System

Abstract

An object of a system according to an embodiment is to analyze agricultural data and weather data and propose an optimal timing for cultivation and harvest of a crop.SOLUTION: A system includes a data collection unit, a data analysis unit, and a proposal unit. The data collection part collects agricultural data and weather data. The data analysis unit is configured to analyze the agricultural data and the weather data collected by the data collection unit. The proposal unit proposes an optimal timing for cultivation and harvest of the crop based on the data analyzed by the data analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately propose optimal timing for growing and harvesting crops based on agricultural and meteorological data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze agricultural data and meteorological data and propose optimal timing for cultivating and harvesting agricultural crops. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, and a proposal unit. The data collection unit collects agricultural data and meteorological data. The data analysis unit analyzes the agricultural data and meteorological data collected by the data collection unit. The proposal unit proposes optimal timing for cultivating and harvesting agricultural crops based on the data analyzed by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze agricultural data and meteorological data to propose optimal timing for cultivating and harvesting agricultural crops. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) The Sustainable Food AI system according to an embodiment of the present invention collects and analyzes agricultural and meteorological data, and uses a generation AI to propose optimal cultivation and harvesting timings, monitor soil health and optimal fertilizer usage, and predict and support food waste management. As a result, the Sustainable Food AI system promotes sustainable food production, achieving both agricultural efficiency and environmental protection.

[0029] The sustainable food AI system according to the embodiment includes a data collection unit, a data analysis unit, and a proposal unit. The data collection unit collects agricultural data and meteorological data. For example, the data collection unit measures soil pH and nutrient content using sensor technology. The data collection unit can also collect meteorological satellite data and ground observation data. The data collection unit can also accept data input from farmers via a smartphone app. The data analysis unit analyzes the agricultural data and meteorological data collected by the data collection unit. For example, the data analysis unit can analyze data trends using statistical analysis. The data analysis unit can also recognize data patterns using machine learning algorithms. The data analysis unit can also predict abnormal weather and disasters. The proposal unit proposes optimal timing for cultivating and harvesting agricultural crops based on the data analyzed by the data analysis unit. For example, the proposal unit can create an optimal cultivation schedule using a generation AI. The proposal unit can also build a harvest timing prediction model using the generation AI. The proposal unit can also propose crop rotation and intercropping methods using the generation AI. As a result, the sustainable food AI system according to the embodiment can promote sustainable food production and achieve both agricultural efficiency and environmental protection.

[0030] The data collection unit monitors crop growth conditions and weather conditions in real time, and the generation AI can use that data to suggest optimal cultivation and harvesting timings. For example, the data collection unit uses sensor technology to monitor crop growth conditions in real time. For example, it installs soil humidity and temperature sensors to collect data. The data collection unit also collects meteorological satellite data and ground observation data to monitor weather conditions in real time. For example, it uses meteorological satellite data to measure precipitation and wind speed. Furthermore, the data collection unit can accept data input from farmers via a smartphone app. The generation AI then suggests optimal cultivation and harvesting timings based on the collected data. For example, the generation AI can analyze data using a deep learning model to create an optimal cultivation schedule. The generation AI can also use natural language generation technology to build a harvest prediction model. Furthermore, the generation AI can predict abnormal weather and disasters and suggest measures to protect crops. This allows for real-time monitoring to suggest optimal cultivation and harvesting timings.

[0031] The data analysis unit measures the soil's pH value and nutrient content, and the generation AI analyzes the data to suggest the type and amount of fertilizer needed. The data analysis unit, for example, uses a pH sensor to measure the soil's pH value. For example, a soil sample is collected and measured with the pH sensor. The data analysis unit also uses a soil analyzer to measure the nutrient content. For example, a soil sample is collected and measured with the soil analyzer. The generation AI then suggests the type and amount of fertilizer needed based on the collected data. For example, the generation AI analyzes the data using a deep learning model to suggest the optimal type of fertilizer. The generation AI can also suggest the amount of fertilizer to use using natural language generation technology. Furthermore, the generation AI can monitor the health of the soil and provide advice to enable long-term agricultural land use. This allows for monitoring the health of the soil and suggesting the optimal fertilizer to use.

[0032] The proposal unit can analyze data on harvest yields and consumption, predict food waste, and propose specific measures to reduce food waste. For example, the proposal unit uses statistical analysis to analyze harvest yield and consumption data. For example, it analyzes trends in harvest times and harvest yields based on harvest yield data. The proposal unit also uses machine learning algorithms to predict consumer demand based on consumption data. For example, it analyzes consumer purchasing patterns based on consumption data. The generation AI predicts food waste based on the collected data and proposes specific measures to reduce food waste. For example, the generation AI uses a deep learning model to analyze the data and identify the causes of food waste. The generation AI can also propose measures to reduce food waste using natural language generation technology. Furthermore, the generation AI can also propose logistics optimization and consumer education programs. This makes it possible to reduce food waste by predicting and managing food waste.

[0033] The proposal unit can propose crop rotation and intercropping methods, as well as appropriate irrigation techniques. The proposal unit uses a generation AI to propose crop rotation and intercropping methods, for example. For example, the generation AI analyzes data using a deep learning model and proposes optimal crop rotation and intercropping methods. The generation AI can also propose irrigation techniques using natural language generation technology. For example, the generation AI creates an irrigation schedule and proposes appropriate irrigation techniques. Furthermore, the generation AI can propose optimal cultivation methods for each growth stage of crops based on the collected data. This makes it possible to provide specific advice for achieving sustainable agriculture.

[0034] The data collection unit can predict abnormal weather and disasters and propose measures to protect crops. For example, the data collection unit collects meteorological satellite data and ground observation data to predict abnormal weather and disasters. For example, meteorological satellite data is used to analyze the risk of heavy rain and drought. The data collection unit also uses a generative AI to predict abnormal weather and disasters. For example, the generative AI analyzes data using a deep learning model to predict abnormal weather and disasters. Furthermore, the generative AI can also propose measures to protect crops using natural language generation technology. For example, the generative AI can propose the installation of windbreaks and the strengthening of irrigation systems. In this way, it is possible to propose measures to protect crops based on the prediction of abnormal weather and disasters.

[0035] The data analysis unit can propose the optimal cultivation method for each stage of crop growth, maximizing harvest yields. The data analysis unit, for example, uses a generative AI to propose the optimal cultivation method for each stage of crop growth. For example, the generative AI analyzes data using a deep learning model and proposes the optimal cultivation method. The generative AI can also create cultivation schedules using natural language generation technology. For example, the generative AI indicates the timing of fertilization and irrigation according to each stage of sowing, growth, flowering, and harvesting. Furthermore, the generative AI can also propose the optimal cultivation method for each stage of crop growth based on the collected data. This makes it possible to propose the optimal cultivation method for each stage of growth, maximizing harvest yields.

[0036] The data collection unit can propose optimal cultivation conditions for urban and indoor agriculture, supporting sustainable agriculture in urban areas. The data collection unit, for example, uses generative AI to propose optimal cultivation conditions for urban and indoor agriculture. For example, the generative AI analyzes data using a deep learning model and proposes optimal cultivation conditions. The generative AI can also create cultivation schedules using natural language generation technology. For example, the generative AI analyzes the light, temperature, and humidity conditions suitable for cultivation on the rooftops of buildings and indoors in urban areas. Furthermore, the generative AI can propose optimal cultivation conditions for urban and indoor agriculture based on the collected data. This can propose optimal cultivation conditions for urban and indoor agriculture and support sustainable agriculture.

[0037] The data collection unit can propose an optimal operation schedule for agricultural machinery and promote efficient use of machinery. The data collection unit, for example, uses generative AI to propose an optimal operation schedule for agricultural machinery. For example, the generative AI analyzes data using a deep learning model and proposes an optimal operation schedule. The generative AI can also create operation schedules using natural language generation technology. For example, the generative AI optimizes the operating hours of tractors and combines to reduce fuel consumption. Furthermore, the generative AI can propose an optimal operation schedule for agricultural machinery based on the collected data. This promotes efficient use of agricultural machinery and optimizes operation schedules.

[0038] The data collection unit can analyze consumer preferences and propose a cultivation plan based on a demand forecast. The data collection unit, for example, uses a generation AI to analyze consumer preferences. For example, the generation AI analyzes data using a deep learning model to identify consumer preferences. The generation AI can also perform demand forecasts using natural language generation technology. For example, the generation AI analyzes data from social media and review sites to identify consumer preferences. Furthermore, the generation AI can propose a cultivation plan based on a demand forecast based on the collected data. This makes it possible to analyze consumer preferences and propose a cultivation plan based on a demand forecast.

[0039] The data collection unit can monitor microbial activity in the soil and propose measures to maintain microbial balance. The data collection unit, for example, uses a generative AI to monitor microbial activity in the soil. For example, the generative AI analyzes data using a deep learning model to identify the type of microorganisms and their activity levels. The generative AI can also propose measures to maintain microbial balance using natural language generation technology. For example, the generative AI can suggest the use of soil conditioners or the introduction of specific cultivation methods. Furthermore, the generative AI can propose measures to maintain microbial balance based on the collected data. This makes it possible to monitor microbial activity in the soil and propose measures to maintain microbial balance.

[0040] The data analysis unit can analyze soil data, predict the risk of specific pests and diseases, and propose preventive measures. The data analysis unit uses a generative AI to analyze the soil data, for example. For example, the generative AI analyzes data using a deep learning model and predicts the risk of specific pests and diseases. The generative AI can also propose preventive measures using natural language generation technology. For example, the generative AI calculates the probability of pests and diseases occurring based on the nutrient balance and humidity of the soil. Furthermore, the generative AI can predict the risk of specific pests and diseases occurring based on the collected data and propose preventive measures. This makes it possible to analyze soil data, predict the risk of pests and diseases occurring, and propose preventive measures.

[0041] The data collection unit can compare soil data from different regions and propose the optimal amount of fertilizer to be used for each region. The data collection unit, for example, uses a generation AI to compare soil data from different regions. For example, the generation AI can analyze data using a deep learning model to identify the characteristics of each region. The generation AI can also propose fertilizer usage amounts using natural language generation technology. For example, the generation AI can analyze the pH value and nutrient content of the soil and propose the optimal amount of fertilizer to be used for each region. Furthermore, the generation AI can compare soil data from different regions based on the collected data and propose the optimal amount of fertilizer to be used for each region. This makes it possible to compare soil data from different regions and propose the optimal amount of fertilizer to be used for each region.

[0042] The data analysis unit can propose the optimal soil improvement method for each crop based on the soil data, thereby improving yields. The data analysis unit uses the generation AI, for example, to propose the optimal soil improvement method for each crop based on the soil data. For example, the generation AI analyzes data using a deep learning model and proposes the optimal soil improvement method. The generation AI can also propose soil improvement methods using natural language generation technology. For example, the generation AI recommends the use of organic fertilizers and soil improvement materials suitable for specific crops. Furthermore, the generation AI can also propose the optimal soil improvement method for each crop based on the collected data. This makes it possible to propose the optimal soil improvement method for each crop based on the soil data, thereby improving yields.

[0043] The data analysis unit can perform a detailed analysis of the causes of food waste and propose specific improvement measures. The data analysis unit, for example, uses a generation AI to perform a detailed analysis of the causes of food waste. For example, the generation AI uses a deep learning model to analyze data and identify the causes. The generation AI can also propose improvement measures using natural language generation technology. For example, the generation AI identifies problems in post-harvest storage conditions and the distribution process. Furthermore, the generation AI can perform a detailed analysis of the causes of food waste based on the collected data and propose specific improvement measures. This makes it possible to perform a detailed analysis of the causes of food waste and propose specific improvement measures.

[0044] The data analysis unit can propose logistics optimization to minimize food waste based on the collected data. The data analysis unit uses the generative AI, for example, to propose logistics optimization to minimize food waste. For example, the generative AI analyzes data using a deep learning model and proposes optimal delivery routes and storage locations. The generative AI can also propose logistics optimization using natural language generation technology. For example, the generative AI analyzes optimal delivery routes and storage locations based on the collected data. Furthermore, the generative AI can also propose logistics optimization to minimize food waste based on the collected data. This makes it possible to propose logistics optimization to minimize food waste based on the collected data.

[0045] The data analysis unit can suggest recycling methods to reduce food waste and promote the reuse of waste. The data analysis unit uses, for example, a generative AI to suggest recycling methods to reduce food waste. For example, the generative AI analyzes data using a deep learning model and suggests recycling methods. The generative AI can also suggest recycling methods using natural language generation technology. For example, the generative AI analyzes methods for composting food waste and technologies for generating biogas. Furthermore, the generative AI can also suggest recycling methods to reduce food waste based on the collected data. This makes it possible to suggest recycling methods to reduce food waste and promote the reuse of waste.

[0046] The data analysis unit can propose educational programs for reducing food waste based on the collected data, thereby raising consumer awareness. The data analysis unit, for example, uses a generative AI to propose educational programs for reducing food waste. For example, the generative AI analyzes data using a deep learning model and proposes educational programs. The generative AI can also propose educational programs using natural language generation technology. For example, the generative AI provides information on the impact of food waste and how to reduce it. Furthermore, the generative AI can also propose educational programs for reducing food waste based on the collected data. This makes it possible to propose educational programs for reducing food waste and raise consumer awareness.

[0047] The data analysis unit can propose new farming methods to improve agricultural sustainability. The data analysis unit uses generative AI, for example, to propose new farming methods to improve agricultural sustainability. For example, the generative AI analyzes data using a deep learning model and proposes new farming methods. The generative AI can also propose new farming methods using natural language generation technology. For example, the generative AI analyzes agroforestry and permaculture techniques. Furthermore, the generative AI can propose new farming methods to improve agricultural sustainability based on the collected data. This makes it possible to propose new farming methods to improve agricultural sustainability.

[0048] The data analysis unit can propose specific measures to minimize the environmental impact of agriculture based on the collected data. The data analysis unit, for example, uses the generative AI to propose specific measures to minimize the environmental impact of agriculture. For example, the generative AI analyzes data using a deep learning model and proposes specific measures. The generative AI can also propose measures using natural language generation technology. For example, the generative AI recommends the use of pesticides and fertilizers with low environmental impact. Furthermore, the generative AI can also propose specific measures to minimize the environmental impact of agriculture based on the collected data. This makes it possible to propose specific measures to minimize the environmental impact of agriculture.

[0049] The data analysis unit can support the formation of a community for sustainable agriculture and promote information sharing. The data analysis unit, for example, uses the generative AI to support the formation of a community for sustainable agriculture. For example, the generative AI analyzes data using a deep learning model and supports the formation of a community. The generative AI can also promote information sharing using natural language generation technology. For example, the generative AI promotes information sharing between farmers through an online platform. Furthermore, the generative AI can support the formation of a community for sustainable agriculture and promote information sharing based on the collected data. This can support the formation of a community for sustainable agriculture and promote information sharing.

[0050] The data analysis unit can make policy recommendations for sustainable agriculture based on the collected data and work in collaboration with the government and local governments. The data analysis unit, for example, uses generative AI to make policy recommendations for sustainable agriculture. For example, the generative AI analyzes data using a deep learning model and makes policy recommendations. The generative AI can also make policy recommendations using natural language generation technology. For example, the generative AI proposes agricultural subsidies and environmental protection policies. Furthermore, the generative AI can make policy recommendations for sustainable agriculture based on the collected data and work in collaboration with the government and local governments. This makes it possible to make policy recommendations for sustainable agriculture and work in collaboration with the government and local governments.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The data analysis unit can also propose training programs to improve the work efficiency of agricultural workers. For example, the generation AI analyzes data using a deep learning model and creates an optimal training program. The generation AI can also propose training programs using natural language generation technology. For example, the generation AI analyzes work performance data of agricultural workers and provides individual training plans. Furthermore, the generation AI can also propose training programs to improve the work efficiency of agricultural workers based on the collected data. This makes it possible to propose training programs to improve the work efficiency of agricultural workers.

[0053] The data analysis unit can also optimize agricultural machinery maintenance schedules and reduce the risk of machinery breakdowns. For example, the generative AI analyzes data using a deep learning model and creates an optimal maintenance schedule. The generative AI can also suggest maintenance schedules using natural language generation technology. For example, the generative AI analyzes agricultural machinery operation data and detects signs of breakdowns. Furthermore, the generative AI can optimize agricultural machinery maintenance schedules based on the collected data and reduce the risk of machinery breakdowns. This makes it possible to optimize agricultural machinery maintenance schedules and reduce the risk of machinery breakdowns.

[0054] The data analysis unit can also propose new farming methods to improve agricultural sustainability. For example, the generative AI analyzes data using a deep learning model and proposes new farming methods. The generative AI can also propose new farming methods using natural language generation technology. For example, the generative AI analyzes agroforestry and permaculture techniques. Furthermore, the generative AI can also propose new farming methods to improve agricultural sustainability based on the collected data. This makes it possible to propose new farming methods to improve agricultural sustainability.

[0055] The data analysis unit can also propose educational programs for reducing food waste based on the collected data, thereby raising consumer awareness. For example, the generative AI can analyze data using a deep learning model and propose educational programs. The generative AI can also propose educational programs using natural language generation technology. For example, the generative AI can provide information on the impact of food waste and how to reduce it. Furthermore, the generative AI can also propose educational programs for reducing food waste based on the collected data. This makes it possible to propose educational programs for reducing food waste and raise consumer awareness.

[0056] The data analysis unit can also propose specific measures to minimize the environmental impact of agriculture based on the collected data. For example, the generative AI analyzes data using a deep learning model and proposes specific measures. The generative AI can also propose measures using natural language generation technology. For example, the generative AI recommends the use of pesticides and fertilizers with low environmental impact. Furthermore, the generative AI can also propose specific measures to minimize the environmental impact of agriculture based on the collected data. This makes it possible to propose specific measures to minimize the environmental impact of agriculture.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The data collection unit collects agricultural and meteorological data. For example, the data collection unit may use sensor technology to measure soil pH and nutrient content. The data collection unit may also collect meteorological satellite data and ground observation data. Furthermore, the data collection unit may accept data input from farmers via a smartphone app. Step 2: The data analysis unit analyzes the agricultural and meteorological data collected by the data collection unit. For example, the data analysis unit may analyze trends in the data using statistical analysis. The data analysis unit may also recognize patterns in the data using machine learning algorithms. Furthermore, the data analysis unit may predict abnormal weather and disasters. Step 3: The proposal unit proposes the optimal timing for cultivating and harvesting agricultural crops based on the data analyzed by the data analysis unit. For example, the proposal unit uses the generation AI to create an optimal cultivation schedule. The proposal unit can also use the generation AI to build a harvest timing prediction model. Furthermore, the proposal unit can also use the generation AI to propose methods for crop rotation and intercropping.

[0059] (Example 2) The Sustainable Food AI system according to an embodiment of the present invention collects and analyzes agricultural and meteorological data, and uses a generation AI to propose optimal cultivation and harvesting timings, monitor soil health and optimal fertilizer usage, and predict and support food waste management. As a result, the Sustainable Food AI system promotes sustainable food production, achieving both agricultural efficiency and environmental protection.

[0060] The sustainable food AI system according to the embodiment includes a data collection unit, a data analysis unit, and a proposal unit. The data collection unit collects agricultural data and meteorological data. For example, the data collection unit measures soil pH and nutrient content using sensor technology. The data collection unit can also collect meteorological satellite data and ground observation data. The data collection unit can also accept data input from farmers via a smartphone app. The data analysis unit analyzes the agricultural data and meteorological data collected by the data collection unit. For example, the data analysis unit can analyze data trends using statistical analysis. The data analysis unit can also recognize data patterns using machine learning algorithms. The data analysis unit can also predict abnormal weather and disasters. The proposal unit proposes optimal timing for cultivating and harvesting agricultural crops based on the data analyzed by the data analysis unit. For example, the proposal unit can create an optimal cultivation schedule using a generation AI. The proposal unit can also build a harvest timing prediction model using the generation AI. The proposal unit can also propose crop rotation and intercropping methods using the generation AI. As a result, the sustainable food AI system according to the embodiment can promote sustainable food production and achieve both agricultural efficiency and environmental protection.

[0061] The data collection unit monitors crop growth conditions and weather conditions in real time, and the generation AI can use that data to suggest optimal cultivation and harvesting timings. For example, the data collection unit uses sensor technology to monitor crop growth conditions in real time. For example, it installs soil humidity and temperature sensors to collect data. The data collection unit also collects meteorological satellite data and ground observation data to monitor weather conditions in real time. For example, it uses meteorological satellite data to measure precipitation and wind speed. Furthermore, the data collection unit can accept data input from farmers via a smartphone app. The generation AI then suggests optimal cultivation and harvesting timings based on the collected data. For example, the generation AI can analyze data using a deep learning model to create an optimal cultivation schedule. The generation AI can also use natural language generation technology to build a harvest prediction model. Furthermore, the generation AI can predict abnormal weather and disasters and suggest measures to protect crops. This allows for real-time monitoring to suggest optimal cultivation and harvesting timings.

[0062] The data analysis unit measures the soil's pH value and nutrient content, and the generation AI analyzes the data to suggest the type and amount of fertilizer needed. The data analysis unit, for example, uses a pH sensor to measure the soil's pH value. For example, a soil sample is collected and measured with the pH sensor. The data analysis unit also uses a soil analyzer to measure the nutrient content. For example, a soil sample is collected and measured with the soil analyzer. The generation AI then suggests the type and amount of fertilizer needed based on the collected data. For example, the generation AI analyzes the data using a deep learning model to suggest the optimal type of fertilizer. The generation AI can also suggest the amount of fertilizer to use using natural language generation technology. Furthermore, the generation AI can monitor the health of the soil and provide advice to enable long-term agricultural land use. This allows for monitoring the health of the soil and suggesting the optimal fertilizer to use.

[0063] The proposal unit can analyze data on harvest yields and consumption, predict food waste, and propose specific measures to reduce food waste. For example, the proposal unit uses statistical analysis to analyze harvest yield and consumption data. For example, it analyzes trends in harvest times and harvest yields based on harvest yield data. The proposal unit also uses machine learning algorithms to predict consumer demand based on consumption data. For example, it analyzes consumer purchasing patterns based on consumption data. The generation AI predicts food waste based on the collected data and proposes specific measures to reduce food waste. For example, the generation AI uses a deep learning model to analyze the data and identify the causes of food waste. The generation AI can also propose measures to reduce food waste using natural language generation technology. Furthermore, the generation AI can also propose logistics optimization and consumer education programs. This makes it possible to reduce food waste by predicting and managing food waste.

[0064] The proposal unit can propose crop rotation and intercropping methods, as well as appropriate irrigation techniques. The proposal unit uses a generation AI to propose crop rotation and intercropping methods, for example. For example, the generation AI analyzes data using a deep learning model and proposes optimal crop rotation and intercropping methods. The generation AI can also propose irrigation techniques using natural language generation technology. For example, the generation AI creates an irrigation schedule and proposes appropriate irrigation techniques. Furthermore, the generation AI can propose optimal cultivation methods for each growth stage of crops based on the collected data. This makes it possible to provide specific advice for achieving sustainable agriculture.

[0065] The data collection unit can predict abnormal weather and disasters and propose measures to protect crops. For example, the data collection unit collects meteorological satellite data and ground observation data to predict abnormal weather and disasters. For example, meteorological satellite data is used to analyze the risk of heavy rain and drought. The data collection unit also uses a generative AI to predict abnormal weather and disasters. For example, the generative AI analyzes data using a deep learning model to predict abnormal weather and disasters. Furthermore, the generative AI can also propose measures to protect crops using natural language generation technology. For example, the generative AI can propose the installation of windbreaks and the strengthening of irrigation systems. In this way, it is possible to propose measures to protect crops based on the prediction of abnormal weather and disasters.

[0066] The data analysis unit can propose the optimal cultivation method for each stage of crop growth, maximizing harvest yields. The data analysis unit, for example, uses a generative AI to propose the optimal cultivation method for each stage of crop growth. For example, the generative AI analyzes data using a deep learning model and proposes the optimal cultivation method. The generative AI can also create cultivation schedules using natural language generation technology. For example, the generative AI indicates the timing of fertilization and irrigation according to each stage of sowing, growth, flowering, and harvesting. Furthermore, the generative AI can also propose the optimal cultivation method for each stage of crop growth based on the collected data. This makes it possible to propose the optimal cultivation method for each stage of growth, maximizing harvest yields.

[0067] The data collection unit can use the emotion estimation function to monitor the stress levels of farmers and propose an appropriate work schedule. The data collection unit, for example, uses the emotion estimation function to monitor the stress levels of farmers. For example, a smartwatch or wearable device can be used to measure heart rate and electrodermal activity and analyze stress levels. The data collection unit can also use the emotion estimation function to monitor the stress levels of farmers and propose an appropriate work schedule. For example, the generation AI can analyze the data using a deep learning model and create an optimal work schedule. The generation AI can also propose a work schedule using natural language generation technology. Furthermore, the generation AI can monitor the stress levels of farmers and propose an appropriate work schedule based on the collected data. This makes it possible to monitor the stress levels of farmers and propose an appropriate work schedule.

[0068] The data collection unit can propose optimal cultivation conditions for urban and indoor agriculture, supporting sustainable agriculture in urban areas. The data collection unit, for example, uses generative AI to propose optimal cultivation conditions for urban and indoor agriculture. For example, the generative AI analyzes data using a deep learning model and proposes optimal cultivation conditions. The generative AI can also create cultivation schedules using natural language generation technology. For example, the generative AI analyzes the light, temperature, and humidity conditions suitable for cultivation on the rooftops of buildings and indoors in urban areas. Furthermore, the generative AI can propose optimal cultivation conditions for urban and indoor agriculture based on the collected data. This can propose optimal cultivation conditions for urban and indoor agriculture and support sustainable agriculture.

[0069] The data collection unit can propose an optimal operation schedule for agricultural machinery and promote efficient use of machinery. The data collection unit, for example, uses generative AI to propose an optimal operation schedule for agricultural machinery. For example, the generative AI analyzes data using a deep learning model and proposes an optimal operation schedule. The generative AI can also create operation schedules using natural language generation technology. For example, the generative AI optimizes the operating hours of tractors and combines to reduce fuel consumption. Furthermore, the generative AI can propose an optimal operation schedule for agricultural machinery based on the collected data. This promotes efficient use of agricultural machinery and optimizes operation schedules.

[0070] The data collection unit can analyze consumer preferences and propose a cultivation plan based on a demand forecast. The data collection unit, for example, uses a generation AI to analyze consumer preferences. For example, the generation AI analyzes data using a deep learning model to identify consumer preferences. The generation AI can also perform demand forecasts using natural language generation technology. For example, the generation AI analyzes data from social media and review sites to identify consumer preferences. Furthermore, the generation AI can propose a cultivation plan based on a demand forecast based on the collected data. This makes it possible to analyze consumer preferences and propose a cultivation plan based on a demand forecast.

[0071] The data collection unit can monitor microbial activity in the soil and propose measures to maintain microbial balance. The data collection unit, for example, uses a generative AI to monitor microbial activity in the soil. For example, the generative AI analyzes data using a deep learning model to identify the type of microorganisms and their activity levels. The generative AI can also propose measures to maintain microbial balance using natural language generation technology. For example, the generative AI can suggest the use of soil conditioners or the introduction of specific cultivation methods. Furthermore, the generative AI can propose measures to maintain microbial balance based on the collected data. This makes it possible to monitor microbial activity in the soil and propose measures to maintain microbial balance.

[0072] The data analysis unit can analyze soil data, predict the risk of specific pests and diseases, and propose preventive measures. The data analysis unit uses a generative AI to analyze the soil data, for example. For example, the generative AI analyzes data using a deep learning model and predicts the risk of specific pests and diseases. The generative AI can also propose preventive measures using natural language generation technology. For example, the generative AI calculates the probability of pests and diseases occurring based on the nutrient balance and humidity of the soil. Furthermore, the generative AI can predict the risk of specific pests and diseases occurring based on the collected data and propose preventive measures. This makes it possible to analyze soil data, predict the risk of pests and diseases occurring, and propose preventive measures.

[0073] The data collection unit can use the emotion estimation function to analyze the emotional state of the farmer and provide advice to reduce stress in soil management. The data collection unit, for example, uses the emotion estimation function to analyze the emotional state of the farmer. For example, a wearable device can be used to measure heart rate and electrodermal activity and analyze stress levels. The data collection unit can also use the emotion estimation function to analyze the emotional state of the farmer and provide advice to reduce stress in soil management. For example, the generation AI can analyze the data using a deep learning model and create an optimal work schedule. The generation AI can also provide advice using natural language generation technology. Furthermore, the generation AI can analyze the emotional state of the farmer based on the collected data and provide advice to reduce stress in soil management. This makes it possible to analyze the emotional state of the farmer and provide advice to reduce stress in soil management.

[0074] The data collection unit can compare soil data from different regions and propose the optimal amount of fertilizer to be used for each region. The data collection unit, for example, uses a generation AI to compare soil data from different regions. For example, the generation AI can analyze data using a deep learning model to identify the characteristics of each region. The generation AI can also propose fertilizer usage amounts using natural language generation technology. For example, the generation AI can analyze the pH value and nutrient content of the soil and propose the optimal amount of fertilizer to be used for each region. Furthermore, the generation AI can compare soil data from different regions based on the collected data and propose the optimal amount of fertilizer to be used for each region. This makes it possible to compare soil data from different regions and propose the optimal amount of fertilizer to be used for each region.

[0075] The data analysis unit can propose the optimal soil improvement method for each crop based on the soil data, thereby improving yields. The data analysis unit uses the generation AI, for example, to propose the optimal soil improvement method for each crop based on the soil data. For example, the generation AI analyzes data using a deep learning model and proposes the optimal soil improvement method. The generation AI can also propose soil improvement methods using natural language generation technology. For example, the generation AI recommends the use of organic fertilizers and soil improvement materials suitable for specific crops. Furthermore, the generation AI can also propose the optimal soil improvement method for each crop based on the collected data. This makes it possible to propose the optimal soil improvement method for each crop based on the soil data, thereby improving yields.

[0076] The data collection unit can use the emotion estimation function to analyze consumers' health preferences and suggest healthy crop cultivation methods. The data collection unit uses the emotion estimation function, for example, to analyze consumers' health preferences. For example, the generation AI can analyze data using a deep learning model to identify consumers' health preferences. The generation AI can also suggest health-conscious cultivation methods using natural language generation technology. For example, the generation AI can analyze data from social media and review sites to identify consumers' health preferences. Furthermore, the generation AI can suggest health-conscious cultivation methods based on the collected data. This makes it possible to analyze consumers' health preferences and suggest health-conscious cultivation methods.

[0077] The data analysis unit can perform a detailed analysis of the causes of food waste and propose specific improvement measures. The data analysis unit, for example, uses a generation AI to perform a detailed analysis of the causes of food waste. For example, the generation AI uses a deep learning model to analyze data and identify the causes. The generation AI can also propose improvement measures using natural language generation technology. For example, the generation AI identifies problems in post-harvest storage conditions and the distribution process. Furthermore, the generation AI can perform a detailed analysis of the causes of food waste based on the collected data and propose specific improvement measures. This makes it possible to perform a detailed analysis of the causes of food waste and propose specific improvement measures.

[0078] The data analysis unit can propose logistics optimization to minimize food waste based on the collected data. The data analysis unit uses the generative AI, for example, to propose logistics optimization to minimize food waste. For example, the generative AI analyzes data using a deep learning model and proposes optimal delivery routes and storage locations. The generative AI can also propose logistics optimization using natural language generation technology. For example, the generative AI analyzes optimal delivery routes and storage locations based on the collected data. Furthermore, the generative AI can also propose logistics optimization to minimize food waste based on the collected data. This makes it possible to propose logistics optimization to minimize food waste based on the collected data.

[0079] The data collection unit can use the emotion estimation function to analyze consumer purchasing intent and propose a production plan based on demand forecasts. The data collection unit, for example, uses the emotion estimation function to analyze consumer purchasing intent. For example, the generation AI can analyze data using a deep learning model to identify consumer purchasing intent. The generation AI can also perform demand forecasts using natural language generation technology. For example, the generation AI can analyze data from social media and review sites to identify consumer purchasing intent. Furthermore, the generation AI can propose a production plan based on demand forecasts based on the collected data. This makes it possible to analyze consumer purchasing intent and propose a production plan based on demand forecasts.

[0080] The data analysis unit can suggest recycling methods to reduce food waste and promote the reuse of waste. The data analysis unit uses, for example, a generative AI to suggest recycling methods to reduce food waste. For example, the generative AI analyzes data using a deep learning model and suggests recycling methods. The generative AI can also suggest recycling methods using natural language generation technology. For example, the generative AI analyzes methods for composting food waste and technologies for generating biogas. Furthermore, the generative AI can also suggest recycling methods to reduce food waste based on the collected data. This makes it possible to suggest recycling methods to reduce food waste and promote the reuse of waste.

[0081] The data analysis unit can propose educational programs for reducing food waste based on the collected data, thereby raising consumer awareness. The data analysis unit, for example, uses a generative AI to propose educational programs for reducing food waste. For example, the generative AI analyzes data using a deep learning model and proposes educational programs. The generative AI can also propose educational programs using natural language generation technology. For example, the generative AI provides information on the impact of food waste and how to reduce it. Furthermore, the generative AI can also propose educational programs for reducing food waste based on the collected data. This makes it possible to propose educational programs for reducing food waste and raise consumer awareness.

[0082] The data collection unit can use the emotion estimation function to analyze consumers' emotional responses and propose marketing strategies for reducing food waste. The data collection unit, for example, uses the emotion estimation function to analyze consumers' emotional responses. For example, the generation AI can analyze data using a deep learning model to identify consumers' emotional responses. The generation AI can also propose marketing strategies using natural language generation technology. For example, the generation AI can analyze data from social media and review sites to identify emotional responses regarding food waste reduction. Furthermore, the generation AI can propose marketing strategies for reducing food waste based on the collected data. This makes it possible to analyze consumers' emotional responses and propose marketing strategies for reducing food waste.

[0083] The data analysis unit can propose new farming methods to improve agricultural sustainability. The data analysis unit uses generative AI, for example, to propose new farming methods to improve agricultural sustainability. For example, the generative AI analyzes data using a deep learning model and proposes new farming methods. The generative AI can also propose new farming methods using natural language generation technology. For example, the generative AI analyzes agroforestry and permaculture techniques. Furthermore, the generative AI can propose new farming methods to improve agricultural sustainability based on the collected data. This makes it possible to propose new farming methods to improve agricultural sustainability.

[0084] The data analysis unit can propose specific measures to minimize the environmental impact of agriculture based on the collected data. The data analysis unit, for example, uses the generative AI to propose specific measures to minimize the environmental impact of agriculture. For example, the generative AI analyzes data using a deep learning model and proposes specific measures. The generative AI can also propose measures using natural language generation technology. For example, the generative AI recommends the use of pesticides and fertilizers with low environmental impact. Furthermore, the generative AI can also propose specific measures to minimize the environmental impact of agriculture based on the collected data. This makes it possible to propose specific measures to minimize the environmental impact of agriculture.

[0085] The data collection unit can use the emotion estimation function to analyze the motivation of farmers and provide support for practicing sustainable agriculture. The data collection unit, for example, uses the emotion estimation function to analyze the motivation of farmers. For example, the generation AI analyzes the data using a deep learning model to identify the motivation of farmers. The generation AI can also provide support using natural language generation technology. For example, the generation AI measures heart rate and electrodermal activity using a wearable device to analyze the motivation level. Furthermore, the generation AI can analyze the motivation of farmers based on the collected data and provide support for practicing sustainable agriculture. This makes it possible to analyze the motivation of farmers and provide support for practicing sustainable agriculture.

[0086] The data analysis unit can support the formation of a community for sustainable agriculture and promote information sharing. The data analysis unit, for example, uses the generative AI to support the formation of a community for sustainable agriculture. For example, the generative AI analyzes data using a deep learning model and supports the formation of a community. The generative AI can also promote information sharing using natural language generation technology. For example, the generative AI promotes information sharing between farmers through an online platform. Furthermore, the generative AI can support the formation of a community for sustainable agriculture and promote information sharing based on the collected data. This can support the formation of a community for sustainable agriculture and promote information sharing.

[0087] The data analysis unit can make policy recommendations for sustainable agriculture based on the collected data and work in collaboration with the government and local governments. The data analysis unit, for example, uses generative AI to make policy recommendations for sustainable agriculture. For example, the generative AI analyzes data using a deep learning model and makes policy recommendations. The generative AI can also make policy recommendations using natural language generation technology. For example, the generative AI proposes agricultural subsidies and environmental protection policies. Furthermore, the generative AI can make policy recommendations for sustainable agriculture based on the collected data and work in collaboration with the government and local governments. This makes it possible to make policy recommendations for sustainable agriculture and work in collaboration with the government and local governments.

[0088] The data collection unit can use the emotion estimation function to analyze consumers' environmental awareness and propose a marketing strategy for sustainable agricultural products. The data collection unit uses the emotion estimation function, for example, to analyze consumers' environmental awareness. For example, the generation AI can analyze data using a deep learning model to identify consumers' environmental awareness. The generation AI can also propose a marketing strategy using natural language generation technology. For example, the generation AI can analyze data from social media and review sites to identify emotional responses related to the environment. Furthermore, the generation AI can propose a marketing strategy for sustainable agricultural products based on the collected data. This makes it possible to analyze consumers' environmental awareness and propose a marketing strategy for sustainable agricultural products.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The data collection unit can also monitor the health of agricultural workers and suggest appropriate break times and workload adjustments. For example, a wearable device can be used to measure heart rate and body temperature to detect signs of overwork. The data collection unit can also monitor the health of agricultural workers and suggest appropriate break times. For example, the generation AI can analyze data using a deep learning model to create optimal break times. The generation AI can also suggest break times using natural language generation technology. Furthermore, the generation AI can monitor the health of agricultural workers based on the collected data and suggest appropriate break times. This makes it possible to monitor the health of agricultural workers and suggest appropriate break times.

[0091] The data analysis unit can also propose training programs to improve the work efficiency of agricultural workers. For example, the generation AI analyzes data using a deep learning model and creates an optimal training program. The generation AI can also propose training programs using natural language generation technology. For example, the generation AI analyzes work performance data of agricultural workers and provides individual training plans. Furthermore, the generation AI can also propose training programs to improve the work efficiency of agricultural workers based on the collected data. This makes it possible to propose training programs to improve the work efficiency of agricultural workers.

[0092] The data collection unit can also use emotion estimation functions to analyze consumer purchasing intent and propose production plans based on demand forecasts. For example, the generation AI can analyze data using a deep learning model to identify consumer purchasing intent. The generation AI can also perform demand forecasts using natural language generation technology. For example, the generation AI can analyze data from social media and review sites to identify consumer purchasing intent. Furthermore, the generation AI can propose production plans based on demand forecasts using the collected data. This makes it possible to analyze consumer purchasing intent and propose production plans based on demand forecasts.

[0093] The data analysis unit can also optimize agricultural machinery maintenance schedules and reduce the risk of machinery breakdowns. For example, the generative AI analyzes data using a deep learning model and creates an optimal maintenance schedule. The generative AI can also suggest maintenance schedules using natural language generation technology. For example, the generative AI analyzes agricultural machinery operation data and detects signs of breakdowns. Furthermore, the generative AI can optimize agricultural machinery maintenance schedules based on the collected data and reduce the risk of machinery breakdowns. This makes it possible to optimize agricultural machinery maintenance schedules and reduce the risk of machinery breakdowns.

[0094] The data collection unit can also use an emotion estimation function to analyze farmers' motivation and provide support for practicing sustainable agriculture. For example, the generative AI can analyze data using a deep learning model to identify farmers' motivation. The generative AI can also provide support using natural language generation technology. For example, the generative AI can measure heart rate and electrodermal activity using a wearable device to analyze motivation levels. Furthermore, the generative AI can analyze farmers' motivation based on the collected data and provide support for practicing sustainable agriculture. This makes it possible to analyze farmers' motivation and provide support for practicing sustainable agriculture.

[0095] The data analysis unit can also propose new farming methods to improve agricultural sustainability. For example, the generative AI analyzes data using a deep learning model and proposes new farming methods. The generative AI can also propose new farming methods using natural language generation technology. For example, the generative AI analyzes agroforestry and permaculture techniques. Furthermore, the generative AI can also propose new farming methods to improve agricultural sustainability based on the collected data. This makes it possible to propose new farming methods to improve agricultural sustainability.

[0096] The data collection unit can also use the emotion estimation function to analyze consumers' health preferences and suggest healthy crop cultivation methods. For example, the generation AI can analyze data using a deep learning model to identify consumers' health preferences. The generation AI can also suggest health-conscious cultivation methods using natural language generation technology. For example, the generation AI can analyze data from social media and review sites to identify consumers' health preferences. The generation AI can also suggest health-conscious cultivation methods based on the collected data. This makes it possible to analyze consumers' health preferences and suggest health-conscious crop cultivation methods.

[0097] The data analysis unit can also propose educational programs for reducing food waste based on the collected data, thereby raising consumer awareness. For example, the generative AI can analyze data using a deep learning model and propose educational programs. The generative AI can also propose educational programs using natural language generation technology. For example, the generative AI can provide information on the impact of food waste and how to reduce it. Furthermore, the generative AI can also propose educational programs for reducing food waste based on the collected data. This makes it possible to propose educational programs for reducing food waste and raise consumer awareness.

[0098] The data collection unit can also use the emotion estimation function to analyze consumers' environmental awareness and propose a marketing strategy for sustainable agricultural products. For example, the generative AI can analyze data using a deep learning model to identify consumers' environmental awareness. The generative AI can also propose a marketing strategy using natural language generation technology. For example, the generative AI can analyze data from social media and review sites to identify emotional responses related to the environment. Furthermore, the generative AI can propose a marketing strategy for sustainable agricultural products based on the collected data. This makes it possible to analyze consumers' environmental awareness and propose a marketing strategy for sustainable agricultural products.

[0099] The data analysis unit can also propose specific measures to minimize the environmental impact of agriculture based on the collected data. For example, the generative AI analyzes data using a deep learning model and proposes specific measures. The generative AI can also propose measures using natural language generation technology. For example, the generative AI recommends the use of pesticides and fertilizers with low environmental impact. Furthermore, the generative AI can also propose specific measures to minimize the environmental impact of agriculture based on the collected data. This makes it possible to propose specific measures to minimize the environmental impact of agriculture.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The data collection unit collects agricultural and meteorological data. For example, the data collection unit may use sensor technology to measure soil pH and nutrient content. The data collection unit may also collect meteorological satellite data and ground observation data. Furthermore, the data collection unit may accept data input from farmers via a smartphone app. Step 2: The data analysis unit analyzes the agricultural and meteorological data collected by the data collection unit. For example, the data analysis unit may analyze trends in the data using statistical analysis. The data analysis unit may also recognize patterns in the data using machine learning algorithms. Furthermore, the data analysis unit may predict abnormal weather and disasters. Step 3: The proposal unit proposes the optimal timing for cultivating and harvesting agricultural crops based on the data analyzed by the data analysis unit. For example, the proposal unit uses the generation AI to create an optimal cultivation schedule. The proposal unit can also use the generation AI to build a harvest timing prediction model. Furthermore, the proposal unit can also use the generation AI to propose methods for crop rotation and intercropping.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit for collecting agricultural data and meteorological data; a data analysis unit that analyzes the agricultural data and the meteorological data collected by the data collection unit; a suggestion unit that suggests optimal timing for cultivating and harvesting agricultural products based on the data analyzed by the data analysis unit. A system characterized by:

2. The data analysis unit Measure the pH value and nutrient content of the soil, The generative AI analyzes the data and suggests the type and amount of fertilizer needed. The system of claim 1 .

3. The proposal unit Propose methods for crop rotation and intercropping of the above crops, as well as appropriate irrigation techniques. The system of claim 1 .

4. The data collection unit Monitor soil microbial activity and propose measures to maintain microbial balance The system of claim 1 .

5. The data analysis unit Conduct a detailed analysis of the causes of food waste and propose specific measures to improve it The system of claim 1 .

6. The data analysis unit Propose the new farming method to improve agricultural sustainability The system of claim 1 .

7. The data collection unit Emotion estimation function is used to monitor the stress levels of agricultural workers, Suggest a work schedule based on the stress level The system of claim 1 .

8. The data collection unit Analyzing the emotional state of farmers using emotion estimation and providing advice to reduce stress in soil management The system of claim 1 .

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