System

Abstract

An object of a system according to an embodiment is to efficiently monitor a growth condition of a crop and propose an optimal timing of irrigation or fertilization.SOLUTION: A system includes a monitoring unit and a proposal unit. The monitoring unit monitors a growth condition of the crop using the generated AI. The suggestion unit suggests an optimal irrigation or fertilization timing based on the growth condition of the crop monitored by the monitoring 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 technology has the problem that it is difficult to efficiently monitor the growth status of agricultural crops and determine the optimal timing for irrigation and fertilization.

[0005] The system according to the embodiment aims to efficiently monitor the growth status of agricultural crops and propose optimal timing for irrigation and fertilization. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit and a proposing unit. The monitoring unit monitors the growth status of agricultural crops using a generating AI. The proposing unit proposes optimal timing for irrigation and fertilization based on the growth status of the agricultural crops monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently monitor the growth status of agricultural crops and propose optimal timing for irrigation and fertilization. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The agricultural support system according to an embodiment of the present invention is a system that uses generative AI to streamline various agricultural tasks and realize optimal agricultural management through data analysis. As a result, the agricultural support system enables farmers, agricultural companies, and government agencies to farm efficiently and maximize harvests.

[0029] An agricultural support system according to an embodiment includes a monitoring unit that monitors the growth status of agricultural crops using a generation AI, and a proposal unit. The monitoring unit monitors the growth status of agricultural crops using the generation AI. For example, the generation AI monitors the leaf color and growth rate of the crops and detects the presence or absence of disease. The generation AI can also analyze meteorological and soil data to comprehensively evaluate the growth status of the crops. The generation AI uses, for example, deep learning-based AI or an AI with image recognition capabilities. The proposal unit proposes optimal irrigation and fertilization timing based on the growth status of the crops monitored by the monitoring unit. For example, the proposal unit proposes irrigation timing and fertilization amounts based on data analyzed by the generation AI. The proposal unit can also propose optimal crop selection and cultivation methods by using the generation AI to analyze past harvest data and meteorological data. This allows for efficient monitoring of crop growth status and proposal of optimal irrigation and fertilization timing, thereby improving the efficiency of agricultural work.

[0030] The monitoring unit monitors the growth status of agricultural crops in real time and can immediately issue an alert if an abnormality is detected. The monitoring unit, for example, uses generation AI to monitor the growth status of agricultural crops in real time. For example, generation AI detects changes in leaf color and shape to detect the occurrence of pests and diseases early. Generation AI also analyzes weather data and soil data in real time and immediately issues an alert if an abnormality is detected. Alerts are sent to farmers, for example, via email notification or app notification. This allows for a rapid response by issuing an alert immediately if an abnormality is detected.

[0031] When monitoring the growth status of agricultural crops, the monitoring unit uses a drone to collect image data from the air, allowing for a more accurate understanding of the growth status. The monitoring unit, for example, uses a drone to monitor the growth status of agricultural crops. For example, the drone collects high-resolution image data from the air, and the generation AI analyzes that image data. The drone can also be equipped with an infrared camera to obtain a detailed understanding of the health of the crops. The generation AI evaluates the growth status of the crops based on the collected image data and identifies any abnormalities. This makes it possible to use a drone to scan a wide area of ​​farmland in a short period of time and identify any abnormalities.

[0032] The proposal unit can monitor not only the growth status of crops but also the health of livestock and propose optimal rearing methods. The proposal unit, for example, uses generative AI to monitor the health of livestock. For example, generative AI analyzes the body temperature and behavior patterns of livestock to detect illness early. Generative AI can also monitor changes in the appetite and weight of livestock and propose optimal rearing methods. Generative AI uses, for example, AI that uses deep learning or AI with image recognition capabilities. This makes it possible to monitor the health of both crops and livestock and propose optimal management methods, thereby improving the efficiency of agriculture as a whole.

[0033] The proposal unit not only monitors the growth status of agricultural crops, but also monitors the nutrient state and moisture content of the soil in real time, and can propose optimal fertilization and irrigation. The proposal unit, for example, uses a generation AI to monitor the nutrient state and moisture content of the soil in real time. For example, the generation AI analyzes data collected from a soil sensor and evaluates the nutrient state and moisture content of the soil. The generation AI also comprehensively analyzes weather data and the growth status of agricultural crops, and proposes optimal fertilization and irrigation timing. This makes it possible to optimize crop growth by monitoring the nutrient state and moisture content of the soil in real time and proposing optimal fertilization and irrigation.

[0034] Based on data, generative AI can optimize agricultural machinery maintenance schedules and prevent breakdowns before they occur. For example, generative AI can analyze agricultural machinery usage history and failure data to propose optimal maintenance schedules. For example, generative AI can monitor the frequency of use and operating conditions of machinery, optimizing the timing of part replacement and inspection. Generative AI can also detect abnormal sounds and malfunctions, and discover early signs of breakdowns. This allows for the optimization of agricultural machinery maintenance schedules and the prevention of breakdowns, thereby improving the efficiency of agricultural work.

[0035] Generative AI can use data to analyze not only agricultural data, but also fisheries and forestry data, and propose optimal management methods. For example, generative AI can analyze fisheries harvest data, seawater temperature, and fish detection data to propose optimal fisheries management methods. Generative AI can also analyze forestry growth data and disease information to propose optimal forestry management methods. Generative AI uses, for example, AI that uses deep learning or AI with image recognition capabilities. This allows for integrated analysis of agricultural data, fisheries data, and forestry data, and proposes comprehensive management methods, thereby improving the efficiency of each industry.

[0036] Based on the data, generative AI can propose optimal agricultural policies for each region and provide them to government agencies. For example, generative AI can analyze local weather and soil data to propose optimal crop selection and cultivation methods. Generative AI can also propose agricultural policies such as subsidy allocation and recommended crop varieties based on local agricultural data. Generative AI uses, for example, AI that uses deep learning or AI with image recognition capabilities. This allows the proposal of optimal agricultural policies for each region and the provision of these to government agencies, thereby improving the efficiency of agriculture in each region.

[0037] Based on data, generative AI can analyze not only harvest data and weather data, but also market trends and consumer preference data to select the most suitable crops. For example, generative AI analyzes past harvest data and weather data to select the most suitable crops. Generative AI can also analyze market trends and consumer preference data to select crops according to demand. Generative AI uses, for example, AI that uses deep learning or AI with image recognition capabilities. This makes it possible to select crops according to demand by analyzing market trends and consumer preference data.

[0038] Based on the data and the results of agricultural data analysis, generative AI can propose optimal agricultural policies for each region and provide them to government agencies. For example, generative AI can analyze regional weather and soil data to propose optimal crop selection and cultivation methods. Generative AI can also propose agricultural policies such as subsidy allocation and recommended crop varieties based on regional agricultural data. Generative AI uses, for example, AI that uses deep learning or AI with image recognition capabilities. This allows for the proposal of optimal agricultural policies for each region and the provision of these to government agencies, thereby improving the efficiency of agriculture in each region.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The agricultural support system can also be equipped with a voice recognition unit to monitor the growth status of agricultural crops. The voice recognition unit collects information on growth status and abnormalities reported verbally by farmers, and the generation AI analyzes that information. For example, if a farmer reports that "the leaves are turning yellow," the voice recognition unit converts that information into text data, and the generation AI analyzes the cause. The voice recognition unit also improves work efficiency by allowing farmers to input information without using their hands while working. Furthermore, the voice recognition unit can accumulate information previously reported by farmers, and the generation AI can use that data to predict future abnormalities.

[0041] The agricultural support system can also be equipped with a temperature sensor unit to monitor the growth status of crops. The temperature sensor unit monitors the temperature around the crops in real time, and the generation AI analyzes the data. For example, the temperature sensor unit can detect sudden temperature changes, and the generation AI evaluates their impact. The temperature sensor unit can also control cooling and heating devices to maintain the optimal growth temperature for crops. This automates temperature management and optimizes the growth environment for crops.

[0042] The agricultural support system can also be equipped with a light sensor unit to monitor the growth status of crops. The light sensor unit monitors the intensity and wavelength of light falling on the crops in real time, and the generation AI analyzes the data. For example, the light sensor unit can detect insufficient or excessive sunlight, and the generation AI evaluates the impact. The light sensor unit can also control shading devices and lighting devices to maintain an optimal light environment for crops. This automates light management and optimizes the growth environment for crops.

[0043] The agricultural support system can also be equipped with a humidity sensor unit to monitor the growth status of agricultural crops. The humidity sensor unit monitors the humidity around the crops in real time, and the generation AI analyzes the data. For example, the humidity sensor unit detects excessive humidity or dryness, and the generation AI evaluates the impact. The humidity sensor unit can also control humidifiers and dehumidifiers to maintain an optimal humidity environment for the crops. This automates humidity management and optimizes the growth environment for the crops.

[0044] The agricultural support system can also be equipped with a wind speed sensor unit to monitor the growth status of agricultural crops. The wind speed sensor unit monitors the wind speed around the crops in real time, and the generation AI analyzes the data. For example, the wind speed sensor unit detects strong winds or no wind, and the generation AI evaluates their impact. The wind speed sensor unit can also control windbreaks and blowers to maintain an optimal wind environment for the crops. This automates wind speed management and optimizes the growth environment for the crops.

[0045] The agricultural support system can also be equipped with a soil pH sensor unit to monitor the growth status of agricultural crops. The soil pH sensor unit monitors the pH value of the soil around the crops in real time, and the generation AI analyzes the data. For example, the soil pH sensor unit detects changes in acidity or alkalinity, and the generation AI evaluates the impact. The soil pH sensor unit can also control acidity and alkalinity regulators to maintain an optimal pH environment for the crops. This automates soil pH management and optimizes the growth environment for the crops.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The monitoring unit uses the generation AI to monitor the growth status of the crops. The generation AI monitors the color and growth rate of the crop leaves and detects the presence or absence of disease. The generation AI also analyzes weather and soil data to comprehensively evaluate the growth status of the crops. The generation AI uses AI that uses deep learning and AI with image recognition capabilities. Step 2: The proposal unit proposes optimal irrigation and fertilization timing based on the crop growth status monitored by the monitoring unit. The proposal unit proposes irrigation timing and fertilization amounts based on the data analyzed by the generation AI. The proposal unit can also propose optimal crop selection and cultivation methods based on the generation AI's analysis of past harvest data and weather data.

[0048] (Example 2) The agricultural support system according to an embodiment of the present invention is a system that uses generative AI to streamline various agricultural tasks and realize optimal agricultural management through data analysis. As a result, the agricultural support system enables farmers, agricultural companies, and government agencies to farm efficiently and maximize harvests.

[0049] An agricultural support system according to an embodiment includes a monitoring unit that monitors the growth status of agricultural crops using a generation AI, and a proposal unit. The monitoring unit monitors the growth status of agricultural crops using the generation AI. For example, the generation AI monitors the leaf color and growth rate of the crops and detects the presence or absence of disease. The generation AI can also analyze meteorological and soil data to comprehensively evaluate the growth status of the crops. The generation AI uses, for example, deep learning-based AI or an AI with image recognition capabilities. The proposal unit proposes optimal irrigation and fertilization timing based on the growth status of the crops monitored by the monitoring unit. For example, the proposal unit proposes irrigation timing and fertilization amounts based on data analyzed by the generation AI. The proposal unit can also propose optimal crop selection and cultivation methods by using the generation AI to analyze past harvest data and meteorological data. This allows for efficient monitoring of crop growth status and proposal of optimal irrigation and fertilization timing, thereby improving the efficiency of agricultural work.

[0050] The monitoring unit monitors the growth status of agricultural crops in real time and can immediately issue an alert if an abnormality is detected. The monitoring unit, for example, uses generation AI to monitor the growth status of agricultural crops in real time. For example, generation AI detects changes in leaf color and shape to detect the occurrence of pests and diseases early. Generation AI also analyzes weather data and soil data in real time and immediately issues an alert if an abnormality is detected. Alerts are sent to farmers, for example, via email notification or app notification. This allows for a rapid response by issuing an alert immediately if an abnormality is detected.

[0051] When monitoring the growth status of agricultural crops, the monitoring unit uses a drone to collect image data from the air, allowing for a more accurate understanding of the growth status. The monitoring unit, for example, uses a drone to monitor the growth status of agricultural crops. For example, the drone collects high-resolution image data from the air, and the generation AI analyzes that image data. The drone can also be equipped with an infrared camera to obtain a detailed understanding of the health of the crops. The generation AI evaluates the growth status of the crops based on the collected image data and identifies any abnormalities. This makes it possible to use a drone to scan a wide area of ​​farmland in a short period of time and identify any abnormalities.

[0052] The proposal unit can monitor not only the growth status of crops but also the health of livestock and propose optimal rearing methods. The proposal unit, for example, uses generative AI to monitor the health of livestock. For example, generative AI analyzes the body temperature and behavior patterns of livestock to detect illness early. Generative AI can also monitor changes in the appetite and weight of livestock and propose optimal rearing methods. Generative AI uses, for example, AI that uses deep learning or AI with image recognition capabilities. This makes it possible to monitor the health of both crops and livestock and propose optimal management methods, thereby improving the efficiency of agriculture as a whole.

[0053] The proposal unit not only monitors the growth status of agricultural crops, but also monitors the nutrient state and moisture content of the soil in real time, and can propose optimal fertilization and irrigation. The proposal unit, for example, uses a generation AI to monitor the nutrient state and moisture content of the soil in real time. For example, the generation AI analyzes data collected from a soil sensor and evaluates the nutrient state and moisture content of the soil. The generation AI also comprehensively analyzes weather data and the growth status of agricultural crops, and proposes optimal fertilization and irrigation timing. This makes it possible to optimize crop growth by monitoring the nutrient state and moisture content of the soil in real time and proposing optimal fertilization and irrigation.

[0054] Based on data, generative AI can optimize agricultural machinery maintenance schedules and prevent breakdowns before they occur. For example, generative AI can analyze agricultural machinery usage history and failure data to propose optimal maintenance schedules. For example, generative AI can monitor the frequency of use and operating conditions of machinery, optimizing the timing of part replacement and inspection. Generative AI can also detect abnormal sounds and malfunctions, and discover early signs of breakdowns. This allows for the optimization of agricultural machinery maintenance schedules and the prevention of breakdowns, thereby improving the efficiency of agricultural work.

[0055] Generative AI can use data to analyze not only agricultural data, but also fisheries and forestry data, and propose optimal management methods. For example, generative AI can analyze fisheries harvest data, seawater temperature, and fish detection data to propose optimal fisheries management methods. Generative AI can also analyze forestry growth data and disease information to propose optimal forestry management methods. Generative AI uses, for example, AI that uses deep learning or AI with image recognition capabilities. This allows for integrated analysis of agricultural data, fisheries data, and forestry data, and proposes comprehensive management methods, thereby improving the efficiency of each industry.

[0056] Based on the data, generative AI can propose optimal agricultural policies for each region and provide them to government agencies. For example, generative AI can analyze local weather and soil data to propose optimal crop selection and cultivation methods. Generative AI can also propose agricultural policies such as subsidy allocation and recommended crop varieties based on local agricultural data. Generative AI uses, for example, AI that uses deep learning or AI with image recognition capabilities. This allows the proposal of optimal agricultural policies for each region and the provision of these to government agencies, thereby improving the efficiency of agriculture in each region.

[0057] The emotion estimation function collects farmers' emotional responses to the results of agricultural data analysis and can improve the accuracy of the analysis results based on the emotional responses. For example, the emotion estimation function captures the farmer's facial expressions with a camera and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions. The emotion estimation function also records the farmer's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The emotion estimation function also collects farmers' biometric data (heart rate and electrodermal activity) with sensors and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on fluctuations in heart rate. This improves the accuracy of the analysis results based on farmers' emotional responses, enabling more practical agricultural management.

[0058] Based on data, generative AI can analyze not only harvest data and weather data, but also market trends and consumer preference data to select the most suitable crops. For example, generative AI analyzes past harvest data and weather data to select the most suitable crops. Generative AI can also analyze market trends and consumer preference data to select crops according to demand. Generative AI uses, for example, AI that uses deep learning or AI with image recognition capabilities. This makes it possible to select crops according to demand by analyzing market trends and consumer preference data.

[0059] The emotion estimation function can analyze farmers' emotional data and suggest crop selection and cultivation methods that are emotionally positive. For example, the emotion estimation function captures the farmer's facial expressions with a camera and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions. The emotion estimation function also records the farmer's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of their voice and calculates an emotion score. The emotion estimation function also collects farmers' biometric data (heart rate and electrodermal activity) with sensors and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on fluctuations in heart rate. This makes it possible to suggest crop selection and cultivation methods that elicit positive emotions based on farmers' emotional data, thereby improving the efficiency of agricultural work.

[0060] Based on the data and the results of agricultural data analysis, generative AI can propose optimal agricultural policies for each region and provide them to government agencies. For example, generative AI can analyze regional weather and soil data to propose optimal crop selection and cultivation methods. Generative AI can also propose agricultural policies such as subsidy allocation and recommended crop varieties based on regional agricultural data. Generative AI uses, for example, AI that uses deep learning or AI with image recognition capabilities. This allows for the proposal of optimal agricultural policies for each region and the provision of these to government agencies, thereby improving the efficiency of agriculture in each region.

[0061] The emotion estimation function collects farmers' emotional responses to the results of agricultural data analysis and can improve the accuracy of the analysis results based on the emotional responses. For example, the emotion estimation function captures the farmer's facial expressions with a camera and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions. The emotion estimation function also records the farmer's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The emotion estimation function also collects farmers' biometric data (heart rate and electrodermal activity) with sensors and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on fluctuations in heart rate. This improves the accuracy of the analysis results based on farmers' emotional responses, enabling more practical agricultural management.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The agricultural support system can also be equipped with a voice recognition unit to monitor the growth status of agricultural crops. The voice recognition unit collects information on growth status and abnormalities reported verbally by farmers, and the generation AI analyzes that information. For example, if a farmer reports that "the leaves are turning yellow," the voice recognition unit converts that information into text data, and the generation AI analyzes the cause. The voice recognition unit also improves work efficiency by allowing farmers to input information without using their hands while working. Furthermore, the voice recognition unit can accumulate information previously reported by farmers, and the generation AI can use that data to predict future abnormalities.

[0064] The agricultural support system can also be equipped with a temperature sensor unit to monitor the growth status of crops. The temperature sensor unit monitors the temperature around the crops in real time, and the generation AI analyzes the data. For example, the temperature sensor unit can detect sudden temperature changes, and the generation AI evaluates their impact. The temperature sensor unit can also control cooling and heating devices to maintain the optimal growth temperature for crops. This automates temperature management and optimizes the growth environment for crops.

[0065] The agricultural support system can also be equipped with an emotion estimation function to monitor the growth status of crops. The emotion estimation function estimates the farmer's emotions and evaluates the growth status of crops based on the estimated emotions. For example, if the farmer is feeling stressed, the emotion estimation function provides that information to the generation AI, which then evaluates the impact on the growth status of crops. Furthermore, if the farmer has positive emotions, the emotion estimation function can also use that information to allow the generation AI to make suggestions to optimize the growth status of crops. This makes it possible to manage agriculture in a way that takes the farmer's emotions into account.

[0066] The agricultural support system can also be equipped with a light sensor unit to monitor the growth status of crops. The light sensor unit monitors the intensity and wavelength of light falling on the crops in real time, and the generation AI analyzes the data. For example, the light sensor unit can detect insufficient or excessive sunlight, and the generation AI evaluates the impact. The light sensor unit can also control shading devices and lighting devices to maintain an optimal light environment for crops. This automates light management and optimizes the growth environment for crops.

[0067] The agricultural support system can also be equipped with an emotion estimation function to monitor the growth status of crops. The emotion estimation function estimates the farmer's emotions and evaluates the growth status of crops based on the estimated emotions. For example, if the farmer is feeling anxious, the emotion estimation function provides that information to the generation AI, which then evaluates the impact on the growth status of crops. Furthermore, if the farmer feels relieved, the emotion estimation function can also use that information to allow the generation AI to make suggestions to optimize the growth status of crops. This makes it possible to manage agriculture in a way that takes farmers' emotions into account.

[0068] The agricultural support system can also be equipped with a humidity sensor unit to monitor the growth status of agricultural crops. The humidity sensor unit monitors the humidity around the crops in real time, and the generation AI analyzes the data. For example, the humidity sensor unit detects excessive humidity or dryness, and the generation AI evaluates the impact. The humidity sensor unit can also control humidifiers and dehumidifiers to maintain an optimal humidity environment for the crops. This automates humidity management and optimizes the growth environment for the crops.

[0069] The agricultural support system can also be equipped with an emotion estimation function to monitor the growth status of crops. The emotion estimation function estimates the farmer's emotions and evaluates the growth status of crops based on the estimated emotions. For example, if the farmer is feeling happy, the emotion estimation function provides that information to the generation AI, which then evaluates the impact on the growth status of crops. Furthermore, if the farmer is feeling sad, the emotion estimation function can also use that information to allow the generation AI to make suggestions to optimize the growth status of crops. This makes it possible to manage agriculture in a way that takes the farmer's emotions into account.

[0070] The agricultural support system can also be equipped with a wind speed sensor unit to monitor the growth status of agricultural crops. The wind speed sensor unit monitors the wind speed around the crops in real time, and the generation AI analyzes the data. For example, the wind speed sensor unit detects strong winds or no wind, and the generation AI evaluates their impact. The wind speed sensor unit can also control windbreaks and blowers to maintain an optimal wind environment for the crops. This automates wind speed management and optimizes the growth environment for the crops.

[0071] The agricultural support system can also be equipped with an emotion estimation function to monitor the growth status of crops. The emotion estimation function estimates the farmer's emotions and evaluates the growth status of crops based on the estimated emotions. For example, if the farmer is excited, the emotion estimation function provides that information to the generation AI, which then evaluates the impact on the growth status of crops. Furthermore, if the farmer is calm, the emotion estimation function can also use that information to allow the generation AI to make suggestions to optimize the growth status of crops. This makes it possible to manage agriculture in a way that takes the farmer's emotions into account.

[0072] The agricultural support system can also be equipped with a soil pH sensor unit to monitor the growth status of agricultural crops. The soil pH sensor unit monitors the pH value of the soil around the crops in real time, and the generation AI analyzes the data. For example, the soil pH sensor unit detects changes in acidity or alkalinity, and the generation AI evaluates the impact. The soil pH sensor unit can also control acidity and alkalinity regulators to maintain an optimal pH environment for the crops. This automates soil pH management and optimizes the growth environment for the crops.

[0073] The processing flow of the second embodiment will be briefly explained below.

[0074] Step 1: The monitoring unit uses the generation AI to monitor the growth status of the crops. The generation AI monitors the color and growth rate of the crop leaves and detects the presence or absence of disease. The generation AI also analyzes weather and soil data to comprehensively evaluate the growth status of the crops. The generation AI uses AI that uses deep learning and AI with image recognition capabilities. Step 2: The proposal unit proposes optimal irrigation and fertilization timing based on the crop growth status monitored by the monitoring unit. The proposal unit proposes irrigation timing and fertilization amounts based on the data analyzed by the generation AI. The proposal unit can also propose optimal crop selection and cultivation methods based on the generation AI's analysis of past harvest data and weather data.

[0075] 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.

[0076] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0077] 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.

[0078] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0088] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0103] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] In the robot 414, the processor 46 performs the identification process. 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 robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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."

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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. [Explanation of symbols]

[0142] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A monitoring unit that uses generative AI to monitor the growth status of agricultural crops; a suggestion unit that suggests optimal timing for irrigation and fertilization based on the growth status of the crops monitored by the monitoring unit. A system characterized by:

2. The monitoring unit When monitoring the growth status of the crops, drones are used to collect image data from the air, enabling a more accurate understanding of the growth status.

2. The system of claim 1.

3. The proposal unit In addition to monitoring the growth status of the crops, the system also monitors the nutrient status and moisture content of the soil in real time, and suggests optimal fertilization and irrigation.

2. The system of claim 1.

4. The generated AI is Optimizing agricultural machinery maintenance schedules based on data and preventing breakdowns 2. The system of claim 1.

5. The generated AI is Based on this data, we analyze not only agricultural data but also fisheries and forestry data to propose optimal management methods.

2. The system of claim 1.

6. The emotion estimation function is Collecting farmers' emotional responses to the results of agricultural data analysis and improving the accuracy of the analysis results based on these emotional responses 2. The system of claim 1.

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