system

Abstract

The system according to this embodiment aims to provide comprehensive support to agricultural beginners, from selecting the optimal crop to securing sales channels and formulating planting plans. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a selection unit, a securing unit, a planning unit, a management unit, and a strategy unit. The collection unit collects multifaceted data such as geographic information, soil data, weather conditions, and market data. The analysis unit analyzes the data collected by the collection unit. The selection unit selects the optimal crop based on the data analyzed by the analysis unit. The securing unit secures sales routes based on the crops selected by the selection unit. The planning unit creates a planting plan based on the sales routes secured by the securing unit. The management unit manages logistics based on the planting plan created by the planning unit. The strategy unit creates a marketing strategy based on the logistics managed by the management unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there were problems such as the selection of crops in agriculture, securing sales routes, and formulating cultivation plans being complex and difficult for beginners.

[0005] The system according to the embodiment aims to comprehensively support even agricultural beginners from the selection of optimal crops to securing sales routes and formulating cultivation plans.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a selection unit, a securing unit, a planning unit, a management unit, and a strategy unit. The data collection unit collects multifaceted data such as geographic information, soil data, weather conditions, and market data. The analysis unit analyzes the data collected by the data collection unit. The selection unit selects the optimal crops based on the data analyzed by the analysis unit. The securing unit secures sales routes based on the crops selected by the selection unit. The planning unit creates a planting plan based on the sales routes secured by the securing unit. The management unit manages logistics based on the planting plan created by the planning unit. The strategy unit develops a marketing strategy based on the logistics managed by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively support even novice farmers, from selecting the optimal crop to securing sales channels and formulating planting plans. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memories (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

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

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] 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 AgriPlanner System according to an embodiment of the present invention is a system that analyzes multifaceted data such as geographic information, soil data, weather conditions, and market data, and autonomously plans everything from selecting the optimal crop to securing sales routes. The AgriPlanner System collects multifaceted data such as geographic information, soil data, weather conditions, and market data, and the AI ​​analyzes it to select the optimal crop. Subsequently, based on the selected crop, it autonomously plans securing sales routes, planting plans, logistics, and marketing strategies. For example, the AgriPlanner System collects multifaceted data such as geographic information, soil data, weather conditions, and market data. Next, the AI ​​analyzes the collected data to select the optimal crop. For example, the AI ​​selects the optimal crop based on criteria such as profitability, growth conditions, and demand. Subsequently, based on the selected crop, it autonomously plans securing sales routes, planting plans, logistics, and marketing strategies. For example, the AI ​​secures sales routes such as direct sales, wholesale, and online sales based on the selected crop. Furthermore, the AgriPlanner system provides comprehensive support for agriculture, including automatic contract generation, guidance on legal procedures, and provision of subsidy and grant information. For example, AI automatically generates contracts and provides guidance on legal procedures. By providing subsidy and grant information, even beginners can start farming with confidence. In addition, the AgriPlanner system supports communication after matching through its community function and promotes collaboration with local communities. For example, AI supports communication after matching through its community function. This reduces the burden of management costs and property taxes on abandoned farmland and eliminates the effort and reliability concerns of finding suitable users. It also solves problems such as lack of access to agricultural know-how and market information, and lack of information on land condition and suitability. In this way, the AgriPlanner system aims to eliminate abandoned farmland and create vibrant agriculture and prosperous local communities. In this way, the AgriPlanner system allows even beginners to start farming with confidence and promotes the effective use of abandoned farmland.

[0029] The agricultural planner system according to this embodiment comprises a collection unit, an analysis unit, a selection unit, a securing unit, a planning unit, a management unit, and a strategy unit. The collection unit collects multifaceted data such as geographic information, soil data, weather conditions, and market data. For example, the collection unit can use GPS data to collect geographic information. It can also use soil sensors to collect soil data. Furthermore, it can use weather sensors to collect weather conditions. For example, the collection unit can use GPS data to collect geographic information, soil sensors to collect soil data, and weather sensors to collect weather conditions. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected geographic information, soil data, weather conditions, and market data to provide information for selecting the optimal crop. The analysis unit can use AI to analyze the collected data. For example, the analysis unit uses AI to analyze the collected geographic information, soil data, weather conditions, and market data to provide information for selecting the optimal crop. The selection unit selects the optimal crop based on the data analyzed by the analysis unit. The selection unit selects the optimal crop based on criteria such as profitability, growth conditions, and demand. The selection unit can use AI to select the optimal crop. For example, the selection unit can use AI to select the optimal crop based on criteria such as profitability, growth conditions, and demand. The securing unit secures sales routes based on the crops selected by the selection unit. The securing unit secures sales routes such as direct sales, wholesale, and online sales. The securing unit can use AI to secure sales routes. For example, the securing unit uses AI to secure sales routes such as direct sales, wholesale, and online sales based on the selected crops. The planning unit creates a planting plan based on the sales routes secured by the securing unit. The planning unit creates a planting plan such as planting time, area, and crop type. The planning unit can use AI to create a planting plan. For example, the planning unit uses AI to create a planting plan such as planting time, area, and crop type based on the secured sales routes. The management department manages logistics based on the planting plan developed by the planning department.The Management Department manages logistics, such as transportation methods, storage methods, and delivery schedules. The Management Department can manage logistics using AI. For example, the Management Department uses AI to manage logistics, such as transportation methods, storage methods, and delivery schedules, based on the planting plan. The Strategy Department develops marketing strategies based on the logistics managed by the Management Department. The Strategy Department develops marketing strategies, such as target markets, promotion methods, and pricing. The Strategy Department can develop marketing strategies using AI. For example, the Strategy Department uses AI to develop marketing strategies, such as target markets, promotion methods, and pricing, based on the managed logistics. As a result, the AgriPlanner system according to this embodiment can analyze multifaceted data such as geographic information, soil data, weather conditions, and market data, and autonomously plan everything from selecting the optimal crops to securing sales routes.

[0030] The data collection unit collects multifaceted data such as geographic information, soil data, weather conditions, and market data. Specifically, it can use GPS data to collect geographic information. GPS data provides detailed geographic information such as the location, area, and topography of farmland. This enables the selection of appropriate crops based on the characteristics and location of the farmland. The data collection unit can also use soil sensors to collect soil data. Soil sensors acquire information such as soil pH, humidity, and nutrient content in real time. This enables the selection of appropriate fertilizers and the development of fertilization plans according to the soil condition. Furthermore, the data collection unit can use weather sensors to collect weather conditions. Weather sensors collect weather data such as temperature, humidity, precipitation, and wind speed, and based on this data, provide information to provide the optimal environment for crop growth. For example, the data collection unit can use GPS data to collect geographic information, soil sensors to collect soil data, and weather sensors to collect weather conditions. In this way, the data collection unit can efficiently collect multifaceted data and provide the information necessary for optimizing agriculture. Furthermore, the data collection unit can also collect market data. Market data includes crop price trends, demand forecasts, and competitive landscapes. This data enables the selection of highly profitable crops and the development of sales strategies. The data collection unit centrally manages this data and can integrate with other systems and departments as needed. For example, collected data is stored on a cloud server, allowing access from the analysis and selection units. Furthermore, adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. Specifically, it analyzes collected geographic information, soil data, weather conditions, and market data to provide information for selecting the optimal crops. The analysis unit can use AI to analyze the collected data. For example, the analysis unit uses AI to analyze collected geographic information, soil data, weather conditions, and market data to provide information for selecting the optimal crops. The AI ​​uses machine learning algorithms to learn from past data and patterns and make future predictions. For example, the AI ​​predicts future weather conditions based on past weather data and identifies the optimal time for crop growth based on that. The AI ​​also analyzes soil data to select appropriate fertilizers and plan fertilization according to the soil condition. Furthermore, the AI ​​analyzes market data to forecast crop price trends and demand, supporting the selection of highly profitable crops. As a result, the analysis unit can quickly and accurately analyze the collected data and provide the information necessary for optimizing agriculture. In addition, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on historical weather and market data, it can predict risk fluctuations in specific regions and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The selection unit selects the optimal crop based on data analyzed by the analysis unit. Specifically, it selects the optimal crop based on criteria such as profitability, growth conditions, and demand. The selection unit can also use AI to select the optimal crop. For example, the selection unit uses AI to select the optimal crop based on criteria such as profitability, growth conditions, and demand. The AI ​​evaluates the profitability of each crop based on the collected data and identifies the most profitable crop. The AI ​​also analyzes growth conditions and selects the optimal crop for specific regions and weather conditions. Furthermore, the AI ​​analyzes market data and identifies crops with high demand. This allows the selection unit to select the optimal crop based on criteria such as profitability, growth conditions, and demand. In addition, the selection unit can formulate a cultivation plan based on the selected crop. For example, the selection unit plans the cultivation period and method for the selected crop and formulates an optimal cultivation plan. This allows the selection unit to not only select highly profitable crops but also formulate optimal cultivation plans, supporting improved efficiency and profitability in agriculture.

[0033] The procurement department secures sales channels based on the crops selected by the selection department. Specifically, it secures sales channels such as direct sales, wholesale, and online sales. The procurement department can use AI to secure sales channels. For example, the procurement department uses AI to secure sales channels such as direct sales, wholesale, and online sales based on the selected crops. The AI ​​analyzes market data and identifies the most effective sales channels. For example, based on past sales data, the AI ​​identifies the most effective regions and times for direct sales and develops a sales plan tailored to those regions and times. The AI ​​also predicts online sales demand and develops an optimal online sales strategy. This allows the procurement department to secure the most effective sales channels based on the selected crops. Furthermore, the procurement department can not only secure sales channels but also develop sales strategies. For example, the procurement department plans the sales price and promotion methods for the selected crops and develops an optimal sales strategy. This allows the procurement department to not only secure sales channels but also develop sales strategies, thereby improving the profitability of agriculture.

[0034] The planning department creates planting plans based on sales channels secured by the procurement department. Specifically, it creates planting plans including planting time, area, and crop types. The planning department can use AI to create planting plans. For example, the planning department can use AI to create planting plans including planting time, area, and crop types based on secured sales channels. The AI ​​identifies the optimal planting time and area based on collected data. For example, the AI ​​analyzes weather data to identify the optimal planting time. The AI ​​also analyzes soil data to identify the optimal planting area. Furthermore, the AI ​​analyzes market data to identify the optimal crop types. This allows the planning department to create optimal planting plans based on secured sales channels. In addition, the planning department can not only create planting plans but also handle their execution. For example, the planning department can create planting schedules based on the planting plans and manage the progress of the work. This allows the planning department to not only create planting plans but also handle their execution, supporting improved efficiency and profitability in agriculture.

[0035] The Management Department manages logistics based on the planting plan developed by the Planning Department. Specifically, it manages logistics such as transportation methods, storage methods, and delivery schedules. The Management Department can use AI to manage logistics. For example, the Management Department uses AI to manage logistics such as transportation methods, storage methods, and delivery schedules based on the planting plan. Based on collected data, the AI ​​identifies the optimal transportation methods and storage methods. For example, the AI ​​analyzes weather data to identify the optimal transportation method. The AI ​​also analyzes soil data to identify the optimal storage method. Furthermore, the AI ​​analyzes market data to identify the optimal delivery schedule. This allows the Management Department to manage optimal logistics based on the planting plan. In addition, the Management Department can not only manage logistics but also optimize them. For example, the Management Department reviews transportation methods and storage methods to improve logistics efficiency and develops an optimal logistics plan. This allows the Management Department to not only manage logistics but also optimize them, supporting improved efficiency and profitability in agriculture.

[0036] The Strategy Department develops marketing strategies based on logistics managed by the Management Department. Specifically, it develops marketing strategies such as target markets, promotion methods, and pricing. The Strategy Department can use AI to develop marketing strategies. For example, the Strategy Department uses AI to develop marketing strategies such as target markets, promotion methods, and pricing based on managed logistics. The AI ​​analyzes market data and identifies the optimal target market. For example, the AI ​​identifies the most effective target market based on past sales data and plans promotion methods tailored to that market. The AI ​​also optimizes pricing to improve profitability. This allows the Strategy Department to develop optimal marketing strategies based on managed logistics. Furthermore, the Strategy Department can not only develop marketing strategies but also handle their execution. For example, the Strategy Department implements promotional activities based on the developed marketing strategies and evaluates their effectiveness. This allows the Strategy Department to handle not only the development but also the execution of marketing strategies, thereby improving the profitability of agriculture.

[0037] The support department can provide services such as automatic contract generation, legal procedure guidance, and subsidy / grant information. For example, the support department can automatically generate contracts. The support department can use AI to automatically generate contracts. For example, the support department can generate contracts using templates or AI. The support department can provide legal procedure guidance. The support department can use AI to provide legal procedure guidance. For example, the support department can provide guidance on procedural steps, required documents, and submission methods. The support department can provide subsidy / grant information. The support department can use AI to provide subsidy / grant information. For example, the support department can provide information on application requirements, application methods, and grant amounts. By providing services such as automatic contract generation, legal procedure guidance, and subsidy / grant information, the support department can offer comprehensive support for agriculture.

[0038] The Community Department can support post-matching communication through community features. The Community Department provides community features such as forums, chat, and group functions. The Community Department can also provide community features using AI. For example, the Community Department provides community features such as forums, chat, and group functions using AI. The Community Department supports post-matching communication. The Community Department can support post-matching communication using AI. For example, the Community Department provides support for messaging, video calls, and event hosting. This allows for support of post-matching communication through community features and promotes collaboration with local communities.

[0039] The data collection unit can analyze past collected data and select the optimal data collection method. For example, the data collection unit can identify the most effective data collection method from past collected data and prioritize its use. The data collection unit can analyze past collected data, find areas for improvement in data collection methods, and optimize them. Based on past collected data, the data collection unit can analyze patterns in data collection methods and collect data at the optimal timing. This allows for the selection of the optimal data collection method and improved data collection efficiency by analyzing past collected data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter data based on the user's current farming situation and areas of interest during data collection. For example, the data collection unit can collect only the necessary data based on the user's current farming situation. The data collection unit can prioritize the collection of highly relevant data based on the user's areas of interest. The data collection unit can adjust the scope of data collection according to the user's farming situation and areas of interest. This allows for the efficient collection of only the necessary data by filtering 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 AI or not. For example, the data collection unit can input data on the user's current farming situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of region-related data based on the user's geographical location information. The data collection unit can prioritize the collection of data related to weather conditions, taking into account the user's geographical location information. The data collection unit can prioritize the collection of soil data based on the user's geographical location information. This enables efficient data collection by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the user's social media activity and collect data related to crops of interest. Based on the user's social media activity, the data collection unit can collect data related to agricultural technologies of interest. The data collection unit can use the user's social media activity as a reference to collect market data of interest. In this way, by analyzing the user's social media activity, data related to crops and agricultural technologies of interest can be efficiently collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a weather forecasting algorithm to weather data. For soil data, it can apply a soil analysis algorithm. For market data, it can apply a market forecasting algorithm. By applying different analysis algorithms depending on the data category, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. The analysis unit can adjust the priority of analysis according to the data collection timing. This allows the analysis 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-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The selection unit can improve the accuracy of its selection process by considering the interrelationships between data. For example, the selection unit can select crops by considering the interrelationships between weather data and soil data. The selection unit can select crops by considering the interrelationships between market data and geographic information. The selection unit can analyze the interrelationships between data and select the optimal crop. This improves the accuracy of the selection process by considering the interrelationships between data. Some or all of the above-described processes in the selection unit may be performed using AI or not. For example, the selection unit can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of the selection.

[0048] The selection unit can make selections while considering the attribute information of the data submitter. For example, if the data submitter is a novice farmer, the selection unit can select crops that are easy to grow. If the data submitter is an experienced farmer, the selection unit can select crops that are highly profitable. The selection unit can select the optimal crop based on the attribute information of the data submitter. In this way, the optimal crop can be selected by considering the attribute information of the data submitter. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the attribute information of the data submitter into a generating AI and have the generating AI perform the selection.

[0049] The selection unit can perform selection while considering the geographical distribution of the data. For example, the selection unit can select a geographically suitable crop. The selection unit can select the optimal crop based on the geographical distribution. The selection unit can perform crop selection while considering geographical conditions. In this way, by considering the geographical distribution of the data, a geographically suitable crop can be selected. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the selection.

[0050] The selection unit can improve the accuracy of its selection by referring to relevant literature during the selection process. For example, the selection unit selects crops by referring to relevant literature. The selection unit can improve the accuracy of its selection based on the information in the relevant literature. The selection unit can analyze the relevant literature and select the optimal crop. In this way, the accuracy of the selection can be improved by referring to relevant literature for the data. Some or all of the above-described processes in the selection unit may be performed using AI or not. For example, the selection unit can input relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of the selection.

[0051] The securing unit can select the optimal securing method by referring to past sales route data when securing inventory. For example, the securing unit can analyze past sales route data and select the optimal securing method. Based on past sales route data, the securing unit can identify areas for improvement in the securing method. The securing unit can analyze patterns of securing methods by referring to past sales route data. As a result, by referring to past sales route data, the optimal securing method can be selected, enabling efficient sales route securing. Some or all of the above processes in the securing unit may be performed using AI, or they may not be performed using AI. For example, the securing unit can input past sales route data into a generating AI and have the generating AI select the optimal securing method.

[0052] The procurement unit can customize the means of sales routes based on the current market situation when securing sales. For example, the procurement unit can analyze the current market situation and customize the optimal sales route. The procurement unit can adjust the means of sales routes according to market demand. The procurement unit can optimize the means of sales routes considering the market supply situation. This makes it possible to secure sales routes efficiently by customizing the means of sales routes based on the current market situation. Some or all of the above processes in the procurement unit may be performed using AI or not. For example, the procurement unit can input the current market situation into a generating AI and have the generating AI perform the customization of the means of sales routes.

[0053] The procurement unit can select the optimal sales route by considering geographical location information during procurement. For example, the procurement unit selects the optimal sales route based on geographical location information. The procurement unit can improve the efficiency of the sales route by considering geographical location information. The procurement unit can optimize the means of the sales route based on geographical location information. As a result, by considering geographical location information, an efficient sales route can be selected. Some or all of the above processing in the procurement unit may be performed using AI or not. For example, the procurement unit can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal sales route.

[0054] The procurement unit can analyze social media activity during procurement and propose sales route methods. For example, the procurement unit analyzes social media activity and proposes the optimal sales route. The procurement unit can customize sales route methods based on social media activity. The procurement unit can optimize sales route methods by referring to social media activity. In this way, by analyzing social media activity, it can propose the optimal sales route method. Some or all of the above processing in the procurement unit may be performed using AI or not. For example, the procurement unit can input social media activity data into a generating AI and have the generating AI execute the proposal of sales route methods.

[0055] The planning department can select the optimal planning method by referring to past planting data during the planning stage. For example, the planning department can analyze past planting data and select the optimal planning method. Based on past planting data, the planning department can identify areas for improvement in the planning method. The planning department can analyze patterns in the planning method by referring to past planting data. As a result, by referring to past planting data, the optimal planning method can be selected and an efficient planting plan can be created. Some or all of the above processes in the planning department may be performed using AI or not. For example, the planning department can input past planting data into a generating AI and have the generating AI select the optimal planning method.

[0056] The planning unit can customize the planting plan based on the current agricultural conditions during the planning stage. For example, the planning unit can analyze the current agricultural conditions and customize the optimal planting plan. The planning unit can adjust the planting plan according to agricultural demand. The planning unit can optimize the planting plan considering the agricultural supply situation. This allows for the creation of an efficient planting plan by customizing the planting plan based on the current agricultural conditions. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input the current agricultural conditions into a generating AI and have the generating AI perform the customization of the planting plan.

[0057] The planning unit can select the optimal planting plan by considering geographical location information during the planning stage. For example, the planning unit selects the optimal planting plan based on geographical location information. The planning unit can improve the efficiency of the planting plan by considering geographical location information. The planning unit can optimize the means of the planting plan based on geographical location information. As a result, by considering geographical location information, an efficient planting plan can be selected. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal planting plan.

[0058] The planning department can analyze social media activity during the planning stage and propose planting plans. For example, the planning department can analyze social media activity and propose an optimal planting plan. The planning department can customize planting plans based on social media activity. The planning department can optimize planting plans by referring to social media activity. In this way, by analyzing social media activity, it is possible to propose an optimal planting plan. Some or all of the above processes in the planning department may be performed using AI or not. For example, the planning department can input social media activity data into a generating AI and have the generating AI propose planting plans.

[0059] The management department can select the optimal management method by referring to past logistics data during management. For example, the management department can analyze past logistics data and select the optimal management method. Based on past logistics data, the management department can identify areas for improvement in management methods. The management department can analyze patterns in management methods by referring to past logistics data. As a result, by referring to past logistics data, the optimal management method can be selected and efficient logistics management can be carried out. Some or all of the above processes in the management department may be performed using AI, or they may not be performed using AI. For example, the management department can input past logistics data into a generating AI and have the generating AI perform the selection of the optimal management method.

[0060] The management department can customize logistics management methods based on the current logistics situation during management. For example, the management department can analyze the current logistics situation and customize the optimal logistics management method. The management department can adjust logistics management methods according to logistics demand. The management department can optimize logistics management methods considering the logistics supply situation. This enables efficient logistics management by customizing logistics management methods based on the current logistics situation. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the current logistics situation into a generating AI and have the generating AI perform the customization of logistics management methods.

[0061] The management department can select the optimal logistics management method while considering geographical location information during management. For example, the management department can select the optimal logistics management method based on geographical location information. The management department can improve the efficiency of logistics management by considering geographical location information. The management department can optimize the means of logistics management based on geographical location information. As a result, efficient logistics management can be performed by considering geographical location information. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal logistics management method.

[0062] The management department can analyze social media activity during management and propose logistics management methods. For example, the management department can analyze social media activity and propose the optimal logistics management method. The management department can customize logistics management methods based on social media activity. The management department can optimize logistics management methods by referring to social media activity. In this way, by analyzing social media activity, the optimal logistics management methods can be proposed. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input social media activity data into a generating AI and have the generating AI execute proposals for logistics management methods.

[0063] The Strategy Department can select the optimal strategic approach by referring to past marketing data when formulating a strategy. For example, the Strategy Department can analyze past marketing data and select the optimal strategic approach. Based on past marketing data, the Strategy Department can identify areas for improvement in the strategic approach. The Strategy Department can analyze patterns in strategic approaches by referring to past marketing data. As a result, by referring to past marketing data, the optimal strategic approach can be selected and an efficient marketing strategy can be formulated. Some or all of the above processes in the Strategy Department may be performed using AI, or they may not be performed using AI. For example, the Strategy Department can input past marketing data into a generating AI and have the generating AI select the optimal strategic approach.

[0064] The Strategy Department can customize the means of marketing strategy based on the current market situation when formulating a strategy. For example, the Strategy Department can analyze the current market situation and customize the optimal marketing strategy. The Strategy Department can adjust the means of marketing strategy according to market demand. The Strategy Department can optimize the means of marketing strategy considering the market supply situation. In this way, an efficient marketing strategy can be formulated by customizing the means of marketing strategy based on the current market situation. Some or all of the above processes in the Strategy Department may be performed using AI or not. For example, the Strategy Department can input the current market situation into a generating AI and have the generating AI perform the customization of the means of marketing strategy.

[0065] The Strategy Department can select the optimal marketing strategy by considering geographical location information when formulating a strategy. For example, the Strategy Department can select the optimal marketing strategy based on geographical location information. The Strategy Department can improve the efficiency of the marketing strategy by considering geographical location information. The Strategy Department can optimize the means of the marketing strategy based on geographical location information. In this way, by considering geographical location information, an efficient marketing strategy can be formulated. Some or all of the above processes in the Strategy Department may be performed using AI or not. For example, the Strategy Department can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal marketing strategy.

[0066] The Strategy Department can analyze social media activity and propose marketing strategy measures when formulating strategies. For example, the Strategy Department can analyze social media activity and propose the optimal marketing strategy. The Strategy Department can customize the marketing strategy measures based on social media activity. The Strategy Department can optimize the marketing strategy measures by referring to social media activity. In this way, by analyzing social media activity, the Strategy Department can propose the optimal marketing strategy measures. Some or all of the above processes in the Strategy Department may be performed using AI or not. For example, the Strategy Department can input social media activity data into a generating AI and have the generating AI execute the proposal of marketing strategy measures.

[0067] The support department can select the optimal support method by referring to past legal procedure data during support. For example, the support department can analyze past legal procedure data and select the optimal support method. Based on past legal procedure data, the support department can identify areas for improvement in support methods. The support department can analyze support method patterns by referring to past legal procedure data. As a result, by referring to past legal procedure data, the optimal support method can be selected and efficient support can be provided. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input past legal procedure data into a generating AI and have the generating AI select the optimal support method.

[0068] The support unit can provide guidance on the most appropriate legal procedure when providing support, taking geographical location information into consideration. For example, the support unit can provide guidance on the most appropriate legal procedure based on geographical location information. The support unit can improve the efficiency of legal procedures by taking geographical location information into consideration. The support unit can optimize the means of legal procedures based on geographical location information. In this way, by taking geographical location information into consideration, it is possible to provide guidance on efficient legal procedures. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input geographical location information into a generating AI and have the generating AI perform the task of providing guidance on the most appropriate legal procedure.

[0069] The Community Department can select the optimal support method by referring to past communication data when providing communication support. For example, the Community Department can analyze past communication data and select the optimal support method. Based on past communication data, the Community Department can identify areas for improvement in support methods. The Community Department can analyze support method patterns by referring to past communication data. As a result, by referring to past communication data, the optimal support method can be selected and efficient communication support can be provided. Some or all of the above processes in the Community Department may be performed using AI or not. For example, the Community Department can input past communication data into a generating AI and have the generating AI select the optimal support method.

[0070] The community department can provide optimal support methods when providing communication support, taking geographical location information into consideration. For example, the community department can provide optimal communication support methods based on geographical location information. The community department can improve the efficiency of communication support by taking geographical location information into consideration. The community department can optimize the means of communication support based on geographical location information. As a result, efficient communication support can be provided by taking geographical location information into consideration. Some or all of the above processing in the community department may be performed using AI or not. For example, the community department can input geographical location information into a generating AI and have the generating AI perform the task of providing optimal communication support methods.

[0071] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0072] The AgriPlanner system can also be equipped with a forecasting unit. This unit can predict future agricultural trends based on collected data. For example, it can predict future weather conditions based on meteorological data and suggest the optimal time for crop growth. It can also predict future market demand based on market data and provide information for selecting profitable crops. Furthermore, it can predict soil changes based on soil data and suggest appropriate fertilizers and improvement methods. Thus, by incorporating a forecasting unit, it becomes possible to predict future agricultural trends and create more effective agricultural plans.

[0073] The AgriPlanner system can also be equipped with a notification unit. This unit can notify users of important information and alerts. For example, it can issue a warning to the user if it detects a sudden change in weather conditions. It can also periodically notify users of information regarding crop growth and harvest times. Furthermore, it can provide real-time notifications regarding market demand and price fluctuations. By incorporating this notification unit, users can receive important information in a timely manner, enabling them to respond quickly.

[0074] The AgriPlanner system can also be equipped with a learning unit. This learning unit can learn from user behavior and choices, improving the system's accuracy. For example, it can analyze the history of crops and sales routes selected by the user and incorporate this into future suggestions. Furthermore, the learning unit can collect user feedback and improve the system's algorithms. It can also learn from the success stories of other users and propose optimal agricultural plans. Thus, incorporating a learning unit enables personalized suggestions tailored to the user's needs.

[0075] The AgriPlanner system can also be equipped with a simulation unit. This unit can simulate agricultural plans based on collected data. For example, it can simulate different crop combinations and planting times to propose an optimal plan. It can also simulate fluctuations in weather conditions and propose measures to minimize risks. Furthermore, it can simulate logistics and sales routes to propose efficient distribution plans. By incorporating this simulation unit, users can verify the effectiveness of their plans in advance and create optimal agricultural plans.

[0076] The AgriPlanner system can also be equipped with an advisory section. This advisory section can provide users with expert advice. For example, it can offer advice on crop selection and cultivation methods. It can also provide advice on pest and disease prevention and control. Furthermore, it can offer advice on post-harvest processing and storage methods. By including this advisory section, users can gain expert knowledge and practice more effective agriculture.

[0077] The following briefly describes the processing flow for example form 1.

[0078] Step 1: The data collection unit collects multifaceted data such as geographic information, soil data, weather conditions, and market data. For example, GPS data can be used to collect geographic information, soil sensors can be used to collect soil data, and weather sensors can be used to collect weather conditions. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses AI to analyze the collected geographic information, soil data, weather conditions, and market data to provide information for selecting the optimal crop. Step 3: The selection unit selects the optimal crop based on the data analyzed by the analysis unit. For example, it uses AI to select the optimal crop based on criteria such as profitability, growth conditions, and demand. Step 4: The procurement department secures sales routes based on the crops selected by the selection department. For example, it uses AI to secure sales routes such as direct sales, wholesale, and online sales. Step 5: The planning department creates a planting plan based on the sales routes secured by the procurement department. For example, they use AI to create planting plans including planting time, area, and crop types. Step 6: The management department manages logistics based on the planting plan developed by the planning department. For example, it uses AI to manage logistics such as transportation methods, storage methods, and delivery schedules. Step 7: The Strategy Department develops marketing strategies based on logistics managed by the Management Department. For example, marketing strategies such as target markets, promotion methods, and pricing are developed using AI.

[0079] (Example of form 2) The AgriPlanner System according to an embodiment of the present invention is a system that analyzes multifaceted data such as geographic information, soil data, weather conditions, and market data, and autonomously plans everything from selecting the optimal crop to securing sales routes. The AgriPlanner System collects multifaceted data such as geographic information, soil data, weather conditions, and market data, and the AI ​​analyzes it to select the optimal crop. Subsequently, based on the selected crop, it autonomously plans securing sales routes, planting plans, logistics, and marketing strategies. For example, the AgriPlanner System collects multifaceted data such as geographic information, soil data, weather conditions, and market data. Next, the AI ​​analyzes the collected data to select the optimal crop. For example, the AI ​​selects the optimal crop based on criteria such as profitability, growth conditions, and demand. Subsequently, based on the selected crop, it autonomously plans securing sales routes, planting plans, logistics, and marketing strategies. For example, the AI ​​secures sales routes such as direct sales, wholesale, and online sales based on the selected crop. Furthermore, the AgriPlanner system provides comprehensive support for agriculture, including automatic contract generation, guidance on legal procedures, and provision of subsidy and grant information. For example, AI automatically generates contracts and provides guidance on legal procedures. By providing subsidy and grant information, even beginners can start farming with confidence. In addition, the AgriPlanner system supports communication after matching through its community function and promotes collaboration with local communities. For example, AI supports communication after matching through its community function. This reduces the burden of management costs and property taxes on abandoned farmland and eliminates the effort and reliability concerns of finding suitable users. It also solves problems such as lack of access to agricultural know-how and market information, and lack of information on land condition and suitability. In this way, the AgriPlanner system aims to eliminate abandoned farmland and create vibrant agriculture and prosperous local communities. In this way, the AgriPlanner system allows even beginners to start farming with confidence and promotes the effective use of abandoned farmland.

[0080] The agricultural planner system according to this embodiment comprises a collection unit, an analysis unit, a selection unit, a securing unit, a planning unit, a management unit, and a strategy unit. The collection unit collects multifaceted data such as geographic information, soil data, weather conditions, and market data. For example, the collection unit can use GPS data to collect geographic information. It can also use soil sensors to collect soil data. Furthermore, it can use weather sensors to collect weather conditions. For example, the collection unit can use GPS data to collect geographic information, soil sensors to collect soil data, and weather sensors to collect weather conditions. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected geographic information, soil data, weather conditions, and market data to provide information for selecting the optimal crop. The analysis unit can use AI to analyze the collected data. For example, the analysis unit uses AI to analyze the collected geographic information, soil data, weather conditions, and market data to provide information for selecting the optimal crop. The selection unit selects the optimal crop based on the data analyzed by the analysis unit. The selection unit selects the optimal crop based on criteria such as profitability, growth conditions, and demand. The selection unit can use AI to select the optimal crop. For example, the selection unit can use AI to select the optimal crop based on criteria such as profitability, growth conditions, and demand. The securing unit secures sales routes based on the crops selected by the selection unit. The securing unit secures sales routes such as direct sales, wholesale, and online sales. The securing unit can use AI to secure sales routes. For example, the securing unit uses AI to secure sales routes such as direct sales, wholesale, and online sales based on the selected crops. The planning unit creates a planting plan based on the sales routes secured by the securing unit. The planning unit creates a planting plan such as planting time, area, and crop type. The planning unit can use AI to create a planting plan. For example, the planning unit uses AI to create a planting plan such as planting time, area, and crop type based on the secured sales routes. The management department manages logistics based on the planting plan developed by the planning department.The Management Department manages logistics, such as transportation methods, storage methods, and delivery schedules. The Management Department can manage logistics using AI. For example, the Management Department uses AI to manage logistics, such as transportation methods, storage methods, and delivery schedules, based on the planting plan. The Strategy Department develops marketing strategies based on the logistics managed by the Management Department. The Strategy Department develops marketing strategies, such as target markets, promotion methods, and pricing. The Strategy Department can develop marketing strategies using AI. For example, the Strategy Department uses AI to develop marketing strategies, such as target markets, promotion methods, and pricing, based on the managed logistics. As a result, the AgriPlanner system according to this embodiment can analyze multifaceted data such as geographic information, soil data, weather conditions, and market data, and autonomously plan everything from selecting the optimal crops to securing sales routes.

[0081] The data collection unit collects multifaceted data such as geographic information, soil data, weather conditions, and market data. Specifically, it can use GPS data to collect geographic information. GPS data provides detailed geographic information such as the location, area, and topography of farmland. This enables the selection of appropriate crops based on the characteristics and location of the farmland. The data collection unit can also use soil sensors to collect soil data. Soil sensors acquire information such as soil pH, humidity, and nutrient content in real time. This enables the selection of appropriate fertilizers and the development of fertilization plans according to the soil condition. Furthermore, the data collection unit can use weather sensors to collect weather conditions. Weather sensors collect weather data such as temperature, humidity, precipitation, and wind speed, and based on this data, provide information to provide the optimal environment for crop growth. For example, the data collection unit can use GPS data to collect geographic information, soil sensors to collect soil data, and weather sensors to collect weather conditions. In this way, the data collection unit can efficiently collect multifaceted data and provide the information necessary for optimizing agriculture. Furthermore, the data collection unit can also collect market data. Market data includes crop price trends, demand forecasts, and competitive landscapes. This data enables the selection of highly profitable crops and the development of sales strategies. The data collection unit centrally manages this data and can integrate with other systems and departments as needed. For example, collected data is stored on a cloud server, allowing access from the analysis and selection units. Furthermore, adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0082] The analysis unit analyzes the data collected by the collection unit. Specifically, it analyzes collected geographic information, soil data, weather conditions, and market data to provide information for selecting the optimal crops. The analysis unit can use AI to analyze the collected data. For example, the analysis unit uses AI to analyze collected geographic information, soil data, weather conditions, and market data to provide information for selecting the optimal crops. The AI ​​uses machine learning algorithms to learn from past data and patterns and make future predictions. For example, the AI ​​predicts future weather conditions based on past weather data and identifies the optimal time for crop growth based on that. The AI ​​also analyzes soil data to select appropriate fertilizers and plan fertilization according to the soil condition. Furthermore, the AI ​​analyzes market data to forecast crop price trends and demand, supporting the selection of highly profitable crops. As a result, the analysis unit can quickly and accurately analyze the collected data and provide the information necessary for optimizing agriculture. In addition, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on historical weather and market data, it can predict risk fluctuations in specific regions and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0083] The selection unit selects the optimal crop based on data analyzed by the analysis unit. Specifically, it selects the optimal crop based on criteria such as profitability, growth conditions, and demand. The selection unit can also use AI to select the optimal crop. For example, the selection unit uses AI to select the optimal crop based on criteria such as profitability, growth conditions, and demand. The AI ​​evaluates the profitability of each crop based on the collected data and identifies the most profitable crop. The AI ​​also analyzes growth conditions and selects the optimal crop for specific regions and weather conditions. Furthermore, the AI ​​analyzes market data and identifies crops with high demand. This allows the selection unit to select the optimal crop based on criteria such as profitability, growth conditions, and demand. In addition, the selection unit can formulate a cultivation plan based on the selected crop. For example, the selection unit plans the cultivation period and method for the selected crop and formulates an optimal cultivation plan. This allows the selection unit to not only select highly profitable crops but also formulate optimal cultivation plans, supporting improved efficiency and profitability in agriculture.

[0084] The procurement department secures sales channels based on the crops selected by the selection department. Specifically, it secures sales channels such as direct sales, wholesale, and online sales. The procurement department can use AI to secure sales channels. For example, the procurement department uses AI to secure sales channels such as direct sales, wholesale, and online sales based on the selected crops. The AI ​​analyzes market data and identifies the most effective sales channels. For example, based on past sales data, the AI ​​identifies the most effective regions and times for direct sales and develops a sales plan tailored to those regions and times. The AI ​​also predicts online sales demand and develops an optimal online sales strategy. This allows the procurement department to secure the most effective sales channels based on the selected crops. Furthermore, the procurement department can not only secure sales channels but also develop sales strategies. For example, the procurement department plans the sales price and promotion methods for the selected crops and develops an optimal sales strategy. This allows the procurement department to not only secure sales channels but also develop sales strategies, thereby improving the profitability of agriculture.

[0085] The planning department creates planting plans based on sales channels secured by the procurement department. Specifically, it creates planting plans including planting time, area, and crop types. The planning department can use AI to create planting plans. For example, the planning department can use AI to create planting plans including planting time, area, and crop types based on secured sales channels. The AI ​​identifies the optimal planting time and area based on collected data. For example, the AI ​​analyzes weather data to identify the optimal planting time. The AI ​​also analyzes soil data to identify the optimal planting area. Furthermore, the AI ​​analyzes market data to identify the optimal crop types. This allows the planning department to create optimal planting plans based on secured sales channels. In addition, the planning department can not only create planting plans but also handle their execution. For example, the planning department can create planting schedules based on the planting plans and manage the progress of the work. This allows the planning department to not only create planting plans but also handle their execution, supporting improved efficiency and profitability in agriculture.

[0086] The Management Department manages logistics based on the planting plan developed by the Planning Department. Specifically, it manages logistics such as transportation methods, storage methods, and delivery schedules. The Management Department can use AI to manage logistics. For example, the Management Department uses AI to manage logistics such as transportation methods, storage methods, and delivery schedules based on the planting plan. Based on collected data, the AI ​​identifies the optimal transportation methods and storage methods. For example, the AI ​​analyzes weather data to identify the optimal transportation method. The AI ​​also analyzes soil data to identify the optimal storage method. Furthermore, the AI ​​analyzes market data to identify the optimal delivery schedule. This allows the Management Department to manage optimal logistics based on the planting plan. In addition, the Management Department can not only manage logistics but also optimize them. For example, the Management Department reviews transportation methods and storage methods to improve logistics efficiency and develops an optimal logistics plan. This allows the Management Department to not only manage logistics but also optimize them, supporting improved efficiency and profitability in agriculture.

[0087] The Strategy Department develops marketing strategies based on logistics managed by the Management Department. Specifically, it develops marketing strategies such as target markets, promotion methods, and pricing. The Strategy Department can use AI to develop marketing strategies. For example, the Strategy Department uses AI to develop marketing strategies such as target markets, promotion methods, and pricing based on managed logistics. The AI ​​analyzes market data and identifies the optimal target market. For example, the AI ​​identifies the most effective target market based on past sales data and plans promotion methods tailored to that market. The AI ​​also optimizes pricing to improve profitability. This allows the Strategy Department to develop optimal marketing strategies based on managed logistics. Furthermore, the Strategy Department can not only develop marketing strategies but also handle their execution. For example, the Strategy Department implements promotional activities based on the developed marketing strategies and evaluates their effectiveness. This allows the Strategy Department to handle not only the development but also the execution of marketing strategies, thereby improving the profitability of agriculture.

[0088] The support department can provide services such as automatic contract generation, legal procedure guidance, and subsidy / grant information. For example, the support department can automatically generate contracts. The support department can use AI to automatically generate contracts. For example, the support department can generate contracts using templates or AI. The support department can provide legal procedure guidance. The support department can use AI to provide legal procedure guidance. For example, the support department can provide guidance on procedural steps, required documents, and submission methods. The support department can provide subsidy / grant information. The support department can use AI to provide subsidy / grant information. For example, the support department can provide information on application requirements, application methods, and grant amounts. By providing services such as automatic contract generation, legal procedure guidance, and subsidy / grant information, the support department can offer comprehensive support for agriculture.

[0089] The Community Department can support post-matching communication through community features. The Community Department provides community features such as forums, chat, and group functions. The Community Department can also provide community features using AI. For example, the Community Department provides community features such as forums, chat, and group functions using AI. The Community Department supports post-matching communication. The Community Department can support post-matching communication using AI. For example, the Community Department provides support for messaging, video calls, and event hosting. This allows for support of post-matching communication through community features and promotes collaboration with local communities.

[0090] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. If the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can speed up the timing of data collection to quickly provide the necessary information. By adjusting the timing of data collection according to the user's emotions, the burden on the user is reduced and efficient data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0091] The data collection unit can analyze past collected data and select the optimal data collection method. For example, the data collection unit can identify the most effective data collection method from past collected data and prioritize its use. The data collection unit can analyze past collected data, find areas for improvement in data collection methods, and optimize them. Based on past collected data, the data collection unit can analyze patterns in data collection methods and collect data at the optimal timing. This allows for the selection of the optimal data collection method and improved data collection efficiency by analyzing past collected data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal data collection method.

[0092] The data collection unit can filter data based on the user's current farming situation and areas of interest during data collection. For example, the data collection unit can collect only the necessary data based on the user's current farming situation. The data collection unit can prioritize the collection of highly relevant data based on the user's areas of interest. The data collection unit can adjust the scope of data collection according to the user's farming situation and areas of interest. This allows for the efficient collection of only the necessary data by filtering 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 AI or not. For example, the data collection unit can input data on the user's current farming situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0093] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting high-priority data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. If the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. In this way, important data can be prioritized 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority.

[0094] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of region-related data based on the user's geographical location information. The data collection unit can prioritize the collection of data related to weather conditions, taking into account the user's geographical location information. The data collection unit can prioritize the collection of soil data based on the user's geographical location information. This enables efficient data collection by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0095] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the user's social media activity and collect data related to crops of interest. Based on the user's social media activity, the data collection unit can collect data related to agricultural technologies of interest. The data collection unit can use the user's social media activity as a reference to collect market data of interest. In this way, by analyzing the user's social media activity, data related to crops and agricultural technologies of interest can be efficiently collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0096] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, the analysis results can be made easier for the user to understand. 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0097] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0098] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a weather forecasting algorithm to weather data. For soil data, it can apply a soil analysis algorithm. For market data, it can apply a market forecasting algorithm. By applying different analysis algorithms depending on the data category, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0099] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a concise analysis result that gets straight to the point. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an analysis result of an appropriate length for the user. 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0100] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. The analysis unit can adjust the priority of analysis according to the data collection timing. This allows the analysis 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-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0101] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0102] The selection unit can estimate the user's emotions and adjust the crop selection criteria based on the estimated emotions. For example, if the user is stressed, the selection unit may prioritize easy-to-grow crops. If the user is relaxed, the selection unit may prioritize highly profitable crops. If the user is in a hurry, the selection unit may prioritize fast-growing crops. In this way, by adjusting the crop selection criteria according to the user's emotions, the system can select crops that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the selection unit may be performed using AI or not. For example, the selection unit can input user emotion data into a generative AI and have the generative AI adjust the crop selection criteria.

[0103] The selection unit can improve the accuracy of its selection process by considering the interrelationships between data. For example, the selection unit can select crops by considering the interrelationships between weather data and soil data. The selection unit can select crops by considering the interrelationships between market data and geographic information. The selection unit can analyze the interrelationships between data and select the optimal crop. This improves the accuracy of the selection process by considering the interrelationships between data. Some or all of the above-described processes in the selection unit may be performed using AI or not. For example, the selection unit can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of the selection.

[0104] The selection unit can make selections while considering the attribute information of the data submitter. For example, if the data submitter is a novice farmer, the selection unit can select crops that are easy to grow. If the data submitter is an experienced farmer, the selection unit can select crops that are highly profitable. The selection unit can select the optimal crop based on the attribute information of the data submitter. In this way, the optimal crop can be selected by considering the attribute information of the data submitter. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the attribute information of the data submitter into a generating AI and have the generating AI perform the selection.

[0105] The selection unit can estimate the user's emotions and adjust the order in which the selection results are displayed based on the estimated emotions. For example, if the user is stressed, the selection unit can display important results first. If the user is relaxed, the selection unit can display detailed results sequentially. If the user is in a hurry, the selection unit can display concise results first. By adjusting the order in which the selection results are displayed according to the user's emotions, the system can provide 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input user emotion data into a generative AI and have the generative AI adjust the order in which the results are displayed.

[0106] The selection unit can perform selection while considering the geographical distribution of the data. For example, the selection unit can select a geographically suitable crop. The selection unit can select the optimal crop based on the geographical distribution. The selection unit can perform crop selection while considering geographical conditions. In this way, by considering the geographical distribution of the data, a geographically suitable crop can be selected. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the selection.

[0107] The selection unit can improve the accuracy of its selection by referring to relevant literature during the selection process. For example, the selection unit selects crops by referring to relevant literature. The selection unit can improve the accuracy of its selection based on the information in the relevant literature. The selection unit can analyze the relevant literature and select the optimal crop. In this way, the accuracy of the selection can be improved by referring to relevant literature for the data. Some or all of the above-described processes in the selection unit may be performed using AI or not. For example, the selection unit can input relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of the selection.

[0108] The sales unit can estimate the user's emotions and adjust the sales route securing method based on the estimated user emotions. For example, if the user is stressed, the sales unit can prioritize securing easy sales routes. If the user is relaxed, the sales unit can prioritize securing highly profitable sales routes. If the user is in a hurry, the sales unit can prioritize securing sales routes that can be secured quickly. In this way, by adjusting the sales route securing method according to the user's emotions, the sales unit can secure sales routes that are appropriate for the user. 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sales unit may be performed using AI or not. For example, the sales unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the sales route securing method.

[0109] The securing unit can select the optimal securing method by referring to past sales route data when securing inventory. For example, the securing unit can analyze past sales route data and select the optimal securing method. Based on past sales route data, the securing unit can identify areas for improvement in the securing method. The securing unit can analyze patterns of securing methods by referring to past sales route data. As a result, by referring to past sales route data, the optimal securing method can be selected, enabling efficient sales route securing. Some or all of the above processes in the securing unit may be performed using AI, or they may not be performed using AI. For example, the securing unit can input past sales route data into a generating AI and have the generating AI select the optimal securing method.

[0110] The procurement unit can customize the means of sales routes based on the current market situation when securing sales. For example, the procurement unit can analyze the current market situation and customize the optimal sales route. The procurement unit can adjust the means of sales routes according to market demand. The procurement unit can optimize the means of sales routes considering the market supply situation. This makes it possible to secure sales routes efficiently by customizing the means of sales routes based on the current market situation. Some or all of the above processes in the procurement unit may be performed using AI or not. For example, the procurement unit can input the current market situation into a generating AI and have the generating AI perform the customization of the means of sales routes.

[0111] The sales unit can estimate the user's emotions and determine the priority of sales route acquisition based on the estimated emotions. For example, if the user is stressed, the sales unit can prioritize securing important sales routes. If the user is relaxed, the sales unit can prioritize securing highly profitable sales routes. If the user is in a hurry, the sales unit can prioritize securing sales routes that can be secured quickly. In this way, by determining the priority of sales route acquisition according to the user's emotions, important sales routes can be prioritized. 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 sales unit may be performed using AI or not. For example, the sales unit can input user emotion data into a generative AI and have the generative AI perform the determination of sales route acquisition priorities.

[0112] The procurement unit can select the optimal sales route by considering geographical location information during procurement. For example, the procurement unit selects the optimal sales route based on geographical location information. The procurement unit can improve the efficiency of the sales route by considering geographical location information. The procurement unit can optimize the means of the sales route based on geographical location information. As a result, by considering geographical location information, an efficient sales route can be selected. Some or all of the above processing in the procurement unit may be performed using AI or not. For example, the procurement unit can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal sales route.

[0113] The procurement unit can analyze social media activity during procurement and propose sales route methods. For example, the procurement unit analyzes social media activity and proposes the optimal sales route. The procurement unit can customize sales route methods based on social media activity. The procurement unit can optimize sales route methods by referring to social media activity. In this way, by analyzing social media activity, it can propose the optimal sales route method. Some or all of the above processing in the procurement unit may be performed using AI or not. For example, the procurement unit can input social media activity data into a generating AI and have the generating AI execute the proposal of sales route methods.

[0114] The planning unit can estimate the user's emotions and adjust the planting plan based on the estimated emotions. For example, if the user is stressed, the planning unit will prioritize a simple planting plan. If the user is relaxed, the planning unit can create a detailed planting plan. If the user is in a hurry, the planning unit can create a planting plan quickly. In this way, by adjusting the planting plan according to the user's emotions, an appropriate planting plan can be created 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 planning unit may be performed using AI or not. For example, the planning unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the planting plan.

[0115] The planning department can select the optimal planning method by referring to past planting data during the planning stage. For example, the planning department can analyze past planting data and select the optimal planning method. Based on past planting data, the planning department can identify areas for improvement in the planning method. The planning department can analyze patterns in the planning method by referring to past planting data. As a result, by referring to past planting data, the optimal planning method can be selected and an efficient planting plan can be created. Some or all of the above processes in the planning department may be performed using AI or not. For example, the planning department can input past planting data into a generating AI and have the generating AI select the optimal planning method.

[0116] The planning unit can customize the planting plan based on the current agricultural conditions during the planning stage. For example, the planning unit can analyze the current agricultural conditions and customize the optimal planting plan. The planning unit can adjust the planting plan according to agricultural demand. The planning unit can optimize the planting plan considering the agricultural supply situation. This allows for the creation of an efficient planting plan by customizing the planting plan based on the current agricultural conditions. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input the current agricultural conditions into a generating AI and have the generating AI perform the customization of the planting plan.

[0117] The planning unit can estimate the user's emotions and determine the priority of planting plans based on the estimated emotions. For example, if the user is stressed, the planning unit can prioritize important planting plans. If the user is relaxed, the planning unit can prioritize detailed planting plans. If the user is in a hurry, the planning unit can prioritize planting plans that can be created quickly. In this way, by determining the priority of planting plans according to the user's emotions, important planting plans can be prioritized. 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 planning unit may be performed using AI or not. For example, the planning unit can input user emotion data into a generative AI and have the generative AI perform the determination of planting plan priorities.

[0118] The planning unit can select the optimal planting plan by considering geographical location information during the planning stage. For example, the planning unit selects the optimal planting plan based on geographical location information. The planning unit can improve the efficiency of the planting plan by considering geographical location information. The planning unit can optimize the means of the planting plan based on geographical location information. As a result, by considering geographical location information, an efficient planting plan can be selected. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal planting plan.

[0119] The planning department can analyze social media activity during the planning stage and propose planting plans. For example, the planning department can analyze social media activity and propose an optimal planting plan. The planning department can customize planting plans based on social media activity. The planning department can optimize planting plans by referring to social media activity. In this way, by analyzing social media activity, it is possible to propose an optimal planting plan. Some or all of the above processes in the planning department may be performed using AI or not. For example, the planning department can input social media activity data into a generating AI and have the generating AI propose planting plans.

[0120] The management department can estimate the user's emotions and adjust the logistics management method based on the estimated emotions. For example, if the user is stressed, the management department can prioritize using a simple logistics management method. If the user is relaxed, the management department can use a detailed logistics management method. If the user is in a hurry, the management department can use a method that allows for rapid logistics management. In this way, by adjusting the logistics management method according to the user's emotions, appropriate logistics management can be provided to 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 management department may be performed using AI or not. For example, the management department can input user emotion data into a generative AI and have the generative AI perform the adjustment of the logistics management method.

[0121] The management department can select the optimal management method by referring to past logistics data during management. For example, the management department can analyze past logistics data and select the optimal management method. Based on past logistics data, the management department can identify areas for improvement in management methods. The management department can analyze patterns in management methods by referring to past logistics data. As a result, by referring to past logistics data, the optimal management method can be selected and efficient logistics management can be carried out. Some or all of the above processes in the management department may be performed using AI, or they may not be performed using AI. For example, the management department can input past logistics data into a generating AI and have the generating AI perform the selection of the optimal management method.

[0122] The management department can customize logistics management methods based on the current logistics situation during management. For example, the management department can analyze the current logistics situation and customize the optimal logistics management method. The management department can adjust logistics management methods according to logistics demand. The management department can optimize logistics management methods considering the logistics supply situation. This enables efficient logistics management by customizing logistics management methods based on the current logistics situation. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the current logistics situation into a generating AI and have the generating AI perform the customization of logistics management methods.

[0123] The management department can estimate the user's emotions and determine logistics management priorities based on those estimated emotions. For example, if the user is stressed, the management department can prioritize critical logistics management. If the user is relaxed, the management department can prioritize detailed logistics management. If the user is in a hurry, the management department can prioritize logistics management that can be done quickly. In this way, critical logistics management can be prioritized by determining logistics management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input user emotion data into a generative AI and have the generative AI determine logistics management priorities.

[0124] The management department can select the optimal logistics management method while considering geographical location information during management. For example, the management department can select the optimal logistics management method based on geographical location information. The management department can improve the efficiency of logistics management by considering geographical location information. The management department can optimize the means of logistics management based on geographical location information. As a result, efficient logistics management can be performed by considering geographical location information. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal logistics management method.

[0125] The management department can analyze social media activity during management and propose logistics management methods. For example, the management department can analyze social media activity and propose the optimal logistics management method. The management department can customize logistics management methods based on social media activity. The management department can optimize logistics management methods by referring to social media activity. In this way, by analyzing social media activity, the optimal logistics management methods can be proposed. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input social media activity data into a generating AI and have the generating AI execute proposals for logistics management methods.

[0126] The Strategy Department can estimate user emotions and adjust marketing strategies based on those emotions. For example, if a user is stressed, the Strategy Department might prioritize simple marketing strategies. If a user is relaxed, it might use more detailed marketing strategies. If a user is in a hurry, it might use methods to quickly develop marketing strategies. This allows for the development of appropriate marketing strategies for users by adjusting them according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 processes in the Strategy Department may be performed using AI or not. For example, the Strategy Department can input user emotion data into a generative AI and have the generative AI adjust marketing strategies.

[0127] The Strategy Department can select the optimal strategic approach by referring to past marketing data when formulating a strategy. For example, the Strategy Department can analyze past marketing data and select the optimal strategic approach. Based on past marketing data, the Strategy Department can identify areas for improvement in the strategic approach. The Strategy Department can analyze patterns in strategic approaches by referring to past marketing data. As a result, by referring to past marketing data, the optimal strategic approach can be selected and an efficient marketing strategy can be formulated. Some or all of the above processes in the Strategy Department may be performed using AI, or they may not be performed using AI. For example, the Strategy Department can input past marketing data into a generating AI and have the generating AI select the optimal strategic approach.

[0128] The Strategy Department can customize the means of marketing strategy based on the current market situation when formulating a strategy. For example, the Strategy Department can analyze the current market situation and customize the optimal marketing strategy. The Strategy Department can adjust the means of marketing strategy according to market demand. The Strategy Department can optimize the means of marketing strategy considering the market supply situation. In this way, an efficient marketing strategy can be formulated by customizing the means of marketing strategy based on the current market situation. Some or all of the above processes in the Strategy Department may be performed using AI or not. For example, the Strategy Department can input the current market situation into a generating AI and have the generating AI perform the customization of the means of marketing strategy.

[0129] The strategy department can estimate user emotions and prioritize marketing strategies based on those estimated emotions. For example, if a user is stressed, the strategy department can prioritize important marketing strategies. If a user is relaxed, the strategy department can prioritize detailed marketing strategies. If a user is in a hurry, the strategy department can prioritize marketing strategies that can be developed quickly. This allows for prioritizing important marketing strategies according to user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 strategy department may be performed using AI or not. For example, the strategy department can input user emotion data into a generative AI and have the generative AI determine the prioritization of marketing strategies.

[0130] The Strategy Department can select the optimal marketing strategy by considering geographical location information when formulating a strategy. For example, the Strategy Department can select the optimal marketing strategy based on geographical location information. The Strategy Department can improve the efficiency of the marketing strategy by considering geographical location information. The Strategy Department can optimize the means of the marketing strategy based on geographical location information. In this way, by considering geographical location information, an efficient marketing strategy can be formulated. Some or all of the above processes in the Strategy Department may be performed using AI or not. For example, the Strategy Department can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal marketing strategy.

[0131] The Strategy Department can analyze social media activity and propose marketing strategy measures when formulating strategies. For example, the Strategy Department can analyze social media activity and propose the optimal marketing strategy. The Strategy Department can customize the marketing strategy measures based on social media activity. The Strategy Department can optimize the marketing strategy measures by referring to social media activity. In this way, by analyzing social media activity, the Strategy Department can propose the optimal marketing strategy measures. Some or all of the above processes in the Strategy Department may be performed using AI or not. For example, the Strategy Department can input social media activity data into a generating AI and have the generating AI execute the proposal of marketing strategy measures.

[0132] The support unit can estimate the user's emotions and adjust the automatic generation of contracts and legal procedure guidance based on the estimated emotions. For example, if the user is stressed, the support unit will prioritize the automatic generation of a simple contract. If the user is relaxed, the support unit can automatically generate a detailed contract. If the user is in a hurry, the support unit can use a method to quickly automatically generate a contract. This allows the support unit to provide appropriate support to the user by adjusting the automatic generation of contracts and legal procedure guidance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform the automatic generation of contracts and adjust the legal procedure guidance.

[0133] The support department can select the optimal support method by referring to past legal procedure data during support. For example, the support department can analyze past legal procedure data and select the optimal support method. Based on past legal procedure data, the support department can identify areas for improvement in support methods. The support department can analyze support method patterns by referring to past legal procedure data. As a result, by referring to past legal procedure data, the optimal support method can be selected and efficient support can be provided. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input past legal procedure data into a generating AI and have the generating AI select the optimal support method.

[0134] The support unit can estimate the user's emotions and adjust the method of providing subsidy and grant information based on the estimated emotions. For example, if the user is stressed, the support unit will prioritize providing simple subsidy and grant information. If the user is relaxed, the support unit can provide detailed subsidy and grant information. If the user is in a hurry, the support unit can use a method to provide subsidy and grant information quickly. In this way, by adjusting the method of providing subsidy and grant information according to the user's emotions, the support unit can provide information that is appropriate 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 support unit may be performed using AI or not. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the method of providing subsidy and grant information.

[0135] The support unit can provide guidance on the most appropriate legal procedure when providing support, taking geographical location information into consideration. For example, the support unit can provide guidance on the most appropriate legal procedure based on geographical location information. The support unit can improve the efficiency of legal procedures by taking geographical location information into consideration. The support unit can optimize the means of legal procedures based on geographical location information. In this way, by taking geographical location information into consideration, it is possible to provide guidance on efficient legal procedures. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input geographical location information into a generating AI and have the generating AI perform the task of providing guidance on the most appropriate legal procedure.

[0136] The community unit can estimate the user's emotions and adjust its communication support methods based on those emotions. For example, if the user is stressed, the community unit can prioritize simple communication support. If the user is relaxed, the community unit can provide more detailed communication support. If the user is in a hurry, the community unit can use methods to provide rapid communication support. This allows the community unit to provide appropriate communication support to the user by adjusting its methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the community unit may be performed using AI or not. For example, the community unit can input user emotion data into a generative AI and have the generative AI adjust its communication support methods.

[0137] The Community Department can select the optimal support method by referring to past communication data when providing communication support. For example, the Community Department can analyze past communication data and select the optimal support method. Based on past communication data, the Community Department can identify areas for improvement in support methods. The Community Department can analyze support method patterns by referring to past communication data. As a result, by referring to past communication data, the optimal support method can be selected and efficient communication support can be provided. Some or all of the above processes in the Community Department may be performed using AI or not. For example, the Community Department can input past communication data into a generating AI and have the generating AI select the optimal support method.

[0138] The community unit can estimate the user's emotions and prioritize communication support based on those emotions. For example, if the user is stressed, the community unit will prioritize important communication support. If the user is relaxed, the community unit will prioritize detailed communication support. If the user is in a hurry, the community unit will prioritize communication support that can be provided quickly. This allows for prioritizing important communication support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the community unit may be performed using AI or not. For example, the community unit can input user emotion data into a generative AI and have the generative AI determine the priority of communication support.

[0139] The community department can provide optimal support methods when providing communication support, taking geographical location information into consideration. For example, the community department can provide optimal communication support methods based on geographical location information. The community department can improve the efficiency of communication support by taking geographical location information into consideration. The community department can optimize the means of communication support based on geographical location information. As a result, efficient communication support can be provided by taking geographical location information into consideration. Some or all of the above processing in the community department may be performed using AI or not. For example, the community department can input geographical location information into a generating AI and have the generating AI perform the task of providing optimal communication support methods.

[0140] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0141] The AgriPlanner system can also be equipped with a forecasting unit. This unit can predict future agricultural trends based on collected data. For example, it can predict future weather conditions based on meteorological data and suggest the optimal time for crop growth. It can also predict future market demand based on market data and provide information for selecting profitable crops. Furthermore, it can predict soil changes based on soil data and suggest appropriate fertilizers and improvement methods. Thus, by incorporating a forecasting unit, it becomes possible to predict future agricultural trends and create more effective agricultural plans.

[0142] The AgriPlanner system can also be equipped with a notification unit. This unit can notify users of important information and alerts. For example, it can issue a warning to the user if it detects a sudden change in weather conditions. It can also periodically notify users of information regarding crop growth and harvest times. Furthermore, it can provide real-time notifications regarding market demand and price fluctuations. By incorporating this notification unit, users can receive important information in a timely manner, enabling them to respond quickly.

[0143] The AgriPlanner system can also be equipped with a learning unit. This learning unit can learn from user behavior and choices, improving the system's accuracy. For example, it can analyze the history of crops and sales routes selected by the user and incorporate this into future suggestions. Furthermore, the learning unit can collect user feedback and improve the system's algorithms. It can also learn from the success stories of other users and propose optimal agricultural plans. Thus, incorporating a learning unit enables personalized suggestions tailored to the user's needs.

[0144] The AgriPlanner system can also be equipped with a simulation unit. This unit can simulate agricultural plans based on collected data. For example, it can simulate different crop combinations and planting times to propose an optimal plan. It can also simulate fluctuations in weather conditions and propose measures to minimize risks. Furthermore, it can simulate logistics and sales routes to propose efficient distribution plans. By incorporating this simulation unit, users can verify the effectiveness of their plans in advance and create optimal agricultural plans.

[0145] The AgriPlanner system can also be equipped with an advisory section. This advisory section can provide users with expert advice. For example, it can offer advice on crop selection and cultivation methods. It can also provide advice on pest and disease prevention and control. Furthermore, it can offer advice on post-harvest processing and storage methods. By including this advisory section, users can gain expert knowledge and practice more effective agriculture.

[0146] The AgriPlanner system can further utilize emotion estimation to select crops based on the user's emotions. For example, if the user is stressed, it can prioritize easy-to-grow crops. If the user is relaxed, it can select highly profitable crops. Furthermore, if the user is in a hurry, it can select fast-growing crops. In this way, by using emotion estimation, the system can select the optimal crops according to the user's emotions.

[0147] The AgriPlanner system can further utilize its emotion estimation function to secure sales routes based on the user's emotions. For example, if a user is stressed, it can prioritize securing easy sales routes. If a user is relaxed, it can prioritize securing highly profitable sales routes. Furthermore, if a user is in a hurry, it can prioritize securing sales routes that can be secured quickly. In this way, by using the emotion estimation function, the system can secure the optimal sales route according to the user's emotions.

[0148] The AgriPlanner system can further utilize emotion estimation to create planting plans based on the user's emotions. For example, if the user is stressed, a simple planting plan can be prioritized. Conversely, if the user is relaxed, a detailed planting plan can be created. Furthermore, if the user is in a hurry, a planting plan can be created quickly. In this way, by using emotion estimation, the system can create an optimal planting plan that is tailored to the user's emotions.

[0149] The AgriPlanner system can further utilize emotion estimation to manage logistics based on the user's emotions. For example, if a user is stressed, a simple logistics management method can be prioritized. Conversely, if a user is relaxed, a detailed logistics management method can be used. Furthermore, if a user is in a hurry, a method for rapid logistics management can be used. In this way, by using emotion estimation, optimal logistics management can be provided according to the user's emotions.

[0150] The AgriPlanner system can further utilize its emotion estimation function to develop marketing strategies based on user emotions. For example, if a user is stressed, a simple marketing strategy can be prioritized. Conversely, if a user is relaxed, a more detailed marketing strategy can be used. Furthermore, if a user is in a hurry, a method for quickly developing a marketing strategy can be employed. In this way, by using the emotion estimation function, the system can develop the optimal marketing strategy tailored to the user's emotions.

[0151] The following briefly describes the processing flow for example form 2.

[0152] Step 1: The data collection unit collects multifaceted data such as geographic information, soil data, weather conditions, and market data. For example, GPS data can be used to collect geographic information, soil sensors can be used to collect soil data, and weather sensors can be used to collect weather conditions. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses AI to analyze the collected geographic information, soil data, weather conditions, and market data to provide information for selecting the optimal crop. Step 3: The selection unit selects the optimal crop based on the data analyzed by the analysis unit. For example, it uses AI to select the optimal crop based on criteria such as profitability, growth conditions, and demand. Step 4: The procurement department secures sales routes based on the crops selected by the selection department. For example, it uses AI to secure sales routes such as direct sales, wholesale, and online sales. Step 5: The planning department creates a planting plan based on the sales routes secured by the procurement department. For example, they use AI to create planting plans including planting time, area, and crop types. Step 6: The management department manages logistics based on the planting plan developed by the planning department. For example, it uses AI to manage logistics such as transportation methods, storage methods, and delivery schedules. Step 7: The Strategy Department develops marketing strategies based on logistics managed by the Management Department. For example, marketing strategies such as target markets, promotion methods, and pricing are developed using AI.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, securing unit, planning unit, management unit, and strategy unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects geographic information, soil data, and weather conditions using GPS data, soil sensors, and weather sensors from the smart device 14. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 and provides information for selecting the optimal crop. The selection unit selects the optimal crop based on criteria such as profitability, growth conditions, and demand using the specific processing unit 290 of the data processing unit 12. The securing unit secures sales routes based on the crops selected by the specific processing unit 290 of the data processing unit 12. The planning unit creates a planting plan based on the sales routes secured by the specific processing unit 290 of the data processing unit 12. The management unit manages logistics based on the planting plan created by the specific processing unit 290 of the data processing unit 12. The strategy unit develops a marketing strategy based on the logistics managed by the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, securing unit, planning unit, management unit, and strategy unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects geographic information, soil data, and weather conditions using GPS data from the smart glasses 214, soil sensors, and weather sensors. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 and provides information for selecting the optimal crop. The selection unit selects the optimal crop based on criteria such as profitability, growth conditions, and demand using the specific processing unit 290 of the data processing unit 12. The securing unit secures sales routes based on the crops selected by the specific processing unit 290 of the data processing unit 12. The planning unit creates a planting plan based on the sales routes secured by the specific processing unit 290 of the data processing unit 12. The management unit manages logistics based on the planting plan created by the specific processing unit 290 of the data processing unit 12. The strategy unit creates a marketing strategy based on the logistics managed by the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0173] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, securing unit, planning unit, management unit, and strategy unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects geographic information, soil data, and weather conditions using GPS data, soil sensors, and weather sensors from the headset terminal 314. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 and provides information for selecting the optimal crop. The selection unit selects the optimal crop based on criteria such as profitability, growth conditions, and demand using the specific processing unit 290 of the data processing unit 12. The securing unit secures sales routes based on the crop selected by the specific processing unit 290 of the data processing unit 12. The planning unit creates a planting plan based on the sales routes secured by the specific processing unit 290 of the data processing unit 12. The management unit manages logistics based on the planting plan created by the specific processing unit 290 of the data processing unit 12. The Strategy Department develops marketing strategies based on logistics managed by the specific processing unit 290 of the data processing device 12. The correspondence between each department and the devices and control units is not limited to the example described above and can be modified in various ways.

[0189] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.).

[0202] 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.

[0203] 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.

[0204] 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.

[0205] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, securing unit, planning unit, management unit, and strategy unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects geographic information, soil data, and weather conditions using GPS data, soil sensors, and weather sensors from the robot 414. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 and provides information for selecting the optimal crop. The selection unit selects the optimal crop based on criteria such as profitability, growth conditions, and demand using the specific processing unit 290 of the data processing unit 12. The securing unit secures sales routes based on the crops selected by the specific processing unit 290 of the data processing unit 12. The planning unit creates a planting plan based on the sales routes secured by the specific processing unit 290 of the data processing unit 12. The management unit manages logistics based on the planting plan created by the specific processing unit 290 of the data processing unit 12. The strategy unit creates a marketing strategy based on the logistics managed by the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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."

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] (Note 1) The data collection unit collects diverse data such as geographic information, soil data, weather conditions, and market data. An analysis unit analyzes the data collected by the aforementioned collection unit, A selection unit that selects the optimal crop based on the data analyzed by the aforementioned analysis unit, A securing unit that secures sales routes based on the crops selected by the aforementioned selection unit, A planning department that creates a planting plan based on the sales routes secured by the aforementioned securing department, Based on the planting plan established by the aforementioned planning department, the management department manages logistics, The Strategy Department, which develops marketing strategies based on the logistics managed by the aforementioned Management Department, Equipped with A system characterized by the following features. (Note 2) It includes a support department that provides services such as automatic contract generation, guidance on legal procedures, and information on subsidies and grants. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a community department that supports communication after matching through community features. The system described in Appendix 1, characterized by the features described herein. (Note 4) 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 5) The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) 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 8) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) 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 11) 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 12) 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 13) 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 14) 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 15) 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 16) The aforementioned selection unit is The system estimates user sentiment and adjusts crop selection criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned selection unit is When making a selection, consider the interrelationships between data to improve the accuracy of the selection. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned selection unit is When making a selection, the attribute information of the data submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is It estimates the user's emotions and adjusts the order in which the selection results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is When making a selection, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is During the selection process, we refer to relevant literature to improve the accuracy of the selection. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned securing part is, We estimate user sentiment and adjust sales channel acquisition methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned securing part is, When securing a customer, the optimal method of securing the customer is selected by referring to past sales route data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned securing part is, When securing a supply, customize the sales channels based on current market conditions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned securing part is, We estimate user sentiment and determine the priority of securing sales channels based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned securing part is, When securing inventory, the optimal sales route is selected, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned securing part is, During the acquisition process, we analyze social media activity and propose sales channels. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned planning department, The system estimates user sentiment and adjusts the planting plan based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned planning department, During the planning stage, the optimal planning method is selected by referring to past planting data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned planning department, During the planning stage, customize the planting plan based on current agricultural conditions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned planning department, The system estimates user sentiment and determines the priority of the planting plan based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned planning department, During the planning stage, the optimal planting plan is selected by considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned planning department, During the planning stage, we analyze social media activity and propose methods for planting plans. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned management department, The system estimates user emotions and adjusts logistics management methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned management department, During management, the optimal management method is selected by referring to past logistics data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned management department, During management, customize logistics management methods based on the current logistics situation. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned management department, The system estimates user emotions and determines logistics management priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned management department, During management, the optimal logistics management system is selected by considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned management department, During management, we analyze social media activity and propose logistics management strategies. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned Strategy Department, We estimate user sentiment and adjust marketing strategies based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Appendix 41) The strategic department selects an optimal strategic method by referring to past marketing data when formulating a strategy for the system according to Appendix 1, which is characterized in that. (Appendix 42) The strategic department customizes the means of the marketing strategy based on the current market situation when formulating a strategy for the system according to Appendix 1, which is characterized in that. (Appendix 43) The strategic department estimates the user's sentiment and determines the priority of the marketing strategy based on the estimated user's sentiment for the system according to Appendix 1, which is characterized in that. (Appendix 44) The strategic department selects an optimal marketing strategy by considering geographical location information when formulating a strategy for the system according to Appendix 1, which is characterized in that. (Appendix 45) The strategic department analyzes social media activities and proposes means of the marketing strategy when formulating a strategy for the system according to Appendix 1, which is characterized in that. (Appendix 46) The support department estimates the user's sentiment and adjusts the automatic generation of contracts and the guidance of legal procedures based on the estimated user's sentiment for the system according to Appendix 2, which is characterized in that. (Appendix 47) The support department [[ID=5--3]]selects an optimal support method by referring to past legal procedure data when providing support for the system according to Appendix 2, which is characterized in that. (Appendix 48) The support department estimates the user's sentiment and adjusts the method of providing subsidy and grant information based on the estimated user's sentiment The system described in Appendix 2, characterized by the features described herein. (Note 49) The aforementioned support unit is When providing support, we take geographical location information into consideration to guide you through the most appropriate legal procedures. The system described in Appendix 2, characterized by the features described herein. (Note 50) The aforementioned community section, It estimates the user's emotions and adjusts communication support methods based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 51) The aforementioned community section, When providing communication support, past communication data is referenced to select the most suitable support method. The system described in Appendix 3, characterized by the features described herein. (Note 52) The aforementioned community section, It estimates the user's emotions and determines the priority of communication support based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 53) The aforementioned community section, When providing communication support, we take geographical location information into consideration to provide the most suitable support method. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0225] 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. The data collection unit collects diverse data such as geographic information, soil data, weather conditions, and market data. An analysis unit analyzes the data collected by the aforementioned collection unit, A selection unit that selects the optimal crop based on the data analyzed by the aforementioned analysis unit, A securing unit that secures sales routes based on the crops selected by the aforementioned selection unit, A planning department that creates a planting plan based on the sales routes secured by the aforementioned securing department, Based on the planting plan established by the aforementioned planning department, the management department manages logistics, The Strategy Department, which develops marketing strategies based on the logistics managed by the aforementioned Management Department, Equipped with A system characterized by the following features.

2. The company has a support department that provides services such as automated contract generation, guidance on legal procedures, and information on subsidies and grants. The system according to feature 1.

3. It has a community department that supports communication after matching through community features. The system according to feature 1.

4. 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.

5. The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system according to feature 1.

6. 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.

7. 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.

8. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the user's geographical location. The system according to feature 1.

9. The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system according to feature 1.

10. 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 according to feature 1.

Images