System

Abstract

An object of a system according to an embodiment is to enable a new farmer to optimize agricultural work, monitor a health condition of a crop, and appropriately determine a timing of putting the crop on the market.SOLUTION: A system according to an embodiment includes a generation AI, a communication line, and an information communication support service. The generated AI provides new farmers with advice on optimizing agricultural operations, monitoring crop health, and timing the marketing of agricultural products. The communication line provides a high-speed communication line. The information communication support service supports communication with local communities and information communication of market information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for new farmers to optimize agricultural operations, monitor the health of their crops, and determine the right timing for bringing them to market.

[0005] The system according to the embodiment aims to enable new farmers to optimize agricultural work, monitor the health of their crops, and appropriately determine the timing of their market launches. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a communication line, and an information and communication support service. The generation AI provides new farmers with advice on optimizing agricultural work, monitoring crop health, and timing the marketing of agricultural products. The communication line provides a high-speed communication line. The information and communication support service supports communication with local communities and market information. [Effects of the Invention]

[0007] The system according to the embodiment can enable new farmers to optimize their agricultural operations, monitor the health of their crops, and appropriately determine the timing of their market launches. [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 AgriConnect system according to an embodiment of the present invention uses generative AI to provide new farmers with advice on optimizing agricultural work, monitoring crop health, and timing the marketing of agricultural products. It also provides high-speed communication lines and provides communication support services for contact with local communities and market information. As a result, the AgriConnect system can provide comprehensive support to new farmers and contribute to the prosperity of local agricultural communities.

[0029] The AgriConnect system according to an embodiment includes a generating AI, a communication line, and an information and communication support service. The generating AI provides new farmers with advice on optimizing agricultural operations, monitoring crop health, and timing the marketing of agricultural products. For example, the generating AI analyzes information such as soil condition, weather data, and crop type to suggest optimal cultivation methods and fertilization timing. The generating AI also analyzes crop image data and environmental data collected using drones and sensors to detect problems such as pest infestations and nutrient deficiencies. The generating AI also analyzes market demand data and price trends to suggest the most profitable timing. The communication line provides high-speed communication, facilitating communication with local communities and the sharing of market information. For example, market updates and weather forecasts can be accessed via the Internet. The information and communication support service improves access to knowledge and resources. For example, farmers can exchange information with other farmers and experts through online forums and webinars. The generating AI can also provide answers to questions and advice. This allows the AgriConnect system to provide comprehensive support to new farmers and contribute to the prosperity of local agricultural communities.

[0030] The generation AI can analyze information on soil conditions, weather data, and crop types to suggest cultivation methods and fertilization timing. For example, the generation AI can analyze information on soil conditions, weather data, and crop types to suggest optimal cultivation methods and fertilization timing. For example, the generation AI can analyze the user's past work history and provide an individually customized cultivation plan. For example, it can suggest an optimal fertilization schedule based on past fertilization timing and harvest yield data. It can also learn the user's past work history and provide the optimal cultivation method for a specific crop. For example, it can suggest optimal planting intervals and watering frequency based on past harvest data. The generation AI can also generate an individually customized work schedule based on the user's past work history. For example, it can combine past weather data and work history to suggest the optimal work days. This makes it possible to improve the efficiency and optimization of agricultural work.

[0031] Generative AI can analyze crop image data and environmental data collected using drones and sensors to detect pest and disease outbreaks and nutrient deficiency problems. For example, generative AI can collect weather data in real time and provide work instructions in response to sudden weather changes. For example, if sudden rain is forecast, it can suggest bringing forward harvesting work. It can also analyze weather data in real time and immediately instruct the timing of fertilization and irrigation in response to weather changes. For example, it can suggest additional irrigation if dry weather is predicted. It can also automatically generate work schedules in response to sudden weather changes based on real-time weather data. For example, it can suggest windbreak measures if strong winds are predicted. This allows crop health to be monitored in real time and problems to be detected early.

[0032] Generative AI can analyze market demand data and price trends to suggest the best timing to make a profit. Generative AI can, for example, analyze market demand data and price trends to suggest the best timing to make a profit. For example, it can use its emotion estimation function to assess a user's stress level in real time and suggest a work schedule to reduce stress. For example, it can suggest reducing the amount of work during times of high stress. It can also analyze user emotion data to generate a schedule that includes relaxation activities and breaks to reduce stress. For example, it can prioritize light work during times of high stress. It can also use its emotion estimation function to assess a user's stress level and automatically generate a work schedule to reduce stress. For example, it can suggest refreshing activities during times of high stress. This can suggest the optimal timing to go to market and maximize profits.

[0033] The communication lines can provide high-speed communication lines, facilitating communication with the local community and sharing of market information. The communication lines can, for example, provide high-speed communication lines, facilitating communication with the local community and sharing of market information. For example, the generation AI analyzes the success stories of other farmers and suggests the most suitable best practices to the user. For example, it can refer to cultivation methods that have been successful in the same area. In addition, the success stories of other farmers can be compiled into a database, and the generation AI can use this as a basis to provide the most suitable work plan to the user. For example, it can suggest successful fertilization methods and harvest timing. In addition, a system can be built in which the generation AI refers to the success stories of other farmers and suggests the most suitable best practices to the user. For example, it can provide a work schedule based on the success stories. This makes it possible to obtain necessary information in real time, enabling quick decision-making.

[0034] The information and communication support service allows for the exchange of information with farmers and experts through online forums and webinars. The information and communication support service allows for the exchange of information with farmers and experts through online forums and webinars, for example. For example, a generation AI proposes different crop combinations and provides diverse cultivation methods to avoid crop rotation problems. For example, it proposes a crop rotation plan. It also analyzes different crop combinations and generates optimal cultivation methods to avoid crop rotation problems. For example, it proposes a crop that should be planted after a specific crop. It also builds a system where the generation AI provides diverse cultivation methods to avoid crop rotation problems based on different crop combinations. For example, it proposes an alternating crop planting plan. This improves access to knowledge and resources.

[0035] The generation AI can learn the user's past work history and provide an individually customized optimization plan. The generation AI, for example, learns the user's past work history and provides an individually customized optimization plan. For example, the generation AI analyzes the user's past work history and provides an individually customized cultivation plan. For example, it suggests an optimal fertilization schedule based on past fertilization timing and harvest yield data. It also learns the user's past work history and provides the optimal cultivation method for a specific crop. For example, it suggests optimal planting intervals and watering frequency based on past harvest data. The generation AI also generates an individually customized work schedule based on the user's past work history. For example, it combines past weather data and work history to suggest the optimal work days. This makes it possible to provide the user with an optimal agricultural work plan.

[0036] The generation AI can collect weather data in real time and generate instant work instructions that respond to weather fluctuations. For example, the generation AI collects weather data in real time and generates instant work instructions that respond to weather fluctuations. For example, the generation AI collects weather data in real time and provides work instructions that respond to sudden weather fluctuations. For example, if sudden rain is forecast, it may suggest bringing forward harvesting work. It also analyzes weather data in real time and immediately instructs on the timing of fertilization and irrigation in accordance with weather fluctuations. For example, it may suggest additional irrigation if dry weather is predicted. The generation AI also automatically generates a work schedule that responds to sudden weather fluctuations based on real-time weather data. For example, it may suggest windbreak measures if strong winds are forecast. This allows for quick response to sudden weather fluctuations.

[0037] The generation AI can refer to the success stories of other farmers and suggest best practices. The generation AI, for example, refers to the success stories of other farmers and suggests best practices. For example, the generation AI could analyze the success stories of other farmers and suggest the best practices that are optimal for the user. For example, it could refer to cultivation methods that have been successful in the same area. In addition, a database of other farmers' success stories could be created, and the generation AI could use this as a basis to provide the user with the optimal work plan. For example, it could suggest successful fertilization methods or harvest timing. In addition, a system could be built in which the generation AI refers to the success stories of other farmers and suggests the best practices that are optimal for the user. For example, it could provide a work schedule based on success stories. This makes it possible to utilize the success stories of other farmers to suggest the optimal agricultural work.

[0038] The generative AI can propose different crop combinations and provide a variety of cultivation methods to avoid continuous crop damage. For example, the generative AI proposes different crop combinations and provides a variety of cultivation methods to avoid continuous crop damage. For example, the generative AI proposes different crop combinations and provides a variety of cultivation methods to avoid continuous crop damage. For example, it proposes a crop rotation plan. It can also analyze different crop combinations and generate optimal cultivation methods to avoid continuous crop damage. For example, it suggests which crops should be planted after a specific crop. It can also build a system that provides a variety of cultivation methods to avoid continuous crop damage based on different crop combinations. For example, it proposes an alternating crop planting plan. This can avoid continuous crop damage and maintain the health of crops.

[0039] The generative AI can introduce an algorithm that compares it with past crop data to detect anomalies early. For example, the generative AI introduces an algorithm that compares it with past crop data to detect anomalies early. For example, the generative AI detects anomalies based on past pest and disease outbreak data. In addition, an algorithm is developed to analyze past crop data and detect anomalies early. For example, past yield data is compared with current data. In addition, a system is constructed in which the generative AI introduces an algorithm that compares it with past data to detect anomalies early. For example, past weather data is compared with current data. This enables anomalies to be detected early and a prompt response can be taken.

[0040] Generative AI can integrate data from different sensors to perform more accurate health assessments. For example, generative AI can integrate data from different sensors to assess the health of crops with high accuracy. For example, it can combine data from soil sensors and weather sensors. It can also analyze data from different sensors to build a system that assesses the health of crops with high accuracy. For example, it can integrate image data from drones with data from ground sensors. Generative AI can also develop algorithms that assess the health of crops with high accuracy based on data from different sensors. For example, it can combine data from temperature sensors and humidity sensors. This allows for highly accurate assessment of crop health.

[0041] The generative AI can compare data from different regions and propose region-specific pest and disease control measures. The generative AI, for example, compares data from different regions and proposes region-specific pest and disease control measures. For example, the generative AI compares data from different regions and proposes region-specific pest and disease control measures. For example, it analyzes pest and disease occurrence patterns for each region. It also analyzes crop data from different regions and builds a system that proposes region-specific pest and disease control measures. For example, it combines weather data and pest and disease occurrence data for each region. It also develops an algorithm that proposes region-specific pest and disease control measures based on data from different regions. For example, it compares the health of crops in each region. This makes it possible to propose region-specific pest and disease control measures and maintain the health of crops.

[0042] Generative AI can analyze animal behavior data and evaluate the impact on crops. For example, generative AI analyzes animal behavior data and evaluates the impact on crops. For example, generative AI analyzes animal behavior data and evaluates the impact on crops. For example, generative AI analyzes animal behavior data and evaluates the impact on crops. For example, it analyzes the movement patterns of wild animals and predicts damage to crops. In addition, a system can be built based on animal behavior data to evaluate the impact on crop health. For example, it analyzes livestock behavior data and evaluates the impact on crops. In addition, generative AI can analyze animal behavior data and develop algorithms to evaluate the impact on crops. For example, it analyzes bird movement patterns and predicts the impact on crops. This makes it possible to evaluate the impact of animal behavior on crops and take appropriate measures.

[0043] Generative AI can learn from past market data and predict the optimal timing for launching a product to market for each season. For example, generative AI learns from past market data and predicts the optimal timing for launching a product to market for each season. For example, generative AI learns from past market data and predicts the optimal timing for launching a product to market for each season. For example, it suggests the optimal harvest time based on past price trend data. It can also build a system that predicts the optimal timing for launching a product to market by analyzing past market data and learning seasonal demand patterns. For example, it suggests the harvest time for crops that are in high demand during certain seasons. It can also develop an algorithm that predicts the optimal timing for launching a product to market for each season based on past market data. For example, it analyzes past sales data and suggests the timing that will be most profitable. This makes it possible to predict the optimal timing for launching a product to market for each season and maximize profits.

[0044] Generative AI can analyze competitors' trends in real time and propose the best timing to launch a product to secure a competitive advantage. For example, generative AI can analyze competitors' trends in real time and propose the best timing to launch a product to secure a competitive advantage. For example, generative AI can analyze competitors' trends in real time and propose the best timing to launch a product to secure a competitive advantage. For example, it can analyze competitors' harvest times and determine the optimal timing to launch a product to secure a competitive advantage. In addition, a system can be built that collects competitor trend data in real time and uses that data to propose the optimal timing to launch a product to secure a competitive advantage. For example, it can analyze competitors' pricing and propose the best timing to launch a product to secure a competitive advantage. In addition, generative AI can analyze competitors' trends in real time and develop an algorithm that predicts the best timing to launch a product to secure a competitive advantage. For example, it can analyze competitors' sales strategies and propose the optimal timing to launch a product to secure a competitive advantage. This allows you to understand competitors' trends and secure a competitive advantage.

[0045] Generative AI can compare demand data from different markets and select the optimal market. For example, generative AI compares demand data from different markets and selects the optimal market. For example, generative AI compares demand data from different markets and selects the optimal market. For example, it analyzes demand data from domestic and overseas markets and suggests the most profitable market. In addition, a system is built in which demand data from different markets is analyzed and generative AI selects the optimal market. For example, it suggests the optimal market based on demand data for each region. In addition, generative AI develops an algorithm to select the optimal market based on demand data from different markets. For example, it predicts when demand will increase in a particular market and suggests the optimal market. This makes it possible to select the optimal market and maximize profits.

[0046] Generative AI can analyze logistics data and propose the most efficient delivery schedule. Generative AI, for example, analyzes logistics data and proposes the most efficient delivery schedule. For example, generative AI analyzes logistics data and proposes the most efficient delivery schedule. For example, it proposes a schedule that optimizes logistics costs and delivery times. In addition, a system is built in which generative AI proposes optimal delivery schedules based on logistics data. For example, it proposes a schedule that optimizes delivery routes and delivery timings. In addition, generative AI analyzes logistics data and develops an algorithm that predicts the most efficient delivery schedule. For example, it proposes an optimal delivery schedule that takes into account logistics congestion. This makes it possible to propose efficient delivery schedules and reduce logistics costs.

[0047] The communication line can utilize a high-speed communication line to enable real-time video conferencing, allowing for immediate advice from experts in remote locations. For example, a high-speed communication line can be used to realize real-time video conferencing, allowing for immediate advice from experts in remote locations. For example, a high-speed communication line can be used to realize real-time video conferencing, allowing for immediate advice from experts in remote locations. For example, a video conference can be held with an agricultural technology expert and advice can be received in real time. Furthermore, a system can be built using a high-speed communication line to enable real-time video conferencing with experts in remote locations. For example, a video conference can be held with a pest and disease expert and advice can be received on the health of crops. Furthermore, a system can be developed using a high-speed communication line to enable real-time video conferencing, allowing for immediate advice from experts in remote locations. For example, a video conference can be held with a fertilization expert and advice can be received on the optimal fertilization method. This allows for real-time advice to be received from experts in remote locations.

[0048] The communication line can be used to enable the remote control of agricultural machinery and achieve efficient work. For example, a high-speed communication line can be used to enable the remote control of agricultural machinery and achieve efficient work. For example, a high-speed communication line can be used to enable the remote control of agricultural machinery and achieve efficient work. For example, a tractor or drone can be remotely controlled to cultivate or fertilize. A system can also be built using a high-speed communication line to enable the remote control of agricultural machinery. For example, a harvester can be remotely controlled to perform efficient harvesting work. A high-speed communication line can also be used to develop a system that enables the remote control of agricultural machinery and achieves efficient work. For example, an irrigation system can be remotely controlled to perform optimal watering. This allows for efficient work through the remote control of agricultural machinery.

[0049] The communication line can utilize a high-speed communication line to provide cloud storage of agricultural data, thereby enabling safe storage and sharing of data. For example, a high-speed communication line can be utilized to provide cloud storage of agricultural data, thereby enabling safe storage and sharing of data. For example, crop growth data and harvest data can be stored in the cloud. A system can also be constructed using a high-speed communication line to provide cloud storage of agricultural data. For example, soil data and weather data can be stored in the cloud and shared with other farmers. A system can also be developed using a high-speed communication line to provide cloud storage of agricultural data, thereby enabling safe storage and sharing of data. For example, pest and disease data and fertilization data can be stored in the cloud and shared with experts. This enables safe storage and sharing of agricultural data.

[0050] The communication line can utilize a high-speed communication line to provide agricultural education programs online and support the improvement of skills of new farmers. For example, a high-speed communication line can be used to provide agricultural education programs online and support the improvement of skills of new farmers. For example, a high-speed communication line can be used to provide agricultural education programs online and support the improvement of skills of new farmers. For example, agricultural techniques can be learned through online courses and webinars. A system can also be built that uses a high-speed communication line to provide agricultural education programs online. For example, online lectures and practical training by experts can be provided. A system can also be developed that uses a high-speed communication line to provide agricultural education programs online and support the improvement of skills of new farmers. For example, online practical training and feedback can be provided. This can support the improvement of skills of new farmers.

[0051] An information communication support service can use a generation AI to learn past questions and answers in order to provide the best answer to a user's question. For example, an information communication support service uses a generation AI to learn past questions and answers in order to provide the best answer to a user's question. For example, the generation AI learns past questions and answers and provides the best answer to the user's question. For example, answers are quickly generated based on similar questions from the past. In addition, past question and answer data is analyzed, and a system is built in which the generation AI provides the best answer based on that. For example, standard answers to frequently asked questions are automatically generated. In addition, the generation AI learns past questions and answers and provides the best answer to the user's question in real time. For example, specific advice based on past data is provided. In this way, the system can learn past questions and answers and provide the best answer to the user.

[0052] An information communication support service can use a generation AI to automatically recommend related information based on a user's interests and concerns. For example, an information communication support service uses a generation AI to automatically recommend related information based on a user's interests and concerns. For example, the generation AI analyzes a user's interests and concerns and automatically recommends related information. For example, articles and materials related to topics that interest the user are provided. In addition, a system can be built in which the generation AI automatically recommends related information based on user interest and concern data. For example, information can be recommended based on the user's past search history. In addition, the generation AI can analyze a user's interests and concerns and recommend related information in real time. For example, the latest information related to topics that interest the user can be provided. This makes it possible to automatically recommend related information based on a user's interests and concerns.

[0053] Information and communication support services can support communication in different languages ​​and promote international information exchange. Information and communication support services, for example, support communication in different languages ​​and promote international information exchange. For example, a function to support communication in different languages ​​can be added to the information and communication support service to promote international information exchange. For example, a real-time translation function can be provided. Furthermore, a system can be built to support communication in different languages ​​and promote international information exchange. For example, a multilingual chatbot can be provided. Furthermore, a function to support communication in different languages ​​can be added to the information and communication support service to develop a system to promote international information exchange. For example, an automatic translation function between different languages ​​can be provided. This can support communication in different languages ​​and promote international information exchange.

[0054] An information communication support service can utilize visual content to provide information that is easy to understand visually. An information communication support service, for example, utilizes visual content to provide information that is easy to understand visually. For example, a function that utilizes visual content to provide information that is easy to understand visually is added to the information communication support service. For example, information is provided using infographics and videos. In addition, a system that utilizes visual content to provide information that is easy to understand visually is built. For example, information is provided using charts and illustrations. In addition, a system is developed that utilizes visual content to provide information that is easy to understand visually. For example, information is provided using animations and slideshows. This makes it possible to provide information that is easy to understand visually.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The AgriConnect system can also be equipped with a health management unit that monitors the user's health. For example, it collects the user's heart rate and sleep data to evaluate their health. The health management unit can also analyze the user's health data and suggest appropriate times for rest and exercise. For example, it can suggest taking a break after working for a long period of time. The health management unit can also adjust the work schedule based on the user's health. For example, it can prioritize light work if the user is not feeling well. This supports efficient agricultural work while maintaining the user's health.

[0057] The AgriConnect system can also be equipped with an energy management unit. For example, it collects energy consumption data from agricultural machinery and facilities and suggests efficient energy use. The energy management unit can also provide advice to promote the use of renewable energy. For example, it could suggest the introduction of a solar power generation system. The energy management unit can also analyze energy consumption data and generate an optimal energy usage schedule to reduce costs. For example, it could suggest running machinery during times when electricity rates are low. This reduces energy costs and enables sustainable agriculture.

[0058] The AgriConnect system can also be equipped with an education support section. For example, it can provide the latest research results and technical information on agriculture. The education support section can also provide online courses and training programs to help users improve their skills. For example, it can provide courses on pest control and soil improvement. The education support section can also monitor users' learning progress and propose individually customized learning plans. For example, it can suggest complementary courses if a specific skill is lacking. This can improve users' knowledge and skills, supporting the efficiency and quality of agricultural work.

[0059] The AgriConnect system can also be equipped with an environmental monitoring unit. For example, it collects soil pH and water quality data to evaluate environmental conditions. The environmental monitoring unit can also analyze environmental data and provide advice for sustainable agricultural practices. For example, it can suggest the application of lime if the soil is highly acidic. The environmental monitoring unit can also evaluate the health of crops based on environmental data and suggest appropriate measures. For example, it can suggest improving irrigation water if water quality deteriorates. This allows for environmentally friendly agriculture and maintains crop health.

[0060] The AgriConnect system can also be equipped with a marketing support section. For example, it can support agricultural product branding strategies and promotional activities. The marketing support section can also analyze consumer preference data and propose optimal marketing strategies. For example, it can suggest crops that are popular in a specific market. The marketing support section can also promote the use of online sales platforms and help expand agricultural product sales channels. For example, it can suggest sales methods on e-commerce sites. This can promote agricultural product sales and maximize profits.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: Generative AI provides new farmers with advice on optimizing agricultural work, monitoring crop health, and timing the release of agricultural products to market. Specifically, Generative AI analyzes information such as soil conditions, weather data, and crop type to suggest optimal cultivation methods and fertilization timing. It also analyzes crop image data and environmental data collected using drones and sensors to detect problems such as pests and diseases and nutrient deficiencies. It also analyzes market demand data and price trends to suggest the most profitable timing. Step 2: Communication lines provide high-speed communication lines to facilitate communication with the local community and sharing of market information. For example, market updates and weather forecasts can be accessed via the internet. Step 3: Information and communication support services improve access to knowledge and resources. For example, online forums and webinars allow farmers to exchange information with other farmers and experts. Generative AI can also be used to provide answers to questions and advice.

[0063] (Example 2) The AgriConnect system according to an embodiment of the present invention uses generative AI to provide new farmers with advice on optimizing agricultural work, monitoring crop health, and timing the marketing of agricultural products. It also provides high-speed communication lines and provides communication support services for contact with local communities and market information. As a result, the AgriConnect system can provide comprehensive support to new farmers and contribute to the prosperity of local agricultural communities.

[0064] The AgriConnect system according to an embodiment includes a generating AI, a communication line, and an information and communication support service. The generating AI provides new farmers with advice on optimizing agricultural operations, monitoring crop health, and timing the marketing of agricultural products. For example, the generating AI analyzes information such as soil condition, weather data, and crop type to suggest optimal cultivation methods and fertilization timing. The generating AI also analyzes crop image data and environmental data collected using drones and sensors to detect problems such as pest infestations and nutrient deficiencies. The generating AI also analyzes market demand data and price trends to suggest the most profitable timing. The communication line provides high-speed communication, facilitating communication with local communities and the sharing of market information. For example, market updates and weather forecasts can be accessed via the Internet. The information and communication support service improves access to knowledge and resources. For example, farmers can exchange information with other farmers and experts through online forums and webinars. The generating AI can also provide answers to questions and advice. This allows the AgriConnect system to provide comprehensive support to new farmers and contribute to the prosperity of local agricultural communities.

[0065] The generation AI can analyze information on soil conditions, weather data, and crop types to suggest cultivation methods and fertilization timing. For example, the generation AI can analyze information on soil conditions, weather data, and crop types to suggest optimal cultivation methods and fertilization timing. For example, the generation AI can analyze the user's past work history and provide an individually customized cultivation plan. For example, it can suggest an optimal fertilization schedule based on past fertilization timing and harvest yield data. It can also learn the user's past work history and provide the optimal cultivation method for a specific crop. For example, it can suggest optimal planting intervals and watering frequency based on past harvest data. The generation AI can also generate an individually customized work schedule based on the user's past work history. For example, it can combine past weather data and work history to suggest the optimal work days. This makes it possible to improve the efficiency and optimization of agricultural work.

[0066] Generative AI can analyze crop image data and environmental data collected using drones and sensors to detect pest and disease outbreaks and nutrient deficiency problems. For example, generative AI can collect weather data in real time and provide work instructions in response to sudden weather changes. For example, if sudden rain is forecast, it can suggest bringing forward harvesting work. It can also analyze weather data in real time and immediately instruct the timing of fertilization and irrigation in response to weather changes. For example, it can suggest additional irrigation if dry weather is predicted. It can also automatically generate work schedules in response to sudden weather changes based on real-time weather data. For example, it can suggest windbreak measures if strong winds are predicted. This allows crop health to be monitored in real time and problems to be detected early.

[0067] Generative AI can analyze market demand data and price trends to suggest the best timing to make a profit. Generative AI can, for example, analyze market demand data and price trends to suggest the best timing to make a profit. For example, it can use its emotion estimation function to assess a user's stress level in real time and suggest a work schedule to reduce stress. For example, it can suggest reducing the amount of work during times of high stress. It can also analyze user emotion data to generate a schedule that includes relaxation activities and breaks to reduce stress. For example, it can prioritize light work during times of high stress. It can also use its emotion estimation function to assess a user's stress level and automatically generate a work schedule to reduce stress. For example, it can suggest refreshing activities during times of high stress. This can suggest the optimal timing to go to market and maximize profits.

[0068] The communication lines can provide high-speed communication lines, facilitating communication with the local community and sharing of market information. The communication lines can, for example, provide high-speed communication lines, facilitating communication with the local community and sharing of market information. For example, the generation AI analyzes the success stories of other farmers and suggests the most suitable best practices to the user. For example, it can refer to cultivation methods that have been successful in the same area. In addition, the success stories of other farmers can be compiled into a database, and the generation AI can use this as a basis to provide the most suitable work plan to the user. For example, it can suggest successful fertilization methods and harvest timing. In addition, a system can be built in which the generation AI refers to the success stories of other farmers and suggests the most suitable best practices to the user. For example, it can provide a work schedule based on the success stories. This makes it possible to obtain necessary information in real time, enabling quick decision-making.

[0069] The information and communication support service allows for the exchange of information with farmers and experts through online forums and webinars. The information and communication support service allows for the exchange of information with farmers and experts through online forums and webinars, for example. For example, a generation AI proposes different crop combinations and provides diverse cultivation methods to avoid crop rotation problems. For example, it proposes a crop rotation plan. It also analyzes different crop combinations and generates optimal cultivation methods to avoid crop rotation problems. For example, it proposes a crop that should be planted after a specific crop. It also builds a system where the generation AI provides diverse cultivation methods to avoid crop rotation problems based on different crop combinations. For example, it proposes an alternating crop planting plan. This improves access to knowledge and resources.

[0070] The generation AI can learn the user's past work history and provide an individually customized optimization plan. The generation AI, for example, learns the user's past work history and provides an individually customized optimization plan. For example, the generation AI analyzes the user's past work history and provides an individually customized cultivation plan. For example, it suggests an optimal fertilization schedule based on past fertilization timing and harvest yield data. It also learns the user's past work history and provides the optimal cultivation method for a specific crop. For example, it suggests optimal planting intervals and watering frequency based on past harvest data. The generation AI also generates an individually customized work schedule based on the user's past work history. For example, it combines past weather data and work history to suggest the optimal work days. This makes it possible to provide the user with an optimal agricultural work plan.

[0071] The generation AI can collect weather data in real time and generate instant work instructions that respond to weather fluctuations. For example, the generation AI collects weather data in real time and generates instant work instructions that respond to weather fluctuations. For example, the generation AI collects weather data in real time and provides work instructions that respond to sudden weather fluctuations. For example, if sudden rain is forecast, it may suggest bringing forward harvesting work. It also analyzes weather data in real time and immediately instructs on the timing of fertilization and irrigation in accordance with weather fluctuations. For example, it may suggest additional irrigation if dry weather is predicted. The generation AI also automatically generates a work schedule that responds to sudden weather fluctuations based on real-time weather data. For example, it may suggest windbreak measures if strong winds are forecast. This allows for quick response to sudden weather fluctuations.

[0072] The generation AI can use the emotion estimation function to evaluate the user's stress level and propose a work schedule to reduce stress. For example, the generation AI uses the emotion estimation function to evaluate the user's stress level in real time and propose a work schedule to reduce stress. For example, it may propose reducing the amount of work during times of high stress. It can also analyze the user's emotion data and generate a schedule that includes relaxation activities and breaks to reduce stress. For example, it may prioritize light work during times of high stress. It can also use the emotion estimation function to evaluate the user's stress level and automatically generate a work schedule to reduce stress. For example, it may suggest refreshing activities during times of high stress. This reduces the user's stress and improves work efficiency.

[0073] The generation AI can refer to the success stories of other farmers and suggest best practices. The generation AI, for example, refers to the success stories of other farmers and suggests best practices. For example, the generation AI could analyze the success stories of other farmers and suggest the best practices that are optimal for the user. For example, it could refer to cultivation methods that have been successful in the same area. In addition, a database of other farmers' success stories could be created, and the generation AI could use this as a basis to provide the user with the optimal work plan. For example, it could suggest successful fertilization methods or harvest timing. In addition, a system could be built in which the generation AI refers to the success stories of other farmers and suggests the best practices that are optimal for the user. For example, it could provide a work schedule based on success stories. This makes it possible to utilize the success stories of other farmers to suggest the optimal agricultural work.

[0074] The generative AI can propose different crop combinations and provide a variety of cultivation methods to avoid continuous crop damage. For example, the generative AI proposes different crop combinations and provides a variety of cultivation methods to avoid continuous crop damage. For example, the generative AI proposes different crop combinations and provides a variety of cultivation methods to avoid continuous crop damage. For example, it proposes a crop rotation plan. It can also analyze different crop combinations and generate optimal cultivation methods to avoid continuous crop damage. For example, it suggests which crops should be planted after a specific crop. It can also build a system that provides a variety of cultivation methods to avoid continuous crop damage based on different crop combinations. For example, it proposes an alternating crop planting plan. This can avoid continuous crop damage and maintain the health of crops.

[0075] The generation AI can use the emotion estimation function to generate positive feedback to increase the user's motivation and improve work efficiency. The generation AI, for example, uses the emotion estimation function to generate positive feedback to increase the user's motivation. For example, it sends encouraging messages according to the progress of the work. It also analyzes the user's emotion data and builds a system that provides positive feedback to increase motivation. For example, it provides feedback that gives the user a sense of accomplishment. It also uses the emotion estimation function to provide positive feedback in real time to increase the user's motivation. For example, it sends a message praising the results of the work. This increases the user's motivation and improves work efficiency.

[0076] The generative AI can introduce an algorithm that compares it with past crop data to detect anomalies early. For example, the generative AI introduces an algorithm that compares it with past crop data to detect anomalies early. For example, the generative AI detects anomalies based on past pest and disease outbreak data. In addition, an algorithm is developed to analyze past crop data and detect anomalies early. For example, past yield data is compared with current data. In addition, a system is constructed in which the generative AI introduces an algorithm that compares it with past data to detect anomalies early. For example, past weather data is compared with current data. This enables anomalies to be detected early and a prompt response can be taken.

[0077] Generative AI can integrate data from different sensors to perform more accurate health assessments. For example, generative AI can integrate data from different sensors to assess the health of crops with high accuracy. For example, it can combine data from soil sensors and weather sensors. It can also analyze data from different sensors to build a system that assesses the health of crops with high accuracy. For example, it can integrate image data from drones with data from ground sensors. Generative AI can also develop algorithms that assess the health of crops with high accuracy based on data from different sensors. For example, it can combine data from temperature sensors and humidity sensors. This allows for highly accurate assessment of crop health.

[0078] The generation AI can use the emotion estimation function to send reassuring notifications about the health of the crops to reduce the user's anxiety. The generation AI, for example, uses the emotion estimation function to send reassuring notifications about the health of the crops to reduce the user's anxiety. For example, it sends a message informing the user that the crops are healthy. The generation AI also analyzes the user's emotion data and builds a system that provides reassuring notifications to reduce anxiety. For example, it periodically reports on the growth status of the crops. The generation AI also uses the emotion estimation function to send reassuring notifications in real time to reduce the user's anxiety. For example, it sends a message informing the user that the health of the crops is good. This reduces the user's anxiety and provides a sense of security.

[0079] The generative AI can compare data from different regions and propose region-specific pest and disease control measures. The generative AI, for example, compares data from different regions and proposes region-specific pest and disease control measures. For example, the generative AI compares data from different regions and proposes region-specific pest and disease control measures. For example, it analyzes pest and disease occurrence patterns for each region. It also analyzes crop data from different regions and builds a system that proposes region-specific pest and disease control measures. For example, it combines weather data and pest and disease occurrence data for each region. It also develops an algorithm that proposes region-specific pest and disease control measures based on data from different regions. For example, it compares the health of crops in each region. This makes it possible to propose region-specific pest and disease control measures and maintain the health of crops.

[0080] Generative AI can analyze animal behavior data and evaluate the impact on crops. For example, generative AI analyzes animal behavior data and evaluates the impact on crops. For example, generative AI analyzes animal behavior data and evaluates the impact on crops. For example, generative AI analyzes animal behavior data and evaluates the impact on crops. For example, it analyzes the movement patterns of wild animals and predicts damage to crops. In addition, a system can be built based on animal behavior data to evaluate the impact on crop health. For example, it analyzes livestock behavior data and evaluates the impact on crops. In addition, generative AI can analyze animal behavior data and develop algorithms to evaluate the impact on crops. For example, it analyzes bird movement patterns and predicts the impact on crops. This makes it possible to evaluate the impact of animal behavior on crops and take appropriate measures.

[0081] The generation AI can use the emotion estimation function to analyze the emotions a user has about the health of their crops and provide appropriate advice. The generation AI can, for example, use the emotion estimation function to analyze the emotions a user has about the health of their crops and provide appropriate advice. For example, the emotion estimation function can be used to analyze the emotions a user has about the health of their crops and provide appropriate advice. For example, if the user is feeling anxious, the system can provide advice that gives a sense of security. In addition, a system can be built that analyzes user emotion data and provides advice based on the user's emotions about the health of their crops. For example, if the user is worried, the system can suggest specific measures to take. In addition, the emotion estimation function can be used to analyze the user's emotions and provide appropriate advice about the health of their crops in real time. For example, if the user is feeling anxious, the system can send an encouraging message. This makes it possible to provide appropriate advice based on the user's emotions.

[0082] Generative AI can learn from past market data and predict the optimal timing for launching a product to market for each season. For example, generative AI learns from past market data and predicts the optimal timing for launching a product to market for each season. For example, generative AI learns from past market data and predicts the optimal timing for launching a product to market for each season. For example, it suggests the optimal harvest time based on past price trend data. It can also build a system that predicts the optimal timing for launching a product to market by analyzing past market data and learning seasonal demand patterns. For example, it suggests the harvest time for crops that are in high demand during certain seasons. It can also develop an algorithm that predicts the optimal timing for launching a product to market for each season based on past market data. For example, it analyzes past sales data and suggests the timing that will be most profitable. This makes it possible to predict the optimal timing for launching a product to market for each season and maximize profits.

[0083] Generative AI can analyze competitors' trends in real time and propose the best timing to launch a product to secure a competitive advantage. For example, generative AI can analyze competitors' trends in real time and propose the best timing to launch a product to secure a competitive advantage. For example, generative AI can analyze competitors' trends in real time and propose the best timing to launch a product to secure a competitive advantage. For example, it can analyze competitors' harvest times and determine the optimal timing to launch a product to secure a competitive advantage. In addition, a system can be built that collects competitor trend data in real time and uses that data to propose the optimal timing to launch a product to secure a competitive advantage. For example, it can analyze competitors' pricing and propose the best timing to launch a product to secure a competitive advantage. In addition, generative AI can analyze competitors' trends in real time and develop an algorithm that predicts the best timing to launch a product to secure a competitive advantage. For example, it can analyze competitors' sales strategies and propose the optimal timing to launch a product to secure a competitive advantage. This allows you to understand competitors' trends and secure a competitive advantage.

[0084] The generative AI can use the emotion estimation function to evaluate user expectations and propose the timing of a market launch that will meet those expectations. The generative AI, for example, uses the emotion estimation function to evaluate user expectations and propose the timing of a market launch that will meet those expectations. For example, the emotion estimation function can be used to evaluate user expectations and propose the timing of a market launch that will meet those expectations. For example, a market launch can be proposed at a time when users have high expectations. A system can also be built that analyzes user emotion data and proposes the optimal timing of a market launch based on those expectations. For example, a timing can be proposed that will achieve the revenue that the user expects. The emotion estimation function can also be used to evaluate user expectations and propose the timing of a market launch that will meet those expectations in real time. For example, a market launch can be proposed at a time when user expectations are high. This makes it possible to propose a market launch timing that meets user expectations.

[0085] Generative AI can compare demand data from different markets and select the optimal market. For example, generative AI compares demand data from different markets and selects the optimal market. For example, generative AI compares demand data from different markets and selects the optimal market. For example, it analyzes demand data from domestic and overseas markets and suggests the most profitable market. In addition, a system is built in which demand data from different markets is analyzed and generative AI selects the optimal market. For example, it suggests the optimal market based on demand data for each region. In addition, generative AI develops an algorithm to select the optimal market based on demand data from different markets. For example, it predicts when demand will increase in a particular market and suggests the optimal market. This makes it possible to select the optimal market and maximize profits.

[0086] Generative AI can analyze logistics data and propose the most efficient delivery schedule. Generative AI, for example, analyzes logistics data and proposes the most efficient delivery schedule. For example, generative AI analyzes logistics data and proposes the most efficient delivery schedule. For example, it proposes a schedule that optimizes logistics costs and delivery times. In addition, a system is built in which generative AI proposes optimal delivery schedules based on logistics data. For example, it proposes a schedule that optimizes delivery routes and delivery timings. In addition, generative AI analyzes logistics data and develops an algorithm that predicts the most efficient delivery schedule. For example, it proposes an optimal delivery schedule that takes into account logistics congestion. This makes it possible to propose efficient delivery schedules and reduce logistics costs.

[0087] The generation AI can use the emotion estimation function to analyze the emotions that users have regarding market launch and suggest the optimal timing. The generation AI, for example, uses the emotion estimation function to analyze the emotions that users have regarding market launch and suggest the optimal timing. For example, using the emotion estimation function to analyze the emotions that users have regarding market launch and suggest the optimal timing. For example, it can suggest market launch when the user feels confident. A system can also be built that analyzes user emotion data and suggests the optimal timing based on their emotions regarding market launch. For example, it can suggest market launch when the user feels reassured. The emotion estimation function can also be used to analyze user emotions and suggest the optimal timing for market launch in real time. For example, it can suggest market launch when the user is feeling positive. This makes it possible to suggest the optimal market launch timing based on user emotions.

[0088] The communication line can utilize a high-speed communication line to enable real-time video conferencing, allowing for immediate advice from experts in remote locations. For example, a high-speed communication line can be used to realize real-time video conferencing, allowing for immediate advice from experts in remote locations. For example, a high-speed communication line can be used to realize real-time video conferencing, allowing for immediate advice from experts in remote locations. For example, a video conference can be held with an agricultural technology expert and advice can be received in real time. Furthermore, a system can be built using a high-speed communication line to enable real-time video conferencing with experts in remote locations. For example, a video conference can be held with a pest and disease expert and advice can be received on the health of crops. Furthermore, a system can be developed using a high-speed communication line to enable real-time video conferencing, allowing for immediate advice from experts in remote locations. For example, a video conference can be held with a fertilization expert and advice can be received on the optimal fertilization method. This allows for real-time advice to be received from experts in remote locations.

[0089] The communication line can be used to enable the remote control of agricultural machinery and achieve efficient work. For example, a high-speed communication line can be used to enable the remote control of agricultural machinery and achieve efficient work. For example, a high-speed communication line can be used to enable the remote control of agricultural machinery and achieve efficient work. For example, a tractor or drone can be remotely controlled to cultivate or fertilize. A system can also be built using a high-speed communication line to enable the remote control of agricultural machinery. For example, a harvester can be remotely controlled to perform efficient harvesting work. A high-speed communication line can also be used to develop a system that enables the remote control of agricultural machinery and achieves efficient work. For example, an irrigation system can be remotely controlled to perform optimal watering. This allows for efficient work through the remote control of agricultural machinery.

[0090] A communication line can use an emotion estimation function to evaluate a user's communication experience and provide an optimal communication environment. A communication line can use, for example, an emotion estimation function to evaluate a user's communication experience and provide an optimal communication environment. For example, the emotion estimation function can be used to evaluate a user's communication experience and provide an optimal communication environment. For example, the communication speed and connection stability can be evaluated and an optimal communication plan can be proposed. A system can also be built that analyzes the user's emotion data and provides an optimal communication environment based on the communication experience. For example, a communication environment that does not cause stress to the user can be provided. The emotion estimation function can also be used to evaluate a user's communication experience and provide an optimal communication environment in real time. For example, if communication quality deteriorates, a proposal can be made to immediately improve it. In this way, the user's communication experience can be evaluated and an optimal communication environment can be provided.

[0091] The communication line can utilize a high-speed communication line to provide cloud storage of agricultural data, thereby enabling safe storage and sharing of data. For example, a high-speed communication line can be utilized to provide cloud storage of agricultural data, thereby enabling safe storage and sharing of data. For example, crop growth data and harvest data can be stored in the cloud. A system can also be constructed using a high-speed communication line to provide cloud storage of agricultural data. For example, soil data and weather data can be stored in the cloud and shared with other farmers. A system can also be developed using a high-speed communication line to provide cloud storage of agricultural data, thereby enabling safe storage and sharing of data. For example, pest and disease data and fertilization data can be stored in the cloud and shared with experts. This enables safe storage and sharing of agricultural data.

[0092] The communication line can utilize a high-speed communication line to provide agricultural education programs online and support the improvement of skills of new farmers. For example, a high-speed communication line can be used to provide agricultural education programs online and support the improvement of skills of new farmers. For example, a high-speed communication line can be used to provide agricultural education programs online and support the improvement of skills of new farmers. For example, agricultural techniques can be learned through online courses and webinars. A system can also be built that uses a high-speed communication line to provide agricultural education programs online. For example, online lectures and practical training by experts can be provided. A system can also be developed that uses a high-speed communication line to provide agricultural education programs online and support the improvement of skills of new farmers. For example, online practical training and feedback can be provided. This can support the improvement of skills of new farmers.

[0093] A communication line can use an emotion estimation function to analyze a user's emotions regarding their communication experience and use the results to improve the communication service. A communication line can, for example, use the emotion estimation function to analyze a user's emotions regarding their communication experience and use the results to improve the communication service. For example, the emotion estimation function can be used to analyze a user's emotions regarding their communication experience and use the results to improve the communication service. For example, the emotion estimation function can be used to identify points of dissatisfaction among users and propose improvements. A system can also be built that analyzes user emotion data and improves communication services based on their emotions regarding their communication experience. For example, a communication plan that satisfies users can be provided. The emotion estimation function can also be used to analyze a user's emotions regarding their communication experience in real time and use the results to improve the communication service. For example, if communication quality deteriorates, improvements can be immediately proposed. This allows the user's communication experience to be analyzed and used to improve the communication service.

[0094] An information communication support service can use a generation AI to learn past questions and answers in order to provide the best answer to a user's question. For example, an information communication support service uses a generation AI to learn past questions and answers in order to provide the best answer to a user's question. For example, the generation AI learns past questions and answers and provides the best answer to the user's question. For example, answers are quickly generated based on similar questions from the past. In addition, past question and answer data is analyzed, and a system is built in which the generation AI provides the best answer based on that. For example, standard answers to frequently asked questions are automatically generated. In addition, the generation AI learns past questions and answers and provides the best answer to the user's question in real time. For example, specific advice based on past data is provided. In this way, the system can learn past questions and answers and provide the best answer to the user.

[0095] An information communication support service can use a generation AI to automatically recommend related information based on a user's interests and concerns. For example, an information communication support service uses a generation AI to automatically recommend related information based on a user's interests and concerns. For example, the generation AI analyzes a user's interests and concerns and automatically recommends related information. For example, articles and materials related to topics that interest the user are provided. In addition, a system can be built in which the generation AI automatically recommends related information based on user interest and concern data. For example, information can be recommended based on the user's past search history. In addition, the generation AI can analyze a user's interests and concerns and recommend related information in real time. For example, the latest information related to topics that interest the user can be provided. This makes it possible to automatically recommend related information based on a user's interests and concerns.

[0096] The information communication support service can use the emotion estimation function to evaluate the user's emotion in response to a question and provide an answer in an appropriate tone. The information communication support service, for example, uses the emotion estimation function to evaluate the user's emotion in response to a question and provide an answer in an appropriate tone. For example, the emotion estimation function is used to evaluate the user's emotion in response to a question and provide an answer in an appropriate tone. For example, if the user is feeling anxious, the answer is given in a tone that gives a sense of security. Furthermore, a system is constructed that analyzes the user's emotion data and provides an answer in an appropriate tone based on the user's emotion in response to a question. For example, if the user is feeling angry, the answer is given in a calm tone. Furthermore, the emotion estimation function is used to evaluate the user's emotion in response to a question in real time and provide an answer in an appropriate tone. For example, if the user is feeling happy, the answer is given in a tone that shows empathy. This makes it possible to provide an answer in an appropriate tone according to the user's emotion.

[0097] Information and communication support services can support communication in different languages ​​and promote international information exchange. Information and communication support services, for example, support communication in different languages ​​and promote international information exchange. For example, a function to support communication in different languages ​​can be added to the information and communication support service to promote international information exchange. For example, a real-time translation function can be provided. Furthermore, a system can be built to support communication in different languages ​​and promote international information exchange. For example, a multilingual chatbot can be provided. Furthermore, a function to support communication in different languages ​​can be added to the information and communication support service to develop a system to promote international information exchange. For example, an automatic translation function between different languages ​​can be provided. This can support communication in different languages ​​and promote international information exchange.

[0098] An information communication support service can utilize visual content to provide information that is easy to understand visually. An information communication support service, for example, utilizes visual content to provide information that is easy to understand visually. For example, a function that utilizes visual content to provide information that is easy to understand visually is added to the information communication support service. For example, information is provided using infographics and videos. In addition, a system that utilizes visual content to provide information that is easy to understand visually is built. For example, information is provided using charts and illustrations. In addition, a system is developed that utilizes visual content to provide information that is easy to understand visually. For example, information is provided using animations and slideshows. This makes it possible to provide information that is easy to understand visually.

[0099] An information communication support service can use an emotion estimation function to analyze a user's communication experience and use the results to improve the service. An information communication support service can, for example, use an emotion estimation function to analyze a user's communication experience and use the results to improve the service. For example, the emotion estimation function can be used to analyze a user's communication experience and use the results to improve the service. For example, points of dissatisfaction for the user can be identified and improvement measures can be proposed. A system can also be built that analyzes user emotion data and improves services based on the communication experience. For example, a communication method that satisfies the user can be provided. The emotion estimation function can also be used to analyze a user's communication experience in real time and use the results to improve the service. For example, if communication quality deteriorates, improvement measures can be immediately proposed. In this way, the user's communication experience can be analyzed and used to improve the service.

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

[0101] The AgriConnect system can also be equipped with a health management unit that monitors the user's health. For example, it collects the user's heart rate and sleep data to evaluate their health. The health management unit can also analyze the user's health data and suggest appropriate times for rest and exercise. For example, it can suggest taking a break after working for a long period of time. The health management unit can also adjust the work schedule based on the user's health. For example, it can prioritize light work if the user is not feeling well. This supports efficient agricultural work while maintaining the user's health.

[0102] The AgriConnect system can also be equipped with an energy management unit. For example, it collects energy consumption data from agricultural machinery and facilities and suggests efficient energy use. The energy management unit can also provide advice to promote the use of renewable energy. For example, it could suggest the introduction of a solar power generation system. The energy management unit can also analyze energy consumption data and generate an optimal energy usage schedule to reduce costs. For example, it could suggest running machinery during times when electricity rates are low. This reduces energy costs and enables sustainable agriculture.

[0103] The AgriConnect system can also be equipped with an education support section. For example, it can provide the latest research results and technical information on agriculture. The education support section can also provide online courses and training programs to help users improve their skills. For example, it can provide courses on pest control and soil improvement. The education support section can also monitor users' learning progress and propose individually customized learning plans. For example, it can suggest complementary courses if a specific skill is lacking. This can improve users' knowledge and skills, supporting the efficiency and quality of agricultural work.

[0104] The AgriConnect system can also be equipped with an environmental monitoring unit. For example, it collects soil pH and water quality data to evaluate environmental conditions. The environmental monitoring unit can also analyze environmental data and provide advice for sustainable agricultural practices. For example, it can suggest the application of lime if the soil is highly acidic. The environmental monitoring unit can also evaluate the health of crops based on environmental data and suggest appropriate measures. For example, it can suggest improving irrigation water if water quality deteriorates. This allows for environmentally friendly agriculture and maintains crop health.

[0105] The AgriConnect system can also be equipped with a marketing support section. For example, it can support agricultural product branding strategies and promotional activities. The marketing support section can also analyze consumer preference data and propose optimal marketing strategies. For example, it can suggest crops that are popular in a specific market. The marketing support section can also promote the use of online sales platforms and help expand agricultural product sales channels. For example, it can suggest sales methods on e-commerce sites. This can promote agricultural product sales and maximize profits.

[0106] The AgriConnect system can also use its emotion estimation function to provide feedback to increase user motivation. For example, it can send encouraging messages based on the progress of work. The emotion estimation function can also be used to analyze the user's emotional data and provide positive feedback to increase motivation. For example, it can provide feedback that makes the user feel a sense of accomplishment. The emotion estimation function can also be used to provide real-time feedback to increase user motivation. For example, it can send a message praising the results of work. This increases the user's motivation and improves work efficiency.

[0107] The AgriConnect system can also use its emotion estimation function to evaluate a user's stress level and suggest a work schedule to reduce stress. For example, it can suggest reducing the amount of work during times of high stress. The emotion estimation function can also be used to analyze a user's emotional data and generate a schedule that includes relaxation activities and breaks to reduce stress. For example, it can prioritize light work during times of high stress. The emotion estimation function can also be used to evaluate a user's stress level and automatically generate a work schedule to reduce stress. For example, it can suggest refreshing activities during times of high stress. This reduces the user's stress and improves work efficiency.

[0108] The AgriConnect system can further use the emotion estimation function to send reassuring notifications about the health of crops to reduce user anxiety. For example, a message informing the user that the crops are healthy can be sent. The emotion estimation function can also be used to analyze the user's emotion data and provide reassuring notifications to reduce anxiety. For example, regular reports on the growth status of crops can be sent. The emotion estimation function can also be used to send reassuring notifications in real time to reduce user anxiety. For example, a message informing the user that the health of crops is good can be sent. This can reduce user anxiety and provide a sense of security.

[0109] The AgriConnect system can also use an emotion estimation function to analyze the emotions a user has regarding the health of their crops and provide appropriate advice. For example, the emotion estimation function can be used to analyze the emotions a user has regarding the health of their crops and provide appropriate advice. For example, if a user is feeling anxious, advice that gives a sense of security can be provided. The emotion estimation function can also be used to build a system that analyzes user emotional data and provides advice based on their emotions regarding the health of their crops. For example, specific measures can be suggested if a user is worried. The emotion estimation function can also be used to analyze a user's emotions and provide appropriate advice regarding the health of their crops in real time. For example, if a user is feeling anxious, an encouraging message can be sent. This makes it possible to provide appropriate advice based on the user's emotions.

[0110] The AgriConnect system can further use its emotion estimation function to evaluate user expectations and propose market launch timing that will meet those expectations. For example, the emotion estimation function can be used to evaluate user expectations and propose market launch timing that will meet those expectations. For example, it can propose market launch at a time when users have high expectations. The emotion estimation function can also be used to build a system that analyzes user emotion data and proposes the optimal market launch timing based on those expectations. For example, it can propose timing that will achieve the user's expected profits. The emotion estimation function can also be used to evaluate user expectations and propose market launch timing in real time that will meet those expectations. For example, it can propose market launch at a time when user expectations are high. This makes it possible to propose market launch timing that meets user expectations.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: Generative AI provides new farmers with advice on optimizing agricultural work, monitoring crop health, and timing the release of agricultural products to market. Specifically, Generative AI analyzes information such as soil conditions, weather data, and crop type to suggest optimal cultivation methods and fertilization timing. It also analyzes crop image data and environmental data collected using drones and sensors to detect problems such as pests and diseases and nutrient deficiencies. It also analyzes market demand data and price trends to suggest the most profitable timing. Step 2: Communication lines provide high-speed communication lines to facilitate communication with the local community and sharing of market information. For example, market updates and weather forecasts can be accessed via the internet. Step 3: Information and communication support services improve access to knowledge and resources. For example, online forums and webinars allow farmers to exchange information with other farmers and experts. Generative AI can also be used to provide answers to questions and advice.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[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 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.

[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 (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).

[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] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The 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.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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]

[0180] 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. Generative AI will provide new farmers with advice on optimizing farming operations, monitoring crop health, and timing the release of agricultural products to market. a communication line providing high-speed communication lines; and communication support services for contact with local communities and market information. A system characterized by:

2. The generated AI is Analyzes soil conditions, weather data, and information on the type of crop, and suggests cultivation methods and fertilization timing 2. The system of claim 1.

3. The generated AI is Image data and environmental data of the crops collected using drones and sensors are analyzed to detect pest and disease outbreaks and nutritional deficiencies.

2. The system of claim 1.

4. The generated AI is Analyzing market demand data and price trends to suggest profitable timing 2. The system of claim 1.

5. The communication line is Providing said high-speed communication lines to facilitate communication with said local community and sharing of said market information 2. The system of claim 1.

Images