system

Abstract

The system according to this embodiment aims to optimize resource management in agriculture. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a management unit. The data collection unit collects data from sensors. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The management unit manages resources based on the proposals made by the proposal unit.

Description

Technical Field

[0004] ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] <00000!16>

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, resource management in agriculture is not efficiently performed and there is room for improvement.

[0005] The system according to the embodiment aims to optimize resource management in agriculture.

Means for Solving the Problems

[0006] <00!0033>The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a management unit. The collection unit collects data from sensors. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes a proposal based on the analysis result obtained by the analysis unit. The management unit manages resources based on the content proposed by the proposal unit.

Effects of the Invention

[0007] The system according to this embodiment can optimize resource management in agriculture. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 fifty-two. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI-based agricultural optimization system according to an embodiment of the present invention is a system for optimizing Japanese agriculture. This system collects data from sensors, and the AI ​​analyzes that data. Next, based on the analysis results, it optimizes resource use, automates climate control, predicts harvest yields, and detects pests and diseases early. This system addresses the unique challenges of Japanese agriculture and improves agricultural efficiency. In particular, it is a system for addressing small-scale farming and specific environmental problems, providing real-time data analysis and automated decision-making tools. This improves agricultural efficiency and reduces environmental impact. It also functions as a tool to address the aging agricultural population and limited arable land. For example, it monitors crop health, optimizes resource use, automates climate control, predicts harvest yields, and detects pests and diseases early. This improves agricultural efficiency and reduces environmental impact. Thus, the AI-based agricultural optimization system can improve agricultural efficiency and reduce environmental impact.

[0029] The agricultural optimization system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a management unit. The data collection unit collects data from sensors. The data collection unit can collect data from, for example, temperature sensors, humidity sensors, soil sensors, etc. The data collection unit can collect temperature data using, for example, a temperature sensor. The data collection unit can also collect humidity data using a humidity sensor. The data collection unit can also collect soil moisture data using a soil sensor. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, AI. The analysis unit analyzes the data using, for example, a machine learning algorithm. The analysis unit can also analyze the data using a deep learning algorithm. The analysis unit can also analyze the data using statistical analysis. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposal unit makes proposals using, for example, AI. The proposal unit proposes, for example, a method for using resources. The proposal unit can also propose a method for climate control. The proposal unit can also propose a harvest forecast. The management unit manages resources based on the proposals made by the proposal unit. The management unit manages resources using, for example, AI. The management unit manages water usage, for example. The management unit can also manage fertilizer usage. The management unit can also manage energy usage. As a result, the agricultural optimization system according to this embodiment can improve agricultural efficiency.

[0030] The data collection unit collects data from sensors. For example, it can collect data from temperature sensors, humidity sensors, and soil sensors. Specifically, temperature sensors measure the temperature of farmland in real time and transmit this data to a central database. Humidity sensors measure humidity in the air, providing data to maintain humidity conditions suitable for crop growth. Soil sensors measure soil moisture content, supporting the optimal operation of irrigation systems. These sensors are evenly distributed throughout the farmland, allowing for the collection of extensive data. Furthermore, the data collection unit centrally manages the data from these sensors and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and proposal departments. Adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance. Additionally, the data collection unit can utilize mobile sensors such as drones and autonomous vehicles to collect data over a wide area. This allows the data collection unit to achieve more detailed and multifaceted data collection by combining fixed and mobile sensors.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze the data. Specifically, it uses machine learning algorithms to analyze data and predict crop growth patterns and fluctuations in environmental conditions. By using deep learning algorithms, it can analyze more complex data correlations and make highly accurate predictions. For example, it can combine temperature, humidity, and soil moisture data to predict optimal irrigation timing and fertilizer use. Furthermore, statistical analysis can be used to extract trends from past data and evaluate future risks and opportunities. Based on these analysis results, the analysis unit proposes concrete action plans to improve agricultural efficiency. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. Moreover, the analysis unit continuously learns and updates its AI models, ensuring that analysis always reflects the latest data and technology. This enables the analysis unit to achieve highly accurate data analysis for agricultural optimization, improving the efficiency and quality of agricultural production.

[0032] The proposal department makes suggestions based on the analysis results obtained by the analysis department. The proposal department uses AI, for example, to make suggestions. Specifically, it proposes how to use resources and shows concrete actions to maintain optimal environmental conditions for crop growth. For example, it proposes the optimal irrigation schedule and fertilizer usage based on temperature, humidity, and soil moisture data. The proposal department can also propose methods for climate control. For example, it proposes the timing of ventilation and humidification to maintain optimal temperature and humidity in greenhouses. Furthermore, the proposal department can propose harvest predictions. For example, it predicts the optimal harvest time based on historical data and current environmental conditions to improve the efficiency of harvesting work. The proposal department can also predict the risk of pest and disease outbreaks and propose measures to take early action. In this way, the proposal department can provide concrete action plans to improve agricultural efficiency and maximize crop quality and yield. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can adjust the suggestion algorithm based on the results of executing the suggested actions to make more accurate suggestions. Furthermore, the proposal department can simulate multiple scenarios and select the most effective action plan. This allows the proposal department to provide highly accurate proposals for optimizing agriculture and improve the efficiency and quality of agricultural production.

[0033] The management department manages resources based on proposals made by the proposal department. For example, the management department uses AI to manage resources. Specifically, it manages water usage and executes optimal irrigation schedules. For instance, it calculates the required water volume based on data from soil sensors and automatically controls the irrigation system. The management department can also manage fertilizer usage. For example, it monitors soil nutrient levels, calculates the required type and amount of fertilizer, and applies it at the appropriate time. Furthermore, the management department can manage energy usage. For example, it controls energy consumption to minimize the need to maintain optimal temperature and humidity in greenhouses. This enables the management department to achieve efficient resource use and reduce agricultural costs. Additionally, the management department can monitor resource usage in real time and respond immediately to any abnormalities. For example, it can detect abnormalities such as water leaks or excessive fertilizer use and take early corrective action. The management department can also record resource usage history and use it for future planning. This enables the management department to achieve efficient resource management and improve agricultural sustainability. Furthermore, the management department can collaborate with other systems and departments to achieve comprehensive agricultural management. For example, it can work with the data collection, analysis, and proposal departments to share data and coordinate actions. This allows the management department to achieve comprehensive resource management for agricultural optimization and improve the efficiency and quality of agricultural production.

[0034] The data collection unit can monitor the health of crops. For example, the data collection unit can monitor the color of crop leaves. The data collection unit can also monitor the growth rate of crops. The data collection unit can also monitor signs of disease in crops. This allows for appropriate management by monitoring the health of crops. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the color of crop leaves into a generating AI and have the generating AI perform monitoring of the health of crops.

[0035] The analysis unit can analyze the collected data and optimize resource usage. For example, the analysis unit can analyze the collected temperature data. The analysis unit can also analyze the collected humidity data. The analysis unit can also analyze the collected soil data. This optimizes resource usage, enabling efficient agriculture. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the optimization of resource usage.

[0036] The proposed system can automate climate control based on the analysis results. For example, the proposed system can automate temperature control. For example, the proposed system can also automate humidity control. For example, the proposed system can also automate ventilation systems. By automating climate control, the growing environment for crops can be optimized. Some or all of the above-described processes in the proposed system may be performed using AI, for example, or without AI. For example, the proposed system can input the analysis results into a generating AI and have the generating AI perform the automation of climate control.

[0037] The proposed unit can perform harvest forecasting. The proposed unit can perform harvest forecasting using, for example, a crop growth model. The proposed unit can also perform harvest forecasting by analyzing, for example, historical data. The proposed unit can also perform harvest forecasting based on, for example, weather data. By performing harvest forecasting, the timing of harvesting can be optimized. Some or all of the above-described processes in the proposed unit may be performed using, for example, AI, or without using AI. For example, the proposed unit can input data for harvest forecasting into a generating AI and have the generating AI perform the harvest forecasting.

[0038] The proposed unit can perform early detection of pests and diseases. For example, the proposed unit can detect the occurrence of pests using sensors. The proposed unit can also detect signs of disease using sensors. The proposed unit can also perform early detection of pests and diseases using data analysis. This allows for the maintenance of crop health by enabling early detection of pests and diseases. Some or all of the above-described processes in the proposed unit may be performed using AI, for example, or without AI. For example, the proposed unit can input pest and disease data into a generating AI and have the generating AI perform early detection.

[0039] The data collection unit can dynamically change the type of data it collects according to the crop's growth stage. For example, when the crop is in the germination stage, the data collection unit will focus on collecting soil moisture and temperature. When the crop is in the growth stage, the data collection unit can also collect data to measure the efficiency of photosynthesis. When the crop is approaching the harvest stage, the data collection unit can also collect data on the sugar content and acidity of the fruit. This allows for appropriate data collection by changing the type of data according to the crop's growth stage. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the crop's growth stage into a generating AI and have the generating AI change the type of data to collect.

[0040] The data collection unit can integrate data from different sensors to monitor crop health in more detail. For example, the data collection unit can integrate data from soil sensors and weather sensors to assess water stress on crops. The data collection unit can also integrate data from light sensors and temperature sensors to assess photosynthetic efficiency. The data collection unit can also integrate data from pest and disease sensors and crop growth sensors to enable early detection of pests and diseases. This allows for detailed monitoring of crop health by integrating data from different sensors. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from different sensors into a generating AI and have the generating AI perform data integration and crop health monitoring.

[0041] The data collection unit can adjust the range of data to be collected based on geographical conditions. For example, in mountainous areas, the data collection unit may focus on collecting temperature and humidity data. In plains, for example, the data collection unit may focus on collecting wind speed and precipitation data. In coastal areas, for example, the data collection unit may focus on collecting salinity and wind direction data. By adjusting the range of data based on geographical conditions, appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical condition data into a generating AI and have the generating AI adjust the range of data to be collected.

[0042] The data collection unit can improve the accuracy of the data it collects by combining it with weather forecast data. For example, the data collection unit can collect soil moisture data before rainfall based on weather forecast data. For example, the data collection unit can also collect crop temperature data before temperatures rise sharply based on weather forecast data. For example, the data collection unit can also monitor the health of crops before wind speeds increase based on weather forecast data. This improves the accuracy of the data by combining it with weather forecast data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather forecast data into a generating AI and have the generating AI perform the task of improving the accuracy of the data it collects.

[0043] The analysis unit can apply different analysis methods to each type of crop to derive the optimal resource usage method. For example, the analysis unit can apply an analysis method that emphasizes soil moisture and temperature to rice crops. For example, the analysis unit can also apply an analysis method that emphasizes photosynthetic efficiency to vegetable crops. For example, the analysis unit can also apply an analysis method that emphasizes fruit sugar content and acidity to fruit trees. In this way, by applying an analysis method to each type of crop, the optimal resource usage method can be derived. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for each type of crop into a generating AI and have the generating AI perform the application of analysis methods and the derivation of resource usage methods.

[0044] The analysis unit can detect anomalies by comparing them with past data and quickly propose countermeasures. For example, the analysis unit can detect an abnormal temperature rise by comparing it with past data and propose cooling measures. For example, the analysis unit can also detect an abnormal humidity drop by comparing it with past data and propose irrigation measures. For example, the analysis unit can detect an abnormal pest or disease outbreak by comparing it with past data and propose control measures. In this way, by detecting anomalies by comparing them with past data, countermeasures can be proposed quickly. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past and current data into a generating AI and have the generating AI perform anomaly detection and propose countermeasures.

[0045] The analysis unit can integrate information from different data sources to improve the accuracy of the analysis. For example, the analysis unit can integrate data from soil sensors and weather sensors to evaluate water stress in crops. The analysis unit can also integrate data from light sensors and temperature sensors to evaluate the efficiency of photosynthesis. The analysis unit can also integrate data from pest and disease sensors and crop growth sensors to perform early detection of pests and diseases. This improves the accuracy of the analysis by integrating information from different data sources. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information from different data sources into a generating AI and have the generating AI perform data integration and improve the accuracy of the analysis.

[0046] The analysis unit can update analysis results in real time, supporting rapid decision-making. For example, the analysis unit can update soil moisture data in real time and suggest irrigation timing. For example, the analysis unit can update temperature data in real time and suggest cooling measures. For example, the analysis unit can update pest and disease occurrence data in real time and suggest control measures. This enables rapid decision-making by updating analysis results in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input real-time data into a generating AI and have the generating AI perform the updating of analysis results and support decision-making.

[0047] The proposal unit can generate a concrete action plan and provide actionable steps based on the proposed content. For example, the proposal unit can specify the timing and amount of irrigation based on the proposed content. The proposal unit can also specify the timing and amount of fertilization based on the proposed content. The proposal unit can also specify the specific procedures for pest and disease control based on the proposed content. By providing a concrete action plan, actionable steps become clear. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the proposed content into a generating AI and have the generating AI generate an action plan and provide actionable steps.

[0048] The proposal unit can compare the proposed content with past successful cases and select the optimal proposal. For example, the proposal unit can propose the optimal irrigation method by comparing it with past successful cases. For example, the proposal unit can also propose the optimal fertilization method by comparing it with past successful cases. For example, the proposal unit can also propose the optimal pest and disease control method by comparing it with past successful cases. In this way, the optimal proposal can be selected by comparing it with past successful cases. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input data on past successful cases into a generating AI and have the generating AI perform the comparison of proposed content and the selection of the optimal proposal.

[0049] The proposal department can provide proposals in different languages, thereby achieving multilingual support. For example, the proposal department can provide proposals in English to serve international users. For example, the proposal department can provide proposals in Chinese to serve users in Chinese-speaking regions. For example, the proposal department can provide proposals in Spanish to serve users in Spanish-speaking regions. In this way, by providing proposals in different languages, it is possible to serve international users. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input proposal content into a generation AI and have the generation AI perform the task of providing multilingual proposal content.

[0050] The proposal section can visualize the proposal content and provide it in an easy-to-understand format. For example, the proposal section can visualize the proposal content using graphs and charts to make it visually easy to understand. For example, the proposal section can display the proposal content on a map to provide geographical information visually. For example, the proposal section can explain the proposal content using a video to make it visually easy to understand. In this way, visualizing the proposal content makes it easier for users to understand. Some or all of the above processing in the proposal section may be performed using AI, for example, or without AI. For example, the proposal section can input the proposal content into a generating AI and have the generating AI perform visualization and provide it in an easy-to-understand format.

[0051] The management department can analyze resource usage history and propose optimal resource allocation. For example, the management department can propose the optimal amount of irrigation based on past resource usage history. For example, the management department can also propose the optimal amount of fertilizer based on past resource usage history. For example, the management department can also propose the optimal amount of energy consumption based on past resource usage history. This makes it possible to achieve optimal resource allocation by analyzing resource usage history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input resource usage history data into a generating AI and have the generating AI execute resource allocation proposals.

[0052] The management unit can monitor resource usage in real time and issue alerts if an anomaly occurs. For example, the management unit can monitor the water volume of an irrigation system in real time and issue alerts if an anomaly occurs. The management unit can also monitor the amount of fertilizer in a fertilization system in real time and issue alerts if an anomaly occurs. The management unit can also monitor energy consumption in real time and issue alerts if an anomaly occurs. This allows for a quick response to anomalies by monitoring resource usage in real time. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input real-time usage data into a generating AI and have the generating AI perform anomaly detection and issue alerts.

[0053] The management department can improve accessibility by displaying resource management results on different devices. For example, the management department can display resource management results on a smartphone so that users can check them while away from the office. The management department can also display resource management results on a tablet so that users can view details on a larger screen. The management department can also display resource management results on a PC so that users can perform detailed analysis in a desktop environment. This allows users to access resource management results from various devices by displaying them on different devices. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input resource management results into a generating AI and have the generating AI perform the display on different devices.

[0054] The management department can store resource management data in the cloud to ensure data security and accessibility. For example, the management department can store resource management data in the cloud to ensure data backup. For example, the management department can store resource management data in the cloud to make it accessible from multiple devices. For example, the management department can store resource management data in the cloud to ensure data security. This improves data security and accessibility by storing resource management data in the cloud. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input resource management data into a generating AI and have the generating AI perform cloud storage and ensure data security and accessibility.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can dynamically change the type of data it collects according to the crop's growth stage. For example, when the crop is in the germination stage, the data collection unit will focus on collecting soil moisture and temperature. When the crop is in the growth stage, the data collection unit can also collect data to measure the efficiency of photosynthesis. When the crop is approaching the harvest stage, the data collection unit can also collect data on the sugar content and acidity of the fruit. This allows for appropriate data collection by changing the type of data according to the crop's growth stage. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the crop's growth stage into a generating AI and have the generating AI change the type of data to collect.

[0057] The analysis unit can integrate information from different data sources to improve the accuracy of the analysis. For example, the analysis unit can integrate data from soil sensors and weather sensors to evaluate water stress in crops. The analysis unit can also integrate data from light sensors and temperature sensors to evaluate the efficiency of photosynthesis. The analysis unit can also integrate data from pest and disease sensors and crop growth sensors to perform early detection of pests and diseases. This improves the accuracy of the analysis by integrating information from different data sources. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information from different data sources into a generating AI and have the generating AI perform data integration and improve the accuracy of the analysis.

[0058] The proposal section can visualize the proposal content and provide it in an easy-to-understand format. For example, the proposal section can visualize the proposal content using graphs and charts to make it visually easy to understand. For example, the proposal section can display the proposal content on a map to provide geographical information visually. For example, the proposal section can explain the proposal content using a video to make it visually easy to understand. In this way, visualizing the proposal content makes it easier for users to understand. Some or all of the above processing in the proposal section may be performed using AI, for example, or without AI. For example, the proposal section can input the proposal content into a generating AI and have the generating AI perform visualization and provide it in an easy-to-understand format.

[0059] The management department can store resource management data in the cloud to ensure data security and accessibility. For example, the management department can store resource management data in the cloud to ensure data backup. For example, the management department can store resource management data in the cloud to make it accessible from multiple devices. For example, the management department can store resource management data in the cloud to ensure data security. This improves data security and accessibility by storing resource management data in the cloud. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input resource management data into a generating AI and have the generating AI perform cloud storage and ensure data security and accessibility.

[0060] The management department can analyze resource usage history and propose optimal resource allocation. For example, the management department can propose the optimal amount of irrigation based on past resource usage history. For example, the management department can also propose the optimal amount of fertilizer based on past resource usage history. For example, the management department can also propose the optimal amount of energy consumption based on past resource usage history. This makes it possible to achieve optimal resource allocation by analyzing resource usage history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input resource usage history data into a generating AI and have the generating AI execute resource allocation proposals.

[0061] The proposal unit can compare the proposed content with past successful cases and select the optimal proposal. For example, the proposal unit can propose the optimal irrigation method by comparing it with past successful cases. For example, the proposal unit can also propose the optimal fertilization method by comparing it with past successful cases. For example, the proposal unit can also propose the optimal pest and disease control method by comparing it with past successful cases. In this way, the optimal proposal can be selected by comparing it with past successful cases. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input data on past successful cases into a generating AI and have the generating AI perform the comparison of proposed content and the selection of the optimal proposal.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects data from sensors. The collection unit can collect data from, for example, temperature sensors, humidity sensors, and soil sensors. Specifically, it collects temperature data using a temperature sensor, humidity data using a humidity sensor, and soil moisture data using a soil sensor. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, AI. Specifically, it analyzes the data using machine learning algorithms, deep learning algorithms, and statistical analysis. Step 3: The proposal team makes proposals based on the analysis results obtained by the analysis team. For example, the proposal team uses AI to propose methods for resource utilization, climate control, and harvest predictions. Step 4: The management department manages resources based on the proposals made by the proposal department. For example, the management department uses AI to manage water usage, fertilizer usage, and energy usage.

[0064] (Example of form 2) The AI-based agricultural optimization system according to an embodiment of the present invention is a system for optimizing Japanese agriculture. This system collects data from sensors, and the AI ​​analyzes that data. Next, based on the analysis results, it optimizes resource use, automates climate control, predicts harvest yields, and detects pests and diseases early. This system addresses the unique challenges of Japanese agriculture and improves agricultural efficiency. In particular, it is a system for addressing small-scale farming and specific environmental problems, providing real-time data analysis and automated decision-making tools. This improves agricultural efficiency and reduces environmental impact. It also functions as a tool to address the aging agricultural population and limited arable land. For example, it monitors crop health, optimizes resource use, automates climate control, predicts harvest yields, and detects pests and diseases early. This improves agricultural efficiency and reduces environmental impact. Thus, the AI-based agricultural optimization system can improve agricultural efficiency and reduce environmental impact.

[0065] The agricultural optimization system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a management unit. The data collection unit collects data from sensors. The data collection unit can collect data from, for example, temperature sensors, humidity sensors, soil sensors, etc. The data collection unit can collect temperature data using, for example, a temperature sensor. The data collection unit can also collect humidity data using a humidity sensor. The data collection unit can also collect soil moisture data using a soil sensor. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, AI. The analysis unit analyzes the data using, for example, a machine learning algorithm. The analysis unit can also analyze the data using a deep learning algorithm. The analysis unit can also analyze the data using statistical analysis. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposal unit makes proposals using, for example, AI. The proposal unit proposes, for example, a method for using resources. The proposal unit can also propose a method for climate control. The proposal unit can also propose a harvest forecast. The management unit manages resources based on the proposals made by the proposal unit. The management unit manages resources using, for example, AI. The management unit manages water usage, for example. The management unit can also manage fertilizer usage. The management unit can also manage energy usage. As a result, the agricultural optimization system according to this embodiment can improve agricultural efficiency.

[0066] The data collection unit collects data from sensors. For example, it can collect data from temperature sensors, humidity sensors, and soil sensors. Specifically, temperature sensors measure the temperature of farmland in real time and transmit this data to a central database. Humidity sensors measure humidity in the air, providing data to maintain humidity conditions suitable for crop growth. Soil sensors measure soil moisture content, supporting the optimal operation of irrigation systems. These sensors are evenly distributed throughout the farmland, allowing for the collection of extensive data. Furthermore, the data collection unit centrally manages the data from these sensors and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and proposal departments. Adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance. Additionally, the data collection unit can utilize mobile sensors such as drones and autonomous vehicles to collect data over a wide area. This allows the data collection unit to achieve more detailed and multifaceted data collection by combining fixed and mobile sensors.

[0067] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze the data. Specifically, it uses machine learning algorithms to analyze data and predict crop growth patterns and fluctuations in environmental conditions. By using deep learning algorithms, it can analyze more complex data correlations and make highly accurate predictions. For example, it can combine temperature, humidity, and soil moisture data to predict optimal irrigation timing and fertilizer use. Furthermore, statistical analysis can be used to extract trends from past data and evaluate future risks and opportunities. Based on these analysis results, the analysis unit proposes concrete action plans to improve agricultural efficiency. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. Moreover, the analysis unit continuously learns and updates its AI models, ensuring that analysis always reflects the latest data and technology. This enables the analysis unit to achieve highly accurate data analysis for agricultural optimization, improving the efficiency and quality of agricultural production.

[0068] The proposal department makes suggestions based on the analysis results obtained by the analysis department. The proposal department uses AI, for example, to make suggestions. Specifically, it proposes how to use resources and shows concrete actions to maintain optimal environmental conditions for crop growth. For example, it proposes the optimal irrigation schedule and fertilizer usage based on temperature, humidity, and soil moisture data. The proposal department can also propose methods for climate control. For example, it proposes the timing of ventilation and humidification to maintain optimal temperature and humidity in greenhouses. Furthermore, the proposal department can propose harvest predictions. For example, it predicts the optimal harvest time based on historical data and current environmental conditions to improve the efficiency of harvesting work. The proposal department can also predict the risk of pest and disease outbreaks and propose measures to take early action. In this way, the proposal department can provide concrete action plans to improve agricultural efficiency and maximize crop quality and yield. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can adjust the suggestion algorithm based on the results of executing the suggested actions to make more accurate suggestions. Furthermore, the proposal department can simulate multiple scenarios and select the most effective action plan. This allows the proposal department to provide highly accurate proposals for optimizing agriculture and improve the efficiency and quality of agricultural production.

[0069] The management department manages resources based on proposals made by the proposal department. For example, the management department uses AI to manage resources. Specifically, it manages water usage and executes optimal irrigation schedules. For instance, it calculates the required water volume based on data from soil sensors and automatically controls the irrigation system. The management department can also manage fertilizer usage. For example, it monitors soil nutrient levels, calculates the required type and amount of fertilizer, and applies it at the appropriate time. Furthermore, the management department can manage energy usage. For example, it controls energy consumption to minimize the need to maintain optimal temperature and humidity in greenhouses. This enables the management department to achieve efficient resource use and reduce agricultural costs. Additionally, the management department can monitor resource usage in real time and respond immediately to any abnormalities. For example, it can detect abnormalities such as water leaks or excessive fertilizer use and take early corrective action. The management department can also record resource usage history and use it for future planning. This enables the management department to achieve efficient resource management and improve agricultural sustainability. Furthermore, the management department can collaborate with other systems and departments to achieve comprehensive agricultural management. For example, it can work with the data collection, analysis, and proposal departments to share data and coordinate actions. This allows the management department to achieve comprehensive resource management for agricultural optimization and improve the efficiency and quality of agricultural production.

[0070] The data collection unit can monitor the health of crops. For example, the data collection unit can monitor the color of crop leaves. The data collection unit can also monitor the growth rate of crops. The data collection unit can also monitor signs of disease in crops. This allows for appropriate management by monitoring the health of crops. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the color of crop leaves into a generating AI and have the generating AI perform monitoring of the health of crops.

[0071] The analysis unit can analyze the collected data and optimize resource usage. For example, the analysis unit can analyze the collected temperature data. The analysis unit can also analyze the collected humidity data. The analysis unit can also analyze the collected soil data. This optimizes resource usage, enabling efficient agriculture. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the optimization of resource usage.

[0072] The proposed system can automate climate control based on the analysis results. For example, the proposed system can automate temperature control. For example, the proposed system can also automate humidity control. For example, the proposed system can also automate ventilation systems. By automating climate control, the growing environment for crops can be optimized. Some or all of the above-described processes in the proposed system may be performed using AI, for example, or without AI. For example, the proposed system can input the analysis results into a generating AI and have the generating AI perform the automation of climate control.

[0073] The proposed unit can perform harvest forecasting. The proposed unit can perform harvest forecasting using, for example, a crop growth model. The proposed unit can also perform harvest forecasting by analyzing, for example, historical data. The proposed unit can also perform harvest forecasting based on, for example, weather data. By performing harvest forecasting, the timing of harvesting can be optimized. Some or all of the above-described processes in the proposed unit may be performed using, for example, AI, or without using AI. For example, the proposed unit can input data for harvest forecasting into a generating AI and have the generating AI perform the harvest forecasting.

[0074] The proposed unit can perform early detection of pests and diseases. For example, the proposed unit can detect the occurrence of pests using sensors. The proposed unit can also detect signs of disease using sensors. The proposed unit can also perform early detection of pests and diseases using data analysis. This allows for the maintenance of crop health by enabling early detection of pests and diseases. Some or all of the above-described processes in the proposed unit may be performed using AI, for example, or without AI. For example, the proposed unit can input pest and disease data into a generating AI and have the generating AI perform early detection.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0076] The data collection unit can dynamically change the type of data it collects according to the crop's growth stage. For example, when the crop is in the germination stage, the data collection unit will focus on collecting soil moisture and temperature. When the crop is in the growth stage, the data collection unit can also collect data to measure the efficiency of photosynthesis. When the crop is approaching the harvest stage, the data collection unit can also collect data on the sugar content and acidity of the fruit. This allows for appropriate data collection by changing the type of data according to the crop's growth stage. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the crop's growth stage into a generating AI and have the generating AI change the type of data to collect.

[0077] The data collection unit can integrate data from different sensors to monitor crop health in more detail. For example, the data collection unit can integrate data from soil sensors and weather sensors to assess water stress on crops. The data collection unit can also integrate data from light sensors and temperature sensors to assess photosynthetic efficiency. The data collection unit can also integrate data from pest and disease sensors and crop growth sensors to enable early detection of pests and diseases. This allows for detailed monitoring of crop health by integrating data from different sensors. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from different sensors into a generating AI and have the generating AI perform data integration and crop health monitoring.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting only important data. If the user is relaxed, the data collection unit may also prioritize collecting detailed data. If the user is in a hurry, the data collection unit may also prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0079] The data collection unit can adjust the range of data to be collected based on geographical conditions. For example, in mountainous areas, the data collection unit may focus on collecting temperature and humidity data. In plains, for example, the data collection unit may focus on collecting wind speed and precipitation data. In coastal areas, for example, the data collection unit may focus on collecting salinity and wind direction data. By adjusting the range of data based on geographical conditions, appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical condition data into a generating AI and have the generating AI adjust the range of data to be collected.

[0080] The data collection unit can improve the accuracy of the data it collects by combining it with weather forecast data. For example, the data collection unit can collect soil moisture data before rainfall based on weather forecast data. For example, the data collection unit can also collect crop temperature data before temperatures rise sharply based on weather forecast data. For example, the data collection unit can also monitor the health of crops before wind speeds increase based on weather forecast data. This improves the accuracy of the data by combining it with weather forecast data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather forecast data into a generating AI and have the generating AI perform the task of improving the accuracy of the data it collects.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simplified analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a rapid analysis result. This allows for the provision of appropriate analysis results by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0082] The analysis unit can apply different analysis methods to each type of crop to derive the optimal resource usage method. For example, the analysis unit can apply an analysis method that emphasizes soil moisture and temperature to rice crops. For example, the analysis unit can also apply an analysis method that emphasizes photosynthetic efficiency to vegetable crops. For example, the analysis unit can also apply an analysis method that emphasizes fruit sugar content and acidity to fruit trees. In this way, by applying an analysis method to each type of crop, the optimal resource usage method can be derived. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for each type of crop into a generating AI and have the generating AI perform the application of analysis methods and the derivation of resource usage methods.

[0083] The analysis unit can detect anomalies by comparing them with past data and quickly propose countermeasures. For example, the analysis unit can detect an abnormal temperature rise by comparing it with past data and propose cooling measures. For example, the analysis unit can also detect an abnormal humidity drop by comparing it with past data and propose irrigation measures. For example, the analysis unit can detect an abnormal pest or disease outbreak by comparing it with past data and propose control measures. In this way, by detecting anomalies by comparing them with past data, countermeasures can be proposed quickly. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past and current data into a generating AI and have the generating AI perform anomaly detection and propose countermeasures.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This allows for the provision of appropriate information by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0085] The analysis unit can integrate information from different data sources to improve the accuracy of the analysis. For example, the analysis unit can integrate data from soil sensors and weather sensors to evaluate water stress in crops. The analysis unit can also integrate data from light sensors and temperature sensors to evaluate the efficiency of photosynthesis. The analysis unit can also integrate data from pest and disease sensors and crop growth sensors to perform early detection of pests and diseases. This improves the accuracy of the analysis by integrating information from different data sources. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information from different data sources into a generating AI and have the generating AI perform data integration and improve the accuracy of the analysis.

[0086] The analysis unit can update analysis results in real time, supporting rapid decision-making. For example, the analysis unit can update soil moisture data in real time and suggest irrigation timing. For example, the analysis unit can update temperature data in real time and suggest cooling measures. For example, the analysis unit can update pest and disease occurrence data in real time and suggest control measures. This enables rapid decision-making by updating analysis results in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input real-time data into a generating AI and have the generating AI perform the updating of analysis results and support decision-making.

[0087] The suggestion unit can estimate the user's emotions and customize the suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide simplified suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide suggestions that can be quickly implemented. This allows for appropriate suggestions by customizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI customize the suggestions.

[0088] The proposal unit can generate a concrete action plan and provide actionable steps based on the proposed content. For example, the proposal unit can specify the timing and amount of irrigation based on the proposed content. The proposal unit can also specify the timing and amount of fertilization based on the proposed content. The proposal unit can also specify the specific procedures for pest and disease control based on the proposed content. By providing a concrete action plan, actionable steps become clear. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the proposed content into a generating AI and have the generating AI generate an action plan and provide actionable steps.

[0089] The proposal unit can compare the proposed content with past successful cases and select the optimal proposal. For example, the proposal unit can propose the optimal irrigation method by comparing it with past successful cases. For example, the proposal unit can also propose the optimal fertilization method by comparing it with past successful cases. For example, the proposal unit can also propose the optimal pest and disease control method by comparing it with past successful cases. In this way, the optimal proposal can be selected by comparing it with past successful cases. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input data on past successful cases into a generating AI and have the generating AI perform the comparison of proposed content and the selection of the optimal proposal.

[0090] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit may prioritize providing only important suggestions. For example, if the user is relaxed, the suggestion unit may prioritize providing detailed suggestions. For example, if the user is in a hurry, the suggestion unit may prioritize providing suggestions that can be acted on quickly. This allows for the prioritization of important suggestions by determining the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0091] The proposal department can provide proposals in different languages, thereby achieving multilingual support. For example, the proposal department can provide proposals in English to serve international users. For example, the proposal department can provide proposals in Chinese to serve users in Chinese-speaking regions. For example, the proposal department can provide proposals in Spanish to serve users in Spanish-speaking regions. In this way, by providing proposals in different languages, it is possible to serve international users. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input proposal content into a generation AI and have the generation AI perform the task of providing multilingual proposal content.

[0092] The proposal section can visualize the proposal content and provide it in an easy-to-understand format. For example, the proposal section can visualize the proposal content using graphs and charts to make it visually easy to understand. For example, the proposal section can display the proposal content on a map to provide geographical information visually. For example, the proposal section can explain the proposal content using a video to make it visually easy to understand. In this way, visualizing the proposal content makes it easier for users to understand. Some or all of the above processing in the proposal section may be performed using AI, for example, or without AI. For example, the proposal section can input the proposal content into a generating AI and have the generating AI perform visualization and provide it in an easy-to-understand format.

[0093] The management unit can estimate the user's emotions and adjust resource management methods based on the estimated emotions. For example, if the user is stressed, the management unit can simplify resource management procedures. For example, if the user is relaxed, the management unit can provide detailed resource management procedures. For example, if the user is in a hurry, the management unit can provide procedures for rapid resource management. This allows for appropriate resource management by adjusting resource management methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI adjust the resource management methods.

[0094] The management department can analyze resource usage history and propose optimal resource allocation. For example, the management department can propose the optimal amount of irrigation based on past resource usage history. For example, the management department can also propose the optimal amount of fertilizer based on past resource usage history. For example, the management department can also propose the optimal amount of energy consumption based on past resource usage history. This makes it possible to achieve optimal resource allocation by analyzing resource usage history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input resource usage history data into a generating AI and have the generating AI execute resource allocation proposals.

[0095] The management unit can monitor resource usage in real time and issue alerts if an anomaly occurs. For example, the management unit can monitor the water volume of an irrigation system in real time and issue alerts if an anomaly occurs. The management unit can also monitor the amount of fertilizer in a fertilization system in real time and issue alerts if an anomaly occurs. The management unit can also monitor energy consumption in real time and issue alerts if an anomaly occurs. This allows for a quick response to anomalies by monitoring resource usage in real time. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input real-time usage data into a generating AI and have the generating AI perform anomaly detection and issue alerts.

[0096] The management unit can estimate the user's emotions and determine resource management priorities based on those estimated emotions. For example, if the user is stressed, the management unit can prioritize only critical resource management. If the user is relaxed, the management unit can also prioritize detailed resource management. If the user is in a hurry, the management unit can also prioritize providing procedures for rapid resource management. This allows for prioritizing critical resource management by determining resource management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI determine resource management priorities.

[0097] The management department can improve accessibility by displaying resource management results on different devices. For example, the management department can display resource management results on a smartphone so that users can check them while away from the office. The management department can also display resource management results on a tablet so that users can view details on a larger screen. The management department can also display resource management results on a PC so that users can perform detailed analysis in a desktop environment. This allows users to access resource management results from various devices by displaying them on different devices. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input resource management results into a generating AI and have the generating AI perform the display on different devices.

[0098] The management department can store resource management data in the cloud to ensure data security and accessibility. For example, the management department can store resource management data in the cloud to ensure data backup. For example, the management department can store resource management data in the cloud to make it accessible from multiple devices. For example, the management department can store resource management data in the cloud to ensure data security. This improves data security and accessibility by storing resource management data in the cloud. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input resource management data into a generating AI and have the generating AI perform cloud storage and ensure data security and accessibility.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The data collection unit can dynamically change the type of data it collects according to the crop's growth stage. For example, when the crop is in the germination stage, the data collection unit will focus on collecting soil moisture and temperature. When the crop is in the growth stage, the data collection unit can also collect data to measure the efficiency of photosynthesis. When the crop is approaching the harvest stage, the data collection unit can also collect data on the sugar content and acidity of the fruit. This allows for appropriate data collection by changing the type of data according to the crop's growth stage. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the crop's growth stage into a generating AI and have the generating AI change the type of data to collect.

[0101] The analysis unit can integrate information from different data sources to improve the accuracy of the analysis. For example, the analysis unit can integrate data from soil sensors and weather sensors to evaluate water stress in crops. The analysis unit can also integrate data from light sensors and temperature sensors to evaluate the efficiency of photosynthesis. The analysis unit can also integrate data from pest and disease sensors and crop growth sensors to perform early detection of pests and diseases. This improves the accuracy of the analysis by integrating information from different data sources. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information from different data sources into a generating AI and have the generating AI perform data integration and improve the accuracy of the analysis.

[0102] The proposal section can visualize the proposal content and provide it in an easy-to-understand format. For example, the proposal section can visualize the proposal content using graphs and charts to make it visually easy to understand. For example, the proposal section can display the proposal content on a map to provide geographical information visually. For example, the proposal section can explain the proposal content using a video to make it visually easy to understand. In this way, visualizing the proposal content makes it easier for users to understand. Some or all of the above processing in the proposal section may be performed using AI, for example, or without AI. For example, the proposal section can input the proposal content into a generating AI and have the generating AI perform visualization and provide it in an easy-to-understand format.

[0103] The management department can store resource management data in the cloud to ensure data security and accessibility. For example, the management department can store resource management data in the cloud to ensure data backup. For example, the management department can store resource management data in the cloud to make it accessible from multiple devices. For example, the management department can store resource management data in the cloud to ensure data security. This improves data security and accessibility by storing resource management data in the cloud. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input resource management data into a generating AI and have the generating AI perform cloud storage and ensure data security and accessibility.

[0104] The suggestion unit can estimate the user's emotions and customize the suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide simplified suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide suggestions that can be quickly implemented. This allows for appropriate suggestions by customizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI customize the suggestions.

[0105] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simplified analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a rapid analysis result. This allows for the provision of appropriate analysis results by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0107] The management department can analyze resource usage history and propose optimal resource allocation. For example, the management department can propose the optimal amount of irrigation based on past resource usage history. For example, the management department can also propose the optimal amount of fertilizer based on past resource usage history. For example, the management department can also propose the optimal amount of energy consumption based on past resource usage history. This makes it possible to achieve optimal resource allocation by analyzing resource usage history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input resource usage history data into a generating AI and have the generating AI execute resource allocation proposals.

[0108] The proposal unit can compare the proposed content with past successful cases and select the optimal proposal. For example, the proposal unit can propose the optimal irrigation method by comparing it with past successful cases. For example, the proposal unit can also propose the optimal fertilization method by comparing it with past successful cases. For example, the proposal unit can also propose the optimal pest and disease control method by comparing it with past successful cases. In this way, the optimal proposal can be selected by comparing it with past successful cases. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input data on past successful cases into a generating AI and have the generating AI perform the comparison of proposed content and the selection of the optimal proposal.

[0109] The management unit can estimate the user's emotions and adjust resource management methods based on the estimated emotions. For example, if the user is stressed, the management unit can simplify resource management procedures. For example, if the user is relaxed, the management unit can provide detailed resource management procedures. For example, if the user is in a hurry, the management unit can provide procedures for rapid resource management. This allows for appropriate resource management by adjusting resource management methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI adjust the resource management methods.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The collection unit collects data from sensors. The collection unit can collect data from, for example, temperature sensors, humidity sensors, and soil sensors. Specifically, it collects temperature data using a temperature sensor, humidity data using a humidity sensor, and soil moisture data using a soil sensor. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, AI. Specifically, it analyzes the data using machine learning algorithms, deep learning algorithms, and statistical analysis. Step 3: The proposal team makes proposals based on the analysis results obtained by the analysis team. For example, the proposal team uses AI to propose methods for resource utilization, climate control, and harvest predictions. Step 4: The management department manages resources based on the proposals made by the proposal department. For example, the management department uses AI to manage water usage, fertilizer usage, and energy usage.

[0112] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0115] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and management unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects temperature, humidity, and soil moisture data using the sensors of the smart device 14. The analysis unit analyzes the data using AI, for example, by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes methods for resource usage and climate control, for example, by the specific processing unit 290 of the data processing unit 12. The management unit manages the usage of water, fertilizer, and energy, for example, by the control unit 46A of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects temperature, humidity, and soil moisture data using the sensors of the smart glasses 214. The analysis unit analyzes the data using AI, for example, by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes methods for resource usage and climate control, for example, by the specific processing unit 290 of the data processing unit 12. The management unit manages the usage of water, fertilizer, and energy, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and management unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects temperature, humidity, and soil moisture data using the sensors of the headset terminal 314. The analysis unit analyzes the data using AI, for example, by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes methods for resource usage and climate control, for example, by the specific processing unit 290 of the data processing unit 12. The management unit manages the usage of water, fertilizer, and energy, for example, by the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and management unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects temperature, humidity, and soil moisture data using the sensors of the robot 414. The analysis unit analyzes the data using AI, for example, by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes methods for resource usage and climate control, for example, by the specific processing unit 290 of the data processing unit 12. The management unit manages the usage of water, fertilizer, and energy, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0165] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A data collection unit that collects data from sensors, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the analysis unit, The system comprises a management unit that manages resources based on the content proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Monitor the health of crops. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected data and optimize resource usage. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Automate climate control based on analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Perform harvest forecast The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Early detection of pests and diseases The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The type of data collected is dynamically changed according to the crop's growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Integrating data from different sensors allows for more detailed monitoring of crop health. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Adjust the scope of data collected based on geographical conditions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is By combining weather forecast data, we improve the accuracy of the data we collect. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, By applying different analytical methods to each crop type, we derive the optimal resource usage method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, By comparing with past data, it detects anomalies and quickly proposes countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Integrating information from different data sources improves the accuracy of analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The analysis results are updated in real time to support rapid decision-making. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and customizes the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, Based on the proposed content, we will generate a concrete action plan and provide actionable steps. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, We will compare the proposed solutions with past successful cases and select the most suitable one. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, Providing proposals in different languages ​​enables multilingual support. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, The proposal is visualized and presented in an easy-to-understand format. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, It estimates user sentiment and adjusts resource management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, It analyzes resource usage history and proposes the optimal resource allocation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, It monitors resource usage in real time and issues alerts if an anomaly occurs. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, It estimates user sentiment and determines resource management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, View resource management results across different devices to improve accessibility. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, Resource management data is stored in the cloud to ensure data security and accessibility. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from sensors, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the analysis unit, The system comprises a management unit that manages resources based on the content proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Monitor the health of crops. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected data and optimize resource usage. The system according to feature 1.

4. The aforementioned proposal section is, Automate climate control based on analysis results. The system according to feature 1.

5. The aforementioned proposal section is, Perform harvest forecast The system according to feature 1.

6. The aforementioned proposal section is, Early detection of pests and diseases The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is The type of data collected is dynamically changed according to the crop's growth stage. The system according to feature 1.

9. The aforementioned collection unit is Integrating data from different sensors allows for more detailed monitoring of crop health. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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