System

Abstract

An object of a system according to an embodiment is to provide information and advice in real time at an agricultural site.SOLUTION: A system according to an embodiment includes MR glasses, a drone, a generation AI, and an information providing unit. The MR glasses are worn by an operator and display digital information superimposed on a real scene. The drone monitors the entire farmland from the air and collects data. The production AI analyses the datum collected by the drone. The information providing unit provides information analyzed by the generation AI to the operator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not provide sufficient real-time information or advice at agricultural sites, and there is room for improvement in efficiency and quality.

[0005] The system according to the embodiment aims to provide information and advice in real time at agricultural sites. [Means for solving the problem]

[0006] The system according to the embodiment includes MR glasses, a drone, a generation AI, and an information providing unit. The MR glasses are worn by a worker and display digital information superimposed on the real landscape. The drone monitors the entire farmland from the air and collects data. The generation AI analyzes the data collected by the drone. The information providing unit provides the worker with information analyzed by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can provide information and advice in real time at agricultural sites. [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 agricultural production support system according to an embodiment of the present invention utilizes MR glasses and drones in agricultural fields to provide real-time information and advice to workers. This enables the smart agricultural production support system to improve the efficiency and quality of agricultural work and promote a shift to sustainable agriculture.

[0029] A smart agricultural production support system according to an embodiment includes MR glasses, a drone, a generation AI, and an information provider. The MR glasses are worn by a worker and display digital information overlaid on a real-world landscape. For example, the MR glasses display real-time information such as the health of crops and the timing of necessary fertilization and irrigation as the worker walks through the field. The drone monitors the entire farmland from the air and collects data. For example, the drone flies over the farmland and collects images captured by a camera. The generation AI analyzes the data collected by the drone. For example, the generation AI analyzes the growth status of crops and the occurrence of pests and diseases based on image data and sensor data collected by the drone. The information provider provides the worker with the information analyzed by the generation AI. For example, the information provider displays the health status of crops and the timing of necessary fertilization and irrigation through the MR glasses. The information provider also displays the occurrence of pests and diseases and appropriate countermeasures based on the data collected by the drone. This enables the smart agricultural production support system according to an embodiment to improve the efficiency and quality of agricultural work and promote a shift to sustainable agriculture.

[0030] MR glasses can use an eye-tracking sensor to automatically display detailed information about the crop in front of the worker's eyes. For example, MR glasses use an eye-tracking sensor to display detailed information about the crop the worker is looking at in real time. For example, when a worker looks at a specific crop, the health status of that crop and the timing of required fertilization and irrigation are displayed. This allows detailed information about the crop the worker is looking at to be displayed in real time.

[0031] The MR glasses can use their motion detection function to display additional information in response to specific movements of the worker. For example, the MR glasses can use their motion detection function to detect when a worker holds their hand over the glasses and display additional information. For example, when a worker holds their hand over the glasses, detailed growth data and fertilization history of that crop are displayed. This allows additional information to be displayed in response to the worker's movements.

[0032] MR glasses can be equipped with a communication function that allows workers to share information with each other in real time. For example, MR glasses can be equipped with a communication function that allows workers to share information with each other in real time. For example, a worker can share information about the crops they are looking at with other workers and work together to come up with countermeasures. This allows workers to share information with each other in real time.

[0033] MR glasses can be equipped with a voice recognition function, allowing workers to call up information using voice commands. For example, if a worker says, "Show the health status of crops," related information will be displayed. This allows information to be called up using voice commands.

[0034] Drones are equipped with multispectral cameras, allowing for detailed analysis of the health of crops.Drones are equipped with multispectral cameras, allowing for detailed analysis of the health of crops.For example, different wavelengths of light can be used to understand the stress and nutritional status of crops.This allows for detailed analysis of the health of crops.

[0035] The drone can generate a crop growth prediction model based on the collected data and predict future growth conditions. For example, the drone can analyze past data and develop an algorithm to predict future growth conditions. This makes it possible to predict future growth conditions of crops.

[0036] Drones can perform 3D mapping of agricultural land and provide detailed information on the topography and soil. Drones can, for example, perform 3D mapping of agricultural land and build systems that provide detailed information on the topography and soil. For example, the elevation difference of the terrain and the quality of the soil can be displayed in a 3D model. This makes it possible to perform 3D mapping of agricultural land and provide detailed information on the topography and soil.

[0037] Drones can collect weather data using sensors and make weather forecasts. For example, drones use sensors to collect weather data and build a system for making weather forecasts. For example, they collect data such as temperature, humidity, and wind speed, and generate a weather forecast model. This allows them to collect weather data and make weather forecasts.

[0038] Based on the location information of the worker, it is possible to prioritize the display of information that is most relevant to the current location. We will build a system that obtains the location information of the worker in real time and prioritizes the display of information that is most relevant to the current location. For example, the health status of the crops in the worker's location and the work that needs to be done can be displayed. This allows the display of information that is most relevant to the current location to be prioritized.

[0039] It is possible to analyze the past behavioral history of workers and provide individually customized advice. We will build a system that analyzes the past behavioral history of workers and provides individually customized advice. For example, it will suggest the optimal timing for fertilization and irrigation based on past work data. This will allow us to provide individually customized advice.

[0040] Real-time information can be provided by voice, reducing the visual burden. A system that provides real-time information by voice can be built to reduce the visual burden on workers. For example, the health status of crops and necessary work can be guided by voice. This allows real-time information to be provided by voice to reduce the visual burden.

[0041] It is possible to add a memo function that allows workers to easily record information. We will build a system that adds a memo function that allows workers to record information in real time. For example, the information that a worker sees can be easily saved as a memo. This allows us to add a memo function that allows workers to easily record information.

[0042] It is possible to analyze worker movement data and propose optimal work procedures. We will build a system that analyzes worker movement data and proposes optimal work procedures. For example, we will monitor worker movements and propose efficient work procedures. This will allow us to analyze worker movement data and propose optimal work procedures.

[0043] It is possible to monitor the fatigue level of workers and suggest the timing of breaks. We will build a system that monitors the fatigue level of workers and suggests the timing of breaks. For example, the fatigue level can be analyzed based on the worker's heart rate and movement data, and breaks can be suggested. This makes it possible to monitor the fatigue level of workers and suggest the timing of breaks.

[0044] It is possible to add a task management function to promote cooperation between workers. A system is constructed that adds a task management function to promote cooperation between workers. For example, workers can jointly manage tasks and work efficiently. This allows the addition of a task management function to promote cooperation between workers.

[0045] It is possible to provide maintenance information for the equipment used by workers in real time. We will build a system that provides maintenance information for the equipment used by workers in real time. For example, we will monitor the usage status of the equipment and suggest the timing of maintenance. This will allow us to provide maintenance information for the equipment used by workers in real time.

[0046] We will build a system that analyzes environmental data and proposes the best crops to grow. For example, we will propose the best crops based on soil quality and climatic conditions. This will allow us to analyze environmental data and propose the best crops to grow.

[0047] It is possible to create long-term agricultural plans based on crop growth data. We will build a system that creates long-term agricultural plans based on crop growth data. For example, we will analyze past growth data and propose future agricultural plans. This will allow us to create long-term agricultural plans based on crop growth data.

[0048] A community function for sharing examples of sustainable agricultural practices can be added. A system can be built that adds a community function for sharing examples of sustainable agricultural practices. For example, a farmer can post a practice example and share the information with other farmers. This allows a community function for sharing examples of sustainable agricultural practices to be added.

[0049] It is possible to add a function that recommends the use of environmentally friendly agricultural materials. Build a system that adds a function that recommends the use of environmentally friendly agricultural materials. For example, suggest the use of environmentally friendly fertilizers and pesticides. This allows the addition of a function that recommends the use of environmentally friendly agricultural materials.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The smart agricultural production support system can also be equipped with a health management unit that monitors the health status of workers. For example, it can monitor the worker's heart rate and body temperature in real time and issue an alert if an abnormality is detected. It can also accumulate worker health data and use it for long-term health management. This allows the system to constantly monitor the worker's health status and take appropriate measures.

[0052] The smart agricultural production support system can also analyze worker movement data and propose optimal work procedures. For example, it can monitor worker movements and propose efficient work procedures. It can also provide work procedures customized for each worker based on past work data. This can improve work efficiency.

[0053] The smart agricultural production support system can also add a task management function to promote cooperation between workers. For example, workers can jointly manage tasks and work efficiently. They can also share information in real time and jointly consider countermeasures. This promotes cooperation between workers and improves work efficiency.

[0054] The smart agricultural production support system can also prioritize the display of information most relevant to the worker's current location based on the worker's location information. For example, it can display the health status of the crops where the worker is and the work that needs to be done. In addition, when the worker moves, it can provide information related to the next work location in real time. This makes it possible to provide information based on the worker's location information.

[0055] The smart agricultural production support system can also add a memo function that allows workers to easily record information. For example, workers can easily save the information they see as a memo. They can also share notes with other workers and manage information collaboratively. This allows workers to easily record information and manage it efficiently.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The MR glasses are worn by a worker and display digital information overlaid on the real landscape. For example, as a worker walks through a field, the MR glasses display real-time information such as the health of crops and the timing of necessary fertilization and irrigation. Step 2: The drone monitors the entire farmland from the air and collects data. For example, the drone flies over the farmland and collects images taken with its camera. Step 3: The generative AI analyzes the data collected by the drone. For example, the generative AI analyzes the growth status of crops and the occurrence of pests and diseases based on the image data and sensor data collected by the drone. Step 4: The information provider provides the information analyzed by the AI ​​to the worker. For example, the information provider displays the health of the crops and the timing of necessary fertilization and irrigation through MR glasses. The information provider also displays the occurrence of pests and diseases and appropriate countermeasures based on the data collected by the drone.

[0058] (Example 2) The smart agricultural production support system according to an embodiment of the present invention utilizes MR glasses and drones in agricultural fields to provide real-time information and advice to workers. This enables the smart agricultural production support system to improve the efficiency and quality of agricultural work and promote a shift to sustainable agriculture.

[0059] A smart agricultural production support system according to an embodiment includes MR glasses, a drone, a generation AI, and an information provider. The MR glasses are worn by a worker and display digital information overlaid on a real-world landscape. For example, the MR glasses display real-time information such as the health of crops and the timing of necessary fertilization and irrigation as the worker walks through the field. The drone monitors the entire farmland from the air and collects data. For example, the drone flies over the farmland and collects images captured by a camera. The generation AI analyzes the data collected by the drone. For example, the generation AI analyzes the growth status of crops and the occurrence of pests and diseases based on image data and sensor data collected by the drone. The information provider provides the worker with the information analyzed by the generation AI. For example, the information provider displays the health status of crops and the timing of necessary fertilization and irrigation through the MR glasses. The information provider also displays the occurrence of pests and diseases and appropriate countermeasures based on the data collected by the drone. This enables the smart agricultural production support system according to an embodiment to improve the efficiency and quality of agricultural work and promote a shift to sustainable agriculture.

[0060] MR glasses can use an eye-tracking sensor to automatically display detailed information about the crop in front of the worker's eyes. For example, MR glasses use an eye-tracking sensor to display detailed information about the crop the worker is looking at in real time. For example, when a worker looks at a specific crop, the health status of that crop and the timing of required fertilization and irrigation are displayed. This allows detailed information about the crop the worker is looking at to be displayed in real time.

[0061] The MR glasses can use their motion detection function to display additional information in response to specific movements of the worker. For example, the MR glasses can use their motion detection function to detect when a worker holds their hand over the glasses and display additional information. For example, when a worker holds their hand over the glasses, detailed growth data and fertilization history of that crop are displayed. This allows additional information to be displayed in response to the worker's movements.

[0062] The MR glasses can use an emotion estimation function to analyze the emotional state of the worker and display advice to help them relax if they are under high stress. For example, the MR glasses are equipped with an emotion estimation function and can analyze the worker's facial expressions and voice to estimate their emotional state. For example, if stress is high, they can display advice or music to help them relax. This makes it possible to display advice to help reduce the worker's stress.

[0063] MR glasses can be equipped with a communication function that allows workers to share information with each other in real time. For example, MR glasses can be equipped with a communication function that allows workers to share information with each other in real time. For example, a worker can share information about the crops they are looking at with other workers and work together to come up with countermeasures. This allows workers to share information with each other in real time.

[0064] MR glasses can be equipped with a voice recognition function, allowing workers to call up information using voice commands. For example, if a worker says, "Show the health status of crops," related information will be displayed. This allows information to be called up using voice commands.

[0065] The MR glasses can use their emotion estimation function to preferentially suggest tasks that the worker feels positive about. For example, the MR glasses can use their emotion estimation function to analyze the worker's emotional state and preferentially suggest tasks that the worker feels positive about. For example, they can preferentially display tasks that the worker enjoys. This allows the glasses to preferentially suggest tasks that the worker feels positive about.

[0066] Drones are equipped with multispectral cameras, allowing for detailed analysis of the health of crops.Drones are equipped with multispectral cameras, allowing for detailed analysis of the health of crops.For example, different wavelengths of light can be used to understand the stress and nutritional status of crops.This allows for detailed analysis of the health of crops.

[0067] The drone can generate a crop growth prediction model based on the collected data and predict future growth conditions. For example, the drone can analyze past data and develop an algorithm to predict future growth conditions. This makes it possible to predict future growth conditions of crops.

[0068] The drone can use the emotion estimation function to add an autopilot function to reduce the stress that workers feel when operating the drone. For example, the drone can use the emotion estimation function to analyze the stress that workers feel when operating the drone and add an autopilot function. For example, if the worker feels stressed, the drone can be operated automatically. This allows the drone to add an autopilot function to reduce worker stress.

[0069] Drones can perform 3D mapping of agricultural land and provide detailed information on the topography and soil. Drones can, for example, perform 3D mapping of agricultural land and build systems that provide detailed information on the topography and soil. For example, the elevation difference of the terrain and the quality of the soil can be displayed in a 3D model. This makes it possible to perform 3D mapping of agricultural land and provide detailed information on the topography and soil.

[0070] Drones can collect weather data using sensors and make weather forecasts. For example, drones use sensors to collect weather data and build a system for making weather forecasts. For example, they collect data such as temperature, humidity, and wind speed, and generate a weather forecast model. This allows them to collect weather data and make weather forecasts.

[0071] The drone can use the emotion estimation function to prioritize displaying data that the worker is most interested in. For example, the drone can use the emotion estimation function to analyze data that the worker is interested in and build a system that prioritizes displaying data that the worker is most interested in. For example, data on crops that the worker is interested in can be prioritized. This allows the data that the worker is most interested in to be prioritized.

[0072] Based on the location information of the worker, it is possible to prioritize the display of information that is most relevant to the current location. We will build a system that obtains the location information of the worker in real time and prioritizes the display of information that is most relevant to the current location. For example, the health status of the crops in the worker's location and the work that needs to be done can be displayed. This allows the display of information that is most relevant to the current location to be prioritized.

[0073] It is possible to analyze the past behavioral history of workers and provide individually customized advice. We will build a system that analyzes the past behavioral history of workers and provides individually customized advice. For example, it will suggest the optimal timing for fertilization and irrigation based on past work data. This will allow us to provide individually customized advice.

[0074] It is possible to adjust the way information is displayed according to the emotional state of the worker. Using the emotion estimation function, we will build a system that adjusts the way information is displayed according to the emotional state of the worker. For example, if a worker is feeling stressed, information can be displayed in colors and fonts that encourage relaxation. This makes it possible to adjust the way information is displayed according to the emotional state of the worker.

[0075] Real-time information can be provided by voice, reducing the visual burden. A system that provides real-time information by voice can be built to reduce the visual burden on workers. For example, the health status of crops and necessary work can be guided by voice. This allows real-time information to be provided by voice to reduce the visual burden.

[0076] It is possible to add a memo function that allows workers to easily record information. We will build a system that adds a memo function that allows workers to record information in real time. For example, the information that a worker sees can be easily saved as a memo. This allows us to add a memo function that allows workers to easily record information.

[0077] Information can be provided to workers at the timing when they are most relaxed. Using emotion estimation functionality, we will build a system that provides information to workers at the timing when they are most relaxed. For example, important information can be displayed when a worker is relaxed. This allows information to be provided to workers at the timing when they are most relaxed.

[0078] It is possible to analyze worker movement data and propose optimal work procedures. We will build a system that analyzes worker movement data and proposes optimal work procedures. For example, we will monitor worker movements and propose efficient work procedures. This will allow us to analyze worker movement data and propose optimal work procedures.

[0079] It is possible to monitor the fatigue level of workers and suggest the timing of breaks. We will build a system that monitors the fatigue level of workers and suggests the timing of breaks. For example, the fatigue level can be analyzed based on the worker's heart rate and movement data, and breaks can be suggested. This makes it possible to monitor the fatigue level of workers and suggest the timing of breaks.

[0080] It is possible to propose an environment in which a worker can work most efficiently. Using the emotion estimation function, we will build a system that proposes an environment in which a worker can work most efficiently. For example, we can analyze the emotional state of a worker and propose the optimal working environment. This makes it possible to propose an environment in which a worker can work most efficiently.

[0081] It is possible to add a task management function to promote cooperation between workers. A system is constructed that adds a task management function to promote cooperation between workers. For example, workers can jointly manage tasks and work efficiently. This allows the addition of a task management function to promote cooperation between workers.

[0082] It is possible to provide maintenance information for the equipment used by workers in real time. We will build a system that provides maintenance information for the equipment used by workers in real time. For example, we will monitor the usage status of the equipment and suggest the timing of maintenance. This will allow us to provide maintenance information for the equipment used by workers in real time.

[0083] It is possible to prioritize suggesting tasks that motivate workers the most. Using emotion estimation functionality, we can analyze the motivation of workers and build a system that prioritizes suggesting tasks that motivate them the most. For example, tasks that interest workers can be displayed preferentially. This allows us to prioritize suggesting tasks that motivate workers the most.

[0084] We will build a system that analyzes environmental data and proposes the best crops to grow. For example, we will propose the best crops based on soil quality and climatic conditions. This will allow us to analyze environmental data and propose the best crops to grow.

[0085] It is possible to create long-term agricultural plans based on crop growth data. We will build a system that creates long-term agricultural plans based on crop growth data. For example, we will analyze past growth data and propose future agricultural plans. This will allow us to create long-term agricultural plans based on crop growth data.

[0086] It is possible to provide educational content to raise workers' awareness of sustainable agriculture. Using the emotion estimation function, a system is constructed that provides educational content to raise workers' awareness of sustainable agriculture. For example, educational content is displayed according to the worker's emotional state. This makes it possible to provide educational content to raise workers' awareness of sustainable agriculture.

[0087] A community function for sharing examples of sustainable agricultural practices can be added. A system can be built that adds a community function for sharing examples of sustainable agricultural practices. For example, a farmer can post a practice example and share the information with other farmers. This allows a community function for sharing examples of sustainable agricultural practices to be added.

[0088] It is possible to add a function that recommends the use of environmentally friendly agricultural materials. Build a system that adds a function that recommends the use of environmentally friendly agricultural materials. For example, suggest the use of environmentally friendly fertilizers and pesticides. This allows the addition of a function that recommends the use of environmentally friendly agricultural materials.

[0089] It is possible to induce workers to have positive feelings toward sustainable agriculture. Using emotion estimation functions, we will build a system that induces workers to have positive feelings toward sustainable agriculture. For example, by displaying messages that elicit positive emotions, it is possible to induce workers to have positive feelings toward sustainable agriculture.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The smart agricultural production support system can also be equipped with a health management unit that monitors the health status of workers. For example, it can monitor the worker's heart rate and body temperature in real time and issue an alert if an abnormality is detected. It can also accumulate worker health data and use it for long-term health management. This allows the system to constantly monitor the worker's health status and take appropriate measures.

[0092] The smart agricultural production support system can also analyze the emotional state of the worker and provide information at the timing when the worker is most relaxed. For example, if a worker is feeling stressed, relaxing music or messages can be displayed. Also, by providing important information when the worker is relaxed, the ease with which the information is received can be improved. This makes it possible to provide information according to the worker's emotional state.

[0093] The smart agricultural production support system can also analyze worker movement data and propose optimal work procedures. For example, it can monitor worker movements and propose efficient work procedures. It can also provide work procedures customized for each worker based on past work data. This can improve work efficiency.

[0094] The smart agricultural production support system can also analyze the emotional state of the worker and prioritize suggesting tasks that evoke positive emotions. For example, it can prioritize tasks that the worker enjoys to maintain their motivation. It can also suggest relaxing tasks if the worker is feeling stressed. This makes it possible to suggest tasks that correspond to the worker's emotional state.

[0095] The smart agricultural production support system can also add a task management function to promote cooperation between workers. For example, workers can jointly manage tasks and work efficiently. They can also share information in real time and jointly consider countermeasures. This promotes cooperation between workers and improves work efficiency.

[0096] The smart agricultural production support system can also analyze the emotional state of the worker and prioritize suggesting tasks that will motivate the worker the most. For example, it can prioritize tasks that interest the worker to maintain their motivation. It can also suggest relaxing tasks if the worker is feeling stressed. This makes it possible to suggest tasks that correspond to the worker's emotional state.

[0097] The smart agricultural production support system can also prioritize the display of information most relevant to the worker's current location based on the worker's location information. For example, it can display the health status of the crops where the worker is and the work that needs to be done. In addition, when the worker moves, it can provide information related to the next work location in real time. This makes it possible to provide information based on the worker's location information.

[0098] The smart agricultural production support system can also analyze the emotional state of workers and induce them to have positive feelings toward sustainable agriculture. For example, it can display messages that elicit positive emotions to raise workers' awareness. It can also increase workers' motivation by sharing examples of sustainable agriculture practices. This can induce workers to have positive feelings toward sustainable agriculture.

[0099] The smart agricultural production support system can also add a memo function that allows workers to easily record information. For example, workers can easily save the information they see as a memo. They can also share notes with other workers and manage information collaboratively. This allows workers to easily record information and manage it efficiently.

[0100] The smart agricultural production support system can also analyze the emotional state of the worker and provide information at the timing when the worker is most relaxed. For example, important information can be displayed when the worker is relaxed, making it easier for the worker to receive the information. Also, if the worker is feeling stressed, relaxing music or messages can be displayed. This makes it possible to provide information according to the worker's emotional state.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The MR glasses are worn by a worker and display digital information overlaid on the real landscape. For example, as a worker walks through a field, the MR glasses display real-time information such as the health of crops and the timing of necessary fertilization and irrigation. Step 2: The drone monitors the entire farmland from the air and collects data. For example, the drone flies over the farmland and collects images taken with its camera. Step 3: The generative AI analyzes the data collected by the drone. For example, the generative AI analyzes the growth status of crops and the occurrence of pests and diseases based on the image data and sensor data collected by the drone. Step 4: The information provider provides the information analyzed by the AI ​​to the worker. For example, the information provider displays the health of the crops and the timing of necessary fertilization and irrigation through MR glasses. The information provider also displays the occurrence of pests and diseases and appropriate countermeasures based on the data collected by the drone.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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. MR glasses and Drones and Generative AI and an information providing unit, The MR glass is The worker wears it and it displays digital information overlaid on the real world. The drone is Monitor the entire farmland from the air, collect data, The generated AI is Analyzing the data collected by the drone; The information providing unit Provide the information analyzed by the generation AI to the worker A system characterized by:

2. The MR glass is Using an eye-tracking sensor, detailed information about the crop in the worker's line of sight is automatically displayed. The system of claim 1 .

3. The drone is Equipped with a multispectral camera, it can analyze the health of crops in detail. The system of claim 1 .

4. Based on the location information of the worker, the information most relevant to the current location is preferentially displayed. The system of claim 1 .

5. Analyzing the worker's motion data and proposing optimal work procedures The system of claim 1 .

6. The MR glass is Analyzing the emotional state of the worker and displaying advice to relax if stress levels are high The system of claim 1 .

Images