System

Abstract

An object of a system according to an embodiment is to provide information on growth of vegetables in a home vegetable garden in real time and promptly take appropriate measures.SOLUTION: A system includes a reception unit, a generation unit, an advice unit, and a detection unit. The reception unit receives an input of a type of vegetable desired to be grown from a user. The generation unit generates raising information based on the information received by the reception unit. The advice unit gives advice in real time based on the growth information generated by the generation unit. The detector detects the occurrence of pests based on the information advised by the adviser and proposes a countermeasure.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that it is difficult to provide real-time information on vegetable cultivation in home gardens, making it difficult to take appropriate measures quickly.

[0005] The system according to the embodiment aims to provide real-time information on growing vegetables in home gardens and to promptly take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, an advising unit, and a detecting unit. The receiving unit receives input from a user about the type of vegetable they wish to grow. The generating unit generates cultivation information based on the information received by the receiving unit. The advising unit provides advice in real time based on the cultivation information generated by the generating unit. The detecting unit detects the occurrence of pests and diseases based on the information advised by the advising unit and proposes countermeasures. [Effects of the Invention]

[0007] The system according to the embodiment provides real-time information about growing vegetables in home gardens, enabling appropriate measures to be taken promptly. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A home gardening support system according to an embodiment of the present invention allows users to receive cultivation information, advice, and suggestions for pest control simply by inputting the type of vegetable they wish to grow. In the home gardening support system, users input the type of vegetable they wish to grow, and a generation AI analyzes the cultivation information for that vegetable and provides information such as cultivation methods, necessary tools, appropriate times, and precautions. Furthermore, the generation AI provides real-time advice during the cultivation process to help users properly cultivate the vegetables. For example, in the home gardening support system, a user inputs the type of vegetable they wish to grow. For example, the user inputs a vegetable such as "tomato" or "cucumber." This information is input into the generation AI. The home gardening support system then analyzes the input information and provides cultivation information for that vegetable. The generation AI analyzes information such as cultivation methods, necessary tools, appropriate times, and precautions, and provides it to the user. For example, in the case of tomatoes, information such as the time to sow seeds, how to prepare the soil, how often to water, and the type of fertilizer is provided. Furthermore, in the home gardening support system, the generation AI provides real-time advice during the cultivation process. For example, the system monitors the plant's growth and provides instructions for watering and adding fertilizer at the appropriate time. It can also detect the occurrence of pests and diseases and suggest countermeasures. This allows users to grow vegetables properly. As a result, the home gardening support system makes it easy for even beginners to enjoy home gardening. For example, users can grow healthy vegetables without complex knowledge by simply following the advice of the generating AI. For example, when growing tomatoes, users can harvest delicious tomatoes by following the instructions of the generating AI to proceed through the process from sowing to harvesting. As a result, the home gardening support system allows users to receive cultivation information, advice, and suggestions for pest and disease control simply by entering the type of vegetable they want to grow. For example, even beginners can enjoy home gardening easily, and users can grow healthy vegetables without complex knowledge by simply following the advice of the generating AI.

[0029] A home gardening support system according to an embodiment includes a receiving unit, a generating unit, an advice unit, and a detecting unit. The receiving unit receives input from a user about the type of vegetable the user wants to grow. Examples of the type of vegetable the user wants to grow include, but are not limited to, tomatoes, cucumbers, and lettuce. The receiving unit receives, for example, input of the type of vegetable in text form from the user. The receiving unit can also receive voice input or image input. The generating unit uses a generation AI to generate cultivation information based on the information received by the receiving unit. The cultivation information includes, for example, cultivation methods, necessary tools, appropriate times, and precautions, but is not limited to, examples. For example, the generation AI generates the cultivation information using a text generation AI (e.g., LLM). The generating unit can also generate the cultivation information using a multimodal generation AI. For example, the generation AI provides information such as sowing methods and timing for thinning as cultivation methods. Information such as shovels, watering cans, and fertilizer as necessary tools. Information such as appropriate times can be provided as seasons, such as spring, summer, autumn, and winter, or specific dates. The system provides precautions such as methods for preventing pests and diseases and appropriate temperature management. The advice unit provides real-time advice based on the cultivation information generated by the generation unit. For example, the advice unit monitors the plant's growth status and instructs the plant to water and add fertilizer at the appropriate time. The advice unit can also detect the occurrence of pests and diseases and suggest countermeasures. For example, the advice unit monitors the plant's growth status using image analysis or sensor measurements. The appropriate timing is determined based on the time of day or the growth stage. The detection unit detects the occurrence of pests and diseases based on the information advised by the advice unit and suggests countermeasures. For example, the detection unit performs detection using image analysis or sensors. Countermeasures include proposing pesticide application methods and physical control methods. As a result, the home gardening support system according to the embodiment allows users to receive cultivation information, advice, and suggestions for pest and disease control measures simply by inputting the type of vegetables they want to grow. For example, even beginners can easily enjoy home gardening, and users can grow healthy vegetables without complex knowledge by simply following the advice provided by the generation AI.

[0030] The generation unit can generate information including cultivation methods, necessary tools, timing, and precautions. The generation unit can provide, for example, a sowing method and timing for thinning as cultivation methods. The generation unit can also provide a shovel, a watering can, fertilizer, etc. as necessary tools. The generation unit can also provide seasons such as spring, summer, autumn, and winter, or specific dates as appropriate times. The generation unit can also provide methods for preventing pests and diseases, appropriate temperature management, etc. as precautions. In this way, the generation unit can provide detailed cultivation information, allowing the user to properly cultivate vegetables. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can cause the generation AI to generate the cultivation information.

[0031] The advice unit can monitor the growth status of the plant and instruct the user to water or add fertilizer at the appropriate time. The advice unit can monitor the growth status of the plant, for example, by image analysis or measurement using a sensor. For example, the advice unit can photograph the growth status of the plant with a camera and evaluate the growth status using image analysis technology. The advice unit can also measure the growth status of the plant with a sensor and evaluate the growth stage. The advice unit can also provide instructions based on the time of day or the growth stage as the appropriate timing. For example, the advice unit can instruct the user on the timing of watering based on the time of day. The advice unit can also instruct the user on the timing of adding fertilizer based on the growth stage. This allows the advice unit to provide appropriate advice in real time, allowing the user to grow vegetables appropriately. Some or all of the above-mentioned processing in the advice unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the advice unit can cause the generation AI to monitor the growth status of the plant.

[0032] The detection unit can detect the occurrence of pests and diseases and propose countermeasures. The detection unit detects the occurrence of pests and diseases, for example, by image analysis or a sensor. For example, the detection unit takes images of plants with a camera and detects the occurrence of pests and diseases using image analysis technology. The detection unit can also detect the occurrence of pests and diseases using a sensor. The detection unit can also propose pesticide application methods or physical control methods as countermeasures. For example, the detection unit can propose pesticide application methods depending on the type of pest and diseases. The detection unit can also propose physical control methods such as installing nets or traps. In this way, the detection unit can detect the occurrence of pests and diseases and propose appropriate countermeasures, allowing the user to grow healthy vegetables. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit can cause the generation AI to detect the occurrence of pests and diseases.

[0033] The reception unit can analyze the user's past input history and select an input method. The reception unit, for example, analyzes the user's past input history using data mining or a machine learning algorithm. For example, the reception unit can automatically display vegetable types that the user has frequently input in the past as candidates. The reception unit can also prioritize input methods (such as voice or text) that the user has used in the past. The reception unit can also predict and recommend vegetables to grow in a specific season based on the user's past input history. For example, if the user inputs vegetables in the spring, the reception unit can prioritize displaying vegetables suitable for spring. If the user lives in a cold region, the reception unit can also recommend vegetables that are easy to grow in cold regions. This allows the user to efficiently input vegetable types by selecting the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can cause a generation AI to analyze the user's past input history.

[0034] When inputting the type of vegetable, the reception unit can filter the vegetable based on the user's current season and region. The reception unit performs filtering based on the user's current season and region, for example. For example, if the user inputs vegetables in spring based on weather data and local agricultural information, the reception unit can preferentially display vegetables suitable for spring. Furthermore, if the user lives in a cold region, the reception unit can also suggest vegetables that are easy to grow in cold regions. Furthermore, if the user lives in an urban area, the reception unit can also suggest vegetables that are easy to grow in urban areas. This allows the user to select appropriate vegetables by suggesting vegetables suitable for the user's season and region. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can cause the generation AI to perform filtering based on the user's current season and region.

[0035] When inputting the type of vegetable, the reception unit can select an input means according to the user's input method. The reception unit selects the optimal input means according to the user's input method (voice, text, image, etc.), for example. For example, when the user inputs "tomato" by voice, the reception unit prioritizes processing the voice input. Also, when the user inputs "cucumber" by text, the reception unit can also prioritize processing the text input. Also, when the user uploads a photo of the vegetable as an image, the reception unit can identify the type of vegetable using image recognition technology. This allows the user to efficiently input the type of vegetable by selecting the optimal means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can cause the generation AI to select the input means according to the user's input method.

[0036] When inputting the type of vegetable, the reception unit can prioritize inputting highly relevant vegetables taking into account the user's geographical location information. The reception unit, for example, considers the user's geographical location information based on GPS data and local climatic conditions. For example, if the user lives in a warm region, the reception unit can suggest vegetables that are easy to grow in a warm climate. Furthermore, if the user lives in a highland, the reception unit can suggest vegetables that are easy to grow in a highland. Furthermore, if the user lives in a coastal region, the reception unit can suggest vegetables that are resistant to salt damage. This allows the user to select appropriate vegetables by suggesting highly relevant vegetables based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can cause a generation AI to suggest vegetables taking into account the user's geographical location information.

[0037] When the user inputs the type of vegetable, the reception unit can analyze the user's social media activity and suggest related vegetables. The reception unit, for example, analyzes the user's social media activity based on the content of posts and the interests of followers. For example, if the user posts about "tomato cultivation" on social media, the reception unit can suggest tomatoes. Furthermore, if the user participates in a group about "home gardening," the reception unit can also suggest vegetables that are popular in home gardens. Furthermore, the reception unit can also suggest vegetables that the user's friends grow based on the vegetables they grow. This allows the user to select vegetables that interest them by suggesting related vegetables based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can cause a generation AI to analyze the user's social media activity.

[0038] The reception unit can customize the input method by reflecting the user's past feedback when inputting the type of vegetable. The reception unit reflects the user's past feedback based on evaluation data and usage history, for example. For example, the reception unit customizes tomato cultivation information based on feedback from the user when growing tomatoes in the past. The reception unit can also customize cucumber cultivation information based on feedback from the user when growing cucumbers in the past. The reception unit can also customize eggplant cultivation information based on feedback from the user when growing eggplants in the past. This allows the user to efficiently input the type of vegetable by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can cause the generation AI to reflect the user's past feedback.

[0039] When generating cultivation information, the generation unit can adjust the level of detail of the information based on the importance of the vegetable. The generation unit, for example, evaluates the importance of the vegetable based on the difficulty of cultivation and the yield. For example, the generation unit provides detailed cultivation information for an important vegetable (e.g., tomato). The generation unit can also provide concise cultivation information for a vegetable with a low importance (e.g., lettuce). The generation unit can also provide detailed cultivation information for a vegetable in which the user is particularly interested. In this way, by adjusting the level of detail of the information based on the importance of the vegetable, the user can appropriately obtain the information he or she needs. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to adjust the level of detail of the information based on the importance of the vegetable.

[0040] When generating cultivation information, the generation unit can apply different generation algorithms depending on the category of the vegetable. For example, the generation unit classifies vegetable categories into leafy vegetables, root vegetables, fruit vegetables, etc. For example, in the case of root vegetables (e.g., carrots), the generation unit can provide information focusing on root growth. In addition, in the case of leafy vegetables (e.g., spinach), the generation unit can also provide information focusing on leaf growth. In addition, in the case of fruit vegetables (e.g., tomatoes), the generation unit can provide information focusing on fruit growth. This allows the user to grow vegetables appropriately by providing appropriate information according to the vegetable category. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to apply a generation algorithm depending on the vegetable category.

[0041] When generating the cultivation information, the generation unit can improve the accuracy of the information by referring to the user's past cultivation results. The generation unit, for example, refers to the user's past cultivation results by using a database or statistical analysis. For example, the generation unit provides similar cultivation information based on cultivation methods that the user has used successfully in the past. The generation unit can also provide information to help the user avoid cultivation methods that have failed them in the past. The generation unit can also analyze the user's past cultivation results and suggest optimal cultivation methods. By referring to the user's past cultivation results, the accuracy of the information can be improved, allowing the user to properly cultivate vegetables. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to execute the processing by referring to the user's past cultivation results.

[0042] When generating the cultivation information, the generation unit can determine the priority of information based on the vegetable cultivation period. The generation unit evaluates the vegetable cultivation period based on, for example, the season, temperature, and sunlight hours. For example, when the sowing period is approaching, the generation unit can prioritize providing information about sowing. Furthermore, the generation unit can prioritize providing information about fertilizer when fertilizer needs to be added. Furthermore, when the harvest period is approaching, the generation unit can prioritize providing information about harvesting. In this way, by determining the priority of information based on the vegetable cultivation period, the user can obtain the necessary information at the appropriate time. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to determine the priority of information based on the vegetable cultivation period.

[0043] When generating the cultivation information, the generation unit can adjust the order of information based on the relevance of vegetables. The generation unit, for example, evaluates the relevance of vegetables based on similarities in cultivation methods or common growth conditions. For example, the generation unit continuously provides information on vegetables of the same family (e.g., Solanaceae). The generation unit can also continuously provide information on vegetables with the same cultivation conditions (e.g., vegetables that require sunlight). The generation unit can also continuously provide information on vegetables that are grown at the same time. In this way, by adjusting the order of information based on the relevance of vegetables, the user can efficiently obtain information. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to adjust the order of information based on the relevance of vegetables.

[0044] When generating the training information, the generation unit can adjust the use of technical terminology in the information according to the user's level of expertise. The generation unit, for example, evaluates the user's level of expertise as beginner, intermediate, advanced, etc. For example, the generation unit provides a simple explanation that avoids technical terminology for beginners. The generation unit can also provide an explanation that includes appropriate technical terminology for intermediate users. The generation unit can also provide an explanation that includes detailed technical terminology for advanced users. In this way, by adjusting the use of technical terminology in the information according to the user's level of expertise, it is possible to provide information that is easy for the user to understand. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to adjust the use of technical terminology in the information according to the user's level of expertise.

[0045] When providing advice, the advice unit can adjust the level of detail of the advice based on the growth stage of the plant. The advice unit, for example, evaluates the growth stage of the plant as a germination stage, a growth stage, a harvest stage, etc. For example, the advice unit provides detailed advice regarding sowing at the sowing stage. The advice unit can also provide detailed advice regarding growth at the growth stage. The advice unit can also provide detailed advice regarding harvesting at the harvest stage. In this way, by adjusting the level of detail of the advice based on the growth stage of the plant, the user can obtain appropriate advice. Some or all of the above-described processing in the advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice unit can cause the generation AI to adjust the level of detail of the advice based on the growth stage of the plant.

[0046] When providing advice, the advice unit can apply different advice algorithms depending on the type of plant. For example, the advice unit classifies plant types into leafy vegetables, root vegetables, fruit vegetables, etc. For example, in the case of tomatoes, the advice unit can provide advice specialized for tomato cultivation. Furthermore, in the case of cucumbers, the advice unit can provide advice specialized for cucumber cultivation. Furthermore, in the case of eggplants, the advice unit can provide advice specialized for eggplant cultivation. This allows the user to grow plants appropriately by providing appropriate advice depending on the type of plant. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice unit can cause the generation AI to apply an advice algorithm depending on the type of plant.

[0047] When providing advice, the advice unit can improve the accuracy of the advice by referring to the user's past cultivation results. The advice unit, for example, refers to the user's past cultivation results by using a database or statistical analysis. For example, the advice unit provides similar advice based on cultivation methods that the user has used successfully in the past. The advice unit can also provide advice to help the user avoid cultivation methods that have failed the user in the past. The advice unit can also analyze the user's past cultivation results and propose optimal advice. By referring to the user's past cultivation results, the accuracy of the advice can be improved, allowing the user to properly cultivate plants. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice unit can cause the generation AI to execute the advice by referring to the user's past cultivation results.

[0048] When providing advice, the advice unit can determine the priority of advice based on the plant's growth period. The advice unit evaluates the plant's growth period based on, for example, the season, temperature, and sunlight hours. For example, when the sowing season is approaching, the advice unit can prioritize providing advice regarding sowing. Furthermore, the advice unit can prioritize providing advice regarding fertilizer when additional fertilizer is needed. Furthermore, when the harvest season is approaching, the advice unit can prioritize providing advice regarding harvesting. In this way, by determining the priority of advice based on the plant's growth period, the user can obtain the necessary advice at the appropriate time. Some or all of the above-described processing in the advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice unit can cause the generation AI to determine the priority of advice based on the plant's growth period.

[0049] When providing advice, the advice unit can adjust the order of advice based on the relevance of the plants. The advice unit, for example, evaluates the relevance of the plants based on similarities in cultivation methods or common growth conditions. For example, the advice unit may continuously provide advice for plants of the same family (e.g., Solanaceae). The advice unit may also continuously provide advice for plants with the same growing conditions (e.g., plants that require sunlight). The advice unit may also continuously provide advice for plants to be grown at the same time of year. In this way, by adjusting the order of advice based on the relevance of the plants, the user can efficiently obtain advice. Some or all of the above-described processing in the advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice unit may cause the generation AI to adjust the order of advice based on the relevance of the plants.

[0050] When providing advice, the advice unit can adjust the use of technical terms in the advice according to the user's level of expertise. The advice unit, for example, evaluates the user's level of expertise as beginner, intermediate, advanced, etc. For example, the advice unit provides simple advice that avoids technical terms for beginners. The advice unit can also provide advice that includes appropriate technical terms for intermediate users. The advice unit can also provide advice that includes detailed technical terms for advanced users. In this way, by adjusting the use of technical terms in the advice according to the user's level of expertise, it is possible to provide advice that is easy for the user to understand. Some or all of the above-described processing in the advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice unit can cause the generation AI to adjust the use of technical terms in the advice according to the user's level of expertise.

[0051] When detecting the occurrence of pests or diseases, the detection unit can adjust the level of detection detail based on the growth stage of the plant. The detection unit, for example, evaluates the growth stage of the plant as a germination stage, a growth stage, a harvest stage, etc. For example, the detection unit provides detection information on the occurrence of pests or diseases related to sowing at the sowing stage. The detection unit can also provide detection information on the occurrence of pests or diseases related to growth at the growth stage. The detection unit can also provide detection information on the occurrence of pests or diseases related to harvest at the harvest stage. In this way, by adjusting the level of detection detail based on the growth stage of the plant, the user can obtain appropriate information. Some or all of the above-described processing in the detection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the detection unit can cause the generation AI to adjust the level of detection detail based on the growth stage of the plant.

[0052] When detecting the occurrence of pests or diseases, the detection unit can apply different detection algorithms depending on the type of plant. The detection unit, for example, classifies plant types into leafy vegetables, root vegetables, fruit vegetables, etc. For example, in the case of tomatoes, the detection unit applies a detection algorithm specialized for tomato pests and diseases. In addition, in the case of cucumbers, the detection unit can also apply a detection algorithm specialized for cucumber pests and diseases. In addition, in the case of eggplants, the detection unit can also apply a detection algorithm specialized for eggplant pests and diseases. This allows the user to grow plants appropriately by applying an appropriate detection algorithm depending on the type of plant. Some or all of the above-mentioned processing in the detection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the detection unit can cause the generation AI to apply a detection algorithm depending on the type of plant.

[0053] When detecting the occurrence of pests or diseases, the detection unit can improve the accuracy of detection by referring to the user's past cultivation results. The detection unit, for example, refers to the user's past cultivation results by using a database or statistical analysis. For example, the detection unit provides similar pest or disease occurrence detection information based on cultivation methods that the user has used successfully in the past. The detection unit can also provide pest or disease occurrence detection information to help the user avoid cultivation methods that have failed in the past. The detection unit can also analyze the user's past cultivation results and suggest optimal pest or disease occurrence detection information. By referring to the user's past cultivation results, the detection accuracy is improved, allowing the user to properly cultivate plants. Some or all of the above-described processing in the detection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the detection unit can cause the generation AI to execute the processing by referring to the user's past cultivation results.

[0054] When detecting the occurrence of pests or diseases, the detection unit can determine the detection priority based on the plant growth period. The detection unit evaluates the plant growth period based on, for example, the season, temperature, and sunlight hours. For example, when the sowing season is approaching, the detection unit can prioritize providing information on the detection of pests or diseases related to sowing. The detection unit can also prioritize providing information on the detection of pests or diseases related to fertilizer when fertilizer addition is required. The detection unit can also prioritize providing information on the detection of pests or diseases related to harvest when the harvest season is approaching. In this way, by determining the detection priority based on the plant growth period, the user can obtain the necessary information at the appropriate time. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the detection unit can cause the generation AI to determine the detection priority based on the plant growth period.

[0055] When detecting the occurrence of pests or diseases, the detection unit can adjust the detection order based on the relevance of the plants. The detection unit, for example, evaluates the relevance of the plants based on similarities in cultivation methods or common growth conditions. For example, the detection unit continuously provides detection information on the occurrence of pests or diseases in plants of the same family (e.g., Solanaceae). The detection unit can also continuously provide detection information on the occurrence of pests or diseases in plants with the same growing conditions (e.g., plants that require sunlight). The detection unit can also continuously provide detection information on the occurrence of pests or diseases in plants grown at the same time. In this way, by adjusting the detection order based on the relevance of the plants, the user can efficiently obtain information. Some or all of the above-mentioned processing in the detection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the detection unit can cause the generation AI to adjust the detection order based on the relevance of the plants.

[0056] The detection unit can adjust the use of technical terminology for detection of pest infestations according to the user's level of expertise. For example, the detection unit evaluates the user's level of expertise as beginner, intermediate, advanced, or the like. For example, the detection unit can provide beginners with simple pest infestation detection information that avoids technical terminology. The detection unit can also provide intermediate users with pest infestation detection information that includes appropriate technical terminology. The detection unit can also provide advanced users with pest infestation detection information that includes detailed technical terminology. In this way, by adjusting the use of technical terminology for detection according to the user's level of expertise, it is possible to provide information that is easy for users to understand. Some or all of the above-described processing in the detection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the detection unit can cause the generation AI to adjust the use of technical terminology for detection according to the user's level of expertise.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The home vegetable gardening support system can also be equipped with a community function. The community function provides a forum where users can share cultivation information and advice with each other. For example, users can post photos of the vegetables they have grown and receive feedback from other users. Users can also participate in forums related to specific vegetables and exchange information with other users. Furthermore, the community function can provide a forum where users can post problems they are having growing and receive solutions from other users. This allows users to enjoy home vegetable gardening while interacting with other users.

[0059] When generating the cultivation information, the generation unit can customize the information taking into account the user's health condition. For example, if the user has an allergy, cultivation information for vegetables related to that allergy can be avoided. Also, if the user needs a specific nutrient, cultivation information for vegetables containing a large amount of that nutrient can be provided preferentially. Furthermore, if the user is linked to a health management app, cultivation information for optimal vegetables can be provided based on that data. This allows the user to live a healthy life by providing cultivation information according to the user's health condition.

[0060] When detecting the occurrence of pests or diseases, the detection unit can improve the accuracy of detection by referring to the user's past cultivation history. For example, if the user has been plagued by a particular pest or disease in the past, the detection unit can strengthen detection of that pest or disease. The detection unit can also suggest similar measures based on control methods that the user has used successfully in the past. Furthermore, the detection unit can analyze the user's past cultivation history and suggest the optimal control method. In this way, by referring to the user's past cultivation history, the accuracy of pest or disease occurrence detection can be improved, allowing the user to cultivate plants appropriately.

[0061] The reception unit can analyze the user's past input history and select an input method. For example, types of vegetables that the user has frequently input in the past can be automatically displayed as candidates. It can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest vegetables that are grown in a specific season based on the user's past input history. For example, if the user inputs vegetables in the spring, vegetables that are suitable for spring can be displayed with priority. It can also suggest vegetables that are easy to grow in cold regions if the user lives in a cold region. This allows the user to efficiently input types of vegetables by selecting the optimal input method based on the user's past input history.

[0062] When a type of vegetable is input, the reception unit can filter the vegetable selection based on the user's current season and region. For example, if the user inputs vegetables for spring based on weather data and local agricultural information, the reception unit can preferentially display vegetables suitable for spring. Furthermore, if the user lives in a cold region, the reception unit can also suggest vegetables that are easy to grow in cold regions. Furthermore, if the user lives in an urban area, the reception unit can also suggest vegetables that are easy to grow in urban areas. This allows the user to select appropriate vegetables by suggesting vegetables suitable for the user's season and region.

[0063] When inputting the type of vegetable, the reception unit can select an input means according to the user's input method. For example, the reception unit selects the most appropriate input means according to the user's input method (voice, text, image, etc.). For example, if the user inputs "tomato" by voice, the voice input can be given priority in processing. Also, if the user inputs "cucumber" by text, the text input can be given priority in processing. Also, if the user uploads a photo of the vegetable as an image, the type of vegetable can be identified using image recognition technology. In this way, by selecting the most appropriate means according to the user's input method, the user can efficiently input the type of vegetable.

[0064] When inputting the type of vegetable, the reception unit can prioritize inputting highly relevant vegetables taking into consideration the user's geographical location information. For example, the user's geographical location information can be considered based on GPS data and local climatic conditions. For example, if the user lives in a warm region, vegetables that are easy to grow in a warm climate can be suggested. Also, if the user lives in a highland, vegetables that are easy to grow in highland can be suggested. Also, if the user lives in a coastal region, vegetables that are resistant to salt damage can be suggested. In this way, highly relevant vegetables can be suggested based on the user's geographical location information, allowing the user to select appropriate vegetables.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives input from the user about the type of vegetable they want to grow. Examples of types of vegetables the user wants to grow include, but are not limited to, tomatoes, cucumbers, and lettuce. The reception unit receives, for example, the user's input of the type of vegetable in text format. The reception unit can also receive voice input or image input. Step 2: The generation unit uses the generation AI to generate cultivation information based on the information received by the reception unit. The cultivation information includes, for example, cultivation methods, necessary tools, appropriate times, and precautions, but is not limited to these examples. For example, the generation AI generates cultivation information using a text generation AI (e.g., LLM). The generation unit can also generate cultivation information using a multimodal generation AI. For example, the generation AI provides information such as sowing methods and timing for thinning as cultivation methods. Information such as shovels, watering cans, and fertilizers as necessary tools. Information such as seasons (spring, summer, autumn, winter, etc.) and specific dates are provided as appropriate times. Information such as methods for preventing pests and diseases and appropriate temperature control are provided as precautions. Step 3: The advice unit provides advice in real time based on the growth information generated by the generation unit. The advice unit, for example, monitors the growth status of the plant and instructs the plant to water or add fertilizer at the appropriate time. The advice unit can also detect the occurrence of pests and diseases and propose countermeasures. For example, the advice unit monitors the growth status of the plant using image analysis or sensor measurements. The advice unit provides instructions based on the time of day, the stage of growth, etc., to determine the appropriate timing. Step 4: The detection unit detects the occurrence of pests and diseases based on the information provided by the advice unit and proposes countermeasures. The detection unit performs detection using image analysis or sensors, for example. Countermeasures proposed include the use of pesticides and physical control methods.

[0067] (Example 2) A home gardening support system according to an embodiment of the present invention allows users to receive cultivation information, advice, and suggestions for pest control simply by inputting the type of vegetable they wish to grow. In the home gardening support system, users input the type of vegetable they wish to grow, and a generation AI analyzes the cultivation information for that vegetable and provides information such as cultivation methods, necessary tools, appropriate times, and precautions. Furthermore, the generation AI provides real-time advice during the cultivation process to help users properly cultivate the vegetables. For example, in the home gardening support system, a user inputs the type of vegetable they wish to grow. For example, the user inputs a vegetable such as "tomato" or "cucumber." This information is input into the generation AI. The home gardening support system then analyzes the input information and provides cultivation information for that vegetable. The generation AI analyzes information such as cultivation methods, necessary tools, appropriate times, and precautions, and provides it to the user. For example, in the case of tomatoes, information such as the time to sow seeds, how to prepare the soil, how often to water, and the type of fertilizer is provided. Furthermore, in the home gardening support system, the generation AI provides real-time advice during the cultivation process. For example, the system monitors the plant's growth and provides instructions for watering and adding fertilizer at the appropriate time. It can also detect the occurrence of pests and diseases and suggest countermeasures. This allows users to grow vegetables properly. As a result, the home gardening support system makes it easy for even beginners to enjoy home gardening. For example, users can grow healthy vegetables without complex knowledge by simply following the advice of the generating AI. For example, when growing tomatoes, users can harvest delicious tomatoes by following the instructions of the generating AI to proceed through the process from sowing to harvesting. As a result, the home gardening support system allows users to receive cultivation information, advice, and suggestions for pest and disease control simply by entering the type of vegetable they want to grow. For example, even beginners can enjoy home gardening easily, and users can grow healthy vegetables without complex knowledge by simply following the advice of the generating AI.

[0068] A home gardening support system according to an embodiment includes a receiving unit, a generating unit, an advice unit, and a detecting unit. The receiving unit receives input from a user about the type of vegetable the user wants to grow. Examples of the type of vegetable the user wants to grow include, but are not limited to, tomatoes, cucumbers, and lettuce. The receiving unit receives, for example, input of the type of vegetable in text form from the user. The receiving unit can also receive voice input or image input. The generating unit uses a generation AI to generate cultivation information based on the information received by the receiving unit. The cultivation information includes, for example, cultivation methods, necessary tools, appropriate times, and precautions, but is not limited to, examples. For example, the generation AI generates the cultivation information using a text generation AI (e.g., LLM). The generating unit can also generate the cultivation information using a multimodal generation AI. For example, the generation AI provides information such as sowing methods and timing for thinning as cultivation methods. Information such as shovels, watering cans, and fertilizer as necessary tools. Information such as appropriate times can be provided as seasons, such as spring, summer, autumn, and winter, or specific dates. The system provides precautions such as methods for preventing pests and diseases and appropriate temperature management. The advice unit provides real-time advice based on the cultivation information generated by the generation unit. For example, the advice unit monitors the plant's growth status and instructs the plant to water and add fertilizer at the appropriate time. The advice unit can also detect the occurrence of pests and diseases and suggest countermeasures. For example, the advice unit monitors the plant's growth status using image analysis or sensor measurements. The appropriate timing is determined based on the time of day or the growth stage. The detection unit detects the occurrence of pests and diseases based on the information advised by the advice unit and suggests countermeasures. For example, the detection unit performs detection using image analysis or sensors. Countermeasures include proposing pesticide application methods and physical control methods. As a result, the home gardening support system according to the embodiment allows users to receive cultivation information, advice, and suggestions for pest and disease control measures simply by inputting the type of vegetables they want to grow. For example, even beginners can easily enjoy home gardening, and users can grow healthy vegetables without complex knowledge by simply following the advice provided by the generation AI.

[0069] The generation unit can generate information including cultivation methods, necessary tools, timing, and precautions. The generation unit can provide, for example, a sowing method and timing for thinning as cultivation methods. The generation unit can also provide a shovel, a watering can, fertilizer, etc. as necessary tools. The generation unit can also provide seasons such as spring, summer, autumn, and winter, or specific dates as appropriate times. The generation unit can also provide methods for preventing pests and diseases, appropriate temperature management, etc. as precautions. In this way, the generation unit can provide detailed cultivation information, allowing the user to properly cultivate vegetables. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can cause the generation AI to generate the cultivation information.

[0070] The advice unit can monitor the growth status of the plant and instruct the user to water or add fertilizer at the appropriate time. The advice unit can monitor the growth status of the plant, for example, by image analysis or measurement using a sensor. For example, the advice unit can photograph the growth status of the plant with a camera and evaluate the growth status using image analysis technology. The advice unit can also measure the growth status of the plant with a sensor and evaluate the growth stage. The advice unit can also provide instructions based on the time of day or the growth stage as the appropriate timing. For example, the advice unit can instruct the user on the timing of watering based on the time of day. The advice unit can also instruct the user on the timing of adding fertilizer based on the growth stage. This allows the advice unit to provide appropriate advice in real time, allowing the user to grow vegetables appropriately. Some or all of the above-mentioned processing in the advice unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the advice unit can cause the generation AI to monitor the growth status of the plant.

[0071] The detection unit can detect the occurrence of pests and diseases and propose countermeasures. The detection unit detects the occurrence of pests and diseases, for example, by image analysis or a sensor. For example, the detection unit takes images of plants with a camera and detects the occurrence of pests and diseases using image analysis technology. The detection unit can also detect the occurrence of pests and diseases using a sensor. The detection unit can also propose pesticide application methods or physical control methods as countermeasures. For example, the detection unit can propose pesticide application methods depending on the type of pest and diseases. The detection unit can also propose physical control methods such as installing nets or traps. In this way, the detection unit can detect the occurrence of pests and diseases and propose appropriate countermeasures, allowing the user to grow healthy vegetables. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit can cause the generation AI to detect the occurrence of pests and diseases.

[0072] The reception unit can estimate the user's emotions and adjust the timing of inputting the vegetable type based on the estimated user's emotions. The reception unit estimates the user's emotions, for example, using facial expression recognition or voice analysis. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. The reception unit can also adjust the timing of inputting the vegetable type based on the estimated user's emotions. For example, if the user is stressed, a simple interface can be provided to minimize the input steps. If the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. If the user is in a hurry, voice input can be prioritized to allow the user to quickly input the vegetable type. This allows the user to input the vegetable type without stress by adjusting the input timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may cause the generation AI to estimate the user's emotion.

[0073] The reception unit can analyze the user's past input history and select an input method. The reception unit, for example, analyzes the user's past input history using data mining or a machine learning algorithm. For example, the reception unit can automatically display vegetable types that the user has frequently input in the past as candidates. The reception unit can also prioritize input methods (such as voice or text) that the user has used in the past. The reception unit can also predict and recommend vegetables to grow in a specific season based on the user's past input history. For example, if the user inputs vegetables in the spring, the reception unit can prioritize displaying vegetables suitable for spring. If the user lives in a cold region, the reception unit can also recommend vegetables that are easy to grow in cold regions. This allows the user to efficiently input vegetable types by selecting the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can cause a generation AI to analyze the user's past input history.

[0074] When inputting the type of vegetable, the reception unit can filter the vegetable based on the user's current season and region. The reception unit performs filtering based on the user's current season and region, for example. For example, if the user inputs vegetables in spring based on weather data and local agricultural information, the reception unit can preferentially display vegetables suitable for spring. Furthermore, if the user lives in a cold region, the reception unit can also suggest vegetables that are easy to grow in cold regions. Furthermore, if the user lives in an urban area, the reception unit can also suggest vegetables that are easy to grow in urban areas. This allows the user to select appropriate vegetables by suggesting vegetables suitable for the user's season and region. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can cause the generation AI to perform filtering based on the user's current season and region.

[0075] When inputting the type of vegetable, the reception unit can select an input means according to the user's input method. The reception unit selects the optimal input means according to the user's input method (voice, text, image, etc.), for example. For example, when the user inputs "tomato" by voice, the reception unit prioritizes processing the voice input. Also, when the user inputs "cucumber" by text, the reception unit can also prioritize processing the text input. Also, when the user uploads a photo of the vegetable as an image, the reception unit can identify the type of vegetable using image recognition technology. This allows the user to efficiently input the type of vegetable by selecting the optimal means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can cause the generation AI to select the input means according to the user's input method.

[0076] The reception unit can estimate the user's emotions and prioritize the vegetables to be input based on the estimated user emotions. The reception unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. The reception unit can also prioritize the vegetables to be input based on the estimated user emotions. For example, if the user is stressed, easy-to-grow vegetables can be prioritized. If the user is relaxed, vegetables that are worth growing can be prioritized. If the user is in a hurry, fast-growing vegetables can be prioritized. This allows the user to select vegetables without stress by prioritizing the vegetables according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may cause the generation AI to estimate the user's emotion.

[0077] When inputting the type of vegetable, the reception unit can prioritize inputting highly relevant vegetables taking into account the user's geographical location information. The reception unit, for example, considers the user's geographical location information based on GPS data and local climatic conditions. For example, if the user lives in a warm region, the reception unit can suggest vegetables that are easy to grow in a warm climate. Furthermore, if the user lives in a highland, the reception unit can suggest vegetables that are easy to grow in a highland. Furthermore, if the user lives in a coastal region, the reception unit can suggest vegetables that are resistant to salt damage. This allows the user to select appropriate vegetables by suggesting highly relevant vegetables based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can cause a generation AI to suggest vegetables taking into account the user's geographical location information.

[0078] When the user inputs the type of vegetable, the reception unit can analyze the user's social media activity and suggest related vegetables. The reception unit, for example, analyzes the user's social media activity based on the content of posts and the interests of followers. For example, if the user posts about "tomato cultivation" on social media, the reception unit can suggest tomatoes. Furthermore, if the user participates in a group about "home gardening," the reception unit can also suggest vegetables that are popular in home gardens. Furthermore, the reception unit can also suggest vegetables that the user's friends grow based on the vegetables they grow. This allows the user to select vegetables that interest them by suggesting related vegetables based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can cause a generation AI to analyze the user's social media activity.

[0079] The reception unit can customize the input method by reflecting the user's past feedback when inputting the type of vegetable. The reception unit reflects the user's past feedback based on evaluation data and usage history, for example. For example, the reception unit customizes tomato cultivation information based on feedback from the user when growing tomatoes in the past. The reception unit can also customize cucumber cultivation information based on feedback from the user when growing cucumbers in the past. The reception unit can also customize eggplant cultivation information based on feedback from the user when growing eggplants in the past. This allows the user to efficiently input the type of vegetable by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can cause the generation AI to reflect the user's past feedback.

[0080] The generation unit can estimate the user's emotions and adjust the presentation method of the development information based on the estimated user emotions. The generation unit estimates the user's emotions, for example, using facial expression recognition or voice analysis. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. The generation unit can also adjust the presentation method of the development information based on the estimated user emotions. For example, if the user is relaxed, detailed development information can be provided. If the user is in a hurry, concise development information that focuses on the main points can be provided. If the user is excited, visually appealing development information can be provided. In this way, by adjusting the presentation method of the development information according to the user's emotions, it is possible to provide information that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may cause the generation AI to estimate the user's emotion.

[0081] When generating cultivation information, the generation unit can adjust the level of detail of the information based on the importance of the vegetable. The generation unit, for example, evaluates the importance of the vegetable based on the difficulty of cultivation and the yield. For example, the generation unit provides detailed cultivation information for an important vegetable (e.g., tomato). The generation unit can also provide concise cultivation information for a vegetable with a low importance (e.g., lettuce). The generation unit can also provide detailed cultivation information for a vegetable in which the user is particularly interested. In this way, by adjusting the level of detail of the information based on the importance of the vegetable, the user can appropriately obtain the information he or she needs. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to adjust the level of detail of the information based on the importance of the vegetable.

[0082] When generating cultivation information, the generation unit can apply different generation algorithms depending on the category of the vegetable. For example, the generation unit classifies vegetable categories into leafy vegetables, root vegetables, fruit vegetables, etc. For example, in the case of root vegetables (e.g., carrots), the generation unit can provide information focusing on root growth. In addition, in the case of leafy vegetables (e.g., spinach), the generation unit can also provide information focusing on leaf growth. In addition, in the case of fruit vegetables (e.g., tomatoes), the generation unit can provide information focusing on fruit growth. This allows the user to grow vegetables appropriately by providing appropriate information according to the vegetable category. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to apply a generation algorithm depending on the vegetable category.

[0083] When generating the cultivation information, the generation unit can improve the accuracy of the information by referring to the user's past cultivation results. The generation unit, for example, refers to the user's past cultivation results by using a database or statistical analysis. For example, the generation unit provides similar cultivation information based on cultivation methods that the user has used successfully in the past. The generation unit can also provide information to help the user avoid cultivation methods that have failed them in the past. The generation unit can also analyze the user's past cultivation results and suggest optimal cultivation methods. By referring to the user's past cultivation results, the accuracy of the information can be improved, allowing the user to properly cultivate vegetables. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to execute the processing by referring to the user's past cultivation results.

[0084] The generation unit can estimate the user's emotions and adjust the length of the development information based on the estimated user emotions. The generation unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. The generation unit can also adjust the length of the development information based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can provide short, to-the-point development information. If the user is relaxed, the generation unit can provide longer development information with detailed explanations. If the user is excited, the generation unit can provide development information with visually stimulating effects. By adjusting the length of the development information according to the user's emotions, it is possible to provide information that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may cause the generation AI to estimate the user's emotion.

[0085] When generating the cultivation information, the generation unit can determine the priority of information based on the vegetable cultivation period. The generation unit evaluates the vegetable cultivation period based on, for example, the season, temperature, and sunlight hours. For example, when the sowing period is approaching, the generation unit can prioritize providing information about sowing. Furthermore, the generation unit can prioritize providing information about fertilizer when fertilizer needs to be added. Furthermore, when the harvest period is approaching, the generation unit can prioritize providing information about harvesting. In this way, by determining the priority of information based on the vegetable cultivation period, the user can obtain the necessary information at the appropriate time. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to determine the priority of information based on the vegetable cultivation period.

[0086] When generating the cultivation information, the generation unit can adjust the order of information based on the relevance of vegetables. The generation unit, for example, evaluates the relevance of vegetables based on similarities in cultivation methods or common growth conditions. For example, the generation unit continuously provides information on vegetables of the same family (e.g., Solanaceae). The generation unit can also continuously provide information on vegetables with the same cultivation conditions (e.g., vegetables that require sunlight). The generation unit can also continuously provide information on vegetables that are grown at the same time. In this way, by adjusting the order of information based on the relevance of vegetables, the user can efficiently obtain information. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to adjust the order of information based on the relevance of vegetables.

[0087] When generating the training information, the generation unit can adjust the use of technical terminology in the information according to the user's level of expertise. The generation unit, for example, evaluates the user's level of expertise as beginner, intermediate, advanced, etc. For example, the generation unit provides a simple explanation that avoids technical terminology for beginners. The generation unit can also provide an explanation that includes appropriate technical terminology for intermediate users. The generation unit can also provide an explanation that includes detailed technical terminology for advanced users. In this way, by adjusting the use of technical terminology in the information according to the user's level of expertise, it is possible to provide information that is easy for the user to understand. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to adjust the use of technical terminology in the information according to the user's level of expertise.

[0088] The advice unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user's emotions. The advice unit estimates the user's emotions, for example, using facial expression recognition or voice analysis. For example, the advice unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The advice unit can also record the user's voice and estimate the emotions using voice analysis technology. The advice unit can also adjust the way the advice is presented based on the estimated user's emotions. For example, if the user is relaxed, detailed advice can be provided. If the user is in a hurry, concise advice that focuses on the main points can be provided. If the user is excited, visually appealing advice can be provided. In this way, by adjusting the way the advice is presented according to the user's emotions, it is possible to provide advice that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the advice unit may cause the generation AI to estimate the user's emotions.

[0089] When providing advice, the advice unit can adjust the level of detail of the advice based on the growth stage of the plant. The advice unit, for example, evaluates the growth stage of the plant as a germination stage, a growth stage, a harvest stage, etc. For example, the advice unit provides detailed advice regarding sowing at the sowing stage. The advice unit can also provide detailed advice regarding growth at the growth stage. The advice unit can also provide detailed advice regarding harvesting at the harvest stage. In this way, by adjusting the level of detail of the advice based on the growth stage of the plant, the user can obtain appropriate advice. Some or all of the above-described processing in the advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice unit can cause the generation AI to adjust the level of detail of the advice based on the growth stage of the plant.

[0090] When providing advice, the advice unit can apply different advice algorithms depending on the type of plant. For example, the advice unit classifies plant types into leafy vegetables, root vegetables, fruit vegetables, etc. For example, in the case of tomatoes, the advice unit can provide advice specialized for tomato cultivation. Furthermore, in the case of cucumbers, the advice unit can provide advice specialized for cucumber cultivation. Furthermore, in the case of eggplants, the advice unit can provide advice specialized for eggplant cultivation. This allows the user to grow plants appropriately by providing appropriate advice depending on the type of plant. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice unit can cause the generation AI to apply an advice algorithm depending on the type of plant.

[0091] When providing advice, the advice unit can improve the accuracy of the advice by referring to the user's past cultivation results. The advice unit, for example, refers to the user's past cultivation results by using a database or statistical analysis. For example, the advice unit provides similar advice based on cultivation methods that the user has used successfully in the past. The advice unit can also provide advice to help the user avoid cultivation methods that have failed the user in the past. The advice unit can also analyze the user's past cultivation results and propose optimal advice. By referring to the user's past cultivation results, the accuracy of the advice can be improved, allowing the user to properly cultivate plants. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice unit can cause the generation AI to execute the advice by referring to the user's past cultivation results.

[0092] The advice unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. The advice unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, the advice unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The advice unit can also record the user's voice and estimate the emotion using voice analysis technology. The advice unit can also adjust the length of the advice based on the estimated user's emotion. For example, if the user is in a hurry, the advice unit can provide short, to-the-point advice. If the user is relaxed, the advice unit can provide longer advice with detailed explanations. If the user is excited, the advice unit can provide advice with visually stimulating effects. In this way, by adjusting the length of the advice according to the user's emotion, it is possible to provide advice that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the advice unit may cause the generation AI to estimate the user's emotions.

[0093] When providing advice, the advice unit can determine the priority of advice based on the plant's growth period. The advice unit evaluates the plant's growth period based on, for example, the season, temperature, and sunlight hours. For example, when the sowing season is approaching, the advice unit can prioritize providing advice regarding sowing. Furthermore, the advice unit can prioritize providing advice regarding fertilizer when additional fertilizer is needed. Furthermore, when the harvest season is approaching, the advice unit can prioritize providing advice regarding harvesting. In this way, by determining the priority of advice based on the plant's growth period, the user can obtain the necessary advice at the appropriate time. Some or all of the above-described processing in the advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice unit can cause the generation AI to determine the priority of advice based on the plant's growth period.

[0094] When providing advice, the advice unit can adjust the order of advice based on the relevance of the plants. The advice unit, for example, evaluates the relevance of the plants based on similarities in cultivation methods or common growth conditions. For example, the advice unit may continuously provide advice for plants of the same family (e.g., Solanaceae). The advice unit may also continuously provide advice for plants with the same growing conditions (e.g., plants that require sunlight). The advice unit may also continuously provide advice for plants to be grown at the same time of year. In this way, by adjusting the order of advice based on the relevance of the plants, the user can efficiently obtain advice. Some or all of the above-described processing in the advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice unit may cause the generation AI to adjust the order of advice based on the relevance of the plants.

[0095] When providing advice, the advice unit can adjust the use of technical terms in the advice according to the user's level of expertise. The advice unit, for example, evaluates the user's level of expertise as beginner, intermediate, advanced, etc. For example, the advice unit provides simple advice that avoids technical terms for beginners. The advice unit can also provide advice that includes appropriate technical terms for intermediate users. The advice unit can also provide advice that includes detailed technical terms for advanced users. In this way, by adjusting the use of technical terms in the advice according to the user's level of expertise, it is possible to provide advice that is easy for the user to understand. Some or all of the above-described processing in the advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the advice unit can cause the generation AI to adjust the use of technical terms in the advice according to the user's level of expertise.

[0096] The detection unit can estimate the user's emotions and adjust the pest infestation detection method based on the estimated user emotions. The detection unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the detection unit can capture the user's facial expressions with a camera and estimate the emotion using facial expression recognition technology. The detection unit can also record the user's voice and estimate the emotion using voice analysis technology. The detection unit can also adjust the pest infestation detection method based on the estimated user emotions. For example, if the user is relaxed, detailed pest infestation detection information can be provided. If the user is in a hurry, concise pest infestation detection information that focuses on the main points can be provided. If the user is excited, visually appealing pest infestation detection information can be provided. By adjusting the pest infestation detection method according to the user's emotions, it is possible to provide information that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the detection unit may cause the generation AI to estimate the user's emotion.

[0097] When detecting the occurrence of pests or diseases, the detection unit can adjust the level of detection detail based on the growth stage of the plant. The detection unit, for example, evaluates the growth stage of the plant as a germination stage, a growth stage, a harvest stage, etc. For example, the detection unit provides detection information on the occurrence of pests or diseases related to sowing at the sowing stage. The detection unit can also provide detection information on the occurrence of pests or diseases related to growth at the growth stage. The detection unit can also provide detection information on the occurrence of pests or diseases related to harvest at the harvest stage. In this way, by adjusting the level of detection detail based on the growth stage of the plant, the user can obtain appropriate information. Some or all of the above-described processing in the detection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the detection unit can cause the generation AI to adjust the level of detection detail based on the growth stage of the plant.

[0098] When detecting the occurrence of pests or diseases, the detection unit can apply different detection algorithms depending on the type of plant. The detection unit, for example, classifies plant types into leafy vegetables, root vegetables, fruit vegetables, etc. For example, in the case of tomatoes, the detection unit applies a detection algorithm specialized for tomato pests and diseases. In addition, in the case of cucumbers, the detection unit can also apply a detection algorithm specialized for cucumber pests and diseases. In addition, in the case of eggplants, the detection unit can also apply a detection algorithm specialized for eggplant pests and diseases. This allows the user to grow plants appropriately by applying an appropriate detection algorithm depending on the type of plant. Some or all of the above-mentioned processing in the detection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the detection unit can cause the generation AI to apply a detection algorithm depending on the type of plant.

[0099] When detecting the occurrence of pests or diseases, the detection unit can improve the accuracy of detection by referring to the user's past cultivation results. The detection unit, for example, refers to the user's past cultivation results by using a database or statistical analysis. For example, the detection unit provides similar pest or disease occurrence detection information based on cultivation methods that the user has used successfully in the past. The detection unit can also provide pest or disease occurrence detection information to help the user avoid cultivation methods that have failed in the past. The detection unit can also analyze the user's past cultivation results and suggest optimal pest or disease occurrence detection information. By referring to the user's past cultivation results, the detection accuracy is improved, allowing the user to properly cultivate plants. Some or all of the above-described processing in the detection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the detection unit can cause the generation AI to execute the processing by referring to the user's past cultivation results.

[0100] The detection unit can estimate the user's emotions and determine the priority of pest infestation detection based on the estimated user emotions. The detection unit estimates the user's emotions, for example, using facial expression recognition or voice analysis. For example, the detection unit can capture the user's facial expressions with a camera and estimate the emotion using facial expression recognition technology. The detection unit can also record the user's voice and estimate the emotion using voice analysis technology. The detection unit can also determine the priority of pest infestation detection based on the estimated user emotions. For example, if the user is stressed, important pest infestation detection information can be provided preferentially. If the user is relaxed, detailed pest infestation detection information can be provided. If the user is in a hurry, concise pest infestation detection information can be provided. In this way, by determining the priority of pest infestation detection according to the user's emotions, the user can obtain important information preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the detection unit may cause the generation AI to estimate the user's emotion.

[0101] When detecting the occurrence of pests or diseases, the detection unit can determine the detection priority based on the plant growth period. The detection unit evaluates the plant growth period based on, for example, the season, temperature, and sunlight hours. For example, when the sowing season is approaching, the detection unit can prioritize providing information on the detection of pests or diseases related to sowing. The detection unit can also prioritize providing information on the detection of pests or diseases related to fertilizer when fertilizer addition is required. The detection unit can also prioritize providing information on the detection of pests or diseases related to harvest when the harvest season is approaching. In this way, by determining the detection priority based on the plant growth period, the user can obtain the necessary information at the appropriate time. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the detection unit can cause the generation AI to determine the detection priority based on the plant growth period.

[0102] When detecting the occurrence of pests or diseases, the detection unit can adjust the detection order based on the relevance of the plants. The detection unit, for example, evaluates the relevance of the plants based on similarities in cultivation methods or common growth conditions. For example, the detection unit continuously provides detection information on the occurrence of pests or diseases in plants of the same family (e.g., Solanaceae). The detection unit can also continuously provide detection information on the occurrence of pests or diseases in plants with the same growing conditions (e.g., plants that require sunlight). The detection unit can also continuously provide detection information on the occurrence of pests or diseases in plants grown at the same time. In this way, by adjusting the detection order based on the relevance of the plants, the user can efficiently obtain information. Some or all of the above-mentioned processing in the detection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the detection unit can cause the generation AI to adjust the detection order based on the relevance of the plants.

[0103] The detection unit can adjust the use of technical terminology for detection of pest infestations according to the user's level of expertise. For example, the detection unit evaluates the user's level of expertise as beginner, intermediate, advanced, or the like. For example, the detection unit can provide beginners with simple pest infestation detection information that avoids technical terminology. The detection unit can also provide intermediate users with pest infestation detection information that includes appropriate technical terminology. The detection unit can also provide advanced users with pest infestation detection information that includes detailed technical terminology. In this way, by adjusting the use of technical terminology for detection according to the user's level of expertise, it is possible to provide information that is easy for users to understand. Some or all of the above-described processing in the detection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the detection unit can cause the generation AI to adjust the use of technical terminology for detection according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, advice unit, and detection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives input from a user via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates cultivation information using a generation AI. The advice unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides advice in real time. The detection unit detects the occurrence of pests and diseases using the camera 42 or sensor of the smart device 14 and proposes countermeasures. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, advice unit, and detection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives input from a user via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates cultivation information using a generation AI. The advice unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides advice in real time. The detection unit detects the occurrence of pests and diseases using the camera 42 or sensor of the smart glasses 214 and suggests countermeasures. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, advice unit, and detection unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit receives input from a user via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates cultivation information using a generation AI. The advice unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and provides advice in real time. The detection unit detects the occurrence of pests and diseases using the camera 42 or sensor of the headset terminal 314 and proposes countermeasures. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, advice unit, and detection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives input from a user via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates cultivation information using a generation AI. The advice unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides advice in real time. The detection unit detects the occurrence of pests and diseases using the camera 42 or sensors of the robot 414 and proposes countermeasures.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The home vegetable gardening support system can also be equipped with a community function. The community function provides a forum where users can share cultivation information and advice with each other. For example, users can post photos of the vegetables they have grown and receive feedback from other users. Users can also participate in forums related to specific vegetables and exchange information with other users. Furthermore, the community function can provide a forum where users can post problems they are having growing and receive solutions from other users. This allows users to enjoy home vegetable gardening while interacting with other users.

[0106] When generating the cultivation information, the generation unit can customize the information taking into account the user's health condition. For example, if the user has an allergy, cultivation information for vegetables related to that allergy can be avoided. Also, if the user needs a specific nutrient, cultivation information for vegetables containing a large amount of that nutrient can be provided preferentially. Furthermore, if the user is linked to a health management app, cultivation information for optimal vegetables can be provided based on that data. This allows the user to live a healthy life by providing cultivation information according to the user's health condition.

[0107] The advice unit can estimate the user's emotions and adjust the tone of the advice based on the estimated user's emotions. For example, if the user is feeling stressed, the advice unit can provide the advice in a gentle tone. If the user is relaxed, the advice unit can provide the advice in a friendly tone. Furthermore, if the user is excited, the advice unit can provide the advice in an energetic tone. In this way, by providing advice in a tone that corresponds to the user's emotions, the user is more likely to accept the advice.

[0108] When detecting the occurrence of pests or diseases, the detection unit can improve the accuracy of detection by referring to the user's past cultivation history. For example, if the user has been plagued by a particular pest or disease in the past, the detection unit can strengthen detection of that pest or disease. The detection unit can also suggest similar measures based on control methods that the user has used successfully in the past. Furthermore, the detection unit can analyze the user's past cultivation history and suggest the optimal control method. In this way, by referring to the user's past cultivation history, the accuracy of pest or disease occurrence detection can be improved, allowing the user to cultivate plants appropriately.

[0109] The reception unit can estimate the user's emotions and adjust the interface design based on the estimated user emotions. For example, if the user is feeling stressed, a simple and calm design can be provided. If the user is relaxed, a colorful and fun design can be provided. Furthermore, if the user is excited, an energetic design can be provided. In this way, the system can provide an interface design that corresponds to the user's emotions, allowing the user to use the system comfortably.

[0110] The reception unit can analyze the user's past input history and select an input method. For example, types of vegetables that the user has frequently input in the past can be automatically displayed as candidates. It can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest vegetables that are grown in a specific season based on the user's past input history. For example, if the user inputs vegetables in the spring, vegetables that are suitable for spring can be displayed with priority. It can also suggest vegetables that are easy to grow in cold regions if the user lives in a cold region. This allows the user to efficiently input types of vegetables by selecting the optimal input method based on the user's past input history.

[0111] When a type of vegetable is input, the reception unit can filter the vegetable selection based on the user's current season and region. For example, if the user inputs vegetables for spring based on weather data and local agricultural information, the reception unit can preferentially display vegetables suitable for spring. Furthermore, if the user lives in a cold region, the reception unit can also suggest vegetables that are easy to grow in cold regions. Furthermore, if the user lives in an urban area, the reception unit can also suggest vegetables that are easy to grow in urban areas. This allows the user to select appropriate vegetables by suggesting vegetables suitable for the user's season and region.

[0112] When inputting the type of vegetable, the reception unit can select an input means according to the user's input method. For example, the reception unit selects the most appropriate input means according to the user's input method (voice, text, image, etc.). For example, if the user inputs "tomato" by voice, the voice input can be given priority in processing. Also, if the user inputs "cucumber" by text, the text input can be given priority in processing. Also, if the user uploads a photo of the vegetable as an image, the type of vegetable can be identified using image recognition technology. In this way, by selecting the most appropriate means according to the user's input method, the user can efficiently input the type of vegetable.

[0113] The reception unit can estimate the user's emotions and determine the priority of vegetables to be input based on the estimated user's emotions. For example, if the user is feeling stressed, vegetables that are easy to grow can be preferentially suggested. Also, if the user is relaxed, vegetables that are worth growing can be suggested. Also, if the user is in a hurry, vegetables that grow quickly can be preferentially suggested. In this way, by determining the priority of vegetables according to the user's emotions, the user can select vegetables without stress.

[0114] When inputting the type of vegetable, the reception unit can prioritize inputting highly relevant vegetables taking into consideration the user's geographical location information. For example, the user's geographical location information can be considered based on GPS data and local climatic conditions. For example, if the user lives in a warm region, vegetables that are easy to grow in a warm climate can be suggested. Also, if the user lives in a highland, vegetables that are easy to grow in highland can be suggested. Also, if the user lives in a coastal region, vegetables that are resistant to salt damage can be suggested. In this way, highly relevant vegetables can be suggested based on the user's geographical location information, allowing the user to select appropriate vegetables.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives input from the user about the type of vegetable they want to grow. Examples of types of vegetables the user wants to grow include, but are not limited to, tomatoes, cucumbers, and lettuce. The reception unit receives, for example, the user's input of the type of vegetable in text format. The reception unit can also receive voice input or image input. Step 2: The generation unit uses the generation AI to generate cultivation information based on the information received by the reception unit. The cultivation information includes, for example, cultivation methods, necessary tools, appropriate times, and precautions, but is not limited to these examples. For example, the generation AI generates cultivation information using a text generation AI (e.g., LLM). The generation unit can also generate cultivation information using a multimodal generation AI. For example, the generation AI provides information such as sowing methods and timing for thinning as cultivation methods. Information such as shovels, watering cans, and fertilizers as necessary tools. Information such as seasons (spring, summer, autumn, winter, etc.) and specific dates are provided as appropriate times. Information such as methods for preventing pests and diseases and appropriate temperature control are provided as precautions. Step 3: The advice unit provides advice in real time based on the growth information generated by the generation unit. The advice unit, for example, monitors the growth status of the plant and instructs the plant to water or add fertilizer at the appropriate time. The advice unit can also detect the occurrence of pests and diseases and propose countermeasures. For example, the advice unit monitors the growth status of the plant using image analysis or sensor measurements. The advice unit provides instructions based on the time of day, the stage of growth, etc., to determine the appropriate timing. Step 4: The detection unit detects the occurrence of pests and diseases based on the information provided by the advice unit and proposes countermeasures. The detection unit performs detection using image analysis or sensors, for example. Countermeasures proposed include the use of pesticides and physical control methods.

[0117] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a 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.

[0155] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] 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.

[0180] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user of the type of vegetable they wish to grow; a generation unit that generates training information based on the information received by the reception unit; an advice unit that provides advice in real time based on the training information generated by the generation unit; a detection unit that detects occurrence of pests and proposes countermeasures based on the information advised by the advice unit; Equipped with A system characterized by:

2. The generation unit Generate information including cultivation methods, necessary tools, timing, and precautions 2. The system of claim 1.

3. The advice unit Monitor plant growth and provide instructions on watering and fertilizing at the right time 2. The system of claim 1.

4. The detection unit Detecting pest outbreaks and proposing countermeasures 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and adjust the timing of vegetable input based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past input history and select the input method 2. The system of claim 1.

7. The reception unit When entering a type of vegetable, it filters it based on the user's current season and region.

2. The system of claim 1.

8. The reception unit When inputting the type of vegetable, select the input method according to the user's input method.

2. The system of claim 1.

Images