System

Abstract

An object of the system according to the embodiment is to optimize the harvest time and method and maximize the harvest amount and quality.SOLUTION: A system includes a harvest prediction data analysis unit, a weather data analysis unit, a soil data analysis unit, and a crop growth data analysis unit. The processor is configured to analyze the generated AI. The weather data analysis unit analyzes weather data based on the harvest prediction data analyzed by the harvest prediction data analysis unit. The soil data analysis unit analyzes the soil data on the basis of the weather data analyzed by the weather data analysis unit. The crop growth data analysis unit analyzes crop growth data on the basis of the soil data analyzed by the soil 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 techniques have not adequately optimized harvesting times and methods, leaving room for improvement in yield and quality.

[0005] The system according to the embodiment aims to optimize harvesting time and method to maximize harvest yield and quality. [Means for solving the problem]

[0006] The system according to the embodiment includes a harvest forecast data analysis unit, a weather data analysis unit, a soil data analysis unit, and a crop growth data analysis unit. The harvest forecast data analysis unit analyzes harvest forecast data using a generation AI. The weather data analysis unit analyzes weather data based on the harvest forecast data analyzed by the harvest forecast data analysis unit. The soil data analysis unit analyzes soil data based on the weather data analyzed by the weather data analysis unit. The crop growth data analysis unit analyzes crop growth data based on the soil data analyzed by the soil data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize harvesting time and method to maximize harvest yield and quality. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The harvest suggestion system according to an embodiment of the present invention uses generative AI to analyze harvest forecast data, and comprehensively analyzes weather data, soil data, and crop growth data to suggest optimal harvest times and methods to farmers. This allows the harvest suggestion system to maximize harvest volume and quality and realize sustainable agriculture.

[0029] A harvest suggestion system according to an embodiment includes a harvest prediction data analysis unit, a weather data analysis unit, a soil data analysis unit, and a crop growth data analysis unit. The harvest prediction data analysis unit analyzes harvest prediction data using a generative AI. For example, the harvest prediction data analysis unit suggests optimal harvest times and methods based on past harvest performance and the current growth status of crops. The weather data analysis unit analyzes weather data based on the harvest prediction data analyzed by the harvest prediction data analysis unit. For example, the weather data analysis unit identifies optimal weather conditions for harvesting based on meteorological data such as temperature and precipitation. The soil data analysis unit analyzes soil data based on the weather data analyzed by the weather data analysis unit. For example, the soil data analysis unit suggests optimal soil management methods for crop growth based on the soil moisture content and nutrient content. The crop growth data analysis unit analyzes crop growth data based on the soil data analyzed by the soil data analysis unit. For example, the crop growth data analysis unit identifies optimal harvest timing based on the leaf color and stem thickness of the crop. This allows the harvest suggestion system according to an embodiment to maximize yield and quality and achieve sustainable agriculture.

[0030] The harvest prediction data analysis unit can perform simulations based on the harvest prediction data and compare different harvest scenarios. For example, the harvest prediction data analysis unit uses generative AI to perform simulations based on the harvest prediction data and compare different harvest scenarios. For example, it can simulate the changes in harvest volume and quality when the harvest time is shifted by one week, and identify the optimal harvest time. It can also compare the changes in efficiency and cost when the harvesting method is changed from manual to mechanical harvesting, and propose the optimal harvesting method. It can also simulate the changes in harvest volume and quality when the harvest time is advanced or delayed, and identify the optimal harvest time. In this way, it can identify the optimal harvest time and method by comparing different harvest scenarios.

[0031] The harvest forecast data analysis unit can compare past harvest data with current data and detect outliers and trend changes. For example, the harvest forecast data analysis unit uses generative AI to compare past harvest data with current data and detect outliers and trend changes. For example, it compares past harvest yields with current harvest yields to detect abnormal increases or decreases and identify the causes. It also compares past weather data with current weather data to detect abnormal weather patterns and reflect them in the harvest plan. It also compares past soil data with current soil data to detect abnormal soil conditions and propose appropriate countermeasures. This improves the accuracy of harvest plans by detecting outliers and trend changes.

[0032] The harvest prediction data analysis unit can anonymously share data from other farmers and learn best practices. For example, when analyzing harvest prediction data using generative AI, the harvest prediction data analysis unit can anonymously share data from other farmers and learn best practices. For example, it can suggest optimal harvest times and methods based on harvest data from other farmers in the same region. It can also suggest optimal harvest methods based on weather conditions and soil conditions based on harvest data from farmers in different regions. It can also suggest optimal harvest plans based on past successes and failures. This makes it possible to suggest optimal harvest times and methods by utilizing data from other farmers.

[0033] The harvest prediction data analysis unit can integrate real-time data from drones or sensors. For example, when analyzing harvest prediction data using generative AI, the harvest prediction data analysis unit integrates real-time data from drones and sensors. For example, it proposes the optimal harvest time and method based on aerial images from drones and soil data from sensors. It also creates a harvest plan based on data on the growth status of crops from drones and weather data from sensors. It also adjusts the harvest time based on data on the occurrence of pests and diseases from drones and soil moisture content from sensors. By integrating real-time data, it becomes possible to make more accurate harvest predictions.

[0034] The weather data analysis unit can learn past abnormal weather patterns and predict future abnormal weather. For example, when analyzing weather data using generative AI, the weather data analysis unit learns past abnormal weather patterns and predicts future abnormal weather. For example, based on data from past typhoons and heavy rains, it predicts the probability of future abnormal weather occurrences. It also predicts the impact of future abnormal weather based on data from past heat waves and cold waves. It also predicts the risk of future abnormal weather based on data from past droughts and floods. In this way, by learning past abnormal weather patterns, it is possible to predict future abnormal weather and reflect this in harvest plans.

[0035] The weather data analysis unit takes into account the microclimate data for each region, enabling more precise forecasts. For example, when analyzing weather data using generative AI, the weather data analysis unit takes into account the microclimate data for each region to make more precise forecasts. For example, a harvest plan is made based on weather data for specific regions, such as mountainous or coastal areas. Weather data for urban and rural areas is also compared to propose a harvest plan that suits the characteristics of each region. Furthermore, the harvest time is adjusted based on weather data that takes into account differences in altitude and topography. In this way, by taking into account the microclimate data for each region, more precise weather forecasts are possible.

[0036] The weather data analysis unit can compare data from other agricultural regions to learn weather patterns in different regions. For example, when analyzing weather data using generative AI, the weather data analysis unit compares it with data from other agricultural regions to learn weather patterns in different regions. For example, it proposes optimal harvest plans based on agricultural data from different climate zones. It also adjusts harvest times and methods based on weather data from different countries and regions. It also proposes optimal harvest plans based on harvest data under different weather conditions. In this way, by utilizing data from other agricultural regions, it is possible to learn weather patterns in different regions and reflect them in harvest plans.

[0037] The weather data analysis unit integrates satellite data and can predict wide-area weather fluctuations. For example, when analyzing weather data using generative AI, the weather data analysis unit integrates satellite data and predicts wide-area weather fluctuations. For example, wide-area weather patterns are analyzed based on satellite images and reflected in harvest plans. Also, based on satellite data, the risk of abnormal weather occurrence is predicted and harvest times are adjusted. Furthermore, based on satellite data, wide-area precipitation and temperature fluctuations are predicted and harvest plans are optimized. In this way, by integrating satellite data, wide-area weather fluctuations can be predicted and reflected in harvest plans.

[0038] The soil data analysis unit takes into account microbial activity in the soil and can propose soil management methods that are optimal for crop growth. For example, when analyzing soil data using generative AI, the soil data analysis unit takes into account microbial activity in the soil and proposes soil management methods that are optimal for crop growth. For example, adding fertilizer when microbial activity is active, using organic fertilizer to promote microbial activity, and proposing appropriate irrigation methods to suppress microbial activity. In this way, by taking microbial activity in the soil into account, it is possible to propose soil management methods that are optimal for crop growth.

[0039] The soil data analysis unit can consider the physical characteristics of the soil and propose the optimal soil improvement method. For example, when analyzing soil data using generative AI, the soil data analysis unit considers the physical characteristics of the soil (e.g., particle size distribution) and proposes the optimal soil improvement method. For example, it proposes an appropriate soil improvement material according to the particle size distribution. It also proposes an appropriate cultivation method based on the particle size distribution. It also proposes an appropriate irrigation method according to the particle size distribution. In this way, it is possible to propose the optimal soil improvement method by considering the physical characteristics of the soil.

[0040] The soil data analysis unit can anonymously share data from other farmers and learn best practices. For example, when analyzing soil data using generative AI, the soil data analysis unit can anonymously share data from other farmers and learn best practices. For example, it can propose optimal soil management methods based on soil data from other farmers in the same region. It can also propose soil management methods that suit local characteristics based on soil data from farmers in different regions. It can also propose optimal soil management plans based on past successes and failures. This makes it possible to propose optimal soil management methods by utilizing data from other farmers.

[0041] The soil data analysis unit can integrate real-time data from drones or sensors. For example, when analyzing soil data using generative AI, the soil data analysis unit integrates real-time data from drones and sensors. For example, it proposes optimal soil management methods based on aerial images from drones and soil moisture data from sensors. It also proposes soil improvement methods based on soil nutrient data from drones and pH value data from sensors. It also proposes soil management plans based on soil temperature data from drones and microbial activity data from sensors. By integrating real-time data, more accurate soil management becomes possible.

[0042] The crop growth data analysis unit can identify optimal growth conditions by taking into account the genetic information of the crop. For example, when analyzing crop growth data using generative AI, the crop growth data analysis unit can identify optimal growth conditions by taking into account the genetic information of the crop. For example, it can propose optimal fertilizer and irrigation methods for crops with specific genes. It can also propose methods for cultivating crops that are resistant to pests and diseases based on the genetic information. It can also propose cultivation methods to maximize yields based on the genetic information. This makes it possible to identify optimal growth conditions by taking into account the genetic information of the crop.

[0043] The crop growth data analysis unit can identify optimal growth conditions by taking into account the physiological characteristics of the crop. For example, when analyzing crop growth data using generative AI, the crop growth data analysis unit can identify optimal growth conditions by taking into account the physiological characteristics of the crop (e.g., photosynthetic efficiency). For example, it can suggest optimal sunlight conditions to maximize photosynthetic efficiency. It can also suggest the optimal type and amount of fertilizer to increase photosynthetic efficiency. It can also suggest the optimal irrigation schedule to maximize photosynthetic efficiency. This makes it possible to identify optimal growth conditions by taking into account the physiological characteristics of the crop.

[0044] The crop growth data analysis unit can anonymously share data from other farmers and learn best practices. For example, when analyzing crop growth data using generative AI, the crop growth data analysis unit can anonymously share data from other farmers and learn best practices. For example, it can propose optimal growth conditions based on growth data from other farmers in the same region. It can also propose growth conditions that suit local characteristics based on growth data from farmers in different regions. It can also propose optimal growth plans based on past successes and failures. This makes it possible to propose optimal crop growth conditions by utilizing data from other farmers.

[0045] The crop growth data analysis unit can integrate real-time data from drones or sensors. For example, when analyzing crop growth data using generative AI, the crop growth data analysis unit integrates real-time data from drones and sensors. For example, it proposes optimal growth conditions based on aerial images from drones and growth data from sensors. It also creates growth plans based on pest and disease occurrence status from drones and soil data from sensors. It also adjusts growth conditions based on crop health data from drones and weather data from sensors. By integrating real-time data, it is possible to propose more accurate crop growth conditions.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The harvest proposal system can further include a market data analysis unit. The market data analysis unit, for example, uses generative AI to analyze market supply and demand data and propose the optimal harvest time. For example, the harvest time can be adjusted based on market price trends. The harvest volume can also be optimized based on market demand forecast data. Furthermore, the harvest plan can be created taking into account the competitive situation in the market. This makes it possible to propose a harvest plan that reflects market trends.

[0048] The harvest suggestion system can further include an energy management unit. The energy management unit, for example, analyzes the amount of energy consumption required for harvesting work and proposes efficient energy usage methods. For example, energy consumption can be reduced by optimizing the operating hours of the harvesting machine. The energy management unit can also make proposals to promote the use of renewable energy. Furthermore, the energy management unit can propose specific measures to reduce energy costs. This can improve energy efficiency and realize sustainable agriculture.

[0049] The harvest suggestion system may further include a communication support unit. The communication support unit may provide a platform for promoting information sharing among farmers, for example, by sharing best practices and success stories related to harvesting. It may also facilitate discussions to share challenges and problems faced by farmers and find solutions. Furthermore, the communication support unit may support collaboration between farmers and experts and provide expert advice. This may promote cooperation and information sharing among farmers and improve the efficiency of harvesting work.

[0050] The harvest suggestion system can further include an environmental monitoring unit. The environmental monitoring unit, for example, analyzes the impact of harvesting on the environment and makes suggestions for achieving sustainable agriculture. For example, it monitors soil degradation and water pollution caused by harvesting and suggests appropriate countermeasures. It also analyzes greenhouse gas emissions caused by harvesting and suggests specific measures to reduce them. Furthermore, the environmental monitoring unit can propose harvest plans that take biodiversity protection into consideration. This makes it possible to achieve sustainable agriculture while minimizing the impact on the environment.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The harvest prediction data analysis unit uses the generation AI to analyze the harvest prediction data. For example, the harvest prediction data analysis unit may suggest optimal harvest times and methods based on past harvest results and the current state of crop growth. Step 2: The weather data analysis unit analyzes weather data based on the harvest forecast data analyzed by the harvest forecast data analysis unit. For example, the weather data analysis unit identifies optimal weather conditions for harvesting based on meteorological data such as temperature and precipitation. Step 3: The soil data analysis unit analyzes the soil data based on the weather data analyzed by the weather data analysis unit. For example, the soil data analysis unit proposes the optimal soil management method for crop growth based on the soil's moisture content and nutrient content. Step 4: The crop growth data analysis unit analyzes the crop growth data based on the soil data analyzed by the soil data analysis unit. For example, the crop growth data analysis unit identifies the optimal timing for harvesting based on the color of the crop's leaves and the thickness of its stems.

[0053] (Example 2) The harvest suggestion system according to an embodiment of the present invention uses generative AI to analyze harvest forecast data, and comprehensively analyzes weather data, soil data, and crop growth data to suggest optimal harvest times and methods to farmers. This allows the harvest suggestion system to maximize harvest volume and quality and realize sustainable agriculture.

[0054] A harvest suggestion system according to an embodiment includes a harvest prediction data analysis unit, a weather data analysis unit, a soil data analysis unit, and a crop growth data analysis unit. The harvest prediction data analysis unit analyzes harvest prediction data using a generative AI. For example, the harvest prediction data analysis unit suggests optimal harvest times and methods based on past harvest performance and the current growth status of crops. The weather data analysis unit analyzes weather data based on the harvest prediction data analyzed by the harvest prediction data analysis unit. For example, the weather data analysis unit identifies optimal weather conditions for harvesting based on meteorological data such as temperature and precipitation. The soil data analysis unit analyzes soil data based on the weather data analyzed by the weather data analysis unit. For example, the soil data analysis unit suggests optimal soil management methods for crop growth based on the soil moisture content and nutrient content. The crop growth data analysis unit analyzes crop growth data based on the soil data analyzed by the soil data analysis unit. For example, the crop growth data analysis unit identifies optimal harvest timing based on the leaf color and stem thickness of the crop. This allows the harvest suggestion system according to an embodiment to maximize yield and quality and achieve sustainable agriculture.

[0055] The harvest prediction data analysis unit can perform simulations based on the harvest prediction data and compare different harvest scenarios. For example, the harvest prediction data analysis unit uses generative AI to perform simulations based on the harvest prediction data and compare different harvest scenarios. For example, it can simulate the changes in harvest volume and quality when the harvest time is shifted by one week, and identify the optimal harvest time. It can also compare the changes in efficiency and cost when the harvesting method is changed from manual to mechanical harvesting, and propose the optimal harvesting method. It can also simulate the changes in harvest volume and quality when the harvest time is advanced or delayed, and identify the optimal harvest time. In this way, it can identify the optimal harvest time and method by comparing different harvest scenarios.

[0056] The harvest forecast data analysis unit can compare past harvest data with current data and detect outliers and trend changes. For example, the harvest forecast data analysis unit uses generative AI to compare past harvest data with current data and detect outliers and trend changes. For example, it compares past harvest yields with current harvest yields to detect abnormal increases or decreases and identify the causes. It also compares past weather data with current weather data to detect abnormal weather patterns and reflect them in the harvest plan. It also compares past soil data with current soil data to detect abnormal soil conditions and propose appropriate countermeasures. This improves the accuracy of harvest plans by detecting outliers and trend changes.

[0057] The harvest prediction data analysis unit can use the emotion estimation function to analyze the emotional state of the farmer and propose a less stressful harvesting plan. The harvest prediction data analysis unit, for example, uses the emotion estimation function to analyze the emotional state of the farmer and propose a less stressful harvesting plan. For example, it collects emotional data from the farmer and suggests harvesting at a time when stress is low. It also suggests a schedule to reduce the burden of harvesting work based on the emotional data from the farmer. It also suggests reducing stress by distributing the harvesting work based on the emotional data from the farmer. In this way, it is possible to propose a less stressful harvesting plan by taking the emotional state of the farmer into consideration.

[0058] The harvest prediction data analysis unit can anonymously share data from other farmers and learn best practices. For example, when analyzing harvest prediction data using generative AI, the harvest prediction data analysis unit can anonymously share data from other farmers and learn best practices. For example, it can suggest optimal harvest times and methods based on harvest data from other farmers in the same region. It can also suggest optimal harvest methods based on weather conditions and soil conditions based on harvest data from farmers in different regions. It can also suggest optimal harvest plans based on past successes and failures. This makes it possible to suggest optimal harvest times and methods by utilizing data from other farmers.

[0059] The harvest prediction data analysis unit can integrate real-time data from drones or sensors. For example, when analyzing harvest prediction data using generative AI, the harvest prediction data analysis unit integrates real-time data from drones and sensors. For example, it proposes the optimal harvest time and method based on aerial images from drones and soil data from sensors. It also creates a harvest plan based on data on the growth status of crops from drones and weather data from sensors. It also adjusts the harvest time based on data on the occurrence of pests and diseases from drones and soil moisture content from sensors. By integrating real-time data, it becomes possible to make more accurate harvest predictions.

[0060] The harvest forecast data analysis unit can use the emotion estimation function to analyze the emotions of farmers when they input harvest forecast data in real time and provide positive feedback. The harvest forecast data analysis unit can, for example, use the emotion estimation function to analyze the emotions of farmers when they input harvest forecast data in real time and provide positive feedback. For example, if a farmer is feeling stressed, an encouraging message can be displayed. Also, if a farmer is feeling anxious, success stories can be introduced. Furthermore, if a farmer is tired, a message urging the farmer to take a break can be displayed. In this way, the emotions of farmers when they input harvest forecast data can be analyzed in real time and positive feedback can be provided, thereby reducing the stress of farmers.

[0061] The weather data analysis unit can learn past abnormal weather patterns and predict future abnormal weather. For example, when analyzing weather data using generative AI, the weather data analysis unit learns past abnormal weather patterns and predicts future abnormal weather. For example, based on data from past typhoons and heavy rains, it predicts the probability of future abnormal weather occurrences. It also predicts the impact of future abnormal weather based on data from past heat waves and cold waves. It also predicts the risk of future abnormal weather based on data from past droughts and floods. In this way, by learning past abnormal weather patterns, it is possible to predict future abnormal weather and reflect this in harvest plans.

[0062] The weather data analysis unit takes into account the microclimate data for each region, enabling more precise forecasts. For example, when analyzing weather data using generative AI, the weather data analysis unit takes into account the microclimate data for each region to make more precise forecasts. For example, a harvest plan is made based on weather data for specific regions, such as mountainous or coastal areas. Weather data for urban and rural areas is also compared to propose a harvest plan that suits the characteristics of each region. Furthermore, the harvest time is adjusted based on weather data that takes into account differences in altitude and topography. In this way, by taking into account the microclimate data for each region, more precise weather forecasts are possible.

[0063] The weather data analysis unit can use the emotion estimation function to provide advice to reduce farmers' anxiety about weather fluctuations. The weather data analysis unit, for example, uses the emotion estimation function to provide advice to reduce farmers' anxiety about weather fluctuations. For example, if a farmer is feeling anxious, specific measures based on weather data can be suggested. Also, if a farmer is feeling stressed, past success stories can be introduced to reassure farmers. Furthermore, if a farmer is worried, a harvest plan based on weather data can be suggested to reassure farmers. In this way, by providing advice to reduce farmers' anxiety about weather fluctuations, farmers' stress can be reduced.

[0064] The weather data analysis unit can compare data from other agricultural regions to learn weather patterns in different regions. For example, when analyzing weather data using generative AI, the weather data analysis unit compares it with data from other agricultural regions to learn weather patterns in different regions. For example, it proposes optimal harvest plans based on agricultural data from different climate zones. It also adjusts harvest times and methods based on weather data from different countries and regions. It also proposes optimal harvest plans based on harvest data under different weather conditions. In this way, by utilizing data from other agricultural regions, it is possible to learn weather patterns in different regions and reflect them in harvest plans.

[0065] The weather data analysis unit integrates satellite data and can predict wide-area weather fluctuations. For example, when analyzing weather data using generative AI, the weather data analysis unit integrates satellite data and predicts wide-area weather fluctuations. For example, wide-area weather patterns are analyzed based on satellite images and reflected in harvest plans. Also, based on satellite data, the risk of abnormal weather occurrence is predicted and harvest times are adjusted. Furthermore, based on satellite data, wide-area precipitation and temperature fluctuations are predicted and harvest plans are optimized. In this way, by integrating satellite data, wide-area weather fluctuations can be predicted and reflected in harvest plans.

[0066] The weather data analysis unit can use the emotion estimation function to analyze the emotions of farmers when they input weather data in real time and provide appropriate advice. The weather data analysis unit can, for example, use the emotion estimation function to analyze the emotions of farmers when they input weather data in real time and provide appropriate advice. For example, if a farmer is feeling anxious, the unit can suggest specific measures. Also, if a farmer is feeling stressed, the unit can provide advice to help them relax. Furthermore, if a farmer is worried, the unit can suggest a harvest plan based on the weather data to give them a sense of security. In this way, the unit can reduce farmers' stress by analyzing the emotions of farmers when they input weather data in real time and providing appropriate advice.

[0067] The soil data analysis unit takes into account microbial activity in the soil and can propose soil management methods that are optimal for crop growth. For example, when analyzing soil data using generative AI, the soil data analysis unit takes into account microbial activity in the soil and proposes soil management methods that are optimal for crop growth. For example, adding fertilizer when microbial activity is active, using organic fertilizer to promote microbial activity, and proposing appropriate irrigation methods to suppress microbial activity. In this way, by taking microbial activity in the soil into account, it is possible to propose soil management methods that are optimal for crop growth.

[0068] The soil data analysis unit can consider the physical characteristics of the soil and propose the optimal soil improvement method. For example, when analyzing soil data using generative AI, the soil data analysis unit considers the physical characteristics of the soil (e.g., particle size distribution) and proposes the optimal soil improvement method. For example, it proposes an appropriate soil improvement material according to the particle size distribution. It also proposes an appropriate cultivation method based on the particle size distribution. It also proposes an appropriate irrigation method according to the particle size distribution. In this way, it is possible to propose the optimal soil improvement method by considering the physical characteristics of the soil.

[0069] The soil data analysis unit can use the emotion estimation function to analyze farmers' emotions regarding soil management and suggest management methods that are less stressful. The soil data analysis unit, for example, uses the emotion estimation function to analyze farmers' emotions regarding soil management and suggest management methods that are less stressful. For example, if a farmer is feeling stressed, it can suggest a simple soil management method. Also, if a farmer is feeling anxious, it can provide specific soil management procedures. Furthermore, if a farmer is worried, it can suggest a management plan based on soil data to give the farmer a sense of security. In this way, by analyzing farmers' emotions regarding soil management and suggesting management methods that are less stressful, it is possible to reduce farmers' stress.

[0070] The soil data analysis unit can anonymously share data from other farmers and learn best practices. For example, when analyzing soil data using generative AI, the soil data analysis unit can anonymously share data from other farmers and learn best practices. For example, it can propose optimal soil management methods based on soil data from other farmers in the same region. It can also propose soil management methods that suit local characteristics based on soil data from farmers in different regions. It can also propose optimal soil management plans based on past successes and failures. This makes it possible to propose optimal soil management methods by utilizing data from other farmers.

[0071] The soil data analysis unit can integrate real-time data from drones or sensors. For example, when analyzing soil data using generative AI, the soil data analysis unit integrates real-time data from drones and sensors. For example, it proposes optimal soil management methods based on aerial images from drones and soil moisture data from sensors. It also proposes soil improvement methods based on soil nutrient data from drones and pH value data from sensors. It also proposes soil management plans based on soil temperature data from drones and microbial activity data from sensors. By integrating real-time data, more accurate soil management becomes possible.

[0072] The soil data analysis unit can use the emotion estimation function to analyze the emotions of farmers when they input soil data in real time and provide positive feedback. The soil data analysis unit can, for example, use the emotion estimation function to analyze the emotions of farmers when they input soil data in real time and provide positive feedback. For example, if a farmer is feeling stressed, an encouraging message can be displayed. Also, if a farmer is feeling anxious, success stories can be introduced. Furthermore, if a farmer is tired, a message urging the farmer to take a break can be displayed. In this way, the emotions of farmers when they input soil data can be analyzed in real time and positive feedback can be provided, thereby reducing farmers' stress.

[0073] The crop growth data analysis unit can identify optimal growth conditions by taking into account the genetic information of the crop. For example, when analyzing crop growth data using generative AI, the crop growth data analysis unit can identify optimal growth conditions by taking into account the genetic information of the crop. For example, it can propose optimal fertilizer and irrigation methods for crops with specific genes. It can also propose methods for cultivating crops that are resistant to pests and diseases based on the genetic information. It can also propose cultivation methods to maximize yields based on the genetic information. This makes it possible to identify optimal growth conditions by taking into account the genetic information of the crop.

[0074] The crop growth data analysis unit can identify optimal growth conditions by taking into account the physiological characteristics of the crop. For example, when analyzing crop growth data using generative AI, the crop growth data analysis unit can identify optimal growth conditions by taking into account the physiological characteristics of the crop (e.g., photosynthetic efficiency). For example, it can suggest optimal sunlight conditions to maximize photosynthetic efficiency. It can also suggest the optimal type and amount of fertilizer to increase photosynthetic efficiency. It can also suggest the optimal irrigation schedule to maximize photosynthetic efficiency. This makes it possible to identify optimal growth conditions by taking into account the physiological characteristics of the crop.

[0075] The crop growth data analysis unit can use the emotion estimation function to analyze farmers' emotions regarding crop management and propose management methods that are less stressful. The crop growth data analysis unit, for example, uses the emotion estimation function to analyze farmers' emotions regarding crop management and propose management methods that are less stressful. For example, if a farmer is feeling stressed, it proposes a simple crop management method. Also, if a farmer is feeling anxious, it provides specific crop management procedures. Furthermore, if a farmer is worried, it proposes a management plan based on crop data to give the farmer a sense of security. In this way, by analyzing farmers' emotions regarding crop management and proposing management methods that are less stressful, it is possible to reduce farmers' stress.

[0076] The crop growth data analysis unit can anonymously share data from other farmers and learn best practices. For example, when analyzing crop growth data using generative AI, the crop growth data analysis unit can anonymously share data from other farmers and learn best practices. For example, it can propose optimal growth conditions based on growth data from other farmers in the same region. It can also propose growth conditions that suit local characteristics based on growth data from farmers in different regions. It can also propose optimal growth plans based on past successes and failures. This makes it possible to propose optimal crop growth conditions by utilizing data from other farmers.

[0077] The crop growth data analysis unit can integrate real-time data from drones or sensors. For example, when analyzing crop growth data using generative AI, the crop growth data analysis unit integrates real-time data from drones and sensors. For example, it proposes optimal growth conditions based on aerial images from drones and growth data from sensors. It also creates growth plans based on pest and disease occurrence status from drones and soil data from sensors. It also adjusts growth conditions based on crop health data from drones and weather data from sensors. By integrating real-time data, it is possible to propose more accurate crop growth conditions.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The harvest suggestion system can further include a health management unit that monitors the farmer's health condition. For example, it can collect the farmer's heart rate and sleep data to detect signs of overwork or stress. The health management unit can also adjust the harvesting work schedule based on the farmer's health condition. Furthermore, the health management unit can suggest appropriate times for rest and nutritional supplementation, taking into account the farmer's health condition. This allows for efficient harvesting work while maintaining the farmer's health.

[0080] The harvest proposal system can further include a market data analysis unit. The market data analysis unit, for example, uses generative AI to analyze market supply and demand data and propose the optimal harvest time. For example, the harvest time can be adjusted based on market price trends. The harvest volume can also be optimized based on market demand forecast data. Furthermore, the harvest plan can be created taking into account the competitive situation in the market. This makes it possible to propose a harvest plan that reflects market trends.

[0081] The harvest suggestion system can further include an energy management unit. The energy management unit, for example, analyzes the amount of energy consumption required for harvesting work and proposes efficient energy usage methods. For example, energy consumption can be reduced by optimizing the operating hours of the harvesting machine. The energy management unit can also make proposals to promote the use of renewable energy. Furthermore, the energy management unit can propose specific measures to reduce energy costs. This can improve energy efficiency and realize sustainable agriculture.

[0082] The harvest suggestion system may further include a communication support unit. The communication support unit may provide a platform for promoting information sharing among farmers, for example, by sharing best practices and success stories related to harvesting. It may also facilitate discussions to share challenges and problems faced by farmers and find solutions. Furthermore, the communication support unit may support collaboration between farmers and experts and provide expert advice. This may promote cooperation and information sharing among farmers and improve the efficiency of harvesting work.

[0083] The harvest suggestion system can further include an environmental monitoring unit. The environmental monitoring unit, for example, analyzes the impact of harvesting on the environment and makes suggestions for achieving sustainable agriculture. For example, it monitors soil degradation and water pollution caused by harvesting and suggests appropriate countermeasures. It also analyzes greenhouse gas emissions caused by harvesting and suggests specific measures to reduce them. Furthermore, the environmental monitoring unit can propose harvest plans that take biodiversity protection into consideration. This makes it possible to achieve sustainable agriculture while minimizing the impact on the environment.

[0084] The harvest suggestion system can also use its emotion estimation function to provide feedback to improve farmers' motivation. For example, if a farmer has positive feelings about harvesting, it will display messages of praise and encouragement. If a farmer has negative feelings about harvesting, it will provide improvement measures and support. Furthermore, based on the farmer's emotion data, it can adjust the harvest schedule and make suggestions to maintain motivation. This will improve farmers' motivation and enable more efficient harvesting.

[0085] The harvesting suggestion system can also use its emotion estimation function to provide customized advice based on the farmer's emotional state. For example, if a farmer is feeling stressed, it can provide relaxation techniques and stress management advice. If a farmer is feeling anxious, it can reassure them by introducing specific measures and success stories. Furthermore, if a farmer is feeling positive, it can provide feedback to help them maintain that emotion. This allows for customized advice to be provided based on the farmer's emotional state, improving the efficiency of harvesting work.

[0086] The harvest suggestion system can also use emotion estimation to provide community support based on the farmer's emotional state. For example, if a farmer feels lonely, it can suggest events or online forums to promote interaction with other farmers. If a farmer feels stressed, it can suggest workshops or seminars for stress management. Furthermore, if a farmer is feeling positive, it can provide a platform for sharing those feelings. This can provide community support according to the farmer's emotional state and improve the efficiency of harvesting work.

[0087] The harvest suggestion system can also use its emotion estimation function to provide health management advice based on the farmer's emotional state. For example, if the farmer is feeling stressed, it can provide relaxation methods and stress management advice. If the farmer is tired, it can suggest appropriate times to take a break or replenish nutrients. Furthermore, if the farmer is feeling positive, it can provide health management advice to maintain that emotion. This makes it possible to provide health management advice according to the farmer's emotional state and improve the efficiency of harvesting work.

[0088] The harvest suggestion system can also use emotion estimation to propose personalized harvest plans based on the farmer's emotional state. For example, if a farmer is feeling stressed, the system can propose a schedule to reduce the burden of harvesting work. If a farmer is feeling anxious, the system can reassure them by introducing specific countermeasures and success stories. Furthermore, if a farmer is feeling positive, the system can propose a harvest plan to maintain that emotion. This makes it possible to propose personalized harvest plans based on the farmer's emotional state and improve the efficiency of harvesting work.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The harvest prediction data analysis unit uses the generation AI to analyze the harvest prediction data. For example, the harvest prediction data analysis unit may suggest optimal harvest times and methods based on past harvest results and the current state of crop growth. Step 2: The weather data analysis unit analyzes weather data based on the harvest forecast data analyzed by the harvest forecast data analysis unit. For example, the weather data analysis unit identifies optimal weather conditions for harvesting based on meteorological data such as temperature and precipitation. Step 3: The soil data analysis unit analyzes the soil data based on the weather data analyzed by the weather data analysis unit. For example, the soil data analysis unit proposes the optimal soil management method for crop growth based on the soil's moisture content and nutrient content. Step 4: The crop growth data analysis unit analyzes the crop growth data based on the soil data analyzed by the soil data analysis unit. For example, the crop growth data analysis unit identifies the optimal timing for harvesting based on the color of the crop's leaves and the thickness of its stems.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] 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.

[0106] 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.

[0107] 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 AI 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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 AI 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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]

[0158] 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 harvest forecast data analysis unit that analyzes the harvest forecast data using the generation AI; a weather data analysis unit that analyzes weather data based on the harvest forecast data analyzed by the harvest forecast data analysis unit; a soil data analysis unit that analyzes soil data based on the weather data analyzed by the weather data analysis unit; a crop growth data analysis unit that analyzes crop growth data based on the soil data analyzed by the soil data analysis unit. A system characterized by:

2. The harvest prediction data analysis unit Simulations based on the harvest forecast data are performed to compare different harvest scenarios.

2. The system of claim 1.

3. The harvest prediction data analysis unit Compare past harvest data with current data to detect outliers and trend changes 2. The system of claim 1.

4. The harvest prediction data analysis unit Analyzing farmers' emotional state and proposing less stressful harvest plans 2. The system of claim 1.

5. The harvest prediction data analysis unit Anonymously share data with other farmers and learn best practices 2. The system of claim 1.

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