system

Abstract

The system according to this embodiment aims to make effective use of farmland by suggesting effective vegetable combinations and cultivation methods based on information about farmland. [Solution] The system according to the embodiment comprises a collection unit, a proposal unit, and a lending unit. The collection unit collects information such as the size of fields and unused farmland, climate, and season. The proposal unit proposes effective vegetable combinations and cultivation methods based on the information collected by the collection unit. The lending unit leases farmland to general users.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, the effective utilization of agricultural land and the proposal of effective ways to grow vegetables have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an effective combination of vegetables and a growing method based on information on agricultural land and effectively utilize the agricultural land.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a proposal unit, and a lending unit. The collection unit collects information such as the area of fields and unused agricultural land, climate, and season. The proposal unit proposes an effective combination of vegetables and a growing method based on the information collected by the collection unit. The lending unit lends agricultural land to general users. [Effects of the Invention]

[0007] The system according to this embodiment can propose effective vegetable combinations and cultivation methods based on information about farmland, thereby enabling effective use of farmland. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] [[ID=ID=19]]The smart device 14 includes a computer 力36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The organic vegetable quality improvement system according to an embodiment of the present invention is a system designed to address the declining agricultural population and meet the increasing demand for organic vegetables. This system proposes a method for improving vegetable quality using companion planting. Companion planting involves planting vegetables that tend to have synergistic growth effects together to achieve effects such as pest prevention and growth promotion. The organic vegetable quality improvement system utilizes AI to advise on effective vegetable combinations and cultivation methods based on the size of current fields and unused farmland, climate, and season. It is also possible to lease farmland to general users and utilize the AI's advice to make effective use of the fields. For example, the organic vegetable quality improvement system first collects information such as the size of fields and unused farmland, climate, and season. Next, based on the collected information, the AI ​​proposes effective vegetable combinations and cultivation methods. For example, planting spinach and turnips together can provide pest prevention. Also, planting corn and pumpkins together can provide growth promotion. In this way, the AI ​​proposes the optimal combination of companion plants, thereby improving the quality of vegetables. Furthermore, by leasing farmland to general users and utilizing AI advice, it is possible to make effective use of fields. For example, ordinary consumers who enjoy home gardening as a hobby can lease farmland and grow vegetables according to AI advice. This can increase the production of organic vegetables while addressing the decline in the agricultural population. Through this system, farmers can produce high-quality vegetables with less effort, and consumers can easily obtain organic vegetables. In addition, the effective use of farmland is promoted, and it is expected that agriculture will be revitalized. In summary, this system for improving the quality of organic vegetables can increase the production of organic vegetables while addressing the decline in the agricultural population.

[0029] The organic vegetable quality improvement system according to this embodiment comprises a collection unit, a suggestion unit, and a lending unit. The collection unit collects information such as the size of fields and unused farmland, climate, and season. For example, the collection unit measures the size of fields, collects climate data, and records seasonal variations. The collection unit can also efficiently collect information on a wide area of ​​farmland using, for example, a drone. The suggestion unit proposes effective vegetable combinations and cultivation methods based on the information collected by the collection unit. For example, the suggestion unit proposes the effect of pest prevention by planting spinach and turnips together. The suggestion unit can also propose the effect of promoting growth by planting corn and pumpkins together. The suggestion unit can also propose the optimal combination of companion plants based on the collected information. For example, the suggestion unit proposes the optimal combination of vegetables based on collected soil component data. The lending unit lends farmland to general users. For example, the lending unit can lend farmland to general consumers who enjoy home gardening as a hobby. The lending department can also collect feedback from users of farmland and use it to improve lending conditions. As a result, the organic vegetable quality improvement system according to the embodiment can collect information such as the size of fields and unused farmland, climate, and season, propose effective vegetable combinations and cultivation methods, and lend farmland to general users.

[0030] The data collection unit collects information such as the size of fields and unused farmland, climate, and seasons. Specifically, it uses GPS technology and Geographic Information Systems (GIS) to measure the size of fields and obtain accurate area data. For climate data collection, weather sensors and weather stations are installed to collect data such as temperature, humidity, precipitation, and wind speed in real time. To record seasonal variations, long-term weather data is accumulated and analyzed to understand seasonal climate patterns. Furthermore, the data collection unit can also efficiently collect information on wide-area farmland using drones. Drones are equipped with high-resolution cameras and multispectral sensors to monitor the condition of the farmland and the health of the crops in detail. This allows the data collection unit to accurately and efficiently collect data on field size, climate, and seasonal variations and store it in the system's overall database. The collected data is stored on a cloud server and made accessible to the proposal and lending units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively and improve the overall performance of the system.

[0031] The proposal department proposes effective vegetable combinations and cultivation methods based on information collected by the data collection department. Specifically, it analyzes collected field size, climate data, and seasonal variation information to determine the optimal vegetable combinations. For example, it may suggest planting spinach and turnips together, as spinach can help prevent turnip pests. It may also suggest planting corn and pumpkins together, as corn can promote pumpkin growth. The proposal department also proposes optimal companion plant combinations based on collected soil composition data. For example, it analyzes soil pH values ​​and nutrient balance to suggest suitable vegetable combinations. Furthermore, the proposal department can use AI to learn from past data and success stories, enabling it to make more accurate suggestions. The AI ​​analyzes the collected data and automatically suggests optimal vegetable combinations and cultivation methods. This allows the proposal department to propose effective vegetable combinations and cultivation methods based on collected information, supporting the improvement of organic vegetable quality.

[0032] The Lending Department leases farmland to general users. Specifically, it can lease farmland to general consumers who enjoy home gardening as a hobby. The Lending Department can also collect feedback from farmland users and use it to improve the lease conditions. For example, it can review farmland usage fees and lease periods based on user feedback. The Lending Department can also provide support to users regarding how to use the farmland and cultivation methods. For example, if a user is using farmland for the first time, it can provide a guidebook explaining basic cultivation methods and precautions. It can also set up online support and consultation services so that users can consult anytime they have a problem. In this way, the Lending Department can provide farmland users with an environment in which they can use farmland with peace of mind and support the improvement of organic vegetable quality. Furthermore, the Lending Department can collect user feedback and use it to improve the overall system. For example, it can improve the functions of the Collection Department and the Proposal Department based on user opinions and requests, providing a more user-friendly system. In this way, the Lending Department can provide high-quality service to farmland users and support the improvement of organic vegetable quality.

[0033] The proposal team can suggest that planting spinach and turnips together can have a pest prevention effect. For example, the proposal team suggests that planting spinach and turnips together can suppress the occurrence of specific pests. The proposal team can also suggest that planting spinach and turnips together can reduce pest damage. Furthermore, the proposal team can suggest that planting spinach and turnips together can prevent the reproduction of pests. In this way, pest prevention can be obtained by planting spinach and turnips together.

[0034] The proposal suggests that planting corn and pumpkins together can promote growth. For example, it suggests that planting corn and pumpkins together can improve their growth rate. It can also suggest that planting corn and pumpkins together can increase yields. Furthermore, it can suggest that planting corn and pumpkins together can improve the health of the plants. As a result, planting corn and pumpkins together can be expected to promote growth.

[0035] The leasing department can lease farmland to general consumers who enjoy home gardening as a hobby. For example, the leasing department can lease farmland to beginners who enjoy home gardening as a hobby. It can also lease farmland to experienced home gardeners. Furthermore, the leasing department can lease farmland to families who enjoy home gardening as a hobby. In this way, leasing farmland to general consumers who enjoy home gardening as a hobby promotes the effective use of farmland.

[0036] The suggestion unit can propose the optimal companion plant combinations based on the collected information. For example, it can propose the optimal vegetable combinations based on collected soil composition data. It can also propose the optimal vegetable combinations based on collected climate data. Furthermore, it can propose the optimal vegetable combinations based on collected seasonal data. In this way, by proposing the optimal companion plant combinations based on the collected information, the quality of vegetables can be improved.

[0037] The data collection unit can collect information such as the size of fields and unused farmland, climate, and seasons. For example, the unit can measure the size of fields, collect climate data, and record seasonal variations. The unit can also efficiently collect information on wide areas of farmland using drones, for example. The unit can also collect information on wide-area climate and seasons using satellite data, for example. By collecting information such as the size of fields and unused farmland, climate, and seasons, it is possible to obtain data that can be used to propose effective vegetable combinations and cultivation methods.

[0038] The data collection unit can analyze the soil composition of fields and unused farmland and collect data to select the optimal combination of vegetables. For example, the data collection unit can measure the pH value of the soil and select vegetables suitable for acidic soil. It can also analyze the nitrogen, phosphorus, and potassium content in the soil and select vegetables with a suitable nutritional balance. Furthermore, the data collection unit can evaluate the soil's water retention capacity and select drought-resistant vegetables. In this way, by analyzing the soil composition of fields and unused farmland, data can be obtained to select the optimal combination of vegetables.

[0039] The data collection unit can monitor climate and seasonal variations in real time, improving the accuracy of the collected data. For example, it can monitor temperature fluctuations in real time to identify the appropriate time to grow vegetables. It can also monitor precipitation fluctuations in real time to determine the need for irrigation. Furthermore, it can monitor sunshine duration fluctuations in real time to select vegetables suitable for photosynthesis. In this way, by monitoring climate and seasonal variations in real time, the accuracy of the collected data can be improved.

[0040] The data collection unit can efficiently collect information from a wide area of ​​farmland using drones. For example, the unit can fly drones to take aerial photographs of the entire farmland to understand the growth status of crops. The unit can also measure soil moisture with sensors mounted on the drones to determine the need for irrigation. Furthermore, the unit can use drones to monitor pest outbreaks and take early countermeasures. In this way, by efficiently collecting information from a wide area of ​​farmland using drones, it is possible to understand the condition of the farmland.

[0041] The data collection unit can use satellite data to collect climate and seasonal information over a wide area. For example, it can use satellite data to understand temperature fluctuations over a wide area and identify the appropriate timing for planting crops. It can also use satellite data to monitor precipitation fluctuations and develop irrigation plans. Furthermore, it can utilize satellite data to understand changes in sunshine duration and select crops suitable for photosynthesis. In this way, by collecting climate and seasonal information over a wide area using satellite data, it is possible to obtain climate and seasonal information over a wide area.

[0042] The proposal department can suggest the optimal vegetable combination by referring to past success stories. For example, it can suggest the previously successful combination of spinach and turnip. It can also suggest the combination of corn and pumpkin based on past data. Furthermore, it can suggest vegetable combinations effective for pest prevention by referring to past success stories. In this way, by referring to past success stories, it can suggest the optimal vegetable combination.

[0043] The proposal unit can suggest effective combinations for pest prevention by considering pest outbreak prediction data. For example, based on pest outbreak prediction data, the proposal unit can suggest a combination of spinach and turnips. It can also suggest a combination of corn and pumpkins, taking pest outbreak prediction data into consideration. Furthermore, the proposal unit can suggest effective vegetable combinations for pest prevention by referring to pest outbreak prediction data. In this way, by considering pest outbreak prediction data, it is possible to suggest effective combinations for pest prevention.

[0044] The proposal department can make more realistic proposals by incorporating the expertise of local agricultural experts. For example, the proposal department could propose a combination of spinach and turnips based on the expertise of local agricultural experts. It could also propose a combination of corn and pumpkins, incorporating advice from local agricultural experts. Furthermore, the proposal department could utilize the expertise of local agricultural experts to propose vegetable combinations that are effective in preventing pests. In this way, incorporating the expertise of local agricultural experts allows for more realistic proposals.

[0045] The suggestion function can provide individually customized suggestions by taking into account the user's past cultivation history. For example, the suggestion function can suggest a combination of spinach and turnips based on the user's past cultivation history. It can also suggest a combination of corn and pumpkins, taking into account the user's past cultivation history. Furthermore, the suggestion function can refer to the user's past cultivation history to suggest vegetable combinations that are effective in preventing pests. In this way, it can provide individually customized suggestions by taking into account the user's past cultivation history.

[0046] The lending department can collect feedback from farmland users and use it to improve lending conditions. For example, the lending department can collect feedback from farmland users and improve lending conditions. Furthermore, the lending department can propose better lending conditions based on user feedback. In addition, the lending department can flexibly adjust lending conditions based on user feedback. Thus, collecting feedback from farmland users can be used to improve lending conditions.

[0047] The Lending Department can provide a platform to facilitate communication among users of farmland. For example, it can provide an online forum where users can exchange information. It can also provide a chat function where users can exchange advice with each other. Furthermore, it can provide a schedule sharing function to enable users to work together. This promotes communication among users and allows them to work together.

[0048] The lending department can provide agricultural education programs to users of farmland. For example, the lending department can offer online courses on agriculture to users. It can also hold workshops where users can experience farm work firsthand. Furthermore, the lending department can provide agricultural books and materials to users. In this way, by providing agricultural education programs to users, the lending department can improve their knowledge of agriculture.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit not only gathers information such as the size of fields and unused farmland, climate, and season, but can also monitor soil microbial activity. For example, it can measure the types of beneficial microorganisms in the soil and their activity levels, and provide advice on maintaining microbial balance. The data collection unit can also assess soil health and suggest the use of organic fertilizers as needed. Furthermore, it can assess the soil's water retention capacity and suggest appropriate irrigation methods. This helps maintain soil health and improve vegetable quality.

[0051] The land lending department can host community events related to agriculture for users of farmland. For example, they can hold harvest festivals and farming experience events to promote interaction among users. The land lending department can also regularly hold seminars and workshops on agriculture to improve users' knowledge. Furthermore, the land lending department can provide a platform for users to exchange information and give advice to each other through online forums. This promotes communication among users of farmland and allows for the sharing of agricultural knowledge.

[0052] The data collection department can not only gather information on the size of fields and unused farmland, climate, and seasons, but also investigate the historical use of farmland. For example, it can investigate what crops were cultivated and what agricultural techniques were used in the past, and reflect this in current cultivation plans. The data collection department can also analyze historical climate data and assess the impact of climate change. Furthermore, the data collection department can refer to historical soil data and track soil changes. This allows for the creation of cultivation plans that take into account the historical use of farmland.

[0053] The collection department not only gathers information on the size of fields and unused farmland, climate, and seasons, but can also assess the biodiversity of farmland. For example, it can survey the types and numbers of plants and animals inhabiting farmland to help conserve biodiversity. Furthermore, the collection department can assess the health of farmland ecosystems and propose ecosystem restoration as needed. In addition, the collection department can propose management methods to maintain farmland biodiversity. This enables the conservation of farmland biodiversity and the realization of sustainable agriculture.

[0054] The data collection unit not only gathers information such as the size of fields and unused farmland, climate, and season, but can also evaluate the management of water resources on farmland. For example, it can assess the efficiency of irrigation systems and propose improvements as needed. The data collection unit can also monitor water quality on farmland and provide advice for water quality improvement. Furthermore, the data collection unit can analyze the utilization of water resources on farmland and propose sustainable water resource management methods. This enables efficient management of water resources on farmland and realizes sustainable agriculture.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection unit gathers information such as the size of fields and unused farmland, climate, and season. For example, it measures the size of fields, collects climate data, and records seasonal variations. The data collection unit can also efficiently collect information on a wide area of ​​farmland using drones. Step 2: The proposal team proposes effective vegetable combinations and cultivation methods based on the information collected by the data collection team. For example, they might suggest planting spinach and turnips together to prevent pests, or planting corn and pumpkins together to promote growth. Furthermore, they can also propose optimal companion plant combinations based on the collected soil composition data. Step 3: The leasing department leases the farmland to general users. For example, it can lease farmland to general consumers who enjoy home gardening as a hobby. The leasing department can also collect feedback from farmland users and use it to improve the leasing conditions.

[0057] (Example of form 2) The organic vegetable quality improvement system according to an embodiment of the present invention is a system designed to address the declining agricultural population and meet the increasing demand for organic vegetables. This system proposes a method for improving vegetable quality using companion planting. Companion planting involves planting vegetables that tend to have synergistic growth effects together to achieve effects such as pest prevention and growth promotion. The organic vegetable quality improvement system utilizes AI to advise on effective vegetable combinations and cultivation methods based on the size of current fields and unused farmland, climate, and season. It is also possible to lease farmland to general users and utilize the AI's advice to make effective use of the fields. For example, the organic vegetable quality improvement system first collects information such as the size of fields and unused farmland, climate, and season. Next, based on the collected information, the AI ​​proposes effective vegetable combinations and cultivation methods. For example, planting spinach and turnips together can provide pest prevention. Also, planting corn and pumpkins together can provide growth promotion. In this way, the AI ​​proposes the optimal combination of companion plants, thereby improving the quality of vegetables. Furthermore, by leasing farmland to general users and utilizing AI advice, it is possible to make effective use of fields. For example, ordinary consumers who enjoy home gardening as a hobby can lease farmland and grow vegetables according to AI advice. This can increase the production of organic vegetables while addressing the decline in the agricultural population. Through this system, farmers can produce high-quality vegetables with less effort, and consumers can easily obtain organic vegetables. In addition, the effective use of farmland is promoted, and it is expected that agriculture will be revitalized. In summary, this system for improving the quality of organic vegetables can increase the production of organic vegetables while addressing the decline in the agricultural population.

[0058] The organic vegetable quality improvement system according to this embodiment comprises a collection unit, a suggestion unit, and a lending unit. The collection unit collects information such as the size of fields and unused farmland, climate, and season. For example, the collection unit measures the size of fields, collects climate data, and records seasonal variations. The collection unit can also efficiently collect information on a wide area of ​​farmland using, for example, a drone. The suggestion unit proposes effective vegetable combinations and cultivation methods based on the information collected by the collection unit. For example, the suggestion unit proposes the effect of pest prevention by planting spinach and turnips together. The suggestion unit can also propose the effect of promoting growth by planting corn and pumpkins together. The suggestion unit can also propose the optimal combination of companion plants based on the collected information. For example, the suggestion unit proposes the optimal combination of vegetables based on collected soil component data. The lending unit lends farmland to general users. For example, the lending unit can lend farmland to general consumers who enjoy home gardening as a hobby. The lending department can also collect feedback from users of farmland and use it to improve lending conditions. As a result, the organic vegetable quality improvement system according to the embodiment can collect information such as the size of fields and unused farmland, climate, and season, propose effective vegetable combinations and cultivation methods, and lend farmland to general users.

[0059] The data collection unit collects information such as the size of fields and unused farmland, climate, and seasons. Specifically, it uses GPS technology and Geographic Information Systems (GIS) to measure the size of fields and obtain accurate area data. For climate data collection, weather sensors and weather stations are installed to collect data such as temperature, humidity, precipitation, and wind speed in real time. To record seasonal variations, long-term weather data is accumulated and analyzed to understand seasonal climate patterns. Furthermore, the data collection unit can also efficiently collect information on wide-area farmland using drones. Drones are equipped with high-resolution cameras and multispectral sensors to monitor the condition of the farmland and the health of the crops in detail. This allows the data collection unit to accurately and efficiently collect data on field size, climate, and seasonal variations and store it in the system's overall database. The collected data is stored on a cloud server and made accessible to the proposal and lending units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively and improve the overall performance of the system.

[0060] The proposal department proposes effective vegetable combinations and cultivation methods based on information collected by the data collection department. Specifically, it analyzes collected field size, climate data, and seasonal variation information to determine the optimal vegetable combinations. For example, it may suggest planting spinach and turnips together, as spinach can help prevent turnip pests. It may also suggest planting corn and pumpkins together, as corn can promote pumpkin growth. The proposal department also proposes optimal companion plant combinations based on collected soil composition data. For example, it analyzes soil pH values ​​and nutrient balance to suggest suitable vegetable combinations. Furthermore, the proposal department can use AI to learn from past data and success stories, enabling it to make more accurate suggestions. The AI ​​analyzes the collected data and automatically suggests optimal vegetable combinations and cultivation methods. This allows the proposal department to propose effective vegetable combinations and cultivation methods based on collected information, supporting the improvement of organic vegetable quality.

[0061] The Lending Department leases farmland to general users. Specifically, it can lease farmland to general consumers who enjoy home gardening as a hobby. The Lending Department can also collect feedback from farmland users and use it to improve the lease conditions. For example, it can review farmland usage fees and lease periods based on user feedback. The Lending Department can also provide support to users regarding how to use the farmland and cultivation methods. For example, if a user is using farmland for the first time, it can provide a guidebook explaining basic cultivation methods and precautions. It can also set up online support and consultation services so that users can consult anytime they have a problem. In this way, the Lending Department can provide farmland users with an environment in which they can use farmland with peace of mind and support the improvement of organic vegetable quality. Furthermore, the Lending Department can collect user feedback and use it to improve the overall system. For example, it can improve the functions of the Collection Department and the Proposal Department based on user opinions and requests, providing a more user-friendly system. In this way, the Lending Department can provide high-quality service to farmland users and support the improvement of organic vegetable quality.

[0062] The proposal team can suggest that planting spinach and turnips together can have a pest prevention effect. For example, the proposal team suggests that planting spinach and turnips together can suppress the occurrence of specific pests. The proposal team can also suggest that planting spinach and turnips together can reduce pest damage. Furthermore, the proposal team can suggest that planting spinach and turnips together can prevent the reproduction of pests. In this way, pest prevention can be obtained by planting spinach and turnips together.

[0063] The proposal suggests that planting corn and pumpkins together can promote growth. For example, it suggests that planting corn and pumpkins together can improve their growth rate. It can also suggest that planting corn and pumpkins together can increase yields. Furthermore, it can suggest that planting corn and pumpkins together can improve the health of the plants. As a result, planting corn and pumpkins together can be expected to promote growth.

[0064] The leasing department can lease farmland to general consumers who enjoy home gardening as a hobby. For example, the leasing department can lease farmland to beginners who enjoy home gardening as a hobby. It can also lease farmland to experienced home gardeners. Furthermore, the leasing department can lease farmland to families who enjoy home gardening as a hobby. In this way, leasing farmland to general consumers who enjoy home gardening as a hobby promotes the effective use of farmland.

[0065] The suggestion unit can propose the optimal companion plant combinations based on the collected information. For example, it can propose the optimal vegetable combinations based on collected soil composition data. It can also propose the optimal vegetable combinations based on collected climate data. Furthermore, it can propose the optimal vegetable combinations based on collected seasonal data. In this way, by proposing the optimal companion plant combinations based on the collected information, the quality of vegetables can be improved.

[0066] The data collection unit can collect information such as the size of fields and unused farmland, climate, and seasons. For example, the unit can measure the size of fields, collect climate data, and record seasonal variations. The unit can also efficiently collect information on wide areas of farmland using drones, for example. The unit can also collect information on wide-area climate and seasons using satellite data, for example. By collecting information such as the size of fields and unused farmland, climate, and seasons, it is possible to obtain data that can be used to propose effective vegetable combinations and cultivation methods.

[0067] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect information when the user is relaxed. The data collection unit can also collect information quickly if the user is focused. Furthermore, if the user is tired, the data collection unit can collect information after the user has rested. This allows for information to be collected at a more appropriate time by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0068] The data collection unit can analyze the soil composition of fields and unused farmland and collect data to select the optimal combination of vegetables. For example, the data collection unit can measure the pH value of the soil and select vegetables suitable for acidic soil. It can also analyze the nitrogen, phosphorus, and potassium content in the soil and select vegetables with a suitable nutritional balance. Furthermore, the data collection unit can evaluate the soil's water retention capacity and select drought-resistant vegetables. In this way, by analyzing the soil composition of fields and unused farmland, data can be obtained to select the optimal combination of vegetables.

[0069] The data collection unit can monitor climate and seasonal variations in real time, improving the accuracy of the collected data. For example, it can monitor temperature fluctuations in real time to identify the appropriate time to grow vegetables. It can also monitor precipitation fluctuations in real time to determine the need for irrigation. Furthermore, it can monitor sunshine duration fluctuations in real time to select vegetables suitable for photosynthesis. In this way, by monitoring climate and seasonal variations in real time, the accuracy of the collected data can be improved.

[0070] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting information that provides a sense of security. Similarly, if the user is excited, the data collection unit can prioritize collecting information that is of interest. Furthermore, if the user is calm, the data collection unit can prioritize collecting detailed information. This allows for the collection of more appropriate information by prioritizing information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The data collection unit can efficiently collect information from a wide area of ​​farmland using drones. For example, the unit can fly drones to take aerial photographs of the entire farmland to understand the growth status of crops. The unit can also measure soil moisture with sensors mounted on the drones to determine the need for irrigation. Furthermore, the unit can use drones to monitor pest outbreaks and take early countermeasures. In this way, by efficiently collecting information from a wide area of ​​farmland using drones, it is possible to understand the condition of the farmland.

[0072] The data collection unit can use satellite data to collect climate and seasonal information over a wide area. For example, it can use satellite data to understand temperature fluctuations over a wide area and identify the appropriate timing for planting crops. It can also use satellite data to monitor precipitation fluctuations and develop irrigation plans. Furthermore, it can utilize satellite data to understand changes in sunshine duration and select crops suitable for photosynthesis. In this way, by collecting climate and seasonal information over a wide area using satellite data, it is possible to obtain climate and seasonal information over a wide area.

[0073] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is feeling anxious, the suggestion unit will present suggestions in a way that provides reassurance. If the user is excited, the suggestion unit can present suggestions in a way that attracts interest. Furthermore, if the user is calm, the suggestion unit can present suggestions in a way that includes detailed information. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The proposal department can suggest the optimal vegetable combination by referring to past success stories. For example, it can suggest the previously successful combination of spinach and turnip. It can also suggest the combination of corn and pumpkin based on past data. Furthermore, it can suggest vegetable combinations effective for pest prevention by referring to past success stories. In this way, by referring to past success stories, it can suggest the optimal vegetable combination.

[0075] The proposal unit can suggest effective combinations for pest prevention by considering pest outbreak prediction data. For example, based on pest outbreak prediction data, the proposal unit can suggest a combination of spinach and turnips. It can also suggest a combination of corn and pumpkins, taking pest outbreak prediction data into consideration. Furthermore, the proposal unit can suggest effective vegetable combinations for pest prevention by referring to pest outbreak prediction data. In this way, by considering pest outbreak prediction data, it is possible to suggest effective combinations for pest prevention.

[0076] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is feeling anxious, the suggestion function will prioritize suggestions that provide reassurance. It can also prioritize suggestions that pique the user's interest if the user is excited. Furthermore, if the user is calm, the suggestion function can prioritize suggestions that contain detailed information. This allows for more appropriate suggestions to be made by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The proposal department can make more realistic proposals by incorporating the expertise of local agricultural experts. For example, the proposal department could propose a combination of spinach and turnips based on the expertise of local agricultural experts. It could also propose a combination of corn and pumpkins, incorporating advice from local agricultural experts. Furthermore, the proposal department could utilize the expertise of local agricultural experts to propose vegetable combinations that are effective in preventing pests. In this way, incorporating the expertise of local agricultural experts allows for more realistic proposals.

[0078] The suggestion function can provide individually customized suggestions by taking into account the user's past cultivation history. For example, the suggestion function can suggest a combination of spinach and turnips based on the user's past cultivation history. It can also suggest a combination of corn and pumpkins, taking into account the user's past cultivation history. Furthermore, the suggestion function can refer to the user's past cultivation history to suggest vegetable combinations that are effective in preventing pests. In this way, it can provide individually customized suggestions by taking into account the user's past cultivation history.

[0079] The lending department can collect feedback from farmland users and use it to improve lending conditions. For example, the lending department can collect feedback from farmland users and improve lending conditions. Furthermore, the lending department can propose better lending conditions based on user feedback. In addition, the lending department can flexibly adjust lending conditions based on user feedback. Thus, collecting feedback from farmland users can be used to improve lending conditions.

[0080] The lending system can estimate the user's emotions and determine lending priorities based on those emotions. For example, if the user is feeling anxious, the lending system will prioritize lending items that provide a sense of security. Similarly, if the user is excited, the lending system may prioritize lending items that pique their interest. Furthermore, if the user is calm, the lending system may prioritize lending items that offer more detail. This allows for more appropriate lending by prioritizing lending according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The Lending Department can provide a platform to facilitate communication among users of farmland. For example, it can provide an online forum where users can exchange information. It can also provide a chat function where users can exchange advice with each other. Furthermore, it can provide a schedule sharing function to enable users to work together. This promotes communication among users and allows them to work together.

[0082] The lending department can provide agricultural education programs to users of farmland. For example, the lending department can offer online courses on agriculture to users. It can also hold workshops where users can experience farm work firsthand. Furthermore, the lending department can provide agricultural books and materials to users. In this way, by providing agricultural education programs to users, the lending department can improve their knowledge of agriculture.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, the suggestion function can suggest growing relaxing herbs. If the user is excited, it can suggest growing challenging crops. Furthermore, if the user is calm, it can suggest a detailed cultivation plan. By adjusting the content of suggestions according to the user's emotions, it can provide more appropriate suggestions.

[0085] The data collection unit not only gathers information such as the size of fields and unused farmland, climate, and season, but can also monitor soil microbial activity. For example, it can measure the types of beneficial microorganisms in the soil and their activity levels, and provide advice on maintaining microbial balance. The data collection unit can also assess soil health and suggest the use of organic fertilizers as needed. Furthermore, it can assess the soil's water retention capacity and suggest appropriate irrigation methods. This helps maintain soil health and improve vegetable quality.

[0086] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is tired, the suggestion function will offer suggestions after the user has rested. If the user is focused, the suggestion function can offer suggestions quickly. Furthermore, if the user is relaxed, the suggestion function can offer suggestions that include detailed information. By adjusting the timing of suggestions according to the user's emotions, suggestions can be delivered at a more appropriate time.

[0087] The land lending department can host community events related to agriculture for users of farmland. For example, they can hold harvest festivals and farming experience events to promote interaction among users. The land lending department can also regularly hold seminars and workshops on agriculture to improve users' knowledge. Furthermore, the land lending department can provide a platform for users to exchange information and give advice to each other through online forums. This promotes communication among users of farmland and allows for the sharing of agricultural knowledge.

[0088] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is feeling anxious, the suggestion function will present suggestions in a way that provides reassurance. If the user is excited, the suggestion function can present suggestions in a way that attracts attention. Furthermore, if the user is calm, the suggestion function can present suggestions in a way that includes detailed information. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made.

[0089] The data collection department can not only gather information on the size of fields and unused farmland, climate, and seasons, but also investigate the historical use of farmland. For example, it can investigate what crops were cultivated and what agricultural techniques were used in the past, and reflect this in current cultivation plans. The data collection department can also analyze historical climate data and assess the impact of climate change. Furthermore, the data collection department can refer to historical soil data and track soil changes. This allows for the creation of cultivation plans that take into account the historical use of farmland.

[0090] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is feeling anxious, the suggestion function will prioritize suggestions that provide reassurance. If the user is excited, the suggestion function may also prioritize suggestions that pique their interest. Furthermore, if the user is calm, the suggestion function may prioritize suggestions that contain detailed information. By prioritizing suggestions according to the user's emotions, the system can provide more appropriate suggestions.

[0091] The collection department not only gathers information on the size of fields and unused farmland, climate, and seasons, but can also assess the biodiversity of farmland. For example, it can survey the types and numbers of plants and animals inhabiting farmland to help conserve biodiversity. Furthermore, the collection department can assess the health of farmland ecosystems and propose ecosystem restoration as needed. In addition, the collection department can propose management methods to maintain farmland biodiversity. This enables the conservation of farmland biodiversity and the realization of sustainable agriculture.

[0092] The suggestion function can estimate the user's emotions and customize the suggestions based on those emotions. For example, if the user is stressed, the suggestion function can suggest growing relaxing herbs. If the user is excited, the suggestion function can suggest growing challenging crops. Furthermore, if the user is calm, the suggestion function can suggest a detailed cultivation plan. By customizing the suggestions according to the user's emotions, it can provide more appropriate recommendations.

[0093] The data collection unit not only gathers information such as the size of fields and unused farmland, climate, and season, but can also evaluate the management of water resources on farmland. For example, it can assess the efficiency of irrigation systems and propose improvements as needed. The data collection unit can also monitor water quality on farmland and provide advice for water quality improvement. Furthermore, the data collection unit can analyze the utilization of water resources on farmland and propose sustainable water resource management methods. This enables efficient management of water resources on farmland and realizes sustainable agriculture.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The data collection unit gathers information such as the size of fields and unused farmland, climate, and season. For example, it measures the size of fields, collects climate data, and records seasonal variations. The data collection unit can also efficiently collect information on a wide area of ​​farmland using drones. Step 2: The proposal team proposes effective vegetable combinations and cultivation methods based on the information collected by the data collection team. For example, they might suggest planting spinach and turnips together to prevent pests, or planting corn and pumpkins together to promote growth. Furthermore, they can also propose optimal companion plant combinations based on the collected soil composition data. Step 3: The leasing department leases the farmland to general users. For example, it can lease farmland to general consumers who enjoy home gardening as a hobby. The leasing department can also collect feedback from farmland users and use it to improve the leasing conditions.

[0096] 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.

[0097] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0098] 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.

[0099] Each of the multiple elements described above, including the collection unit, proposal unit, and lending unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart device 14 or a drone to collect information such as the size of fields and unused farmland, climate, and season. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes effective vegetable combinations and cultivation methods based on the collected information. The lending unit is implemented by, for example, the control unit 46A of the smart device 14 and lends farmland to general users. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

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

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, proposal unit, and lending unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart glasses 214 or a drone to collect information such as the size of fields and unused farmland, climate, and season. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes effective vegetable combinations and cultivation methods based on the collected information. The lending unit is implemented, for example, by the control unit 46A of the smart glasses 214, and lends farmland to general users. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

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

[0119] The 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.

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

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

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

[0123] Figure 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.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0126] In the 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.

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

[0128] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0130] The data processing system 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.

[0131] Each of the multiple elements described above, including the collection unit, proposal unit, and lending unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 of the headset terminal 314 or a drone to collect information such as the size of fields and unused farmland, climate, and season. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and proposes effective vegetable combinations and cultivation methods based on the collected information. The lending unit is implemented by, for example, the control unit 46A of the headset terminal 314, and lends farmland to general users. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

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

[0135] The 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.

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

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, proposal unit, and lending unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 of the robot 414 or a drone to collect information such as the size of fields and unused farmland, climate, and season. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and proposes effective vegetable combinations and cultivation methods based on the collected information. The lending unit is implemented by, for example, the control unit 46A of the robot 414, and leases farmland to general users. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Note 1) The collection department gathers information such as the size of fields and unused farmland, climate, and seasons. Based on the information collected by the aforementioned collection unit, a proposal unit proposes effective vegetable combinations and cultivation methods. It includes a leasing section that leases farmland to general users. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose planting spinach and turnips together to help prevent pests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose planting corn and pumpkins together to promote their growth. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned lending section, We lease farmland to ordinary consumers who enjoy home gardening as a hobby. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the collected information, we propose the optimal combination of companion plants. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Collect information such as the size of fields and unused farmland, climate, and season. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze the soil composition of fields and unused farmland to collect data for selecting the optimal combination of vegetables. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We monitor climate and seasonal variations in real time and improve the accuracy of collected data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Using drones to efficiently collect information on a wide range of farmland. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Using satellite data to collect information on climate and seasons over a wide area. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, We propose the optimal vegetable combination by referring to past success stories. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, We propose effective combinations for pest prevention, taking into account pest outbreak prediction data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, We will incorporate the expertise of local agricultural specialists to make more realistic proposals. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We provide individually customized suggestions, taking into account the user's past cultivation history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned lending section, We collect feedback from users of farmland to help improve rental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned lending section, The system estimates the user's emotions and determines lending priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned lending section, We provide a platform to facilitate communication among users of agricultural land. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned lending section, We provide agricultural education programs to users of farmland. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 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 collection department gathers information such as the size of fields and unused farmland, climate, and seasons. Based on the information collected by the aforementioned collection unit, a proposal unit proposes effective vegetable combinations and cultivation methods. It includes a leasing section that leases farmland to general users. A system characterized by the following features.

2. The aforementioned proposal section is, We propose planting spinach and turnips together to help prevent pests. The system according to feature 1.

3. The aforementioned proposal section is, We propose planting corn and pumpkins together to promote their growth. The system according to feature 1.

4. The aforementioned lending section, We lease farmland to ordinary consumers who enjoy home gardening as a hobby. The system according to feature 1.

5. The aforementioned proposal section is, Based on the collected information, we propose the optimal combination of companion plants. The system according to feature 1.

6. The aforementioned collection unit is Collect information such as the size of fields and unused farmland, climate, and season. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is We analyze the soil composition of fields and unused farmland to collect data for selecting the optimal combination of vegetables. The system according to feature 1.

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