system

Abstract

The system according to this embodiment aims to provide a personalized agricultural strategy based on the user's specific circumstances and experiences. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's situation. The analysis unit analyzes the data collected by the collection unit. The generation unit generates an agricultural strategy based on the analysis results obtained by the analysis unit. The provision unit provides the agricultural strategy generated by the generation unit to the user.

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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a personalized agricultural strategy based on a user's specific situation or experience has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized agricultural strategy based on a user's specific situation or experience.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a provision unit. The data collection unit collects the user's information. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates an agricultural strategy based on the analysis results obtained by the analysis unit. The provision unit provides the agricultural strategy generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can provide a personalized agricultural strategy based on the user's specific circumstances and experiences. [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 labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F manages communications among a plurality of computers. Examples of communication standards applicable 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 expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] The smart device 14 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 strategy generation system according to an embodiment of the present invention is a system that utilizes generation AI to generate personalized agricultural strategies tailored to each user's specific circumstances and accumulated experience. This agricultural strategy generation system generates personalized agricultural strategies based on the user's specific circumstances and accumulated experience. Next, it introduces a dialogue system to provide quick answers to specific problems that the user faces in actual crop management. Furthermore, it integrates disaster risk prediction and advises on optimal countermeasures under specific circumstances. It also generates suggestions to adjust fertilizer and irrigation schedules based on desired crop quality. This promotes more intuitive and effective agricultural management. Thus, the agricultural strategy generation system can generate and provide personalized agricultural strategies based on the user's circumstances.

[0029] The agricultural strategy generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user information. User information includes, but is not limited to, weather, soil conditions, and crop growth stages. The collection unit collects data using, for example, sensors. The collection unit can also collect data manually entered by the user. For example, the collection unit measures soil moisture and nutrient status using a soil sensor. The collection unit can also measure temperature and precipitation using a weather sensor. The collection unit can also collect crop growth status entered by the user through a smartphone application. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, a data analysis algorithm. The analysis unit can also integrate and analyze multiple data sources to improve data accuracy. For example, the analysis unit integrates and analyzes soil data and weather data. The analysis unit can also analyze crop growth data and identify growth patterns. The analysis unit can also detect outliers in the data and perform filtering to improve data quality. The generation unit generates agricultural strategies based on the analysis results obtained by the analysis unit. The generation unit generates agricultural strategies using, for example, generation AI. The generation unit can propose optimal agricultural strategies based on the user's specific circumstances and accumulated experience. For example, the generation unit proposes an optimal fertilizer usage plan based on historical data. The generation unit can also generate suggestions for adjusting irrigation schedules. The generation unit can also generate suggestions for pest and disease control. The delivery unit provides the agricultural strategies generated by the generation unit to the user. The delivery unit provides agricultural strategies by, for example, adjusting notification methods and delivery timing. The delivery unit can also provide agricultural strategies in an optimal display method depending on the user's device. For example, the delivery unit notifies users of agricultural strategies via a smartphone app. The delivery unit can also send agricultural strategies via email. The delivery unit can also display agricultural strategies via a web application. Thus, the agricultural strategy generation system according to the embodiment can generate and provide personalized agricultural strategies based on the user's circumstances.

[0030] The data collection unit collects user information. This information includes, but is not limited to, weather, soil conditions, and crop growth stages. The unit collects data using sensors, for example. Specifically, soil sensors measure soil moisture, temperature, pH, and nutrient content, collecting this data in real time. Weather sensors collect meteorological data such as temperature, humidity, precipitation, wind speed, and sunshine duration. These sensors are installed in farmland, periodically collecting data and transmitting it to a central database. Furthermore, the data collection unit can also collect data manually entered by users. For example, users can use a smartphone app to input crop growth status and pest / disease occurrences. This allows the data collection unit to integrate automated data from sensors with manual data from users to collect more comprehensive information. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the analysis and generation units. Additionally, adjusting the frequency and accuracy of data collection allows for flexible responses 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 uses data analysis algorithms to analyze the data. Specifically, it integrates and analyzes soil data and weather data to identify optimal conditions for crop growth. For instance, it analyzes the relationship between soil moisture and temperature to propose an optimal irrigation schedule. It can also analyze crop growth data to identify growth patterns, enabling the development of optimal fertilizer usage plans according to the crop's growth stage. Furthermore, the analysis unit can detect data anomalies and perform filtering to improve data quality. For example, it can detect sensor failures and data errors, using only accurate data for analysis. The analysis unit can also utilize historical data and statistical information to assess long-term trends and risks. For example, it can predict future weather conditions based on historical weather data and identify factors affecting crop growth. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only provide real-time situational awareness but also handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The generation unit generates agricultural strategies based on the analysis results obtained by the analysis unit. The generation unit generates agricultural strategies using, for example, generation AI. Specifically, the generation AI proposes optimal agricultural strategies based on historical data and the user's specific circumstances. For example, the generation unit proposes an optimal fertilizer usage plan based on historical data. The generation unit can also generate suggestions for adjusting irrigation schedules. It can also generate suggestions for pest and disease control. The generation AI uses machine learning algorithms to learn from past successes and failures and generate optimal strategies. For example, it proposes the optimal type and amount of fertilizer for a specific crop based on historical data. Furthermore, the generation AI can continuously modify strategies based on real-time updated data to respond to the latest situations. For example, it can quickly adjust irrigation schedules and pest and disease control measures in response to sudden weather changes or pest outbreaks. In addition, the generation unit can collect user feedback and continuously improve the accuracy and effectiveness of the generated strategies. This allows the generation unit to generate and provide personalized agricultural strategies based on the user's circumstances.

[0033] The provider unit provides users with agricultural strategies generated by the generation unit. The provider unit provides agricultural strategies by, for example, adjusting the notification method and timing of delivery. Specifically, the provider unit provides agricultural strategies in the most optimal display method depending on the user's device. For example, the provider unit notifies users of agricultural strategies through a smartphone app. The smartphone app provides notifications at the optimal time based on the user's current situation and location information. For example, it notifies users of irrigation timing and fertilizer application timing. The provider unit can also send agricultural strategies via email. The email can include detailed strategy information and reference materials and is provided in a format that is easy for the user to review later. Furthermore, the provider unit can display agricultural strategies through a web application. The web application provides an easy-to-access interface for users and can display detailed data and graphs. This makes it easier for users to visually understand the generated agricultural strategies. The provider unit can collect user feedback and continuously improve the accuracy of the delivery method and content. For example, it can adjust the timing and content of notifications based on user reactions and actions. This allows the provider unit to provide agricultural strategies to users quickly and reliably, maximizing the effectiveness of the agricultural strategy generation system according to the embodiment.

[0034] The agricultural strategy generation system includes a prediction unit that predicts disaster risks. The prediction unit predicts disaster risks. Disaster risks include, but are not limited to, flood risks and drought risks. The prediction unit predicts disaster risks based on, for example, past disaster data. The prediction unit can also predict disaster risks by analyzing meteorological data. The prediction unit can also predict disaster risks by considering geographical information. For example, the prediction unit predicts flood risks based on past flood data. The prediction unit can also predict drought risks by analyzing meteorological data. The prediction unit can also predict disaster risks for specific regions by considering geographical information. This makes it possible to predict disaster risks and propose optimal countermeasures under specific circumstances. Some or all of the above processing in the prediction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the prediction unit can input past disaster data into a generation AI and have the generation AI perform disaster risk predictions.

[0035] The agricultural strategy generation system includes a dialogue unit that provides rapid answers to user questions. Rapid answers include, but are not limited to, response time and processing speed. For example, if a user asks, "What fertilizer is suitable for this time of year?", the dialogue unit will suggest the optimal fertilizer based on historical data and the results of analysis by the generating AI. The dialogue unit can also generate answers to user questions using the generating AI. The dialogue unit can also generate answers to user questions based on historical data. For example, if a user asks, "What fertilizer is suitable for this time of year?", the generating AI will analyze historical data and suggest the optimal fertilizer. The dialogue unit can also provide rapid answers to user questions based on historical data. This allows the system to provide answers that address specific problems users face in actual crop management. Some or all of the above-described processes in the dialogue unit may be performed using the generating AI or not. For example, the dialogue unit can input a user question into the generating AI and have the generating AI generate the answer.

[0036] The generation unit generates suggestions to adjust fertilizer and irrigation schedules based on the desired crop quality. For example, if a user wants to grow high-quality tomatoes, the generation unit analyzes past data and proposes the optimal fertilizer and irrigation schedule. The generation unit can generate suggestions to adjust the type and amount of fertilizer and the frequency and amount of irrigation based on the user's desired crop quality. For example, the generation unit proposes the optimal fertilizer usage plan based on past data. The generation unit can also generate suggestions to adjust the irrigation schedule. The generation unit can also generate suggestions to improve crop quality. For example, if a user wants to grow high-quality tomatoes, the generation AI analyzes past data and proposes the optimal fertilizer and irrigation schedule. The generation unit can also generate suggestions to adjust the type and amount of fertilizer and the frequency and amount of irrigation based on the user's desired crop quality. This allows for the proposal of the optimal fertilizer and irrigation schedule to achieve the desired crop quality. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can have the generation AI generate suggestions for fertilizers and irrigation schedules based on the user's desired crop quality.

[0037] The data collection unit analyzes the user's past agricultural activity history and selects the optimal data collection method. For example, the data collection unit proposes the optimal data collection method based on the fertilizers and irrigation methods the user has used in the past. The data collection unit can also analyze the user's past harvest yield data and determine the types of data to collect. The data collection unit can also adjust the timing of data collection based on the frequency of the user's past agricultural activities. For example, the data collection unit proposes the optimal data collection method based on the fertilizers and irrigation methods the user has used in the past. The data collection unit can also analyze the user's past harvest yield data and determine the types of data to collect. The data collection unit can also adjust the timing of data collection based on the frequency of the user's past agricultural activities. This allows the optimal data collection method to be selected based on the user's past agricultural activity history. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's past agricultural activity history into a generative AI and have the generative AI select the optimal data collection method.

[0038] The data collection unit filters data based on the user's current farming situation and areas of interest. For example, the data collection unit prioritizes collecting data related to the crops the user is currently growing. The data collection unit can also collect data related to agricultural technologies the user is interested in. The data collection unit can also collect relevant data based on the user's current farming situation (e.g., weather, soil conditions). For example, the data collection unit prioritizes collecting data related to the crops the user is currently growing. The data collection unit can also collect data related to agricultural technologies the user is interested in. The data collection unit can also collect relevant data based on the user's current farming situation (e.g., weather, soil conditions). This allows for the priority collection of relevant data based on the user's current farming situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input the user's current farming situation and areas of interest into a generative AI and have the generative AI perform data filtering.

[0039] The data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location. For example, the data collection unit prioritizes the collection of local weather data based on the user's farm location. The data collection unit can also collect region-specific pest and disease information based on the user's geographical location. The data collection unit can also collect nearby agricultural activity data, taking into account the user's location. For example, the data collection unit prioritizes the collection of local weather data based on the user's farm location. The data collection unit can also collect region-specific pest and disease information based on the user's geographical location. The data collection unit can also collect nearby agricultural activity data, taking into account the user's location. This allows for the priority collection of highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the user's geographical location information into a generation AI and have the generation AI collect highly relevant data.

[0040] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit collects relevant data based on agriculture-related posts shared by the user on social media. The data collection unit can also analyze posts from agricultural experts followed by the user and collect relevant data. The data collection unit can also identify agricultural technologies and trends of interest from the user's social media activity and collect data. For example, the data collection unit collects relevant data based on agriculture-related posts shared by the user on social media. The data collection unit can also analyze posts from agricultural experts followed by the user and collect relevant data. The data collection unit can also identify agricultural technologies and trends of interest from the user's social media activity and collect data. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input the user's social media activity into a generative AI and have the generative AI collect the relevant data.

[0041] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for detailed analysis of important data by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance of the data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a weather forecasting algorithm to weather data. The analysis unit can also apply a soil analysis algorithm to soil data. The analysis unit can also apply a crop growth prediction algorithm to crop data. This allows the accuracy of the analysis to be improved by applying the most suitable analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the data category into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0043] The analysis unit determines the priority of analysis based on the data collection timing during analysis. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also prioritize the most recent data while referring to past data. The analysis unit can also adjust the order of analysis according to the data collection timing. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also prioritize the most recent data while referring to past data. The analysis unit can also adjust the order of analysis according to the data collection timing. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the data collection timing into the generation AI and have the generation AI determine the priority of analysis.

[0044] The analysis unit adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also optimize the order of analysis according to the relevance of the data. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also optimize the order of analysis according to the relevance of the data. This allows the analysis unit to prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0045] The generation unit adjusts the level of detail of agricultural strategies based on the importance of the data when generating them. For example, the generation unit generates detailed agricultural strategies based on high-importance data. The generation unit can also generate simplified agricultural strategies based on low-importance data. The generation unit can also determine the priority of strategies according to the importance of the data. For example, the generation unit generates detailed agricultural strategies based on high-importance data. The generation unit can also generate simplified agricultural strategies based on low-importance data. The generation unit can also determine the priority of strategies according to the importance of the data. This allows for the generation of detailed agricultural strategies based on important data by adjusting the level of detail of strategies based on the importance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the importance of the data into the generation AI and have the generation AI perform the adjustment of the level of detail of the strategies.

[0046] The generation unit applies different generation algorithms depending on the data category when generating agricultural strategies. For example, the generation unit can generate agricultural strategies by applying a weather forecasting algorithm to weather data. The generation unit can also generate agricultural strategies by applying a soil analysis algorithm to soil data. The generation unit can also generate agricultural strategies by applying a crop growth prediction algorithm to crop data. This allows for improved accuracy of agricultural strategies by applying the most suitable generation algorithm according to the data category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the data category into the generation AI and have the generation AI perform the application of the generation algorithm.

[0047] The generation unit determines the priority of agricultural strategies based on the data collection timing when generating agricultural strategies. For example, the generation unit generates agricultural strategies based on the latest data. The generation unit can also prioritize the latest data while referring to past data. The generation unit can also adjust the order of strategies according to the data collection timing. For example, the generation unit generates agricultural strategies based on the latest data. The generation unit can also prioritize the latest data while referring to past data. The generation unit can also adjust the order of strategies according to the data collection timing. This makes it possible to generate agricultural strategies based on the latest data by determining the priority of strategies based on the data collection timing. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the data collection timing into the generation AI and have the generation AI perform the determination of strategy priorities.

[0048] The generation unit adjusts the order of strategies based on the relevance of the data when generating agricultural strategies. For example, the generation unit generates agricultural strategies based on highly relevant data. The generation unit can also postpone processing less relevant data. The generation unit can also optimize the order of strategies according to the relevance of the data. For example, the generation unit generates agricultural strategies based on highly relevant data. The generation unit can also postpone processing less relevant data. The generation unit can also optimize the order of strategies according to the relevance of the data. This allows for the generation of agricultural strategies based on highly relevant data by adjusting the order of strategies based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the relevance of the data into a generation AI and have the generation AI perform the adjustment of the order of strategies.

[0049] The service provider selects the optimal delivery method when providing agricultural strategies by referring to the user's past response history. For example, the service provider provides agricultural strategies in the most optimal way based on the delivery methods the user has preferred in the past. The service provider can also analyze the user's past response history and select the most effective delivery method. The service provider can also customize the delivery method based on the user's past response history. For example, the service provider provides agricultural strategies in the most optimal way based on the delivery methods the user has preferred in the past. The service provider can also analyze the user's past response history and select the most effective delivery method. The service provider can also customize the delivery method based on the user's past response history. This allows the service provider to provide agricultural strategies in an effective way for the user by selecting the optimal delivery method based on the user's past response history. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input the user's past response history into a generative AI and have the generative AI select the optimal delivery method.

[0050] The service provider selects the optimal delivery method when providing agricultural strategies, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide the agricultural strategies using a display method adapted to the screen size. If the user is using a tablet, the service provider can also provide the agricultural strategies using a display method optimized for larger screens. If the user is using a personal computer, the service provider can also provide the agricultural strategies using a display method that includes detailed information. By selecting the optimal delivery method based on the user's device information, the service provider can provide agricultural strategies in a way that is easy for the user to understand. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's device information into a generative AI and have the generative AI select the optimal delivery method.

[0051] The prediction unit optimizes its prediction algorithm by referring to past disaster data when predicting disaster risk. For example, the prediction unit optimizes its algorithm for predicting typhoon risk based on past typhoon data. The prediction unit can also optimize its algorithm for predicting drought risk based on past drought data. The prediction unit can also optimize its algorithm for predicting flood risk based on past flood data. For example, the prediction unit optimizes its algorithm for predicting typhoon risk based on past typhoon data. The prediction unit can also optimize its algorithm for predicting drought risk based on past drought data. The prediction unit can also optimize its algorithm for predicting flood risk based on past flood data. In this way, the accuracy of disaster risk prediction can be improved by optimizing the prediction algorithm based on past disaster data. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the prediction unit can input past disaster data into a generative AI and have the generative AI perform the optimization of the prediction algorithm.

[0052] The prediction unit selects the optimal prediction method when predicting disaster risk, taking into account the user's geographical location information. For example, the prediction unit predicts regional disaster risk based on the location information of the user's farm. The prediction unit can also predict region-specific disaster risk based on the user's geographical location. The prediction unit can also predict nearby disaster risk, taking into account the user's location information. For example, the prediction unit predicts regional disaster risk based on the location information of the user's farm. The prediction unit can also predict region-specific disaster risk based on the user's geographical location. The prediction unit can also predict nearby disaster risk, taking into account the user's location information. This makes it possible to predict region-specific disaster risk by selecting the optimal prediction method based on the user's geographical location information. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the prediction unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal prediction method.

[0053] The dialogue unit provides the most appropriate answer during the conversation by referring to the user's past question history. For example, the dialogue unit provides relevant answers based on the content of questions the user has asked in the past. The dialogue unit can also analyze the user's past question history and provide the most effective answer. The dialogue unit can also customize answers based on the user's past question history. For example, the dialogue unit provides relevant answers based on the content of questions the user has asked in the past. The dialogue unit can also analyze the user's past question history and provide the most effective answer. The dialogue unit can also customize answers based on the user's past question history. This allows the dialogue unit to provide the most appropriate answer based on the user's past question history, thereby providing an effective answer for the user. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input the user's past question history into a generative AI and have the generative AI perform the task of providing the most appropriate answer.

[0054] The dialogue unit provides the optimal answer during the conversation, taking into account the user's device information. For example, if the user is using a smartphone, the dialogue unit provides the answer in a display format that matches the screen size. If the user is using a tablet, the dialogue unit can also provide the answer in a display format optimized for a larger screen. If the user is using a PC, the dialogue unit can also provide the answer in a display format that includes detailed information. By providing the optimal answer based on the user's device information, the answer can be provided in a way that is easy for the user to understand. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input the user's device information into a generative AI and have the generative AI perform the task of providing the optimal answer.

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

[0056] The agricultural strategy generation system can also include a health management unit that monitors the user's health status. This unit monitors the user's health and makes suggestions to reduce the burden of agricultural work. For example, it can monitor the user's heart rate and blood pressure and suggest breaks if excessive strain is present. It can also analyze the user's sleep data and suggest appropriate work times. Furthermore, it can collect the user's dietary data and suggest nutritionally balanced meals. This allows for the reduction of agricultural work burden and support of efficient work based on the user's health status.

[0057] The agricultural strategy generation system can also include a communication support section to assist user communication. This section provides functions to facilitate information sharing among users with other farmers and experts. For example, it can provide an online forum where users can post questions and inquiries. It can also provide a function for users to chat with experts in real time. Furthermore, it can provide collaboration tools for users to work on projects together with other farmers. This allows users to effectively communicate with other farmers and experts and share knowledge and experience.

[0058] The agricultural strategy generation system can also include a learning support section to assist user learning. This section provides resources for users to acquire knowledge and skills related to agriculture. For example, it can provide a platform where users can take online courses on agriculture. It can also provide a database where users can access the latest research and technical information on agriculture. Furthermore, it can provide practical guides and tutorials for users to learn through actual agricultural work. This allows users to effectively acquire knowledge and skills related to agriculture and improve the quality of their agricultural work.

[0059] The agricultural strategy generation system can also include a safety management unit to ensure user safety. This unit provides suggestions for users to perform agricultural work safely. For example, it can suggest that users use appropriate protective equipment when performing hazardous tasks. It can also notify users of points to be aware of during work. Furthermore, it can provide emergency contact information and first aid procedures to enable users to respond quickly in emergencies. This allows users to perform agricultural work safely and prevent accidents and injuries.

[0060] The agricultural strategy generation system may also include an energy management unit that monitors the user's energy consumption. The energy management unit monitors the user's energy consumption and suggests efficient energy use. For example, the energy management unit can suggest ways for the user to save energy. It can also suggest schedules for the user to use energy efficiently. Furthermore, the energy management unit can suggest equipment and technologies to minimize energy consumption. This allows the user to use energy efficiently and reduce the cost of agricultural operations.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects user information. This information includes weather, soil conditions, and crop growth stage. The data collection unit collects data using sensors, as well as data manually entered by the user. For example, it uses soil sensors to measure soil moisture and nutrient levels, and weather sensors to measure temperature and precipitation. It also collects crop growth information entered by the user via a smartphone app. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using data analysis algorithms and can also integrate and analyze multiple data sources. For example, it can integrate and analyze soil data and weather data, and analyze crop growth data to identify growth patterns. It can also detect outliers in the data and perform filtering to improve data quality. Step 3: The generation unit generates agricultural strategies based on the analysis results obtained by the analysis unit. The generation unit generates agricultural strategies using generation AI and proposes the optimal agricultural strategy based on the user's specific situation and accumulated experience. For example, it proposes an optimal fertilizer usage plan based on past data, and generates suggestions for adjusting irrigation schedules and pest and disease control measures. Step 4: The provider unit provides the agricultural strategy generated by the generator unit to the user. The provider unit provides the agricultural strategy by adjusting the notification method and timing, and provides the agricultural strategy in the most optimal display method depending on the user's device. For example, the agricultural strategy may be notified via a smartphone app, sent via email, or displayed via a web application.

[0063] (Example of form 2) The agricultural strategy generation system according to an embodiment of the present invention is a system that utilizes generation AI to generate personalized agricultural strategies tailored to each user's specific circumstances and accumulated experience. This agricultural strategy generation system generates personalized agricultural strategies based on the user's specific circumstances and accumulated experience. Next, it introduces a dialogue system to provide quick answers to specific problems that the user faces in actual crop management. Furthermore, it integrates disaster risk prediction and advises on optimal countermeasures under specific circumstances. It also generates suggestions to adjust fertilizer and irrigation schedules based on desired crop quality. This promotes more intuitive and effective agricultural management. Thus, the agricultural strategy generation system can generate and provide personalized agricultural strategies based on the user's circumstances.

[0064] The agricultural strategy generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user information. User information includes, but is not limited to, weather, soil conditions, and crop growth stages. The collection unit collects data using, for example, sensors. The collection unit can also collect data manually entered by the user. For example, the collection unit measures soil moisture and nutrient status using a soil sensor. The collection unit can also measure temperature and precipitation using a weather sensor. The collection unit can also collect crop growth status entered by the user through a smartphone application. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, a data analysis algorithm. The analysis unit can also integrate and analyze multiple data sources to improve data accuracy. For example, the analysis unit integrates and analyzes soil data and weather data. The analysis unit can also analyze crop growth data and identify growth patterns. The analysis unit can also detect outliers in the data and perform filtering to improve data quality. The generation unit generates agricultural strategies based on the analysis results obtained by the analysis unit. The generation unit generates agricultural strategies using, for example, generation AI. The generation unit can propose optimal agricultural strategies based on the user's specific circumstances and accumulated experience. For example, the generation unit proposes an optimal fertilizer usage plan based on historical data. The generation unit can also generate suggestions for adjusting irrigation schedules. The generation unit can also generate suggestions for pest and disease control. The delivery unit provides the agricultural strategies generated by the generation unit to the user. The delivery unit provides agricultural strategies by, for example, adjusting notification methods and delivery timing. The delivery unit can also provide agricultural strategies in an optimal display method depending on the user's device. For example, the delivery unit notifies users of agricultural strategies via a smartphone app. The delivery unit can also send agricultural strategies via email. The delivery unit can also display agricultural strategies via a web application. Thus, the agricultural strategy generation system according to the embodiment can generate and provide personalized agricultural strategies based on the user's circumstances.

[0065] The data collection unit collects user information. This information includes, but is not limited to, weather, soil conditions, and crop growth stages. The unit collects data using sensors, for example. Specifically, soil sensors measure soil moisture, temperature, pH, and nutrient content, collecting this data in real time. Weather sensors collect meteorological data such as temperature, humidity, precipitation, wind speed, and sunshine duration. These sensors are installed in farmland, periodically collecting data and transmitting it to a central database. Furthermore, the data collection unit can also collect data manually entered by users. For example, users can use a smartphone app to input crop growth status and pest / disease occurrences. This allows the data collection unit to integrate automated data from sensors with manual data from users to collect more comprehensive information. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the analysis and generation units. Additionally, adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0066] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses data analysis algorithms to analyze the data. Specifically, it integrates and analyzes soil data and weather data to identify optimal conditions for crop growth. For instance, it analyzes the relationship between soil moisture and temperature to propose an optimal irrigation schedule. It can also analyze crop growth data to identify growth patterns, enabling the development of optimal fertilizer usage plans according to the crop's growth stage. Furthermore, the analysis unit can detect data anomalies and perform filtering to improve data quality. For example, it can detect sensor failures and data errors, using only accurate data for analysis. The analysis unit can also utilize historical data and statistical information to assess long-term trends and risks. For example, it can predict future weather conditions based on historical weather data and identify factors affecting crop growth. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only provide real-time situational awareness but also handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0067] The generation unit generates agricultural strategies based on the analysis results obtained by the analysis unit. The generation unit generates agricultural strategies using, for example, generation AI. Specifically, the generation AI proposes optimal agricultural strategies based on historical data and the user's specific circumstances. For example, the generation unit proposes an optimal fertilizer usage plan based on historical data. The generation unit can also generate suggestions for adjusting irrigation schedules. It can also generate suggestions for pest and disease control. The generation AI uses machine learning algorithms to learn from past successes and failures and generate optimal strategies. For example, it proposes the optimal type and amount of fertilizer for a specific crop based on historical data. Furthermore, the generation AI can continuously modify strategies based on real-time updated data to respond to the latest situations. For example, it can quickly adjust irrigation schedules and pest and disease control measures in response to sudden weather changes or pest outbreaks. In addition, the generation unit can collect user feedback and continuously improve the accuracy and effectiveness of the generated strategies. This allows the generation unit to generate and provide personalized agricultural strategies based on the user's circumstances.

[0068] The provider unit provides users with agricultural strategies generated by the generation unit. The provider unit provides agricultural strategies by, for example, adjusting the notification method and timing of delivery. Specifically, the provider unit provides agricultural strategies in the most optimal display method depending on the user's device. For example, the provider unit notifies users of agricultural strategies through a smartphone app. The smartphone app provides notifications at the optimal time based on the user's current situation and location information. For example, it notifies users of irrigation timing and fertilizer application timing. The provider unit can also send agricultural strategies via email. The email can include detailed strategy information and reference materials and is provided in a format that is easy for the user to review later. Furthermore, the provider unit can display agricultural strategies through a web application. The web application provides an easy-to-access interface for users and can display detailed data and graphs. This makes it easier for users to visually understand the generated agricultural strategies. The provider unit can collect user feedback and continuously improve the accuracy of the delivery method and content. For example, it can adjust the timing and content of notifications based on user reactions and actions. This allows the provider unit to provide agricultural strategies to users quickly and reliably, maximizing the effectiveness of the agricultural strategy generation system according to the embodiment.

[0069] The agricultural strategy generation system includes a prediction unit that predicts disaster risks. The prediction unit predicts disaster risks. Disaster risks include, but are not limited to, flood risks and drought risks. The prediction unit predicts disaster risks based on, for example, past disaster data. The prediction unit can also predict disaster risks by analyzing meteorological data. The prediction unit can also predict disaster risks by considering geographical information. For example, the prediction unit predicts flood risks based on past flood data. The prediction unit can also predict drought risks by analyzing meteorological data. The prediction unit can also predict disaster risks for specific regions by considering geographical information. This makes it possible to predict disaster risks and propose optimal countermeasures under specific circumstances. Some or all of the above processing in the prediction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the prediction unit can input past disaster data into a generation AI and have the generation AI perform disaster risk predictions.

[0070] The agricultural strategy generation system includes a dialogue unit that provides rapid answers to user questions. Rapid answers include, but are not limited to, response time and processing speed. For example, if a user asks, "What fertilizer is suitable for this time of year?", the dialogue unit will suggest the optimal fertilizer based on historical data and the results of analysis by the generating AI. The dialogue unit can also generate answers to user questions using the generating AI. The dialogue unit can also generate answers to user questions based on historical data. For example, if a user asks, "What fertilizer is suitable for this time of year?", the generating AI will analyze historical data and suggest the optimal fertilizer. The dialogue unit can also provide rapid answers to user questions based on historical data. This allows the system to provide answers that address specific problems users face in actual crop management. Some or all of the above-described processes in the dialogue unit may be performed using the generating AI or not. For example, the dialogue unit can input a user question into the generating AI and have the generating AI generate the answer.

[0071] The generation unit generates suggestions to adjust fertilizer and irrigation schedules based on the desired crop quality. For example, if a user wants to grow high-quality tomatoes, the generation unit analyzes past data and proposes the optimal fertilizer and irrigation schedule. The generation unit can generate suggestions to adjust the type and amount of fertilizer and the frequency and amount of irrigation based on the user's desired crop quality. For example, the generation unit proposes the optimal fertilizer usage plan based on past data. The generation unit can also generate suggestions to adjust the irrigation schedule. The generation unit can also generate suggestions to improve crop quality. For example, if a user wants to grow high-quality tomatoes, the generation AI analyzes past data and proposes the optimal fertilizer and irrigation schedule. The generation unit can also generate suggestions to adjust the type and amount of fertilizer and the frequency and amount of irrigation based on the user's desired crop quality. This allows for the proposal of the optimal fertilizer and irrigation schedule to achieve the desired crop quality. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can have the generation AI generate suggestions for fertilizers and irrigation schedules based on the user's desired crop quality.

[0072] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of data collection to lessen the user's burden. If the user is relaxed, the data collection unit can also collect detailed data to obtain more accurate information. If the user is in a hurry, the data collection unit can collect only the minimum necessary data to provide information quickly. For example, if the user is stressed, the data collection unit reduces the frequency of data collection to lessen the user's burden. If the user is relaxed, the data collection unit can also collect detailed data to obtain more accurate information. If the user is in a hurry, the data collection unit can also collect only the minimum necessary 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, such as an emotion engine or generative AI. Generative AI includes, 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 data collection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The data collection unit analyzes the user's past agricultural activity history and selects the optimal data collection method. For example, the data collection unit proposes the optimal data collection method based on the fertilizers and irrigation methods the user has used in the past. The data collection unit can also analyze the user's past harvest yield data and determine the types of data to collect. The data collection unit can also adjust the timing of data collection based on the frequency of the user's past agricultural activities. For example, the data collection unit proposes the optimal data collection method based on the fertilizers and irrigation methods the user has used in the past. The data collection unit can also analyze the user's past harvest yield data and determine the types of data to collect. The data collection unit can also adjust the timing of data collection based on the frequency of the user's past agricultural activities. This allows the optimal data collection method to be selected based on the user's past agricultural activity history. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's past agricultural activity history into a generative AI and have the generative AI select the optimal data collection method.

[0074] The data collection unit filters data based on the user's current farming situation and areas of interest. For example, the data collection unit prioritizes collecting data related to the crops the user is currently growing. The data collection unit can also collect data related to agricultural technologies the user is interested in. The data collection unit can also collect relevant data based on the user's current farming situation (e.g., weather, soil conditions). For example, the data collection unit prioritizes collecting data related to the crops the user is currently growing. The data collection unit can also collect data related to agricultural technologies the user is interested in. The data collection unit can also collect relevant data based on the user's current farming situation (e.g., weather, soil conditions). This allows for the priority collection of relevant data based on the user's current farming situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input the user's current farming situation and areas of interest into a generative AI and have the generative AI perform data filtering.

[0075] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes collecting only high-priority data. If the user is relaxed, the data collection unit may also prioritize collecting detailed data. If the user is in a hurry, the data collection unit may also prioritize data that can be collected quickly. For example, if the user is stressed, the data collection unit prioritizes collecting only high-priority data. If the user is relaxed, the data collection unit may also prioritize collecting detailed data. If the user is in a hurry, the data collection unit may also prioritize data that can be collected quickly. This allows for the priority collection of important data by determining the priority of data to collect 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using or without generative AI. For example, the data collection unit can input user emotion data into a generating AI, allowing the generating AI to perform emotion estimation.

[0076] The data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location. For example, the data collection unit prioritizes the collection of local weather data based on the user's farm location. The data collection unit can also collect region-specific pest and disease information based on the user's geographical location. The data collection unit can also collect nearby agricultural activity data, taking into account the user's location. For example, the data collection unit prioritizes the collection of local weather data based on the user's farm location. The data collection unit can also collect region-specific pest and disease information based on the user's geographical location. The data collection unit can also collect nearby agricultural activity data, taking into account the user's location. This allows for the priority collection of highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the user's geographical location information into a generation AI and have the generation AI collect highly relevant data.

[0077] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit collects relevant data based on agriculture-related posts shared by the user on social media. The data collection unit can also analyze posts from agricultural experts followed by the user and collect relevant data. The data collection unit can also identify agricultural technologies and trends of interest from the user's social media activity and collect data. For example, the data collection unit collects relevant data based on agriculture-related posts shared by the user on social media. The data collection unit can also analyze posts from agricultural experts followed by the user and collect relevant data. The data collection unit can also identify agricultural technologies and trends of interest from the user's social media activity and collect data. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input the user's social media activity into a generative AI and have the generative AI collect the relevant data.

[0078] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides simple and visually easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. For example, if the user is stressed, the analysis unit provides simple and visually easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. 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 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-described processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0079] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for detailed analysis of important data by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance of the data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0080] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a weather forecasting algorithm to weather data. The analysis unit can also apply a soil analysis algorithm to soil data. The analysis unit can also apply a crop growth prediction algorithm to crop data. This allows the accuracy of the analysis to be improved by applying the most suitable analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the data category into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0081] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also summarize the analysis result concisely for quick understanding. For example, if the user is stressed, the analysis unit provides a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also summarize the analysis result concisely for quick understanding. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an analysis result of an appropriate length. 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-described processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0082] The analysis unit determines the priority of analysis based on the data collection timing during analysis. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also prioritize the most recent data while referring to past data. The analysis unit can also adjust the order of analysis according to the data collection timing. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also prioritize the most recent data while referring to past data. The analysis unit can also adjust the order of analysis according to the data collection timing. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the data collection timing into the generation AI and have the generation AI determine the priority of analysis.

[0083] The analysis unit adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also optimize the order of analysis according to the relevance of the data. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also optimize the order of analysis according to the relevance of the data. This allows the analysis unit to prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0084] The generation unit estimates the user's emotions and adjusts the way the generated farming strategy is presented based on the estimated user emotions. For example, if the user is stressed, the generation unit generates a simple and visually easy-to-understand farming strategy. If the user is relaxed, the generation unit can also generate a detailed farming strategy. If the user is in a hurry, the generation unit can also generate a concise and to-the-point farming strategy. For example, if the user is stressed, the generation unit generates a simple and visually easy-to-understand farming strategy. If the user is relaxed, the generation unit can also generate a detailed farming strategy. If the user is in a hurry, the generation unit can also generate a concise and to-the-point farming strategy. This allows for the provision of farming strategies that are easy for the user to understand by adjusting the presentation of the farming strategy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0085] The generation unit adjusts the level of detail of agricultural strategies based on the importance of the data when generating them. For example, the generation unit generates detailed agricultural strategies based on high-importance data. The generation unit can also generate simplified agricultural strategies based on low-importance data. The generation unit can also determine the priority of strategies according to the importance of the data. For example, the generation unit generates detailed agricultural strategies based on high-importance data. The generation unit can also generate simplified agricultural strategies based on low-importance data. The generation unit can also determine the priority of strategies according to the importance of the data. This allows for the generation of detailed agricultural strategies based on important data by adjusting the level of detail of strategies based on the importance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the importance of the data into the generation AI and have the generation AI perform the adjustment of the level of detail of the strategies.

[0086] The generation unit applies different generation algorithms depending on the data category when generating agricultural strategies. For example, the generation unit can generate agricultural strategies by applying a weather forecasting algorithm to weather data. The generation unit can also generate agricultural strategies by applying a soil analysis algorithm to soil data. The generation unit can also generate agricultural strategies by applying a crop growth prediction algorithm to crop data. This allows for improved accuracy of agricultural strategies by applying the most suitable generation algorithm according to the data category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the data category into the generation AI and have the generation AI perform the application of the generation algorithm.

[0087] The generation unit estimates the user's emotions and adjusts the length of the agricultural strategy it generates based on the estimated emotions. For example, if the user is stressed, the generation unit generates a short, concise agricultural strategy. If the user is relaxed, the generation unit can also generate a detailed agricultural strategy. If the user is in a hurry, the generation unit can also summarize the agricultural strategy concisely for quick understanding. For example, if the user is stressed, the generation unit generates a short, concise agricultural strategy. If the user is relaxed, the generation unit can also generate a detailed agricultural strategy. If the user is in a hurry, the generation unit can also summarize the agricultural strategy concisely for quick understanding. By adjusting the length of the agricultural strategy according to the user's emotions, it is possible to provide the user with an agricultural strategy of an appropriate length. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The 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 generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0088] The generation unit determines the priority of agricultural strategies based on the data collection timing when generating agricultural strategies. For example, the generation unit generates agricultural strategies based on the latest data. The generation unit can also prioritize the latest data while referring to past data. The generation unit can also adjust the order of strategies according to the data collection timing. For example, the generation unit generates agricultural strategies based on the latest data. The generation unit can also prioritize the latest data while referring to past data. The generation unit can also adjust the order of strategies according to the data collection timing. This makes it possible to generate agricultural strategies based on the latest data by determining the priority of strategies based on the data collection timing. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the data collection timing into the generation AI and have the generation AI perform the determination of strategy priorities.

[0089] The generation unit adjusts the order of strategies based on the relevance of the data when generating agricultural strategies. For example, the generation unit generates agricultural strategies based on highly relevant data. The generation unit can also postpone processing less relevant data. The generation unit can also optimize the order of strategies according to the relevance of the data. For example, the generation unit generates agricultural strategies based on highly relevant data. The generation unit can also postpone processing less relevant data. The generation unit can also optimize the order of strategies according to the relevance of the data. This allows for the generation of agricultural strategies based on highly relevant data by adjusting the order of strategies based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the relevance of the data into a generation AI and have the generation AI perform the adjustment of the order of strategies.

[0090] The service provider estimates the user's emotions and adjusts how the farming strategy is delivered based on the estimated emotions. For example, if the user is stressed, the service provider delivers the farming strategy in a simple and visually easy-to-understand manner. If the user is relaxed, the service provider can also deliver a detailed farming strategy. If the user is in a hurry, the service provider can also deliver the farming strategy in a concise and to-the-point manner. For example, if the user is stressed, the service provider delivers the farming strategy in a simple and visually easy-to-understand manner. If the user is relaxed, the service provider can also deliver a detailed farming strategy. If the user is in a hurry, the service provider can also deliver the farming strategy in a concise and to-the-point manner. By adjusting how the farming strategy is delivered according to the user's emotions, the farming strategy can be delivered in a way that is 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 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 service provider may be performed using generative AI or not. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0091] The service provider selects the optimal delivery method when providing agricultural strategies by referring to the user's past response history. For example, the service provider provides agricultural strategies in the most optimal way based on the delivery methods the user has preferred in the past. The service provider can also analyze the user's past response history and select the most effective delivery method. The service provider can also customize the delivery method based on the user's past response history. For example, the service provider provides agricultural strategies in the most optimal way based on the delivery methods the user has preferred in the past. The service provider can also analyze the user's past response history and select the most effective delivery method. The service provider can also customize the delivery method based on the user's past response history. This allows the service provider to provide agricultural strategies in an effective way for the user by selecting the optimal delivery method based on the user's past response history. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input the user's past response history into a generative AI and have the generative AI select the optimal delivery method.

[0092] The service provider estimates the user's emotions and adjusts the timing of providing agricultural strategies based on the estimated emotions. For example, if the user is stressed, the service provider will provide agricultural strategies at a time when the user is relaxed. If the user is relaxed, the service provider may also provide detailed agricultural strategies. If the user is in a hurry, the service provider may also provide agricultural strategies quickly. For example, if the user is stressed, the service provider will provide agricultural strategies at a time when the user is relaxed. If the user is relaxed, the service provider may also provide detailed agricultural strategies. If the user is in a hurry, the service provider may also provide agricultural strategies quickly. By adjusting the timing of providing agricultural strategies according to the user's emotions, agricultural strategies can be provided at the appropriate time for the user. 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 service provider may be performed using generative AI or not. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0093] The service provider selects the optimal delivery method when providing agricultural strategies, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide the agricultural strategies using a display method adapted to the screen size. If the user is using a tablet, the service provider can also provide the agricultural strategies using a display method optimized for larger screens. If the user is using a personal computer, the service provider can also provide the agricultural strategies using a display method that includes detailed information. By selecting the optimal delivery method based on the user's device information, the service provider can provide agricultural strategies in a way that is easy for the user to understand. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's device information into a generative AI and have the generative AI select the optimal delivery method.

[0094] The prediction unit estimates the user's emotions and adjusts the disaster risk prediction method based on the estimated user emotions. For example, if the user is stressed, the prediction unit provides a simple and visually easy-to-understand disaster risk prediction. If the user is relaxed, the prediction unit can also provide a detailed disaster risk prediction. If the user is in a hurry, the prediction unit can also provide a concise and to-the-point disaster risk prediction. For example, if the user is stressed, the prediction unit provides a simple and visually easy-to-understand disaster risk prediction. If the user is relaxed, the prediction unit can also provide a detailed disaster risk prediction. If the user is in a hurry, the prediction unit can also provide a concise and to-the-point disaster risk prediction. By adjusting the disaster risk prediction method according to the user's emotions, it is possible to provide a disaster risk prediction that is 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 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 prediction unit may be performed using generative AI or not. For example, the prediction unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0095] The prediction unit optimizes its prediction algorithm by referring to past disaster data when predicting disaster risk. For example, the prediction unit optimizes its algorithm for predicting typhoon risk based on past typhoon data. The prediction unit can also optimize its algorithm for predicting drought risk based on past drought data. The prediction unit can also optimize its algorithm for predicting flood risk based on past flood data. For example, the prediction unit optimizes its algorithm for predicting typhoon risk based on past typhoon data. The prediction unit can also optimize its algorithm for predicting drought risk based on past drought data. The prediction unit can also optimize its algorithm for predicting flood risk based on past flood data. In this way, the accuracy of disaster risk prediction can be improved by optimizing the prediction algorithm based on past disaster data. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the prediction unit can input past disaster data into a generative AI and have the generative AI perform the optimization of the prediction algorithm.

[0096] The prediction unit estimates the user's emotions and determines the priority of disaster risk predictions based on the estimated user emotions. For example, if the user is stressed, the prediction unit prioritizes predicting high-priority disaster risks. If the user is relaxed, the prediction unit can also perform detailed disaster risk predictions. If the user is in a hurry, the prediction unit can also prioritize disaster risks that can be predicted quickly. For example, if the user is stressed, the prediction unit prioritizes predicting high-priority disaster risks. If the user is relaxed, the prediction unit can also perform detailed disaster risk predictions. If the user is in a hurry, the prediction unit can also prioritize disaster risks that can be predicted quickly. This allows for prioritizing important disaster risks by determining the priority of disaster risk predictions 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using generative AI or not. For example, the prediction unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0097] The prediction unit selects the optimal prediction method when predicting disaster risk, taking into account the user's geographical location information. For example, the prediction unit predicts regional disaster risk based on the location information of the user's farm. The prediction unit can also predict region-specific disaster risk based on the user's geographical location. The prediction unit can also predict nearby disaster risk, taking into account the user's location information. For example, the prediction unit predicts regional disaster risk based on the location information of the user's farm. The prediction unit can also predict region-specific disaster risk based on the user's geographical location. The prediction unit can also predict nearby disaster risk, taking into account the user's location information. This makes it possible to predict region-specific disaster risk by selecting the optimal prediction method based on the user's geographical location information. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the prediction unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal prediction method.

[0098] The dialogue unit estimates the user's emotions and adjusts the way the dialogue is presented based on the estimated emotions. For example, if the user is stressed, the dialogue unit provides a simple and visually easy-to-understand dialogue. If the user is relaxed, the dialogue unit can also provide a detailed dialogue. If the user is in a hurry, the dialogue unit can also provide a concise dialogue that gets straight to the point. For example, if the user is stressed, the dialogue unit provides a simple and visually easy-to-understand dialogue. If the user is relaxed, the dialogue unit can also provide a detailed dialogue. If the user is in a hurry, the dialogue unit can also provide a concise dialogue that gets straight to the point. By adjusting the way the dialogue is presented according to the user's emotions, it is possible to provide a dialogue that is 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 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 dialogue unit may be performed using generative AI or not. For example, the dialogue unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0099] The dialogue unit provides the most appropriate answer during the conversation by referring to the user's past question history. For example, the dialogue unit provides relevant answers based on the content of questions the user has asked in the past. The dialogue unit can also analyze the user's past question history and provide the most effective answer. The dialogue unit can also customize answers based on the user's past question history. For example, the dialogue unit provides relevant answers based on the content of questions the user has asked in the past. The dialogue unit can also analyze the user's past question history and provide the most effective answer. The dialogue unit can also customize answers based on the user's past question history. This allows the dialogue unit to provide the most appropriate answer based on the user's past question history, thereby providing an effective answer for the user. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input the user's past question history into a generative AI and have the generative AI perform the task of providing the most appropriate answer.

[0100] The dialogue unit estimates the user's emotions and determines the priority of the dialogue based on the estimated emotions. For example, if the user is stressed, the dialogue unit will prioritize answering high-priority questions. If the user is relaxed, the dialogue unit may also provide detailed answers. If the user is in a hurry, the dialogue unit may also prioritize questions that can be answered quickly. For example, if the user is stressed, the dialogue unit will prioritize answering high-priority questions. If the user is relaxed, the dialogue unit may also provide detailed answers. If the user is in a hurry, the dialogue unit may also prioritize questions that can be answered quickly. This allows for prioritizing important questions by determining the priority of the dialogue 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using generative AI or not. For example, the dialogue unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0101] The dialogue unit provides the optimal answer during the conversation, taking into account the user's device information. For example, if the user is using a smartphone, the dialogue unit provides the answer in a display format that matches the screen size. If the user is using a tablet, the dialogue unit can also provide the answer in a display format optimized for a larger screen. If the user is using a PC, the dialogue unit can also provide the answer in a display format that includes detailed information. By providing the optimal answer based on the user's device information, the answer can be provided in a way that is easy for the user to understand. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input the user's device information into a generative AI and have the generative AI perform the task of providing the optimal answer.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The agricultural strategy generation system can also include a health management unit that monitors the user's health status. This unit monitors the user's health and makes suggestions to reduce the burden of agricultural work. For example, it can monitor the user's heart rate and blood pressure and suggest breaks if excessive strain is present. It can also analyze the user's sleep data and suggest appropriate work times. Furthermore, it can collect the user's dietary data and suggest nutritionally balanced meals. This allows for the reduction of agricultural work burden and support of efficient work based on the user's health status.

[0104] The agricultural strategy generation system can also include a motivation management unit that estimates the user's emotions and makes suggestions to improve motivation for agricultural work based on those estimated emotions. If the user is feeling stressed, the motivation management unit can suggest relaxing music or refreshing methods. If the user is discouraged, it can provide encouraging messages and success stories. Furthermore, if the user is highly motivated, it can suggest setting challenging goals to help them achieve a sense of accomplishment. This allows for increased motivation according to the user's emotions, thereby improving the efficiency of agricultural work.

[0105] The agricultural strategy generation system can also include a communication support section to assist user communication. This section provides functions to facilitate information sharing among users with other farmers and experts. For example, it can provide an online forum where users can post questions and inquiries. It can also provide a function for users to chat with experts in real time. Furthermore, it can provide collaboration tools for users to work on projects together with other farmers. This allows users to effectively communicate with other farmers and experts and share knowledge and experience.

[0106] The agricultural strategy generation system may also include a stress management unit that estimates the user's emotions and makes suggestions to reduce the stress of agricultural work based on those estimated emotions. The stress management unit can suggest a relaxing environment if the user is feeling stressed. For example, it can provide natural sounds or scenery if the user is stressed. It can also suggest deep breathing or meditation techniques to help the user relax. Furthermore, it can suggest exercises or stretches to reduce stress. This allows for stress reduction in accordance with the user's emotions, thereby increasing the efficiency of agricultural work.

[0107] The agricultural strategy generation system can also include a learning support section to assist user learning. This section provides resources for users to acquire knowledge and skills related to agriculture. For example, it can provide a platform where users can take online courses on agriculture. It can also provide a database where users can access the latest research and technical information on agriculture. Furthermore, it can provide practical guides and tutorials for users to learn through actual agricultural work. This allows users to effectively acquire knowledge and skills related to agriculture and improve the quality of their agricultural work.

[0108] The agricultural strategy generation system may also include an efficiency management unit that estimates the user's emotions and makes suggestions to improve the efficiency of agricultural work based on those emotions. The efficiency management unit can suggest an environment that enhances concentration when the user is relaxed. For example, it can provide music or ambient sounds to improve concentration when the user is relaxed. It can also suggest break times to help the user maintain concentration. Furthermore, the efficiency management unit can provide task management tools to help the user work efficiently. This allows for improved work efficiency and higher quality agricultural work in accordance with the user's emotions.

[0109] The agricultural strategy generation system can also include a safety management unit to ensure user safety. This unit provides suggestions for users to perform agricultural work safely. For example, it can suggest that users use appropriate protective equipment when performing hazardous tasks. It can also notify users of points to be aware of during work. Furthermore, it can provide emergency contact information and first aid procedures to enable users to respond quickly in emergencies. This allows users to perform agricultural work safely and prevent accidents and injuries.

[0110] The agricultural strategy generation system can also include a satisfaction management unit that estimates the user's emotions and makes suggestions to improve satisfaction with agricultural work based on those estimated emotions. The satisfaction management unit suggests a work environment that will give the user satisfaction. For example, if the user is relaxed, the satisfaction management unit can provide music or podcasts to enhance their enjoyment of the work. It can also suggest goal setting that will give the user a sense of accomplishment. Furthermore, the satisfaction management unit can provide tools that allow the user to visualize their work progress and feel a sense of accomplishment. This will improve work satisfaction in accordance with the user's emotions and enhance the quality of agricultural work.

[0111] The agricultural strategy generation system may also include an energy management unit that monitors the user's energy consumption. The energy management unit monitors the user's energy consumption and suggests efficient energy use. For example, the energy management unit can suggest ways for the user to save energy. It can also suggest schedules for the user to use energy efficiently. Furthermore, the energy management unit can suggest equipment and technologies to minimize energy consumption. This allows the user to use energy efficiently and reduce the cost of agricultural operations.

[0112] The agricultural strategy generation system can also include a communication management unit that estimates the user's emotions and makes suggestions to facilitate communication in agricultural work based on those estimated emotions. The communication management unit makes suggestions to help the user communicate smoothly with other farmers and experts. For example, if the user is feeling stressed, the communication management unit can suggest a more relaxing environment for communication. If the user is relaxed, the communication management unit can also suggest ways to actively exchange opinions. Furthermore, if the user is in a hurry, the communication management unit can provide tools for quickly sharing information. This allows for smoother communication tailored to the user's emotions, thereby increasing the efficiency of agricultural work.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects user information. This information includes weather, soil conditions, and crop growth stage. The data collection unit collects data using sensors, as well as data manually entered by the user. For example, it uses soil sensors to measure soil moisture and nutrient levels, and weather sensors to measure temperature and precipitation. It also collects crop growth information entered by the user via a smartphone app. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using data analysis algorithms and can also integrate and analyze multiple data sources. For example, it can integrate and analyze soil data and weather data, and analyze crop growth data to identify growth patterns. It can also detect outliers in the data and perform filtering to improve data quality. Step 3: The generation unit generates agricultural strategies based on the analysis results obtained by the analysis unit. The generation unit generates agricultural strategies using generation AI and proposes the optimal agricultural strategy based on the user's specific situation and accumulated experience. For example, it proposes an optimal fertilizer usage plan based on past data, and generates suggestions for adjusting irrigation schedules and pest and disease control measures. Step 4: The provider unit provides the agricultural strategy generated by the generator unit to the user. The provider unit provides the agricultural strategy by adjusting the notification method and timing, and provides the agricultural strategy in the most optimal display method depending on the user's device. For example, the agricultural strategy may be notified via a smartphone app, sent via email, or displayed via a web application.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, provision unit, prediction unit, and dialogue unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data through the sensors of the smart device 14 and user input. The analysis unit analyzes the data using the specific processing unit 290 of the data processing unit 12. The generation unit generates agricultural strategies using the specific processing unit 290 of the data processing unit 12. The provision unit provides agricultural strategies to the user through the control unit 46A of the smart device 14. The prediction unit predicts disaster risks using the specific processing unit 290 of the data processing unit 12. The dialogue unit quickly answers user questions through the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, provision unit, prediction unit, and dialogue unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data through the sensors of the smart glasses 214 and user input. The analysis unit analyzes the data using the specific processing unit 290 of the data processing unit 12. The generation unit generates agricultural strategies using the specific processing unit 290 of the data processing unit 12. The provision unit provides agricultural strategies to the user through the control unit 46A of the smart glasses 214. The prediction unit predicts disaster risks using the specific processing unit 290 of the data processing unit 12. The dialogue unit quickly answers user questions through the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, provision unit, prediction unit, and dialogue unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data through sensors and user input of the headset terminal 314. The analysis unit analyzes the data using the specific processing unit 290 of the data processing unit 12. The generation unit generates agricultural strategies using the specific processing unit 290 of the data processing unit 12. The provision unit provides agricultural strategies to the user through the control unit 46A of the headset terminal 314. The prediction unit predicts disaster risks using the specific processing unit 290 of the data processing unit 12. The dialogue unit quickly answers user questions through the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, provision unit, prediction unit, and dialogue unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit collects data through the robot 414's sensors and user input. The analysis unit analyzes the data using the specific processing unit 290 of the data processing unit 12. The generation unit generates agricultural strategies using the specific processing unit 290 of the data processing unit 12. The provision unit provides agricultural strategies to the user through the control unit 46A of the robot 414. The prediction unit predicts disaster risks using the specific processing unit 290 of the data processing unit 12. The dialogue unit quickly answers user questions through the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A collection unit that collects user information, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates an agricultural strategy based on the analysis results obtained by the analysis unit, The system comprises a providing unit that provides the agricultural strategy generated by the generation unit to the user. A system characterized by the following features. (Note 2) Equipped with a prediction unit to predict disaster risks. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features an interactive section that provides quick answers to user questions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generates suggestions to adjust fertilizer and irrigation schedules based on desired crop quality. The system described in Appendix 1, characterized by the features described herein. (Note 5) 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 6) The aforementioned collection unit is Analyze the user's past agricultural activity history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, filtering is performed based on the user's current agricultural situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) 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 10) 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 11) 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 12) 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 13) 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 14) 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 15) 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 16) 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 17) The generating unit is We estimate user sentiment and adjust the way agricultural strategies are represented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating agricultural strategies, adjust the level of detail of the strategy based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating agricultural strategies, different generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the length of the agricultural strategy generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When developing agricultural strategies, prioritize strategies based on when data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating agricultural strategies, adjust the order of strategies based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, We estimate user sentiment and adjust how agricultural strategies are delivered based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing agricultural strategies, the optimal delivery method is selected by referring to the user's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates user sentiment and adjusts the timing of agricultural strategy delivery based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing agricultural strategies, the optimal delivery method is selected by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, We estimate user sentiment and adjust the disaster risk prediction method based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 28) The prediction unit, When predicting disaster risk, the prediction algorithm is optimized by referring to past disaster data. The system described in Appendix 2, characterized by the features described herein. (Note 29) The prediction unit, The system estimates user sentiment and prioritizes disaster risk predictions based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 30) The prediction unit, When predicting disaster risks, the optimal prediction method is selected by considering the user's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned dialogue unit, During the conversation, the system provides the best possible answer by referring to the user's past question history. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned dialogue unit, During the conversation, the system provides the optimal answer while taking into account the user's device information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0187] 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 collection unit that collects user information, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates an agricultural strategy based on the analysis results obtained by the analysis unit, The system comprises a providing unit that provides the agricultural strategy generated by the generation unit to the user. A system characterized by the following features.

2. Equipped with a prediction unit to predict disaster risks. The system according to feature 1.

3. It features an interactive section that provides quick answers to user questions. The system according to feature 1.

4. The generating unit is Generates suggestions to adjust fertilizer and irrigation schedules based on desired crop quality. The system according to feature 1.

5. 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.

6. The aforementioned collection unit is Analyze the user's past agricultural activity history and select the optimal data collection method. The system according to feature 1.

7. The aforementioned collection unit is During data collection, filtering is performed based on the user's current agricultural situation and areas of interest. The system according to feature 1.

8. 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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