System

Abstract

An object of a system according to an embodiment is to improve the efficiency of a process from agricultural production planning to harvesting and sales.SOLUTION: A system includes a data analysis part, a planting plan drafting part, a cultivation management part, a harvest management part, and a sales management part. FIG. 1 is a block diagram illustrating a configuration of a AI generation system according to a first embodiment; The planting plan creator creates an optimal planting plan based on the data analyzed by the data analyzer. The cultivation management unit analyzes the data collected from the sensors and instructs the timing of optimal irrigation, fertilization, and pest control. The harvest management unit analyzes the growth state of the crop and determines an optimal harvest time. The sales management unit sells the crop through the online sales platform.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not efficiently manage the agricultural process from production planning to harvesting and sales, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of the agricultural process from production planning to harvesting and sales. [Means for solving the problem]

[0006] The system according to the embodiment includes a data analysis unit, a crop planning unit, a cultivation management unit, a harvest management unit, and a sales management unit. The data analysis unit uses generative AI to analyze weather data, soil data, and past harvest data. The crop planning unit creates an optimal crop plan based on the data analyzed by the data analysis unit. The cultivation management unit analyzes data collected from sensors and indicates the optimal timing for irrigation, fertilization, and pest control. The harvest management unit analyzes the growth status of crops and determines the optimal harvest time. The sales management unit sells crops through an online sales platform. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of the agricultural process from production planning to harvesting and sales. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The agricultural automation system according to an embodiment of the present invention is a system that combines automation technology and online sales to streamline the entire agricultural process, from production planning to harvesting and sales, and provides agricultural products to the market fully automatically. As a result, the agricultural automation system is expected to improve food self-sufficiency, solve the problem of a declining workforce due to aging, and reclaim abandoned farmland.

[0029] An agricultural automation system according to an embodiment includes a generation AI, a data analysis unit, a crop planning unit, a cultivation management unit, a harvest management unit, and a sales management unit. The generation AI analyzes weather data, soil data, and past harvest data. For example, the generation AI predicts the optimal time for crop growth based on weather data. The generation AI can also analyze soil data and develop an appropriate fertilization plan. The generation AI can also predict harvest yields based on past harvest data. The data analysis unit develops an optimal crop plan based on data analyzed by the generation AI. For example, the data analysis unit combines weather data and soil data to determine the optimal planting time for crops. The data analysis unit can also predict harvest yields based on past harvest data. The crop planning unit develops a specific crop plan based on the crop plan developed by the data analysis unit. For example, the crop planning unit determines the type of crop and the planting time. The crop planning unit can also determine the cultivation area for the crop. The cultivation management department analyzes data collected from the sensors and provides instructions for optimal timing of irrigation, fertilization, and pest control. For example, the cultivation management department monitors the soil moisture content and provides instructions for irrigation as needed. The cultivation management department can also monitor the growth status of crops and provide instructions for appropriate fertilization. The cultivation management department can also monitor the occurrence of pests and diseases and provide instructions for appropriate pest control. The harvest management department analyzes the growth status of crops and determines the optimal harvest time. For example, the harvest management department monitors the growth status of crops and determines the optimal harvest time. The harvest management department can also control harvesting robots and provide instructions for the optimal harvesting method. The sales management department sells agricultural products through an online sales platform. For example, the sales management department lists harvested agricultural products on an online store and begins sales. The sales management department can also analyze consumer purchase histories and develop personalized sales strategies. The sales management department can also monitor market price trends in real time and set optimal prices. As a result, the agricultural automation system according to the embodiment automates the entire process from agricultural production planning to harvesting and sales, enabling agricultural products to be efficiently delivered to the market.For example, agricultural automation systems are expected to improve food self-sufficiency, solve the problem of a declining workforce due to aging, and reuse abandoned farmland.

[0030] The crop planning unit analyzes weather data, soil data, regional agricultural policies, and market demand forecast data to create more detailed crop planning. For example, the crop planning unit uses generative AI to comprehensively analyze weather data, soil data, regional agricultural policies, and market demand forecast data to create optimal crop planning. For example, it prioritizes planting crops recommended by regional agricultural policies and selects profitable crops based on market demand forecasts. This allows for the creation of detailed crop planning that takes into account regional agricultural policies and market demand forecast data.

[0031] The crop planning unit analyzes the genetic data of crops and selects the optimal variety, thereby maximizing yield and quality. For example, the generative AI in the crop planning unit analyzes the genetic data of crops and selects the variety that is best suited to the local climate and soil conditions. For example, it selects varieties that are resistant to drought or pests and diseases. In this way, by analyzing the genetic data of crops and selecting the optimal variety, it is possible to maximize yield and quality.

[0032] The cultivation management department can analyze microbial activity data, monitor the soil health in real time, and develop optimal fertilization plans. For example, the cultivation management department will build a system in which generative AI analyzes microbial activity data and monitors the soil health in real time. For example, it will suggest fertilization when microbial activity decreases. This will allow the analysis of microbial activity data, monitoring the soil health in real time, and developing optimal fertilization plans.

[0033] The cultivation management department will use drones to monitor the entire farmland, enabling early detection of pests and taking measures. For example, the cultivation management department will build a system in which generative AI will use drones to monitor the entire farmland and enable early detection of pests and diseases. For example, it will analyze images taken by the drone's camera to detect the occurrence of pests and diseases. This will enable drones to monitor the entire farmland, enabling early detection of pests and diseases and taking measures.

[0034] The harvesting management unit controls the harvesting robot and can instruct the optimal harvesting method in real time. For example, the generative AI controls the harvesting robot, analyzes the growth status of agricultural crops in real time, and instructs the optimal harvesting method. For example, it analyzes the ripeness of tomatoes and instructs the optimal harvesting timing. This allows the harvesting robot to be controlled and instructed the optimal harvesting method in real time.

[0035] The sales management department can analyze consumers' purchasing history and develop personalized sales strategies. For example, the sales management department can build a system in which generative AI analyzes consumers' purchasing history and develops the optimal sales strategy for each individual consumer. For example, it can analyze preference trends from past purchasing history and suggest personalized products. This allows the department to analyze consumers' purchasing history and develop personalized sales strategies.

[0036] The sales management department can monitor market price trends in real time and set optimal prices. For example, the sales management department will build a system in which generative AI monitors market price trends in real time and sets optimal prices. For example, it will adjust prices based on the balance between supply and demand. This will allow market price trends to be monitored in real time and optimal prices to be set.

[0037] The sales management department can compare different sales channels and select the most effective one. For example, the sales management department can build a system in which generation AI compares different sales channels and selects the most effective channel. For example, it compares channels such as online stores, physical stores, and marketplaces. This allows different sales channels to be compared and the most effective channel to be selected.

[0038] The sales management department can develop a promotion strategy to increase the brand value of agricultural products. For example, the sales management department will build a system in which generative AI will develop a promotion strategy to increase the brand value of agricultural products. For example, it will create advertisements that emphasize the brand story. This will allow the department to develop a promotion strategy to increase the brand value of agricultural products.

[0039] The system can analyze regional agricultural data and propose optimal agricultural land use plans. For example, a system can be constructed in which generative AI analyzes regional agricultural data and proposes optimal agricultural land use plans. For example, a plan can be created to reuse abandoned farmland. This allows the system to analyze regional agricultural data and propose optimal agricultural land use plans.

[0040] The system can design agricultural machinery that can be operated by the elderly and people with disabilities, ensuring diversity among agricultural workers. For example, the system uses generative AI to build a system that designs agricultural machinery that can be operated by the elderly and people with disabilities. For example, it designs machinery with an easy-to-operate and intuitive interface. This allows the design of agricultural machinery that can be operated by the elderly and people with disabilities, ensuring diversity among agricultural workers.

[0041] The system can compare agricultural data from different regions and introduce the most suitable agricultural technology. For example, the system uses generative AI to compare agricultural data from different regions and build a system that introduces the most suitable agricultural technology. For example, it selects technology that is suitable for climate and soil conditions. This makes it possible to compare agricultural data from different regions and introduce the most suitable agricultural technology.

[0042] The system can automate agricultural education programs and train the next generation of agricultural workers. For example, generative AI can be used to automate agricultural education programs and build a system to train the next generation of agricultural workers. For example, it can provide online courses and interactive learning tools. This can automate agricultural education programs and train the next generation of agricultural workers.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The agricultural automation system can also include an agricultural education department, which provides online educational programs to train the next generation of agricultural workers. For example, the agricultural education department can provide interactive courses on topics such as how to analyze weather data, soil management techniques, and how to operate the latest agricultural machinery. The agricultural education department can also provide hands-on training opportunities and support in applying the knowledge learned in actual agricultural fields. This is expected to train the next generation of agricultural workers and increase the sustainability of agriculture.

[0045] The Crop Planning Department can also create crop plans that take into account local culture and traditions. For example, it can select crops that match traditional local festivals and events. It can also create crop plans that utilize local specialties and increase the brand value of the region. It can also promote cooperation with local residents and create plans to revitalize agricultural activities throughout the region. This is expected to help achieve sustainable agriculture while respecting local culture and traditions.

[0046] The cultivation management unit can also analyze plant physiological data to provide the optimal cultivation environment. For example, it can monitor the plant's photosynthesis and transpiration rates in real time and adjust the optimal amount of light and water. It can also analyze the amount of growth hormone secreted by the plant and create a fertilization plan to promote growth. It can also analyze the plant's stress response and take measures to prevent the occurrence of pests and diseases. This is expected to utilize plant physiological data to provide the optimal cultivation environment and maximize yield and quality.

[0047] The harvest management department can also perform post-harvest quality control. For example, it can monitor the freshness and quality of harvested crops in real time and provide instructions on appropriate storage methods. It can also monitor the temperature and humidity of harvested crops during transportation and implement measures to maintain quality. It can also analyze quality data on harvested crops and reflect it in plans for the next harvest. This is expected to enable thorough post-harvest quality control and provide high-quality crops to the market.

[0048] The sales management department can also introduce a subscription model to ensure regular revenue. For example, they can provide a subscription service that delivers agricultural products to consumers on a regular basis. The subscription model can also be used to analyze consumers' purchasing history and make personalized product recommendations. Furthermore, the subscription model can be used to build long-term relationships with consumers and increase brand loyalty. This is expected to ensure regular revenue and create a stable business model.

[0049] The system can also link with local tourism resources to provide agricultural experience tours. For example, agricultural experience tours can be planned in collaboration with local tourist destinations to convey the appeal of agriculture to tourists. The agricultural experience tours can also be used to promote local specialties and promote sales. Furthermore, feedback from participants in agricultural experience tours can be collected and the content of the tours can be continuously improved. This is expected to revitalize the local economy while linking with local tourism resources and promoting the appeal of agriculture.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The data analysis unit uses the generation AI to analyze weather data, soil data, and past harvest data. For example, the generation AI can predict the optimal time for crop growth based on weather data, analyze soil data to create an appropriate fertilization plan, and predict harvest yields based on past harvest data. Step 2: The crop planning unit creates an optimal crop planning plan based on the data analyzed by the data analysis unit. For example, the crop planning unit determines the type of crop, planting time, and cultivation area. Step 3: The cultivation management unit analyzes the data collected from the sensors and gives instructions on the optimal timing for irrigation, fertilization, and pest control. For example, it monitors the soil moisture level and gives instructions on irrigation, monitors the growth status of crops and gives instructions on appropriate fertilization, and monitors the occurrence of pests and diseases and gives instructions on control. Step 4: The harvesting management unit analyzes the growth status of the crops and determines the optimal harvesting time. For example, it monitors the growth status of the crops to determine the optimal harvesting time, and controls the harvesting robot to instruct it on the optimal harvesting method. Step 5: The sales management department sells agricultural products through an online sales platform, for example, by listing the harvested agricultural products on the online store, starting sales, analyzing consumer purchasing history to develop personalized sales strategies, and monitoring market price trends in real time to set optimal prices.

[0052] (Example 2) The agricultural automation system according to an embodiment of the present invention is a system that combines automation technology and online sales to streamline the entire agricultural process, from production planning to harvesting and sales, and provides agricultural products to the market fully automatically. As a result, the agricultural automation system is expected to improve food self-sufficiency, solve the problem of a declining workforce due to aging, and reclaim abandoned farmland.

[0053] An agricultural automation system according to an embodiment includes a generation AI, a data analysis unit, a crop planning unit, a cultivation management unit, a harvest management unit, and a sales management unit. The generation AI analyzes weather data, soil data, and past harvest data. For example, the generation AI predicts the optimal time for crop growth based on weather data. The generation AI can also analyze soil data and develop an appropriate fertilization plan. The generation AI can also predict harvest yields based on past harvest data. The data analysis unit develops an optimal crop plan based on data analyzed by the generation AI. For example, the data analysis unit combines weather data and soil data to determine the optimal planting time for crops. The data analysis unit can also predict harvest yields based on past harvest data. The crop planning unit develops a specific crop plan based on the crop plan developed by the data analysis unit. For example, the crop planning unit determines the type of crop and the planting time. The crop planning unit can also determine the cultivation area for the crop. The cultivation management department analyzes data collected from the sensors and provides instructions for optimal timing of irrigation, fertilization, and pest control. For example, the cultivation management department monitors the soil moisture content and provides instructions for irrigation as needed. The cultivation management department can also monitor the growth status of crops and provide instructions for appropriate fertilization. The cultivation management department can also monitor the occurrence of pests and diseases and provide instructions for appropriate pest control. The harvest management department analyzes the growth status of crops and determines the optimal harvest time. For example, the harvest management department monitors the growth status of crops and determines the optimal harvest time. The harvest management department can also control harvesting robots and provide instructions for the optimal harvesting method. The sales management department sells agricultural products through an online sales platform. For example, the sales management department lists harvested agricultural products on an online store and begins sales. The sales management department can also analyze consumer purchase histories and develop personalized sales strategies. The sales management department can also monitor market price trends in real time and set optimal prices. As a result, the agricultural automation system according to the embodiment automates the entire process from agricultural production planning to harvesting and sales, enabling agricultural products to be efficiently delivered to the market.For example, agricultural automation systems are expected to improve food self-sufficiency, solve the problem of a declining workforce due to aging, and reuse abandoned farmland.

[0054] The crop planning unit analyzes weather data, soil data, regional agricultural policies, and market demand forecast data to create more detailed crop planning. For example, the crop planning unit uses generative AI to comprehensively analyze weather data, soil data, regional agricultural policies, and market demand forecast data to create optimal crop planning. For example, it prioritizes planting crops recommended by regional agricultural policies and selects profitable crops based on market demand forecasts. This allows for the creation of detailed crop planning that takes into account regional agricultural policies and market demand forecast data.

[0055] The crop planning unit analyzes the genetic data of crops and selects the optimal variety, thereby maximizing yield and quality. For example, the generative AI in the crop planning unit analyzes the genetic data of crops and selects the variety that is best suited to the local climate and soil conditions. For example, it selects varieties that are resistant to drought or pests and diseases. In this way, by analyzing the genetic data of crops and selecting the optimal variety, it is possible to maximize yield and quality.

[0056] The cultivation management department can analyze microbial activity data, monitor the soil health in real time, and develop optimal fertilization plans. For example, the cultivation management department will build a system in which generative AI analyzes microbial activity data and monitors the soil health in real time. For example, it will suggest fertilization when microbial activity decreases. This will allow the analysis of microbial activity data, monitoring the soil health in real time, and developing optimal fertilization plans.

[0057] The cultivation management department will use drones to monitor the entire farmland, enabling early detection of pests and taking measures. For example, the cultivation management department will build a system in which generative AI will use drones to monitor the entire farmland and enable early detection of pests and diseases. For example, it will analyze images taken by the drone's camera to detect the occurrence of pests and diseases. This will enable drones to monitor the entire farmland, enabling early detection of pests and diseases and taking measures.

[0058] The harvesting management unit controls the harvesting robot and can instruct the optimal harvesting method in real time. For example, the generative AI controls the harvesting robot, analyzes the growth status of agricultural crops in real time, and instructs the optimal harvesting method. For example, it analyzes the ripeness of tomatoes and instructs the optimal harvesting timing. This allows the harvesting robot to be controlled and instructed the optimal harvesting method in real time.

[0059] The sales management department can analyze consumers' purchasing history and develop personalized sales strategies. For example, the sales management department can build a system in which generative AI analyzes consumers' purchasing history and develops the optimal sales strategy for each individual consumer. For example, it can analyze preference trends from past purchasing history and suggest personalized products. This allows the department to analyze consumers' purchasing history and develop personalized sales strategies.

[0060] The sales management department can monitor market price trends in real time and set optimal prices. For example, the sales management department will build a system in which generative AI monitors market price trends in real time and sets optimal prices. For example, it will adjust prices based on the balance between supply and demand. This will allow market price trends to be monitored in real time and optimal prices to be set.

[0061] The sales management department can compare different sales channels and select the most effective one. For example, the sales management department can build a system in which generation AI compares different sales channels and selects the most effective channel. For example, it compares channels such as online stores, physical stores, and marketplaces. This allows different sales channels to be compared and the most effective channel to be selected.

[0062] The sales management department can develop a promotion strategy to increase the brand value of agricultural products. For example, the sales management department will build a system in which generative AI will develop a promotion strategy to increase the brand value of agricultural products. For example, it will create advertisements that emphasize the brand story. This will allow the department to develop a promotion strategy to increase the brand value of agricultural products.

[0063] The sales management department can use the emotion estimation function to collect consumer feedback and continuously improve sales strategies. For example, the sales management department can use the emotion estimation function to collect consumer feedback in real time and build a system that continuously improves sales strategies. For example, the sales management department can adjust promotions based on consumer emotion data. This allows the sales management department to collect consumer feedback and continuously improve sales strategies.

[0064] The system can analyze regional agricultural data and propose optimal agricultural land use plans. For example, a system can be constructed in which generative AI analyzes regional agricultural data and proposes optimal agricultural land use plans. For example, a plan can be created to reuse abandoned farmland. This allows the system to analyze regional agricultural data and propose optimal agricultural land use plans.

[0065] The system can design agricultural machinery that can be operated by the elderly and people with disabilities, ensuring diversity among agricultural workers. For example, the system uses generative AI to build a system that designs agricultural machinery that can be operated by the elderly and people with disabilities. For example, it designs machinery with an easy-to-operate and intuitive interface. This allows the design of agricultural machinery that can be operated by the elderly and people with disabilities, ensuring diversity among agricultural workers.

[0066] The system uses the emotion estimation function to analyze the emotions of local residents and improve the acceptability of agricultural projects. For example, the system uses the emotion estimation function to collect emotion data from local residents and build a system to improve the acceptability of agricultural projects. For example, the system adjusts the content of the project based on the emotion data of residents. This makes it possible to analyze the emotions of local residents and improve the acceptability of agricultural projects.

[0067] The system can compare agricultural data from different regions and introduce the most suitable agricultural technology. For example, the system uses generative AI to compare agricultural data from different regions and build a system that introduces the most suitable agricultural technology. For example, it selects technology that is suitable for climate and soil conditions. This makes it possible to compare agricultural data from different regions and introduce the most suitable agricultural technology.

[0068] The system can automate agricultural education programs and train the next generation of agricultural workers. For example, generative AI can be used to automate agricultural education programs and build a system to train the next generation of agricultural workers. For example, it can provide online courses and interactive learning tools. This can automate agricultural education programs and train the next generation of agricultural workers.

[0069] The system can use the emotion estimation function to understand the needs of local residents and propose strategies for establishing agricultural projects in the community. For example, the system can use the emotion estimation function to understand the needs of local residents in real time and propose strategies for establishing agricultural projects in the community. For example, the system can adjust the content of the project based on residents' emotion data. This makes it possible to understand the needs of local residents and propose strategies for establishing agricultural projects in the community.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The agricultural automation system can also include an agricultural education department, which provides online educational programs to train the next generation of agricultural workers. For example, the agricultural education department can provide interactive courses on topics such as how to analyze weather data, soil management techniques, and how to operate the latest agricultural machinery. The agricultural education department can also provide hands-on training opportunities and support in applying the knowledge learned in actual agricultural fields. This is expected to train the next generation of agricultural workers and increase the sustainability of agriculture.

[0072] The Crop Planning Department can also create crop plans that take into account local culture and traditions. For example, it can select crops that match traditional local festivals and events. It can also create crop plans that utilize local specialties and increase the brand value of the region. It can also promote cooperation with local residents and create plans to revitalize agricultural activities throughout the region. This is expected to help achieve sustainable agriculture while respecting local culture and traditions.

[0073] The crop planning unit can use the emotion estimation function to create crop plans that increase the motivation of farmers. For example, the emotion estimation function can be used to analyze the stress levels of farmers and set appropriate rest periods. It can also select crops that take into account the preferences and interests of farmers, improving their work satisfaction. Furthermore, the emotion estimation function can be used to collect feedback from farmers and continuously improve crop plans. This is expected to increase the motivation of farmers and realize efficient agricultural activities.

[0074] The cultivation management unit can also analyze plant physiological data to provide the optimal cultivation environment. For example, it can monitor the plant's photosynthesis and transpiration rates in real time and adjust the optimal amount of light and water. It can also analyze the amount of growth hormone secreted by the plant and create a fertilization plan to promote growth. It can also analyze the plant's stress response and take measures to prevent the occurrence of pests and diseases. This is expected to utilize plant physiological data to provide the optimal cultivation environment and maximize yield and quality.

[0075] The cultivation management department can use the emotion estimation function to analyze the emotional state of the farmer and provide instructions to improve work efficiency. For example, the emotion estimation function can be used to analyze the farmer's fatigue level and suggest appropriate breaks. It can also analyze the farmer's stress level and improve the work environment to reduce stress. Furthermore, it can adjust the work allocation based on the farmer's emotional data to achieve efficient work. This is expected to improve work efficiency by taking the farmer's emotional state into consideration.

[0076] The harvest management department can also perform post-harvest quality control. For example, it can monitor the freshness and quality of harvested crops in real time and provide instructions on appropriate storage methods. It can also monitor the temperature and humidity of harvested crops during transportation and implement measures to maintain quality. It can also analyze quality data on harvested crops and reflect it in plans for the next harvest. This is expected to enable thorough post-harvest quality control and provide high-quality crops to the market.

[0077] The sales management department can use the emotion estimation function to plan promotion strategies to increase consumer purchasing motivation. For example, the emotion estimation function can be used to analyze consumer purchasing motivation in real time and implement promotions at the optimal timing. It can also create personalized advertisements based on consumer emotional data to increase purchasing motivation. Furthermore, it can collect consumer feedback and continuously improve promotion strategies. This is expected to increase consumer purchasing motivation and realize effective promotions.

[0078] The sales management department can also introduce a subscription model to ensure regular revenue. For example, they can provide a subscription service that delivers agricultural products to consumers on a regular basis. The subscription model can also be used to analyze consumers' purchasing history and make personalized product recommendations. Furthermore, the subscription model can be used to build long-term relationships with consumers and increase brand loyalty. This is expected to ensure regular revenue and create a stable business model.

[0079] The sales management department can use the emotion estimation function to provide customer support that improves consumer satisfaction. For example, the emotion estimation function can be used to analyze consumer complaints and problems in real time and respond quickly. It can also improve customer support response methods and increase satisfaction based on consumer emotion data. Furthermore, it can collect consumer feedback and continuously improve the quality of service. This is expected to improve consumer satisfaction and provide more reliable customer support.

[0080] The system can also link with local tourism resources to provide agricultural experience tours. For example, agricultural experience tours can be planned in collaboration with local tourist destinations to convey the appeal of agriculture to tourists. The agricultural experience tours can also be used to promote local specialties and promote sales. Furthermore, feedback from participants in agricultural experience tours can be collected and the content of the tours can be continuously improved. This is expected to revitalize the local economy while linking with local tourism resources and promoting the appeal of agriculture.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The data analysis unit uses the generation AI to analyze weather data, soil data, and past harvest data. For example, the generation AI can predict the optimal time for crop growth based on weather data, analyze soil data to create an appropriate fertilization plan, and predict harvest yields based on past harvest data. Step 2: The crop planning unit creates an optimal crop planning plan based on the data analyzed by the data analysis unit. For example, the crop planning unit determines the type of crop, planting time, and cultivation area. Step 3: The cultivation management unit analyzes the data collected from the sensors and gives instructions on the optimal timing for irrigation, fertilization, and pest control. For example, it monitors the soil moisture level and gives instructions on irrigation, monitors the growth status of crops and gives instructions on appropriate fertilization, and monitors the occurrence of pests and diseases and gives instructions on control. Step 4: The harvesting management unit analyzes the growth status of the crops and determines the optimal harvesting time. For example, it monitors the growth status of the crops to determine the optimal harvesting time, and controls the harvesting robot to instruct it on the optimal harvesting method. Step 5: The sales management department sells agricultural products through an online sales platform, for example, by listing the harvested agricultural products on the online store, starting sales, analyzing consumer purchasing history to develop personalized sales strategies, and monitoring market price trends in real time to set optimal prices.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

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

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. Using generative AI, a data analysis department that analyzes weather data, soil data, and past harvest data; a crop planning unit that formulates an optimal crop planning plan based on the data analyzed by the data analysis unit; A cultivation management unit analyzes data collected from sensors and provides instructions on optimal timing for irrigation, fertilization, and pest control. The harvest management department analyzes the growth status of crops and determines the optimal harvest time. A sales management department that sells agricultural products through an online sales platform. A system characterized by:

2. The cultivation management department Analyzing microbial activity data, monitoring soil health in real time, and developing optimal fertilization plans 2. The system of claim 1.

3. The harvest management department Controlling harvesting robots and providing real-time instructions on optimal harvesting methods 2. The system of claim 1.

4. The sales management department Analyzing consumer purchasing history and developing personalized sales strategies 2. The system of claim 1.

5. The system comprises: Analyzing local residents' sentiments to improve agricultural project acceptance 2. The system of claim 1.

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