System

Abstract

An object of a system according to an embodiment is to analyze environmental data, a growth record, and a work history, generate a crop simulation, and communicate with a user.SOLUTION: A system includes a data collection unit, an analysis unit, a simulation generation unit, and a communication unit. The data collection unit collects environmental data, growth records, and work history data. The analysis unit analyzes the data collected by the data collection unit. The simulation generation unit generates a crop simulation based on the data analyzed by the analysis unit. The communication unit communicates with the user based on the crop simulation generated by the simulation generation 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 technology does not effectively utilize environmental data, growth records, and work history to simulate crop growth and communicate with users, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze environmental data, growth records, and work history, generate crop simulations, and communicate with users. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a simulation generation unit, and a communication unit. The data collection unit collects environmental data, growth records, and work history data. The analysis unit analyzes the data collected by the data collection unit. The simulation generation unit generates a crop simulation based on the data analyzed by the analysis unit. The communication unit communicates with a user based on the crop simulation generated by the simulation generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze environmental data, growth records, and work history, generate crop simulations, and communicate with users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​system according to the embodiment of the present invention collects environmental data, growth records, and work history data, and generates a crop simulation based on the results of the analysis by the generation AI, and communicates with the user. This allows the AI ​​system to simulate the growth process of crops and optimal cultivation methods, and provide the results to the user.

[0029] The AI ​​system according to the embodiment includes a data collection unit, an analysis unit, a simulation generation unit, and a communication unit. The data collection unit collects environmental data, growth records, and work history data. For example, the data collection unit measures temperature and humidity in real time using sensors installed in farmland and collects the data. The data collection unit can also allow users to input work history using smartphones or tablets. The data collection unit can also acquire data from external databases, such as weather data and soil data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses a generation AI to analyze the impact of temperature and humidity fluctuations on crop growth. The analysis unit can also use the generation AI to evaluate the risk of pest infestation. The analysis unit can also use the generation AI to suggest optimal fertilization and irrigation timing. The simulation generation unit generates a crop simulation based on the data analyzed by the analysis unit. For example, the simulation generation unit uses the generation AI to simulate the crop growth process. The simulation generation unit can also use the generation AI to predict harvests. The simulation generation unit can also simulate the risk of pest outbreaks and countermeasures using the generation AI. The communication unit communicates with the user based on the crop simulation generated by the simulation generation unit. For example, the communication unit allows the user to ask the generation AI questions, and the generation AI provides appropriate answers to those questions. The communication unit also allows the generation AI to periodically report simulation results to the user and provide cultivation advice. The communication unit also allows the generation AI to receive user feedback and improve the accuracy of the simulation. As a result, the AI ​​system according to the embodiment can generate a crop simulation based on environmental data, growth records, and work history data, and communicate with the user. For example, the user can receive advice from the generation AI about the crop growth process and optimal cultivation methods.Users can also ask questions to the AI ​​to resolve any doubts they have about cultivation, and can improve the accuracy of the simulation by providing feedback to the AI.

[0030] The data collection unit uses drones to collect environmental data from the air over the entire farmland in real time and transmits it to the generation AI. For example, the data collection unit uses sensors mounted on the drones to collect environmental data such as temperature, humidity, and soil moisture content over the entire farmland in real time. The collected data is transmitted to the generation AI via wireless communication. The data collection unit also uses drones to fly over the farmland periodically to collect environmental data. For example, the drones fly at a set time each day to collect data and transmit it to the generation AI. The data collection unit also uses drones to focus on monitoring specific areas of the farmland and immediately transmit the data to the generation AI if an abnormality is detected. For example, an alert is issued if there is a sudden change in soil moisture content. This makes it possible to use drones to collect environmental data over the entire farmland in real time and transmit it to the generation AI.

[0031] The data collection unit automatically collects samples of crop leaves and soil and feeds back the results of lab analysis to the generation AI. The data collection unit, for example, uses an automatic sample collection device to periodically collect crop leaf and soil samples and send them to a lab. The results of lab analysis are digitized and fed back to the generation AI. The data collection unit also uses a sample collection device to patrol the farmland and collect leaf and soil samples at specific points. The collected samples are analyzed in the lab and then sent as data to the generation AI. The data collection unit also uploads the results of lab analysis to the cloud, building a system in which the generation AI obtains that data in real time. This makes it possible to automatically collect crop leaf and soil samples and feed back the results of lab analysis to the generation AI.

[0032] The data collection unit collects marine environment data, sends it to the generation AI, and applies it to the marine life growth simulation. The data collection unit, for example, uses sensors installed in the ocean to collect environmental data such as water temperature, salinity, and oxygen concentration. The collected data is sent to the generation AI and used in the marine life growth simulation. The data collection unit also collects marine environment data using drones or autonomous underwater robots. For example, it patrols specific ocean areas, collects data, and sends it to the generation AI. The data collection unit also works with marine research institutions to obtain data from existing marine environment databases. The obtained data is imported into the generation AI and applied to the marine life growth simulation. In this way, marine environment data can be collected and applied to the marine life growth simulation.

[0033] The data collection unit collects urban environmental data, sends it to the generation AI, and uses it to optimize urban agriculture and green roofs. For example, the data collection unit uses sensors installed in the city to collect environmental data such as temperature, humidity, and carbon dioxide concentration. The collected data is sent to the generation AI and used to optimize urban agriculture and green roofs. The data collection unit also uses drones to collect environmental data from urban rooftops and green spaces. For example, drones fly periodically to collect data and send it to the generation AI. The data collection unit also works with smart city projects to obtain data from existing urban environmental databases. The obtained data is incorporated into the generation AI and applied to the optimization of urban agriculture and green roofs. This allows urban environmental data to be collected and used to optimize urban agriculture and green roofs.

[0034] The analysis department compares past weather data with current data and develops algorithms to predict the long-term impacts of climate change. For example, the analysis department collects weather data from the past several decades and develops algorithms to compare it with current data. For example, it analyzes patterns of temperature and precipitation fluctuations to predict the impacts of climate change. The analysis department also builds models to simulate the impacts of climate change based on weather data. For example, it predicts future temperature increases and changes in precipitation and evaluates the impact on crop growth. The analysis department also integrates weather data and crop growth data to develop a system to predict the long-term impacts of climate change. For example, it analyzes the impact of rising temperatures on specific crops. This makes it possible to predict the impacts of climate change over the long term.

[0035] The analysis unit analyzes the genetic data of crops and identifies varieties that are best suited to specific environmental conditions. For example, the analysis unit collects genetic data of crops and develops algorithms to identify varieties that are best suited to specific environmental conditions. For example, it identifies varieties that are resistant to drought or pests. The analysis unit also integrates genetic data with environmental data to build a system that identifies optimal varieties. For example, it suggests optimal varieties depending on temperature and humidity. The analysis unit also develops a model based on the genetic data to simulate varieties that adapt to specific environmental conditions. For example, it develops new varieties using gene editing technology. This makes it possible to identify varieties that are best suited to specific environmental conditions.

[0036] The simulation generation unit visualizes the growth process of crops using a 3D model, allowing users to experience it in virtual reality. The simulation generation unit, for example, develops a system that visualizes the growth process of crops using a 3D model. For example, the growth process from sowing to harvesting is displayed in real time. The simulation generation unit also integrates the 3D model into a VR environment, allowing users to experience the growth of crops in virtual reality. For example, the growth of crops is observed using a VR headset. The simulation generation unit also simulates the growth process of crops and displays it as a 3D model. For example, the growth process under different cultivation conditions is compared. This makes it possible to visualize the growth process of crops using a 3D model, allowing users to experience it in virtual reality.

[0037] The simulation generation unit provides an interactive simulation that allows users to try out different cultivation methods and environmental conditions in the simulation. The simulation generation unit, for example, develops an interactive simulation system that allows users to set different cultivation methods and environmental conditions. For example, the simulation unit runs a simulation by changing the amount of fertilizer or irrigation schedule. The simulation generation unit also provides an interface that allows users to change environmental conditions during the simulation. For example, the temperature or humidity can be adjusted to observe the growth of crops. The simulation generation unit also builds a simulation model that allows users to try out different cultivation methods and environmental conditions. For example, a simulation is run to compare organic and conventional farming methods. This makes it possible to provide an interactive simulation that allows users to try out different cultivation methods and environmental conditions.

[0038] The communication department uses natural language processing technology to provide answers to user questions in more natural language. The communication department, for example, uses natural language processing technology to develop a system that analyzes user questions and generates appropriate answers. For example, it understands agricultural terminology and answers in natural language. The communication department also develops algorithms that enable the generation AI to answer user questions in natural language. For example, it understands the context and provides appropriate answers. The communication department also uses natural language processing technology to build a system that generates answers to user questions and provides them in natural language. For example, it accurately understands the user's intent and generates appropriate answers. This makes it possible to provide answers to user questions in more natural language.

[0039] The communication department analyzes the user's past question history and provides personalized advice. For example, the communication department develops an algorithm that collects and analyzes the user's past question history. For example, it provides personalized advice based on the content of the past questions. The communication department also builds a system that understands the user's interests and needs based on the question history and provides personalized advice. For example, it provides advice about a specific crop. The communication department also analyzes the user's question history and develops a model that provides personalized advice. For example, it suggests the optimal cultivation method based on the content of the past questions. This makes it possible to analyze the user's past question history and provide personalized advice.

[0040] The simulation generation unit collects detailed data on the work actually performed by the user and compares it with simulation results to improve accuracy. The simulation generation unit, for example, builds a system that collects detailed data on the work actually performed by the user. For example, it records the timing and amount of fertilization and irrigation. The simulation generation unit also compares the collected work data with simulation results and develops an algorithm to improve accuracy. For example, it adjusts a simulation model based on actual data. The simulation generation unit also builds a system that collects user work data in real time and compares it with simulation results. For example, the work data is input using a smartphone app. This allows detailed data on the work actually performed by the user to be collected and compared with simulation results to improve accuracy.

[0041] The simulation generation unit allows the generation AI to automatically learn based on the feedback data and reflect this in the next simulation. The simulation generation unit, for example, collects feedback data from users and builds a system in which the generation AI automatically learns. For example, it updates the simulation model based on the results of fertilization and irrigation. The simulation generation unit also analyzes the feedback data and develops an algorithm that the generation AI will reflect in the next simulation. For example, it will propose the optimal cultivation method based on the results of actual work. The simulation generation unit also collects user feedback data in real time and builds a system in which the generation AI automatically learns. For example, feedback is entered using a smartphone app. This allows the generation AI to automatically learn based on the feedback data and reflect this in the next simulation.

[0042] The simulation generation unit collects feedback on the medical simulation results and improves the accuracy of the treatment plan. The simulation generation unit, for example, builds a system that collects feedback on the medical simulation results. For example, it records the patient's treatment results and the doctor's opinions. The simulation generation unit also develops an algorithm that improves the accuracy of the treatment plan based on the collected feedback data. For example, it updates the simulation model based on the actual treatment results. The simulation generation unit also collects feedback on the medical simulation results in real time and builds a system that improves the accuracy of the treatment plan. For example, it collects feedback in cooperation with an electronic medical record system. This makes it possible to collect feedback on the medical simulation results and improve the accuracy of the treatment plan.

[0043] The simulation generation unit collects feedback on the educational simulation results and improves the accuracy of the learning plan. The simulation generation unit, for example, builds a system that collects feedback on the educational simulation results. For example, it records students' learning outcomes and teachers' opinions. The simulation generation unit also develops an algorithm that improves the accuracy of the learning plan based on the collected feedback data. For example, it updates the simulation model based on actual learning outcomes. The simulation generation unit also builds a system that collects feedback on the educational simulation results in real time and improves the accuracy of the learning plan. For example, it collects feedback in cooperation with an online learning platform. This makes it possible to collect feedback on the educational simulation results and improve the accuracy of the learning plan.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The data collection unit can also improve work safety by collecting health data on agricultural workers and sending it to the generation AI. For example, it can monitor heart rate and body temperature using a wearable device and issue an alert if an abnormality is detected. The data collection unit can also collect data on the agricultural workers' work environment and send it to the generation AI to make suggestions for improving the work environment. For example, it can monitor the temperature and humidity of the work area and make suggestions for maintaining an appropriate work environment. The data collection unit can also improve work efficiency by collecting the agricultural workers' work history and sending it to the generation AI. For example, it can record the frequency and time of work and suggest an optimal work schedule.

[0046] The data collection unit can also assess the health of the soil by collecting microbial data from farmland and sending it to the generation AI. For example, it can collect soil samples and analyze the types and numbers of microorganisms. The data collection unit can also allow the generation AI to suggest optimal soil improvement methods based on the microbial data from farmland. For example, if there is a shortage of certain microorganisms, it can suggest appropriate fertilizers and amendments. The data collection unit can also monitor the microbial data from farmland over the long term and track changes in soil health. For example, it can collect soil samples regularly and analyze microbial fluctuations.

[0047] The data collection unit can collect urban environmental data, send it to the generation AI, and use it to optimize urban agriculture and green roofs. For example, sensors installed in the city can be used to collect environmental data such as temperature, humidity, and carbon dioxide concentration. The collected data is sent to the generation AI and used to optimize urban agriculture and green roofs. The data collection unit can also use drones to collect environmental data on urban rooftops and green spaces. For example, drones can fly periodically to collect data and send it to the generation AI. The data collection unit can also work with smart city projects to obtain data from existing urban environmental databases. The obtained data is incorporated into the generation AI and applied to the optimization of urban agriculture and green roofs.

[0048] The analysis department can also compare past weather data with current data to develop algorithms that predict the long-term impacts of climate change. For example, it can collect weather data from the past several decades and develop algorithms that compare it with current data. For example, it can analyze patterns of temperature and precipitation fluctuations to predict the impacts of climate change. The analysis department can also build models that simulate the impacts of climate change based on weather data. For example, it can predict future increases in temperature and changes in precipitation and evaluate the impact on crop growth. The analysis department can also integrate weather data and crop growth data to develop systems that predict the long-term impacts of climate change. For example, it can analyze the impact of rising temperatures on specific crops.

[0049] The analysis unit can also analyze the genetic data of crops to identify varieties that are best suited to specific environmental conditions. For example, it can collect genetic data of crops and develop algorithms to identify varieties that are best suited to specific environmental conditions. For example, it can identify varieties that are resistant to drought or pests. The analysis unit can also integrate genetic data with environmental data to build a system that identifies optimal varieties. For example, it can suggest optimal varieties depending on temperature and humidity. The analysis unit can also develop models based on genetic data to simulate varieties that adapt to specific environmental conditions. For example, it can develop new varieties using gene editing technology.

[0050] The simulation generation unit can also visualize the growth process of crops using a 3D model, allowing users to experience it in virtual reality. For example, we will develop a system that visualizes the growth process of crops using a 3D model. For example, we will display the growth process from sowing to harvesting in real time. The simulation generation unit can also integrate the 3D model into a VR environment, allowing users to experience the growth of crops in virtual reality. For example, we will use a VR headset to observe the growth of crops. The simulation generation unit can also simulate the growth process of crops and display it as a 3D model. For example, we will compare the growth process under different cultivation conditions.

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

[0052] Step 1: The data collection unit collects environmental data, growth records, and work history data. For example, sensors installed on farmland can be used to measure temperature and humidity in real time and collect this data. Users can also input work history using smartphones or tablets. Data can also be obtained from external databases, such as weather data and soil data. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, generative AI can be used to analyze the impact of temperature and humidity fluctuations on crop growth. It can also assess the risk of pest and disease outbreaks and suggest optimal fertilization and irrigation timing. Step 3: The simulation generation unit generates a crop simulation based on the data analyzed by the analysis unit. For example, the generation AI can be used to simulate the crop growth process, predicting yields, risk of pest outbreaks, and countermeasures. Step 4: The communication unit communicates with the user based on the crop simulation generated by the simulation generation unit. For example, the user can ask the generation AI a question, and the generation AI will provide an appropriate answer. The communication unit can also periodically report the simulation results and provide cultivation advice. Furthermore, the communication unit can receive user feedback to improve the accuracy of the simulation.

[0053] (Example 2) The AI ​​system according to the embodiment of the present invention collects environmental data, growth records, and work history data, and generates a crop simulation based on the results of the analysis by the generation AI, and communicates with the user. This allows the AI ​​system to simulate the growth process of crops and optimal cultivation methods, and provide the results to the user.

[0054] The AI ​​system according to the embodiment includes a data collection unit, an analysis unit, a simulation generation unit, and a communication unit. The data collection unit collects environmental data, growth records, and work history data. For example, the data collection unit measures temperature and humidity in real time using sensors installed in farmland and collects the data. The data collection unit can also allow users to input work history using smartphones or tablets. The data collection unit can also acquire data from external databases, such as weather data and soil data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses a generation AI to analyze the impact of temperature and humidity fluctuations on crop growth. The analysis unit can also use the generation AI to evaluate the risk of pest infestation. The analysis unit can also use the generation AI to suggest optimal fertilization and irrigation timing. The simulation generation unit generates a crop simulation based on the data analyzed by the analysis unit. For example, the simulation generation unit uses the generation AI to simulate the crop growth process. The simulation generation unit can also use the generation AI to predict harvests. The simulation generation unit can also simulate the risk of pest outbreaks and countermeasures using the generation AI. The communication unit communicates with the user based on the crop simulation generated by the simulation generation unit. For example, the communication unit allows the user to ask the generation AI questions, and the generation AI provides appropriate answers to those questions. The communication unit also allows the generation AI to periodically report simulation results to the user and provide cultivation advice. The communication unit also allows the generation AI to receive user feedback and improve the accuracy of the simulation. As a result, the AI ​​system according to the embodiment can generate a crop simulation based on environmental data, growth records, and work history data, and communicate with the user. For example, the user can receive advice from the generation AI about the crop growth process and optimal cultivation methods.Users can also ask questions to the AI ​​to resolve any doubts they have about cultivation, and can improve the accuracy of the simulation by providing feedback to the AI.

[0055] The data collection unit uses drones to collect environmental data from the air over the entire farmland in real time and transmits it to the generation AI. For example, the data collection unit uses sensors mounted on the drones to collect environmental data such as temperature, humidity, and soil moisture content over the entire farmland in real time. The collected data is transmitted to the generation AI via wireless communication. The data collection unit also uses drones to fly over the farmland periodically to collect environmental data. For example, the drones fly at a set time each day to collect data and transmit it to the generation AI. The data collection unit also uses drones to focus on monitoring specific areas of the farmland and immediately transmit the data to the generation AI if an abnormality is detected. For example, an alert is issued if there is a sudden change in soil moisture content. This makes it possible to use drones to collect environmental data over the entire farmland in real time and transmit it to the generation AI.

[0056] The data collection unit automatically collects samples of crop leaves and soil and feeds back the results of lab analysis to the generation AI. The data collection unit, for example, uses an automatic sample collection device to periodically collect crop leaf and soil samples and send them to a lab. The results of lab analysis are digitized and fed back to the generation AI. The data collection unit also uses a sample collection device to patrol the farmland and collect leaf and soil samples at specific points. The collected samples are analyzed in the lab and then sent as data to the generation AI. The data collection unit also uploads the results of lab analysis to the cloud, building a system in which the generation AI obtains that data in real time. This makes it possible to automatically collect crop leaf and soil samples and feed back the results of lab analysis to the generation AI.

[0057] The data collection unit uses an emotion estimation function to measure the stress levels of farmers and inputs that data into the generation AI to optimize work efficiency. The data collection unit, for example, uses a wearable device worn by the farmer to measure their heart rate and electrodermal activity and estimate their stress levels. The estimated data is sent to the generation AI. The data collection unit also uses a smartphone app to analyze the farmer's emotional state in real time and measure their stress levels. The measurement results are fed back to the generation AI. The data collection unit also introduces a system that analyzes the farmer's facial expressions and voice while they are working to estimate their stress levels. The estimated stress data is input into the generation AI and used to optimize work efficiency. This makes it possible to measure the farmer's stress levels and optimize work efficiency.

[0058] The data collection unit collects marine environment data, sends it to the generation AI, and applies it to the marine life growth simulation. The data collection unit, for example, uses sensors installed in the ocean to collect environmental data such as water temperature, salinity, and oxygen concentration. The collected data is sent to the generation AI and used in the marine life growth simulation. The data collection unit also collects marine environment data using drones or autonomous underwater robots. For example, it patrols specific ocean areas, collects data, and sends it to the generation AI. The data collection unit also works with marine research institutions to obtain data from existing marine environment databases. The obtained data is imported into the generation AI and applied to the marine life growth simulation. In this way, marine environment data can be collected and applied to the marine life growth simulation.

[0059] The data collection unit collects urban environmental data, sends it to the generation AI, and uses it to optimize urban agriculture and green roofs. For example, the data collection unit uses sensors installed in the city to collect environmental data such as temperature, humidity, and carbon dioxide concentration. The collected data is sent to the generation AI and used to optimize urban agriculture and green roofs. The data collection unit also uses drones to collect environmental data from urban rooftops and green spaces. For example, drones fly periodically to collect data and send it to the generation AI. The data collection unit also works with smart city projects to obtain data from existing urban environmental databases. The obtained data is incorporated into the generation AI and applied to the optimization of urban agriculture and green roofs. This allows urban environmental data to be collected and used to optimize urban agriculture and green roofs.

[0060] The data collection unit uses the emotion estimation function to collect consumer preference data and send it to the generation AI to help improve crop varieties. The data collection unit collects preference data, for example, through a smartphone app used by consumers. For example, consumers input their preferred types of crops and their taste preferences. The data collection unit also uses the emotion estimation function to analyze the facial expressions and voices of consumers when they taste crops to collect preference data. The collected data is sent to the generation AI and used for improving crop varieties. The data collection unit also analyzes consumer purchase history and reviews to build a system for collecting preference data. For example, it analyzes data from online shopping sites to understand consumer preferences. This allows the collection of consumer preference data to be used for improving crop varieties.

[0061] The analysis department compares past weather data with current data and develops algorithms to predict the long-term impacts of climate change. For example, the analysis department collects weather data from the past several decades and develops algorithms to compare it with current data. For example, it analyzes patterns of temperature and precipitation fluctuations to predict the impacts of climate change. The analysis department also builds models to simulate the impacts of climate change based on weather data. For example, it predicts future temperature increases and changes in precipitation and evaluates the impact on crop growth. The analysis department also integrates weather data and crop growth data to develop a system to predict the long-term impacts of climate change. For example, it analyzes the impact of rising temperatures on specific crops. This makes it possible to predict the impacts of climate change over the long term.

[0062] The analysis unit analyzes the genetic data of crops and identifies varieties that are best suited to specific environmental conditions. For example, the analysis unit collects genetic data of crops and develops algorithms to identify varieties that are best suited to specific environmental conditions. For example, it identifies varieties that are resistant to drought or pests. The analysis unit also integrates genetic data with environmental data to build a system that identifies optimal varieties. For example, it suggests optimal varieties depending on temperature and humidity. The analysis unit also develops a model based on the genetic data to simulate varieties that adapt to specific environmental conditions. For example, it develops new varieties using gene editing technology. This makes it possible to identify varieties that are best suited to specific environmental conditions.

[0063] The analysis unit uses the emotion estimation function to analyze the emotion data of farmers and make suggestions to improve their work motivation. The analysis unit, for example, develops an algorithm to collect and analyze the emotion data of farmers. For example, it analyzes facial expressions and voices while working to understand their emotional state. The analysis unit also builds a system that makes suggestions to improve work motivation based on the emotion data. For example, it provides positive feedback and encouraging messages. The analysis unit also analyzes the emotion data in real time and suggests actions to improve work motivation. For example, it provides advice on how to relax when stress is high. In this way, it is possible to analyze the emotion data of farmers and make suggestions to improve their work motivation.

[0064] The simulation generation unit visualizes the growth process of crops using a 3D model, allowing users to experience it in virtual reality. The simulation generation unit, for example, develops a system that visualizes the growth process of crops using a 3D model. For example, the growth process from sowing to harvesting is displayed in real time. The simulation generation unit also integrates the 3D model into a VR environment, allowing users to experience the growth of crops in virtual reality. For example, the growth of crops is observed using a VR headset. The simulation generation unit also simulates the growth process of crops and displays it as a 3D model. For example, the growth process under different cultivation conditions is compared. This makes it possible to visualize the growth process of crops using a 3D model, allowing users to experience it in virtual reality.

[0065] The simulation generation unit provides an interactive simulation that allows users to try out different cultivation methods and environmental conditions in the simulation. The simulation generation unit, for example, develops an interactive simulation system that allows users to set different cultivation methods and environmental conditions. For example, the simulation unit runs a simulation by changing the amount of fertilizer or irrigation schedule. The simulation generation unit also provides an interface that allows users to change environmental conditions during the simulation. For example, the temperature or humidity can be adjusted to observe the growth of crops. The simulation generation unit also builds a simulation model that allows users to try out different cultivation methods and environmental conditions. For example, a simulation is run to compare organic and conventional farming methods. This makes it possible to provide an interactive simulation that allows users to try out different cultivation methods and environmental conditions.

[0066] The simulation generation unit uses the emotion estimation function to customize the simulation results according to the user's emotions. The simulation generation unit, for example, collects user emotion data and develops a system that customizes the simulation results. For example, it provides simulation results that elicit positive emotions. The simulation generation unit also uses the emotion estimation function to generate simulation results according to the user's emotional state. For example, it provides a simulation that has a relaxing effect when the user is highly stressed. The simulation generation unit also analyzes the user's emotion data in real time and builds a model that dynamically adjusts the simulation results. For example, it adjusts the difficulty of the simulation according to the emotion score. This makes it possible to customize the simulation results according to the user's emotions.

[0067] The communication department uses natural language processing technology to provide answers to user questions in more natural language. The communication department, for example, uses natural language processing technology to develop a system that analyzes user questions and generates appropriate answers. For example, it understands agricultural terminology and answers in natural language. The communication department also develops algorithms that enable the generation AI to answer user questions in natural language. For example, it understands the context and provides appropriate answers. The communication department also uses natural language processing technology to build a system that generates answers to user questions and provides them in natural language. For example, it accurately understands the user's intent and generates appropriate answers. This makes it possible to provide answers to user questions in more natural language.

[0068] The communication department analyzes the user's past question history and provides personalized advice. For example, the communication department develops an algorithm that collects and analyzes the user's past question history. For example, it provides personalized advice based on the content of the past questions. The communication department also builds a system that understands the user's interests and needs based on the question history and provides personalized advice. For example, it provides advice about a specific crop. The communication department also analyzes the user's question history and develops a model that provides personalized advice. For example, it suggests the optimal cultivation method based on the content of the past questions. This makes it possible to analyze the user's past question history and provide personalized advice.

[0069] The communication unit uses the emotion estimation function to select a communication style according to the user's emotions. The communication unit, for example, collects user emotion data and develops a system that customizes communication styles. For example, it provides communication that elicits positive emotions. The communication unit also uses the emotion estimation function to develop an algorithm that selects a communication style according to the user's emotional state. For example, it provides communication that has a relaxing effect when the user is under high stress. The communication unit also analyzes the user's emotion data in real time and builds a model that dynamically adjusts the communication style. For example, it changes the tone of communication according to the emotion score. This makes it possible to select a communication style according to the user's emotions.

[0070] The simulation generation unit collects detailed data on the work actually performed by the user and compares it with simulation results to improve accuracy. The simulation generation unit, for example, builds a system that collects detailed data on the work actually performed by the user. For example, it records the timing and amount of fertilization and irrigation. The simulation generation unit also compares the collected work data with simulation results and develops an algorithm to improve accuracy. For example, it adjusts a simulation model based on actual data. The simulation generation unit also builds a system that collects user work data in real time and compares it with simulation results. For example, the work data is input using a smartphone app. This allows detailed data on the work actually performed by the user to be collected and compared with simulation results to improve accuracy.

[0071] The simulation generation unit allows the generation AI to automatically learn based on the feedback data and reflect this in the next simulation. The simulation generation unit, for example, collects feedback data from users and builds a system in which the generation AI automatically learns. For example, it updates the simulation model based on the results of fertilization and irrigation. The simulation generation unit also analyzes the feedback data and develops an algorithm that the generation AI will reflect in the next simulation. For example, it will propose the optimal cultivation method based on the results of actual work. The simulation generation unit also collects user feedback data in real time and builds a system in which the generation AI automatically learns. For example, feedback is entered using a smartphone app. This allows the generation AI to automatically learn based on the feedback data and reflect this in the next simulation.

[0072] The simulation generation unit uses the emotion estimation function to provide feedback based on the user's emotions, improving the reliability of the simulation. The simulation generation unit, for example, collects user emotion data and develops a system that customizes feedback. For example, it provides feedback that elicits positive emotions. The simulation generation unit also uses the emotion estimation function to develop an algorithm that provides feedback according to the user's emotional state. For example, it provides feedback that has a relaxing effect when stress is high. The simulation generation unit also analyzes the user's emotion data in real time and builds a model that dynamically adjusts feedback. For example, it changes the content of the feedback depending on the emotion score. This makes it possible to provide feedback based on the user's emotions and improve the reliability of the simulation.

[0073] The simulation generation unit collects feedback on the medical simulation results and improves the accuracy of the treatment plan. The simulation generation unit, for example, builds a system that collects feedback on the medical simulation results. For example, it records the patient's treatment results and the doctor's opinions. The simulation generation unit also develops an algorithm that improves the accuracy of the treatment plan based on the collected feedback data. For example, it updates the simulation model based on the actual treatment results. The simulation generation unit also collects feedback on the medical simulation results in real time and builds a system that improves the accuracy of the treatment plan. For example, it collects feedback in cooperation with an electronic medical record system. This makes it possible to collect feedback on the medical simulation results and improve the accuracy of the treatment plan.

[0074] The simulation generation unit collects feedback on the educational simulation results and improves the accuracy of the learning plan. The simulation generation unit, for example, builds a system that collects feedback on the educational simulation results. For example, it records students' learning outcomes and teachers' opinions. The simulation generation unit also develops an algorithm that improves the accuracy of the learning plan based on the collected feedback data. For example, it updates the simulation model based on actual learning outcomes. The simulation generation unit also builds a system that collects feedback on the educational simulation results in real time and improves the accuracy of the learning plan. For example, it collects feedback in cooperation with an online learning platform. This makes it possible to collect feedback on the educational simulation results and improve the accuracy of the learning plan.

[0075] The simulation generation unit uses the emotion estimation function to collect feedback on the marketing simulation results and improve the accuracy of the strategy. The simulation generation unit, for example, builds a system that collects feedback on the marketing simulation results. For example, it records customers' emotional reactions and purchasing behavior. The simulation generation unit also develops an algorithm that improves the accuracy of the marketing strategy based on the collected emotion data. For example, it proposes an advertising strategy that elicits positive emotions. The simulation generation unit also builds a system that collects feedback on the marketing simulation results in real time and improves the accuracy of the strategy. For example, it collects feedback in cooperation with an online advertising platform. This makes it possible to collect feedback on the marketing simulation results and improve the accuracy of the strategy.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The data collection unit can also improve work safety by collecting health data on agricultural workers and sending it to the generation AI. For example, it can monitor heart rate and body temperature using a wearable device and issue an alert if an abnormality is detected. The data collection unit can also collect data on the agricultural workers' work environment and send it to the generation AI to make suggestions for improving the work environment. For example, it can monitor the temperature and humidity of the work area and make suggestions for maintaining an appropriate work environment. The data collection unit can also improve work efficiency by collecting the agricultural workers' work history and sending it to the generation AI. For example, it can record the frequency and time of work and suggest an optimal work schedule.

[0078] The data collection unit can also assess the health of the soil by collecting microbial data from farmland and sending it to the generation AI. For example, it can collect soil samples and analyze the types and numbers of microorganisms. The data collection unit can also allow the generation AI to suggest optimal soil improvement methods based on the microbial data from farmland. For example, if there is a shortage of certain microorganisms, it can suggest appropriate fertilizers and amendments. The data collection unit can also monitor the microbial data from farmland over the long term and track changes in soil health. For example, it can collect soil samples regularly and analyze microbial fluctuations.

[0079] The data collection unit can use the emotion estimation function to measure consumers' purchasing intent and incorporate that data into the generation AI to optimize marketing strategies. For example, it can analyze the facial expressions and voice of consumers when they browse products to estimate their purchasing intent. The data collection unit can also use a smartphone app to analyze consumers' purchasing behavior in real time and measure their purchasing intent. The measurement results are fed back to the generation AI and used to optimize marketing strategies. The data collection unit can also introduce a system that analyzes consumers' purchasing history and reviews to estimate their purchasing intent. For example, it can analyze data from online shopping sites to understand consumers' purchasing intent.

[0080] The data collection unit can also use its emotion estimation function to measure farmers' motivation and input that data into the generation AI to optimize work efficiency. For example, a wearable device worn by the farmer can be used to measure heart rate and electrodermal activity to estimate motivation. The estimated data is sent to the generation AI. The data collection unit can also use a smartphone app to analyze the farmer's emotional state in real time and measure motivation. The measurement results are fed back to the generation AI and used to optimize work efficiency. The data collection unit can also introduce a system that analyzes the farmers' facial expressions and voices while they are working to estimate their motivation. The estimated motivation data is input into the generation AI and used to optimize work efficiency.

[0081] The data collection unit can also use the emotion estimation function to collect consumer preference data and send it to the generation AI to help improve crop varieties. For example, preference data can be collected through a smartphone app used by consumers. For example, consumers can input their preferred types of crops and their taste preferences. The data collection unit can also use the emotion estimation function to analyze facial expressions and voices when consumers taste crops to collect preference data. The collected data is sent to the generation AI and used for improving varieties. The data collection unit can also analyze consumer purchase history and reviews to build a system that collects preference data. For example, data from online shopping sites can be analyzed to understand consumer preferences.

[0082] The data collection unit can collect urban environmental data, send it to the generation AI, and use it to optimize urban agriculture and green roofs. For example, sensors installed in the city can be used to collect environmental data such as temperature, humidity, and carbon dioxide concentration. The collected data is sent to the generation AI and used to optimize urban agriculture and green roofs. The data collection unit can also use drones to collect environmental data on urban rooftops and green spaces. For example, drones can fly periodically to collect data and send it to the generation AI. The data collection unit can also work with smart city projects to obtain data from existing urban environmental databases. The obtained data is incorporated into the generation AI and applied to the optimization of urban agriculture and green roofs.

[0083] The analysis department can also compare past weather data with current data to develop algorithms that predict the long-term impacts of climate change. For example, it can collect weather data from the past several decades and develop algorithms that compare it with current data. For example, it can analyze patterns of temperature and precipitation fluctuations to predict the impacts of climate change. The analysis department can also build models that simulate the impacts of climate change based on weather data. For example, it can predict future increases in temperature and changes in precipitation and evaluate the impact on crop growth. The analysis department can also integrate weather data and crop growth data to develop systems that predict the long-term impacts of climate change. For example, it can analyze the impact of rising temperatures on specific crops.

[0084] The analysis unit can also analyze the genetic data of crops to identify varieties that are best suited to specific environmental conditions. For example, it can collect genetic data of crops and develop algorithms to identify varieties that are best suited to specific environmental conditions. For example, it can identify varieties that are resistant to drought or pests. The analysis unit can also integrate genetic data with environmental data to build a system that identifies optimal varieties. For example, it can suggest optimal varieties depending on temperature and humidity. The analysis unit can also develop models based on genetic data to simulate varieties that adapt to specific environmental conditions. For example, it can develop new varieties using gene editing technology.

[0085] The analysis unit can also use the emotion estimation function to analyze the emotional data of farmers and make suggestions to improve their motivation to work. For example, an algorithm can be developed to collect and analyze the emotional data of farmers. For example, facial expressions and voices while working can be analyzed to understand the emotional state. The analysis unit can also build a system that makes suggestions to improve motivation to work based on the emotional data. For example, it can provide positive feedback or encouraging messages. The analysis unit can also analyze the emotional data in real time and suggest actions to improve motivation to work. For example, it can provide advice on relaxing when stress is high.

[0086] The simulation generation unit can also visualize the growth process of crops using a 3D model, allowing users to experience it in virtual reality. For example, we will develop a system that visualizes the growth process of crops using a 3D model. For example, we will display the growth process from sowing to harvesting in real time. The simulation generation unit can also integrate the 3D model into a VR environment, allowing users to experience the growth of crops in virtual reality. For example, we will use a VR headset to observe the growth of crops. The simulation generation unit can also simulate the growth process of crops and display it as a 3D model. For example, we will compare the growth process under different cultivation conditions.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The data collection unit collects environmental data, growth records, and work history data. For example, sensors installed on farmland can be used to measure temperature and humidity in real time and collect this data. Users can also input work history using smartphones or tablets. Data can also be obtained from external databases, such as weather data and soil data. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, generative AI can be used to analyze the impact of temperature and humidity fluctuations on crop growth. It can also assess the risk of pest and disease outbreaks and suggest optimal fertilization and irrigation timing. Step 3: The simulation generation unit generates a crop simulation based on the data analyzed by the analysis unit. For example, the generation AI can be used to simulate the crop growth process, predicting yields, risk of pest outbreaks, and countermeasures. Step 4: The communication unit communicates with the user based on the crop simulation generated by the simulation generation unit. For example, the user can ask the generation AI a question, and the generation AI will provide an appropriate answer. The communication unit can also periodically report the simulation results and provide cultivation advice. Furthermore, the communication unit can receive user feedback to improve the accuracy of the simulation.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0103] 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.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects environmental data, growth records, and work history data; an analysis unit that analyzes the data collected by the data collection unit; a simulation generation unit that generates a crop simulation based on the data analyzed by the analysis unit; a communication unit that communicates with a user based on the crop simulation generated by the simulation generation unit. A system characterized by:

2. The data collection unit Using drones, environmental data of the entire farmland is collected from the air in real time and sent to the generation AI.

2. The system of claim 1.

3. The data collection unit Crop leaf and soil samples are automatically collected and the results of laboratory analysis are fed back to the generative AI.

2. The system of claim 1.

4. The data collection unit Optimizing work efficiency by measuring the stress levels of agricultural workers and incorporating that data into the generative AI 2. The system of claim 1.

5. The data collection unit Collect marine environment data, send it to the AI ​​generator, and use it to simulate the growth of marine life.

2. The system of claim 1.

6. The data collection unit Collect urban environmental data, send it to the AI ​​generator, and use it to optimize urban agriculture and green roofs.

2. The system of claim 1.

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