System

Abstract

An object of a system according to an embodiment is to analyze information related to farmland and crops and provide appropriate advice.SOLUTION: A system includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information on farmland and crops. The analysis unit analyzes the information collected by the collection unit. The providing unit provides advice based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

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 are not sufficient in efficiently collecting and analyzing information about farmland and crops and providing appropriate advice, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze information about farmland and crops and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information about farmland and crops. The analysis unit analyzes the information collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze information about farmland and crops and provide appropriate advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An agricultural support system according to an embodiment of the present invention collects information about farmland and crops, analyzes it using a generating AI, and provides optimal advice. The agricultural support system collects information about farmland and crops, analyzes it using a generating AI, and provides optimal advice based on the results. For example, the agricultural support system uses sensors to collect detailed data such as soil conditions and crop growth status. The generating AI then analyzes the collected data to analyze the farmland condition and crop growth status. For example, the system proposes optimal cultivation methods based on data such as soil moisture, nutrient content, and crop leaf color and growth rate. Furthermore, based on the analysis results of the generating AI, the system explains specific cultivation methods and provides advice in a format that is easy for farmers to implement. This allows farmers, especially new farmers, to acquire agricultural expertise while farming, contributing to an increase in the number of farmers entering the agricultural industry. For example, by performing farm work while receiving advice from the generating AI, new farmers can learn farming efficiently and increase their chances of success. This agricultural support system allows farmers, especially new farmers, to acquire agricultural expertise while farming, contributing to an increase in the number of farmers entering the agricultural industry. For example, by performing farm work while receiving advice from the generating AI, new farmers can learn farming efficiently and increase their chances of success.

[0029] An agricultural support system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information about farmland and crops. The collection unit can collect information about soil conditions and crop growth status using sensors, for example. The collection unit can collect data about soil humidity, nutrient content, crop leaf color, and growth rate. The collection unit can also collect farmland data over a wide area using drones. The analysis unit analyzes the information collected by the collection unit. The analysis unit uses a generation AI to analyze the farmland condition and crop growth status based on the collected data. For example, the generation AI suggests irrigation timing when soil humidity is low, and suggests the appropriate type and amount of fertilizer when nutrients are lacking. The provision unit provides advice based on the analysis results obtained by the analysis unit. The provision unit suggests optimal cultivation methods based on the analysis results of the generation AI. For example, the generation AI specifically explains the cultivation methods proposed by the generation AI and provides advice in a format that is easy for farmers to implement. This allows farmers to perform appropriate farming work even without specialized knowledge. As a result, the agricultural support system of the embodiment collects and analyzes information about farmland and crops and provides optimal advice, allowing farmers, especially new farmers, to farm while acquiring agricultural expertise.

[0030] The collection unit can collect information on the condition of the soil or the growth status of the crops using sensors. For example, the collection unit can collect information on the humidity and nutrient content of the soil using a soil sensor. The collection unit can also collect information on the temperature of the soil or the crops using a temperature sensor. Furthermore, the collection unit can collect information on the humidity in the air using a humidity sensor. For example, the collection unit can measure the humidity of the soil in real time using a soil sensor and collect data. The temperature sensor can measure the temperature of the soil or the crops and collect data. The humidity sensor can measure the humidity in the air and collect data. In this way, by using sensors, detailed information on the condition of the soil and the growth status of the crops can be collected. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data acquired from the sensor into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the condition of the farmland and the growth status of the crops based on the collected data. The analysis unit, for example, analyzes the condition of the soil based on collected soil humidity data. For example, the analysis unit suggests the timing of irrigation when the soil humidity is low. The analysis unit can also suggest the appropriate type and amount of fertilizer based on collected nutrient data. For example, the analysis unit suggests the appropriate type and amount of fertilizer when the soil is lacking nutrients. The analysis unit can also analyze the growth status of the crops based on collected crop growth data. For example, the analysis unit analyzes the color and growth rate of the crop leaves and suggests the optimal cultivation method. In this way, the analysis unit can suggest the optimal cultivation method by analyzing the condition of the farmland and the growth status of the crops based on the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI analyze the data.

[0032] The providing unit can suggest a cultivation method based on the analysis results of the generating AI. For example, the providing unit can suggest the timing of irrigation if the soil moisture is low based on the analysis results of the generating AI. The providing unit can also suggest the appropriate type and amount of fertilizer if there is a nutrient deficiency based on the analysis results of the generating AI. For example, the providing unit can suggest the appropriate type and amount of fertilizer based on the analysis results of the generating AI. The providing unit can also suggest a cultivation method according to the growth status of the crop based on the analysis results of the generating AI. For example, the providing unit can suggest a cultivation method according to the leaf color and growth rate of the crop based on the analysis results of the generating AI. This allows farmers to efficiently perform farm work by suggesting the optimal cultivation method based on the analysis results of the generating AI. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generating AI. For example, the providing unit can suggest the optimal cultivation method based on the analysis results of the generating AI.

[0033] The providing unit can explain the cultivation method proposed by the generation AI and provide advice in a format that is easy for farmers to implement. For example, the providing unit can explain the cultivation method proposed by the generation AI step by step. The providing unit can also explain the cultivation method proposed by the generation AI using a video tutorial. For example, the providing unit can specifically explain the cultivation method proposed by the generation AI and provide advice in a format that is easy for farmers to implement. The providing unit can also explain the cultivation method proposed by the generation AI in text format. For example, the providing unit can explain the cultivation method proposed by the generation AI in text format and provide advice in a format that is easy for farmers to implement. This allows farmers to perform appropriate farm work even without specialized knowledge by specifically explaining the cultivation method proposed by the generation AI and providing advice in a format that is easy for farmers to implement. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can provide advice in a format that is easy for farmers to implement based on the cultivation method proposed by the generation AI.

[0034] When collecting information on soil conditions and crop growth status, the collection unit can acquire weather data in real time and reflect it in the collected data. For example, when it is raining, the collection unit can acquire soil humidity data in real time and reflect it in the collected data. Furthermore, when it is sunny, the collection unit can also acquire crop growth rate in real time and reflect it in the collected data. For example, the collection unit can update the collected data in real time in response to changes in weather and provide accurate data. This enables more accurate data collection by acquiring weather data in real time and reflecting it in the collected data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather data to a generation AI and have the generation AI reflect the weather data in the collected data.

[0035] The collection unit can integrate data from different sensors to understand the state of the farmland in more detail. For example, the collection unit can integrate data from a soil sensor and a temperature sensor to understand the state of the farmland in more detail. The collection unit can also integrate data from a crop growth sensor and a humidity sensor to understand the growth status of the crops in more detail. For example, the collection unit can integrate data from different sensors in real time to accurately understand the state of the farmland. In this way, by integrating data from different sensors, the state of the farmland can be understood in more detail. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from different sensors into a generation AI and have the generation AI integrate the data.

[0036] The collection unit can detect abnormal values ​​by comparing with past data and issue an alert when an abnormality occurs. For example, the collection unit can issue an alert when soil moisture is abnormally low compared to past data. The collection unit can also issue an alert when crop growth rate is abnormally slow compared to past data. For example, the collection unit can detect abnormal values ​​in real time and issue an alert immediately when an abnormality occurs. This allows for rapid response by detecting abnormal values ​​by comparing with past data and issuing an alert when an abnormality occurs. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can cause the generation AI to perform an analysis to compare with past data to detect abnormal values.

[0037] The collection unit can use a drone to collect farmland data over a wide area and analyze it in combination with ground sensors. For example, the collection unit can use a drone to collect aerial photography data of a wide area of ​​farmland and analyze it in combination with ground sensor data. The collection unit can also use a drone to collect information on crop growth conditions over a wide area and analyze it in combination with ground sensor data. For example, the collection unit can use a drone to collect information on soil conditions over a wide area and analyze it in combination with ground sensor data. In this way, by using a drone to collect farmland data over a wide area and analyzing it in combination with ground sensors, it is possible to understand the farmland condition in more detail. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data acquired from a drone into a generation AI and have the generation AI analyze the data.

[0038] The collection unit can collect data specialized for a specific region by taking into account the geographic information of the farmland. For example, the collection unit collects soil data for a specific region based on the geographic information of the farmland. The collection unit can also collect crop growth data for a specific region based on the geographic information of the farmland. For example, the collection unit collects weather data for a specific region based on the geographic information of the farmland. This makes it possible to collect data specialized for a specific region by taking into account the geographic information of the farmland. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographic information to the generation AI and cause the generation AI to collect data specialized for a specific region.

[0039] The collection unit can customize the collection method by reflecting the farmer's past feedback. For example, the collection unit customizes the type of data to be collected based on the farmer's past feedback. The collection unit can also customize the frequency of data collection based on the farmer's past feedback. For example, the collection unit customizes the timing of data collection based on the farmer's past feedback. This allows the collection method to be customized by reflecting the farmer's past feedback, enabling more appropriate data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the farmer's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0040] The analysis unit can predict the risk of pests and diseases of crops based on the collected data and propose preventive measures. For example, the analysis unit can predict the risk of pests and diseases of crops based on the collected data and propose appropriate preventive measures. The analysis unit can also predict the risk of insect damage to crops based on the collected data and propose appropriate preventive measures. For example, the analysis unit can comprehensively predict the risk of pests and diseases of crops based on the collected data and propose optimal preventive measures. This makes it possible to maintain the health of crops by predicting the risk of pests and diseases of crops based on the collected data and proposing preventive measures. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI, for example. For example, the analysis unit can input the collected data into the generation AI and cause the generation AI to predict the risk of pests and diseases and propose preventive measures.

[0041] The analysis unit can compare the growth patterns of different crops and derive the optimal cultivation method. For example, the analysis unit can compare growth data of different crops and derive the optimal cultivation method. The analysis unit can also analyze the growth patterns of different crops and propose the optimal cultivation method. For example, the analysis unit derives the optimal cultivation method based on the growth data of different crops. In this way, the optimal cultivation method can be derived by comparing the growth patterns of different crops. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input growth data of different crops into the generation AI and cause the generation AI to derive the optimal cultivation method.

[0042] The analysis unit can learn from past analysis results and improve the analysis accuracy. The analysis unit can improve the analysis accuracy, for example, based on past analysis results. The analysis unit can also learn from past analysis results and improve the analysis algorithm. For example, the analysis unit continuously improves the analysis accuracy based on past analysis results. In this way, the analysis accuracy can be improved by learning from past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the analysis accuracy.

[0043] The analysis unit can propose a water management plan for agricultural land based on the collected data. The analysis unit, for example, proposes an optimal irrigation schedule based on the collected data. The analysis unit can also propose a plan to optimize water usage based on the collected data. For example, the analysis unit comprehensively proposes a water management plan for agricultural land based on the collected data. This enables efficient water management by proposing a water management plan for agricultural land based on the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI execute the proposal of a water management plan.

[0044] The analysis unit can propose the optimal amount and timing of fertilizer use based on the collected data. For example, the analysis unit can propose the optimal amount of fertilizer use based on the collected data. The analysis unit can also propose the optimal timing of fertilizer use based on the collected data. For example, the analysis unit comprehensively proposes the amount and timing of fertilizer use based on the collected data. This enables efficient use of fertilizer by proposing the optimal amount and timing of fertilizer use based on the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI suggest the amount and timing of fertilizer use.

[0045] The analysis unit can predict the harvest time of crops based on the collected data and propose the optimal harvest timing. The analysis unit, for example, predicts the optimal harvest time of crops based on the collected data. The analysis unit can also propose the harvest timing of crops based on the collected data. For example, the analysis unit comprehensively predicts the harvest time of crops based on the collected data and proposes the optimal timing. This improves harvest efficiency by predicting the harvest time of crops based on the collected data and proposing the optimal harvest timing. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected data into the generation AI and cause the generation AI to predict the harvest time and propose the harvest timing.

[0046] The providing unit can provide specific work procedures to farmers based on the analysis results. The providing unit, for example, provides specific irrigation procedures based on the analysis results. The providing unit can also provide specific fertilizer application procedures based on the analysis results. For example, the providing unit provides specific pest prevention procedures based on the analysis results. This allows farmers to work efficiently by providing specific work procedures based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the analysis results into the generation AI and have the generation AI provide specific work procedures.

[0047] The providing unit can provide farmers with a list of necessary materials and equipment based on the analysis results. The providing unit, for example, provides a list of necessary fertilizers based on the analysis results. The providing unit can also provide a list of necessary irrigation equipment based on the analysis results. For example, the providing unit provides a list of necessary pest control materials based on the analysis results. By providing a list of necessary materials and equipment based on the analysis results, farmers can efficiently prepare what they need. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the analysis results into the generation AI and cause the generation AI to provide a list of necessary materials and equipment.

[0048] The providing unit can propose a long-term cultivation plan to the farmer based on the analysis results. The providing unit, for example, proposes a long-term irrigation plan based on the analysis results. The providing unit can also propose a long-term fertilizer use plan based on the analysis results. For example, the providing unit proposes a long-term pest prevention plan based on the analysis results. This allows farmers to practice sustainable agriculture by proposing a long-term cultivation plan based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the analysis results into the generation AI and have the generation AI execute the proposal of a long-term cultivation plan.

[0049] The providing unit can provide farmers with information about local agricultural events and training based on the analysis results. For example, the providing unit provides information about local agricultural events based on the analysis results. The providing unit can also provide information about local agricultural training based on the analysis results. For example, the providing unit provides information about local agricultural seminars based on the analysis results. This allows farmers to obtain the latest information by providing information about local agricultural events and training based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the analysis results into the generation AI and cause the generation AI to provide information about local agricultural events and training.

[0050] The providing unit can provide farmers with a market demand forecast based on the analysis results and propose a sales strategy for the crop. For example, the providing unit can provide a market demand forecast based on the analysis results and propose an optimal sales strategy for the crop. The providing unit can also predict crop price trends based on the analysis results and propose a sales strategy. For example, the providing unit can propose crops with high demand based on the analysis results and provide a sales strategy. This allows farmers to efficiently conduct sales activities by providing a market demand forecast based on the analysis results and proposing a sales strategy for the crop. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, the generation AI, for example. For example, the providing unit can input the analysis results into the generation AI and have the generation AI execute the market demand forecast and propose a sales strategy.

[0051] The providing unit can propose environmentally friendly cultivation methods to farmers based on the analysis results. For example, the providing unit can propose environmentally friendly fertilizer usage methods based on the analysis results. The providing unit can also propose sustainable irrigation methods based on the analysis results. For example, the providing unit can propose environmentally friendly pest prevention methods based on the analysis results. In this way, sustainable agriculture can be achieved by proposing environmentally friendly cultivation methods based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the analysis results into the generation AI and cause the generation AI to propose environmentally friendly cultivation methods.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The analysis unit can analyze the condition of the farmland and the growth status of the crops based on the collected data. The analysis unit, for example, analyzes the condition of the soil based on collected soil humidity data. For example, the analysis unit suggests the timing of irrigation when the soil humidity is low. The analysis unit can also suggest the appropriate type and amount of fertilizer based on collected nutrient data. For example, the analysis unit suggests the appropriate type and amount of fertilizer when the soil is lacking nutrients. The analysis unit can also analyze the growth status of the crops based on collected crop growth data. For example, the analysis unit analyzes the color and growth rate of the crop leaves and suggests the optimal cultivation method. In this way, the analysis unit can suggest the optimal cultivation method by analyzing the condition of the farmland and the growth status of the crops based on the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI analyze the data.

[0054] The providing unit can suggest a cultivation method based on the analysis results of the generating AI. For example, the providing unit can suggest the timing of irrigation if the soil moisture is low based on the analysis results of the generating AI. The providing unit can also suggest the appropriate type and amount of fertilizer if there is a nutrient deficiency based on the analysis results of the generating AI. For example, the providing unit can suggest the appropriate type and amount of fertilizer based on the analysis results of the generating AI. The providing unit can also suggest a cultivation method according to the growth status of the crop based on the analysis results of the generating AI. For example, the providing unit can suggest a cultivation method according to the leaf color and growth rate of the crop based on the analysis results of the generating AI. This allows farmers to efficiently perform farm work by suggesting the optimal cultivation method based on the analysis results of the generating AI. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generating AI. For example, the providing unit can suggest the optimal cultivation method based on the analysis results of the generating AI.

[0055] The providing unit can explain the cultivation method proposed by the generation AI and provide advice in a format that is easy for farmers to implement. For example, the providing unit can explain the cultivation method proposed by the generation AI step by step. The providing unit can also explain the cultivation method proposed by the generation AI using a video tutorial. For example, the providing unit can specifically explain the cultivation method proposed by the generation AI and provide advice in a format that is easy for farmers to implement. The providing unit can also explain the cultivation method proposed by the generation AI in text format. For example, the providing unit can explain the cultivation method proposed by the generation AI in text format and provide advice in a format that is easy for farmers to implement. This allows farmers to perform appropriate farm work even without specialized knowledge by specifically explaining the cultivation method proposed by the generation AI and providing advice in a format that is easy for farmers to implement. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can provide advice in a format that is easy for farmers to implement based on the cultivation method proposed by the generation AI.

[0056] When collecting information on soil conditions and crop growth status, the collection unit can acquire weather data in real time and reflect it in the collected data. For example, when it is raining, the collection unit can acquire soil humidity data in real time and reflect it in the collected data. Furthermore, when it is sunny, the collection unit can also acquire crop growth rate in real time and reflect it in the collected data. For example, the collection unit can update the collected data in real time in response to changes in weather and provide accurate data. This enables more accurate data collection by acquiring weather data in real time and reflecting it in the collected data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather data to a generation AI and have the generation AI reflect the weather data in the collected data.

[0057] The collection unit can integrate data from different sensors to understand the state of the farmland in more detail. For example, the collection unit can integrate data from a soil sensor and a temperature sensor to understand the state of the farmland in more detail. The collection unit can also integrate data from a crop growth sensor and a humidity sensor to understand the growth status of the crops in more detail. For example, the collection unit can integrate data from different sensors in real time to accurately understand the state of the farmland. In this way, by integrating data from different sensors, the state of the farmland can be understood in more detail. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from different sensors into a generation AI and have the generation AI integrate the data.

[0058] The collection unit can detect abnormal values ​​by comparing with past data and issue an alert when an abnormality occurs. For example, the collection unit can issue an alert when soil moisture is abnormally low compared to past data. The collection unit can also issue an alert when crop growth rate is abnormally slow compared to past data. For example, the collection unit can detect abnormal values ​​in real time and issue an alert immediately when an abnormality occurs. This allows for rapid response by detecting abnormal values ​​by comparing with past data and issuing an alert when an abnormality occurs. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can cause the generation AI to perform an analysis to compare with past data to detect abnormal values.

[0059] The analysis unit can predict the risk of pests and diseases of crops based on the collected data and propose preventive measures. For example, the analysis unit can predict the risk of pests and diseases of crops based on the collected data and propose appropriate preventive measures. The analysis unit can also predict the risk of insect damage to crops based on the collected data and propose appropriate preventive measures. For example, the analysis unit can comprehensively predict the risk of pests and diseases of crops based on the collected data and propose optimal preventive measures. This makes it possible to maintain the health of crops by predicting the risk of pests and diseases of crops based on the collected data and proposing preventive measures. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI, for example. For example, the analysis unit can input the collected data into the generation AI and cause the generation AI to predict the risk of pests and diseases and propose preventive measures.

[0060] The analysis unit can compare the growth patterns of different crops and derive the optimal cultivation method. For example, the analysis unit can compare growth data of different crops and derive the optimal cultivation method. The analysis unit can also analyze the growth patterns of different crops and propose the optimal cultivation method. For example, the analysis unit derives the optimal cultivation method based on the growth data of different crops. In this way, the optimal cultivation method can be derived by comparing the growth patterns of different crops. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input growth data of different crops into the generation AI and cause the generation AI to derive the optimal cultivation method.

[0061] The analysis unit can learn from past analysis results and improve the analysis accuracy. The analysis unit can improve the analysis accuracy, for example, based on past analysis results. The analysis unit can also learn from past analysis results and improve the analysis algorithm. For example, the analysis unit continuously improves the analysis accuracy based on past analysis results. In this way, the analysis accuracy can be improved by learning from past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the analysis accuracy.

[0062] The analysis unit can propose a water management plan for agricultural land based on the collected data. The analysis unit, for example, proposes an optimal irrigation schedule based on the collected data. The analysis unit can also propose a plan to optimize water usage based on the collected data. For example, the analysis unit comprehensively proposes a water management plan for agricultural land based on the collected data. This enables efficient water management by proposing a water management plan for agricultural land based on the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI execute the proposal of a water management plan.

[0063] The analysis unit can propose the optimal amount and timing of fertilizer use based on the collected data. For example, the analysis unit can propose the optimal amount of fertilizer use based on the collected data. The analysis unit can also propose the optimal timing of fertilizer use based on the collected data. For example, the analysis unit comprehensively proposes the amount and timing of fertilizer use based on the collected data. This enables efficient use of fertilizer by proposing the optimal amount and timing of fertilizer use based on the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI suggest the amount and timing of fertilizer use.

[0064] The analysis unit can predict the harvest time of crops based on the collected data and propose the optimal harvest timing. The analysis unit, for example, predicts the optimal harvest time of crops based on the collected data. The analysis unit can also propose the harvest timing of crops based on the collected data. For example, the analysis unit comprehensively predicts the harvest time of crops based on the collected data and proposes the optimal timing. This improves harvest efficiency by predicting the harvest time of crops based on the collected data and proposing the optimal harvest timing. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected data into the generation AI and cause the generation AI to predict the harvest time and propose the harvest timing.

[0065] The providing unit can provide specific work procedures to farmers based on the analysis results. The providing unit, for example, provides specific irrigation procedures based on the analysis results. The providing unit can also provide specific fertilizer application procedures based on the analysis results. For example, the providing unit provides specific pest prevention procedures based on the analysis results. This allows farmers to work efficiently by providing specific work procedures based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the analysis results into the generation AI and have the generation AI provide specific work procedures.

[0066] The providing unit can provide farmers with a list of necessary materials and equipment based on the analysis results. The providing unit, for example, provides a list of necessary fertilizers based on the analysis results. The providing unit can also provide a list of necessary irrigation equipment based on the analysis results. For example, the providing unit provides a list of necessary pest control materials based on the analysis results. By providing a list of necessary materials and equipment based on the analysis results, farmers can efficiently prepare what they need. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the analysis results into the generation AI and cause the generation AI to provide a list of necessary materials and equipment.

[0067] The providing unit can propose a long-term cultivation plan to the farmer based on the analysis results. The providing unit, for example, proposes a long-term irrigation plan based on the analysis results. The providing unit can also propose a long-term fertilizer use plan based on the analysis results. For example, the providing unit proposes a long-term pest prevention plan based on the analysis results. This allows farmers to practice sustainable agriculture by proposing a long-term cultivation plan based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the analysis results into the generation AI and have the generation AI execute the proposal of a long-term cultivation plan.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The collection unit collects information about farmland and crops. The collection unit can use sensors to collect information about soil conditions and crop growth. Specifically, it collects data such as soil moisture and nutrient content, and crop leaf color and growth rate. It can also use drones to collect data over a wide area of ​​farmland. Step 2: The analysis unit analyzes the information collected by the collection unit. Using generative AI, the analysis unit analyzes the condition of the farmland and the growth of the crops based on the collected data. For example, if the soil moisture is low, it will suggest the timing of irrigation, and if there is a nutrient deficiency, it will suggest the appropriate type and amount of fertilizer. Step 3: The provision unit provides advice based on the analysis results obtained by the analysis unit. The provision unit proposes the optimal cultivation method based on the analysis results of the generation AI. For example, the provision unit provides specific explanations of the cultivation method proposed by the generation AI and provides advice in a format that is easy for farmers to implement.

[0070] (Example 2) An agricultural support system according to an embodiment of the present invention collects information about farmland and crops, analyzes it using a generating AI, and provides optimal advice. The agricultural support system collects information about farmland and crops, analyzes it using a generating AI, and provides optimal advice based on the results. For example, the agricultural support system uses sensors to collect detailed data such as soil conditions and crop growth status. The generating AI then analyzes the collected data to analyze the farmland condition and crop growth status. For example, the system proposes optimal cultivation methods based on data such as soil moisture, nutrient content, and crop leaf color and growth rate. Furthermore, based on the analysis results of the generating AI, the system explains specific cultivation methods and provides advice in a format that is easy for farmers to implement. This allows farmers, especially new farmers, to acquire agricultural expertise while farming, contributing to an increase in the number of farmers entering the agricultural industry. For example, by performing farm work while receiving advice from the generating AI, new farmers can learn farming efficiently and increase their chances of success. This agricultural support system allows farmers, especially new farmers, to acquire agricultural expertise while farming, contributing to an increase in the number of farmers entering the agricultural industry. For example, by performing farm work while receiving advice from the generating AI, new farmers can learn farming efficiently and increase their chances of success.

[0071] An agricultural support system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information about farmland and crops. The collection unit can collect information about soil conditions and crop growth status using sensors, for example. The collection unit can collect data about soil humidity, nutrient content, crop leaf color, and growth rate. The collection unit can also collect farmland data over a wide area using drones. The analysis unit analyzes the information collected by the collection unit. The analysis unit uses a generation AI to analyze the farmland condition and crop growth status based on the collected data. For example, the generation AI suggests irrigation timing when soil humidity is low, and suggests the appropriate type and amount of fertilizer when nutrients are lacking. The provision unit provides advice based on the analysis results obtained by the analysis unit. The provision unit suggests optimal cultivation methods based on the analysis results of the generation AI. For example, the generation AI specifically explains the cultivation methods proposed by the generation AI and provides advice in a format that is easy for farmers to implement. This allows farmers to perform appropriate farming work even without specialized knowledge. As a result, the agricultural support system of the embodiment collects and analyzes information about farmland and crops and provides optimal advice, allowing farmers, especially new farmers, to farm while acquiring agricultural expertise.

[0072] The collection unit can collect information on the condition of the soil or the growth status of the crops using sensors. For example, the collection unit can collect information on the humidity and nutrient content of the soil using a soil sensor. The collection unit can also collect information on the temperature of the soil or the crops using a temperature sensor. Furthermore, the collection unit can collect information on the humidity in the air using a humidity sensor. For example, the collection unit can measure the humidity of the soil in real time using a soil sensor and collect data. The temperature sensor can measure the temperature of the soil or the crops and collect data. The humidity sensor can measure the humidity in the air and collect data. In this way, by using sensors, detailed information on the condition of the soil and the growth status of the crops can be collected. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data acquired from the sensor into a generation AI and have the generation AI analyze the data.

[0073] The analysis unit can analyze the condition of the farmland and the growth status of the crops based on the collected data. The analysis unit, for example, analyzes the condition of the soil based on collected soil humidity data. For example, the analysis unit suggests the timing of irrigation when the soil humidity is low. The analysis unit can also suggest the appropriate type and amount of fertilizer based on collected nutrient data. For example, the analysis unit suggests the appropriate type and amount of fertilizer when the soil is lacking nutrients. The analysis unit can also analyze the growth status of the crops based on collected crop growth data. For example, the analysis unit analyzes the color and growth rate of the crop leaves and suggests the optimal cultivation method. In this way, the analysis unit can suggest the optimal cultivation method by analyzing the condition of the farmland and the growth status of the crops based on the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI analyze the data.

[0074] The providing unit can suggest a cultivation method based on the analysis results of the generating AI. For example, the providing unit can suggest the timing of irrigation if the soil moisture is low based on the analysis results of the generating AI. The providing unit can also suggest the appropriate type and amount of fertilizer if there is a nutrient deficiency based on the analysis results of the generating AI. For example, the providing unit can suggest the appropriate type and amount of fertilizer based on the analysis results of the generating AI. The providing unit can also suggest a cultivation method according to the growth status of the crop based on the analysis results of the generating AI. For example, the providing unit can suggest a cultivation method according to the leaf color and growth rate of the crop based on the analysis results of the generating AI. This allows farmers to efficiently perform farm work by suggesting the optimal cultivation method based on the analysis results of the generating AI. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generating AI. For example, the providing unit can suggest the optimal cultivation method based on the analysis results of the generating AI.

[0075] The providing unit can explain the cultivation method proposed by the generation AI and provide advice in a format that is easy for farmers to implement. For example, the providing unit can explain the cultivation method proposed by the generation AI step by step. The providing unit can also explain the cultivation method proposed by the generation AI using a video tutorial. For example, the providing unit can specifically explain the cultivation method proposed by the generation AI and provide advice in a format that is easy for farmers to implement. The providing unit can also explain the cultivation method proposed by the generation AI in text format. For example, the providing unit can explain the cultivation method proposed by the generation AI in text format and provide advice in a format that is easy for farmers to implement. This allows farmers to perform appropriate farm work even without specialized knowledge by specifically explaining the cultivation method proposed by the generation AI and providing advice in a format that is easy for farmers to implement. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can provide advice in a format that is easy for farmers to implement based on the cultivation method proposed by the generation AI.

[0076] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the collection unit can adjust the timing of data collection to quickly collect necessary data. This adjusts the timing of data collection according to the user's emotions, reducing the user's burden and enabling efficient data collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0077] When collecting information on soil conditions and crop growth status, the collection unit can acquire weather data in real time and reflect it in the collected data. For example, when it is raining, the collection unit can acquire soil humidity data in real time and reflect it in the collected data. Furthermore, when it is sunny, the collection unit can also acquire crop growth rate in real time and reflect it in the collected data. For example, the collection unit can update the collected data in real time in response to changes in weather and provide accurate data. This enables more accurate data collection by acquiring weather data in real time and reflecting it in the collected data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather data to a generation AI and have the generation AI reflect the weather data in the collected data.

[0078] The collection unit can integrate data from different sensors to understand the state of the farmland in more detail. For example, the collection unit can integrate data from a soil sensor and a temperature sensor to understand the state of the farmland in more detail. The collection unit can also integrate data from a crop growth sensor and a humidity sensor to understand the growth status of the crops in more detail. For example, the collection unit can integrate data from different sensors in real time to accurately understand the state of the farmland. In this way, by integrating data from different sensors, the state of the farmland can be understood in more detail. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from different sensors into a generation AI and have the generation AI integrate the data.

[0079] The collection unit can detect abnormal values ​​by comparing with past data and issue an alert when an abnormality occurs. For example, the collection unit can issue an alert when soil moisture is abnormally low compared to past data. The collection unit can also issue an alert when crop growth rate is abnormally slow compared to past data. For example, the collection unit can detect abnormal values ​​in real time and issue an alert immediately when an abnormality occurs. This allows for rapid response by detecting abnormal values ​​by comparing with past data and issuing an alert when an abnormality occurs. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can cause the generation AI to perform an analysis to compare with past data to detect abnormal values.

[0080] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting only important data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. For example, when the user is in a hurry, the collection unit prioritizes collecting data that can be collected quickly. This enables efficient data collection by determining the priority of data to be collected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0081] The collection unit can use a drone to collect farmland data over a wide area and analyze it in combination with ground sensors. For example, the collection unit can use a drone to collect aerial photography data of a wide area of ​​farmland and analyze it in combination with ground sensor data. The collection unit can also use a drone to collect information on crop growth conditions over a wide area and analyze it in combination with ground sensor data. For example, the collection unit can use a drone to collect information on soil conditions over a wide area and analyze it in combination with ground sensor data. In this way, by using a drone to collect farmland data over a wide area and analyzing it in combination with ground sensors, it is possible to understand the farmland condition in more detail. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data acquired from a drone into a generation AI and have the generation AI analyze the data.

[0082] The collection unit can collect data specialized for a specific region by taking into account the geographic information of the farmland. For example, the collection unit collects soil data for a specific region based on the geographic information of the farmland. The collection unit can also collect crop growth data for a specific region based on the geographic information of the farmland. For example, the collection unit collects weather data for a specific region based on the geographic information of the farmland. This makes it possible to collect data specialized for a specific region by taking into account the geographic information of the farmland. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographic information to the generation AI and cause the generation AI to collect data specialized for a specific region.

[0083] The collection unit can customize the collection method by reflecting the farmer's past feedback. For example, the collection unit customizes the type of data to be collected based on the farmer's past feedback. The collection unit can also customize the frequency of data collection based on the farmer's past feedback. For example, the collection unit customizes the timing of data collection based on the farmer's past feedback. This allows the collection method to be customized by reflecting the farmer's past feedback, enabling more appropriate data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the farmer's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit provides analysis results that focus on the main points. By adjusting the presentation method of the analysis results according to the user's emotions, analysis results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis results.

[0085] The analysis unit can predict the risk of pests and diseases of crops based on the collected data and propose preventive measures. For example, the analysis unit can predict the risk of pests and diseases of crops based on the collected data and propose appropriate preventive measures. The analysis unit can also predict the risk of insect damage to crops based on the collected data and propose appropriate preventive measures. For example, the analysis unit can comprehensively predict the risk of pests and diseases of crops based on the collected data and propose optimal preventive measures. This makes it possible to maintain the health of crops by predicting the risk of pests and diseases of crops based on the collected data and proposing preventive measures. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI, for example. For example, the analysis unit can input the collected data into the generation AI and cause the generation AI to predict the risk of pests and diseases and propose preventive measures.

[0086] The analysis unit can compare the growth patterns of different crops and derive the optimal cultivation method. For example, the analysis unit can compare growth data of different crops and derive the optimal cultivation method. The analysis unit can also analyze the growth patterns of different crops and propose the optimal cultivation method. For example, the analysis unit derives the optimal cultivation method based on the growth data of different crops. In this way, the optimal cultivation method can be derived by comparing the growth patterns of different crops. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input growth data of different crops into the generation AI and cause the generation AI to derive the optimal cultivation method.

[0087] The analysis unit can learn from past analysis results and improve the analysis accuracy. The analysis unit can improve the analysis accuracy, for example, based on past analysis results. The analysis unit can also learn from past analysis results and improve the analysis algorithm. For example, the analysis unit continuously improves the analysis accuracy based on past analysis results. In this way, the analysis accuracy can be improved by learning from past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the analysis accuracy.

[0088] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide concise analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. By adjusting the level of detail of the analysis results according to the user's emotions, optimal information can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the level of detail of the analysis results.

[0089] The analysis unit can propose a water management plan for agricultural land based on the collected data. The analysis unit, for example, proposes an optimal irrigation schedule based on the collected data. The analysis unit can also propose a plan to optimize water usage based on the collected data. For example, the analysis unit comprehensively proposes a water management plan for agricultural land based on the collected data. This enables efficient water management by proposing a water management plan for agricultural land based on the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI execute the proposal of a water management plan.

[0090] The analysis unit can propose the optimal amount and timing of fertilizer use based on the collected data. For example, the analysis unit can propose the optimal amount of fertilizer use based on the collected data. The analysis unit can also propose the optimal timing of fertilizer use based on the collected data. For example, the analysis unit comprehensively proposes the amount and timing of fertilizer use based on the collected data. This enables efficient use of fertilizer by proposing the optimal amount and timing of fertilizer use based on the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI suggest the amount and timing of fertilizer use.

[0091] The analysis unit can predict the harvest time of crops based on the collected data and propose the optimal harvest timing. The analysis unit, for example, predicts the optimal harvest time of crops based on the collected data. The analysis unit can also propose the harvest timing of crops based on the collected data. For example, the analysis unit comprehensively predicts the harvest time of crops based on the collected data and proposes the optimal timing. This improves harvest efficiency by predicting the harvest time of crops based on the collected data and proposing the optimal harvest timing. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected data into the generation AI and cause the generation AI to predict the harvest time and propose the harvest timing.

[0092] The providing unit can estimate the user's emotions and adjust the way advice is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide simple, highly visible advice. The providing unit can also provide detailed advice if the user is relaxed. For example, if the user is in a hurry, the providing unit can provide advice that focuses on the main points. This allows the user to be provided with advice that is easy to understand by adjusting the way the advice is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the advice is presented.

[0093] The providing unit can provide specific work procedures to farmers based on the analysis results. The providing unit, for example, provides specific irrigation procedures based on the analysis results. The providing unit can also provide specific fertilizer application procedures based on the analysis results. For example, the providing unit provides specific pest prevention procedures based on the analysis results. This allows farmers to work efficiently by providing specific work procedures based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the analysis results into the generation AI and have the generation AI provide specific work procedures.

[0094] The providing unit can provide farmers with a list of necessary materials and equipment based on the analysis results. The providing unit, for example, provides a list of necessary fertilizers based on the analysis results. The providing unit can also provide a list of necessary irrigation equipment based on the analysis results. For example, the providing unit provides a list of necessary pest control materials based on the analysis results. By providing a list of necessary materials and equipment based on the analysis results, farmers can efficiently prepare what they need. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the analysis results into the generation AI and cause the generation AI to provide a list of necessary materials and equipment.

[0095] The providing unit can propose a long-term cultivation plan to the farmer based on the analysis results. The providing unit, for example, proposes a long-term irrigation plan based on the analysis results. The providing unit can also propose a long-term fertilizer use plan based on the analysis results. For example, the providing unit proposes a long-term pest prevention plan based on the analysis results. This allows farmers to practice sustainable agriculture by proposing a long-term cultivation plan based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the analysis results into the generation AI and have the generation AI execute the proposal of a long-term cultivation plan.

[0096] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing important advice. Furthermore, if the user is relaxed, the providing unit can prioritize providing detailed advice. For example, if the user is in a hurry, the providing unit can prioritize providing advice that can be implemented quickly. In this way, by determining the priority of advice according to the user's emotions, the advice that is most important to the user can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of advice.

[0097] The providing unit can provide farmers with information about local agricultural events and training based on the analysis results. For example, the providing unit provides information about local agricultural events based on the analysis results. The providing unit can also provide information about local agricultural training based on the analysis results. For example, the providing unit provides information about local agricultural seminars based on the analysis results. This allows farmers to obtain the latest information by providing information about local agricultural events and training based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the analysis results into the generation AI and cause the generation AI to provide information about local agricultural events and training.

[0098] The providing unit can provide farmers with a market demand forecast based on the analysis results and propose a sales strategy for the crop. For example, the providing unit can provide a market demand forecast based on the analysis results and propose an optimal sales strategy for the crop. The providing unit can also predict crop price trends based on the analysis results and propose a sales strategy. For example, the providing unit can propose crops with high demand based on the analysis results and provide a sales strategy. This allows farmers to efficiently conduct sales activities by providing a market demand forecast based on the analysis results and proposing a sales strategy for the crop. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, the generation AI, for example. For example, the providing unit can input the analysis results into the generation AI and have the generation AI execute the market demand forecast and propose a sales strategy.

[0099] The providing unit can propose environmentally friendly cultivation methods to farmers based on the analysis results. For example, the providing unit can propose environmentally friendly fertilizer usage methods based on the analysis results. The providing unit can also propose sustainable irrigation methods based on the analysis results. For example, the providing unit can propose environmentally friendly pest prevention methods based on the analysis results. In this way, sustainable agriculture can be achieved by proposing environmentally friendly cultivation methods based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the analysis results into the generation AI and cause the generation AI to propose environmentally friendly cultivation methods. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 collection unit collects information on the soil condition and the growth status of the crops using the camera 42 and sensors of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the collected data is analyzed by a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and proposes an optimal cultivation method based on the analysis results of the generation AI. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 collection unit collects information on the soil condition and the growth status of crops using the camera 42 and sensors of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the collected data is analyzed by a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and proposes an optimal cultivation method based on the analysis results of the generation AI. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 collection unit collects information on the condition of the soil and the growth status of the crops using the camera 42 and sensors of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the collected data is analyzed by a generation AI. The provision unit is realized, for example, by the control unit 46A of the headset terminal 314, and proposes an optimal cultivation method based on the results of the analysis by the generation AI. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information on the condition of the soil and the growth status of the crops using the camera 42 and sensors of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the collected data is analyzed by the generation AI. The provision unit is realized, for example, by the control unit 46A of the robot 414, and proposes an optimal cultivation method based on the results of the analysis by the generation AI.

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

[0101] The analysis unit can analyze the condition of the farmland and the growth status of the crops based on the collected data. The analysis unit, for example, analyzes the condition of the soil based on collected soil humidity data. For example, the analysis unit suggests the timing of irrigation when the soil humidity is low. The analysis unit can also suggest the appropriate type and amount of fertilizer based on collected nutrient data. For example, the analysis unit suggests the appropriate type and amount of fertilizer when the soil is lacking nutrients. The analysis unit can also analyze the growth status of the crops based on collected crop growth data. For example, the analysis unit analyzes the color and growth rate of the crop leaves and suggests the optimal cultivation method. In this way, the analysis unit can suggest the optimal cultivation method by analyzing the condition of the farmland and the growth status of the crops based on the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI analyze the data.

[0102] The providing unit can suggest a cultivation method based on the analysis results of the generating AI. For example, the providing unit can suggest the timing of irrigation if the soil moisture is low based on the analysis results of the generating AI. The providing unit can also suggest the appropriate type and amount of fertilizer if there is a nutrient deficiency based on the analysis results of the generating AI. For example, the providing unit can suggest the appropriate type and amount of fertilizer based on the analysis results of the generating AI. The providing unit can also suggest a cultivation method according to the growth status of the crop based on the analysis results of the generating AI. For example, the providing unit can suggest a cultivation method according to the leaf color and growth rate of the crop based on the analysis results of the generating AI. This allows farmers to efficiently perform farm work by suggesting the optimal cultivation method based on the analysis results of the generating AI. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generating AI. For example, the providing unit can suggest the optimal cultivation method based on the analysis results of the generating AI.

[0103] The providing unit can explain the cultivation method proposed by the generation AI and provide advice in a format that is easy for farmers to implement. For example, the providing unit can explain the cultivation method proposed by the generation AI step by step. The providing unit can also explain the cultivation method proposed by the generation AI using a video tutorial. For example, the providing unit can specifically explain the cultivation method proposed by the generation AI and provide advice in a format that is easy for farmers to implement. The providing unit can also explain the cultivation method proposed by the generation AI in text format. For example, the providing unit can explain the cultivation method proposed by the generation AI in text format and provide advice in a format that is easy for farmers to implement. This allows farmers to perform appropriate farm work even without specialized knowledge by specifically explaining the cultivation method proposed by the generation AI and providing advice in a format that is easy for farmers to implement. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can provide advice in a format that is easy for farmers to implement based on the cultivation method proposed by the generation AI.

[0104] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the collection unit can adjust the timing of data collection to quickly collect necessary data. This adjusts the timing of data collection according to the user's emotions, reducing the user's burden and enabling efficient data collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0105] When collecting information on soil conditions and crop growth status, the collection unit can acquire weather data in real time and reflect it in the collected data. For example, when it is raining, the collection unit can acquire soil humidity data in real time and reflect it in the collected data. Furthermore, when it is sunny, the collection unit can also acquire crop growth rate in real time and reflect it in the collected data. For example, the collection unit can update the collected data in real time in response to changes in weather and provide accurate data. This enables more accurate data collection by acquiring weather data in real time and reflecting it in the collected data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather data to a generation AI and have the generation AI reflect the weather data in the collected data.

[0106] The collection unit can integrate data from different sensors to understand the state of the farmland in more detail. For example, the collection unit can integrate data from a soil sensor and a temperature sensor to understand the state of the farmland in more detail. The collection unit can also integrate data from a crop growth sensor and a humidity sensor to understand the growth status of the crops in more detail. For example, the collection unit can integrate data from different sensors in real time to accurately understand the state of the farmland. In this way, by integrating data from different sensors, the state of the farmland can be understood in more detail. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from different sensors into a generation AI and have the generation AI integrate the data.

[0107] The collection unit can detect abnormal values ​​by comparing with past data and issue an alert when an abnormality occurs. For example, the collection unit can issue an alert when soil moisture is abnormally low compared to past data. The collection unit can also issue an alert when crop growth rate is abnormally slow compared to past data. For example, the collection unit can detect abnormal values ​​in real time and issue an alert immediately when an abnormality occurs. This allows for rapid response by detecting abnormal values ​​by comparing with past data and issuing an alert when an abnormality occurs. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can cause the generation AI to perform an analysis to compare with past data to detect abnormal values.

[0108] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting only important data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. For example, when the user is in a hurry, the collection unit prioritizes collecting data that can be collected quickly. This enables efficient data collection by determining the priority of data to be collected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0109] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit provides analysis results that focus on the main points. By adjusting the presentation method of the analysis results according to the user's emotions, analysis results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis results.

[0110] The analysis unit can predict the risk of pests and diseases of crops based on the collected data and propose preventive measures. For example, the analysis unit can predict the risk of pests and diseases of crops based on the collected data and propose appropriate preventive measures. The analysis unit can also predict the risk of insect damage to crops based on the collected data and propose appropriate preventive measures. For example, the analysis unit can comprehensively predict the risk of pests and diseases of crops based on the collected data and propose optimal preventive measures. This makes it possible to maintain the health of crops by predicting the risk of pests and diseases of crops based on the collected data and proposing preventive measures. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI, for example. For example, the analysis unit can input the collected data into the generation AI and cause the generation AI to predict the risk of pests and diseases and propose preventive measures.

[0111] The analysis unit can compare the growth patterns of different crops and derive the optimal cultivation method. For example, the analysis unit can compare growth data of different crops and derive the optimal cultivation method. The analysis unit can also analyze the growth patterns of different crops and propose the optimal cultivation method. For example, the analysis unit derives the optimal cultivation method based on the growth data of different crops. In this way, the optimal cultivation method can be derived by comparing the growth patterns of different crops. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input growth data of different crops into the generation AI and cause the generation AI to derive the optimal cultivation method.

[0112] The analysis unit can learn from past analysis results and improve the analysis accuracy. The analysis unit can improve the analysis accuracy, for example, based on past analysis results. The analysis unit can also learn from past analysis results and improve the analysis algorithm. For example, the analysis unit continuously improves the analysis accuracy based on past analysis results. In this way, the analysis accuracy can be improved by learning from past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the analysis accuracy.

[0113] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide concise analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. By adjusting the level of detail of the analysis results according to the user's emotions, optimal information can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the level of detail of the analysis results.

[0114] The analysis unit can propose a water management plan for agricultural land based on the collected data. The analysis unit, for example, proposes an optimal irrigation schedule based on the collected data. The analysis unit can also propose a plan to optimize water usage based on the collected data. For example, the analysis unit comprehensively proposes a water management plan for agricultural land based on the collected data. This enables efficient water management by proposing a water management plan for agricultural land based on the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI execute the proposal of a water management plan.

[0115] The analysis unit can propose the optimal amount and timing of fertilizer use based on the collected data. For example, the analysis unit can propose the optimal amount of fertilizer use based on the collected data. The analysis unit can also propose the optimal timing of fertilizer use based on the collected data. For example, the analysis unit comprehensively proposes the amount and timing of fertilizer use based on the collected data. This enables efficient use of fertilizer by proposing the optimal amount and timing of fertilizer use based on the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI suggest the amount and timing of fertilizer use.

[0116] The analysis unit can predict the harvest time of crops based on the collected data and propose the optimal harvest timing. The analysis unit, for example, predicts the optimal harvest time of crops based on the collected data. The analysis unit can also propose the harvest timing of crops based on the collected data. For example, the analysis unit comprehensively predicts the harvest time of crops based on the collected data and proposes the optimal timing. This improves harvest efficiency by predicting the harvest time of crops based on the collected data and proposing the optimal harvest timing. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected data into the generation AI and cause the generation AI to predict the harvest time and propose the harvest timing.

[0117] The providing unit can estimate the user's emotions and adjust the way advice is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide simple, highly visible advice. The providing unit can also provide detailed advice if the user is relaxed. For example, if the user is in a hurry, the providing unit can provide advice that focuses on the main points. This allows the user to be provided with advice that is easy to understand by adjusting the way the advice is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the advice is presented.

[0118] The providing unit can provide specific work procedures to farmers based on the analysis results. The providing unit, for example, provides specific irrigation procedures based on the analysis results. The providing unit can also provide specific fertilizer application procedures based on the analysis results. For example, the providing unit provides specific pest prevention procedures based on the analysis results. This allows farmers to work efficiently by providing specific work procedures based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the analysis results into the generation AI and have the generation AI provide specific work procedures.

[0119] The providing unit can provide farmers with a list of necessary materials and equipment based on the analysis results. The providing unit, for example, provides a list of necessary fertilizers based on the analysis results. The providing unit can also provide a list of necessary irrigation equipment based on the analysis results. For example, the providing unit provides a list of necessary pest control materials based on the analysis results. By providing a list of necessary materials and equipment based on the analysis results, farmers can efficiently prepare what they need. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the analysis results into the generation AI and cause the generation AI to provide a list of necessary materials and equipment.

[0120] The providing unit can propose a long-term cultivation plan to the farmer based on the analysis results. The providing unit, for example, proposes a long-term irrigation plan based on the analysis results. The providing unit can also propose a long-term fertilizer use plan based on the analysis results. For example, the providing unit proposes a long-term pest prevention plan based on the analysis results. This allows farmers to practice sustainable agriculture by proposing a long-term cultivation plan based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the analysis results into the generation AI and have the generation AI execute the proposal of a long-term cultivation plan.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection unit collects information about farmland and crops. The collection unit can use sensors to collect information about soil conditions and crop growth. Specifically, it collects data such as soil moisture and nutrient content, and crop leaf color and growth rate. It can also use drones to collect data over a wide area of ​​farmland. Step 2: The analysis unit analyzes the information collected by the collection unit. Using generative AI, the analysis unit analyzes the condition of the farmland and the growth of the crops based on the collected data. For example, if the soil moisture is low, it will suggest the timing of irrigation, and if there is a nutrient deficiency, it will suggest the appropriate type and amount of fertilizer. Step 3: The provision unit provides advice based on the analysis results obtained by the analysis unit. The provision unit proposes the optimal cultivation method based on the analysis results of the generation AI. For example, the provision unit provides specific explanations of the cultivation method proposed by the generation AI and provides advice in a format that is easy for farmers to implement.

[0123] 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.

[0124] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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, to avoid confusion and 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 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 collection department that collects information on farmland and crops; an analysis unit that analyzes the information collected by the collection unit; a providing unit that provides advice based on the analysis result obtained by the analyzing unit; Equipped with A system characterized by:

2. The collecting unit Use sensors to collect information on soil conditions or crop growth 2. The system of claim 1.

3. The analysis unit Analyzing the condition of farmland and the growth of crops based on collected data 2. The system of claim 1.

4. The providing unit Propose cultivation methods based on the results of the generative AI analysis 2. The system of claim 1.

5. The providing unit The generative AI explains the cultivation methods it proposes and provides advice in a format that is easy for farmers to implement.

2. The system of claim 1.

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit When collecting information on soil conditions and crop growth, weather data is acquired in real time and reflected in the collected data.

2. The system of claim 1.

8. The collecting unit Integrating data from different sensors to understand the condition of farmland in more detail 2. The system of claim 1.

9. The collecting unit Compare with past data to detect abnormal values ​​and issue an alert if an abnormality occurs.

2. The system of claim 1.

Images