allocated data in the cloud and share the data with other farms. The allocating unit can store the allocated data in the cloud and share the data with other farms, for example, using a generation AI. For example, the allocating unit can automatically upload the allocated data to the cloud using the generation AI. For example, the generation AI can store the allocated data in the cloud and share the data with other farms. The allocating unit can also share data with other farms and jointly optimize operations. For example, the generation AI can share data with other farms and jointly optimize operations. Furthermore, the allocating unit can evaluate the work efficiency of the entire region based on the data on the cloud. For example, the generation AI evaluates the work efficiency of the entire region based on the data on the cloud. In this way, by storing the allocated data in the cloud, it is possible to share the data with other farms and jointly optimize operations. Some or all of the above-mentioned processing in the allocating unit may be performed using, or without, the generation AI. For example, the allocating unit can input the allocated data into the generation AI, which can analyze the data, store it in the cloud, and share the data with other farms. === Hard Collateral 1-1 === Each of the multiple elements, including the monitoring unit, proposal unit, working unit, harvesting unit, detection unit, and allocation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit collects crop growth status in real time using sensors in the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The proposal unit proposes an optimal cultivation method using a generation AI by the specific processing unit 290 of the data processing device 12. The working unit automatically performs agricultural work based on the cultivation method proposed by the robot in the smart device 14. The harvesting unit performs harvesting work using the robot in the smart device 14 and properly stores the harvested crops. The detection unit monitors the occurrence of pests and diseases in real time using sensors in the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The allocation unit optimally allocates the work of each robot using AI by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the monitoring unit, proposal unit, working unit, harvesting unit, detection unit, and allocation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit collects crop growth status in real time using sensors in the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The proposal unit proposes an optimal cultivation method using generated AI by the specific processing unit 290 of the data processing device 12. The working unit automatically performs agricultural work based on the proposed cultivation method by the robot in the smart glasses 214. The harvesting unit performs harvesting work using the robot in the smart glasses 214 and properly stores the harvested crops. The detection unit monitors the occurrence of pests and diseases in real time using sensors in the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The allocation unit optimally allocates the work of each robot using AI by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the monitoring unit, proposal unit, working unit, harvesting unit, detection unit, and allocation unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the monitoring unit collects information on the growth status of agricultural crops in real time using sensors in the headset terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The proposal unit proposes an optimal cultivation method using generated AI by the specific processing unit 290 of the data processing device 12. The working unit automatically performs agricultural work based on the cultivation method proposed by the robot in the headset terminal 314. The harvesting unit performs harvesting work using the robot in the headset terminal 314 and properly stores the harvested crops. The detection unit monitors the occurrence of pests and diseases in real time using sensors in the headset terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The allocation unit optimally allocates the work of each robot using AI by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the monitoring unit, proposal unit, working unit, harvesting unit, detection unit, and allocation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit collects information on the growth status of agricultural crops in real time using sensors in the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The proposal unit proposes an optimal cultivation method using generated AI by the specific processing unit 290 of the data processing device 12. The working unit automatically performs agricultural work based on the cultivation method proposed by the robot 414. The harvesting unit performs harvesting work using the robot 414 and appropriately stores the harvested products. The detection unit monitors the occurrence of pests and diseases in real time using sensors in the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The allocation unit optimally allocates the work of each robot using AI by the specific processing unit 290 of the data processing device 12.

[0113] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0114] The monitoring unit not only monitors the growth status of crops in real time, but can also monitor the nutritional status and microbial activity of the soil. For example, a soil sensor is used to measure the nutrient level in the soil and send the data to the generation AI. The generation AI analyzes this data and evaluates the nutritional status of the soil. It also uses a microbial sensor to monitor the activity of microorganisms in the soil and send the data to the generation AI. The generation AI analyzes this data and evaluates the microbial activity status. This allows for a more detailed understanding of the crop growth environment and the proposal of optimal cultivation methods.

[0115] The monitoring unit not only collects information on the growth status of agricultural crops in real time, but also combines it with weather data to make growth predictions. For example, weather sensors are used to collect data such as temperature, humidity, and precipitation, and this data is sent to the generation AI. The generation AI analyzes this data and predicts future weather conditions. It can also compare past weather data with current data to predict future growth conditions. This makes it possible to propose optimal cultivation methods that take weather conditions into account.

[0116] The suggestion unit not only suggests appropriate cultivation methods based on the collected data, but also estimates the user's emotions and adjusts the suggestions based on the estimated user emotions. For example, facial expression recognition technology is used to estimate the user's emotions, and the generation AI analyzes the data to estimate the emotions. If the user is feeling stressed, the generation AI will make simple and easy-to-implement suggestions. Also, if the user is relaxed, the generation AI can make suggestions with detailed explanations. This allows the system to provide optimal suggestions for the user.

[0117] The working unit not only waters and fertilizes based on the proposed cultivation method, but also provides real-time feedback on the results of the work and reflects them in the next work. For example, it can use generative AI to analyze the work results in real time and optimize the next work based on the results. It can also upload the work results to the cloud and share them with other robots, improving the accuracy of the work.

[0118] When it's time to harvest, the harvesting department not only harvests the produce and stores it properly, but also provides real-time feedback on the harvest results, which can be reflected in the next harvest. For example, generative AI can be used to analyze the harvest results in real time and optimize the next harvest based on the results. Harvesting results can also be uploaded to the cloud and shared with other robots, improving harvesting accuracy.

[0119] The detection unit not only detects pest infestations early, but can also estimate the user's emotions and adjust the frequency of detection based on the estimated user emotions. For example, facial expression recognition technology can be used to estimate the user's emotions, and the generation AI can analyze that data to estimate the emotion. If the user is feeling stressed, the generation AI can reduce the frequency of detection and tone down notifications. Also, if the user is relaxed, the generation AI can increase the frequency of detection and provide more detailed data. This reduces the burden on the user.

[0120] The allocation unit not only allocates tasks to resolve labor shortages, but also optimally allocates tasks by taking into account the capabilities of each robot. For example, it allocates tasks by taking into account the speed and accuracy of each robot. The generation AI analyzes the speed and accuracy of each robot and optimally allocates tasks based on that data. It can also allocate tasks by taking into account the remaining battery life of each robot. This allows the capabilities of each robot to be utilized to the fullest.

[0121] The monitoring unit estimates the user's emotions and can adjust the frequency of monitoring based on the estimated user emotions, as well as the way the monitoring results are displayed. For example, if the user is feeling stressed, the generation AI can display the monitoring results in a simple graph. Alternatively, if the user is relaxed, the generation AI can display the monitoring results with detailed data. This can help the user understand.

[0122] The proposal unit not only proposes appropriate cultivation methods based on collected data, but can also select the optimal cultivation method by referring to past success stories. For example, it can use generative AI to analyze past data and select cultivation methods with a high success rate. It can also propose the optimal cultivation method under similar conditions based on past success stories. This makes it possible to select the optimal cultivation method by utilizing past success stories.

[0123] The working unit not only waters and fertilizes plants based on the proposed cultivation method, but can also estimate the user's emotions and adjust the timing of tasks based on the estimated user emotions. For example, facial expression recognition technology can be used to estimate the user's emotions, and the generation AI can analyze that data to estimate the emotions. If the user is feeling stressed, the generation AI can delay the timing of tasks. Also, if the user is relaxed, the generation AI can speed up the timing of tasks. This reduces the burden on the user.

[0124] The processing flow of the second embodiment will be briefly explained below.

[0125] Step 1: The monitoring unit monitors the growth status of the crops in real time. For example, the monitoring unit collects information on the growth status of the crops in real time using a temperature sensor, humidity sensor, light sensor, etc. The temperature sensor measures the temperature around the crops, the humidity sensor measures the surrounding humidity, and the light sensor measures the amount of light hitting the crops. Step 2: The proposal unit proposes the optimal cultivation method based on the data collected by the monitoring unit. For example, the proposal unit analyzes the data using a generative AI and proposes the optimal cultivation method. Generative AI includes text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The working unit automatically performs farm work based on the cultivation method proposed by the proposing unit. For example, the working unit uses a robot to water and fertilize. The robot adjusts the timing and amount of watering and fertilization based on data from sensors. Step 4: The harvesting department performs harvesting work based on the results of the farming work performed by the working department. For example, when it is time to harvest, the harvesting department uses a robot to harvest and stores the harvested produce at the appropriate temperature and humidity.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] [Explanation of symbols]

[0198] 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 monitoring unit that monitors the growth status of agricultural crops in real time; a suggestion unit that suggests an appropriate cultivation method based on the data collected by the monitoring unit; a working unit that automatically performs farm work based on the cultivation method proposed by the proposing unit; a harvesting unit that performs harvesting work based on the results of the agricultural work performed by the working unit; Equipped with A system characterized by:

2. Equipped with a detection unit that detects the occurrence of pests at an early stage 2. The system of claim 1.

3. Establishing divisions to share work loads to resolve labor shortages 2. The system of claim 1.

4. The monitoring unit Using sensors to collect real-time information on crop growth 2. The system of claim 1.

5. The proposal unit Proposes appropriate cultivation methods based on collected data 2. The system of claim 1.

6. The working unit includes: Watering and fertilizing according to the proposed cultivation method 2. The system of claim 1.

7. The harvesting unit is When it's time to harvest, they harvest and store the produce properly.

2. The system of claim 1.

8. The monitoring unit Estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions.

2. The system of claim 1.

9. The monitoring unit Combining different sensors to understand growth conditions from multiple angles 2. The system of claim 1.

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