system

Abstract

The system according to this embodiment aims to centrally manage data collection and analysis, material procurement, fundraising, and automation of operations in agricultural management. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, a management unit, and an automation unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes optimal proposals based on the analysis results obtained by the analysis unit. The management unit handles material procurement and fundraising. The automation unit automates tasks.

Description

Technical Field

[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, and includes 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to manage data collection and analysis, material procurement, financing, work automation, etc. in agricultural management in a unified manner, and there is room for improvement.

[0005] The system according to the embodiment aims to manage data collection and analysis, material procurement, financing, and work automation in agricultural management in a unified manner.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a management unit, and an automation unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes optimal proposals based on the analysis results obtained by the analysis unit. The management unit handles material procurement and fundraising. The automation unit automates tasks. [Effects of the Invention]

[0007] The system according to this embodiment can centrally manage data collection and analysis, material procurement, fundraising, and automation of operations in agricultural management. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 three or more matters are connected and expressed 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 agricultural management support system according to an embodiment of the present invention is a platform that supports agricultural management by providing real-time crop growth data, weather forecasts, and price forecasts using AI and IoT technology. This agricultural management support system uses AI to analyze crop growth data, weather forecasts, and price forecasts in real time, and centralizes the purchase of materials, sales management, and fundraising through cashless payments. It also supports the automation of tasks using robotics. For example, the AI ​​analyzes weather forecasts, crop growth, and market prices in real time to propose optimal agricultural plans and material management. The AI ​​analyzes weather data and proposes the optimal harvest time to maximize yields. The AI ​​predicts market prices and proposes the optimal sales timing to improve profitability. Furthermore, it improves farmers' cash flow through material purchases via cashless payments and rapid fundraising. The AI ​​analyzes inventory data and predicts and notifies farmers of the timing for purchasing necessary materials, thereby reducing unnecessary costs. Funding can be secured quickly through cashless payments. Automation using robotics is also an important element. For example, robots automating farm work can alleviate labor shortages and enable efficient agricultural management. This provides a unique value proposition by centrally addressing diverse challenges in the agricultural sector and realizing sustainable agricultural management. As a result, agricultural management support systems can comprehensively support agricultural management, leading to improved profitability and sustainable agricultural operations.

[0029] The agricultural management support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a management unit, and an automation unit. The data collection unit collects data. The data collection unit can collect, for example, weather data and crop growth data. The data collection unit can collect, for example, weather data such as temperature, precipitation, and wind speed. The data collection unit can also collect crop growth data such as crop growth rate, yield, and disease information. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, analyze the collected weather data and make weather forecasts. The analysis unit can also analyze the collected crop growth data and make crop growth forecasts. The proposal unit makes optimal proposals based on the analysis results obtained by the analysis unit. The proposal unit can, for example, propose the optimal harvest time based on the analysis results. The proposal unit can also propose the optimal sales timing based on the analysis results. The management unit handles material purchases and fundraising. The management unit can, for example, analyze inventory data and predict and notify the timing of necessary material purchases. Furthermore, the management unit can also secure funds quickly through cashless payments. The automation unit automates tasks. For example, the automation unit can perform agricultural work automatically using robots. For example, the automation unit can automate crop harvesting using harvesting robots. The automation unit can also automate crop planting using planting robots. As a result, the agricultural management support system according to this embodiment can comprehensively support agricultural management and realize improved profitability and sustainable agricultural management.

[0030] The data collection unit collects data. For example, the data collection unit can collect weather data and crop growth data. Specifically, it installs weather sensors and weather stations to collect weather data such as temperature, precipitation, and wind speed, and acquires this data in real time. This allows for a detailed understanding of microclimate fluctuations in specific areas of the farm. In addition, drones and ground sensors are used to collect crop growth data such as crop growth rate, yield, and disease information. Drones photograph the health of crops from above, and image analysis technology is used for early detection of diseases and monitoring of growth status. Ground sensors measure soil moisture, nutrient concentration, pH value, etc., to understand the condition of crop roots and soil environment in detail. This data is transmitted to a cloud server and stored in a central database. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, the collected data can be made accessible to the analysis unit and the proposal unit, enabling real-time data sharing. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze collected weather data and make weather forecasts. Specifically, it uses AI to compare past weather data with current weather conditions and predict future weather with high accuracy. For example, it uses machine learning algorithms to analyze fluctuation patterns of temperature, precipitation, and wind speed to predict the weather for the next few days. The analysis unit can also analyze collected crop growth data and make crop growth forecasts. The AI ​​analyzes the growth rate and health of crops and suggests the optimal timing for fertilization and irrigation. Furthermore, it analyzes disease information and provides information for taking early countermeasures. The analysis unit integrates this data to optimize overall agricultural management. For example, it combines weather forecasts and crop growth forecasts to optimize harvest time and fertilization timing. The analysis unit can also utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past weather data and crop growth data, it can predict fluctuations in risk in specific regions and time periods and formulate future countermeasures. 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, thereby improving the reliability and safety of the entire system.

[0032] The proposal department makes optimal suggestions based on the analysis results obtained by the analysis department. For example, the proposal department can suggest the optimal harvest time based on the analysis results. Specifically, it combines crop growth data and weather forecasts to calculate the optimal timing for harvesting and notifies farmers. The proposal department can also suggest the optimal timing for sales based on the analysis results. It analyzes market supply and demand data and recommends selling at the most profitable time. Furthermore, the proposal department suggests the optimal timing for fertilization and irrigation and provides specific action plans to optimize crop health. For example, it suggests the necessary type and amount of fertilizer and optimizes irrigation timing based on soil nutrient data. It also suggests specific control methods for early intervention based on disease information. The proposal department notifies farmers of these suggestions and provides tools and resources to support their implementation. For example, it notifies farmers of the suggestions via a smartphone app and provides links to purchase necessary materials and work procedures. In this way, the proposal department can support farmers in making optimal decisions and improving profitability.

[0033] The management department handles material procurement and fundraising. For example, it can analyze inventory data to predict and notify farmers of the timing for purchasing necessary materials. Specifically, it uses an inventory management system to grasp the current inventory status in real time and automatically places orders before necessary materials run out. The management department also proposes the best methods for fundraising and supports rapid fundraising. For example, it proposes methods for quickly raising necessary funds by utilizing crowdfunding or agricultural-specific lending platforms. Furthermore, the management department provides various tools and resources to improve the efficiency of agricultural management. For example, it uses expense management tools to conduct detailed expense analysis and proposes concrete action plans for cost reduction. It also provides tools to support labor management and schedule optimization, thereby improving the efficiency of agricultural management. In this way, the management department can support farmers in efficiently managing materials and quickly raising necessary funds.

[0034] The automation unit automates tasks. For example, it can automate agricultural work using robots. Specifically, it can automate crop harvesting using harvesting robots. Harvesting robots use AI to determine the maturity of crops and harvest at the optimal time. It can also automate crop planting using planting robots. Planting robots detect soil conditions with sensors and plant seeds at the optimal depth and spacing. Furthermore, the automation unit improves the efficiency of agricultural work using weeding robots and irrigation systems. Weeding robots use AI to identify weeds and selectively remove them. Irrigation systems automatically supply water at the optimal time based on soil moisture data. As a result, the automation unit can improve the efficiency of agricultural work, reduce labor, and improve the profitability of agricultural management. In addition, the automation unit maintains and updates these robots and systems to ensure they are always operating in optimal condition. For example, it sets up regular maintenance schedules and replaces necessary parts and updates software. This allows the automation unit to continuously support the automation of agricultural work, thereby improving the efficiency and sustainability of agricultural management.

[0035] The data collection unit can collect weather data and crop growth data. For example, the data collection unit can collect weather data such as temperature, precipitation, and wind speed. The data collection unit can also collect crop growth data such as crop growth rate, yield, and disease information. For example, the data collection unit can collect weather data using weather sensors. The data collection unit can also collect crop growth data using cameras and sensors to monitor crop growth. By collecting weather data and crop growth data, it is possible to provide information necessary for agricultural management. 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 acquired from weather sensors into a generating AI and have the generating AI perform analysis of the weather data.

[0036] The analysis unit can analyze collected data in real time. For example, the analysis unit can analyze collected weather data in real time and make weather forecasts. The analysis unit can also analyze collected crop growth data in real time and make crop growth forecasts. The analysis unit can analyze collected data in real time using AI, for example. For example, the analysis unit can input collected weather data into AI and have the AI ​​perform weather forecasts. The analysis unit can also input collected crop growth data into AI and have the AI ​​perform crop growth forecasts. This enables rapid decision-making by analyzing data in real time. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0037] The proposal unit can propose the optimal harvest time and sales timing based on the analysis results. For example, the proposal unit can propose the optimal harvest time based on the analysis results. The proposal unit can also propose the optimal sales timing based on the analysis results. For example, the proposal unit can use AI to make optimal suggestions based on the analysis results. For example, the proposal unit can input the analysis results into AI and have the AI ​​make suggestions for the optimal harvest time and sales timing. This improves profitability by proposing the optimal harvest time and sales timing. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0038] The management department can analyze inventory data and predict and notify when necessary materials should be purchased. For example, the management department can analyze inventory data and predict when necessary materials should be purchased. The management department can also analyze inventory data and notify when necessary materials should be purchased. For example, the management department can use AI to analyze inventory data and predict when necessary materials should be purchased. For example, the management department can input inventory data into AI and have the AI ​​predict when necessary materials should be purchased. This allows for the reduction of unnecessary costs by analyzing inventory data and predicting when materials should be purchased. Some or all of the above processes in the management department may be performed using AI, or without AI.

[0039] The automation unit can perform agricultural work automatically using robots. For example, the automation unit can automate crop harvesting using harvesting robots. The automation unit can also automate crop planting using planting robots. For example, the automation unit can control the robot's movements using AI. For example, the automation unit can input the movements of a harvesting robot into the AI ​​and have the AI ​​perform the automated harvesting work. The automation unit can also input the movements of a planting robot into the AI ​​and have the AI ​​perform the automated planting work. This solves the labor shortage by automating agricultural work using robots. Some or all of the above processes in the automation unit may be performed using AI, for example, or without using AI.

[0040] The data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can select the most efficient collection method based on past data collection history. The data collection unit can also analyze past data collection results and optimize collection frequency and timing. The data collection unit can also improve collection methods by referring to successful past data collection examples. In this way, the optimal collection method can be selected by analyzing past data. 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 past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0041] The data collection unit can filter data based on specific crops or regions during data collection. For example, the data collection unit can collect only data related to a specific crop and filter out other data. The data collection unit can also collect only data related to a specific region and filter out data from other regions. The data collection unit can also customize the types of data to collect depending on the type of crop or region. This allows for efficient collection of necessary information by filtering data based on specific crops or regions. 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 related to specific crops or regions into a generating AI and have the generating AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant weather data based on the user's current location. For example, the data collection unit can also prioritize the collection of region-specific crop growth data based on the user's farm location information. For example, the data collection unit can select the optimal collection point by considering the user's geographical location information. This allows for the efficient collection of highly relevant data by considering the user's geographical location information. 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 the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media posts and collect relevant agricultural data. The data collection unit can also collect data on crops or regions of interest from a user's social media activity. For example, the data collection unit can analyze posts from a user's social media followers and friends and collect relevant data. This allows for the efficient collection of relevant data by analyzing a user's social media activity. 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 user social media activity data into a generating AI and have the generating AI collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on other data. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit can apply multiple analysis methods to high-importance data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a weather forecasting algorithm to weather data and a growth forecasting algorithm to crop growth data. For example, the analysis unit can also apply a market forecasting algorithm to price data and an inventory management algorithm to inventory data. The analysis unit can also select the optimal analysis algorithm depending on the data category. By applying the optimal analysis algorithm according to the data category, the accuracy of the analysis is improved. 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 the data category into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data and postpone the analysis of older data. For example, the analysis unit may also perform rapid analysis on data where the collection timing is important. The analysis unit can also adjust the analysis schedule based on the data collection timing. This allows for the rapid provision of the latest information by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also optimize the order of analysis based on the relevance of the data. For example, the analysis unit can perform a more detailed analysis on highly relevant data. By adjusting the order of analysis based on the relevance of the data, efficient analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The proposal unit can adjust the level of detail of its proposals based on the importance of the analysis results. For example, it can provide detailed proposals for important analysis results and simplified proposals for other results. The proposal unit can also determine the priority of proposals based on the importance of the analysis results. For example, it can provide multiple proposals for high-importance analysis results. This allows for efficient proposals by adjusting the level of detail of proposals based on the importance of the analysis results. 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 importance of the analysis results into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0049] The proposal unit can apply different proposal algorithms depending on the category of the analysis results when making a proposal. For example, the proposal unit can apply a weather forecasting algorithm to weather analysis results and a growth forecasting algorithm to crop growth analysis results. The proposal unit can also apply a market forecasting algorithm to price analysis results and an inventory management algorithm to inventory analysis results. The proposal unit can also select the optimal proposal algorithm depending on the category of the analysis results. By applying the optimal proposal algorithm according to the category of the analysis results, the accuracy of the proposal is improved. 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 category of the analysis results into a generating AI and have the generating AI execute the application of the optimal proposal algorithm.

[0050] The proposal unit can determine the priority of proposals based on the timing of analysis result collection when making a proposal. For example, the proposal unit may prioritize the latest analysis results and postpone older ones. For example, the proposal unit may also make proposals quickly for analysis results where the collection timing is important. The proposal unit may also adjust the proposal schedule based on the timing of analysis result collection. This allows for the rapid provision of the latest information by determining the priority of proposals based on the timing of analysis result collection. 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 may input the timing of analysis result collection into a generating AI and have the generating AI determine the priority of proposals.

[0051] The proposal unit can adjust the order of proposals based on the relevance of the analysis results during the proposal process. For example, the proposal unit can prioritize proposing highly relevant analysis results and postpone less relevant ones. The proposal unit can also optimize the order of proposals based on the relevance of the analysis results. For example, the proposal unit can provide detailed suggestions for highly relevant analysis results. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the analysis results. 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 relevance of the analysis results into a generating AI and have the generating AI adjust the order of proposals.

[0052] The management department can analyze past inventory data to select the optimal inventory management method during inventory management. For example, the management department can select the optimal inventory management method based on past inventory data. The management department can also analyze past inventory data to calculate the appropriate amount of inventory. For example, the management department can adjust the inventory management schedule by referring to past inventory data. In this way, the optimal inventory management method can be selected by analyzing past inventory data. 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 past inventory data into a generating AI and have the generating AI select the optimal inventory management method.

[0053] The management department can customize inventory management methods based on specific materials or regions. For example, the management department can provide optimal inventory management methods for specific materials. The management department can also customize inventory management methods based on specific regions. For example, the management department can adjust inventory management methods according to the type of material or region. This enables efficient inventory management by customizing inventory management methods based on specific materials or regions. 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 data on specific materials or regions into a generating AI and have the generating AI perform the customization of the management methods.

[0054] The management department can select the optimal inventory management method by considering the user's geographical location information during inventory management. For example, the management department can provide the optimal inventory management method based on the user's current location. For example, the management department can also provide a region-specific inventory management method based on the user's farm location information. For example, the management department can adjust the inventory management schedule by considering the user's geographical location information. This allows for the provision of the optimal inventory management method by considering the user's geographical location information. 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 the user's geographical location information into a generating AI and have the generating AI select the optimal inventory management method.

[0055] The management department can analyze users' social media activity and propose inventory management methods during inventory management. For example, the management department can analyze users' social media posts and propose relevant inventory management methods. For example, the management department can also propose inventory management methods related to materials of interest based on users' social media activity. For example, the management department can analyze posts from users' social media followers and friends and propose relevant inventory management methods. This allows for the efficient proposal of relevant inventory management methods by analyzing users' social media activity. 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 user social media activity data into a generating AI and have the generating AI propose relevant inventory management methods.

[0056] The automation unit can analyze past work data to select the optimal automation method during automation. For example, the automation unit selects the optimal automation method based on past work data. The automation unit can also analyze past work data and adjust the automation schedule. For example, the automation unit can improve the automation means by referring to past work data. In this way, the optimal automation method can be selected by analyzing past work data. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input past work data into a generating AI and have the generating AI select the optimal automation method.

[0057] The automation unit can customize the automation means based on specific tasks or regions during automation. For example, the automation unit provides the optimal automation means for a specific task. The automation unit can also customize the automation means based on a specific region. For example, the automation unit can adjust the automation means according to the type of work or region. This enables efficient automation by customizing the automation means based on specific tasks or regions. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input data about specific tasks or regions into a generating AI and have the generating AI perform the customization of the automation means.

[0058] The automation unit can select the optimal automation method by considering the user's geographical location information during automation. For example, the automation unit can provide the optimal automation method based on the user's current location. For example, the automation unit can also provide a region-specific automation method based on the user's farm location information. For example, the automation unit can adjust the automation schedule by considering the user's geographical location information. This allows the system to provide the optimal automation method by considering the user's geographical location information. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal automation method.

[0059] The automation unit can analyze the user's social media activity and propose automation methods during automation. For example, the automation unit can analyze the user's social media posts and propose relevant automation methods. The automation unit can also propose automation methods related to tasks of interest based on the user's social media activity. For example, the automation unit can analyze posts from the user's social media followers and friends and propose relevant automation methods. This allows for the efficient proposal of relevant automation methods by analyzing the user's social media activity. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's social media activity data into a generating AI and have the generating AI execute suggestions for relevant automation methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analysis unit can analyze collected data in real time. For example, it can analyze collected weather data in real time and make weather forecasts. It can also analyze collected crop growth data in real time and make crop growth forecasts. Furthermore, it can input collected data into an AI and have the AI ​​perform weather forecasts and crop growth forecasts. This enables rapid decision-making by analyzing data in real time. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0062] The proposal unit can suggest the optimal harvest time and sales timing based on the analysis results. For example, it can suggest the optimal harvest time based on the analysis results. It can also suggest the optimal sales timing based on the analysis results. Furthermore, the analysis results can be input into an AI, which can then make suggestions for the optimal harvest time and sales timing. This allows for improved profitability by suggesting the optimal harvest time and sales timing. Some or all of the above-described processes in the proposal unit may be performed using AI, or they may be performed without using AI.

[0063] The management department can analyze inventory data and predict and notify when necessary materials need to be purchased. For example, it can analyze inventory data and predict when necessary materials need to be purchased. It can also analyze inventory data and notify when necessary materials need to be purchased. Furthermore, inventory data can be input into AI, and the AI ​​can perform the prediction of when necessary materials need to be purchased. By analyzing inventory data and predicting when materials need to be purchased, unnecessary costs can be reduced. Some or all of the above processes in the management department may be performed using AI, or they may not be performed using AI.

[0064] The automation unit can perform agricultural tasks automatically using robots. For example, it can automate crop harvesting using harvesting robots. It can also automate crop planting using planting robots. Furthermore, it can control the robots' movements using AI. For example, the movements of a harvesting robot can be input into the AI, allowing the AI ​​to perform automated harvesting. Similarly, the movements of a planting robot can be input into the AI, allowing the AI ​​to perform automated planting. This automates agricultural work using robots, thereby alleviating labor shortages. Some or all of the above-mentioned processes in the automation unit may be performed using AI, or they may be performed without AI.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The data collection unit collects data. The data collection unit can collect, for example, weather data and crop growth data. Specifically, it collects weather data such as temperature, precipitation, and wind speed, and crop growth data such as crop growth rate, yield, and disease information. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected weather data and make weather forecasts. It can also analyze the collected crop growth data and make crop growth forecasts. Step 3: The proposal unit makes optimal suggestions based on the analysis results obtained by the analysis unit. For example, the proposal unit can suggest the optimal harvest time and sales timing based on the analysis results. Step 4: The management department handles material procurement and fundraising. For example, the management department can analyze inventory data to predict and notify when necessary materials need to be purchased. They can also raise funds quickly through cashless payments. Step 5: The automation unit automates the work. The automation unit can, for example, automate agricultural work using robots. Specifically, it can automate crop harvesting using harvesting robots, or automate crop planting using planting robots.

[0067] (Example of form 2) The agricultural management support system according to an embodiment of the present invention is a platform that supports agricultural management by providing real-time crop growth data, weather forecasts, and price forecasts using AI and IoT technology. This agricultural management support system uses AI to analyze crop growth data, weather forecasts, and price forecasts in real time, and centralizes the purchase of materials, sales management, and fundraising through cashless payments. It also supports the automation of tasks using robotics. For example, the AI ​​analyzes weather forecasts, crop growth, and market prices in real time to propose optimal agricultural plans and material management. The AI ​​analyzes weather data and proposes the optimal harvest time to maximize yields. The AI ​​predicts market prices and proposes the optimal sales timing to improve profitability. Furthermore, it improves farmers' cash flow through material purchases via cashless payments and rapid fundraising. The AI ​​analyzes inventory data and predicts and notifies farmers of the timing for purchasing necessary materials, thereby reducing unnecessary costs. Funding can be secured quickly through cashless payments. Automation using robotics is also an important element. For example, robots automating farm work can alleviate labor shortages and enable efficient agricultural management. This provides a unique value proposition by centrally addressing diverse challenges in the agricultural sector and realizing sustainable agricultural management. As a result, agricultural management support systems can comprehensively support agricultural management, leading to improved profitability and sustainable agricultural operations.

[0068] The agricultural management support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a management unit, and an automation unit. The data collection unit collects data. The data collection unit can collect, for example, weather data and crop growth data. The data collection unit can collect, for example, weather data such as temperature, precipitation, and wind speed. The data collection unit can also collect crop growth data such as crop growth rate, yield, and disease information. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, analyze the collected weather data and make weather forecasts. The analysis unit can also analyze the collected crop growth data and make crop growth forecasts. The proposal unit makes optimal proposals based on the analysis results obtained by the analysis unit. The proposal unit can, for example, propose the optimal harvest time based on the analysis results. The proposal unit can also propose the optimal sales timing based on the analysis results. The management unit handles material purchases and fundraising. The management unit can, for example, analyze inventory data and predict and notify the timing of necessary material purchases. Furthermore, the management unit can also secure funds quickly through cashless payments. The automation unit automates tasks. For example, the automation unit can perform agricultural work automatically using robots. For example, the automation unit can automate crop harvesting using harvesting robots. The automation unit can also automate crop planting using planting robots. As a result, the agricultural management support system according to this embodiment can comprehensively support agricultural management and realize improved profitability and sustainable agricultural management.

[0069] The data collection unit collects data. For example, the data collection unit can collect weather data and crop growth data. Specifically, it installs weather sensors and weather stations to collect weather data such as temperature, precipitation, and wind speed, and acquires this data in real time. This allows for a detailed understanding of microclimate fluctuations in specific areas of the farm. In addition, drones and ground sensors are used to collect crop growth data such as crop growth rate, yield, and disease information. Drones photograph the health of crops from above, and image analysis technology is used for early detection of diseases and monitoring of growth status. Ground sensors measure soil moisture, nutrient concentration, pH value, etc., to understand the condition of crop roots and soil environment in detail. This data is transmitted to a cloud server and stored in a central database. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, the collected data can be made accessible to the analysis unit and the proposal unit, enabling real-time data sharing. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0070] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze collected weather data and make weather forecasts. Specifically, it uses AI to compare past weather data with current weather conditions and predict future weather with high accuracy. For example, it uses machine learning algorithms to analyze fluctuation patterns of temperature, precipitation, and wind speed to predict the weather for the next few days. The analysis unit can also analyze collected crop growth data and make crop growth forecasts. The AI ​​analyzes the growth rate and health of crops and suggests the optimal timing for fertilization and irrigation. Furthermore, it analyzes disease information and provides information for taking early countermeasures. The analysis unit integrates this data to optimize overall agricultural management. For example, it combines weather forecasts and crop growth forecasts to optimize harvest time and fertilization timing. The analysis unit can also utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past weather data and crop growth data, it can predict fluctuations in risk in specific regions and time periods and formulate future countermeasures. 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, thereby improving the reliability and safety of the entire system.

[0071] The proposal department makes optimal suggestions based on the analysis results obtained by the analysis department. For example, the proposal department can suggest the optimal harvest time based on the analysis results. Specifically, it combines crop growth data and weather forecasts to calculate the optimal timing for harvesting and notifies farmers. The proposal department can also suggest the optimal timing for sales based on the analysis results. It analyzes market supply and demand data and recommends selling at the most profitable time. Furthermore, the proposal department suggests the optimal timing for fertilization and irrigation and provides specific action plans to optimize crop health. For example, it suggests the necessary type and amount of fertilizer and optimizes irrigation timing based on soil nutrient data. It also suggests specific control methods for early intervention based on disease information. The proposal department notifies farmers of these suggestions and provides tools and resources to support their implementation. For example, it notifies farmers of the suggestions via a smartphone app and provides links to purchase necessary materials and work procedures. In this way, the proposal department can support farmers in making optimal decisions and improving profitability.

[0072] The management department handles material procurement and fundraising. For example, it can analyze inventory data to predict and notify farmers of the timing for purchasing necessary materials. Specifically, it uses an inventory management system to grasp the current inventory status in real time and automatically places orders before necessary materials run out. The management department also proposes the best methods for fundraising and supports rapid fundraising. For example, it proposes methods for quickly raising necessary funds by utilizing crowdfunding or agricultural-specific lending platforms. Furthermore, the management department provides various tools and resources to improve the efficiency of agricultural management. For example, it uses expense management tools to conduct detailed expense analysis and proposes concrete action plans for cost reduction. It also provides tools to support labor management and schedule optimization, thereby improving the efficiency of agricultural management. In this way, the management department can support farmers in efficiently managing materials and quickly raising necessary funds.

[0073] The automation unit automates tasks. For example, it can automate agricultural work using robots. Specifically, it can automate crop harvesting using harvesting robots. Harvesting robots use AI to determine the maturity of crops and harvest at the optimal time. It can also automate crop planting using planting robots. Planting robots detect soil conditions with sensors and plant seeds at the optimal depth and spacing. Furthermore, the automation unit improves the efficiency of agricultural work using weeding robots and irrigation systems. Weeding robots use AI to identify weeds and selectively remove them. Irrigation systems automatically supply water at the optimal time based on soil moisture data. As a result, the automation unit can improve the efficiency of agricultural work, reduce labor, and improve the profitability of agricultural management. In addition, the automation unit maintains and updates these robots and systems to ensure they are always operating in optimal condition. For example, it sets up regular maintenance schedules and replaces necessary parts and updates software. This allows the automation unit to continuously support the automation of agricultural work, thereby improving the efficiency and sustainability of agricultural management.

[0074] The data collection unit can collect weather data and crop growth data. For example, the data collection unit can collect weather data such as temperature, precipitation, and wind speed. The data collection unit can also collect crop growth data such as crop growth rate, yield, and disease information. For example, the data collection unit can collect weather data using weather sensors. The data collection unit can also collect crop growth data using cameras and sensors to monitor crop growth. By collecting weather data and crop growth data, it is possible to provide information necessary for agricultural management. 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 acquired from weather sensors into a generating AI and have the generating AI perform analysis of the weather data.

[0075] The analysis unit can analyze collected data in real time. For example, the analysis unit can analyze collected weather data in real time and make weather forecasts. The analysis unit can also analyze collected crop growth data in real time and make crop growth forecasts. The analysis unit can analyze collected data in real time using AI, for example. For example, the analysis unit can input collected weather data into AI and have the AI ​​perform weather forecasts. The analysis unit can also input collected crop growth data into AI and have the AI ​​perform crop growth forecasts. This enables rapid decision-making by analyzing data in real time. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0076] The proposal unit can propose the optimal harvest time and sales timing based on the analysis results. For example, the proposal unit can propose the optimal harvest time based on the analysis results. The proposal unit can also propose the optimal sales timing based on the analysis results. For example, the proposal unit can use AI to make optimal suggestions based on the analysis results. For example, the proposal unit can input the analysis results into AI and have the AI ​​make suggestions for the optimal harvest time and sales timing. This improves profitability by proposing the optimal harvest time and sales timing. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0077] The management department can analyze inventory data and predict and notify when necessary materials should be purchased. For example, the management department can analyze inventory data and predict when necessary materials should be purchased. The management department can also analyze inventory data and notify when necessary materials should be purchased. For example, the management department can use AI to analyze inventory data and predict when necessary materials should be purchased. For example, the management department can input inventory data into AI and have the AI ​​predict when necessary materials should be purchased. This allows for the reduction of unnecessary costs by analyzing inventory data and predicting when materials should be purchased. Some or all of the above processes in the management department may be performed using AI, or without AI.

[0078] The automation unit can perform agricultural work automatically using robots. For example, the automation unit can automate crop harvesting using harvesting robots. The automation unit can also automate crop planting using planting robots. For example, the automation unit can control the robot's movements using AI. For example, the automation unit can input the movements of a harvesting robot into the AI ​​and have the AI ​​perform the automated harvesting work. The automation unit can also input the movements of a planting robot into the AI ​​and have the AI ​​perform the automated planting work. This solves the labor shortage by automating agricultural work using robots. Some or all of the above processes in the automation unit may be performed using AI, for example, or without using AI.

[0079] 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 collect more detailed data to provide more information. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data to provide information quickly. 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, for example, using 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can select the most efficient collection method based on past data collection history. The data collection unit can also analyze past data collection results and optimize collection frequency and timing. The data collection unit can also improve collection methods by referring to successful past data collection examples. In this way, the optimal collection method can be selected by analyzing past data. 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 past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0081] The data collection unit can filter data based on specific crops or regions during data collection. For example, the data collection unit can collect only data related to a specific crop and filter out other data. The data collection unit can also collect only data related to a specific region and filter out data from other regions. The data collection unit can also customize the types of data to collect depending on the type of crop or region. This allows for efficient collection of necessary information by filtering data based on specific crops or regions. 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 related to specific crops or regions into a generating AI and have the generating AI perform the filtering.

[0082] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit may prioritize collecting only important data. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit may 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 using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant weather data based on the user's current location. For example, the data collection unit can also prioritize the collection of region-specific crop growth data based on the user's farm location information. For example, the data collection unit can select the optimal collection point by considering the user's geographical location information. This allows for the efficient collection of highly relevant data by considering the user's geographical location information. 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 the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0084] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media posts and collect relevant agricultural data. The data collection unit can also collect data on crops or regions of interest from a user's social media activity. For example, the data collection unit can analyze posts from a user's social media followers and friends and collect relevant data. This allows for the efficient collection of relevant data by analyzing a user's social media activity. 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 user social media activity data into a generating AI and have the generating AI collect relevant data.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on other data. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit can apply multiple analysis methods to high-importance data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a weather forecasting algorithm to weather data and a growth forecasting algorithm to crop growth data. For example, the analysis unit can also apply a market forecasting algorithm to price data and an inventory management algorithm to inventory data. The analysis unit can also select the optimal analysis algorithm depending on the data category. By applying the optimal analysis algorithm according to the data category, the accuracy of the analysis is improved. 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 the data category into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise 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 excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis result. 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 the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0089] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data and postpone the analysis of older data. For example, the analysis unit may also perform rapid analysis on data where the collection timing is important. The analysis unit can also adjust the analysis schedule based on the data collection timing. This allows for the rapid provision of the latest information by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0090] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also optimize the order of analysis based on the relevance of the data. For example, the analysis unit can perform a more detailed analysis on highly relevant data. By adjusting the order of analysis based on the relevance of the data, efficient analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0091] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable 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 concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. 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 facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The proposal unit can adjust the level of detail of its proposals based on the importance of the analysis results. For example, it can provide detailed proposals for important analysis results and simplified proposals for other results. The proposal unit can also determine the priority of proposals based on the importance of the analysis results. For example, it can provide multiple proposals for high-importance analysis results. This allows for efficient proposals by adjusting the level of detail of proposals based on the importance of the analysis results. 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 importance of the analysis results into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0093] The proposal unit can apply different proposal algorithms depending on the category of the analysis results when making a proposal. For example, the proposal unit can apply a weather forecasting algorithm to weather analysis results and a growth forecasting algorithm to crop growth analysis results. The proposal unit can also apply a market forecasting algorithm to price analysis results and an inventory management algorithm to inventory analysis results. The proposal unit can also select the optimal proposal algorithm depending on the category of the analysis results. By applying the optimal proposal algorithm according to the category of the analysis results, the accuracy of the proposal is improved. 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 category of the analysis results into a generating AI and have the generating AI execute the application of the optimal proposal algorithm.

[0094] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is excited, the suggestion unit can also provide visually stimulating suggestions. By adjusting the length of suggestions according to the user's emotions, the system can provide the most suitable suggestions for the user. 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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The proposal unit can determine the priority of proposals based on the timing of analysis result collection when making a proposal. For example, the proposal unit may prioritize the latest analysis results and postpone older ones. For example, the proposal unit may also make proposals quickly for analysis results where the collection timing is important. The proposal unit may also adjust the proposal schedule based on the timing of analysis result collection. This allows for the rapid provision of the latest information by determining the priority of proposals based on the timing of analysis result collection. 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 may input the timing of analysis result collection into a generating AI and have the generating AI determine the priority of proposals.

[0096] The proposal unit can adjust the order of proposals based on the relevance of the analysis results during the proposal process. For example, the proposal unit can prioritize proposing highly relevant analysis results and postpone less relevant ones. The proposal unit can also optimize the order of proposals based on the relevance of the analysis results. For example, the proposal unit can provide detailed suggestions for highly relevant analysis results. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the analysis results. 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 relevance of the analysis results into a generating AI and have the generating AI adjust the order of proposals.

[0097] The management unit can estimate the user's emotions and adjust inventory management methods based on the estimated emotions. For example, if the user is stressed, the management unit can provide a simple inventory management method. For example, if the user is relaxed, the management unit can also provide a detailed inventory management method. For example, if the user is in a hurry, the management unit can also provide a method for quick inventory management. In this way, by adjusting inventory management methods according to the user's emotions, the optimal inventory management can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using 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, for example, or not using AI. For example, the management unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The management department can analyze past inventory data to select the optimal inventory management method during inventory management. For example, the management department can select the optimal inventory management method based on past inventory data. The management department can also analyze past inventory data to calculate the appropriate amount of inventory. For example, the management department can adjust the inventory management schedule by referring to past inventory data. In this way, the optimal inventory management method can be selected by analyzing past inventory data. 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 past inventory data into a generating AI and have the generating AI select the optimal inventory management method.

[0099] The management department can customize inventory management methods based on specific materials or regions. For example, the management department can provide optimal inventory management methods for specific materials. The management department can also customize inventory management methods based on specific regions. For example, the management department can adjust inventory management methods according to the type of material or region. This enables efficient inventory management by customizing inventory management methods based on specific materials or regions. 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 data on specific materials or regions into a generating AI and have the generating AI perform the customization of the management methods.

[0100] The management department can estimate the user's emotions and determine inventory management priorities based on the estimated emotions. For example, if the user is stressed, the management department can prioritize managing only critical inventory. If the user is relaxed, the management department can perform detailed inventory management. If the user is in a hurry, the management department can prioritize managing inventory that can be managed quickly. This allows for the prioritization of critical inventory by determining inventory 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 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 department may be performed using AI or not. For example, the management department can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The management department can select the optimal inventory management method by considering the user's geographical location information during inventory management. For example, the management department can provide the optimal inventory management method based on the user's current location. For example, the management department can also provide a region-specific inventory management method based on the user's farm location information. For example, the management department can adjust the inventory management schedule by considering the user's geographical location information. This allows for the provision of the optimal inventory management method by considering the user's geographical location information. 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 the user's geographical location information into a generating AI and have the generating AI select the optimal inventory management method.

[0102] The management department can analyze users' social media activity and propose inventory management methods during inventory management. For example, the management department can analyze users' social media posts and propose relevant inventory management methods. For example, the management department can also propose inventory management methods related to materials of interest based on users' social media activity. For example, the management department can analyze posts from users' social media followers and friends and propose relevant inventory management methods. This allows for the efficient proposal of relevant inventory management methods by analyzing users' social media activity. 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 user social media activity data into a generating AI and have the generating AI propose relevant inventory management methods.

[0103] The automation unit can estimate the user's emotions and adjust the automation method based on the estimated user emotions. For example, if the user is stressed, the automation unit can provide a simple automation method. For example, if the user is relaxed, the automation unit can also provide a more detailed automation method. For example, if the user is in a hurry, the automation unit can also provide a method that allows for rapid automation. This allows for optimal automation for the user by adjusting the automation method 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 automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The automation unit can analyze past work data to select the optimal automation method during automation. For example, the automation unit selects the optimal automation method based on past work data. The automation unit can also analyze past work data and adjust the automation schedule. For example, the automation unit can improve the automation means by referring to past work data. In this way, the optimal automation method can be selected by analyzing past work data. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input past work data into a generating AI and have the generating AI select the optimal automation method.

[0105] The automation unit can customize the automation means based on specific tasks or regions during automation. For example, the automation unit provides the optimal automation means for a specific task. The automation unit can also customize the automation means based on a specific region. For example, the automation unit can adjust the automation means according to the type of work or region. This enables efficient automation by customizing the automation means based on specific tasks or regions. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input data about specific tasks or regions into a generating AI and have the generating AI perform the customization of the automation means.

[0106] The automation unit can estimate the user's emotions and determine automation priorities based on the estimated emotions. For example, if the user is stressed, the automation unit will prioritize automating only important tasks. For example, if the user is relaxed, the automation unit can perform more detailed automation. For example, if the user is in a hurry, the automation unit can prioritize automating tasks that can be automated quickly. This allows for the prioritization of important tasks by determining automation 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-described processes in the automation unit may be performed using AI or not using AI. For example, the automation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0107] The automation unit can select the optimal automation method by considering the user's geographical location information during automation. For example, the automation unit can provide the optimal automation method based on the user's current location. For example, the automation unit can also provide a region-specific automation method based on the user's farm location information. For example, the automation unit can adjust the automation schedule by considering the user's geographical location information. This allows the system to provide the optimal automation method by considering the user's geographical location information. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal automation method.

[0108] The automation unit can analyze the user's social media activity and propose automation methods during automation. For example, the automation unit can analyze the user's social media posts and propose relevant automation methods. The automation unit can also propose automation methods related to tasks of interest based on the user's social media activity. For example, the automation unit can analyze posts from the user's social media followers and friends and propose relevant automation methods. This allows for the efficient proposal of relevant automation methods by analyzing the user's social media activity. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's social media activity data into a generating AI and have the generating AI execute suggestions for relevant automation methods.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] 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 frequency of data collection can be reduced to lessen the user's burden. Conversely, if the user is relaxed, more detailed data can be collected to provide more information. Furthermore, if the user is in a hurry, only important data can be prioritized and information can be provided quickly. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation can be achieved, for example, using an emotion engine or generative AI. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0111] The analysis unit can analyze collected data in real time. For example, it can analyze collected weather data in real time and make weather forecasts. It can also analyze collected crop growth data in real time and make crop growth forecasts. Furthermore, it can input collected data into an AI and have the AI ​​perform weather forecasts and crop growth forecasts. This enables rapid decision-making by analyzing data in real time. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0112] The proposal unit can suggest the optimal harvest time and sales timing based on the analysis results. For example, it can suggest the optimal harvest time based on the analysis results. It can also suggest the optimal sales timing based on the analysis results. Furthermore, the analysis results can be input into an AI, which can then make suggestions for the optimal harvest time and sales timing. This allows for improved profitability by suggesting the optimal harvest time and sales timing. Some or all of the above-described processes in the proposal unit may be performed using AI, or they may be performed without using AI.

[0113] The management department can analyze inventory data and predict and notify when necessary materials need to be purchased. For example, it can analyze inventory data and predict when necessary materials need to be purchased. It can also analyze inventory data and notify when necessary materials need to be purchased. Furthermore, inventory data can be input into AI, and the AI ​​can perform the prediction of when necessary materials need to be purchased. By analyzing inventory data and predicting when materials need to be purchased, unnecessary costs can be reduced. Some or all of the above processes in the management department may be performed using AI, or they may not be performed using AI.

[0114] The automation unit can perform agricultural tasks automatically using robots. For example, it can automate crop harvesting using harvesting robots. It can also automate crop planting using planting robots. Furthermore, it can control the robots' movements using AI. For example, the movements of a harvesting robot can be input into the AI, allowing the AI ​​to perform automated harvesting. Similarly, the movements of a planting robot can be input into the AI, allowing the AI ​​to perform automated planting. This automates agricultural work using robots, thereby alleviating labor shortages. Some or all of the above-mentioned processes in the automation unit may be performed using AI, or they may be performed without AI.

[0115] 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 frequency of data collection can be reduced to lessen the user's burden. Conversely, if the user is relaxed, more detailed data can be collected to provide more information. Furthermore, if the user is in a hurry, only important data can be prioritized and information can be provided quickly. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation can be achieved, for example, using an emotion engine or generative AI. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0116] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0117] The suggestion section can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, it can provide simple and highly visible suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions. By adjusting the presentation of suggestions according to the user's emotions, it is possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the processing described above in the suggestion section may be performed using AI or not.

[0118] The management department can estimate the user's emotions and adjust inventory management methods based on those emotions. For example, if the user is stressed, a simple inventory management method can be provided. If the user is relaxed, a more detailed inventory management method can be provided. Furthermore, if the user is in a hurry, a method for quick inventory management can be provided. In this way, by adjusting inventory management methods according to the user's emotions, the system can provide optimal inventory management for the user. Emotion estimation can be achieved, for example, using an emotion engine or generative AI. Some or all of the above processing in the management department may be performed using AI or not.

[0119] The automation unit can estimate the user's emotions and adjust the automation method based on the estimated emotions. For example, if the user is stressed, a simple automation method can be provided. If the user is relaxed, a more detailed automation method can be provided. Furthermore, if the user is in a hurry, a method that allows for rapid automation can be provided. In this way, by adjusting the automation method according to the user's emotions, the optimal automation for the user can be provided. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the above processing in the automation unit may be performed using AI or not using AI.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The data collection unit collects data. The data collection unit can collect, for example, weather data and crop growth data. Specifically, it collects weather data such as temperature, precipitation, and wind speed, and crop growth data such as crop growth rate, yield, and disease information. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected weather data and make weather forecasts. It can also analyze the collected crop growth data and make crop growth forecasts. Step 3: The proposal unit makes optimal suggestions based on the analysis results obtained by the analysis unit. For example, the proposal unit can suggest the optimal harvest time and sales timing based on the analysis results. Step 4: The management department handles material procurement and fundraising. For example, the management department can analyze inventory data to predict and notify when necessary materials need to be purchased. They can also raise funds quickly through cashless payments. Step 5: The automation unit automates the work. The automation unit can, for example, automate agricultural work using robots. Specifically, it can automate crop harvesting using harvesting robots, or automate crop planting using planting robots.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, management unit, and automation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects weather data and crop growth data using the camera 42 and sensors of the smart device 14. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 to make weather forecasts and crop growth forecasts. The proposal unit proposes the optimal harvest time and sales timing based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The management unit analyzes inventory data using, for example, the control unit 46A of the smart device 14 to predict and notify the timing of purchasing necessary materials. The automation unit controls robots using, for example, the control unit 46A of the smart device 14 to automate farm work. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, management unit, and automation 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 weather data and crop growth data using the camera 42 and sensors of the smart glasses 214. The analysis unit analyzes the collected data using, for example, the identification processing unit 290 of the data processing unit 12 to make weather forecasts and crop growth forecasts. The proposal unit proposes the optimal harvest time and sales timing based on the analysis results using, for example, the identification processing unit 290 of the data processing unit 12. The management unit analyzes inventory data using, for example, the control unit 46A of the smart glasses 214 to predict and notify the timing of purchasing necessary materials. The automation unit controls robots using, for example, the control unit 46A of the smart glasses 214 to automate farm work. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, management unit, and automation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects weather data and crop growth data using the camera 42 and sensors of the headset terminal 314. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 to make weather forecasts and crop growth forecasts. The proposal unit proposes the optimal harvest time and sales timing based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The management unit analyzes inventory data using, for example, the control unit 46A of the headset terminal 314 to predict and notify the timing of purchasing necessary materials. The automation unit controls robots using, for example, the control unit 46A of the headset terminal 314 to automate farm work. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, management unit, and automation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects weather data and crop growth data using the camera 42 and sensors of the robot 414. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 to make weather forecasts and crop growth forecasts. The proposal unit proposes the optimal harvest time and sales timing based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The management unit analyzes inventory data using, for example, the control unit 46A of the robot 414 to predict and notify the timing of purchasing necessary materials. The automation unit controls the robot using, for example, the control unit 46A of the robot 414 to automate farm work. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes an optimal proposal based on the analysis results obtained by the analysis unit, The management department is responsible for purchasing materials and securing funding, It includes an automation unit that automates the work. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect weather data and crop growth data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we propose the optimal harvest time and sales timing. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Analyze inventory data to predict and notify you of the timing for purchasing necessary materials. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned automation unit, Using robots to automate agricultural tasks 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 Analyze past collected data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on specific crops or regions. 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 During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. 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 the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. 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 adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the analysis results. 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 adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on when the analysis results were collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, It estimates user sentiment and adjusts inventory 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, When managing inventory, analyze past inventory data to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, When managing inventory, customize management methods based on specific materials or regions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, The system estimates user sentiment and prioritizes inventory management based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When managing inventory, the optimal management method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, When managing inventory, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned automation unit, It estimates the user's emotions and adjusts the automation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned automation unit, When automating processes, past work data is analyzed to select the optimal automation method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned automation unit, When automating, customize the automation methods based on specific tasks or regions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned automation unit, It estimates user emotions and determines automation priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned automation unit, When automating processes, the system selects the optimal automation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned automation unit, During automation, the system analyzes users' social media activity and proposes automation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 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, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes an optimal proposal based on the analysis results obtained by the analysis unit, The management department is responsible for purchasing materials and securing funding, It includes an automation unit that automates the work. A system characterized by the following features.

2. The aforementioned collection unit is Collect weather data and crop growth data. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected data in real time. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we propose the optimal harvest time and sales timing. The system according to feature 1.

5. The aforementioned management department, Analyze inventory data to predict and notify you of the timing for purchasing necessary materials. The system according to feature 1.

6. The aforementioned automation unit, Using robots to automate agricultural tasks 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 Analyze past collected data and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on specific crops or regions. 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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