System

Abstract

An object of a system according to an embodiment is to efficiently monitor a growth condition of a crop and manage an appropriate agricultural schedule.SOLUTION: A system includes a growing condition monitoring part, a schedule management part, and a knowledge providing part. The growth condition monitoring unit monitors a growth condition of a crop. The schedule manager is configured to manage an agricultural schedule based on the growing condition monitored by the growing condition monitor. The knowledge providing unit provides knowledge related to agriculture based on the schedule managed by the schedule managing 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 had the problem of making it difficult to efficiently monitor the growth status of agricultural crops and manage appropriate agricultural schedules.

[0005] The system according to the embodiment aims to efficiently monitor the growth status of agricultural crops and manage an appropriate agricultural schedule. [Means for solving the problem]

[0006] The system according to the embodiment includes a growth status monitoring unit, a schedule management unit, and a knowledge providing unit. The growth status monitoring unit monitors the growth status of agricultural crops. The schedule management unit manages an agricultural schedule based on the growth status monitored by the growth status monitoring unit. The knowledge providing unit provides knowledge about agriculture based on the schedule managed by the schedule management unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently monitor the growth status of agricultural crops and manage an appropriate agricultural schedule. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The smart agriculture system according to an embodiment of the present invention is a system that enables individual farmers to farm efficiently and at low cost. This system uses AI technology to monitor the growth status of agricultural crops, manage farming schedules, and provide agricultural knowledge. As a result, the smart agriculture system enables individual farmers to farm efficiently and at low cost, and promotes the spread of smart agriculture.

[0029] The smart agriculture system according to the embodiment includes a growth status monitoring unit, a schedule management unit, and a knowledge provision unit. The growth status monitoring unit monitors the growth status of agricultural crops. For example, it monitors the growth status of agricultural crops using cameras and sensors and indicates the timing of necessary fertilization and irrigation. The growth status monitoring unit can also monitor microbial activity and evaluate the health of soil. For example, it can monitor microbial activity in real time using soil sensors and evaluate the health of soil. The schedule management unit manages an agricultural schedule based on the growth status monitored by the growth status monitoring unit. For example, it analyzes weather data and crop growth data to propose an optimal work schedule. The schedule management unit can also propose a reasonable schedule taking into account the farmer's physical condition and labor availability. For example, it determines work priorities taking into account the farmer's health condition and labor availability. The knowledge provision unit provides agricultural knowledge based on the schedule managed by the schedule management unit. For example, it provides answers to questions about agriculture and allows users to learn agricultural techniques and knowledge. The knowledge provision unit can also provide agricultural techniques and traditional knowledge specific to each region. For example, feedback from local farmers and historical data are collected to build a knowledge base. As a result, the smart agriculture system according to the embodiment allows individual farmers to farm efficiently and at low cost. For example, AI can monitor the growth status of crops and suggest optimal cultivation methods, allowing individual farmers to produce high-quality crops. AI can also manage agricultural schedules and improve work efficiency, allowing individual farmers to work efficiently. Furthermore, AI is expected to provide agricultural knowledge and help individual farmers improve their skills, leading to improvements in agricultural technology.

[0030] The growth status monitoring unit can monitor the growth status of crops using cameras or sensors and instruct the timing of fertilization and irrigation. The growth status monitoring unit can monitor the growth status of crops using cameras or sensors, for example. For example, a camera can capture the color and shape of the crop's leaves, and AI can analyze the data to evaluate the growth status. In addition, sensors can measure the moisture content and nutrient content of the soil, and AI can use that data to instruct the timing of fertilization and irrigation. This can optimize crop growth.

[0031] The schedule management unit can analyze weather data or crop growth data and propose a work schedule. The schedule management unit, for example, analyzes weather data or crop growth data and proposes an optimal work schedule. For example, weather data such as temperature, precipitation, and wind speed are collected, and the AI ​​determines the work schedule based on that data. In addition, crop growth data such as plant height, number of leaves, and fruit size are collected, and the AI ​​proposes a work schedule based on that data. This improves work efficiency.

[0032] The knowledge provision unit provides answers to questions about agriculture, allowing farmers to learn agricultural techniques and knowledge. The knowledge provision unit provides answers to questions about agriculture, for example. For example, if a farmer inputs a question such as, "What is the best way to cultivate this crop?", the AI ​​will suggest the best cultivation method for that question. The knowledge provision unit can also provide teaching materials and video tutorials for learning agricultural techniques and knowledge. For example, it can provide video tutorials on cultivation techniques and disease control, allowing farmers to learn visually. This is expected to lead to improvements in agricultural techniques.

[0033] The growth status monitoring unit can monitor the activity of microorganisms and evaluate the health of the soil. The growth status monitoring unit, for example, uses a soil sensor to monitor the activity of microorganisms in real time and evaluate the health of the soil. For example, it analyzes the types and numbers of microorganisms in the soil and suggests the timing of fertilization and irrigation to maintain a healthy soil environment. This allows the health of the soil to be maintained.

[0034] The growth status monitoring unit can detect plant stress responses in real time and propose countermeasures. For example, the growth status monitoring unit uses cameras and sensors to monitor changes in the color and shape of plant leaves in real time and detects stress responses. For example, if the leaves turn yellow, it identifies nutritional deficiencies or the occurrence of pests and diseases and proposes appropriate countermeasures. This allows the plant to maintain its health.

[0035] The growth status monitoring unit uses drones to monitor a wide area of ​​farmland, enabling efficient monitoring. The growth status monitoring unit, for example, uses drones to monitor a wide area of ​​farmland and grasps the growth status of crops in real time. For example, cameras and sensors mounted on the drones are used to detect the growth status of crops and the occurrence of pests and diseases. This allows efficient monitoring of a wide area of ​​farmland.

[0036] The growth status monitoring unit can consider the interactions between different crops and propose the optimal combination of mixed planting or crop rotation. For example, the growth status monitoring unit analyzes the interactions between different crops and proposes the optimal combination of mixed planting or crop rotation. For example, if certain crops have a mutually beneficial relationship, it will recommend that combination. This makes it possible to propose the optimal cultivation method that takes into account the interactions between crops.

[0037] The schedule management unit can propose a reasonable schedule taking into consideration the farmer's physical condition or labor situation. The schedule management unit, for example, analyzes the farmer's physical condition data and labor situation and proposes a reasonable farming schedule. For example, it determines the priority of work taking into consideration the farmer's health condition and available labor time. This reduces the burden on the farmer.

[0038] The schedule management unit can predict work efficiency based on past data and propose the optimal work sequence. For example, the schedule management unit analyzes past agricultural data and develops an algorithm to predict work efficiency. For example, it proposes the optimal work sequence based on past work history. This improves work efficiency.

[0039] The knowledge provider can provide agricultural techniques and traditional knowledge specific to the region. For example, the knowledge provider can collect feedback and historical data from local farmers and build a knowledge base to provide agricultural techniques and traditional knowledge specific to the region. For example, it can introduce cultivation methods and traditional techniques specific to the region. This allows local knowledge to be utilized.

[0040] The knowledge providing unit can provide information customized according to the farmer's experience level. For example, the knowledge providing unit analyzes the farmer's experience level and collects the farmer's past work history and feedback in order to provide customized agricultural knowledge. For example, it can provide basic knowledge for beginners to advanced techniques for advanced farmers. This makes it possible to provide information according to the farmer's experience.

[0041] The knowledge providing unit can make the information visually easy to understand by using video tutorials or interactive learning tools. The knowledge providing unit can make the information visually easy to understand by using, for example, video tutorials. For example, agricultural work procedures can be explained using videos to enable visual learning. The knowledge providing unit can also provide agricultural knowledge by using interactive learning tools. For example, quiz-style learning or simulation tools can be used to enable farmers to learn in a fun way. This makes it possible to provide information that is visually easy to understand.

[0042] The knowledge sharing unit can set up an online discussion forum with other farmers to promote knowledge sharing. The knowledge sharing unit can, for example, set up an online discussion forum with other farmers and manage and moderate the forum to promote knowledge sharing. For example, it can promote discussions on specific topics. This can promote knowledge sharing among farmers.

[0043] The schedule management unit can adjust the schedule to allow farmers to participate, taking into account the dates of local events and festivals. The schedule management unit works with the local calendar to adjust the agricultural schedule, taking into account the dates of local events and festivals, for example. For example, it reduces work on days when important events are held. This makes it easier for farmers to participate in local events.

[0044] The schedule management unit can propose cooperative work with other farmers and work together efficiently. For example, to propose cooperative work with other farmers, the schedule management unit builds a database of local farmers and matches them with farmers who can cooperate. For example, working together during harvest season. This promotes cooperative work between farmers.

[0045] The market analysis unit can perform a detailed analysis of consumer purchasing history or preferences and propose individual sales strategies. For example, the market analysis unit analyzes consumer purchasing history and performs a detailed analysis of consumer preferences and purchasing patterns in order to propose individual sales strategies. For example, it proposes an optimal sales strategy for a specific consumer group. This makes it possible to propose a sales strategy based on consumer preferences.

[0046] The market analysis department can propose competitive sales strategies by taking into account the trends and pricing strategies of competitors. For example, the market analysis department collects competitors' pricing strategies and sales data in order to analyze competitors' trends and propose competitive sales strategies. For example, it monitors competitors' price fluctuations in real time. This allows it to propose competitive sales strategies.

[0047] The market analysis department compares market data from different regions and countries and can propose global sales strategies. For example, the market analysis department collects market data from different regions and countries and builds a database to propose global sales strategies. For example, it analyzes the market needs and consumer preferences of each region. This allows it to propose global sales strategies.

[0048] The market analysis department can integrate online or offline sales channels and propose the optimal sales method. For example, the market analysis department collects and analyzes sales data from each channel in order to integrate online and offline sales channels and propose the optimal sales method. For example, the market analysis department proposes the optimal sales strategy based on data on online sales and in-store sales. This makes it possible to propose the optimal sales method.

[0049] The resource management unit records the resource usage history in detail and can predict future usage. The resource management unit, for example, builds a resource management system to record the resource usage history in detail and predict future usage. For example, it proposes an optimal usage plan based on the usage history of water and fertilizer. This makes it possible to predict resource usage.

[0050] The resource management unit can consider the resource supply situation and price fluctuations to propose the optimal purchasing timing. For example, the resource management unit collects resource supply data to analyze the resource supply situation and price fluctuations and propose the optimal purchasing timing. For example, it monitors price fluctuations of fertilizer and seeds in real time. This allows it to propose the optimal purchasing timing.

[0051] The resource management department can propose methods for reusing and recycling resources and promote sustainable agriculture. The resource management department, for example, builds a resource management system to propose methods for reusing and recycling resources. For example, it proposes methods for reusing water and recycling fertilizer. This can promote sustainable agriculture.

[0052] The resource management unit can propose resource sharing with other farmers to reduce costs. For example, to propose resource sharing with other farmers, the resource management unit builds a database of local farmers and matches them with shareable resources. For example, surplus fertilizer or seeds can be shared. This allows for cost reduction through resource sharing.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The growth status monitoring unit can detect plant stress responses in real time and propose countermeasures. For example, it can use cameras and sensors to monitor changes in the color and shape of plant leaves in real time to detect stress responses. For example, if the leaves turn yellow, it can identify nutritional deficiencies or the occurrence of pests and diseases and propose appropriate countermeasures. This helps maintain the health of plants.

[0055] The schedule management department can predict work efficiency based on past data and propose the optimal work sequence. For example, it can analyze past agricultural data and develop an algorithm to predict work efficiency. For example, it can propose the optimal work sequence based on past work history. This can improve work efficiency.

[0056] The Knowledge Provision Department can set up online discussion forums with other farmers to promote knowledge sharing. For example, the Knowledge Provision Department can set up online discussion forums with other farmers to promote knowledge sharing, managing and moderating the forums. For example, it can facilitate discussions on specific topics. This can promote knowledge sharing among farmers.

[0057] The Resource Management Department can propose ways to reuse and recycle resources and promote sustainable agriculture. For example, a resource management system can be built to propose ways to reuse and recycle resources. For example, it can propose ways to reuse water and recycle fertilizer. This can promote sustainable agriculture.

[0058] The Market Analysis Department can propose competitive sales strategies by taking into account the trends and pricing strategies of competitors. For example, in order to analyze competitors' trends and propose competitive sales strategies, they collect competitors' pricing strategies and sales data. For example, they monitor competitors' price fluctuations in real time. This allows them to propose competitive sales strategies.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The growth status monitoring unit monitors the growth status of crops. For example, it uses cameras and sensors to monitor the growth status of crops and indicates the timing of necessary fertilization and irrigation. The growth status monitoring unit can also monitor the activity of microorganisms and evaluate the health of the soil. For example, it uses soil sensors to monitor the activity of microorganisms in real time and evaluate the health of the soil. Step 2: The schedule management unit manages the agricultural schedule based on the growth conditions monitored by the growth condition monitoring unit. For example, it analyzes weather data and crop growth data to propose an optimal work schedule. The schedule management unit can also propose a reasonable schedule taking into account the farmer's physical condition and labor availability. For example, it determines the priority of work by taking into account the farmer's health condition and labor availability. Step 3: The knowledge provision unit provides agricultural knowledge based on a schedule managed by the schedule management unit. For example, it provides answers to agricultural questions and allows participants to learn agricultural techniques and knowledge. The knowledge provision unit can also provide agricultural techniques and traditional knowledge specific to the region. For example, it collects feedback and historical data from local farmers and builds a knowledge base.

[0061] (Example 2) The smart agriculture system according to an embodiment of the present invention is a system that enables individual farmers to farm efficiently and at low cost. This system uses AI technology to monitor the growth status of agricultural crops, manage farming schedules, and provide agricultural knowledge. As a result, the smart agriculture system enables individual farmers to farm efficiently and at low cost, and promotes the spread of smart agriculture.

[0062] The smart agriculture system according to the embodiment includes a growth status monitoring unit, a schedule management unit, and a knowledge provision unit. The growth status monitoring unit monitors the growth status of agricultural crops. For example, it monitors the growth status of agricultural crops using cameras and sensors and indicates the timing of necessary fertilization and irrigation. The growth status monitoring unit can also monitor microbial activity and evaluate the health of soil. For example, it can monitor microbial activity in real time using soil sensors and evaluate the health of soil. The schedule management unit manages an agricultural schedule based on the growth status monitored by the growth status monitoring unit. For example, it analyzes weather data and crop growth data to propose an optimal work schedule. The schedule management unit can also propose a reasonable schedule taking into account the farmer's physical condition and labor availability. For example, it determines work priorities taking into account the farmer's health condition and labor availability. The knowledge provision unit provides agricultural knowledge based on the schedule managed by the schedule management unit. For example, it provides answers to questions about agriculture and allows users to learn agricultural techniques and knowledge. The knowledge provision unit can also provide agricultural techniques and traditional knowledge specific to each region. For example, feedback from local farmers and historical data are collected to build a knowledge base. As a result, the smart agriculture system according to the embodiment allows individual farmers to farm efficiently and at low cost. For example, AI can monitor the growth status of crops and suggest optimal cultivation methods, allowing individual farmers to produce high-quality crops. AI can also manage agricultural schedules and improve work efficiency, allowing individual farmers to work efficiently. Furthermore, AI is expected to provide agricultural knowledge and help individual farmers improve their skills, leading to improvements in agricultural technology.

[0063] The growth status monitoring unit can monitor the growth status of crops using cameras or sensors and instruct the timing of fertilization and irrigation. The growth status monitoring unit can monitor the growth status of crops using cameras or sensors, for example. For example, a camera can capture the color and shape of the crop's leaves, and AI can analyze the data to evaluate the growth status. In addition, sensors can measure the moisture content and nutrient content of the soil, and AI can use that data to instruct the timing of fertilization and irrigation. This can optimize crop growth.

[0064] The schedule management unit can analyze weather data or crop growth data and propose a work schedule. The schedule management unit, for example, analyzes weather data or crop growth data and proposes an optimal work schedule. For example, weather data such as temperature, precipitation, and wind speed are collected, and the AI ​​determines the work schedule based on that data. In addition, crop growth data such as plant height, number of leaves, and fruit size are collected, and the AI ​​proposes a work schedule based on that data. This improves work efficiency.

[0065] The knowledge provision unit provides answers to questions about agriculture, allowing farmers to learn agricultural techniques and knowledge. The knowledge provision unit provides answers to questions about agriculture, for example. For example, if a farmer inputs a question such as, "What is the best way to cultivate this crop?", the AI ​​will suggest the best cultivation method for that question. The knowledge provision unit can also provide teaching materials and video tutorials for learning agricultural techniques and knowledge. For example, it can provide video tutorials on cultivation techniques and disease control, allowing farmers to learn visually. This is expected to lead to improvements in agricultural techniques.

[0066] The growth status monitoring unit can monitor the activity of microorganisms and evaluate the health of the soil. The growth status monitoring unit, for example, uses a soil sensor to monitor the activity of microorganisms in real time and evaluate the health of the soil. For example, it analyzes the types and numbers of microorganisms in the soil and suggests the timing of fertilization and irrigation to maintain a healthy soil environment. This allows the health of the soil to be maintained.

[0067] The growth status monitoring unit can detect plant stress responses in real time and propose countermeasures. For example, the growth status monitoring unit uses cameras and sensors to monitor changes in the color and shape of plant leaves in real time and detects stress responses. For example, if the leaves turn yellow, it identifies nutritional deficiencies or the occurrence of pests and diseases and proposes appropriate countermeasures. This allows the plant to maintain its health.

[0068] The growth status monitoring unit uses drones to monitor a wide area of ​​farmland, enabling efficient monitoring. The growth status monitoring unit, for example, uses drones to monitor a wide area of ​​farmland and grasps the growth status of crops in real time. For example, cameras and sensors mounted on the drones are used to detect the growth status of crops and the occurrence of pests and diseases. This allows efficient monitoring of a wide area of ​​farmland.

[0069] The growth status monitoring unit can consider the interactions between different crops and propose the optimal combination of mixed planting or crop rotation. For example, the growth status monitoring unit analyzes the interactions between different crops and proposes the optimal combination of mixed planting or crop rotation. For example, if certain crops have a mutually beneficial relationship, it will recommend that combination. This makes it possible to propose the optimal cultivation method that takes into account the interactions between crops.

[0070] The growth status monitoring unit can use the emotion estimation function to analyze farmers' emotions regarding the growth status of crops and propose cultivation methods that farmers can feel most comfortable with. For example, to analyze farmers' emotions regarding the growth status of crops, the growth status monitoring unit collects farmers' feedback and comments and calculates an emotion score using an emotion estimation algorithm. For example, it prioritizes the proposal of cultivation methods that farmers feel comfortable with. This makes it possible to propose cultivation methods that farmers can feel comfortable with.

[0071] The schedule management unit can propose a reasonable schedule taking into consideration the farmer's physical condition or labor situation. The schedule management unit, for example, analyzes the farmer's physical condition data and labor situation and proposes a reasonable farming schedule. For example, it determines the priority of work taking into consideration the farmer's health condition and available labor time. This reduces the burden on the farmer.

[0072] The schedule management unit can predict work efficiency based on past data and propose the optimal work sequence. For example, the schedule management unit analyzes past agricultural data and develops an algorithm to predict work efficiency. For example, it proposes the optimal work sequence based on past work history. This improves work efficiency.

[0073] The schedule management unit can use the emotion estimation function to analyze farmers' emotions regarding agricultural schedules and propose schedules that will cause the least stress to farmers. For example, to analyze farmers' emotions, the schedule management unit collects feedback and comments from farmers and calculates emotion scores using an emotion estimation algorithm. For example, it proposes schedules that will cause the farmers less stress. This can reduce stress for farmers.

[0074] The knowledge provider can provide agricultural techniques and traditional knowledge specific to the region. For example, the knowledge provider can collect feedback and historical data from local farmers and build a knowledge base to provide agricultural techniques and traditional knowledge specific to the region. For example, it can introduce cultivation methods and traditional techniques specific to the region. This allows local knowledge to be utilized.

[0075] The knowledge providing unit can provide information customized according to the farmer's experience level. For example, the knowledge providing unit analyzes the farmer's experience level and collects the farmer's past work history and feedback in order to provide customized agricultural knowledge. For example, it can provide basic knowledge for beginners to advanced techniques for advanced farmers. This makes it possible to provide information according to the farmer's experience.

[0076] The knowledge provider can use the emotion estimation function to analyze farmers' emotions regarding agricultural knowledge and provide topics that farmers are most interested in with priority. For example, to analyze farmers' emotions, the knowledge provider collects feedback and comments from farmers and calculates an emotion score using an emotion estimation algorithm. For example, the knowledge provider can provide topics that farmers are most interested in with priority. This allows the knowledge provider to provide topics that farmers are interested in with priority.

[0077] The knowledge providing unit can make the information visually easy to understand by using video tutorials or interactive learning tools. The knowledge providing unit can make the information visually easy to understand by using, for example, video tutorials. For example, agricultural work procedures can be explained using videos to enable visual learning. The knowledge providing unit can also provide agricultural knowledge by using interactive learning tools. For example, quiz-style learning or simulation tools can be used to enable farmers to learn in a fun way. This makes it possible to provide information that is visually easy to understand.

[0078] The knowledge sharing unit can set up an online discussion forum with other farmers to promote knowledge sharing. The knowledge sharing unit can, for example, set up an online discussion forum with other farmers and manage and moderate the forum to promote knowledge sharing. For example, it can promote discussions on specific topics. This can promote knowledge sharing among farmers.

[0079] The knowledge providing unit can use the emotion estimation function to analyze consumer emotions regarding agricultural knowledge and also provide information for consumer education. For example, the knowledge providing unit collects consumer feedback and reviews to analyze consumer emotions, and calculates an emotion score using an emotion estimation algorithm. For example, the knowledge providing unit provides information for consumer education. This makes it possible to provide information for consumer education.

[0080] The schedule management unit can adjust the schedule to allow farmers to participate, taking into account the dates of local events and festivals. The schedule management unit works with the local calendar to adjust the agricultural schedule, taking into account the dates of local events and festivals, for example. For example, it reduces work on days when important events are held. This makes it easier for farmers to participate in local events.

[0081] The schedule management unit can propose cooperative work with other farmers and work together efficiently. For example, to propose cooperative work with other farmers, the schedule management unit builds a database of local farmers and matches them with farmers who can cooperate. For example, working together during harvest season. This promotes cooperative work between farmers.

[0082] The schedule management unit can use the emotion estimation function to analyze family members' emotions regarding the agricultural schedule and propose a schedule that encourages cooperation among all family members. For example, to analyze family members' emotions, the schedule management unit collects feedback and comments from all family members and calculates an emotion score using an emotion estimation algorithm. For example, it proposes a schedule that encourages cooperation among all family members. This makes it possible to provide a schedule that encourages cooperation among all family members.

[0083] The market analysis unit can perform a detailed analysis of consumer purchasing history or preferences and propose individual sales strategies. For example, the market analysis unit analyzes consumer purchasing history and performs a detailed analysis of consumer preferences and purchasing patterns in order to propose individual sales strategies. For example, it proposes an optimal sales strategy for a specific consumer group. This makes it possible to propose a sales strategy based on consumer preferences.

[0084] The market analysis department can propose competitive sales strategies by taking into account the trends and pricing strategies of competitors. For example, the market analysis department collects competitors' pricing strategies and sales data in order to analyze competitors' trends and propose competitive sales strategies. For example, it monitors competitors' price fluctuations in real time. This allows it to propose competitive sales strategies.

[0085] The market analysis unit can use the emotion estimation function to analyze consumer emotions and propose a marketing strategy based on the emotions. For example, the market analysis unit collects consumer feedback and reviews to analyze consumer emotions, and calculates an emotion score using an emotion estimation algorithm. For example, the market analysis unit proposes a marketing strategy based on the emotions. This makes it possible to propose a marketing strategy based on consumer emotions.

[0086] The market analysis department compares market data from different regions and countries and can propose global sales strategies. For example, the market analysis department collects market data from different regions and countries and builds a database to propose global sales strategies. For example, it analyzes the market needs and consumer preferences of each region. This allows it to propose global sales strategies.

[0087] The market analysis department can integrate online or offline sales channels and propose the optimal sales method. For example, the market analysis department collects and analyzes sales data from each channel in order to integrate online and offline sales channels and propose the optimal sales method. For example, the market analysis department proposes the optimal sales strategy based on data on online sales and in-store sales. This makes it possible to propose the optimal sales method.

[0088] The market analysis unit can use the emotion estimation function to monitor consumer emotions in real time and carry out promotions according to those emotions. For example, to monitor consumer emotions in real time, the market analysis unit collects consumer feedback and reviews and calculates an emotion score using an emotion estimation algorithm. For example, promotions can be carried out according to the emotions. This makes it possible to carry out promotions according to consumer emotions.

[0089] The resource management unit records the resource usage history in detail and can predict future usage. The resource management unit, for example, builds a resource management system to record the resource usage history in detail and predict future usage. For example, it proposes an optimal usage plan based on the usage history of water and fertilizer. This makes it possible to predict resource usage.

[0090] The resource management unit can consider the resource supply situation and price fluctuations to propose the optimal purchasing timing. For example, the resource management unit collects resource supply data to analyze the resource supply situation and price fluctuations and propose the optimal purchasing timing. For example, it monitors price fluctuations of fertilizer and seeds in real time. This allows it to propose the optimal purchasing timing.

[0091] The resource management unit can use the emotion estimation function to analyze farmers' emotions regarding resource management and propose a resource management method that gives farmers the most peace of mind. For example, to analyze farmers' emotions, the resource management unit collects farmers' feedback and comments and calculates an emotion score using an emotion estimation algorithm. For example, it can propose a resource management method that gives farmers the most peace of mind. This makes it possible to propose a resource management method that gives farmers the most peace of mind.

[0092] The resource management department can propose methods for reusing and recycling resources and promote sustainable agriculture. The resource management department, for example, builds a resource management system to propose methods for reusing and recycling resources. For example, it proposes methods for reusing water and recycling fertilizer. This can promote sustainable agriculture.

[0093] The resource management unit can propose resource sharing with other farmers to reduce costs. For example, to propose resource sharing with other farmers, the resource management unit builds a database of local farmers and matches them with shareable resources. For example, surplus fertilizer or seeds can be shared. This allows for cost reduction through resource sharing.

[0094] The resource management unit can use the emotion estimation function to analyze the emotions of the local community regarding resource management and propose resource management for the entire community. For example, to analyze the emotions of the local community, the resource management unit collects feedback and comments from community members and calculates an emotion score using an emotion estimation algorithm. For example, the resource management unit proposes a resource management method for the entire community. This promotes resource management for the entire local community.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The growth status monitoring unit can detect plant stress responses in real time and propose countermeasures. For example, it can use cameras and sensors to monitor changes in the color and shape of plant leaves in real time to detect stress responses. For example, if the leaves turn yellow, it can identify nutritional deficiencies or the occurrence of pests and diseases and propose appropriate countermeasures. This helps maintain the health of plants.

[0097] The schedule management department can predict work efficiency based on past data and propose the optimal work sequence. For example, it can analyze past agricultural data and develop an algorithm to predict work efficiency. For example, it can propose the optimal work sequence based on past work history. This can improve work efficiency.

[0098] The Knowledge Provision Department can set up online discussion forums with other farmers to promote knowledge sharing. For example, the Knowledge Provision Department can set up online discussion forums with other farmers to promote knowledge sharing, managing and moderating the forums. For example, it can facilitate discussions on specific topics. This can promote knowledge sharing among farmers.

[0099] The Resource Management Department can propose ways to reuse and recycle resources and promote sustainable agriculture. For example, a resource management system can be built to propose ways to reuse and recycle resources. For example, it can propose ways to reuse water and recycle fertilizer. This can promote sustainable agriculture.

[0100] The Market Analysis Department can propose competitive sales strategies by taking into account the trends and pricing strategies of competitors. For example, in order to analyze competitors' trends and propose competitive sales strategies, they collect competitors' pricing strategies and sales data. For example, they monitor competitors' price fluctuations in real time. This allows them to propose competitive sales strategies.

[0101] The growth status monitoring unit can use the emotion estimation function to analyze farmers' emotions regarding the growth status of crops and propose cultivation methods that farmers can feel most comfortable with. For example, to analyze farmers' emotions regarding the growth status of crops, feedback and comments from farmers are collected and an emotion estimation algorithm is used to calculate an emotion score. For example, the unit can prioritize and propose cultivation methods that farmers feel comfortable with. This makes it possible to propose cultivation methods that farmers can feel comfortable with.

[0102] The schedule management unit can use the emotion estimation function to analyze farmers' emotions regarding agricultural schedules and propose schedules that will cause the least stress to farmers. For example, to analyze farmers' emotions, the unit collects feedback and comments from farmers and calculates an emotion score using an emotion estimation algorithm. For example, it proposes schedules that will cause the least stress to farmers. This can reduce stress for farmers.

[0103] The knowledge provider can use the emotion estimation function to analyze farmers' emotions regarding agricultural knowledge and provide them with priority on topics that farmers are most interested in. For example, to analyze farmers' emotions, the knowledge provider can collect farmers' feedback and comments and calculate an emotion score using an emotion estimation algorithm. For example, the knowledge provider can provide them with priority on topics that farmers are most interested in. This allows the knowledge provider to provide them with priority on topics that farmers are interested in.

[0104] The market analysis unit can use the emotion estimation function to analyze consumer emotions and propose emotion-based marketing strategies. For example, to analyze consumer emotions, consumer feedback and reviews are collected and an emotion estimation algorithm is used to calculate an emotion score. For example, a marketing strategy based on emotions is proposed. This makes it possible to propose a marketing strategy based on consumer emotions.

[0105] The resource management unit can use the emotion estimation function to analyze farmers' emotions regarding resource management and propose resource management methods that farmers can feel most comfortable with. For example, to analyze farmers' emotions, the unit collects farmers' feedback and comments and calculates an emotion score using an emotion estimation algorithm. For example, it can propose resource management methods that farmers can feel most comfortable with. This makes it possible to propose resource management methods that farmers can feel most comfortable with.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The growth status monitoring unit monitors the growth status of crops. For example, it uses cameras and sensors to monitor the growth status of crops and indicates the timing of necessary fertilization and irrigation. The growth status monitoring unit can also monitor the activity of microorganisms and evaluate the health of the soil. For example, it uses soil sensors to monitor the activity of microorganisms in real time and evaluate the health of the soil. Step 2: The schedule management unit manages the agricultural schedule based on the growth conditions monitored by the growth condition monitoring unit. For example, it analyzes weather data and crop growth data to propose an optimal work schedule. The schedule management unit can also propose a reasonable schedule taking into account the farmer's physical condition and labor availability. For example, it determines the priority of work by taking into account the farmer's health condition and labor availability. Step 3: The knowledge provision unit provides agricultural knowledge based on a schedule managed by the schedule management unit. For example, it provides answers to agricultural questions and allows participants to learn agricultural techniques and knowledge. The knowledge provision unit can also provide agricultural techniques and traditional knowledge specific to the region. For example, it collects feedback and historical data from local farmers and builds a knowledge base.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0152] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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]

[0175] 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 growth status monitoring unit that monitors the growth status of agricultural crops; a schedule management unit that manages an agricultural schedule based on the growth status monitored by the growth status monitoring unit; a knowledge providing unit that provides knowledge about agriculture based on the schedule managed by the schedule management unit. A system characterized by:

2. The growth status monitoring unit Using cameras or sensors to monitor the growth of the crops and provide instructions on when to fertilize or irrigate them.

2. The system of claim 1.

3. The schedule management unit Analyzing weather data or growth data of the crops and proposing work schedules 2. The system of claim 1.

4. The knowledge providing unit Providing local agricultural techniques and traditional knowledge 2. The system of claim 1.

5. The growth status monitoring unit Drones are used to monitor farmland over a wide area for efficient monitoring.

2. The system of claim 1.

6. The schedule management unit Considering the health and labor situation of the farmer, we propose a reasonable schedule.

2. The system of claim 1.

7. The Market Analysis Department Analyze consumer emotions using emotion estimation and propose marketing strategies based on those emotions 2. The system of claim 1.

8. The resource management department Using emotion estimation function, we analyze farmers' feelings about resource management and propose resource management methods that give them the most peace of mind.

2. The system of claim 1.

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