System

Abstract

An object of a system according to an embodiment is to create employment in a depopulated area by efficiently producing and selling agricultural crops using AI.SOLUTION: A system includes a monitoring part, an instruction part, a sales part, an improvement part, and an job creation part. The monitoring unit monitors a growth condition of a crop. The instruction unit instructs an optimal timing of irrigation or fertilization based on the growth condition monitored by the monitoring unit. The sales department sells the agricultural products using an existing sales network. The improvement part performs breed improvement. The job creation department creates employment in depopulated areas.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] With conventional technology, there was a problem that efficient production and sales of agricultural products were difficult due to the decline in the number of agricultural workers and their aging.

[0005] The system of the embodiment aims to use AI to improve the efficiency of agricultural production and sales and create jobs in depopulated areas. [Means for solving the problem]

[0006] The system according to the embodiment comprises a monitoring unit, an instruction unit, a sales unit, an improvement unit, and a job creation unit. The monitoring unit monitors the growth status of agricultural crops. The instruction unit instructs the optimal timing for irrigation and fertilization based on the growth status monitored by the monitoring unit. The sales unit sells agricultural products using existing sales networks. The improvement unit improves varieties. The job creation unit creates jobs in depopulated areas. [Effects of the Invention]

[0007] The system of the embodiment utilizes AI to improve the efficiency of agricultural production and sales, and can create jobs in depopulated areas. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The agricultural efficiency system according to an embodiment of the present invention is a system that aims to reduce the number of workers and improve efficiency by introducing AI technology in response to the decline and aging of agricultural workers. As a result, the agricultural efficiency system can improve agricultural efficiency, reduce the number of workers, utilize sales networks, improve varieties, and create jobs in depopulated areas.

[0029] An agricultural efficiency system according to an embodiment includes a monitoring unit, an instruction unit, a sales unit, an improvement unit, and a job creation unit. The monitoring unit monitors the growth status of agricultural crops. For example, the monitoring unit uses sensors to monitor the growth rate, leaf color, and presence of disease of agricultural crops. The monitoring unit can also monitor a wide area of ​​farmland using drones. The monitoring unit can also evaluate the health of agricultural crops using image analysis technology. The instruction unit provides instructions on optimal irrigation and fertilization timing based on the growth status monitored by the monitoring unit. For example, the instruction unit optimizes the irrigation and fertilization timing based on soil moisture, nutrient levels, and weather forecasts. The instruction unit can also calculate the optimal amount of fertilizer to apply for crop growth using AI. The instruction unit can also provide specific work instructions to farmers. The sales unit sells agricultural products using existing sales networks. For example, the sales unit sells agricultural products directly to consumers through online markets. The sales unit can also sell agricultural products through local markets and supermarket chains. Furthermore, the sales department can use an e-commerce platform to improve the efficiency of agricultural product sales. The improvement department performs breeding. For example, the improvement department analyzes genetic data and proposes optimal breeding methods. The improvement department can also analyze growth data during the breeding process and select varieties with superior characteristics. The improvement department can also use AI to improve the efficiency of new variety development. The employment creation department creates jobs in depopulated areas. For example, the employment creation department trains personnel who can operate agricultural machinery and analyze data using AI. The employment creation department can also support the launch of new agricultural projects. The employment creation department can also promote the introduction of remote work. As a result, the agricultural efficiency system according to the embodiment can achieve agricultural efficiency, personnel reduction, utilization of sales networks, variety improvement, and job creation in depopulated areas. For example, it reduces the burden on agricultural workers and improves agricultural productivity. It also improves the efficiency of agricultural product sales and increases sales. It also improves the efficiency of new variety development and strengthens the competitiveness of agriculture. Job creation in depopulated areas is expected to contribute to regional revitalization.

[0030] The instruction unit can analyze microbial activity in the soil and propose optimal soil improvement methods. For example, the instruction unit collects soil samples and builds a system in which AI analyzes microbial activity. For example, it analyzes the type and activity of microorganisms and proposes optimal fertilizers and improvement materials. The instruction unit also develops a system in which AI collects soil microbial activity data in real time and proposes optimal soil improvement methods based on that data. For example, it uses sensors to constantly monitor the condition of the soil. The instruction unit also builds a system in which AI proposes specific steps for soil improvement based on the results of the microbial activity analysis. For example, it proposes fertilization methods to increase specific microorganisms. This makes it possible to maintain soil health and optimize crop growth.

[0031] The instruction unit can visualize the growth status of agricultural crops using a 3D model and make growth predictions. The instruction unit, for example, uses drones and sensors to build a system that visualizes the growth status of agricultural crops using a 3D model. For example, it generates a 3D model based on image data taken periodically. The instruction unit also develops a system in which AI analyzes crop growth data and makes growth predictions based on the 3D model. For example, it predicts growth patterns based on past data and suggests harvest times. The instruction unit also builds a system that uses the 3D model to monitor the growth status of agricultural crops in real time and make growth predictions. For example, it reflects data from sensors in real time. This allows agricultural crop growth to be visually grasped and appropriate management to be carried out.

[0032] The instruction unit can predict agricultural machinery failures and suggest maintenance in advance. For example, the instruction unit will attach sensors to agricultural machinery and build a system in which AI predicts failures. For example, it will analyze vibration and temperature data to detect signs of failure. The instruction unit will also develop a system in which AI analyzes agricultural machinery usage data and predicts failures. For example, it will build a predictive model based on past failure data and suggest maintenance times. The instruction unit will also build a system in which AI automatically generates agricultural machinery maintenance schedules based on failure predictions. For example, it will suggest replacement times for parts with a high risk of failure. This will prevent agricultural machinery failures before they occur and reduce work interruptions.

[0033] The instruction unit can predict the market price of agricultural products and suggest the optimal harvest time. The instruction unit, for example, collects market data and builds a system in which AI predicts the price of agricultural products. For example, it predicts prices based on past price data and demand forecasts. The instruction unit also develops a system in which AI suggests the optimal harvest time based on the market price prediction results. For example, it predicts when prices will rise and gives instructions to harvest at that time. The instruction unit also collects market price prediction data in real time and builds a system in which AI dynamically suggests the optimal harvest time. For example, it adjusts the harvest schedule according to price fluctuations. This makes it possible to optimize the harvest time based on market prices and maximize profits.

[0034] The sales department can analyze logistics data and propose optimal delivery routes. For example, the sales department collects logistics data and builds a system in which AI proposes optimal delivery routes. For example, it calculates the shortest route based on traffic conditions and weather data. The sales department also develops a system in which AI analyzes logistics data and proposes routes to maximize delivery efficiency. For example, it calculates a route that efficiently travels around multiple delivery destinations. The sales department also collects logistics data in real time and builds a system in which AI dynamically proposes optimal delivery routes. For example, it adjusts routes based on traffic congestion and accident information. This makes it possible to maximize logistics efficiency and reduce delivery costs.

[0035] The sales department can analyze consumers' purchasing history and make personalized product suggestions. For example, the sales department collects consumer purchasing history data and builds a system in which AI makes personalized product suggestions. For example, it suggests related products based on past purchase history. The sales department also develops a system in which AI analyzes consumers' purchasing history and makes product suggestions tailored to individual preferences. For example, it makes suggestions based on specific brands or categories. The sales department also collects purchasing history data in real time and builds a system in which AI dynamically makes personalized product suggestions. For example, it makes suggestions tailored to seasons or events. This makes it possible to optimize product suggestions based on consumer preferences and increase purchasing motivation.

[0036] The sales department can monitor the quality of agricultural products in real time and perform quality assurance. For example, the sales department could use sensors to collect quality data on agricultural products in real time, and build a system where AI analyzes that data to perform quality assurance. For example, quality could be evaluated based on temperature and humidity data. The sales department could also develop a system where AI analyzes the quality data on agricultural products and proposes specific steps for quality assurance. For example, it could propose countermeasures if the quality deteriorates. The sales department could also collect quality data in real time, and build a system where AI issues alerts for quality assurance. For example, it could send a notification if the quality falls below a certain standard. This would allow the quality of agricultural products to be maintained and consumer trust to be secured.

[0037] The sales department can analyze consumer reviews and provide feedback for product improvement. For example, the sales department could collect consumer reviews and build a system where AI analyzes the data to provide feedback for product improvement. For example, by classifying reviews into positive and negative. The sales department could also develop a system where AI analyzes consumer reviews and suggests specific product improvements. For example, it could identify frequently pointed out problems and propose solutions. The sales department could also build a system where review data is collected in real time and AI dynamically provides feedback for product improvement. For example, it could analyze each time a new review is posted. This allows the quality of the product to be improved by reflecting consumer opinions.

[0038] The improvement department can analyze climate change data and propose varieties adapted to future climates. For example, the improvement department will collect climate change data and build a system in which AI proposes varieties adapted to future climates. For example, varieties will be selected based on predicted temperature and precipitation data. The improvement department will also develop a system in which AI analyzes climate change data and proposes varieties suitable for specific regions. For example, it will propose drought-resistant varieties suitable for arid regions. The improvement department will also collect climate change data in real time and build a system in which AI dynamically proposes varieties adapted to future climates. For example, it will select varieties suitable for regions that are susceptible to the effects of climate change. This will make it possible to provide varieties that are adaptable to climate change and improve the sustainability of agriculture.

[0039] The improvement department can use gene editing technology to develop varieties that are resistant to specific pests and diseases. For example, the improvement department collects genetic data and builds a system in which AI develops varieties that are resistant to specific pests and diseases. For example, it identifies resistance genes to pests and diseases and improves varieties using gene editing technology. The improvement department also develops a system in which AI analyzes genetic data and suggests varieties that are resistant to specific pests and diseases. For example, it uses gene editing technology to enhance resistance to pests and diseases. The improvement department also builds a system in which AI uses gene editing technology to develop varieties that are resistant to specific pests and diseases. For example, it introduces resistance genes based on pest and disease occurrence data. This makes it possible to provide varieties that are resistant to pests and diseases and improve crop yields.

[0040] The improvement department can analyze agricultural data from different regions and improve varieties specific to the region. For example, the improvement department will collect agricultural data from different regions and build a system in which AI will improve varieties specific to the region. For example, it will select varieties based on the climate and soil conditions of each region. The improvement department will also develop a system in which AI will analyze agricultural data from different regions and propose improvements to varieties suitable for specific regions. For example, it will propose varieties that are resistant to pests and diseases specific to the region. The improvement department will also collect agricultural data from each region in real time and build a system in which AI will dynamically propose improvements to varieties specific to the region. For example, it will select varieties that are suited to climate change in the region. This will enable the provision of varieties that are suitable for local conditions and improve agricultural efficiency.

[0041] The improvement department can analyze growth data from the breeding process and propose optimal cultivation methods. For example, the improvement department will collect growth data from the breeding process and build a system in which AI proposes optimal cultivation methods. For example, it will adjust cultivation methods based on data such as temperature, humidity, and light intensity. The improvement department will also develop a system in which AI analyzes growth data from the breeding process and proposes cultivation methods suitable for specific varieties. For example, it will propose the optimal timing for irrigation and fertilization. The improvement department will also collect growth data in real time and build a system in which AI will dynamically propose optimal cultivation methods. For example, it will adjust cultivation methods based on weather data. This will enable cultivation methods to be optimized based on data from the breeding process, improving the efficiency of variety improvement.

[0042] The Ministry of Employment Creation can analyze resources in depopulated areas and propose agricultural projects unique to the area. For example, the Ministry of Employment Creation will collect resource data from depopulated areas and build a system in which AI proposes agricultural projects unique to the area. For example, projects will be selected based on the region's climate and soil conditions. The Ministry of Employment Creation will also develop a system in which AI analyzes resource data from depopulated areas and proposes agricultural projects suitable for specific areas. For example, it will propose a project to cultivate crops unique to the area. The Ministry of Employment Creation will also collect resource data from depopulated areas in real time and build a system in which AI dynamically proposes agricultural projects unique to the area. For example, it will select projects that respond to climate change in the area. This will make it possible to utilize resources in depopulated areas and promote agricultural projects unique to the area.

[0043] The Ministry of Employment Creation can provide a system that uses remote control technology to operate advanced agricultural machinery even in depopulated areas. For example, the Ministry of Employment Creation could build a system that uses remote control technology to operate advanced agricultural machinery even in depopulated areas. For example, agricultural machinery could be remotely operated via the internet. The Ministry of Employment Creation could also develop a system that uses AI remote control technology to provide farmers in depopulated areas with instructions on how to operate advanced agricultural machinery. For example, it could provide a remote control training program. The Ministry of Employment Creation could also build a system that uses remote control technology to monitor agricultural machinery in depopulated areas in real time and provide AI support for operation. For example, it could provide an alert function to prevent operating errors. This would enable advanced agricultural machinery to be operated even in depopulated areas, improving agricultural efficiency.

[0044] The Job Creation Department can analyze tourism resources in depopulated areas and propose new business models that combine agriculture and tourism. For example, the Job Creation Department could collect tourism resource data in depopulated areas and build a system in which AI would propose business models that combine agriculture and tourism. For example, it could propose tourist farms and agricultural experience tours. The Job Creation Department could also develop a system in which AI would analyze tourism resource data in depopulated areas and propose business models that combine agriculture and tourism that are suitable for specific regions. For example, it could propose tourism programs that use crops unique to the region. The Job Creation Department could also collect tourism resource data in depopulated areas in real time and build a system in which AI would dynamically propose business models that combine agriculture and tourism. For example, it could propose tourism programs tailored to the seasons and events. This could lead to the proposal of new business models that combine agriculture and tourism and promote regional revitalization.

[0045] The Ministry of Employment Creation can analyze educational data from depopulated areas and provide agricultural technology educational programs. For example, the Ministry of Employment Creation could collect educational data from depopulated areas and build a system in which AI provides agricultural technology educational programs. For example, it could provide online courses or on-site training programs. The Ministry of Employment Creation could also develop a system in which AI analyzes educational data from depopulated areas and proposes agricultural technology educational programs suitable for specific areas. For example, it could provide a program that teaches crop cultivation techniques specific to the area. The Ministry of Employment Creation could also collect educational data from depopulated areas in real time and build a system in which AI dynamically provides agricultural technology educational programs. For example, it could provide educational programs that are in line with the latest technological trends. This could provide agricultural technology educational programs based on educational data from depopulated areas and improve local agricultural technology.

[0046] The Ministry of Employment Creation will be able to analyze agricultural data and propose optimal agricultural policies. For example, the Ministry of Employment Creation will build a system that collects agricultural data and uses AI to propose optimal agricultural policies. For example, policies will be proposed based on weather data and growth data. The Ministry of Employment Creation will also develop a system that uses AI to analyze agricultural data and propose agricultural policies suitable for specific regions. For example, policies will be proposed based on the region's unique climate and soil conditions. The Ministry of Employment Creation will also build a system that collects agricultural data in real time and uses AI to dynamically propose optimal agricultural policies. For example, policies will be adjusted in response to climate change and market trends. This will make it possible to propose optimal agricultural policies based on agricultural data and improve agricultural efficiency.

[0047] The Job Creation Department will be able to analyze the entire agricultural supply chain and improve efficiency. For example, the Job Creation Department will collect agricultural supply chain data and build a system where AI will analyze that data to improve efficiency. For example, it will propose optimization of logistics and inventory management. The Job Creation Department will also develop a system where AI will analyze agricultural supply chain data and propose improvements to the efficiency of specific processes. For example, optimizing the process from harvesting to shipping. The Job Creation Department will also build a system where supply chain data will be collected in real time and AI will dynamically make suggestions for efficiency. For example, adjusting production plans based on demand forecasts. This will make it possible to improve the efficiency of the entire agricultural supply chain and reduce costs.

[0048] The Ministry of Employment Creation will be able to combine different agricultural technologies and propose new agricultural methods. For example, the Ministry of Employment Creation will collect data on different agricultural technologies and build a system in which AI will propose new agricultural methods based on that data. For example, it will propose a method that combines pesticide-free cultivation and hydroponic cultivation. The Ministry of Employment Creation will also develop a system in which AI will analyze data on different agricultural technologies and propose agricultural methods suitable for specific conditions. For example, it will propose a combination of technologies according to climate and soil conditions. The Ministry of Employment Creation will also collect data on different agricultural technologies in real time and build a system in which AI will dynamically propose new agricultural methods. For example, it will adjust methods as technology evolves. This will allow new agricultural methods to be proposed by combining different agricultural technologies, improving agricultural efficiency.

[0049] The Ministry of Job Creation will be able to analyze agricultural data and propose sustainable agricultural models. For example, the Ministry of Job Creation will build a system that collects agricultural data and uses AI to propose sustainable agricultural models. For example, it will propose cultivation methods that minimize environmental impact. The Ministry of Job Creation will also develop a system that uses AI to analyze agricultural data and propose sustainable agricultural models suitable for specific regions. For example, it will propose sustainable methods based on the region's climate and soil conditions. The Ministry of Job Creation will also build a system that collects agricultural data in real time and uses AI to dynamically propose sustainable agricultural models. For example, it will adjust the model according to climate change and market trends. This will make it possible to propose sustainable agricultural models based on agricultural data and minimize environmental impact.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The instruction unit can analyze microbial activity in the soil and propose optimal soil improvement methods. For example, a system could be built in which soil samples are collected and AI analyzes microbial activity. For example, the type and activity of microorganisms could be analyzed and optimal fertilizers and amendments could be proposed. The instruction unit could also develop a system in which data on microbial activity in the soil is collected in real time and AI uses that data to propose optimal soil improvement methods. For example, sensors could be used to constantly monitor the condition of the soil. The instruction unit could also build a system in which AI proposes specific steps for soil improvement based on the results of the microbial activity analysis. For example, it could propose fertilization methods to increase specific microorganisms. This could help maintain soil health and optimize crop growth.

[0052] The command unit can visualize the growth status of agricultural crops using a 3D model and make growth predictions. For example, a system can be built using drones and sensors to visualize the growth status of agricultural crops using a 3D model. For example, a 3D model can be generated based on image data taken regularly. The command unit can also develop a system in which AI analyzes crop growth data and makes growth predictions based on the 3D model. For example, it can predict growth patterns based on past data and suggest harvest times. The command unit can also build a system that uses the 3D model to monitor the growth status of agricultural crops in real time and make growth predictions. For example, it can reflect data from sensors in real time. This allows agricultural crop growth to be visually grasped and appropriate management can be carried out.

[0053] The instruction unit can predict agricultural machinery failures and suggest maintenance in advance. For example, a system is built in which sensors are attached to agricultural machinery and AI predicts failures. For example, vibration and temperature data is analyzed to detect signs of failure. The instruction unit also develops a system in which AI analyzes agricultural machinery usage data and predicts failures. For example, a predictive model is built based on past failure data and maintenance timing is suggested. The instruction unit also builds a system in which AI automatically generates agricultural machinery maintenance schedules based on failure predictions. For example, it suggests replacement times for parts with a high risk of failure. This makes it possible to prevent agricultural machinery failures before they occur and reduce work interruptions.

[0054] The instruction unit can predict the market price of agricultural products and suggest the optimal harvest time. For example, a system is built in which market data is collected and AI predicts the price of agricultural products. For example, prices are predicted based on past price data and demand forecasts. The instruction unit also develops a system in which AI suggests the optimal harvest time based on the market price prediction results. For example, it predicts when prices will rise and instructs to harvest at that time. The instruction unit also collects market price prediction data in real time and builds a system in which AI dynamically suggests the optimal harvest time. For example, it adjusts the harvest schedule according to price fluctuations. This makes it possible to optimize the harvest time based on market prices and maximize profits.

[0055] The sales department can analyze logistics data and propose optimal delivery routes. For example, a system is built in which logistics data is collected and AI proposes optimal delivery routes. For example, the shortest route is calculated based on traffic conditions and weather data. The sales department also develops a system in which AI analyzes logistics data and proposes routes to maximize delivery efficiency. For example, it calculates a route that efficiently travels around multiple delivery destinations. The sales department also collects logistics data in real time and builds a system in which AI dynamically proposes optimal delivery routes. For example, it adjusts routes based on traffic congestion and accident information. This makes it possible to maximize logistics efficiency and reduce delivery costs.

[0056] The sales department can analyze consumers' purchasing history and make personalized product suggestions. For example, a system can be built where AI collects consumer purchasing history data and makes personalized product suggestions. For example, related products can be suggested based on past purchase history. The sales department can also develop a system where AI analyzes consumers' purchasing history and makes product suggestions tailored to individual preferences. For example, suggestions can be made based on specific brands or categories. The sales department can also collect purchasing history data in real time and build a system where AI dynamically makes personalized product suggestions. For example, suggestions can be made based on seasons or events. This makes it possible to optimize product suggestions based on consumer preferences and increase purchasing motivation.

[0057] The sales department can monitor the quality of agricultural products in real time and perform quality assurance. For example, they could build a system that uses sensors to collect quality data on agricultural products in real time and AI to analyze that data and perform quality assurance. For example, they could evaluate quality based on temperature and humidity data. The sales department could also develop a system that uses AI to analyze the quality data on agricultural products and propose specific steps for quality assurance. For example, they could propose countermeasures if the quality deteriorates. The sales department could also build a system that collects quality data in real time and uses AI to issue alerts for quality assurance. For example, they could send a notification if the quality falls below a certain standard. This would allow them to maintain the quality of agricultural products and ensure consumer trust.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The monitoring unit monitors the growth status of crops. For example, sensors are used to monitor the growth rate, leaf color, and presence of disease. Drones can also be used to monitor large areas of farmland. Furthermore, image analysis technology can be used to assess the health of crops. Step 2: The instruction unit instructs the optimal timing for irrigation and fertilization based on the growth conditions monitored by the monitoring unit. For example, it optimizes the timing of irrigation and fertilization based on soil moisture, nutrient levels, and weather forecasts. It can also use AI to calculate the optimal amount of fertilizer to apply for crop growth. It can also provide specific work instructions to farmers. Step 3: The sales department leverages existing sales channels to sell the produce. For example, they can sell the produce directly to consumers through online markets, or they can leverage local markets or supermarket chains to sell the produce. They can also use e-commerce platforms to streamline the sale of the produce. Step 4: The breeding department improves the varieties. For example, they analyze genetic data and propose optimal breeding methods. They can also analyze growth data during the breeding process to select varieties with superior characteristics. They can also use AI to make the development of new varieties more efficient. Step 5: The Job Creation Department will create jobs in depopulated areas. For example, it will train personnel to operate agricultural machinery using AI and perform data analysis. It can also support the launch of new agricultural projects. It can also promote the introduction of remote work.

[0060] (Example 2) The agricultural efficiency system according to an embodiment of the present invention is a system that aims to reduce the number of workers and improve efficiency by introducing AI technology in response to the decline and aging of agricultural workers. As a result, the agricultural efficiency system can improve agricultural efficiency, reduce the number of workers, utilize sales networks, improve varieties, and create jobs in depopulated areas.

[0061] An agricultural efficiency system according to an embodiment includes a monitoring unit, an instruction unit, a sales unit, an improvement unit, and a job creation unit. The monitoring unit monitors the growth status of agricultural crops. For example, the monitoring unit uses sensors to monitor the growth rate, leaf color, and presence of disease of agricultural crops. The monitoring unit can also monitor a wide area of ​​farmland using drones. The monitoring unit can also evaluate the health of agricultural crops using image analysis technology. The instruction unit provides instructions on optimal irrigation and fertilization timing based on the growth status monitored by the monitoring unit. For example, the instruction unit optimizes the irrigation and fertilization timing based on soil moisture, nutrient levels, and weather forecasts. The instruction unit can also calculate the optimal amount of fertilizer to apply for crop growth using AI. The instruction unit can also provide specific work instructions to farmers. The sales unit sells agricultural products using existing sales networks. For example, the sales unit sells agricultural products directly to consumers through online markets. The sales unit can also sell agricultural products through local markets and supermarket chains. Furthermore, the sales department can use an e-commerce platform to improve the efficiency of agricultural product sales. The improvement department performs breeding. For example, the improvement department analyzes genetic data and proposes optimal breeding methods. The improvement department can also analyze growth data during the breeding process and select varieties with superior characteristics. The improvement department can also use AI to improve the efficiency of new variety development. The employment creation department creates jobs in depopulated areas. For example, the employment creation department trains personnel who can operate agricultural machinery and analyze data using AI. The employment creation department can also support the launch of new agricultural projects. The employment creation department can also promote the introduction of remote work. As a result, the agricultural efficiency system according to the embodiment can achieve agricultural efficiency, personnel reduction, utilization of sales networks, variety improvement, and job creation in depopulated areas. For example, it reduces the burden on agricultural workers and improves agricultural productivity. It also improves the efficiency of agricultural product sales and increases sales. It also improves the efficiency of new variety development and strengthens the competitiveness of agriculture. Job creation in depopulated areas is expected to contribute to regional revitalization.

[0062] When monitoring the growth status of agricultural crops, the monitoring unit uses an emotion estimation function to monitor the stress levels of agricultural workers in real time and automatically adjust work when stress levels rise. The monitoring unit, for example, analyzes the facial expressions and voices of agricultural workers to build a system that estimates stress levels in real time. For example, it uses a camera and microphone to analyze the workers' emotions and reduces their workload when stress levels rise. The monitoring unit also uses the emotion estimation function to develop a system that monitors the stress levels of agricultural workers and automatically suggests taking a break when stress levels rise. For example, it sends a notification encouraging a break when the stress score exceeds a certain value. The monitoring unit also builds a system that monitors stress levels in real time and automatically adjusts work content when stress levels rise. For example, it instructs workers to switch to lighter work when stress levels are high. This reduces agricultural workers' stress and improves work efficiency.

[0063] The instruction unit can analyze microbial activity in the soil and propose optimal soil improvement methods. For example, the instruction unit collects soil samples and builds a system in which AI analyzes microbial activity. For example, it analyzes the type and activity of microorganisms and proposes optimal fertilizers and improvement materials. The instruction unit also develops a system in which AI collects soil microbial activity data in real time and proposes optimal soil improvement methods based on that data. For example, it uses sensors to constantly monitor the condition of the soil. The instruction unit also builds a system in which AI proposes specific steps for soil improvement based on the results of the microbial activity analysis. For example, it proposes fertilization methods to increase specific microorganisms. This makes it possible to maintain soil health and optimize crop growth.

[0064] The instruction unit can visualize the growth status of agricultural crops using a 3D model and make growth predictions. The instruction unit, for example, uses drones and sensors to build a system that visualizes the growth status of agricultural crops using a 3D model. For example, it generates a 3D model based on image data taken periodically. The instruction unit also develops a system in which AI analyzes crop growth data and makes growth predictions based on the 3D model. For example, it predicts growth patterns based on past data and suggests harvest times. The instruction unit also builds a system that uses the 3D model to monitor the growth status of agricultural crops in real time and make growth predictions. For example, it reflects data from sensors in real time. This allows agricultural crop growth to be visually grasped and appropriate management to be carried out.

[0065] The instruction unit can predict agricultural machinery failures and suggest maintenance in advance. For example, the instruction unit will attach sensors to agricultural machinery and build a system in which AI predicts failures. For example, it will analyze vibration and temperature data to detect signs of failure. The instruction unit will also develop a system in which AI analyzes agricultural machinery usage data and predicts failures. For example, it will build a predictive model based on past failure data and suggest maintenance times. The instruction unit will also build a system in which AI automatically generates agricultural machinery maintenance schedules based on failure predictions. For example, it will suggest replacement times for parts with a high risk of failure. This will prevent agricultural machinery failures before they occur and reduce work interruptions.

[0066] The instruction unit can predict the market price of agricultural products and suggest the optimal harvest time. The instruction unit, for example, collects market data and builds a system in which AI predicts the price of agricultural products. For example, it predicts prices based on past price data and demand forecasts. The instruction unit also develops a system in which AI suggests the optimal harvest time based on the market price prediction results. For example, it predicts when prices will rise and gives instructions to harvest at that time. The instruction unit also collects market price prediction data in real time and builds a system in which AI dynamically suggests the optimal harvest time. For example, it adjusts the harvest schedule according to price fluctuations. This makes it possible to optimize the harvest time based on market prices and maximize profits.

[0067] The instruction unit can use the emotion estimation function to analyze consumer preferences and create a production plan for agricultural products according to demand. The instruction unit, for example, analyzes consumer reviews and social media posts and builds a system that analyzes preferences using the emotion estimation function. For example, it identifies agricultural products with a high percentage of positive emotions. The instruction unit also uses the emotion estimation function to develop a system that forecasts demand based on consumer preference data. For example, it forecasts demand according to specific seasons or events. The instruction unit also builds a system in which AI creates a production plan for agricultural products according to demand based on consumer preference data. For example, it instructs to prioritize cultivation of varieties with high demand. This makes it possible to optimize production plans based on consumer preferences and provide agricultural products according to demand.

[0068] The sales department can use the emotion estimation function to analyze consumer purchasing intent in real time and propose optimal sales strategies. The sales department, for example, analyzes consumer behavior data on an e-commerce platform and builds a system that uses the emotion estimation function to analyze purchasing intent in real time. For example, the sales department adjusts the sales strategy based on the emotion score of products added to a cart. The sales department also uses the emotion estimation function to develop a system that analyzes consumer purchasing intent and proposes optimal sales strategies. For example, it proposes promotions tailored to specific time periods or days of the week. The sales department also collects consumer purchasing intent data in real time and builds a system that dynamically proposes optimal sales strategies based on the emotion estimation function. For example, it prioritizes promotion of products with high emotion scores. This makes it possible to optimize sales strategies based on consumer purchasing intent and increase sales.

[0069] The sales department can analyze logistics data and propose optimal delivery routes. For example, the sales department collects logistics data and builds a system in which AI proposes optimal delivery routes. For example, it calculates the shortest route based on traffic conditions and weather data. The sales department also develops a system in which AI analyzes logistics data and proposes routes to maximize delivery efficiency. For example, it calculates a route that efficiently travels around multiple delivery destinations. The sales department also collects logistics data in real time and builds a system in which AI dynamically proposes optimal delivery routes. For example, it adjusts routes based on traffic congestion and accident information. This makes it possible to maximize logistics efficiency and reduce delivery costs.

[0070] The sales department can analyze consumers' purchasing history and make personalized product suggestions. For example, the sales department collects consumer purchasing history data and builds a system in which AI makes personalized product suggestions. For example, it suggests related products based on past purchase history. The sales department also develops a system in which AI analyzes consumers' purchasing history and makes product suggestions tailored to individual preferences. For example, it makes suggestions based on specific brands or categories. The sales department also collects purchasing history data in real time and builds a system in which AI dynamically makes personalized product suggestions. For example, it makes suggestions tailored to seasons or events. This makes it possible to optimize product suggestions based on consumer preferences and increase purchasing motivation.

[0071] The sales department can monitor the quality of agricultural products in real time and perform quality assurance. For example, the sales department could use sensors to collect quality data on agricultural products in real time, and build a system where AI analyzes that data to perform quality assurance. For example, quality could be evaluated based on temperature and humidity data. The sales department could also develop a system where AI analyzes the quality data on agricultural products and proposes specific steps for quality assurance. For example, it could propose countermeasures if the quality deteriorates. The sales department could also collect quality data in real time, and build a system where AI issues alerts for quality assurance. For example, it could send a notification if the quality falls below a certain standard. This would allow the quality of agricultural products to be maintained and consumer trust to be secured.

[0072] The sales department can analyze consumer reviews and provide feedback for product improvement. For example, the sales department could collect consumer reviews and build a system where AI analyzes the data to provide feedback for product improvement. For example, by classifying reviews into positive and negative. The sales department could also develop a system where AI analyzes consumer reviews and suggests specific product improvements. For example, it could identify frequently pointed out problems and propose solutions. The sales department could also build a system where review data is collected in real time and AI dynamically provides feedback for product improvement. For example, it could analyze each time a new review is posted. This allows the quality of the product to be improved by reflecting consumer opinions.

[0073] The sales department can use the emotion estimation function to implement a marketing campaign based on consumer emotions. For example, the sales department collects consumer emotion data and builds a system that implements a marketing campaign using the emotion estimation function. For example, the sales department runs a campaign targeting a target demographic with a high percentage of positive emotions. The sales department also uses the emotion estimation function to develop a system that adjusts the content of a marketing campaign based on consumer emotion data. For example, the sales department runs a promotion focusing on products with high emotion scores. The sales department also collects consumer emotion data in real time and builds a system that dynamically adjusts a marketing campaign based on the emotion estimation function. For example, the effectiveness of the campaign is monitored in real time and the content is changed as necessary. This makes it possible to optimize the marketing campaign based on consumer emotions and maximize its effectiveness.

[0074] The improvement department can use the emotion estimation function to analyze consumer preferences and improve varieties according to demand. For example, the improvement department analyzes consumer reviews and social media posts and builds a system that analyzes preferences using the emotion estimation function. For example, it identifies varieties with a high proportion of positive emotions. The improvement department also uses the emotion estimation function to develop a system that forecasts demand based on consumer preference data. For example, it forecasts demand according to specific seasons or events. The improvement department also builds a system in which AI improves varieties according to demand based on consumer preference data. For example, it prioritizes improving varieties with characteristics that are in high demand. This makes it possible to optimize breeding based on consumer preferences and provide varieties that meet demand.

[0075] The improvement department can analyze climate change data and propose varieties adapted to future climates. For example, the improvement department will collect climate change data and build a system in which AI proposes varieties adapted to future climates. For example, varieties will be selected based on predicted temperature and precipitation data. The improvement department will also develop a system in which AI analyzes climate change data and proposes varieties suitable for specific regions. For example, it will propose drought-resistant varieties suitable for arid regions. The improvement department will also collect climate change data in real time and build a system in which AI dynamically proposes varieties adapted to future climates. For example, it will select varieties suitable for regions that are susceptible to the effects of climate change. This will make it possible to provide varieties that are adaptable to climate change and improve the sustainability of agriculture.

[0076] The improvement department can use gene editing technology to develop varieties that are resistant to specific pests and diseases. For example, the improvement department collects genetic data and builds a system in which AI develops varieties that are resistant to specific pests and diseases. For example, it identifies resistance genes to pests and diseases and improves varieties using gene editing technology. The improvement department also develops a system in which AI analyzes genetic data and suggests varieties that are resistant to specific pests and diseases. For example, it uses gene editing technology to enhance resistance to pests and diseases. The improvement department also builds a system in which AI uses gene editing technology to develop varieties that are resistant to specific pests and diseases. For example, it introduces resistance genes based on pest and disease occurrence data. This makes it possible to provide varieties that are resistant to pests and diseases and improve crop yields.

[0077] The improvement department can analyze agricultural data from different regions and improve varieties specific to the region. For example, the improvement department will collect agricultural data from different regions and build a system in which AI will improve varieties specific to the region. For example, it will select varieties based on the climate and soil conditions of each region. The improvement department will also develop a system in which AI will analyze agricultural data from different regions and propose improvements to varieties suitable for specific regions. For example, it will propose varieties that are resistant to pests and diseases specific to the region. The improvement department will also collect agricultural data from each region in real time and build a system in which AI will dynamically propose improvements to varieties specific to the region. For example, it will select varieties that are suited to climate change in the region. This will enable the provision of varieties that are suitable for local conditions and improve agricultural efficiency.

[0078] The improvement department can analyze growth data from the breeding process and propose optimal cultivation methods. For example, the improvement department will collect growth data from the breeding process and build a system in which AI proposes optimal cultivation methods. For example, it will adjust cultivation methods based on data such as temperature, humidity, and light intensity. The improvement department will also develop a system in which AI analyzes growth data from the breeding process and proposes cultivation methods suitable for specific varieties. For example, it will propose the optimal timing for irrigation and fertilization. The improvement department will also collect growth data in real time and build a system in which AI will dynamically propose optimal cultivation methods. For example, it will adjust cultivation methods based on weather data. This will enable cultivation methods to be optimized based on data from the breeding process, improving the efficiency of variety improvement.

[0079] The improvement department can use the emotion estimation function to collect the opinions of farmers and reflect them in breeding. For example, the improvement department builds a system that collects the opinions of farmers and analyzes them using the emotion estimation function. For example, positive opinions are preferentially reflected in breeding. The improvement department also uses the emotion estimation function to develop a system that performs breeding based on agricultural worker opinion data. For example, it identifies areas for improvement based on the emotion scores of farmers. The improvement department also builds a system that collects agricultural worker opinion data in real time and dynamically performs breeding based on the emotion estimation function. For example, it adjusts areas for improvement based on farmer feedback. This makes it possible to reflect the opinions of farmers and improve the quality of breeding.

[0080] The employment creation department can use the emotion estimation function to analyze the needs of residents in depopulated areas and propose optimal employment opportunities. For example, the employment creation department builds a system that collects the opinions of residents in depopulated areas and analyzes their needs using the emotion estimation function. For example, it proposes employment opportunities based on positive opinions. The employment creation department also develops a system that uses the emotion estimation function to propose optimal employment opportunities based on needs data of residents in depopulated areas. For example, it identifies employment opportunities based on residents' emotion scores. The employment creation department also builds a system that collects needs data of residents in depopulated areas in real time and dynamically proposes optimal employment opportunities based on the emotion estimation function. For example, it adjusts employment opportunities based on resident feedback. This makes it possible to optimize employment opportunities based on the needs of residents in depopulated areas and promote regional revitalization.

[0081] The Ministry of Employment Creation can analyze resources in depopulated areas and propose agricultural projects unique to the area. For example, the Ministry of Employment Creation will collect resource data from depopulated areas and build a system in which AI proposes agricultural projects unique to the area. For example, projects will be selected based on the region's climate and soil conditions. The Ministry of Employment Creation will also develop a system in which AI analyzes resource data from depopulated areas and proposes agricultural projects suitable for specific areas. For example, it will propose a project to cultivate crops unique to the area. The Ministry of Employment Creation will also collect resource data from depopulated areas in real time and build a system in which AI dynamically proposes agricultural projects unique to the area. For example, it will select projects that respond to climate change in the area. This will make it possible to utilize resources in depopulated areas and promote agricultural projects unique to the area.

[0082] The Ministry of Employment Creation can provide a system that uses remote control technology to operate advanced agricultural machinery even in depopulated areas. For example, the Ministry of Employment Creation could build a system that uses remote control technology to operate advanced agricultural machinery even in depopulated areas. For example, agricultural machinery could be remotely operated via the internet. The Ministry of Employment Creation could also develop a system that uses AI remote control technology to provide farmers in depopulated areas with instructions on how to operate advanced agricultural machinery. For example, it could provide a remote control training program. The Ministry of Employment Creation could also build a system that uses remote control technology to monitor agricultural machinery in depopulated areas in real time and provide AI support for operation. For example, it could provide an alert function to prevent operating errors. This would enable advanced agricultural machinery to be operated even in depopulated areas, improving agricultural efficiency.

[0083] The Job Creation Department can analyze tourism resources in depopulated areas and propose new business models that combine agriculture and tourism. For example, the Job Creation Department could collect tourism resource data in depopulated areas and build a system in which AI would propose business models that combine agriculture and tourism. For example, it could propose tourist farms and agricultural experience tours. The Job Creation Department could also develop a system in which AI would analyze tourism resource data in depopulated areas and propose business models that combine agriculture and tourism that are suitable for specific regions. For example, it could propose tourism programs that use crops unique to the region. The Job Creation Department could also collect tourism resource data in depopulated areas in real time and build a system in which AI would dynamically propose business models that combine agriculture and tourism. For example, it could propose tourism programs tailored to the seasons and events. This could lead to the proposal of new business models that combine agriculture and tourism and promote regional revitalization.

[0084] The Ministry of Employment Creation can analyze educational data from depopulated areas and provide agricultural technology educational programs. For example, the Ministry of Employment Creation could collect educational data from depopulated areas and build a system in which AI provides agricultural technology educational programs. For example, it could provide online courses or on-site training programs. The Ministry of Employment Creation could also develop a system in which AI analyzes educational data from depopulated areas and proposes agricultural technology educational programs suitable for specific areas. For example, it could provide a program that teaches crop cultivation techniques specific to the area. The Ministry of Employment Creation could also collect educational data from depopulated areas in real time and build a system in which AI dynamically provides agricultural technology educational programs. For example, it could provide educational programs that are in line with the latest technological trends. This could provide agricultural technology educational programs based on educational data from depopulated areas and improve local agricultural technology.

[0085] The Job Creation Department can use the emotion estimation function to monitor the satisfaction of residents in depopulated areas and evaluate the effectiveness of job creation. For example, the Job Creation Department collects the opinions of residents in depopulated areas and builds a system that monitors satisfaction using the emotion estimation function. For example, the effectiveness of job creation is evaluated based on positive opinions. The Job Creation Department also uses the emotion estimation function to develop a system that evaluates the effectiveness of job creation based on satisfaction data of residents in depopulated areas. For example, the effectiveness is determined based on residents' emotion scores. The Job Creation Department also collects satisfaction data of residents in depopulated areas in real time and builds a system that dynamically evaluates the effectiveness of job creation based on the emotion estimation function. For example, the effectiveness is adjusted based on resident feedback. In this way, the satisfaction of residents in depopulated areas can be monitored and the effectiveness of job creation can be evaluated, thereby promoting regional revitalization.

[0086] The Ministry of Employment Creation will be able to monitor agricultural workers' motivation in real time and provide them with an optimal working environment. The Ministry of Employment Creation will build a system that analyzes the facial expressions and voices of agricultural workers to estimate their motivation in real time. For example, it will use cameras and microphones to analyze workers' emotions and improve the working environment if their motivation drops. The Ministry of Employment Creation will also develop a system that uses emotion estimation to monitor agricultural workers' motivation and automatically suggest breaks when their motivation drops. For example, it will send a notification encouraging them to take a break if their motivation score falls below a certain value. The Ministry of Employment Creation will also build a system that monitors motivation in real time and automatically adjusts the work content when motivation drops. For example, it will instruct workers to switch to lighter work if their motivation is low. This will help maintain agricultural workers' motivation and improve work efficiency.

[0087] The Ministry of Employment Creation will be able to analyze agricultural data and propose optimal agricultural policies. For example, the Ministry of Employment Creation will build a system that collects agricultural data and uses AI to propose optimal agricultural policies. For example, policies will be proposed based on weather data and growth data. The Ministry of Employment Creation will also develop a system that uses AI to analyze agricultural data and propose agricultural policies suitable for specific regions. For example, policies will be proposed based on the region's unique climate and soil conditions. The Ministry of Employment Creation will also build a system that collects agricultural data in real time and uses AI to dynamically propose optimal agricultural policies. For example, policies will be adjusted in response to climate change and market trends. This will make it possible to propose optimal agricultural policies based on agricultural data and improve agricultural efficiency.

[0088] The Job Creation Department will be able to analyze the entire agricultural supply chain and improve efficiency. For example, the Job Creation Department will collect agricultural supply chain data and build a system where AI will analyze that data to improve efficiency. For example, it will propose optimization of logistics and inventory management. The Job Creation Department will also develop a system where AI will analyze agricultural supply chain data and propose improvements to the efficiency of specific processes. For example, optimizing the process from harvesting to shipping. The Job Creation Department will also build a system where supply chain data will be collected in real time and AI will dynamically make suggestions for efficiency. For example, adjusting production plans based on demand forecasts. This will make it possible to improve the efficiency of the entire agricultural supply chain and reduce costs.

[0089] The Ministry of Employment Creation will be able to combine different agricultural technologies and propose new agricultural methods. For example, the Ministry of Employment Creation will collect data on different agricultural technologies and build a system in which AI will propose new agricultural methods based on that data. For example, it will propose a method that combines pesticide-free cultivation and hydroponic cultivation. The Ministry of Employment Creation will also develop a system in which AI will analyze data on different agricultural technologies and propose agricultural methods suitable for specific conditions. For example, it will propose a combination of technologies according to climate and soil conditions. The Ministry of Employment Creation will also collect data on different agricultural technologies in real time and build a system in which AI will dynamically propose new agricultural methods. For example, it will adjust methods as technology evolves. This will allow new agricultural methods to be proposed by combining different agricultural technologies, improving agricultural efficiency.

[0090] The Ministry of Job Creation will be able to analyze agricultural data and propose sustainable agricultural models. For example, the Ministry of Job Creation will build a system that collects agricultural data and uses AI to propose sustainable agricultural models. For example, it will propose cultivation methods that minimize environmental impact. The Ministry of Job Creation will also develop a system that uses AI to analyze agricultural data and propose sustainable agricultural models suitable for specific regions. For example, it will propose sustainable methods based on the region's climate and soil conditions. The Ministry of Job Creation will also build a system that collects agricultural data in real time and uses AI to dynamically propose sustainable agricultural models. For example, it will adjust the model according to climate change and market trends. This will make it possible to propose sustainable agricultural models based on agricultural data and minimize environmental impact.

[0091] The Ministry of Job Creation can use the emotion estimation function to collect consumer opinions and determine the direction of the agricultural revolution. For example, the Ministry of Job Creation builds a system that collects consumer opinions and analyzes them using the emotion estimation function. For example, it determines the direction of the agricultural revolution based on positive opinions. The Ministry of Job Creation also develops a system that uses the emotion estimation function to determine the direction of the agricultural revolution based on consumer opinion data. For example, it identifies the direction based on consumer emotion scores. The Ministry of Job Creation also builds a system that collects consumer opinion data in real time and dynamically determines the direction of the agricultural revolution based on the emotion estimation function. For example, it adjusts the direction based on consumer feedback. This makes it possible to determine the direction of the agricultural revolution based on consumer opinions and promote agriculture that meets consumer needs.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] When monitoring the growth status of agricultural crops, the monitoring unit uses emotion estimation functionality to monitor the stress levels of agricultural workers in real time and automatically adjust work when stress levels rise. For example, a system is built that analyzes the facial expressions and voices of agricultural workers to estimate their stress levels in real time. For example, a camera and microphone are used to analyze the workers' emotions, and the workload is reduced when stress levels rise. The monitoring unit also uses emotion estimation functionality to develop a system that monitors the stress levels of agricultural workers and automatically suggests taking a break when stress levels rise. For example, a notification is sent encouraging workers to take a break when the stress score exceeds a certain value. The monitoring unit also builds a system that monitors stress levels in real time and automatically adjusts work content when stress levels rise. For example, it instructs workers to switch to lighter work when stress levels are high. This reduces agricultural workers' stress and improves work efficiency.

[0094] The instruction unit can analyze microbial activity in the soil and propose optimal soil improvement methods. For example, a system could be built in which soil samples are collected and AI analyzes microbial activity. For example, the type and activity of microorganisms could be analyzed and optimal fertilizers and amendments could be proposed. The instruction unit could also develop a system in which data on microbial activity in the soil is collected in real time and AI uses that data to propose optimal soil improvement methods. For example, sensors could be used to constantly monitor the condition of the soil. The instruction unit could also build a system in which AI proposes specific steps for soil improvement based on the results of the microbial activity analysis. For example, it could propose fertilization methods to increase specific microorganisms. This could help maintain soil health and optimize crop growth.

[0095] The command unit can visualize the growth status of agricultural crops using a 3D model and make growth predictions. For example, a system can be built using drones and sensors to visualize the growth status of agricultural crops using a 3D model. For example, a 3D model can be generated based on image data taken regularly. The command unit can also develop a system in which AI analyzes crop growth data and makes growth predictions based on the 3D model. For example, it can predict growth patterns based on past data and suggest harvest times. The command unit can also build a system that uses the 3D model to monitor the growth status of agricultural crops in real time and make growth predictions. For example, it can reflect data from sensors in real time. This allows agricultural crop growth to be visually grasped and appropriate management can be carried out.

[0096] The instruction unit can predict agricultural machinery failures and suggest maintenance in advance. For example, a system is built in which sensors are attached to agricultural machinery and AI predicts failures. For example, vibration and temperature data is analyzed to detect signs of failure. The instruction unit also develops a system in which AI analyzes agricultural machinery usage data and predicts failures. For example, a predictive model is built based on past failure data and maintenance timing is suggested. The instruction unit also builds a system in which AI automatically generates agricultural machinery maintenance schedules based on failure predictions. For example, it suggests replacement times for parts with a high risk of failure. This makes it possible to prevent agricultural machinery failures before they occur and reduce work interruptions.

[0097] The instruction unit can predict the market price of agricultural products and suggest the optimal harvest time. For example, a system is built in which market data is collected and AI predicts the price of agricultural products. For example, prices are predicted based on past price data and demand forecasts. The instruction unit also develops a system in which AI suggests the optimal harvest time based on the market price prediction results. For example, it predicts when prices will rise and instructs to harvest at that time. The instruction unit also collects market price prediction data in real time and builds a system in which AI dynamically suggests the optimal harvest time. For example, it adjusts the harvest schedule according to price fluctuations. This makes it possible to optimize the harvest time based on market prices and maximize profits.

[0098] The instruction unit can use the emotion estimation function to analyze consumer preferences and create a production plan for agricultural products according to demand. For example, a system can be built that analyzes consumer reviews and social media posts and uses the emotion estimation function to analyze preferences. For example, agricultural products with a high proportion of positive emotions can be identified. The instruction unit can also use the emotion estimation function to develop a system that forecasts demand based on consumer preference data. For example, it can predict demand according to specific seasons or events. The instruction unit can also build a system in which AI creates a production plan for agricultural products according to demand based on consumer preference data. For example, it can instruct to prioritize the cultivation of varieties with high demand. This makes it possible to optimize production plans based on consumer preferences and provide agricultural products according to demand.

[0099] The sales department can use the emotion estimation function to analyze consumer purchasing intent in real time and propose optimal sales strategies. For example, a system is built that analyzes consumer behavior data on an e-commerce platform and uses the emotion estimation function to analyze purchasing intent in real time. For example, the sales strategy is adjusted based on the emotion score of products added to a cart. The sales department also uses the emotion estimation function to develop a system that analyzes consumer purchasing intent and proposes optimal sales strategies. For example, it proposes promotions tailored to specific times of day or days of the week. The sales department also collects consumer purchasing intent data in real time and builds a system that dynamically proposes optimal sales strategies based on the emotion estimation function. For example, it prioritizes promotion of products with high emotion scores. This makes it possible to optimize sales strategies based on consumer purchasing intent and increase sales.

[0100] The sales department can analyze logistics data and propose optimal delivery routes. For example, a system is built in which logistics data is collected and AI proposes optimal delivery routes. For example, the shortest route is calculated based on traffic conditions and weather data. The sales department also develops a system in which AI analyzes logistics data and proposes routes to maximize delivery efficiency. For example, it calculates a route that efficiently travels around multiple delivery destinations. The sales department also collects logistics data in real time and builds a system in which AI dynamically proposes optimal delivery routes. For example, it adjusts routes based on traffic congestion and accident information. This makes it possible to maximize logistics efficiency and reduce delivery costs.

[0101] The sales department can analyze consumers' purchasing history and make personalized product suggestions. For example, a system can be built where AI collects consumer purchasing history data and makes personalized product suggestions. For example, related products can be suggested based on past purchase history. The sales department can also develop a system where AI analyzes consumers' purchasing history and makes product suggestions tailored to individual preferences. For example, suggestions can be made based on specific brands or categories. The sales department can also collect purchasing history data in real time and build a system where AI dynamically makes personalized product suggestions. For example, suggestions can be made based on seasons or events. This makes it possible to optimize product suggestions based on consumer preferences and increase purchasing motivation.

[0102] The sales department can monitor the quality of agricultural products in real time and perform quality assurance. For example, they could build a system that uses sensors to collect quality data on agricultural products in real time and AI to analyze that data and perform quality assurance. For example, they could evaluate quality based on temperature and humidity data. The sales department could also develop a system that uses AI to analyze the quality data on agricultural products and propose specific steps for quality assurance. For example, they could propose countermeasures if the quality deteriorates. The sales department could also build a system that collects quality data in real time and uses AI to issue alerts for quality assurance. For example, they could send a notification if the quality falls below a certain standard. This would allow them to maintain the quality of agricultural products and ensure consumer trust.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The monitoring unit monitors the growth status of crops. For example, sensors are used to monitor the growth rate, leaf color, and presence of disease. Drones can also be used to monitor large areas of farmland. Furthermore, image analysis technology can be used to assess the health of crops. Step 2: The instruction unit instructs the optimal timing for irrigation and fertilization based on the growth conditions monitored by the monitoring unit. For example, it optimizes the timing of irrigation and fertilization based on soil moisture, nutrient levels, and weather forecasts. It can also use AI to calculate the optimal amount of fertilizer to apply for crop growth. It can also provide specific work instructions to farmers. Step 3: The sales department leverages existing sales channels to sell the produce. For example, they can sell the produce directly to consumers through online markets, or they can leverage local markets or supermarket chains to sell the produce. They can also use e-commerce platforms to streamline the sale of the produce. Step 4: The breeding department improves the varieties. For example, they analyze genetic data and propose optimal breeding methods. They can also analyze growth data during the breeding process to select varieties with superior characteristics. They can also use AI to make the development of new varieties more efficient. Step 5: The Job Creation Department will create jobs in depopulated areas. For example, it will train personnel to operate agricultural machinery using AI and perform data analysis. It can also support the launch of new agricultural projects. It can also promote the introduction of remote work.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[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 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.

[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 (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).

[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] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, 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 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 specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] In the robot 414, 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 robot 414 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a monitoring unit that monitors the growth status of agricultural crops; an instruction unit that instructs optimal timing for irrigation and fertilization based on the growth status monitored by the monitoring unit; A sales department that sells agricultural products using existing sales networks, The breeding department, which conducts breeding improvements, An employment creation department that creates jobs in depopulated areas. A system characterized by:

2. The monitoring unit When monitoring the growth status of the crops, the stress level of the farmer is monitored in real time, and work is automatically adjusted when the stress level increases.

2. The system of claim 1.

3. The instruction unit Analyzing microbial activity in soil and proposing optimal soil improvement methods 2. The system of claim 1.

4. The instruction unit The growth status of the crops is visualized using the 3D model, and growth predictions are made.

2. The system of claim 1.

5. The instruction unit Predicting agricultural machinery breakdowns and proposing maintenance in advance 2. The system of claim 1.

6. The instruction unit Predict the market price of the crop and suggest the best harvest time 2. The system of claim 1.

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