system

Abstract

The system according to this embodiment aims to provide knowledge and advice on home gardening and horticulture, and to support successful home gardening and horticultural experiences. [Solution] The system according to the embodiment comprises a selection unit, an advice unit, a countermeasure unit, a guide unit, a monitoring unit, and a sharing unit. The selection unit selects crops. The advice unit provides cultivation advice for the crops selected by the selection unit. The countermeasure unit implements pest control measures based on the advice provided by the advice unit. The guide unit provides seasonal guides. The monitoring unit monitors the growth of crops. The sharing unit facilitates the sharing of knowledge and information.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that knowledge and advice on home gardening and horticulture are insufficient, and it is difficult for beginners and advanced users to find appropriate crop selection and cultivation methods.

[0005] The system according to the embodiment aims to provide knowledge and advice on home gardening and horticulture and support a successful home gardening and horticulture experience.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a selection unit, an advice unit, a countermeasure unit, a guide unit, a monitoring unit, and a sharing unit. The selection unit selects crops. The advice unit provides cultivation advice for the crops selected by the selection unit. The countermeasure unit implements pest control measures based on the advice provided by the advice unit. The guide unit provides seasonal guides. The monitoring unit monitors crop growth. The sharing unit facilitates the sharing of knowledge and information. [Effects of the Invention]

[0007] The system according to this embodiment can provide knowledge and advice on home gardening and horticulture, and can support successful home gardening and horticultural experiences. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The home gardening and horticulture support system according to an embodiment of the present invention is a system that provides advice and information on home gardening and horticulture. This system utilizes generative AI to support home gardening enthusiasts and horticulture lovers, from beginners to advanced users. When a user inputs the conditions for the crop they want to grow, the generative AI suggests the most suitable crop. Next, the generative AI provides specific cultivation advice for the selected crop, such as the timing of sowing, the frequency of watering, and the type and amount of fertilizer. The generative AI also identifies common pests and provides countermeasures. Furthermore, the generative AI provides appropriate crop cultivation and maintenance methods for each season. The generative AI can monitor the growth of the crops being grown by the user and automatically adjust the optimal amount of watering and fertilizer. In addition, the generative AI can publish the collected data and results on the internet and social media, allowing users to share information with other gardeners. This makes home gardening easier even for beginners, enabling them to enjoy the benefits of improved quality, reduced effort, and stable cultivation, while seriously growing crops at home. For example, a home gardening and horticulture support system allows users to input the conditions for the crops they want to grow, and a generating AI suggests the most suitable crops. The generating AI selects the optimal crops by considering, for example, the user's local climate data and soil conditions. It can also suggest low-maintenance or rewarding crops based on the user's cultivation experience and lifestyle. Next, the home gardening and horticulture support system provides specific cultivation advice for the selected crops. For example, the generating AI provides detailed advice on planting time and method, watering frequency and amount, and fertilizer type and amount. The generating AI also identifies common pests and provides countermeasures. For example, the generating AI suggests optimal pest control measures considering the types and timing of pests in each region. Furthermore, the home gardening and horticulture support system provides seasonal crop cultivation and maintenance methods. For example, the generating AI guides users on how to select and cultivate crops appropriate for each season: spring, summer, autumn, and winter. The generating AI can also monitor the growth of the crops being grown in real time and automatically adjust the optimal amount of water and fertilizer.For example, the generating AI uses sensors to monitor the growth status of crops and adjusts watering and fertilizer amounts as needed. Furthermore, the home gardening and horticulture support system can publish the collected data and results on the internet and social media, allowing users to share information with other gardeners. This enables users to interact with other gardeners and share knowledge and experience. For instance, the home gardening and horticulture support system allows users to publish growth data and cultivation results of the crops they are growing on social media, sharing information with other users. This makes home gardening easier even for beginners, allowing them to enjoy benefits such as improved quality, reduced effort, and stable cultivation, enabling them to grow crops seriously at home. In this way, the home gardening and horticulture support system can provide advice and information on home gardening and horticulture, supporting home gardening enthusiasts and gardening lovers from beginners to advanced levels.

[0029] The home garden and horticulture support system according to this embodiment comprises a selection unit, an advice unit, a countermeasure unit, a guide unit, a monitoring unit, and a sharing unit. The selection unit selects crops. For example, when the user inputs the conditions for the crops they want to grow, the generation AI suggests the optimal crops. The selection unit can select the optimal crops by considering, for example, the climate data and soil conditions of the user's region. The selection unit can also suggest low-maintenance crops or rewarding crops based on the user's cultivation experience and lifestyle. The advice unit provides cultivation advice for the crops selected by the selection unit. For example, the advice unit provides detailed advice on the timing and method of sowing seeds, the frequency and amount of watering, and the type and amount of fertilizer. The advice unit can provide specific cultivation advice to the user using the generation AI. The countermeasure unit implements pest control measures based on the advice provided by the advice unit. For example, the countermeasure unit identifies pests that are likely to occur and provides countermeasures. The countermeasure unit can propose optimal pest control measures by considering the types and timing of pest occurrences in each region using the generation AI. The guide unit provides seasonal guides. For example, the guide unit guides users on how to select and cultivate crops suitable for each season, spring, summer, autumn, and winter. The guide unit can use generative AI to provide seasonally appropriate crop cultivation and maintenance methods. The monitoring unit monitors crop growth. For example, the monitoring unit uses sensors to monitor the growth status of crops and adjusts watering and fertilizer amounts as needed. The monitoring unit can use generative AI to monitor crop growth in real time and automatically adjust the optimal amount of watering and fertilizer. The sharing unit facilitates the sharing of knowledge and information. For example, the sharing unit publishes collected data and results on the internet and social media to share information with other gardeners. The sharing unit can use generative AI to publish growth data and cultivation results of crops grown by users on social media to share information with other users. As a result, the home gardening and horticulture support system according to this embodiment can provide advice and information on home gardening and horticulture, supporting home gardening enthusiasts and gardening lovers from beginners to advanced levels.

[0030] The selection unit selects crops. For example, when a user inputs the conditions for the crop they want to grow, the generating AI suggests the most suitable crop. Specifically, the user inputs detailed conditions such as the type of crop they want to grow, desired harvest time, size of cultivation space, and sunlight conditions through an application or web interface. Based on these conditions, the generating AI selects the most suitable crop, taking into account the user's local climate data and soil conditions. For example, it collects data such as the region's annual rainfall, average temperature, sunshine hours, soil pH value, and nutrient content, analyzes this data, and suggests the most suitable crop for the user. The selection unit can also suggest low-maintenance crops or challenging crops based on the user's cultivation experience and lifestyle. For example, it might suggest low-maintenance herbs or leafy greens for salads to beginners, and challenging crops such as fruit trees or root vegetables to experienced users. Furthermore, the selection unit can learn from the user's past cultivation history and success / failure data to make more accurate suggestions. As a result, the selection unit can select the most suitable crop for the user's needs and conditions, increasing the success rate of home gardening and horticulture.

[0031] The advice unit provides cultivation advice for the crop selected by the selection unit. For example, the advice unit provides detailed advice on things like the timing and method of sowing, the frequency and amount of watering, and the type and amount of fertilizer. Specifically, the generating AI considers the user's local climate data, soil conditions, and the characteristics of the selected crop to suggest the optimal cultivation method. For example, regarding the timing of sowing, it suggests the optimal time based on local temperature and rainfall, and for the sowing method, it provides detailed procedures such as soil preparation, seed depth, and spacing. Regarding the frequency and amount of watering, it advises on the appropriate timing and amount of watering considering the crop's growth stage and weather conditions. Regarding the type and amount of fertilizer, it suggests the optimal type and timing of fertilization based on the balance of nutrients in the soil and the crop's nutritional requirements. Furthermore, the advice unit can use the generating AI to provide specific cultivation advice to the user. For example, when a user inputs a question through the application, the generating AI provides an appropriate answer to that question, resolving the user's doubts and problems. The advice unit can also monitor the user's cultivation status and update the advice as needed. This allows the advice unit to provide users with continuous and specific cultivation advice, supporting their success in home gardening and horticulture.

[0032] The pest control department implements pest control measures based on advice provided by the advisory department. For example, the department identifies pests that are likely to occur and provides countermeasures. Specifically, the AI ​​generates data considering the types and timing of pest outbreaks in each region to propose optimal pest control measures. For example, it analyzes regional climate data and past pest outbreak data to predict pests that are likely to occur at specific times. Based on this, it proposes preventative measures to the user. For example, during periods when specific pests are likely to occur, it proposes the use of appropriate pesticides or physical control methods (such as setting up nets or traps). The pest control department can also monitor crop growth and environmental conditions to detect and control pests early. For example, it uses sensors to monitor the condition of crop leaves and stems, and if an abnormality is detected, the AI ​​generates data to analyze the cause and propose appropriate countermeasures. Furthermore, the pest control department can learn from the user's past pest control history and propose more effective countermeasures. In this way, the pest control department can provide users with effective pest control measures and maintain crop health and yield.

[0033] The Guide section provides seasonal guides. For example, it guides users on how to select and cultivate crops suitable for each season: spring, summer, autumn, and winter. Specifically, the generating AI considers seasonal climate data and crop characteristics to suggest the optimal crop selection and cultivation methods. For example, it suggests early-maturing vegetables and flowers in spring, heat-tolerant crops in summer, late-harvesting crops in autumn, and cold-tolerant crops in winter. It also provides detailed guidance on seasonal cultivation methods, including soil preparation, sowing timing, watering frequency, and fertilization. Furthermore, the Guide section can use the generating AI to provide seasonally appropriate crop cultivation and maintenance methods. For example, it suggests methods to raise soil temperature in spring, methods to block sunlight in summer, soil care after harvest in autumn, and cold protection measures in winter. In this way, the Guide section can provide users with the optimal cultivation methods for each season and support the success of home gardening and horticulture.

[0034] The monitoring unit monitors crop growth. For example, it uses sensors to monitor crop growth and adjusts watering and fertilizer amounts as needed. Specifically, it uses soil sensors and environmental sensors to collect data such as soil moisture, temperature, nutrient content, and ambient temperature and humidity. This data is analyzed by a generating AI to monitor crop growth in real time. For example, if soil moisture decreases, it automatically waters the plants, and if soil nutrients are deficient, it adds appropriate fertilizer. The monitoring unit can also make adjustments to maintain optimal environmental conditions according to the crop's growth stage. For example, it maintains high humidity during the germination stage, ensures adequate sunlight during the growth stage, and maintains appropriate temperatures during the harvest stage. Furthermore, the monitoring unit can notify users about crop growth status and necessary care. For example, it can send notifications about crop growth status and necessary watering and fertilizer amounts via a smartphone app, supporting users in providing care at the appropriate time. In this way, the monitoring unit can optimally manage crop growth and support success in home gardening and horticulture.

[0035] The sharing section facilitates the sharing of knowledge and information. For example, it allows users to publish collected data and results on the internet and social media, sharing information with other gardeners. Specifically, a generating AI analyzes the growth data and cultivation results of crops grown by users and displays them as visually easy-to-understand graphs and charts. With the user's permission, this data can be posted to social media and dedicated community sites, allowing users to share information with others. For example, sharing information such as the growth process, yield, and effectiveness of pest control measures for specific crops can be helpful to other users. The sharing section also provides features to promote communication among users. For example, users can exchange opinions and advice through comment and messaging functions. Furthermore, the sharing section can use the generating AI to analyze information posted by users and automatically extract and provide information that is useful to other users. In this way, the sharing section can promote the sharing of knowledge and experience among users and revitalize the home gardening and horticulture community.

[0036] The selection unit can suggest the optimal crop based on the user's conditions. For example, the selection unit selects the optimal crop by considering the user's local climate data and soil conditions. The selection unit can also suggest the optimal crop based on the user's conditions using generative AI. For example, the selection unit can suggest low-maintenance crops or rewarding crops based on the user's cultivation experience and lifestyle. This increases the success rate of home gardening by suggesting the optimal crop according to the user's conditions.

[0037] The advice unit can provide specific cultivation advice for selected crops. For example, it can provide detailed advice on the timing and method of sowing seeds, the frequency and amount of watering, and the type and amount of fertilizer. The advice unit can provide specific cultivation advice to the user using generated AI. For example, it can also provide advice on fertilization methods and methods for preventing pests and diseases. In this way, by providing specific cultivation advice, users can grow crops in the appropriate way.

[0038] The pest control department can identify common pests and provide countermeasures. For example, it can identify common pests and provide countermeasures. Using AI generation, the department can propose optimal pest control measures, taking into account the types and timing of pest outbreaks in each region. The department can also advise on things like the type of pesticide to use and the timing of application. By providing pest control measures, it is possible to maintain crop health and increase yields.

[0039] The guide function can provide information on how to grow and maintain crops appropriate for each season. For example, it can guide users on how to select and cultivate crops suitable for each season: spring, summer, autumn, and winter. Using generative AI, the guide function can provide information on how to grow and maintain crops appropriate for each season. For example, it can also advise on how to select and cultivate crops for each season. By providing seasonal guides, users can grow the right crops at the right time.

[0040] The monitoring unit can monitor crop growth in real time and adjust the optimal amount of watering and fertilizer. For example, it can use sensors to monitor crop growth and adjust watering and fertilizer as needed. The monitoring unit can also use AI to monitor crop growth in real time and automatically adjust the optimal amount of watering and fertilizer. Furthermore, the monitoring unit can provide advice on methods for measuring growth and monitoring frequency. This allows for optimal management of crop growth through real-time monitoring.

[0041] The sharing function allows users to publish collected data and results on the internet and social media, sharing information with other gardeners. For example, the sharing function can use generative AI to publish growth data and cultivation results of crops being grown by users on social media, sharing information with other users. The sharing function can also provide advice on the types of information to share and the sharing platforms to use. This facilitates interaction with other gardeners through information sharing, leading to improved knowledge.

[0042] The selection unit can analyze the user's past cultivation history and suggest the most suitable crop. For example, the selection unit can suggest crops that can be grown under similar conditions based on crops the user has successfully grown in the past. The selection unit can also suggest crops that the user has failed to grow in the past, for example. The selection unit can also suggest crops suitable for each season based on the user's past cultivation history. This increases the success rate by suggesting the most suitable crop based on past cultivation history. Some or all of the above processing in the selection unit may be performed using generative AI, or it may be performed without using generative AI.

[0043] The selection unit can make suggestions when selecting crops, taking into account the climate data of the user's region. For example, the selection unit can suggest suitable crops based on the annual rainfall of the user's region. The selection unit can also suggest suitable crops based on the average temperature of the user's region. The selection unit can also suggest suitable crops based on the soil conditions of the user's region. In this way, suitable crops can be selected by taking regional climate data into consideration. Some or all of the above processing in the selection unit may be performed using generative AI, or it may be performed without using generative AI.

[0044] The selection unit can make suggestions when selecting crops, taking into account the user's lifestyle and available time. For example, if the user is busy, the selection unit can suggest crops that require little effort. If the user has plenty of time, the selection unit can also suggest crops that require more effort but offer a more enjoyable harvest. If the user only has time on weekends, the selection unit can also suggest crops that require maintenance on weekends. By suggesting crops that suit the user's lifestyle, the success rate of home gardening can be increased. Some or all of the above processing in the selection unit may be performed using generative AI, or it may be performed without using generative AI.

[0045] The selection unit can analyze the user's social media activity when selecting crops and suggest relevant crops. For example, the selection unit can suggest crops that the user frequently talks about on social media. For example, the selection unit can suggest crops grown by gardeners that the user follows. For example, the selection unit can suggest crops that are popular in gardening communities that the user participates in. This allows the user to select crops that match their interests by suggesting crops based on their social media activity. Some or all of the above processing in the selection unit may be performed using generative AI, or it may be performed without using generative AI.

[0046] The advice unit can provide different advice depending on the stage of crop growth. For example, at the time of sowing, it can advise on how to properly prepare the soil. During the growing season, it can also advise on the frequency of watering and the type of fertilizer to use. During the harvest season, it can also advise on the timing and method of harvesting. By providing advice according to the stage of growth, appropriate cultivation methods can be implemented. Some or all of the above processing in the advice unit may be performed using generation AI, or it may be performed without generation AI.

[0047] The advice unit can adjust the level of detail of the advice based on the user's cultivation experience when providing advice. For example, the advice unit can provide basic advice to beginners. For example, the advice unit can provide slightly more detailed advice to intermediate users. For example, the advice unit can provide expert advice to advanced users. This allows for a deeper understanding of the user by providing advice tailored to their cultivation experience. Some or all of the above processing in the advice unit may be performed using generative AI, or it may be performed without using generative AI.

[0048] The advice unit can provide advice while taking into account the weather forecast for the user's area. For example, if rain is expected, the advice unit may advise reducing the frequency of watering. For example, if drought is expected, the advice unit may advise increasing the frequency of watering. For example, if a cold snap is expected, the advice unit may advise on how to protect crops. This allows for the implementation of appropriate cultivation methods by providing advice that takes weather forecasts into account. Some or all of the above processing in the advice unit may be performed using generative AI, or it may be performed without using generative AI.

[0049] The advice unit can analyze the user's social media activity and provide relevant advice when offering it. For example, the advice unit can provide advice on crops that the user is discussing on social media. For example, the advice unit can refer to advice from gardeners that the user follows. For example, the advice unit can provide advice shared within gardening communities that the user participates in. This allows the advice unit to provide advice that is tailored to the user's interests by providing advice based on social media activity. Some or all of the above processing in the advice unit may be performed using generative AI, or it may be performed without using generative AI.

[0050] The pest control unit can propose optimal countermeasures by referring to past pest outbreak data when implementing pest control measures. For example, the unit can propose optimal countermeasures based on the types of pests that have occurred in the past. The unit can also propose preventive measures based on the timing of past pest outbreaks. The unit can also propose optimal countermeasures based on the effectiveness of past pest control measures. This enables effective countermeasures by providing optimal pest control measures based on past data. Some or all of the above processing in the pest control unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0051] The pest control unit can provide different pest control methods depending on the type of crop. For example, the unit can propose specific pest control measures for tomatoes. For example, the unit can propose different pest control measures for basil. For example, the unit can propose specialized pest control measures for strawberries. By providing pest control measures tailored to the type of crop, effective control becomes possible. Some or all of the above processing in the pest control unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0052] The pest control unit can take into account the climate data of the user's region when implementing pest control measures. For example, in areas with heavy rainfall, the unit can propose pest control measures that are resistant to humidity. In dry areas, the unit can also propose pest control measures that are resistant to drought. In cold regions, the unit can also propose pest control measures that are resistant to cold. By providing pest control measures that take regional climate data into account, effective measures become possible. Some or all of the above processing in the pest control unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0053] The pest control department can analyze a user's social media activity and provide relevant solutions when implementing pest control measures. For example, the department can provide solutions related to pests that the user is discussing on social media. The department can also refer to pest control measures from gardeners that the user follows. The department can also provide pest control measures shared within gardening communities that the user participates in. By providing pest control measures based on social media activity, the department can offer solutions that are tailored to the user's interests. Some or all of the above processing in the pest control department may be performed using generative AI, or it may not be performed using generative AI.

[0054] The guide unit can provide optimal guidance by referring to seasonal climate data when providing guidance. For example, in spring, the guide unit can provide guidance on how to grow crops suitable for spring. For example, in summer, the guide unit can provide guidance on how to grow crops suitable for summer. For example, in autumn, the guide unit can provide guidance on how to grow crops suitable for autumn. In this way, by providing guidance based on seasonal climate data, appropriate cultivation methods can be implemented. Some or all of the above processing in the guide unit may be performed using generative AI, or it may be performed without using generative AI.

[0055] The guide unit can adjust the level of detail of the guide based on the user's cultivation experience when providing the guide. For example, the guide unit can provide a basic guide to beginners. For example, the guide unit can provide a slightly more detailed guide to intermediate users. For example, the guide unit can provide a specialized guide to advanced users. This allows for a deeper understanding of the user by providing a guide tailored to their cultivation experience. Some or all of the above processing in the guide unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0056] The guide unit can provide guidance while taking into account the weather forecast for the user's area. For example, if rain is expected, the guide unit can provide guidance on rain countermeasures. For example, if dry conditions are expected, the guide unit can also provide guidance on drought countermeasures. For example, if a cold wave is expected, the guide unit can also provide guidance on cold weather countermeasures. By providing guidance that takes weather forecasts into account, appropriate cultivation methods can be implemented. Some or all of the above processing in the guide unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0057] The guide unit can analyze the user's social media activity when providing guides and provide relevant guides. For example, the guide unit can provide guides about crops that the user is talking about on social media. The guide unit can also refer to guides from gardeners that the user follows. The guide unit can also provide guides shared within gardening communities that the user participates in. By providing guides based on social media activity, the guide unit can provide guides that match the user's interests. Some or all of the above processing in the guide unit may be performed using generative AI, or it may be performed without using generative AI.

[0058] The monitoring unit can apply different monitoring methods depending on the growth stage of the crop during monitoring. For example, during the sowing period, the monitoring unit will focus on monitoring germination. During the growing period, the monitoring unit can also monitor the color and shape of the leaves. During the harvest period, the monitoring unit can also monitor the size and color of the fruit. This allows for the implementation of appropriate cultivation methods by providing monitoring tailored to the growth stage. Some or all of the above-described processes in the monitoring unit may be performed using or without generating AI.

[0059] The monitoring unit can select the optimal monitoring method by referring to the user's cultivation history during monitoring. For example, the monitoring unit can suggest a similar monitoring method based on cultivation methods that the user has successfully used in the past. For example, the monitoring unit can adjust the monitoring method to avoid cultivation methods that the user has failed at in the past. For example, the monitoring unit can suggest an optimal monitoring frequency based on the user's past cultivation history. This allows for the implementation of appropriate cultivation methods by providing monitoring based on cultivation history. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0060] The monitoring unit can perform monitoring while taking into account the climate data of the user's region. For example, in areas with heavy rainfall, the monitoring unit can propose a monitoring method that is resistant to humidity. For example, in dry areas, the monitoring unit can propose a monitoring method that is resistant to drought. For example, in cold regions, the monitoring unit can propose a monitoring method that is resistant to cold. By providing monitoring that takes into account the local climate data, appropriate cultivation methods can be implemented. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0061] The monitoring unit can analyze the user's social media activity during monitoring and perform relevant monitoring. For example, the monitoring unit can monitor crops that the user is discussing on social media. The monitoring unit can also refer to monitoring methods used by gardeners that the user follows. For example, the monitoring unit can provide monitoring methods shared within gardening communities that the user participates in. This allows the monitoring unit to provide monitoring tailored to the user's interests by providing monitoring based on social media activity. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0062] The sharing unit can select the most suitable information by referring to the user's cultivation history when sharing. For example, the sharing unit can share similar information based on cultivation methods that the user has successfully used in the past. For example, the sharing unit can also adjust the information to help the user avoid cultivation methods that have failed in the past. For example, the sharing unit can share the most suitable information based on the user's past cultivation history. This allows for the practice of appropriate cultivation methods by providing information sharing based on cultivation history. Some or all of the above processing in the sharing unit may be performed using generative AI, or it may be performed without using generative AI.

[0063] The sharing section can provide information while considering the user's regional climate data. For example, in areas with heavy rainfall, the sharing section can share information on crops resistant to humidity. In dry areas, for example, the sharing section can also share information on crops resistant to drought. In cold regions, for example, the sharing section can also share information on crops resistant to cold. This allows for the implementation of appropriate cultivation methods by providing information that takes regional climate data into consideration. Some or all of the above processing in the sharing section may be performed using generative AI, or it may be performed without using generative AI.

[0064] The sharing function can analyze the user's social media activity and provide relevant information when sharing. For example, the sharing function can share information about crops that the user is talking about on social media. The sharing function can also refer to information from gardeners that the user follows. The sharing function can also provide information shared within gardening communities that the user participates in. This allows the function to provide information that is tailored to the user's interests by providing information based on social media activity. Some or all of the above processing in the sharing function may be performed using generative AI or not.

[0065] The shared section can update information by referring to feedback from other gardeners during the sharing process. For example, the shared section updates information based on feedback provided by other gardeners. The shared section can also update information by referring to the success stories of other gardeners. The shared section can also update information by referring to the failure stories of other gardeners. By updating information based on feedback from other gardeners, more accurate and useful information can be provided. Some or all of the above processing in the shared section may be performed using generative AI, or it may be performed without using generative AI.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The home garden and horticultural support system can further monitor the user's health and adjust crop selection and cultivation advice based on that health condition. For example, if a user has allergies, it can suggest crops that do not contain allergens. It can also suggest crops rich in specific nutrients if the user needs them. Furthermore, if a user is feeling unwell, it can suggest low-maintenance crops. This allows the system to support the user's health by providing optimal crop selection and cultivation advice tailored to their individual needs.

[0068] Home gardening and gardening support systems can further suggest crop options that take into account the user's hobbies and interests. For example, if a user enjoys cooking, the system can suggest herbs and vegetables suitable for cooking. If a user is interested in growing flowers, it can suggest ornamental flowers. Furthermore, if a user is interested in ecology, it can suggest environmentally friendly crops and sustainable cultivation methods. This enhances the enjoyment of home gardening by providing crop options tailored to the user's hobbies and interests.

[0069] The home gardening and horticultural support system can further adjust crop selection and cultivation advice to take into account the user's home environment. For example, if the user has pets, it can suggest pet-safe crops. Similarly, if the user has young children, it can suggest crops that are safe for children to touch. Furthermore, if the user cultivates on a balcony or indoors, it can suggest crops that can be grown in limited space. This allows for the provision of optimal crop selection and cultivation advice tailored to the user's home environment, enabling a safe and comfortable home garden.

[0070] The home garden and horticultural support system can further tailor crop selection and cultivation advice based on the user's cultivation goals. For example, if the user wants to maximize yield, it can suggest high-yielding crops. If the user prioritizes quality, it can suggest high-quality crops. Furthermore, if the user wants to harvest to coincide with a specific event, it can suggest crops suitable for that event. In this way, it can support the user in achieving their goals by providing optimal crop selection and cultivation advice tailored to their specific cultivation objectives.

[0071] Home gardening and horticultural support systems can further strengthen connections with users' local communities. For example, they can suggest events for interaction with local gardeners. They can also provide information on local farmers' markets and direct sales outlets. Furthermore, they can offer cultivation advice based on local climate and soil conditions. By strengthening connections with local communities, users can share information with local gardeners and increase the overall success rate of home gardening in the area.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The selection section selects the crop. The user inputs the conditions for the crop they want to grow, and the AI ​​generates suggestions for the optimal crop. The selection section can choose the best crop by considering the user's local climate data and soil conditions. It can also suggest low-maintenance or rewarding crops based on the user's cultivation experience and lifestyle. Step 2: The advice unit provides cultivation advice for the crop selected by the selection unit. It provides detailed advice on the timing and method of sowing, the frequency and amount of watering, and the type and amount of fertilizer. Using generation AI, it can provide specific cultivation advice to the user. Step 3: The countermeasures department implements pest control measures based on the advice provided by the advice department. They identify common pests and provide countermeasures. Using generation AI, they can propose optimal pest control measures, taking into account the types and timing of pest outbreaks in each region. Step 4: The guide section provides seasonal guides. It guides users on how to select and cultivate crops suitable for each season: spring, summer, autumn, and winter. Using generation AI, it can provide information on how to grow and maintain crops that are appropriate for each season. Step 5: The monitoring unit monitors crop growth. It uses sensors to monitor the growth status of the crops and adjusts the amount of watering and fertilizer as needed. Using generated AI, it can monitor crop growth in real time and automatically adjust the optimal amount of watering and fertilizer. Step 6: The sharing section involves sharing knowledge and information. Collected data and results are published on the internet and social media to share information with other gardeners. Using generative AI, users can publish growth data and cultivation results of the crops they are growing on social media to share information with other users.

[0074] (Example of form 2) The home gardening and horticulture support system according to an embodiment of the present invention is a system that provides advice and information on home gardening and horticulture. This system utilizes generative AI to support home gardening enthusiasts and horticulture lovers, from beginners to advanced users. When a user inputs the conditions for the crop they want to grow, the generative AI suggests the most suitable crop. Next, the generative AI provides specific cultivation advice for the selected crop, such as the timing of sowing, the frequency of watering, and the type and amount of fertilizer. The generative AI also identifies common pests and provides countermeasures. Furthermore, the generative AI provides appropriate crop cultivation and maintenance methods for each season. The generative AI can monitor the growth of the crops being grown by the user and automatically adjust the optimal amount of watering and fertilizer. In addition, the generative AI can publish the collected data and results on the internet and social media, allowing users to share information with other gardeners. This makes home gardening easier even for beginners, enabling them to enjoy the benefits of improved quality, reduced effort, and stable cultivation, while seriously growing crops at home. For example, a home gardening and horticulture support system allows users to input the conditions for the crops they want to grow, and a generating AI suggests the most suitable crops. The generating AI selects the optimal crops by considering, for example, the user's local climate data and soil conditions. It can also suggest low-maintenance or rewarding crops based on the user's cultivation experience and lifestyle. Next, the home gardening and horticulture support system provides specific cultivation advice for the selected crops. For example, the generating AI provides detailed advice on planting time and method, watering frequency and amount, and fertilizer type and amount. The generating AI also identifies common pests and provides countermeasures. For example, the generating AI suggests optimal pest control measures considering the types and timing of pests in each region. Furthermore, the home gardening and horticulture support system provides seasonal crop cultivation and maintenance methods. For example, the generating AI guides users on how to select and cultivate crops appropriate for each season: spring, summer, autumn, and winter. The generating AI can also monitor the growth of the crops being grown in real time and automatically adjust the optimal amount of water and fertilizer.For example, the generating AI uses sensors to monitor the growth status of crops and adjusts watering and fertilizer amounts as needed. Furthermore, the home gardening and horticulture support system can publish the collected data and results on the internet and social media, allowing users to share information with other gardeners. This enables users to interact with other gardeners and share knowledge and experience. For instance, the home gardening and horticulture support system allows users to publish growth data and cultivation results of the crops they are growing on social media, sharing information with other users. This makes home gardening easier even for beginners, allowing them to enjoy benefits such as improved quality, reduced effort, and stable cultivation, enabling them to grow crops seriously at home. In this way, the home gardening and horticulture support system can provide advice and information on home gardening and horticulture, supporting home gardening enthusiasts and gardening lovers from beginners to advanced levels.

[0075] The home garden and horticulture support system according to this embodiment comprises a selection unit, an advice unit, a countermeasure unit, a guide unit, a monitoring unit, and a sharing unit. The selection unit selects crops. For example, when the user inputs the conditions for the crops they want to grow, the generation AI suggests the optimal crops. The selection unit can select the optimal crops by considering, for example, the climate data and soil conditions of the user's region. The selection unit can also suggest low-maintenance crops or rewarding crops based on the user's cultivation experience and lifestyle. The advice unit provides cultivation advice for the crops selected by the selection unit. For example, the advice unit provides detailed advice on the timing and method of sowing seeds, the frequency and amount of watering, and the type and amount of fertilizer. The advice unit can provide specific cultivation advice to the user using the generation AI. The countermeasure unit implements pest control measures based on the advice provided by the advice unit. For example, the countermeasure unit identifies pests that are likely to occur and provides countermeasures. The countermeasure unit can propose optimal pest control measures by considering the types and timing of pest occurrences in each region using the generation AI. The guide unit provides seasonal guides. For example, the guide unit guides users on how to select and cultivate crops suitable for each season, spring, summer, autumn, and winter. The guide unit can use generative AI to provide seasonally appropriate crop cultivation and maintenance methods. The monitoring unit monitors crop growth. For example, the monitoring unit uses sensors to monitor the growth status of crops and adjusts watering and fertilizer amounts as needed. The monitoring unit can use generative AI to monitor crop growth in real time and automatically adjust the optimal amount of watering and fertilizer. The sharing unit facilitates the sharing of knowledge and information. For example, the sharing unit publishes collected data and results on the internet and social media to share information with other gardeners. The sharing unit can use generative AI to publish growth data and cultivation results of crops grown by users on social media to share information with other users. As a result, the home gardening and horticulture support system according to this embodiment can provide advice and information on home gardening and horticulture, supporting home gardening enthusiasts and gardening lovers from beginners to advanced levels.

[0076] The selection unit selects crops. For example, when a user inputs the conditions for the crop they want to grow, the generating AI suggests the most suitable crop. Specifically, the user inputs detailed conditions such as the type of crop they want to grow, desired harvest time, size of cultivation space, and sunlight conditions through an application or web interface. Based on these conditions, the generating AI selects the most suitable crop, taking into account the user's local climate data and soil conditions. For example, it collects data such as the region's annual rainfall, average temperature, sunshine hours, soil pH value, and nutrient content, analyzes this data, and suggests the most suitable crop for the user. The selection unit can also suggest low-maintenance crops or challenging crops based on the user's cultivation experience and lifestyle. For example, it might suggest low-maintenance herbs or leafy greens for salads to beginners, and challenging crops such as fruit trees or root vegetables to experienced users. Furthermore, the selection unit can learn from the user's past cultivation history and success / failure data to make more accurate suggestions. As a result, the selection unit can select the most suitable crop for the user's needs and conditions, increasing the success rate of home gardening and horticulture.

[0077] The advice unit provides cultivation advice for the crop selected by the selection unit. For example, the advice unit provides detailed advice on things like the timing and method of sowing, the frequency and amount of watering, and the type and amount of fertilizer. Specifically, the generating AI considers the user's local climate data, soil conditions, and the characteristics of the selected crop to suggest the optimal cultivation method. For example, regarding the timing of sowing, it suggests the optimal time based on local temperature and rainfall, and for the sowing method, it provides detailed procedures such as soil preparation, seed depth, and spacing. Regarding the frequency and amount of watering, it advises on the appropriate timing and amount of watering considering the crop's growth stage and weather conditions. Regarding the type and amount of fertilizer, it suggests the optimal type and timing of fertilization based on the balance of nutrients in the soil and the crop's nutritional requirements. Furthermore, the advice unit can use the generating AI to provide specific cultivation advice to the user. For example, when a user inputs a question through the application, the generating AI provides an appropriate answer to that question, resolving the user's doubts and problems. The advice unit can also monitor the user's cultivation status and update the advice as needed. This allows the advice unit to provide users with continuous and specific cultivation advice, supporting their success in home gardening and horticulture.

[0078] The pest control department implements pest control measures based on advice provided by the advisory department. For example, the department identifies pests that are likely to occur and provides countermeasures. Specifically, the AI ​​generates data considering the types and timing of pest outbreaks in each region to propose optimal pest control measures. For example, it analyzes regional climate data and past pest outbreak data to predict pests that are likely to occur at specific times. Based on this, it proposes preventative measures to the user. For example, during periods when specific pests are likely to occur, it proposes the use of appropriate pesticides or physical control methods (such as setting up nets or traps). The pest control department can also monitor crop growth and environmental conditions to detect and control pests early. For example, it uses sensors to monitor the condition of crop leaves and stems, and if an abnormality is detected, the AI ​​generates data to analyze the cause and propose appropriate countermeasures. Furthermore, the pest control department can learn from the user's past pest control history and propose more effective countermeasures. In this way, the pest control department can provide users with effective pest control measures and maintain crop health and yield.

[0079] The Guide section provides seasonal guides. For example, it guides users on how to select and cultivate crops suitable for each season: spring, summer, autumn, and winter. Specifically, the generating AI considers seasonal climate data and crop characteristics to suggest the optimal crop selection and cultivation methods. For example, it suggests early-maturing vegetables and flowers in spring, heat-tolerant crops in summer, late-harvesting crops in autumn, and cold-tolerant crops in winter. It also provides detailed guidance on seasonal cultivation methods, including soil preparation, sowing timing, watering frequency, and fertilization. Furthermore, the Guide section can use the generating AI to provide seasonally appropriate crop cultivation and maintenance methods. For example, it suggests methods to raise soil temperature in spring, methods to block sunlight in summer, soil care after harvest in autumn, and cold protection measures in winter. In this way, the Guide section can provide users with the optimal cultivation methods for each season and support the success of home gardening and horticulture.

[0080] The monitoring unit monitors crop growth. For example, it uses sensors to monitor crop growth and adjusts watering and fertilizer amounts as needed. Specifically, it uses soil sensors and environmental sensors to collect data such as soil moisture, temperature, nutrient content, and ambient temperature and humidity. This data is analyzed by a generating AI to monitor crop growth in real time. For example, if soil moisture decreases, it automatically waters the plants, and if soil nutrients are deficient, it adds appropriate fertilizer. The monitoring unit can also make adjustments to maintain optimal environmental conditions according to the crop's growth stage. For example, it maintains high humidity during the germination stage, ensures adequate sunlight during the growth stage, and maintains appropriate temperatures during the harvest stage. Furthermore, the monitoring unit can notify users about crop growth status and necessary care. For example, it can send notifications about crop growth status and necessary watering and fertilizer amounts via a smartphone app, supporting users in providing care at the appropriate time. In this way, the monitoring unit can optimally manage crop growth and support success in home gardening and horticulture.

[0081] The sharing section facilitates the sharing of knowledge and information. For example, it allows users to publish collected data and results on the internet and social media, sharing information with other gardeners. Specifically, a generating AI analyzes the growth data and cultivation results of crops grown by users and displays them as visually easy-to-understand graphs and charts. With the user's permission, this data can be posted to social media and dedicated community sites, allowing users to share information with others. For example, sharing information such as the growth process, yield, and effectiveness of pest control measures for specific crops can be helpful to other users. The sharing section also provides features to promote communication among users. For example, users can exchange opinions and advice through comment and messaging functions. Furthermore, the sharing section can use the generating AI to analyze information posted by users and automatically extract and provide information that is useful to other users. In this way, the sharing section can promote the sharing of knowledge and experience among users and revitalize the home gardening and horticulture community.

[0082] The selection unit can suggest the optimal crop based on the user's conditions. For example, the selection unit selects the optimal crop by considering the user's local climate data and soil conditions. The selection unit can also suggest the optimal crop based on the user's conditions using generative AI. For example, the selection unit can suggest low-maintenance crops or rewarding crops based on the user's cultivation experience and lifestyle. This increases the success rate of home gardening by suggesting the optimal crop according to the user's conditions.

[0083] The advice unit can provide specific cultivation advice for selected crops. For example, it can provide detailed advice on the timing and method of sowing seeds, the frequency and amount of watering, and the type and amount of fertilizer. The advice unit can provide specific cultivation advice to the user using generated AI. For example, it can also provide advice on fertilization methods and methods for preventing pests and diseases. In this way, by providing specific cultivation advice, users can grow crops in the appropriate way.

[0084] The pest control department can identify common pests and provide countermeasures. For example, it can identify common pests and provide countermeasures. Using AI generation, the department can propose optimal pest control measures, taking into account the types and timing of pest outbreaks in each region. The department can also advise on things like the type of pesticide to use and the timing of application. By providing pest control measures, it is possible to maintain crop health and increase yields.

[0085] The guide function can provide information on how to grow and maintain crops appropriate for each season. For example, it can guide users on how to select and cultivate crops suitable for each season: spring, summer, autumn, and winter. Using generative AI, the guide function can provide information on how to grow and maintain crops appropriate for each season. For example, it can also advise on how to select and cultivate crops for each season. By providing seasonal guides, users can grow the right crops at the right time.

[0086] The monitoring unit can monitor crop growth in real time and adjust the optimal amount of watering and fertilizer. For example, it can use sensors to monitor crop growth and adjust watering and fertilizer as needed. The monitoring unit can also use AI to monitor crop growth in real time and automatically adjust the optimal amount of watering and fertilizer. Furthermore, the monitoring unit can provide advice on methods for measuring growth and monitoring frequency. This allows for optimal management of crop growth through real-time monitoring.

[0087] The sharing function allows users to publish collected data and results on the internet and social media, sharing information with other gardeners. For example, the sharing function can use generative AI to publish growth data and cultivation results of crops being grown by users on social media, sharing information with other users. The sharing function can also provide advice on the types of information to share and the sharing platforms to use. This facilitates interaction with other gardeners through information sharing, leading to improved knowledge.

[0088] The selection unit can estimate the user's emotions and adjust crop options based on those emotions. For example, if the user is stressed, the selection unit will prioritize suggesting low-maintenance crops. If the user is relaxed, the selection unit may suggest crops that are more challenging to grow. If the user is excited, the selection unit may suggest challenging crops. By providing crop options that match the user's emotions, user satisfaction can be improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The selection unit can analyze the user's past cultivation history and suggest the most suitable crop. For example, the selection unit can suggest crops that can be grown under similar conditions based on crops the user has successfully grown in the past. The selection unit can also suggest crops that the user has failed to grow in the past, for example. The selection unit can also suggest crops suitable for each season based on the user's past cultivation history. This increases the success rate by suggesting the most suitable crop based on past cultivation history. Some or all of the above processing in the selection unit may be performed using generative AI, or it may be performed without using generative AI.

[0090] The selection unit can make suggestions when selecting crops, taking into account the climate data of the user's region. For example, the selection unit can suggest suitable crops based on the annual rainfall of the user's region. The selection unit can also suggest suitable crops based on the average temperature of the user's region. The selection unit can also suggest suitable crops based on the soil conditions of the user's region. In this way, suitable crops can be selected by taking regional climate data into consideration. Some or all of the above processing in the selection unit may be performed using generative AI, or it may be performed without using generative AI.

[0091] The selection unit can estimate the user's emotions and determine the order in which crops are selected based on those emotions. For example, if the user is stressed, the selection unit might suggest low-maintenance crops first. If the user is relaxed, for example, the selection unit might suggest challenging crops first. If the user is excited, for example, the selection unit might suggest difficult crops first. This improves user satisfaction by providing a crop selection order that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The selection unit can make suggestions when selecting crops, taking into account the user's lifestyle and available time. For example, if the user is busy, the selection unit can suggest crops that require little effort. If the user has plenty of time, the selection unit can also suggest crops that require more effort but offer a more enjoyable harvest. If the user only has time on weekends, the selection unit can also suggest crops that require maintenance on weekends. By suggesting crops that suit the user's lifestyle, the success rate of home gardening can be increased. Some or all of the above processing in the selection unit may be performed using generative AI, or it may be performed without using generative AI.

[0093] The selection unit can analyze the user's social media activity when selecting crops and suggest relevant crops. For example, the selection unit can suggest crops that the user frequently talks about on social media. For example, the selection unit can suggest crops grown by gardeners that the user follows. For example, the selection unit can suggest crops that are popular in gardening communities that the user participates in. This allows the user to select crops that match their interests by suggesting crops based on their social media activity. Some or all of the above processing in the selection unit may be performed using generative AI, or it may be performed without using generative AI.

[0094] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is stressed, the advice unit will provide concise and easy-to-understand advice. If the user is relaxed, the advice unit may also provide detailed advice. If the user is excited, the advice unit may also provide advice that includes words of encouragement. This allows for a deeper understanding of the user by providing advice tailored to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The advice unit can provide different advice depending on the stage of crop growth. For example, at the time of sowing, it can advise on how to properly prepare the soil. During the growing season, it can also advise on the frequency of watering and the type of fertilizer to use. During the harvest season, it can also advise on the timing and method of harvesting. By providing advice according to the stage of growth, appropriate cultivation methods can be implemented. Some or all of the above processing in the advice unit may be performed using generation AI, or it may be performed without generation AI.

[0096] The advice unit can adjust the level of detail of the advice based on the user's cultivation experience when providing advice. For example, the advice unit can provide basic advice to beginners. For example, the advice unit can provide slightly more detailed advice to intermediate users. For example, the advice unit can provide expert advice to advanced users. This allows for a deeper understanding of the user by providing advice tailored to their cultivation experience. Some or all of the above processing in the advice unit may be performed using generative AI, or it may be performed without using generative AI.

[0097] The advice unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, the advice unit will provide the most important advice first. If the user is relaxed, the advice unit may provide detailed advice in sequence. If the user is excited, the advice unit may provide advice including words of encouragement first. This improves user satisfaction by providing advice that is prioritized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The advice unit can provide advice while taking into account the weather forecast for the user's area. For example, if rain is expected, the advice unit may advise reducing the frequency of watering. For example, if drought is expected, the advice unit may advise increasing the frequency of watering. For example, if a cold snap is expected, the advice unit may advise on how to protect crops. This allows for the implementation of appropriate cultivation methods by providing advice that takes weather forecasts into account. Some or all of the above processing in the advice unit may be performed using generative AI, or it may be performed without using generative AI.

[0099] The advice unit can analyze the user's social media activity and provide relevant advice when offering it. For example, the advice unit can provide advice on crops that the user is discussing on social media. For example, the advice unit can refer to advice from gardeners that the user follows. For example, the advice unit can provide advice shared within gardening communities that the user participates in. This allows the advice unit to provide advice that is tailored to the user's interests by providing advice based on social media activity. Some or all of the above processing in the advice unit may be performed using generative AI, or it may be performed without using generative AI.

[0100] The pest control unit can estimate the user's emotions and adjust the pest control method based on the estimated emotions. For example, if the user is stressed, the unit can suggest a simple and effective pest control method. If the user is relaxed, the unit can suggest a more detailed pest control method. If the user is excited, the unit can suggest a more challenging pest control method. By providing pest control methods tailored to the user's emotions, user satisfaction can be improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The pest control unit can propose optimal countermeasures by referring to past pest outbreak data when implementing pest control measures. For example, the unit can propose optimal countermeasures based on the types of pests that have occurred in the past. The unit can also propose preventive measures based on the timing of past pest outbreaks. The unit can also propose optimal countermeasures based on the effectiveness of past pest control measures. This enables effective countermeasures by providing optimal pest control measures based on past data. Some or all of the above processing in the pest control unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0102] The pest control unit can provide different pest control methods depending on the type of crop. For example, the unit can propose specific pest control measures for tomatoes. For example, the unit can propose different pest control measures for basil. For example, the unit can propose specialized pest control measures for strawberries. By providing pest control measures tailored to the type of crop, effective control becomes possible. Some or all of the above processing in the pest control unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0103] The pest control unit can estimate the user's emotions and determine the priority of pest control measures based on those emotions. For example, if the user is stressed, the unit will suggest the most effective measures first. If the user is relaxed, the unit can suggest detailed measures in sequence. If the user is excited, the unit can suggest challenging measures first. This improves user satisfaction by providing pest control priorities that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The pest control unit can take into account the climate data of the user's region when implementing pest control measures. For example, in areas with heavy rainfall, the unit can propose pest control measures that are resistant to humidity. In dry areas, the unit can also propose pest control measures that are resistant to drought. In cold regions, the unit can also propose pest control measures that are resistant to cold. By providing pest control measures that take regional climate data into account, effective measures become possible. Some or all of the above processing in the pest control unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0105] The pest control department can analyze a user's social media activity and provide relevant solutions when implementing pest control measures. For example, the department can provide solutions related to pests that the user is discussing on social media. The department can also refer to pest control measures from gardeners that the user follows. The department can also provide pest control measures shared within gardening communities that the user participates in. By providing pest control measures based on social media activity, the department can offer solutions that are tailored to the user's interests. Some or all of the above processing in the pest control department may be performed using generative AI, or it may not be performed using generative AI.

[0106] The guide unit can estimate the user's emotions and adjust the way the guide is presented based on the estimated emotions. For example, if the user is stressed, the guide unit can provide a concise and easy-to-understand guide. For example, if the user is relaxed, the guide unit can provide a detailed guide. For example, if the user is excited, the guide unit can provide a guide that includes words of encouragement. This allows for a deeper understanding of the user by providing a guide that is tailored to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The guide unit can provide optimal guidance by referring to seasonal climate data when providing guidance. For example, in spring, the guide unit can provide guidance on how to grow crops suitable for spring. For example, in summer, the guide unit can provide guidance on how to grow crops suitable for summer. For example, in autumn, the guide unit can provide guidance on how to grow crops suitable for autumn. In this way, by providing guidance based on seasonal climate data, appropriate cultivation methods can be implemented. Some or all of the above processing in the guide unit may be performed using generative AI, or it may be performed without using generative AI.

[0108] The guide unit can adjust the level of detail of the guide based on the user's cultivation experience when providing the guide. For example, the guide unit can provide a basic guide to beginners. For example, the guide unit can provide a slightly more detailed guide to intermediate users. For example, the guide unit can provide a specialized guide to advanced users. This allows for a deeper understanding of the user by providing a guide tailored to their cultivation experience. Some or all of the above processing in the guide unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0109] The guide unit can estimate the user's emotions and determine the priority of the guide based on the estimated emotions. For example, if the user is stressed, the guide unit will provide the most important guide first. If the user is relaxed, the guide unit may provide detailed guides in order. If the user is excited, the guide unit may provide guides containing words of encouragement first. This improves user satisfaction by providing guide priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The guide unit can provide guidance while taking into account the weather forecast for the user's area. For example, if rain is expected, the guide unit can provide guidance on rain countermeasures. For example, if dry conditions are expected, the guide unit can also provide guidance on drought countermeasures. For example, if a cold wave is expected, the guide unit can also provide guidance on cold weather countermeasures. By providing guidance that takes weather forecasts into account, appropriate cultivation methods can be implemented. Some or all of the above processing in the guide unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0111] The guide unit can analyze the user's social media activity when providing guides and provide relevant guides. For example, the guide unit can provide guides about crops that the user is talking about on social media. The guide unit can also refer to guides from gardeners that the user follows. The guide unit can also provide guides shared within gardening communities that the user participates in. By providing guides based on social media activity, the guide unit can provide guides that match the user's interests. Some or all of the above processing in the guide unit may be performed using generative AI, or it may be performed without using generative AI.

[0112] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit can monitor frequently to provide reassurance. For example, if the user is relaxed, the monitoring unit can monitor at a moderate frequency. For example, if the user is excited, the monitoring unit can perform detailed monitoring. This improves user satisfaction by providing a monitoring frequency that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The monitoring unit can apply different monitoring methods depending on the growth stage of the crop during monitoring. For example, during the sowing period, the monitoring unit will focus on monitoring germination. During the growing period, the monitoring unit can also monitor the color and shape of the leaves. During the harvest period, the monitoring unit can also monitor the size and color of the fruit. This allows for the implementation of appropriate cultivation methods by providing monitoring tailored to the growth stage. Some or all of the above-described processes in the monitoring unit may be performed using or without generating AI.

[0114] The monitoring unit can select the optimal monitoring method by referring to the user's cultivation history during monitoring. For example, the monitoring unit can suggest a similar monitoring method based on cultivation methods that the user has successfully used in the past. For example, the monitoring unit can adjust the monitoring method to avoid cultivation methods that the user has failed at in the past. For example, the monitoring unit can suggest an optimal monitoring frequency based on the user's past cultivation history. This allows for the implementation of appropriate cultivation methods by providing monitoring based on cultivation history. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0115] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is stressed, the monitoring unit will perform the most important monitoring items first. If the user is relaxed, the monitoring unit can perform detailed monitoring sequentially. If the user is excited, the monitoring unit can provide monitoring results including words of encouragement first. This improves user satisfaction by providing monitoring priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The monitoring unit can perform monitoring while taking into account the climate data of the user's region. For example, in areas with heavy rainfall, the monitoring unit can propose a monitoring method that is resistant to humidity. For example, in dry areas, the monitoring unit can propose a monitoring method that is resistant to drought. For example, in cold regions, the monitoring unit can propose a monitoring method that is resistant to cold. By providing monitoring that takes into account the local climate data, appropriate cultivation methods can be implemented. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0117] The monitoring unit can analyze the user's social media activity during monitoring and perform relevant monitoring. For example, the monitoring unit can monitor crops that the user is discussing on social media. The monitoring unit can also refer to monitoring methods used by gardeners that the user follows. For example, the monitoring unit can provide monitoring methods shared within gardening communities that the user participates in. This allows the monitoring unit to provide monitoring tailored to the user's interests by providing monitoring based on social media activity. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0118] The sharing function can estimate the user's emotions and adjust the way information is presented based on the estimated emotions. For example, if the user is stressed, the sharing function will share concise and easy-to-understand information. If the user is relaxed, the sharing function may also share detailed information. If the user is excited, the sharing function may also share information that includes words of encouragement. This allows for a deeper understanding of the user by providing information tailored to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The sharing unit can select the most suitable information by referring to the user's cultivation history when sharing. For example, the sharing unit can share similar information based on cultivation methods that the user has successfully used in the past. For example, the sharing unit can also adjust the information to help the user avoid cultivation methods that have failed in the past. For example, the sharing unit can share the most suitable information based on the user's past cultivation history. This allows for the practice of appropriate cultivation methods by providing information sharing based on cultivation history. Some or all of the above processing in the sharing unit may be performed using generative AI, or it may be performed without using generative AI.

[0120] The sharing section can provide information while considering the user's regional climate data. For example, in areas with heavy rainfall, the sharing section can share information on crops resistant to humidity. In dry areas, for example, the sharing section can also share information on crops resistant to drought. In cold regions, for example, the sharing section can also share information on crops resistant to cold. This allows for the implementation of appropriate cultivation methods by providing information that takes regional climate data into consideration. Some or all of the above processing in the sharing section may be performed using generative AI, or it may be performed without using generative AI.

[0121] The sharing function can estimate the user's emotions and prioritize the information to share based on those emotions. For example, if the user is stressed, the sharing function will share the most important information first. If the user is relaxed, the sharing function may share detailed information sequentially. If the user is excited, the sharing function may share information containing words of encouragement first. This can improve user satisfaction by providing information prioritization according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0122] The sharing function can analyze the user's social media activity and provide relevant information when sharing. For example, the sharing function can share information about crops that the user is talking about on social media. The sharing function can also refer to information from gardeners that the user follows. The sharing function can also provide information shared within gardening communities that the user participates in. This allows the function to provide information that is tailored to the user's interests by providing information based on social media activity. Some or all of the above processing in the sharing function may be performed using generative AI or not.

[0123] The shared section can update information by referring to feedback from other gardeners during the sharing process. For example, the shared section updates information based on feedback provided by other gardeners. The shared section can also update information by referring to the success stories of other gardeners. The shared section can also update information by referring to the failure stories of other gardeners. By updating information based on feedback from other gardeners, more accurate and useful information can be provided. Some or all of the above processing in the shared section may be performed using generative AI, or it may be performed without using generative AI.

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

[0125] The home garden and horticultural support system can further monitor the user's health and adjust crop selection and cultivation advice based on that health condition. For example, if a user has allergies, it can suggest crops that do not contain allergens. It can also suggest crops rich in specific nutrients if the user needs them. Furthermore, if a user is feeling unwell, it can suggest low-maintenance crops. This allows the system to support the user's health by providing optimal crop selection and cultivation advice tailored to their individual needs.

[0126] Home gardening and gardening support systems can further suggest crop options that take into account the user's hobbies and interests. For example, if a user enjoys cooking, the system can suggest herbs and vegetables suitable for cooking. If a user is interested in growing flowers, it can suggest ornamental flowers. Furthermore, if a user is interested in ecology, it can suggest environmentally friendly crops and sustainable cultivation methods. This enhances the enjoyment of home gardening by providing crop options tailored to the user's hobbies and interests.

[0127] The home gardening and horticultural support system can further adjust crop selection and cultivation advice to take into account the user's home environment. For example, if the user has pets, it can suggest pet-safe crops. Similarly, if the user has young children, it can suggest crops that are safe for children to touch. Furthermore, if the user cultivates on a balcony or indoors, it can suggest crops that can be grown in limited space. This allows for the provision of optimal crop selection and cultivation advice tailored to the user's home environment, enabling a safe and comfortable home garden.

[0128] The home garden and horticultural support system can further tailor crop selection and cultivation advice based on the user's cultivation goals. For example, if the user wants to maximize yield, it can suggest high-yielding crops. If the user prioritizes quality, it can suggest high-quality crops. Furthermore, if the user wants to harvest to coincide with a specific event, it can suggest crops suitable for that event. In this way, it can support the user in achieving their goals by providing optimal crop selection and cultivation advice tailored to their specific cultivation objectives.

[0129] Home gardening and horticultural support systems can further strengthen connections with users' local communities. For example, they can suggest events for interaction with local gardeners. They can also provide information on local farmers' markets and direct sales outlets. Furthermore, they can offer cultivation advice based on local climate and soil conditions. By strengthening connections with local communities, users can share information with local gardeners and increase the overall success rate of home gardening in the area.

[0130] The home gardening and gardening support system can estimate the user's emotions and adjust the timing of cultivation advice based on those emotions. For example, if the user is stressed, it can provide advice at a time when they can relax. If the user is excited, it can provide advice that can be implemented immediately. Furthermore, if the user is depressed, it can provide advice that includes words of encouragement. By providing cultivation advice at the optimal time according to the user's emotions, it can improve user satisfaction.

[0131] The home gardening and horticultural support system can estimate the user's emotions and adjust its pest control approach based on those emotions. For example, if the user is stressed, it can suggest simple and effective pest control methods. If the user is relaxed, it can suggest more detailed methods. Furthermore, if the user is excited, it can suggest more challenging pest control methods. By providing optimal pest control methods tailored to the user's emotions, it can improve user satisfaction.

[0132] The home gardening and horticultural support system can estimate the user's emotions and adjust the guide content based on those emotions. For example, if the user is stressed, it can provide a concise and easy-to-understand guide. If the user is relaxed, it can provide a detailed guide. Furthermore, if the user is excited, it can provide a guide that includes words of encouragement. This allows for a deeper understanding of the user by providing the most appropriate guide according to their emotions.

[0133] The home gardening and horticultural support system can estimate the user's emotions and adjust the monitoring frequency based on those emotions. For example, if the user is stressed, it can monitor more frequently to provide reassurance. If the user is relaxed, it can monitor at a moderate frequency. Furthermore, if the user is excited, it can monitor in detail. By providing an optimal monitoring frequency tailored to the user's emotions, this system can improve user satisfaction.

[0134] The home gardening and horticultural support system can estimate the user's emotions and adjust the way information is presented based on those emotions. For example, if the user is stressed, it can share concise and easy-to-understand information. If the user is relaxed, it can share more detailed information. Furthermore, if the user is excited, it can share information that includes words of encouragement. This allows for a deeper understanding of the user by providing optimal information tailored to their emotions.

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

[0136] Step 1: The selection section selects the crop. The user inputs the conditions for the crop they want to grow, and the AI ​​generates suggestions for the optimal crop. The selection section can choose the best crop by considering the user's local climate data and soil conditions. It can also suggest low-maintenance or rewarding crops based on the user's cultivation experience and lifestyle. Step 2: The advice unit provides cultivation advice for the crop selected by the selection unit. It provides detailed advice on the timing and method of sowing, the frequency and amount of watering, and the type and amount of fertilizer. Using generation AI, it can provide specific cultivation advice to the user. Step 3: The countermeasures department implements pest control measures based on the advice provided by the advice department. They identify common pests and provide countermeasures. Using generation AI, they can propose optimal pest control measures, taking into account the types and timing of pest outbreaks in each region. Step 4: The guide section provides seasonal guides. It guides users on how to select and cultivate crops suitable for each season: spring, summer, autumn, and winter. Using generation AI, it can provide information on how to grow and maintain crops that are appropriate for each season. Step 5: The monitoring unit monitors crop growth. It uses sensors to monitor the growth status of the crops and adjusts the amount of watering and fertilizer as needed. Using generated AI, it can monitor crop growth in real time and automatically adjust the optimal amount of watering and fertilizer. Step 6: The sharing section involves sharing knowledge and information. Collected data and results are published on the internet and social media to share information with other gardeners. Using generative AI, users can publish growth data and cultivation results of the crops they are growing on social media to share information with other users.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0139] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the selection unit, advice unit, countermeasure unit, guide unit, monitoring unit, and sharing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14, and when the user inputs the conditions for the crop they want to grow, the generating AI suggests the optimal crop. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12, and provides specific cultivation advice for the selected crop. The countermeasure unit is implemented by the control unit 46A of the smart device 14, and identifies pests that are likely to occur and provides countermeasures. The guide unit is implemented by the identification processing unit 290 of the data processing unit 12, and provides crop cultivation and maintenance methods suitable for each season. The monitoring unit is implemented by the control unit 46A of the smart device 14, and monitors crop growth in real time and automatically adjusts the optimal amount of watering and fertilizer. The shared section is implemented, for example, by the specific processing unit 290 of the data processing device 12, which publishes the collected data and results on the internet and social media, allowing information to be shared with other gardeners. The correspondence between each section and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0142] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the selection unit, advice unit, countermeasure unit, guide unit, monitoring unit, and sharing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart glasses 214, and when the user inputs the conditions for the crop they want to grow, the generating AI suggests the optimal crop. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12, and provides specific cultivation advice for the selected crop. The countermeasure unit is implemented by the control unit 46A of the smart glasses 214, and identifies common pests and provides countermeasures. The guide unit is implemented by the identification processing unit 290 of the data processing unit 12, and provides crop cultivation and maintenance methods suitable for each season. The monitoring unit is implemented by the control unit 46A of the smart glasses 214, and monitors crop growth in real time and automatically adjusts the optimal amount of watering and fertilizer. The shared section is implemented, for example, by the specific processing unit 290 of the data processing device 12, which publishes the collected data and results on the internet and social media, allowing information to be shared with other gardeners. The correspondence between each section and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the selection unit, advice unit, countermeasure unit, guide unit, monitoring unit, and sharing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the headset terminal 314, and when the user inputs the conditions for the crop they want to grow, the generating AI suggests the optimal crop. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12, and provides specific cultivation advice for the selected crop. The countermeasure unit is implemented by the control unit 46A of the headset terminal 314, and identifies common pests and provides countermeasures. The guide unit is implemented by the identification processing unit 290 of the data processing unit 12, and provides crop cultivation and maintenance methods suitable for each season. The monitoring unit is implemented by the control unit 46A of the headset terminal 314, and monitors crop growth in real time and automatically adjusts the optimal amount of watering and fertilizer. The shared section is implemented, for example, by the specific processing unit 290 of the data processing device 12, which publishes the collected data and results on the internet and social media, allowing information to be shared with other gardeners. The correspondence between each section and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0179] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0180] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0181] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0182] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0183] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0184] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0185] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0187] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0189] Each of the multiple elements described above, including the selection unit, advice unit, countermeasure unit, guide unit, monitoring unit, and sharing unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the robot 414, and when the user inputs the conditions for the crop they want to grow, the generating AI suggests the optimal crop. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12, and provides specific cultivation advice for the selected crop. The countermeasure unit is implemented by the control unit 46A of the robot 414, and identifies pests that are likely to occur and provides countermeasures. The guide unit is implemented by the identification processing unit 290 of the data processing unit 12, and provides crop cultivation and maintenance methods suitable for each season. The monitoring unit is implemented by the control unit 46A of the robot 414, and monitors crop growth in real time and automatically adjusts the optimal amount of watering and fertilizer. The shared section is implemented, for example, by the specific processing unit 290 of the data processing device 12, which publishes the collected data and results on the internet and social media, allowing information to be shared with other gardeners. The correspondence between each section and the device or control unit is not limited to the example described above and can be modified in various ways.

[0190] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0192] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0193] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0194] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0198] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0199] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0200] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0201] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0202] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0203] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0205] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0206] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0207] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0208] (Note 1) A selection unit that selects crops, An advice unit that provides cultivation advice for the crop selected by the selection unit, A pest control unit that implements pest control measures based on the advice provided by the aforementioned advisory unit, The guide department provides seasonal guides, The monitoring department monitors crop growth, It includes a sharing section for sharing knowledge and information. A system characterized by the following features. (Note 2) The aforementioned selection unit is We suggest the optimal crop based on the user's requirements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned advice section, Provide specific cultivation advice for selected crops. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned countermeasures unit, We identify common pests and provide methods for controlling them. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned guide portion is We provide information on how to grow and maintain crops that are suitable for each season. The system described in Appendix 1, characterized by the features described herein. (Note 6) The monitoring unit, It monitors crop growth in real time and adjusts the optimal amount of watering and fertilizer. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned shared portion is, We publish the collected data and results on the internet and social media to share information with other gardeners. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned selection unit is It estimates the user's emotions and adjusts crop choices based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned selection unit is It analyzes the user's past cultivation history and suggests the optimal crop. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned selection unit is When selecting crops, the system makes suggestions that take into account the user's local climate data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned selection unit is The system estimates the user's emotions and determines the crop selection order based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned selection unit is When selecting crops, we make suggestions that take into account the user's lifestyle and available time. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned selection unit is When selecting crops, the system analyzes the user's social media activity and suggests relevant crops. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned advice section, When providing advice, we offer different advice depending on the stage of crop growth. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice section, When providing advice, the level of detail in the advice is adjusted based on the user's cultivation experience. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, When providing advice, we take into account the weather forecast for the user's area. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, When providing advice, we analyze the user's social media activity and provide relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned countermeasures unit, The system estimates the user's emotions and adjusts pest control methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned countermeasures unit, When implementing pest control measures, we refer to past pest outbreak data to propose the most suitable solutions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned countermeasures unit, When dealing with pests, we offer different control methods depending on the type of crop. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned countermeasures unit, It estimates the user's emotions and determines the priority of pest control based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned countermeasures unit, When implementing pest control measures, we take into account the user's local climate data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned countermeasures unit, When implementing pest control measures, we analyze users' social media activity and provide relevant solutions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned guide portion is The system estimates the user's emotions and adjusts the way the guide is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned guide portion is When providing a guide, we refer to seasonal climate data to provide the most suitable guide. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned guide portion is When providing the guide, adjust the level of detail based on the user's cultivation experience. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned guide portion is It estimates the user's emotions and determines the priority of the guide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned guide portion is When providing a guide, we will take into account the weather forecast for the user's area. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned guide portion is When providing guides, we analyze users' social media activity and provide relevant guides. The system described in Appendix 1, characterized by the features described herein. (Note 32) The monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The monitoring unit, During monitoring, different monitoring methods are applied depending on the stage of crop growth. The system described in Appendix 1, characterized by the features described herein. (Note 34) The monitoring unit, During monitoring, the optimal monitoring method is selected by referring to the user's cultivation history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The monitoring unit, During monitoring, the system takes into account the user's local climate data. The system described in Appendix 1, characterized by the features described herein. (Note 37) The monitoring unit, During monitoring, we analyze users' social media activity and perform relevant monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned shared portion is, It estimates the user's emotions and adjusts how shared information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned shared portion is, When sharing, the system selects the most relevant information by referring to the user's cultivation history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned shared portion is, When sharing, the information will be provided taking into account the user's local climate data. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned shared portion is, It estimates the user's emotions and prioritizes the information to share based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned shared portion is, When sharing, the system analyzes the user's social media activity and provides relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned shared portion is, When sharing, update the information by referring to feedback from other gardeners. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A selection unit that selects crops, An advice unit that provides cultivation advice for the crop selected by the selection unit, A pest control unit that implements pest control measures based on the advice provided by the aforementioned advisory unit, The guide department provides seasonal guides, The monitoring department monitors crop growth, It includes a sharing section for sharing knowledge and information. A system characterized by the following features.

2. The aforementioned selection unit is We suggest the optimal crop based on the user's requirements. The system according to feature 1.

3. The aforementioned advice section, Provide specific cultivation advice for selected crops. The system according to feature 1.

4. The aforementioned countermeasures unit, We identify common pests and provide methods for controlling them. The system according to feature 1.

5. The aforementioned guide portion is We provide information on how to grow and maintain crops that are suitable for each season. The system according to feature 1.

6. The monitoring unit, It monitors crop growth in real time and adjusts the optimal amount of watering and fertilizer. The system according to feature 1.

7. The aforementioned shared portion is, We publish the collected data and results on the internet and social media to share information with other gardeners. The system according to feature 1.

8. The aforementioned selection unit is It estimates the user's emotions and adjusts crop choices based on those estimated emotions. The system according to feature 1.

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