system

Abstract

The system according to this embodiment aims to achieve efficient agricultural investment by forecasting the market and investigating shortages of vegetables. [Solution] The system according to the embodiment comprises a market forecasting unit, an information provision unit, a farmland acquisition unit, a cultivation unit, a buying and selling unit, and a profit distribution unit. The market forecasting unit forecasts the market or investigates shortages of vegetables. The information provision unit provides the user with the results obtained by the market forecasting unit. The farmland acquisition unit receives investments based on the information provided by the information provision unit and leases or purchases farmland. The cultivation unit cultivates crops on the farmland acquired by the farmland acquisition unit. The buying and selling unit buys and sells vegetables harvested by the cultivation unit. The profit distribution unit distributes the profits obtained by the buying and selling unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that market prediction and investigation of insufficient vegetables have not been sufficiently carried out, and the efficiency of agricultural investment is low.

[0005] The system according to the embodiment aims to perform market prediction and investigation of insufficient vegetables and realize efficient agricultural investment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a market forecasting unit, an information provision unit, a farmland acquisition unit, a cultivation unit, a buying and selling unit, and a profit distribution unit. The market forecasting unit forecasts the market or investigates shortages of vegetables. The information provision unit provides the user with the results obtained by the market forecasting unit. The farmland acquisition unit receives investments based on the information provided by the information provision unit and leases or purchases farmland. The cultivation unit cultivates crops on the farmland acquired by the farmland acquisition unit. The buying and selling unit buys and sells vegetables harvested by the cultivation unit. The profit distribution unit distributes the profits obtained by the buying and selling unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can perform market forecasting and surveys of shortages of vegetables, thereby enabling efficient agricultural investment. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An agricultural support system according to an embodiment of the present invention is a system that utilizes generative AI to make market predictions and investigate shortages of vegetables, and provides the results to users to encourage investment. This agricultural support system uses generative AI to make market predictions and investigate shortages of vegetables, provides the results to users to encourage investment. Upon receiving investment from users, the system leases or purchases farmland and engages in cultivation and sales. Profits are distributed to users according to the amount earned. In addition, for individuals who wish to experience agriculture, a training platform using generative AI is provided to provide efficient agricultural know-how. For example, the generative AI makes market predictions and investigates shortages of vegetables. The generative AI analyzes past market data and weather data to predict future market trends and vegetables with high demand. This makes it possible to determine which vegetables should be cultivated. Next, the generative AI provides the user with the prediction results obtained. Users make investments based on this information. For example, if it is predicted that the demand for a particular vegetable will increase, they can invest in cultivating that vegetable. Upon receiving investment from users, the system leases or purchases farmland. For example, appropriate farmland is selected according to the investment amount and leased or purchased. This allows for the securing of land necessary for cultivation. Next, vegetables are cultivated on the secured farmland. Efficient cultivation methods are introduced using generative AI. For example, the generative AI proposes the optimal sowing time and fertilizer amount, and cultivation is carried out based on that. The harvested vegetables are bought and sold in the market. The generative AI monitors market trends in real time and proposes the optimal selling time. This allows for the maximum possible profit. The profits earned are distributed to users as revenue. For example, revenue is distributed according to the investment amount and returned to the users. This allows users to feel the results of their investment. In addition, a training platform using generative AI is provided for individuals who want to experience agriculture. For example, they can learn basic agricultural knowledge and efficient cultivation methods online. This allows even beginners to farm efficiently. In this way, by utilizing generative AI, it is possible to consistently manage everything from market forecasting to cultivation, buying and selling, and revenue distribution, realizing efficient agricultural management.This allows agricultural support systems to utilize generative AI to handle everything from market forecasting to cultivation, trading, and profit distribution, enabling efficient agricultural management.

[0029] The agricultural support system according to this embodiment comprises a market forecasting unit, an information provision unit, a farmland acquisition unit, a cultivation unit, a buying and selling unit, and a profit distribution unit. The market forecasting unit forecasts the market or investigates shortages of vegetables. The market forecasting unit, for example, analyzes past market data and weather data to predict future market trends and vegetables in high demand. For example, the market forecasting unit collects past market data and analyzes it using a generating AI. The market forecasting unit can also collect weather data and analyze it using a generating AI. The information provision unit provides the user with the results obtained by the market forecasting unit. For example, the information provision unit provides the user with the results predicted by the generating AI. For example, the information provision unit notifies the user of market trends and information on vegetables in high demand predicted by the generating AI. The farmland acquisition unit receives investments based on the information provided by the information provision unit and leases or purchases farmland. For example, the farmland acquisition unit selects appropriate farmland according to the investment amount and leases or purchases it. For example, the farmland acquisition unit selects farmland based on the investment amount and concludes a contract. The cultivation unit cultivates crops on farmland acquired by the farmland acquisition unit. The cultivation unit cultivates crops based on, for example, the optimal sowing time and fertilizer amount suggested by the generation AI. For example, the cultivation unit sows seeds based on the sowing time suggested by the generation AI. The cultivation unit can also apply fertilizer based on the amount suggested by the generation AI. The trading unit buys and sells vegetables harvested by the cultivation unit. For example, the trading unit uses the generation AI to monitor market trends in real time and suggest the optimal selling timing. For example, the trading unit uses the generation AI to monitor market trends and determine the timing of the sale. The profit distribution unit distributes the profits earned by the trading unit to the users. For example, the profit distribution unit distributes profits according to the investment amount and returns them to the users. For example, the profit distribution unit calculates profits based on the investment amount and distributes them to the users. As a result, the agricultural support system according to this embodiment can consistently perform everything from market forecasting to cultivation, trading, and profit distribution, enabling efficient agricultural management.

[0030] The agricultural support system includes a training unit that provides a training platform using generative AI. The training unit provides a training platform using generative AI. For example, the training unit provides a platform where users can learn basic agricultural knowledge and efficient cultivation methods online. For example, the training unit uses generative AI to provide learning materials for learning basic agricultural knowledge. The training unit can also use generative AI to provide training programs for learning efficient cultivation methods. This allows individuals to efficiently learn agricultural know-how. Some or all of the above-described processes in the training unit may be performed using generative AI, or not. For example, the training unit can use generative AI to monitor the user's learning progress in real time and provide appropriate feedback. Furthermore, the training unit can use generative AI to analyze the user's learning history and provide individually customized training programs. For example, the training unit proposes optimal training content based on the user's learning history. This allows the training unit to provide efficient training tailored to the user's needs.

[0031] The market forecasting unit can analyze historical market data or weather data to predict future market trends and vegetables in high demand. For example, the market forecasting unit can collect historical market data and analyze it using generative AI. For example, the market forecasting unit can predict future market trends based on historical market data. The market forecasting unit can also collect weather data and analyze it using generative AI. For example, the market forecasting unit can predict vegetables in high demand based on weather data. In this way, by analyzing historical data, it is possible to accurately predict future market trends and vegetables in high demand. Some or all of the above processing in the market forecasting unit may be performed using generative AI, or not. For example, the market forecasting unit can input historical market data into the generative AI and have the generative AI perform predictions of future market trends.

[0032] The information provision unit can provide users with the results predicted by the generating AI. For example, the information provision unit can notify users of market trends and information on vegetables in high demand predicted by the generating AI. For example, the information provision unit can provide users with appropriate investment information based on the results predicted by the generating AI. In this way, by providing users with the results predicted by the generating AI, users can make appropriate investment decisions. Some or all of the above processing in the information provision unit may be performed using the generating AI, for example, or without using the generating AI. For example, the information provision unit can build a system to notify users of the results predicted by the generating AI.

[0033] The farmland acquisition unit can select farmland according to the investment amount and lease or purchase it. For example, the farmland acquisition unit selects farmland based on the investment amount and concludes a contract. For example, the farmland acquisition unit selects appropriate farmland according to the investment amount and leases or purchases it. This makes efficient farmland acquisition possible by selecting appropriate farmland according to the investment amount. Some or all of the above processing in the farmland acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the farmland acquisition unit can have a generation AI perform the selection of farmland based on the investment amount.

[0034] The cultivation unit can perform cultivation based on the sowing time or fertilizer amount suggested by the generating AI. For example, the cultivation unit sows seeds based on the sowing time suggested by the generating AI. For example, the cultivation unit applies fertilizer based on the fertilizer amount suggested by the generating AI. This enables efficient cultivation by performing cultivation based on the optimal sowing time and fertilizer amount suggested by the generating AI. Some or all of the above processes in the cultivation unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the cultivation unit performs cultivation based on the sowing time and fertilizer amount suggested by the generating AI.

[0035] The trading department can use a generating AI to monitor market trends in real time and suggest selling timings. For example, the trading department can use a generating AI to monitor market trends and determine the timing of a sale. For example, the trading department can use a generating AI to monitor market trends in real time and suggest the optimal selling timing. This allows the generating AI to monitor market trends in real time and suggest the optimal selling timing, thereby maximizing profits. Some or all of the above processes in the trading department may be performed using a generating AI, or not. For example, the trading department can build a system in which a generating AI monitors market trends and suggests selling timings.

[0036] The revenue distribution unit can distribute profits according to the investment amount and return them to the user. For example, the revenue distribution unit calculates profits based on the investment amount and distributes them to the user. For example, the revenue distribution unit distributes profits according to the investment amount and returns them to the user. This allows users to feel the results of their investment by distributing profits according to the investment amount. Some or all of the above processing in the revenue distribution unit may be performed using, for example, a generation AI, or without a generation AI. For example, the revenue distribution unit distributes profits to the user based on the profits calculated by the generation AI.

[0037] The market forecasting unit can analyze social media trend data in addition to historical market data to make more accurate market forecasts. For example, the market forecasting unit can use a generating AI to analyze trend data collected from social media such as X (formerly Twitter®) and Facebook® to predict fluctuations in demand. For example, the market forecasting unit can use a generating AI to analyze Instagram® post data to evaluate consumer interest in a particular vegetable. The market forecasting unit can also use a generating AI to analyze YouTube® viewing data to predict vegetables with high demand based on the popularity of cooking videos. This makes it possible to make more accurate market forecasts by analyzing social media trend data. Some or all of the above processing in the market forecasting unit may be performed using a generating AI, for example, or without using a generating AI. For example, the market forecasting unit can input social media trend data into a generating AI and have the generating AI perform demand forecasting.

[0038] The market forecasting unit can customize its forecast results by considering regional consumption trends when making market forecasts. For example, the market forecasting unit's generating AI can analyze regional consumption data and predict vegetables that are in high demand in a particular region. For example, the market forecasting unit's generating AI can consider regional weather data and predict the demand for vegetables suitable for the climate. The market forecasting unit's generating AI can also analyze regional demographic data and predict consumption trends according to age group. This allows for more accurate market forecasts by considering regional consumption trends. Some or all of the above processing in the market forecasting unit may be performed using, for example, the generating AI, or without the generating AI. For example, the market forecasting unit can input regional consumption data into the generating AI and have the generating AI customize the forecast results.

[0039] The market forecasting unit can provide individually customized forecasts that take into account the user's purchase history when forecasting the market. For example, the market forecasting unit's generating AI can analyze the user's past purchase history and predict the demand for a specific vegetable. For example, the market forecasting unit's generating AI can analyze the user's purchasing patterns and suggest vegetables with high demand. The market forecasting unit's generating AI can also predict seasonal demand from the user's purchase history. This makes it possible to provide individually customized market forecasts by taking into account the user's purchase history. Some or all of the above processing in the market forecasting unit may be performed using, for example, the generating AI, or without the generating AI. For example, the market forecasting unit can input user purchase history data into the generating AI and have the generating AI execute a customized forecast.

[0040] The market forecasting unit can analyze soil data in addition to weather data when forecasting the market to make more accurate predictions. For example, the market forecasting unit's generating AI can analyze soil data for each region and evaluate the suitability for cultivating specific vegetables. For example, the market forecasting unit's generating AI can analyze soil nutrient data and predict vegetables suitable for cultivation. The market forecasting unit's generating AI can also analyze soil moisture data and predict vegetables with high demand. By analyzing soil data in addition to weather data, more accurate market forecasts become possible. Some or all of the above processing in the market forecasting unit may be performed using, for example, the generating AI, or without the generating AI. For example, the market forecasting unit can input soil data into the generating AI and have the generating AI perform the predictions.

[0041] The information provision unit can provide optimal information by considering the user's past investment history when providing information. For example, the information provision unit can use a generating AI to analyze the user's past investment history and provide relevant information. For example, the information provision unit can use a generating AI to analyze the user's investment patterns and provide optimal investment information. The information provision unit can also use a generating AI to provide information useful for future investments from the user's investment history. In this way, optimal information can be provided by considering the user's past investment history. Some or all of the above processing in the information provision unit may be performed using a generating AI, for example, or without using a generating AI. For example, the information provision unit can input the user's investment history data into a generating AI and have the generating AI perform the optimal information provision.

[0042] The information provision unit can adjust the priority of information based on the user's areas of interest when providing information. For example, the information provision unit can use a generative AI to analyze the user's areas of interest and prioritize the provision of relevant information. For example, the information provision unit can use a generative AI to analyze the user's past search history and provide information of high interest. The information provision unit can also use a generative AI to analyze the user's social media activity and provide relevant information. By adjusting the priority of information based on the user's areas of interest, the information provision unit can prioritize the provision of information that is important to the user. Some or all of the above processing in the information provision unit may be performed using a generative AI, or not. For example, the information provision unit can input user area of ​​interest data into a generative AI and have the generative AI prioritize information.

[0043] The information provision unit can provide highly relevant information by considering the user's geographical location when providing information. For example, the information provision unit can use a generating AI to analyze the user's current location and provide information relevant to that region. For example, the information provision unit can use a generating AI to analyze the user's past location information and provide relevant information. The information provision unit can also use a generating AI to analyze the user's movement patterns and provide optimal information. In this way, highly relevant information can be provided by considering the user's geographical location information. Some or all of the above processing in the information provision unit may be performed using a generating AI, for example, or without using a generating AI. For example, the information provision unit can input the user's geographical location information into a generating AI and have the generating AI perform the provision of highly relevant information.

[0044] The information provision unit can analyze a user's social media activity and provide relevant information when providing information. For example, the information provision unit can use a generative AI to analyze a user's posts on X (formerly Twitter) and provide relevant information. For example, the information provision unit can use a generative AI to analyze a user's activity on Facebook and provide information of high interest. The information provision unit can also use a generative AI to analyze a user's posts on Instagram and provide relevant information. In this way, relevant information can be provided by analyzing a user's social media activity. Some or all of the above processing in the information provision unit may be performed using a generative AI, or without using a generative AI. For example, the information provision unit can input the user's social media activity data into a generative AI and have the generative AI perform the information provision.

[0045] The farmland acquisition unit can select the optimal farmland by analyzing past farmland use data when acquiring farmland. For example, the farmland acquisition unit can use a generating AI to analyze past farmland use data and select farmland with high profitability. For example, the farmland acquisition unit can use a generating AI to analyze the past use history of farmland and select the optimal farmland. The farmland acquisition unit can also use a generating AI to analyze past harvest data of farmland and select farmland with high profitability. In this way, the optimal farmland can be selected by analyzing past farmland use data. Some or all of the above processing in the farmland acquisition unit may be performed using a generating AI, for example, or without using a generating AI. For example, the farmland acquisition unit can input past farmland use data into a generating AI and have the generating AI perform the optimal farmland selection.

[0046] The land acquisition unit can adjust its acquisition plan when acquiring land, taking into account local agricultural policies. For example, the land acquisition unit can use a generating AI to analyze local agricultural policies and prioritize the acquisition of land eligible for subsidies. For example, the land acquisition unit can use a generating AI to consider local agricultural policies and prioritize the acquisition of land with fewer regulations. Furthermore, the land acquisition unit can use a generating AI to analyze local agricultural policies and acquire land with future potential. This allows for the creation of a more appropriate land acquisition plan by considering local agricultural policies. Some or all of the above processes in the land acquisition unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the land acquisition unit can input local agricultural policy data into a generating AI and have the generating AI perform the adjustment of the acquisition plan.

[0047] The farmland acquisition unit can select the optimal farmland by considering the user's geographical location information when acquiring farmland. For example, the farmland acquisition unit's generating AI can analyze the user's current location and prioritize acquiring nearby farmland. For example, the farmland acquisition unit's generating AI can analyze the user's past location information and select the optimal farmland. Furthermore, the farmland acquisition unit's generating AI can analyze the user's movement patterns and select the optimal farmland. In this way, the optimal farmland can be selected by considering the user's geographical location information. Some or all of the above processing in the farmland acquisition unit may be performed using, for example, the generating AI, or without using the generating AI. For example, the farmland acquisition unit can input the user's geographical location information into the generating AI and have the generating AI perform the optimal farmland selection.

[0048] The farmland acquisition unit can adjust the size of the farmland acquired based on the user's investment amount. For example, the farmland acquisition unit can use a generating AI to analyze the user's investment amount and acquire farmland of an appropriate size. For example, the farmland acquisition unit can use a generating AI to analyze the user's investment pattern and acquire farmland of the optimal size. The farmland acquisition unit can also use a generating AI to acquire highly profitable farmland based on the user's investment amount. In this way, by adjusting the size of the farmland based on the user's investment amount, it is possible to acquire farmland of an appropriate size. Some or all of the above processing in the farmland acquisition unit may be performed using a generating AI, for example, or without using a generating AI. For example, the farmland acquisition unit can input user investment amount data into a generating AI and have the generating AI perform the farmland size adjustment.

[0049] The cultivation unit can analyze past cultivation data during cultivation to select the optimal cultivation method. For example, the cultivation unit can use a generating AI to analyze past cultivation data and select a highly profitable cultivation method. For example, the cultivation unit can use a generating AI to analyze past cultivation history and select the optimal cultivation method. The cultivation unit can also use a generating AI to analyze past harvest data and select a highly profitable cultivation method. In this way, the optimal cultivation method can be selected by analyzing past cultivation data. Some or all of the above processes in the cultivation unit may be performed using a generating AI, for example, or without using a generating AI. For example, the cultivation unit can input past cultivation data into a generating AI and have the generating AI select the optimal cultivation method.

[0050] The cultivation unit can adjust its cultivation plan during cultivation, taking into account local weather conditions. For example, the cultivation unit can use a generating AI to analyze local weather data and propose the optimal cultivation time. For example, the cultivation unit can use a generating AI to consider local weather conditions and propose an appropriate cultivation method. The cultivation unit can also use a generating AI to analyze local weather data and propose a highly profitable cultivation plan. This allows for the creation of a more appropriate cultivation plan by considering local weather conditions. Some or all of the above processes in the cultivation unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the cultivation unit can input local weather data into a generating AI and have the generating AI perform the adjustment of the cultivation plan.

[0051] The cultivation unit can select the optimal cultivation method during cultivation, taking into account the user's geographical location information. For example, the cultivation unit's generating AI can analyze the user's current location and propose a cultivation method suitable for the region. For example, the cultivation unit's generating AI can analyze the user's past location information and select the optimal cultivation method. Furthermore, the cultivation unit's generating AI can analyze the user's movement patterns and select the optimal cultivation method. In this way, the optimal cultivation method can be selected by taking into account the user's geographical location information. Some or all of the above-described processes in the cultivation unit may be performed using, for example, the generating AI, or without using the generating AI. For example, the cultivation unit can input the user's geographical location information into the generating AI and have the generating AI select the optimal cultivation method.

[0052] The cultivation unit can adjust the types of vegetables to cultivate based on the user's investment amount during cultivation. For example, the cultivation unit's generating AI can analyze the user's investment amount and cultivate the appropriate types of vegetables. For example, the cultivation unit's generating AI can analyze the user's investment pattern and cultivate the optimal types of vegetables. The cultivation unit can also have the generating AI cultivate highly profitable vegetables based on the user's investment amount. In this way, by adjusting the types of vegetables to cultivate based on the user's investment amount, the appropriate types of vegetables can be cultivated. Some or all of the above processes in the cultivation unit may be performed using a generating AI, for example, or without a generating AI. For example, the cultivation unit can input the user's investment amount data into a generating AI and have the generating AI determine the types of vegetables to cultivate.

[0053] The trading unit can select the optimal trading strategy by analyzing historical market data at the time of trading. For example, the trading unit can use a generative AI to analyze historical market data and select a highly profitable trading strategy. For example, the trading unit can use a generative AI to analyze past market trends and select the optimal trading strategy. Alternatively, the trading unit can use a generative AI to analyze historical market price data and select a highly profitable trading strategy. In this way, the optimal trading strategy can be selected by analyzing historical market data. Some or all of the above processing in the trading unit may be performed using a generative AI, or without using a generative AI. For example, the trading unit can input historical market data into a generative AI and have the generative AI select the optimal trading strategy.

[0054] The trading department can adjust its trading plan when trading, taking into account regional market trends. For example, the trading department can use a generating AI to analyze regional market data and propose the optimal trading timing. For example, the trading department can use a generating AI to consider regional market trends and propose an appropriate trading strategy. The trading department can also use a generating AI to analyze regional market data and propose a highly profitable trading plan. This allows for the creation of more appropriate trading plans by considering regional market trends. Some or all of the above processes in the trading department may be performed using a generating AI, or not. For example, the trading department can input regional market data into a generating AI and have the generating AI perform the adjustment of the trading plan.

[0055] The trading unit can select the optimal trading strategy when trading, taking into account the user's geographical location information. For example, the trading unit can use a generative AI to analyze the user's current location and propose a trading strategy suitable for the region. For example, the trading unit can use a generative AI to analyze the user's past location information and select the optimal trading strategy. The trading unit can also use a generative AI to analyze the user's movement patterns and select the optimal trading strategy. In this way, the optimal trading strategy can be selected by taking into account the user's geographical location information. Some or all of the above processing in the trading unit may be performed using a generative AI, or without using a generative AI. For example, the trading unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal trading strategy.

[0056] The trading unit can adjust the types of vegetables traded based on the user's investment amount at the time of trading. For example, the trading unit can use a generating AI to analyze the user's investment amount and trade appropriate types of vegetables. For example, the trading unit can use a generating AI to analyze the user's investment pattern and trade the optimal types of vegetables. The trading unit can also use a generating AI to trade highly profitable vegetables based on the user's investment amount. In this way, by adjusting the types of vegetables traded based on the user's investment amount, appropriate types of vegetables can be traded. Some or all of the above processing in the trading unit may be performed using a generating AI, for example, or without using a generating AI. For example, the trading unit can input the user's investment amount data into a generating AI and have the generating AI determine the types of vegetables to trade.

[0057] The revenue distribution unit can analyze past revenue data and select the optimal distribution method when distributing revenue. For example, the revenue distribution unit can use a generation AI to analyze past revenue data and select a highly profitable distribution method. For example, the revenue distribution unit can use a generation AI to analyze past revenue history and select the optimal distribution method. Alternatively, the revenue distribution unit can use a generation AI to analyze past revenue data and select a highly profitable distribution method. In this way, the optimal distribution method can be selected by analyzing past revenue data. Some or all of the above processing in the revenue distribution unit may be performed using a generation AI, for example, or without using a generation AI. For example, the revenue distribution unit can input past revenue data into a generation AI and have the generation AI select the optimal distribution method.

[0058] The revenue distribution unit can adjust the distribution plan based on the user's investment amount when distributing revenue. For example, the revenue distribution unit can use a generating AI to analyze the user's investment amount and propose an appropriate distribution plan. For example, the revenue distribution unit can use a generating AI to analyze the user's investment pattern and propose an optimal distribution plan. Alternatively, the revenue distribution unit can use a generating AI to propose a highly profitable distribution plan based on the user's investment amount. In this way, an appropriate distribution plan can be created by adjusting the distribution plan based on the user's investment amount. Some or all of the above processing in the revenue distribution unit may be performed using a generating AI, or without using a generating AI. For example, the revenue distribution unit can input user investment data into a generating AI and have the generating AI perform the adjustment of the distribution plan.

[0059] The revenue distribution unit can select the optimal distribution method when distributing revenue, taking into account the user's geographical location information. For example, the revenue distribution unit can use a generating AI to analyze the user's current location and propose a distribution method suitable for the region. For example, the revenue distribution unit can use a generating AI to analyze the user's past location information and select the optimal distribution method. The revenue distribution unit can also use a generating AI to analyze the user's movement patterns and select the optimal distribution method. In this way, the optimal distribution method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the revenue distribution unit may be performed using a generating AI, for example, or without using a generating AI. For example, the revenue distribution unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal distribution method.

[0060] The revenue distribution unit can adjust the percentage of revenue distributed based on the user's investment amount when distributing revenue. For example, the revenue distribution unit can use a generating AI to analyze the user's investment amount and distribute an appropriate percentage of revenue. For example, the revenue distribution unit can use a generating AI to analyze the user's investment pattern and distribute the optimal percentage of revenue. The revenue distribution unit can also use a generating AI to propose a highly profitable distribution ratio based on the user's investment amount. This makes it possible to distribute revenue appropriately by adjusting the percentage of revenue distributed based on the user's investment amount. Some or all of the above processing in the revenue distribution unit may be performed using a generating AI, for example, or without using a generating AI. For example, the revenue distribution unit can input user investment amount data into a generating AI and have the generating AI perform the revenue ratio adjustment.

[0061] The training provision unit can analyze past training data and select the optimal training method when providing training. For example, the training provision unit can use a generative AI to analyze past training data and select an effective training method. For example, the training provision unit can use a generative AI to analyze past training history and select the optimal training method. Alternatively, the training provision unit can use a generative AI to analyze past training performance data and select an effective training method. In this way, the optimal training method can be selected by analyzing past training data. Some or all of the above processing in the training provision unit may be performed using a generative AI, or without using a generative AI. For example, the training provision unit can input past training data into a generative AI and have the generative AI select the optimal training method.

[0062] The training delivery unit can customize training content based on the user's areas of interest when providing training. For example, the training delivery unit can use a generative AI to analyze the user's areas of interest and provide relevant training content. For example, the training delivery unit can use a generative AI to analyze the user's past search history and provide training content of high interest. The training delivery unit can also use a generative AI to analyze the user's social media activity and provide relevant training content. By customizing training content based on the user's areas of interest, more effective training can be provided. Some or all of the above processing in the training delivery unit may be performed using a generative AI, or not. For example, the training delivery unit can input user area of ​​interest data into a generative AI and have the generative AI perform the customization of training content.

[0063] The training provision unit can select the optimal training method by considering the user's geographical location information when providing training. For example, the training provision unit can use a generative AI to analyze the user's current location and propose a training method suitable for the region. For example, the training provision unit can use a generative AI to analyze the user's past location information and select the optimal training method. The training provision unit can also use a generative AI to analyze the user's movement patterns and select the optimal training method. In this way, the optimal training method can be selected by considering the user's geographical location information. Some or all of the above processing in the training provision unit may be performed using a generative AI, or without using a generative AI. For example, the training provision unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal training method.

[0064] The training provision unit can adjust the training content based on the user's investment amount when providing training. For example, the training provision unit can use a generative AI to analyze the user's investment amount and provide appropriate training content. For example, the training provision unit can use a generative AI to analyze the user's investment pattern and provide optimal training content. The training provision unit can also use a generative AI to provide highly profitable training content based on the user's investment amount. In this way, appropriate training content can be provided by adjusting the training content based on the user's investment amount. Some or all of the above processing in the training provision unit may be performed using a generative AI, or without using a generative AI. For example, the training provision unit can input user investment data into a generative AI and have the generative AI perform the adjustment of the training content.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The agricultural support system can also include a health management unit that monitors the user's health status. This unit can, for example, monitor the user's heart rate and blood pressure in real time and suggest agricultural tasks tailored to their health condition. For instance, if the heart rate is high, it can suggest a break. Similarly, if blood pressure is low, it can suggest lighter tasks. This allows for a safer and more efficient agricultural experience by tailoring tasks to the user's health.

[0067] The agricultural support system can also include a work scheduling unit that analyzes the user's past farming history and proposes an optimal work schedule. For example, the work scheduling unit can suggest optimal work and rest times based on past work history. If, for instance, past work history reveals high efficiency during specific time periods, work can be concentrated during those times. Furthermore, based on past work history, the system can suggest avoiding times when fatigue is likely to accumulate. This enables the creation of an efficient work schedule.

[0068] The agricultural support system can also include a regional information provision unit that takes into account the user's geographical location and provides region-specific agricultural information. For example, the regional information provision unit can suggest optimal cultivation methods based on local weather and soil data. For instance, it can suggest the optimal sowing time based on local weather data, or the appropriate amount of fertilizer based on local soil data. This allows for more accurate agricultural support by utilizing region-specific information.

[0069] The agricultural support system can also include an investment strategy unit that analyzes the user's past investment history and proposes the optimal investment strategy. For example, the investment strategy unit can suggest low-risk investments based on past investment history. For example, it can suggest highly profitable investments based on past investment history. It can also suggest the timing of investments based on past investment history. This allows for the provision of more effective investment strategies by utilizing the user's investment history.

[0070] The agricultural support system can also include a farmland selection unit that selects the optimal farmland by considering the user's geographical location information. For example, the farmland selection unit can prioritize nearby farmland based on the user's current location. Alternatively, it can select the optimal farmland based on the user's past location information. Furthermore, it can analyze the user's movement patterns to select the optimal farmland. This enables efficient farmland selection by utilizing the user's geographical location information.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The market forecasting department conducts market forecasts or investigates shortages of vegetables. For example, they analyze historical market data and weather data to predict future market trends and vegetables in high demand. The market forecasting department uses generative AI to analyze this data. Step 2: The information provision unit provides users with the results obtained by the market forecasting unit. For example, it notifies users of market trends predicted by the generation AI and information on vegetables with high demand. Step 3: The Farmland Acquisition Department receives investments based on information provided by the Information Provision Department and leases or purchases farmland. For example, it selects appropriate farmland according to the investment amount and concludes a contract. Step 4: The cultivation department carries out cultivation on the farmland acquired by the farmland acquisition department. For example, cultivation is carried out based on the optimal sowing time and fertilizer amount suggested by the generation AI. Step 5: The trading department buys and sells the vegetables harvested by the cultivation department. For example, a generation AI monitors market trends in real time and suggests the optimal selling time. Step 6: The profit distribution unit distributes the profits earned by the trading unit to the users. For example, it calculates profits based on the investment amount and distributes them to the users.

[0073] (Example of form 2) An agricultural support system according to an embodiment of the present invention is a system that utilizes generative AI to make market predictions and investigate shortages of vegetables, and provides the results to users to encourage investment. This agricultural support system uses generative AI to make market predictions and investigate shortages of vegetables, provides the results to users to encourage investment. Upon receiving investment from users, the system leases or purchases farmland and engages in cultivation and sales. Profits are distributed to users according to the amount earned. In addition, for individuals who wish to experience agriculture, a training platform using generative AI is provided to provide efficient agricultural know-how. For example, the generative AI makes market predictions and investigates shortages of vegetables. The generative AI analyzes past market data and weather data to predict future market trends and vegetables with high demand. This makes it possible to determine which vegetables should be cultivated. Next, the generative AI provides the user with the prediction results obtained. Users make investments based on this information. For example, if it is predicted that the demand for a particular vegetable will increase, they can invest in cultivating that vegetable. Upon receiving investment from users, the system leases or purchases farmland. For example, appropriate farmland is selected according to the investment amount and leased or purchased. This allows for the securing of land necessary for cultivation. Next, vegetables are cultivated on the secured farmland. Efficient cultivation methods are introduced using generative AI. For example, the generative AI proposes the optimal sowing time and fertilizer amount, and cultivation is carried out based on that. The harvested vegetables are bought and sold in the market. The generative AI monitors market trends in real time and proposes the optimal selling time. This allows for the maximum possible profit. The profits earned are distributed to users as revenue. For example, revenue is distributed according to the investment amount and returned to the users. This allows users to feel the results of their investment. In addition, a training platform using generative AI is provided for individuals who want to experience agriculture. For example, they can learn basic agricultural knowledge and efficient cultivation methods online. This allows even beginners to farm efficiently. In this way, by utilizing generative AI, it is possible to consistently manage everything from market forecasting to cultivation, buying and selling, and revenue distribution, realizing efficient agricultural management.This allows agricultural support systems to utilize generative AI to handle everything from market forecasting to cultivation, trading, and profit distribution, enabling efficient agricultural management.

[0074] The agricultural support system according to this embodiment comprises a market forecasting unit, an information provision unit, a farmland acquisition unit, a cultivation unit, a buying and selling unit, and a profit distribution unit. The market forecasting unit forecasts the market or investigates shortages of vegetables. The market forecasting unit, for example, analyzes past market data and weather data to predict future market trends and vegetables in high demand. For example, the market forecasting unit collects past market data and analyzes it using a generating AI. The market forecasting unit can also collect weather data and analyze it using a generating AI. The information provision unit provides the user with the results obtained by the market forecasting unit. For example, the information provision unit provides the user with the results predicted by the generating AI. For example, the information provision unit notifies the user of market trends and information on vegetables in high demand predicted by the generating AI. The farmland acquisition unit receives investments based on the information provided by the information provision unit and leases or purchases farmland. For example, the farmland acquisition unit selects appropriate farmland according to the investment amount and leases or purchases it. For example, the farmland acquisition unit selects farmland based on the investment amount and concludes a contract. The cultivation unit cultivates crops on farmland acquired by the farmland acquisition unit. The cultivation unit cultivates crops based on, for example, the optimal sowing time and fertilizer amount suggested by the generation AI. For example, the cultivation unit sows seeds based on the sowing time suggested by the generation AI. The cultivation unit can also apply fertilizer based on the amount suggested by the generation AI. The trading unit buys and sells vegetables harvested by the cultivation unit. For example, the trading unit uses the generation AI to monitor market trends in real time and suggest the optimal selling timing. For example, the trading unit uses the generation AI to monitor market trends and determine the timing of the sale. The profit distribution unit distributes the profits earned by the trading unit to the users. For example, the profit distribution unit distributes profits according to the investment amount and returns them to the users. For example, the profit distribution unit calculates profits based on the investment amount and distributes them to the users. As a result, the agricultural support system according to this embodiment can consistently perform everything from market forecasting to cultivation, trading, and profit distribution, enabling efficient agricultural management.

[0075] The agricultural support system includes a training unit that provides a training platform using generative AI. The training unit provides a training platform using generative AI. For example, the training unit provides a platform where users can learn basic agricultural knowledge and efficient cultivation methods online. For example, the training unit uses generative AI to provide learning materials for learning basic agricultural knowledge. The training unit can also use generative AI to provide training programs for learning efficient cultivation methods. This allows individuals to efficiently learn agricultural know-how. Some or all of the above-described processes in the training unit may be performed using generative AI, or not. For example, the training unit can use generative AI to monitor the user's learning progress in real time and provide appropriate feedback. Furthermore, the training unit can use generative AI to analyze the user's learning history and provide individually customized training programs. For example, the training unit proposes optimal training content based on the user's learning history. This allows the training unit to provide efficient training tailored to the user's needs.

[0076] The market forecasting unit can analyze historical market data or weather data to predict future market trends and vegetables in high demand. For example, the market forecasting unit can collect historical market data and analyze it using generative AI. For example, the market forecasting unit can predict future market trends based on historical market data. The market forecasting unit can also collect weather data and analyze it using generative AI. For example, the market forecasting unit can predict vegetables in high demand based on weather data. In this way, by analyzing historical data, it is possible to accurately predict future market trends and vegetables in high demand. Some or all of the above processing in the market forecasting unit may be performed using generative AI, or not. For example, the market forecasting unit can input historical market data into the generative AI and have the generative AI perform predictions of future market trends.

[0077] The information provision unit can provide users with the results predicted by the generating AI. For example, the information provision unit can notify users of market trends and information on vegetables in high demand predicted by the generating AI. For example, the information provision unit can provide users with appropriate investment information based on the results predicted by the generating AI. In this way, by providing users with the results predicted by the generating AI, users can make appropriate investment decisions. Some or all of the above processing in the information provision unit may be performed using the generating AI, for example, or without using the generating AI. For example, the information provision unit can build a system to notify users of the results predicted by the generating AI.

[0078] The farmland acquisition unit can select farmland according to the investment amount and lease or purchase it. For example, the farmland acquisition unit selects farmland based on the investment amount and concludes a contract. For example, the farmland acquisition unit selects appropriate farmland according to the investment amount and leases or purchases it. This makes efficient farmland acquisition possible by selecting appropriate farmland according to the investment amount. Some or all of the above processing in the farmland acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the farmland acquisition unit can have a generation AI perform the selection of farmland based on the investment amount.

[0079] The cultivation unit can perform cultivation based on the sowing time or fertilizer amount suggested by the generating AI. For example, the cultivation unit sows seeds based on the sowing time suggested by the generating AI. For example, the cultivation unit applies fertilizer based on the fertilizer amount suggested by the generating AI. This enables efficient cultivation by performing cultivation based on the optimal sowing time and fertilizer amount suggested by the generating AI. Some or all of the above processes in the cultivation unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the cultivation unit performs cultivation based on the sowing time and fertilizer amount suggested by the generating AI.

[0080] The trading department can use a generating AI to monitor market trends in real time and suggest selling timings. For example, the trading department can use a generating AI to monitor market trends and determine the timing of a sale. For example, the trading department can use a generating AI to monitor market trends in real time and suggest the optimal selling timing. This allows the generating AI to monitor market trends in real time and suggest the optimal selling timing, thereby maximizing profits. Some or all of the above processes in the trading department may be performed using a generating AI, or not. For example, the trading department can build a system in which a generating AI monitors market trends and suggests selling timings.

[0081] The revenue distribution unit can distribute profits according to the investment amount and return them to the user. For example, the revenue distribution unit calculates profits based on the investment amount and distributes them to the user. For example, the revenue distribution unit distributes profits according to the investment amount and returns them to the user. This allows users to feel the results of their investment by distributing profits according to the investment amount. Some or all of the above processing in the revenue distribution unit may be performed using, for example, a generation AI, or without a generation AI. For example, the revenue distribution unit distributes profits to the user based on the profits calculated by the generation AI.

[0082] The market forecasting unit can estimate user sentiment and adjust the accuracy of market forecasts based on the estimated user sentiment. For example, if the user is optimistic, the generating AI in the market forecasting unit can enhance the risk assessment of the market forecast and make a more cautious prediction. For example, if the user is anxious, the generating AI in the market forecasting unit can analyze additional data sources to improve the accuracy of the market forecast. Also, if the user is excited, the generating AI in the market forecasting unit can adjust the market forecast results to be more conservative. This allows for more accurate market forecasts by adjusting the accuracy of market forecasts based on user sentiment. Some or all of the above processing in the market forecasting unit may be performed using the generating AI, or not. For example, the market forecasting unit can input user sentiment data into the generating AI and have the generating AI perform the adjustment of the accuracy of the market forecast.

[0083] The market forecasting unit can analyze social media trend data in addition to historical market data to make more accurate market forecasts. For example, the market forecasting unit can use a generative AI to analyze trend data collected from social media such as X (formerly Twitter) and Facebook to predict fluctuations in demand. For example, the market forecasting unit can use a generative AI to analyze Instagram post data to evaluate consumer interest in a particular vegetable. The market forecasting unit can also use a generative AI to analyze YouTube viewing data to predict vegetables with high demand based on the popularity of cooking videos. This makes it possible to make more accurate market forecasts by analyzing social media trend data. Some or all of the above processing in the market forecasting unit may be performed using a generative AI, or not. For example, the market forecasting unit can input social media trend data into a generative AI and have the generative AI perform demand forecasting.

[0084] The market forecasting unit can customize its forecast results by considering regional consumption trends when making market forecasts. For example, the market forecasting unit's generating AI can analyze regional consumption data and predict vegetables that are in high demand in a particular region. For example, the market forecasting unit's generating AI can consider regional weather data and predict the demand for vegetables suitable for the climate. The market forecasting unit's generating AI can also analyze regional demographic data and predict consumption trends according to age group. This allows for more accurate market forecasts by considering regional consumption trends. Some or all of the above processing in the market forecasting unit may be performed using, for example, the generating AI, or without the generating AI. For example, the market forecasting unit can input regional consumption data into the generating AI and have the generating AI customize the forecast results.

[0085] The market forecasting unit can estimate the user's emotions and adjust the display method of the market forecast results based on the estimated user emotions. For example, if the user is nervous, the generating AI can provide a simple and highly visible display method. For example, if the user is relaxed, the generating AI can provide a display method that includes detailed information. Also, if the user is in a hurry, the generating AI can provide a display method that gets straight to the point. In this way, by adjusting the display method of the market forecast results based on the user's emotions, a display that is easy for the user to understand becomes possible. Some or all of the above processing in the market forecasting unit may be performed using the generating AI, or not. For example, the market forecasting unit can input user emotion data into the generating AI and have the generating AI perform the adjustment of the display method.

[0086] The market forecasting unit can provide individually customized forecasts that take into account the user's purchase history when forecasting the market. For example, the market forecasting unit's generating AI can analyze the user's past purchase history and predict the demand for a specific vegetable. For example, the market forecasting unit's generating AI can analyze the user's purchasing patterns and suggest vegetables with high demand. The market forecasting unit's generating AI can also predict seasonal demand from the user's purchase history. This makes it possible to provide individually customized market forecasts by taking into account the user's purchase history. Some or all of the above processing in the market forecasting unit may be performed using, for example, the generating AI, or without the generating AI. For example, the market forecasting unit can input user purchase history data into the generating AI and have the generating AI execute a customized forecast.

[0087] The market forecasting unit can analyze soil data in addition to weather data when forecasting the market to make more accurate predictions. For example, the market forecasting unit's generating AI can analyze soil data for each region and evaluate the suitability for cultivating specific vegetables. For example, the market forecasting unit's generating AI can analyze soil nutrient data and predict vegetables suitable for cultivation. The market forecasting unit's generating AI can also analyze soil moisture data and predict vegetables with high demand. By analyzing soil data in addition to weather data, more accurate market forecasts become possible. Some or all of the above processing in the market forecasting unit may be performed using, for example, the generating AI, or without the generating AI. For example, the market forecasting unit can input soil data into the generating AI and have the generating AI perform the predictions.

[0088] The information provision unit can estimate the user's emotions and adjust the timing of information provision based on the estimated emotions. For example, if the user is stressed, the information provision unit's generating AI can reduce the frequency of information provision and provide only important information. For example, if the user is relaxed, the information provision unit's generating AI can provide detailed information. Also, if the user is in a hurry, the information provision unit's generating AI can provide information quickly. In this way, by adjusting the timing of information provision based on the user's emotions, information can be provided at the optimal time for the user. Some or all of the above processing in the information provision unit may be performed using the generating AI, or not. For example, the information provision unit can input user emotion data into the generating AI and have the generating AI execute the timing of information provision.

[0089] The information provision unit can provide optimal information by considering the user's past investment history when providing information. For example, the information provision unit can use a generating AI to analyze the user's past investment history and provide relevant information. For example, the information provision unit can use a generating AI to analyze the user's investment patterns and provide optimal investment information. The information provision unit can also use a generating AI to provide information useful for future investments from the user's investment history. In this way, optimal information can be provided by considering the user's past investment history. Some or all of the above processing in the information provision unit may be performed using a generating AI, for example, or without using a generating AI. For example, the information provision unit can input the user's investment history data into a generating AI and have the generating AI perform the optimal information provision.

[0090] The information provision unit can adjust the priority of information based on the user's areas of interest when providing information. For example, the information provision unit can use a generative AI to analyze the user's areas of interest and prioritize the provision of relevant information. For example, the information provision unit can use a generative AI to analyze the user's past search history and provide information of high interest. The information provision unit can also use a generative AI to analyze the user's social media activity and provide relevant information. By adjusting the priority of information based on the user's areas of interest, the information provision unit can prioritize the provision of information that is important to the user. Some or all of the above processing in the information provision unit may be performed using a generative AI, or not. For example, the information provision unit can input user area of ​​interest data into a generative AI and have the generative AI prioritize information.

[0091] The information provision unit can estimate the user's emotions and adjust the format of the information provision based on the estimated emotions. For example, if the user is nervous, the information provision unit's generating AI can provide information in a simple and easy-to-read format. For example, if the user is relaxed, the information provision unit's generating AI can provide information in a format that includes detailed information. Also, if the user is in a hurry, the information provision unit's generating AI can provide information in a concise format. In this way, by adjusting the format of the information provision based on the user's emotions, information can be provided in a format that is easy for the user to understand. Some or all of the above processing in the information provision unit may be performed using the generating AI, for example, or without the generating AI. For example, the information provision unit can input user emotion data into the generating AI and have the generating AI execute the format of the information provision.

[0092] The information provision unit can provide highly relevant information by considering the user's geographical location when providing information. For example, the information provision unit can use a generating AI to analyze the user's current location and provide information relevant to that region. For example, the information provision unit can use a generating AI to analyze the user's past location information and provide relevant information. The information provision unit can also use a generating AI to analyze the user's movement patterns and provide optimal information. In this way, highly relevant information can be provided by considering the user's geographical location information. Some or all of the above processing in the information provision unit may be performed using a generating AI, for example, or without using a generating AI. For example, the information provision unit can input the user's geographical location information into a generating AI and have the generating AI perform the provision of highly relevant information.

[0093] The information provision unit can analyze a user's social media activity and provide relevant information when providing information. For example, the information provision unit can use a generative AI to analyze a user's posts on X (formerly Twitter) and provide relevant information. For example, the information provision unit can use a generative AI to analyze a user's activity on Facebook and provide information of high interest. The information provision unit can also use a generative AI to analyze a user's posts on Instagram and provide relevant information. In this way, relevant information can be provided by analyzing a user's social media activity. Some or all of the above processing in the information provision unit may be performed using a generative AI, or without using a generative AI. For example, the information provision unit can input the user's social media activity data into a generative AI and have the generative AI perform the information provision.

[0094] The farmland acquisition unit can estimate the user's emotions and determine the priority of farmland acquisition based on the estimated emotions. For example, if the user is optimistic, the generating AI in the farmland acquisition unit will prioritize acquiring high-risk farmland. For example, if the user is anxious, the generating AI in the farmland acquisition unit will prioritize acquiring low-risk farmland. Also, if the user is excited, the generating AI in the farmland acquisition unit will prioritize acquiring highly profitable farmland. This allows for more appropriate farmland acquisition by determining the priority of farmland acquisition based on the user's emotions. Some or all of the above processing in the farmland acquisition unit may be performed using the generating AI, or not. For example, the farmland acquisition unit can input user emotion data into the generating AI and have the generating AI execute the farmland acquisition priority.

[0095] The farmland acquisition unit can select the optimal farmland by analyzing past farmland use data when acquiring farmland. For example, the farmland acquisition unit can use a generating AI to analyze past farmland use data and select farmland with high profitability. For example, the farmland acquisition unit can use a generating AI to analyze the past use history of farmland and select the optimal farmland. The farmland acquisition unit can also use a generating AI to analyze past harvest data of farmland and select farmland with high profitability. In this way, the optimal farmland can be selected by analyzing past farmland use data. Some or all of the above processing in the farmland acquisition unit may be performed using a generating AI, for example, or without using a generating AI. For example, the farmland acquisition unit can input past farmland use data into a generating AI and have the generating AI perform the optimal farmland selection.

[0096] The land acquisition unit can adjust its acquisition plan when acquiring land, taking into account local agricultural policies. For example, the land acquisition unit can use a generating AI to analyze local agricultural policies and prioritize the acquisition of land eligible for subsidies. For example, the land acquisition unit can use a generating AI to consider local agricultural policies and prioritize the acquisition of land with fewer regulations. Furthermore, the land acquisition unit can use a generating AI to analyze local agricultural policies and acquire land with future potential. This allows for the creation of a more appropriate land acquisition plan by considering local agricultural policies. Some or all of the above processes in the land acquisition unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the land acquisition unit can input local agricultural policy data into a generating AI and have the generating AI perform the adjustment of the acquisition plan.

[0097] The farmland acquisition unit can estimate the user's emotions and adjust the timing of farmland acquisition based on the estimated emotions. For example, if the user is stressed, the generating AI in the farmland acquisition unit can delay the timing of farmland acquisition. For example, if the user is relaxed, the generating AI in the farmland acquisition unit can speed up the timing of farmland acquisition. Also, if the user is in a hurry, the generating AI in the farmland acquisition unit can acquire farmland quickly. In this way, by adjusting the timing of farmland acquisition based on the user's emotions, farmland can be acquired at a more appropriate time. Some or all of the above processing in the farmland acquisition unit may be performed using the generating AI, or not. For example, the farmland acquisition unit can input user emotion data into the generating AI and have the generating AI execute the timing of farmland acquisition.

[0098] The farmland acquisition unit can select the optimal farmland by considering the user's geographical location information when acquiring farmland. For example, the farmland acquisition unit's generating AI can analyze the user's current location and prioritize acquiring nearby farmland. For example, the farmland acquisition unit's generating AI can analyze the user's past location information and select the optimal farmland. Furthermore, the farmland acquisition unit's generating AI can analyze the user's movement patterns and select the optimal farmland. In this way, the optimal farmland can be selected by considering the user's geographical location information. Some or all of the above processing in the farmland acquisition unit may be performed using, for example, the generating AI, or without using the generating AI. For example, the farmland acquisition unit can input the user's geographical location information into the generating AI and have the generating AI perform the optimal farmland selection.

[0099] The farmland acquisition unit can adjust the size of the farmland acquired based on the user's investment amount. For example, the farmland acquisition unit can use a generating AI to analyze the user's investment amount and acquire farmland of an appropriate size. For example, the farmland acquisition unit can use a generating AI to analyze the user's investment pattern and acquire farmland of the optimal size. The farmland acquisition unit can also use a generating AI to acquire highly profitable farmland based on the user's investment amount. In this way, by adjusting the size of the farmland based on the user's investment amount, it is possible to acquire farmland of an appropriate size. Some or all of the above processing in the farmland acquisition unit may be performed using a generating AI, for example, or without using a generating AI. For example, the farmland acquisition unit can input user investment amount data into a generating AI and have the generating AI perform the farmland size adjustment.

[0100] The cultivation unit can estimate the user's emotions and adjust the cultivation method based on those emotions. For example, if the user is feeling optimistic, the generation AI can suggest a high-risk cultivation method. For example, if the user is feeling anxious, the generation AI can suggest a low-risk cultivation method. Also, if the user is excited, the generation AI can suggest a highly profitable cultivation method. This allows for the selection of a more appropriate cultivation method by adjusting the cultivation method based on the user's emotions. Some or all of the above processing in the cultivation unit may be performed using the generation AI, or not. For example, the cultivation unit can input user emotion data into the generation AI and have the generation AI adjust the cultivation method.

[0101] The cultivation unit can analyze past cultivation data during cultivation to select the optimal cultivation method. For example, the cultivation unit can use a generating AI to analyze past cultivation data and select a highly profitable cultivation method. For example, the cultivation unit can use a generating AI to analyze past cultivation history and select the optimal cultivation method. The cultivation unit can also use a generating AI to analyze past harvest data and select a highly profitable cultivation method. In this way, the optimal cultivation method can be selected by analyzing past cultivation data. Some or all of the above processes in the cultivation unit may be performed using a generating AI, for example, or without using a generating AI. For example, the cultivation unit can input past cultivation data into a generating AI and have the generating AI select the optimal cultivation method.

[0102] The cultivation unit can adjust its cultivation plan during cultivation, taking into account local weather conditions. For example, the cultivation unit can use a generating AI to analyze local weather data and propose the optimal cultivation time. For example, the cultivation unit can use a generating AI to consider local weather conditions and propose an appropriate cultivation method. The cultivation unit can also use a generating AI to analyze local weather data and propose a highly profitable cultivation plan. This allows for the creation of a more appropriate cultivation plan by considering local weather conditions. Some or all of the above processes in the cultivation unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the cultivation unit can input local weather data into a generating AI and have the generating AI perform the adjustment of the cultivation plan.

[0103] The cultivation unit can estimate the user's emotions and determine cultivation priorities based on those emotions. For example, if the user is stressed, the generating AI will prioritize cultivating low-risk crops. For example, if the user is relaxed, the generating AI can prioritize cultivating high-profit crops. Also, if the user is in a hurry, the generating AI can prioritize cultivating crops that can be harvested quickly. This allows for more appropriate cultivation by determining cultivation priorities based on the user's emotions. Some or all of the above processes in the cultivation unit may be performed using the generating AI, or not. For example, the cultivation unit can input user emotion data into the generating AI and have the generating AI execute the cultivation priorities.

[0104] The cultivation unit can select the optimal cultivation method during cultivation, taking into account the user's geographical location information. For example, the cultivation unit's generating AI can analyze the user's current location and propose a cultivation method suitable for the region. For example, the cultivation unit's generating AI can analyze the user's past location information and select the optimal cultivation method. Furthermore, the cultivation unit's generating AI can analyze the user's movement patterns and select the optimal cultivation method. In this way, the optimal cultivation method can be selected by taking into account the user's geographical location information. Some or all of the above-described processes in the cultivation unit may be performed using, for example, the generating AI, or without using the generating AI. For example, the cultivation unit can input the user's geographical location information into the generating AI and have the generating AI select the optimal cultivation method.

[0105] The cultivation unit can adjust the types of vegetables to cultivate based on the user's investment amount during cultivation. For example, the cultivation unit's generating AI can analyze the user's investment amount and cultivate the appropriate types of vegetables. For example, the cultivation unit's generating AI can analyze the user's investment pattern and cultivate the optimal types of vegetables. The cultivation unit can also have the generating AI cultivate highly profitable vegetables based on the user's investment amount. In this way, by adjusting the types of vegetables to cultivate based on the user's investment amount, the appropriate types of vegetables can be cultivated. Some or all of the above processes in the cultivation unit may be performed using a generating AI, for example, or without a generating AI. For example, the cultivation unit can input the user's investment amount data into a generating AI and have the generating AI determine the types of vegetables to cultivate.

[0106] The trading unit can estimate the user's emotions and adjust the timing of trades based on those emotions. For example, if the user is optimistic, the trading unit's generating AI can execute trades at high-risk times. For example, if the user is anxious, the trading unit's generating AI can execute trades at low-risk times. Also, if the user is excited, the trading unit's generating AI can execute trades at high-profit times. In this way, by adjusting the timing of trades based on the user's emotions, trades can be executed at more appropriate times. Some or all of the above processing in the trading unit may be performed using a generating AI, or not. For example, the trading unit can input user emotion data into a generating AI and have the generating AI execute trades at the appropriate times.

[0107] The trading unit can select the optimal trading strategy by analyzing historical market data at the time of trading. For example, the trading unit can use a generative AI to analyze historical market data and select a highly profitable trading strategy. For example, the trading unit can use a generative AI to analyze past market trends and select the optimal trading strategy. Alternatively, the trading unit can use a generative AI to analyze historical market price data and select a highly profitable trading strategy. In this way, the optimal trading strategy can be selected by analyzing historical market data. Some or all of the above processing in the trading unit may be performed using a generative AI, or without using a generative AI. For example, the trading unit can input historical market data into a generative AI and have the generative AI select the optimal trading strategy.

[0108] The trading department can adjust its trading plan when trading, taking into account regional market trends. For example, the trading department can use a generating AI to analyze regional market data and propose the optimal trading timing. For example, the trading department can use a generating AI to consider regional market trends and propose an appropriate trading strategy. The trading department can also use a generating AI to analyze regional market data and propose a highly profitable trading plan. This allows for the creation of more appropriate trading plans by considering regional market trends. Some or all of the above processes in the trading department may be performed using a generating AI, or not. For example, the trading department can input regional market data into a generating AI and have the generating AI perform the adjustment of the trading plan.

[0109] The trading unit can estimate the user's emotions and determine trading priorities based on those emotions. For example, if the user is stressed, the generating AI will prioritize low-risk trades. If the user is relaxed, the generating AI will prioritize high-profit trades. If the user is in a hurry, the generating AI can execute trades quickly. This allows for more appropriate trading by determining trading priorities based on the user's emotions. Some or all of the above processing in the trading unit may be performed using the generating AI, or not. For example, the trading unit can input user emotion data into the generating AI and have the generating AI execute trading priorities.

[0110] The trading unit can select the optimal trading strategy when trading, taking into account the user's geographical location information. For example, the trading unit can use a generative AI to analyze the user's current location and propose a trading strategy suitable for the region. For example, the trading unit can use a generative AI to analyze the user's past location information and select the optimal trading strategy. The trading unit can also use a generative AI to analyze the user's movement patterns and select the optimal trading strategy. In this way, the optimal trading strategy can be selected by taking into account the user's geographical location information. Some or all of the above processing in the trading unit may be performed using a generative AI, or without using a generative AI. For example, the trading unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal trading strategy.

[0111] The trading unit can adjust the types of vegetables traded based on the user's investment amount at the time of trading. For example, the trading unit can use a generating AI to analyze the user's investment amount and trade appropriate types of vegetables. For example, the trading unit can use a generating AI to analyze the user's investment pattern and trade the optimal types of vegetables. The trading unit can also use a generating AI to trade highly profitable vegetables based on the user's investment amount. In this way, by adjusting the types of vegetables traded based on the user's investment amount, appropriate types of vegetables can be traded. Some or all of the above processing in the trading unit may be performed using a generating AI, for example, or without using a generating AI. For example, the trading unit can input the user's investment amount data into a generating AI and have the generating AI determine the types of vegetables to trade.

[0112] The revenue distribution unit can estimate the user's emotions and adjust the revenue distribution method based on the estimated emotions. For example, if the user is feeling optimistic, the revenue distribution unit can have the generating AI suggest a high-risk revenue distribution method. For example, if the user is feeling anxious, the revenue distribution unit can have the generating AI suggest a low-risk revenue distribution method. Also, if the user is excited, the revenue distribution unit can have the generating AI suggest a high-profit revenue distribution method. By adjusting the revenue distribution method based on the user's emotions, a more appropriate revenue distribution becomes possible. Some or all of the above processing in the revenue distribution unit may be performed using the generating AI, for example, or without the generating AI. For example, the revenue distribution unit can input user emotion data into the generating AI and have the generating AI execute the revenue distribution method.

[0113] The revenue distribution unit can analyze past revenue data and select the optimal distribution method when distributing revenue. For example, the revenue distribution unit can use a generation AI to analyze past revenue data and select a highly profitable distribution method. For example, the revenue distribution unit can use a generation AI to analyze past revenue history and select the optimal distribution method. Alternatively, the revenue distribution unit can use a generation AI to analyze past revenue data and select a highly profitable distribution method. In this way, the optimal distribution method can be selected by analyzing past revenue data. Some or all of the above processing in the revenue distribution unit may be performed using a generation AI, for example, or without using a generation AI. For example, the revenue distribution unit can input past revenue data into a generation AI and have the generation AI select the optimal distribution method.

[0114] The revenue distribution unit can adjust the distribution plan based on the user's investment amount when distributing revenue. For example, the revenue distribution unit can use a generating AI to analyze the user's investment amount and propose an appropriate distribution plan. For example, the revenue distribution unit can use a generating AI to analyze the user's investment pattern and propose an optimal distribution plan. Alternatively, the revenue distribution unit can use a generating AI to propose a highly profitable distribution plan based on the user's investment amount. In this way, an appropriate distribution plan can be created by adjusting the distribution plan based on the user's investment amount. Some or all of the above processing in the revenue distribution unit may be performed using a generating AI, or without using a generating AI. For example, the revenue distribution unit can input user investment data into a generating AI and have the generating AI perform the adjustment of the distribution plan.

[0115] The revenue distribution unit can estimate the user's emotions and adjust the timing of revenue distribution based on the estimated emotions. For example, if the user is tense, the revenue distribution unit's generating AI can delay the timing of revenue distribution. For example, if the user is relaxed, the revenue distribution unit's generating AI can speed up the timing of revenue distribution. Also, if the user is in a hurry, the revenue distribution unit's generating AI can distribute revenue quickly. In this way, by adjusting the timing of revenue distribution based on the user's emotions, revenue distribution can be performed at a more appropriate time. Some or all of the above processing in the revenue distribution unit may be performed using a generating AI, for example, or without a generating AI. For example, the revenue distribution unit can input user emotion data into a generating AI and have the generating AI execute the timing of revenue distribution.

[0116] The revenue distribution unit can select the optimal distribution method when distributing revenue, taking into account the user's geographical location information. For example, the revenue distribution unit can use a generating AI to analyze the user's current location and propose a distribution method suitable for the region. For example, the revenue distribution unit can use a generating AI to analyze the user's past location information and select the optimal distribution method. The revenue distribution unit can also use a generating AI to analyze the user's movement patterns and select the optimal distribution method. In this way, the optimal distribution method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the revenue distribution unit may be performed using a generating AI, for example, or without using a generating AI. For example, the revenue distribution unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal distribution method.

[0117] The revenue distribution unit can adjust the percentage of revenue distributed based on the user's investment amount when distributing revenue. For example, the revenue distribution unit can use a generating AI to analyze the user's investment amount and distribute an appropriate percentage of revenue. For example, the revenue distribution unit can use a generating AI to analyze the user's investment pattern and distribute the optimal percentage of revenue. The revenue distribution unit can also use a generating AI to propose a highly profitable distribution ratio based on the user's investment amount. This makes it possible to distribute revenue appropriately by adjusting the percentage of revenue distributed based on the user's investment amount. Some or all of the above processing in the revenue distribution unit may be performed using a generating AI, for example, or without using a generating AI. For example, the revenue distribution unit can input user investment amount data into a generating AI and have the generating AI perform the revenue ratio adjustment.

[0118] The training delivery unit can estimate the user's emotions and adjust the training content based on those emotions. For example, if the user is feeling optimistic, the training delivery unit's generative AI can suggest high-risk training content. For example, if the user is feeling anxious, the training delivery unit's generative AI can suggest low-risk training content. Also, if the user is excited, the training delivery unit's generative AI can suggest high-profit training content. In this way, by adjusting the training content based on the user's emotions, more appropriate training can be provided. Some or all of the above processing in the training delivery unit may be performed using, for example, the generative AI, or without the generative AI. For example, the training delivery unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the training content.

[0119] The training provision unit can analyze past training data and select the optimal training method when providing training. For example, the training provision unit can use a generative AI to analyze past training data and select an effective training method. For example, the training provision unit can use a generative AI to analyze past training history and select the optimal training method. Alternatively, the training provision unit can use a generative AI to analyze past training performance data and select an effective training method. In this way, the optimal training method can be selected by analyzing past training data. Some or all of the above processing in the training provision unit may be performed using a generative AI, or without using a generative AI. For example, the training provision unit can input past training data into a generative AI and have the generative AI select the optimal training method.

[0120] The training delivery unit can customize training content based on the user's areas of interest when providing training. For example, the training delivery unit can use a generative AI to analyze the user's areas of interest and provide relevant training content. For example, the training delivery unit can use a generative AI to analyze the user's past search history and provide training content of high interest. The training delivery unit can also use a generative AI to analyze the user's social media activity and provide relevant training content. By customizing training content based on the user's areas of interest, more effective training can be provided. Some or all of the above processing in the training delivery unit may be performed using a generative AI, or not. For example, the training delivery unit can input user area of ​​interest data into a generative AI and have the generative AI perform the customization of training content.

[0121] The training delivery unit can estimate the user's emotions and determine training priorities based on those emotions. For example, if the user is nervous, the training delivery unit can use a generative AI to prioritize low-risk training. For example, if the user is relaxed, the training delivery unit can use a generative AI to prioritize high-profit training. Also, if the user is in a hurry, the training delivery unit can use a generative AI to prioritize training that can be completed quickly. This allows for the provision of more appropriate training by prioritizing training based on the user's emotions. Some or all of the above processes in the training delivery unit may be performed using a generative AI, or not. For example, the training delivery unit can input user emotion data into a generative AI and have the generative AI prioritize training.

[0122] The training provision unit can select the optimal training method by considering the user's geographical location information when providing training. For example, the training provision unit can use a generative AI to analyze the user's current location and propose a training method suitable for the region. For example, the training provision unit can use a generative AI to analyze the user's past location information and select the optimal training method. The training provision unit can also use a generative AI to analyze the user's movement patterns and select the optimal training method. In this way, the optimal training method can be selected by considering the user's geographical location information. Some or all of the above processing in the training provision unit may be performed using a generative AI, or without using a generative AI. For example, the training provision unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal training method.

[0123] The training provision unit can adjust the training content based on the user's investment amount when providing training. For example, the training provision unit can use a generative AI to analyze the user's investment amount and provide appropriate training content. For example, the training provision unit can use a generative AI to analyze the user's investment pattern and provide optimal training content. The training provision unit can also use a generative AI to provide highly profitable training content based on the user's investment amount. In this way, appropriate training content can be provided by adjusting the training content based on the user's investment amount. Some or all of the above processing in the training provision unit may be performed using a generative AI, or without using a generative AI. For example, the training provision unit can input user investment data into a generative AI and have the generative AI perform the adjustment of the training content. === Hard Collateral 1-1 === Each of the multiple elements described above, including the market forecasting unit, information provision unit, farmland acquisition unit, cultivation unit, buying and selling unit, profit distribution unit, and training provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the market forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes historical market data and weather data. The information provision unit is implemented by the control unit 46A of the smart device 14 and provides the user with the results predicted by the generating AI. The farmland acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects appropriate farmland according to the investment amount. The cultivation unit is implemented by the control unit 46A of the smart device 14 and performs cultivation based on the optimal sowing time and fertilizer amount proposed by the generating AI. The buying and selling unit is implemented by the specific processing unit 290 of the data processing unit 12 and the generating AI monitors market trends in real time and proposes the optimal selling timing. The profit distribution unit is implemented by the specific processing unit 290 of the data processing unit 12 and distributes profits according to the investment amount. The training provision unit is implemented by the control unit 46A of the smart device 14 and provides a platform where users can learn basic agricultural knowledge and efficient cultivation methods online. === Hard Collateral 1-2 === Each of the multiple elements mentioned above, including the market forecasting unit, information provision unit, farmland acquisition unit, cultivation unit, buying and selling unit, profit distribution unit, and training provision unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the market forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes historical market data and weather data. The information provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the user with the results predicted by the generating AI. The farmland acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects appropriate farmland according to the investment amount. The cultivation unit is implemented by the control unit 46A of the smart glasses 214 and performs cultivation based on the optimal sowing time and fertilizer amount proposed by the generating AI. The buying and selling unit is implemented by the specific processing unit 290 of the data processing unit 12 and the generating AI monitors market trends in real time and proposes the optimal selling timing. The profit distribution unit is implemented by the specific processing unit 290 of the data processing unit 12 and distributes profits according to the investment amount. The training provision unit is implemented by the control unit 46A of the smart glasses 214 and provides a platform where users can learn basic agricultural knowledge and efficient cultivation methods online. === Hard Collateral 1-3 === Each of the multiple elements described above, including the market forecasting unit, information provision unit, farmland acquisition unit, cultivation unit, buying and selling unit, profit distribution unit, and training provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the market forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes historical market data and weather data. The information provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the user with the results predicted by the generating AI. The farmland acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects appropriate farmland according to the investment amount. The cultivation unit is implemented by the control unit 46A of the headset terminal 314 and performs cultivation based on the optimal sowing time and fertilizer amount proposed by the generating AI. The buying and selling unit is implemented by the specific processing unit 290 of the data processing unit 12 and the generating AI monitors market trends in real time and proposes the optimal selling timing. The profit distribution unit is implemented by the specific processing unit 290 of the data processing unit 12 and distributes profits according to the investment amount. The training provision unit is implemented by the control unit 46A of the headset terminal 314 and provides a platform where users can learn basic agricultural knowledge and efficient cultivation methods online. === Hard Collateral 1-4 === Each of the multiple elements described above, including the market forecasting unit, information provision unit, farmland acquisition unit, cultivation unit, buying and selling unit, profit distribution unit, and training provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the market forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes historical market data and weather data. The information provision unit is implemented by the control unit 46A of the robot 414 and provides the user with the results predicted by the generating AI. The farmland acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects appropriate farmland according to the investment amount. The cultivation unit is implemented by the control unit 46A of the robot 414 and performs cultivation based on the optimal sowing time and fertilizer amount proposed by the generating AI. The buying and selling unit is implemented by the specific processing unit 290 of the data processing unit 12 and the generating AI monitors market trends in real time and proposes the optimal selling timing. The profit distribution unit is implemented by the specific processing unit 290 of the data processing unit 12 and distributes profits according to the investment amount. The training provision unit is implemented by the control unit 46A of the robot 414 and provides a platform where users can learn basic agricultural knowledge and efficient cultivation methods online.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The agricultural support system can also include a health management unit that monitors the user's health status. This unit can, for example, monitor the user's heart rate and blood pressure in real time and suggest agricultural tasks tailored to their health condition. For instance, if the heart rate is high, it can suggest a break. Similarly, if blood pressure is low, it can suggest lighter tasks. This allows for a safer and more efficient agricultural experience by tailoring tasks to the user's health.

[0126] The agricultural support system can also include an emotion adjustment unit that estimates the user's emotions and adjusts the difficulty of farm work based on those emotions. For example, if the user is feeling stressed, the emotion adjustment unit can suggest easy tasks. For example, if the user is relaxed, it can suggest slightly more difficult tasks. It can also suggest challenging tasks if the user is excited. This allows for a more satisfying farming experience by tailoring farm work to the user's emotions.

[0127] The agricultural support system can also include a work scheduling unit that analyzes the user's past farming history and proposes an optimal work schedule. For example, the work scheduling unit can suggest optimal work and rest times based on past work history. If, for instance, past work history reveals high efficiency during specific time periods, work can be concentrated during those times. Furthermore, based on past work history, the system can suggest avoiding times when fatigue is likely to accumulate. This enables the creation of an efficient work schedule.

[0128] The agricultural support system may also include a music provider that estimates the user's emotions and selects music for farm work based on those emotions. For example, if the user is relaxed, the music provider can provide relaxing music. If the user is stressed, it can provide music that reduces stress. It can also provide music that enhances concentration if the user wants to concentrate. By providing music that matches the user's emotions, the efficiency and satisfaction of farm work can be improved.

[0129] The agricultural support system can also include a regional information provision unit that takes into account the user's geographical location and provides region-specific agricultural information. For example, the regional information provision unit can suggest optimal cultivation methods based on local weather and soil data. For instance, it can suggest the optimal sowing time based on local weather data, or the appropriate amount of fertilizer based on local soil data. This allows for more accurate agricultural support by utilizing region-specific information.

[0130] The agricultural support system may also include a feedback adjustment unit that estimates the user's emotions and adjusts feedback on farm work based on those emotions. For example, if the user is feeling optimistic, the feedback adjustment unit will emphasize positive feedback. If the user is feeling anxious, it can provide encouraging feedback. It can also provide calming feedback if the user is excited. This makes it easier to maintain motivation by providing feedback that matches the user's emotions.

[0131] The agricultural support system can also include an investment strategy unit that analyzes the user's past investment history and proposes the optimal investment strategy. For example, the investment strategy unit can suggest low-risk investments based on past investment history. For example, it can suggest highly profitable investments based on past investment history. It can also suggest the timing of investments based on past investment history. This allows for the provision of more effective investment strategies by utilizing the user's investment history.

[0132] The agricultural support system may also include an information provision adjustment unit that estimates the user's emotions and adjusts the way investment information is provided based on those emotions. For example, if the user is stressed, the information provision adjustment unit can provide simple and easy-to-understand information. For example, if the user is relaxed, it can provide detailed information. If the user is in a hurry, it can provide concise information. In this way, the system can support investment decisions by providing information that is tailored to the user's emotions.

[0133] The agricultural support system can also include a farmland selection unit that selects the optimal farmland by considering the user's geographical location information. For example, the farmland selection unit can prioritize nearby farmland based on the user's current location. Alternatively, it can select the optimal farmland based on the user's past location information. Furthermore, it can analyze the user's movement patterns to select the optimal farmland. This enables efficient farmland selection by utilizing the user's geographical location information.

[0134] The agricultural support system may further include a revenue distribution adjustment unit that estimates the user's emotions and adjusts the revenue distribution method based on the estimated emotions. For example, if the user is optimistic, the revenue distribution adjustment unit may suggest a high-risk revenue distribution method. For example, if the user is anxious, it may suggest a low-risk revenue distribution method. It may also suggest a high-profit revenue distribution method if the user is excited. This allows for more appropriate revenue distribution by providing a revenue distribution method that responds to the user's emotions.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The market forecasting department conducts market forecasts or investigates shortages of vegetables. For example, they analyze historical market data and weather data to predict future market trends and vegetables in high demand. The market forecasting department uses generative AI to analyze this data. Step 2: The information provision unit provides users with the results obtained by the market forecasting unit. For example, it notifies users of market trends predicted by the generation AI and information on vegetables with high demand. Step 3: The Farmland Acquisition Department receives investments based on information provided by the Information Provision Department and leases or purchases farmland. For example, it selects appropriate farmland according to the investment amount and concludes a contract. Step 4: The cultivation department carries out cultivation on the farmland acquired by the farmland acquisition department. For example, cultivation is carried out based on the optimal sowing time and fertilizer amount suggested by the generation AI. Step 5: The trading department buys and sells the vegetables harvested by the cultivation department. For example, a generation AI monitors market trends in real time and suggests the optimal selling time. Step 6: The profit distribution unit distributes the profits earned by the trading unit to the users. For example, it calculates profits based on the investment amount and distributes them to the users.

[0137] 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.

[0138] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0139] 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.

[0140] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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. 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.

[0155] 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.

[0156] 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] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[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 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.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[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 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.

[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 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.

[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 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.

[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. 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 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.

[0172] 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] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[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 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.

[0177] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[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 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).

[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] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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. 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.

[0188] 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.

[0189] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Explanation of symbols]

[0209] 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 market forecasting department conducts market forecasts or research on shortages of vegetables, An information provision unit that provides the user with the results obtained by the market forecasting unit, Based on the information provided by the aforementioned information provision department, the farmland acquisition department receives investments and leases or purchases farmland, The cultivation unit cultivates on the farmland acquired by the aforementioned farmland acquisition unit, A trading unit that buys and sells vegetables harvested by the aforementioned cultivation unit, A revenue distribution unit that distributes the profits obtained by the aforementioned trading unit to the users, Equipped with A system characterized by the following features.

2. We provide a training platform using generative AI and have a training provision department. The system according to feature 1.

3. The aforementioned market forecasting department, By analyzing past market data or weather data, we predict future market trends and high-demand vegetables. The system according to feature 1.

4. The aforementioned information provision unit, The AI ​​generates and predicts results that are then provided to the user. The system according to feature 1.

5. The aforementioned farmland acquisition unit is, Farmland is selected and leased or purchased according to the investment amount. The system according to feature 1.

6. The aforementioned cultivation section is Cultivation is carried out based on the sowing time or fertilizer amount suggested by the generating AI. The system according to feature 1.

7. The aforementioned sales department, The AI ​​generates data, monitors market trends in real time, and suggests optimal selling times. The system according to feature 1.

8. The aforementioned revenue distribution unit is, Profits are distributed to users according to their investment amount. The system according to feature 1.

9. The aforementioned market forecasting department, It estimates user sentiment and adjusts the accuracy of market forecasts based on the estimated user sentiment. The system according to feature 1.

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