system

Abstract

The system according to this embodiment aims to analyze weather and soil data in a home garden and provide optimal gardening advice. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a diagnostic unit. The reception unit receives input information about the home garden. The analysis unit analyzes weather data and soil data based on the information received by the reception unit. The provision unit provides optimal gardening advice based on the results analyzed by the analysis unit. The diagnostic unit analyzes photographs of plants to diagnose diseases and suggest modifications to cultivation methods.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to provide optimal gardening advice by utilizing weather and soil data in a home garden, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze weather and soil data in a home garden and provide optimal gardening advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a diagnostic unit. The reception unit receives input information about the home garden. The analysis unit analyzes weather data and soil data based on the information received by the reception unit. The provision unit provides optimal gardening advice based on the results analyzed by the analysis unit. The diagnostic unit analyzes photographs of plants to diagnose diseases and suggest modifications to cultivation methods. [Effects of the Invention]

[0007] The system according to this embodiment can analyze weather and soil data in a home garden and provide optimal gardening advice. [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 manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The home gardening assistant system according to an embodiment of the present invention is a state-of-the-art AI assistant for people who enjoy home gardening. This home gardening assistant system is designed to suit various scenarios, such as apartment balconies, detached house gardens, and even indoor gardening. The home gardening assistant system supports all tasks related to home gardening, including planting, fertilizing, watering, harvesting schedule management, and pest and disease control. The home gardening assistant system uses generative AI to analyze weather and soil data, monitor plant growth, and provide optimal gardening advice in real time. The home gardening assistant system also diagnoses diseases and suggests adjustments to cultivation methods by taking photos of plants, providing an optimal home gardening experience tailored to the user's living environment. The home gardening assistant system supports home gardening in limited spaces and also suggests ways to enjoy gardening without making a mess on balconies or indoors. For example, the home gardening assistant system receives information from the user about their home garden. For example, the user inputs information such as which plants they want to grow and what kind of environment they want to grow them in. This information is input into the generative AI. Next, the home gardening assistant system uses a generating AI to analyze input information and provide optimal gardening advice based on weather and soil data. For example, the generating AI analyzes weather data to suggest the optimal planting time and watering schedule. It also analyzes soil data to suggest the optimal amount and type of fertilizer. Furthermore, the home gardening assistant system allows users to take photos of their plants, and the generating AI will diagnose diseases and suggest improvements to cultivation methods. For example, the generating AI analyzes plant photos to detect signs of disease and suggest appropriate countermeasures. If there are problems with cultivation methods, the generating AI will suggest corrections and propose the optimal cultivation method. This system allows users to enjoy home gardening efficiently. The home gardening assistant system also supports home gardening in limited spaces and suggests ways to enjoy gardening without making a mess on balconies or indoors. For example, it suggests dedicated gardening sets and stain-resistant mats to keep balconies and indoors clean. In this way, by utilizing generating AI, it is possible to support all tasks related to home gardening and improve the user's home gardening experience.This allows the home gardening assistant system to support all tasks related to home gardening and improve the user's home gardening experience.

[0029] The home gardening assistant system according to this embodiment comprises a reception unit, an analysis unit, a supply unit, and a diagnostic unit. The reception unit inputs information about the home garden. This information includes, but is not limited to, plant species, cultivation methods, and soil conditions. The reception unit can, for example, input information about the plants the user wants to grow and the environment. The analysis unit analyzes weather data and soil data based on the information input by the reception unit. This weather data includes, but is not limited to, temperature, precipitation, and humidity. The analysis unit can, for example, analyze weather data and suggest the optimal planting time and watering timing. The analysis unit can also analyze soil data and suggest the optimal amount and type of fertilizer. This soil data includes, for example, pH value, nutrient content, and moisture content. The supply unit provides optimal gardening advice based on the results analyzed by the analysis unit. This gardening advice includes, but is not limited to, planting time, watering frequency, and fertilizer type. The supply unit can, for example, suggest the optimal planting time and watering timing based on weather data. It can also suggest the optimal amount and type of fertilizer based on soil data. The diagnostic unit analyzes plant photographs to diagnose diseases and suggest modifications to cultivation methods. For example, the diagnostic unit can analyze plant photographs to detect signs of disease and suggest appropriate countermeasures. Furthermore, if there are problems with cultivation methods, the diagnostic unit can suggest modifications and propose optimal cultivation methods. As a result, the home gardening assistant system according to this embodiment can efficiently analyze information related to home gardening and provide optimal gardening advice.

[0030] The reception desk receives information about the home garden. This information includes, but is not limited to, plant species, cultivation methods, and soil conditions. Users can input the name and variety of the plant they want to grow, detailed cultivation methods, and current soil conditions. For example, if a user wants to grow tomatoes, they would input the tomato variety, planned planting date, current soil pH value, and nutrient content. Furthermore, the reception desk organizes the information entered by the user and sends it to the analysis department. The user interface is intuitive and easy to use, designed so that even beginners can easily input information. For example, dropdown menus and checkboxes are used to allow users to easily make selections. In addition, a voice input function is included, allowing users to input information by voice even when their hands are dirty or they want to save time. In this way, the reception desk provides an environment in which users can easily and quickly input the necessary information, making home garden management more efficient.

[0031] The analysis unit analyzes weather and soil data based on information entered by the reception unit. Weather data includes, but is not limited to, temperature, precipitation, and humidity. For example, the analysis unit can analyze weather data to suggest the optimal planting time and watering schedule. Specifically, it predicts future temperature and precipitation fluctuations based on past weather data and weather forecasts, providing optimal conditions for plant growth. The analysis unit can also analyze soil data to suggest the optimal amount and type of fertilizer. Soil data includes, but is not limited to, pH value, nutrient content, and moisture content. Based on this data, the analysis unit calculates the balance of nutrients necessary for plant growth and suggests the appropriate type and amount of fertilizer. Furthermore, the analysis unit uses AI to analyze data and provide more accurate suggestions. For example, it can use machine learning algorithms to learn optimal cultivation methods from past data and provide optimal advice based on new data. As a result, the analysis unit can optimize the management of home gardens and support the healthy growth of plants based on the information entered by the user.

[0032] The service provider provides optimal gardening advice based on the results analyzed by the analysis unit. This advice may include, but is not limited to, planting times, watering frequency, and fertilizer types. For example, the service provider can suggest optimal planting and watering times based on weather data. Specifically, it can identify the best time for plant growth based on temperature and rainfall forecasts and advise planting during that period. The service provider can also suggest optimal fertilizer amounts and types based on soil data. For example, if the soil pH is acidic, it might suggest using alkaline fertilizer. Furthermore, the service provider provides users with specific action instructions, such as weekly watering or monthly fertilizer application. The service provider can also collect user feedback to continuously improve the accuracy of its advice. For example, by inputting data on actual work performed and its results, the service provider analyzes this data and incorporates it into future advice. This allows the service provider to provide users with optimal gardening advice and support the success of their home gardens.

[0033] The diagnostic unit analyzes plant photos to diagnose diseases and suggest improvements to cultivation methods. For example, it can analyze plant photos to detect signs of disease and suggest appropriate countermeasures. Specifically, it utilizes AI-based image recognition technology to detect abnormalities appearing on plant leaves and stems. For instance, if leaves change color or spots appear, it analyzes the symptoms and identifies the type of disease. The diagnostic unit can also suggest improvements and propose optimal cultivation methods if there are problems with cultivation. For example, if watering is too frequent or the amount of fertilizer is inappropriate, it will point out the problem and suggest appropriate countermeasures. Furthermore, the diagnostic unit allows users to upload photos they have taken to the cloud and share them with other users and experts. This allows users to receive advice from other home gardening enthusiasts and experts. The diagnostic unit also saves past diagnostic results and countermeasure history for future reference. In this way, the diagnostic unit can continuously monitor the health of plants and support the success of home gardening by suggesting appropriate countermeasures.

[0034] The reception unit allows users to input information about the plants and environment they wish to cultivate. For example, the reception unit can input information about the plants and environment the user wishes to cultivate, such as the type of plant, cultivation location, and sunlight conditions. This allows users to efficiently input information about the plants and environment they wish to cultivate. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the information entered by the user into a generating AI and have the generating AI perform the analysis of the information.

[0035] The analysis unit can analyze weather data and propose the optimal planting time and watering timing. For example, the analysis unit analyzes weather data and proposes the optimal planting time and watering timing. For example, temperature, precipitation, and local climate may be used as criteria. In this way, by analyzing weather data, the optimal planting time and watering timing can be proposed. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the analysis unit can input weather data into a generation AI and have the generation AI propose the optimal planting time and watering timing.

[0036] The analysis unit can analyze soil data and propose the optimal amount and type of fertilizer. For example, the analysis unit can analyze soil data and propose the optimal amount and type of fertilizer. For example, the nutrient content of the soil and the type of plant may be used as criteria. In this way, by analyzing soil data, the optimal amount and type of fertilizer can be proposed. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input soil data into a generating AI and have the generating AI propose the optimal amount and type of fertilizer.

[0037] The diagnostic unit can analyze plant photographs, detect signs of disease, and propose appropriate countermeasures. For example, the diagnostic unit can analyze plant photographs, detect signs of disease, and propose appropriate countermeasures. For example, leaf discoloration, spots, and wilting may be detected as signs of disease. Thus, by analyzing plant photographs, signs of disease can be detected and appropriate countermeasures can be proposed. Some or all of the above processing in the diagnostic unit may be performed using, for example, a generative AI, or without a generative AI. For example, the diagnostic unit can input plant photographs into a generative AI and have the generative AI perform the detection of disease signs and propose countermeasures.

[0038] The diagnostic unit can suggest corrective actions and propose optimal cultivation methods if problems are found in the cultivation process. For example, if problems are found in the cultivation process, the diagnostic unit can suggest corrective actions and propose optimal cultivation methods. For example, problems such as excessive or insufficient watering, excessive or insufficient fertilizer, and inappropriate sunlight conditions may be detected. As a result, if problems are found in the cultivation process, the diagnostic unit can suggest corrective actions and propose optimal cultivation methods. Some or all of the above processing in the diagnostic unit may be performed using, for example, a generating AI, or without a generating AI. For example, the diagnostic unit can input information about the cultivation process into a generating AI and have the generating AI execute corrective action suggestions and proposals for optimal cultivation methods.

[0039] The service provider can offer suggestions to support home gardening in limited spaces. For example, it can offer suggestions to support home gardening in limited spaces, such as balconies, indoors, or small gardens. By offering suggestions to support home gardening in limited spaces, the service provider improves the user's home gardening experience. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input information about the limited space into a generative AI and have the generative AI execute support suggestions.

[0040] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display plant and environmental information that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest plants to grow in a particular season based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI suggest the optimal input method.

[0041] The reception unit can add features to support voice and gesture input when users are entering information. For example, the reception unit can allow users to input the name of a plant or the environment in which they want to grow it using their voice. It can also allow users to select the type of plant or the place where they want to grow it using gestures. Furthermore, the reception unit can allow users to combine voice and gestures to input information more intuitively. This streamlines the user's input process by supporting voice and gesture input. Some or all of the above processes in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input voice data and gesture data into a generating AI and have the generating AI execute the input support functions.

[0042] The input unit can prioritize inputting information about plants and the environment specific to the region, taking into account the user's geographical location. For example, the input unit can display plants that are easy to grow in the region as candidates based on the user's current location. The input unit can also display input items that take into account the region's specific climate conditions, based on the user's geographical location. Furthermore, the input unit can suggest plants suitable for the region's soil characteristics, based on the user's geographical location. In this way, by considering the user's geographical location, information about plants and the environment specific to the region can be input efficiently. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input geographical location information into a generating AI and have the generating AI suggest region-specific information.

[0043] The reception desk can analyze the user's social media activity and automatically input relevant plant and environmental information. For example, the reception desk can automatically complete input fields based on information about plants shared by the user on social media. It can also analyze the user's social media posts and suggest relevant gardening information. Furthermore, the reception desk can automatically set input fields based on information about gardening accounts that the user follows. This allows for efficient input of relevant plant and environmental information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input social media activity data into a generating AI and have the generating AI input the relevant information.

[0044] The analysis unit can predict future weather by referring to past weather patterns when analyzing weather data. For example, the analysis unit can predict the weather for the next week based on past weather data. The analysis unit can also analyze past weather patterns and predict seasonal weather trends. Furthermore, the analysis unit can predict weather conditions suitable for specific plants based on past weather data. In this way, future weather can be predicted by referring to past weather patterns. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past weather data into a generative AI and have the generative AI perform a future weather forecast.

[0045] The analysis unit can propose the optimal type of fertilizer when analyzing soil data, taking into account the microbial activity in the soil. For example, the analysis unit can analyze the types and activity of microorganisms in the soil and propose the optimal fertilizer. Furthermore, the analysis unit can propose a fertilizer suitable for a specific plant based on the microbial activity in the soil. In addition, the analysis unit can propose the timing of fertilizer application, taking into account the microbial activity in the soil. This allows for the proposal of the optimal type of fertilizer by considering the microbial activity in the soil. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input soil data into a generating AI and have the generating AI propose the optimal type of fertilizer.

[0046] The analysis unit can perform analyses of weather data while considering regional climate characteristics. For example, the analysis unit can suggest the optimal planting time based on the user's regional climate characteristics. It can also suggest watering timing considering the user's regional climate characteristics. Furthermore, the analysis unit can suggest cultivation methods suitable for specific plants based on the user's regional climate characteristics. This allows for more accurate analysis results by considering regional climate characteristics. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input regional climate characteristic data into a generative AI and have the generative AI perform the analysis.

[0047] The analysis unit can improve the accuracy of its analysis by referring to the user's past cultivation history when analyzing soil data. For example, the analysis unit can analyze soil changes based on the user's past cultivation history. The analysis unit can also suggest the optimal type of fertilizer by referring to the user's past cultivation history. Furthermore, the analysis unit can suggest methods for improving the soil based on the user's past cultivation history. In this way, the accuracy of the analysis is improved by referring to the user's past cultivation history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input past cultivation history data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0048] The service provider can adjust the level of detail of the advice it provides according to the user's experience level. For example, it can provide basic advice to beginner users, detailed advice to intermediate users, and expert advice to advanced users. By adjusting the level of detail of the advice according to the user's experience level, it can provide appropriate advice. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input user experience level data into a generative AI and have the generative AI adjust the level of detail of the advice.

[0049] The service provider can add seasonal gardening information to the advice it provides. For example, in spring, it can advise on how to grow plants suitable for spring. In summer, it can also advise on watering methods suitable for summer. Furthermore, in autumn, it can advise on fertilizing methods suitable for autumn. By adding seasonal gardening information, it can provide more appropriate advice. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or not using a generating AI. For example, the service provider can input seasonal gardening information into a generating AI and have the generating AI add the advice.

[0050] The service provider can add region-specific information to the advice it provides, taking into account the user's geographical location. For example, the service provider can advise on the optimal planting time based on the climate characteristics of the user's region. It can also advise on the timing of watering, taking into account the climate characteristics of the user's region. Furthermore, it can advise on the appropriate cultivation methods for specific plants, based on the climate characteristics of the user's region. In this way, region-specific information can be provided by taking into account the user's geographical location. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input geographical location information into a generative AI and have the generative AI perform the addition of region-specific information.

[0051] The service provider can add relevant information to the advice it provides by analyzing the user's social media activity. For example, the service provider can provide advice based on information about plants that the user has shared on social media. It can also analyze the content of the user's social media posts and provide relevant gardening information as advice. Furthermore, the service provider can provide advice based on information about gardening accounts that the user follows. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input social media activity data into a generative AI and have the generative AI perform the addition of relevant information.

[0052] The diagnostic unit can improve the accuracy of its diagnosis by referring to a database of past diseases when analyzing plant photographs. For example, the diagnostic unit can detect signs of plant disease based on the database of past diseases. It can also suggest countermeasures for specific diseases by referring to the database of past diseases. Furthermore, the diagnostic unit can predict the progression of a disease based on the database of past diseases. In this way, the accuracy of the diagnosis is improved by referring to the database of past diseases. Some or all of the above processes in the diagnostic unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the diagnostic unit can input the database of past diseases into a generative AI and have the generative AI perform the task of improving the accuracy of the diagnosis.

[0053] The diagnostic unit can analyze plant photographs and perform diagnoses while considering the plant's growth stage. For example, the diagnostic unit can detect signs of disease based on the plant's growth stage. It can also suggest appropriate countermeasures while considering the plant's growth stage. Furthermore, the diagnostic unit can predict the progression of disease based on the plant's growth stage. This allows for more accurate diagnoses by considering the plant's growth stage. Some or all of the above processing in the diagnostic unit may be performed using, for example, a generative AI, or without a generative AI. For example, the diagnostic unit can input plant growth stage data into a generative AI and have the generative AI perform the diagnosis.

[0054] The diagnostic unit can perform diagnoses by considering regional climatic characteristics when analyzing plant photographs. For example, the diagnostic unit can detect signs of disease based on the user's regional climatic characteristics. It can also suggest appropriate countermeasures considering the user's regional climatic characteristics. Furthermore, the diagnostic unit can predict the progression of disease based on the user's regional climatic characteristics. This allows for more accurate diagnoses by considering regional climatic characteristics. Some or all of the above processing in the diagnostic unit may be performed using, for example, a generative AI, or without a generative AI. For example, the diagnostic unit can input regional climatic data into a generative AI and have the generative AI perform the diagnosis.

[0055] The diagnostic unit can improve the accuracy of its diagnosis by referring to the user's past cultivation history when analyzing plant photographs. For example, the diagnostic unit can detect signs of disease based on the user's past cultivation history. It can also suggest appropriate countermeasures by referring to the user's past cultivation history. Furthermore, the diagnostic unit can predict the progression of disease based on the user's past cultivation history. In this way, the accuracy of the diagnosis is improved by referring to the user's past cultivation history. Some or all of the above processes in the diagnostic unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the diagnostic unit can input past cultivation history data into a generative AI and have the generative AI perform the task of improving the accuracy of the diagnosis.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The home gardening assistant system can also feature a smart connectivity unit that supports gardening activities by integrating with other smart devices in the user's home. For example, the smart connectivity unit can work with a smart speaker to provide gardening advice via voice commands. It can also integrate with smart lighting to automatically adjust lighting conditions to optimize plant growth. Furthermore, it can integrate with a smart irrigation system to automatically water plants based on weather and soil data. This integration with other smart devices in the home provides a more efficient and convenient gardening experience.

[0058] The home gardening assistant system can also include a progress management unit that records the user's gardening activities and visualizes their progress. For example, the progress management unit can record the gardening tasks performed by the user and display them in a calendar format. It can also visualize the growth of plants with photos and graphs, allowing the user to track their progress. Furthermore, the progress management unit can display the user's level of achievement against their set goals and provide feedback to maintain motivation. This allows the system to enhance the enjoyment of gardening by recording the user's activities and visualizing their progress.

[0059] The home gardening assistant system can also include an education section that provides educational content to support users' gardening activities. For example, the education section could offer online courses to learn basic gardening knowledge and techniques. It could also provide detailed guides and tutorials on plants that users wish to grow. Furthermore, the education section could provide the latest research findings and trend information related to gardening. This would allow users to deepen their gardening knowledge and engage in gardening activities more effectively.

[0060] The home gardening assistant system can also include a reminder unit that provides reminder functions to support the user's gardening activities. The reminder unit notifies the user of important tasks such as planting, watering, and fertilizing. It can also send reminders at appropriate times based on a schedule set by the user. Furthermore, the reminder unit can provide a function to automatically remind users of tasks they tend to forget. This ensures that users remember to perform important gardening tasks and maintain the health of their plants.

[0061] The home gardening assistant system can also include a customization section that provides customization features to further support the user's gardening activities. For example, the customization section allows users to customize system settings according to the plants they want to grow and their environment. Furthermore, the customization section can adjust the interface design and display content according to the user's preferences. In addition, the customization section can provide a function for users to set priorities for specific gardening tasks. This allows users to customize the system according to their needs and preferences, enabling them to engage in gardening activities more effectively.

[0062] The home gardening assistant system can also include an eco-suggestion section that offers eco-friendly suggestions to support the user's gardening activities. For example, the eco-suggestion section might suggest the use of environmentally friendly fertilizers and pesticides. It could also recommend the use of recyclable gardening materials. Furthermore, the eco-suggestion section could provide advice to users on adopting energy-efficient gardening methods. This allows users to engage in environmentally conscious gardening activities and achieve sustainable home gardening.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk inputs information about the home garden. This information includes, for example, the type of plant, cultivation method, and soil condition. Users can input information about the plants they want to grow and the environment they want to cultivate. Step 2: The analysis unit analyzes weather and soil data based on the information entered by the reception unit. Weather data includes temperature, precipitation, and humidity, while soil data includes pH value, nutrient content, and moisture content. The analysis unit analyzes the weather data to suggest the optimal planting time and watering schedule. It also analyzes the soil data to suggest the optimal amount and type of fertilizer. Step 3: The service department provides optimal gardening advice based on the results analyzed by the analysis department. This advice includes planting time, watering frequency, and fertilizer type. The service department suggests the optimal planting time and watering timing based on weather data, and the optimal amount and type of fertilizer based on soil data. Step 4: The diagnostic department analyzes plant photographs to diagnose diseases and suggest improvements to cultivation methods. The diagnostic department analyzes plant photographs to detect signs of disease and proposes appropriate countermeasures. If there are problems with cultivation methods, it will also suggest improvements and propose the optimal cultivation method.

[0065] (Example of form 2) The home gardening assistant system according to an embodiment of the present invention is a state-of-the-art AI assistant for people who enjoy home gardening. This home gardening assistant system is designed to suit various scenarios, such as apartment balconies, detached house gardens, and even indoor gardening. The home gardening assistant system supports all tasks related to home gardening, including planting, fertilizing, watering, harvesting schedule management, and pest and disease control. The home gardening assistant system uses generative AI to analyze weather and soil data, monitor plant growth, and provide optimal gardening advice in real time. The home gardening assistant system also diagnoses diseases and suggests adjustments to cultivation methods by taking photos of plants, providing an optimal home gardening experience tailored to the user's living environment. The home gardening assistant system supports home gardening in limited spaces and also suggests ways to enjoy gardening without making a mess on balconies or indoors. For example, the home gardening assistant system receives information from the user about their home garden. For example, the user inputs information such as which plants they want to grow and what kind of environment they want to grow them in. This information is input into the generative AI. Next, the home gardening assistant system uses a generating AI to analyze input information and provide optimal gardening advice based on weather and soil data. For example, the generating AI analyzes weather data to suggest the optimal planting time and watering schedule. It also analyzes soil data to suggest the optimal amount and type of fertilizer. Furthermore, the home gardening assistant system allows users to take photos of their plants, and the generating AI will diagnose diseases and suggest improvements to cultivation methods. For example, the generating AI analyzes plant photos to detect signs of disease and suggest appropriate countermeasures. If there are problems with cultivation methods, the generating AI will suggest corrections and propose the optimal cultivation method. This system allows users to enjoy home gardening efficiently. The home gardening assistant system also supports home gardening in limited spaces and suggests ways to enjoy gardening without making a mess on balconies or indoors. For example, it suggests dedicated gardening sets and stain-resistant mats to keep balconies and indoors clean. In this way, by utilizing generating AI, it is possible to support all tasks related to home gardening and improve the user's home gardening experience.This allows the home gardening assistant system to support all tasks related to home gardening and improve the user's home gardening experience.

[0066] The home gardening assistant system according to this embodiment comprises a reception unit, an analysis unit, a supply unit, and a diagnostic unit. The reception unit inputs information about the home garden. This information includes, but is not limited to, plant species, cultivation methods, and soil conditions. The reception unit can, for example, input information about the plants the user wants to grow and the environment. The analysis unit analyzes weather data and soil data based on the information input by the reception unit. This weather data includes, but is not limited to, temperature, precipitation, and humidity. The analysis unit can, for example, analyze weather data and suggest the optimal planting time and watering timing. The analysis unit can also analyze soil data and suggest the optimal amount and type of fertilizer. This soil data includes, for example, pH value, nutrient content, and moisture content. The supply unit provides optimal gardening advice based on the results analyzed by the analysis unit. This gardening advice includes, but is not limited to, planting time, watering frequency, and fertilizer type. The supply unit can, for example, suggest the optimal planting time and watering timing based on weather data. It can also suggest the optimal amount and type of fertilizer based on soil data. The diagnostic unit analyzes plant photographs to diagnose diseases and suggest modifications to cultivation methods. For example, the diagnostic unit can analyze plant photographs to detect signs of disease and suggest appropriate countermeasures. Furthermore, if there are problems with cultivation methods, the diagnostic unit can suggest modifications and propose optimal cultivation methods. As a result, the home gardening assistant system according to this embodiment can efficiently analyze information related to home gardening and provide optimal gardening advice.

[0067] The reception desk receives information about the home garden. This information includes, but is not limited to, plant species, cultivation methods, and soil conditions. Users can input the name and variety of the plant they want to grow, detailed cultivation methods, and current soil conditions. For example, if a user wants to grow tomatoes, they would input the tomato variety, planned planting date, current soil pH value, and nutrient content. Furthermore, the reception desk organizes the information entered by the user and sends it to the analysis department. The user interface is intuitive and easy to use, designed so that even beginners can easily input information. For example, dropdown menus and checkboxes are used to allow users to easily make selections. In addition, a voice input function is included, allowing users to input information by voice even when their hands are dirty or they want to save time. In this way, the reception desk provides an environment in which users can easily and quickly input the necessary information, making home garden management more efficient.

[0068] The analysis unit analyzes weather and soil data based on information entered by the reception unit. Weather data includes, but is not limited to, temperature, precipitation, and humidity. For example, the analysis unit can analyze weather data to suggest the optimal planting time and watering schedule. Specifically, it predicts future temperature and precipitation fluctuations based on past weather data and weather forecasts, providing optimal conditions for plant growth. The analysis unit can also analyze soil data to suggest the optimal amount and type of fertilizer. Soil data includes, but is not limited to, pH value, nutrient content, and moisture content. Based on this data, the analysis unit calculates the balance of nutrients necessary for plant growth and suggests the appropriate type and amount of fertilizer. Furthermore, the analysis unit uses AI to analyze data and provide more accurate suggestions. For example, it can use machine learning algorithms to learn optimal cultivation methods from past data and provide optimal advice based on new data. As a result, the analysis unit can optimize the management of home gardens and support the healthy growth of plants based on the information entered by the user.

[0069] The service provider provides optimal gardening advice based on the results analyzed by the analysis unit. This advice may include, but is not limited to, planting times, watering frequency, and fertilizer types. For example, the service provider can suggest optimal planting and watering times based on weather data. Specifically, it can identify the best time for plant growth based on temperature and rainfall forecasts and advise planting during that period. The service provider can also suggest optimal fertilizer amounts and types based on soil data. For example, if the soil pH is acidic, it might suggest using alkaline fertilizer. Furthermore, the service provider provides users with specific action instructions, such as weekly watering or monthly fertilizer application. The service provider can also collect user feedback to continuously improve the accuracy of its advice. For example, by inputting data on actual work performed and its results, the service provider analyzes this data and incorporates it into future advice. This allows the service provider to provide users with optimal gardening advice and support the success of their home gardens.

[0070] The diagnostic unit analyzes plant photos to diagnose diseases and suggest improvements to cultivation methods. For example, it can analyze plant photos to detect signs of disease and suggest appropriate countermeasures. Specifically, it utilizes AI-based image recognition technology to detect abnormalities appearing on plant leaves and stems. For instance, if leaves change color or spots appear, it analyzes the symptoms and identifies the type of disease. The diagnostic unit can also suggest improvements and propose optimal cultivation methods if there are problems with cultivation. For example, if watering is too frequent or the amount of fertilizer is inappropriate, it will point out the problem and suggest appropriate countermeasures. Furthermore, the diagnostic unit allows users to upload photos they have taken to the cloud and share them with other users and experts. This allows users to receive advice from other home gardening enthusiasts and experts. The diagnostic unit also saves past diagnostic results and countermeasure history for future reference. In this way, the diagnostic unit can continuously monitor the health of plants and support the success of home gardening by suggesting appropriate countermeasures.

[0071] The reception unit allows users to input information about the plants and environment they wish to cultivate. For example, the reception unit can input information about the plants and environment the user wishes to cultivate, such as the type of plant, cultivation location, and sunlight conditions. This allows users to efficiently input information about the plants and environment they wish to cultivate. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the information entered by the user into a generating AI and have the generating AI perform the analysis of the information.

[0072] The analysis unit can analyze weather data and propose the optimal planting time and watering timing. For example, the analysis unit analyzes weather data and proposes the optimal planting time and watering timing. For example, temperature, precipitation, and local climate may be used as criteria. In this way, by analyzing weather data, the optimal planting time and watering timing can be proposed. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the analysis unit can input weather data into a generation AI and have the generation AI propose the optimal planting time and watering timing.

[0073] The analysis unit can analyze soil data and propose the optimal amount and type of fertilizer. For example, the analysis unit can analyze soil data and propose the optimal amount and type of fertilizer. For example, the nutrient content of the soil and the type of plant may be used as criteria. In this way, by analyzing soil data, the optimal amount and type of fertilizer can be proposed. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input soil data into a generating AI and have the generating AI propose the optimal amount and type of fertilizer.

[0074] The diagnostic unit can analyze plant photographs, detect signs of disease, and propose appropriate countermeasures. For example, the diagnostic unit can analyze plant photographs, detect signs of disease, and propose appropriate countermeasures. For example, leaf discoloration, spots, and wilting may be detected as signs of disease. Thus, by analyzing plant photographs, signs of disease can be detected and appropriate countermeasures can be proposed. Some or all of the above processing in the diagnostic unit may be performed using, for example, a generative AI, or without a generative AI. For example, the diagnostic unit can input plant photographs into a generative AI and have the generative AI perform the detection of disease signs and propose countermeasures.

[0075] The diagnostic unit can suggest corrective actions and propose optimal cultivation methods if problems are found in the cultivation process. For example, if problems are found in the cultivation process, the diagnostic unit can suggest corrective actions and propose optimal cultivation methods. For example, problems such as excessive or insufficient watering, excessive or insufficient fertilizer, and inappropriate sunlight conditions may be detected. As a result, if problems are found in the cultivation process, the diagnostic unit can suggest corrective actions and propose optimal cultivation methods. Some or all of the above processing in the diagnostic unit may be performed using, for example, a generating AI, or without a generating AI. For example, the diagnostic unit can input information about the cultivation process into a generating AI and have the generating AI execute corrective action suggestions and proposals for optimal cultivation methods.

[0076] The service provider can offer suggestions to support home gardening in limited spaces. For example, it can offer suggestions to support home gardening in limited spaces, such as balconies, indoors, or small gardens. By offering suggestions to support home gardening in limited spaces, the service provider improves the user's home gardening experience. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input information about the limited space into a generative AI and have the generative AI execute support suggestions.

[0077] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. This makes the user's input process more comfortable by adjusting the input interface design based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the design of the input interface.

[0078] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display plant and environmental information that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest plants to grow in a particular season based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI suggest the optimal input method.

[0079] The reception unit can add features to support voice and gesture input when users are entering information. For example, the reception unit can allow users to input the name of a plant or the environment in which they want to grow it using their voice. It can also allow users to select the type of plant or the place where they want to grow it using gestures. Furthermore, the reception unit can allow users to combine voice and gestures to input information more intuitively. This streamlines the user's input process by supporting voice and gesture input. Some or all of the above processes in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input voice data and gesture data into a generating AI and have the generating AI execute the input support functions.

[0080] The reception desk can estimate the user's emotions and determine the priority of inputs based on the estimated emotions. For example, if the user is stressed, the reception desk will display the simplest input items first. If the user is relaxed, the reception desk can also prioritize displaying more detailed input items. Furthermore, if the user is in a hurry, the reception desk can display only the minimum necessary input items. This streamlines the user's input process by prioritizing inputs based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of inputs.

[0081] The input unit can prioritize inputting information about plants and the environment specific to the region, taking into account the user's geographical location. For example, the input unit can display plants that are easy to grow in the region as candidates based on the user's current location. The input unit can also display input items that take into account the region's specific climate conditions, based on the user's geographical location. Furthermore, the input unit can suggest plants suitable for the region's soil characteristics, based on the user's geographical location. In this way, by considering the user's geographical location, information about plants and the environment specific to the region can be input efficiently. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input geographical location information into a generating AI and have the generating AI suggest region-specific information.

[0082] The reception desk can analyze the user's social media activity and automatically input relevant plant and environmental information. For example, the reception desk can automatically complete input fields based on information about plants shared by the user on social media. It can also analyze the user's social media posts and suggest relevant gardening information. Furthermore, the reception desk can automatically set input fields based on information about gardening accounts that the user follows. This allows for efficient input of relevant plant and environmental information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input social media activity data into a generating AI and have the generating AI input the relevant information.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. This helps the user understand the results by adjusting the display method of the analysis results based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0084] The analysis unit can predict future weather by referring to past weather patterns when analyzing weather data. For example, the analysis unit can predict the weather for the next week based on past weather data. The analysis unit can also analyze past weather patterns and predict seasonal weather trends. Furthermore, the analysis unit can predict weather conditions suitable for specific plants based on past weather data. In this way, future weather can be predicted by referring to past weather patterns. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past weather data into a generative AI and have the generative AI perform a future weather forecast.

[0085] The analysis unit can propose the optimal type of fertilizer when analyzing soil data, taking into account the microbial activity in the soil. For example, the analysis unit can analyze the types and activity of microorganisms in the soil and propose the optimal fertilizer. Furthermore, the analysis unit can propose a fertilizer suitable for a specific plant based on the microbial activity in the soil. In addition, the analysis unit can propose the timing of fertilizer application, taking into account the microbial activity in the soil. This allows for the proposal of the optimal type of fertilizer by considering the microbial activity in the soil. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input soil data into a generating AI and have the generating AI propose the optimal type of fertilizer.

[0086] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying the most important analysis results. It can also prioritize displaying detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can display only the minimum necessary analysis results. This helps the user understand the system by prioritizing the analysis results based on 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. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI prioritize the analysis results.

[0087] The analysis unit can perform analyses of weather data while considering regional climate characteristics. For example, the analysis unit can suggest the optimal planting time based on the user's regional climate characteristics. It can also suggest watering timing considering the user's regional climate characteristics. Furthermore, the analysis unit can suggest cultivation methods suitable for specific plants based on the user's regional climate characteristics. This allows for more accurate analysis results by considering regional climate characteristics. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input regional climate characteristic data into a generative AI and have the generative AI perform the analysis.

[0088] The analysis unit can improve the accuracy of its analysis by referring to the user's past cultivation history when analyzing soil data. For example, the analysis unit can analyze soil changes based on the user's past cultivation history. The analysis unit can also suggest the optimal type of fertilizer by referring to the user's past cultivation history. Furthermore, the analysis unit can suggest methods for improving the soil based on the user's past cultivation history. In this way, the accuracy of the analysis is improved by referring to the user's past cultivation history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input past cultivation history data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0089] The service provider can estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, if the user is nervous, the service provider can provide simple and easy-to-understand advice. If the user is relaxed, the service provider can also provide advice that includes more detailed information. Furthermore, if the user is in a hurry, the service provider can provide concise advice. This helps the user understand the advice by adjusting its presentation based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the presentation of the advice.

[0090] The service provider can adjust the level of detail of the advice it provides according to the user's experience level. For example, it can provide basic advice to beginner users, detailed advice to intermediate users, and expert advice to advanced users. By adjusting the level of detail of the advice according to the user's experience level, it can provide appropriate advice. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input user experience level data into a generative AI and have the generative AI adjust the level of detail of the advice.

[0091] The service provider can add seasonal gardening information to the advice it provides. For example, in spring, it can advise on how to grow plants suitable for spring. In summer, it can also advise on watering methods suitable for summer. Furthermore, in autumn, it can advise on fertilizing methods suitable for autumn. By adding seasonal gardening information, it can provide more appropriate advice. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or not using a generating AI. For example, the service provider can input seasonal gardening information into a generating AI and have the generating AI add the advice.

[0092] The service provider can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, the service provider will prioritize displaying the most important advice. If the user is relaxed, the service provider can also prioritize displaying more detailed advice. Furthermore, if the user is in a hurry, the service provider can display only the essential advice. This helps the user understand the situation by prioritizing advice based on 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. Some or all of the above-described processes in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI prioritize advice.

[0093] The service provider can add region-specific information to the advice it provides, taking into account the user's geographical location. For example, the service provider can advise on the optimal planting time based on the climate characteristics of the user's region. It can also advise on the timing of watering, taking into account the climate characteristics of the user's region. Furthermore, it can advise on the appropriate cultivation methods for specific plants, based on the climate characteristics of the user's region. In this way, region-specific information can be provided by taking into account the user's geographical location. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input geographical location information into a generative AI and have the generative AI perform the addition of region-specific information.

[0094] The service provider can add relevant information to the advice it provides by analyzing the user's social media activity. For example, the service provider can provide advice based on information about plants that the user has shared on social media. It can also analyze the content of the user's social media posts and provide relevant gardening information as advice. Furthermore, the service provider can provide advice based on information about gardening accounts that the user follows. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input social media activity data into a generative AI and have the generative AI perform the addition of relevant information.

[0095] The diagnostic unit can estimate the user's emotions and adjust the display method of the diagnostic results based on the estimated emotions. For example, if the user is tense, the diagnostic unit can provide a simple and highly visible display method. If the user is relaxed, the diagnostic unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the diagnostic unit can provide a concise display method. This helps the user understand the results by adjusting the display method of the diagnostic results based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the diagnostic unit may be performed using a generative AI, or not using a generative AI. For example, the diagnostic unit can input the user's emotion data into a generative AI and have the generative AI adjust the display method of the diagnostic results.

[0096] The diagnostic unit can improve the accuracy of its diagnosis by referring to a database of past diseases when analyzing plant photographs. For example, the diagnostic unit can detect signs of plant disease based on the database of past diseases. It can also suggest countermeasures for specific diseases by referring to the database of past diseases. Furthermore, the diagnostic unit can predict the progression of a disease based on the database of past diseases. In this way, the accuracy of the diagnosis is improved by referring to the database of past diseases. Some or all of the above processes in the diagnostic unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the diagnostic unit can input the database of past diseases into a generative AI and have the generative AI perform the task of improving the accuracy of the diagnosis.

[0097] The diagnostic unit can analyze plant photographs and perform diagnoses while considering the plant's growth stage. For example, the diagnostic unit can detect signs of disease based on the plant's growth stage. It can also suggest appropriate countermeasures while considering the plant's growth stage. Furthermore, the diagnostic unit can predict the progression of disease based on the plant's growth stage. This allows for more accurate diagnoses by considering the plant's growth stage. Some or all of the above processing in the diagnostic unit may be performed using, for example, a generative AI, or without a generative AI. For example, the diagnostic unit can input plant growth stage data into a generative AI and have the generative AI perform the diagnosis.

[0098] The diagnostic unit can estimate the user's emotions and prioritize diagnostic results based on the estimated emotions. For example, if the user is stressed, the diagnostic unit will prioritize displaying the most important diagnostic results. It can also prioritize displaying detailed diagnostic results if the user is relaxed. Furthermore, if the user is in a hurry, the diagnostic unit can display only the essential diagnostic results. This helps the user understand the situation by prioritizing diagnostic results based on 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. Some or all of the above-described processes in the diagnostic unit may be performed using, for example, a generative AI, or not. For example, the diagnostic unit can input user emotion data into a generative AI and have the generative AI prioritize the diagnostic results.

[0099] The diagnostic unit can perform diagnoses by considering regional climatic characteristics when analyzing plant photographs. For example, the diagnostic unit can detect signs of disease based on the user's regional climatic characteristics. It can also suggest appropriate countermeasures considering the user's regional climatic characteristics. Furthermore, the diagnostic unit can predict the progression of disease based on the user's regional climatic characteristics. This allows for more accurate diagnoses by considering regional climatic characteristics. Some or all of the above processing in the diagnostic unit may be performed using, for example, a generative AI, or without a generative AI. For example, the diagnostic unit can input regional climatic data into a generative AI and have the generative AI perform the diagnosis.

[0100] The diagnostic unit can improve the accuracy of its diagnosis by referring to the user's past cultivation history when analyzing plant photographs. For example, the diagnostic unit can detect signs of disease based on the user's past cultivation history. It can also suggest appropriate countermeasures by referring to the user's past cultivation history. Furthermore, the diagnostic unit can predict the progression of disease based on the user's past cultivation history. In this way, the accuracy of the diagnosis is improved by referring to the user's past cultivation history. Some or all of the above processes in the diagnostic unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the diagnostic unit can input past cultivation history data into a generative AI and have the generative AI perform the task of improving the accuracy of the diagnosis.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The home gardening assistant system can also include a health management unit that monitors the user's health status and suggests optimal gardening activities. For example, the health management unit can monitor the user's heart rate and stress level and suggest appropriate gardening activities. If the user is stressed, it might suggest growing plants with relaxing properties. It could also suggest light tasks if the user's heart rate is high, and slightly more strenuous tasks if their heart rate is stable. Furthermore, the health management unit can adjust the time and frequency of gardening activities based on the user's health status. This allows the system to support the user's health maintenance by suggesting optimal gardening activities tailored to their individual needs.

[0103] The home gardening assistant system can also include a hobby integration section that suggests gardening activities based on the user's hobbies and interests. For example, if the user enjoys music, the hobby integration section can suggest playlists to listen to while gardening. If the user enjoys cooking, it can also suggest recipes using the plants they've grown. Furthermore, if the user enjoys photography, the hobby integration section can provide advice on taking photos to document their gardening progress. This makes the gardening experience more enjoyable by suggesting gardening activities tailored to the user's hobbies and interests.

[0104] The home gardening assistant system can also feature a smart connectivity unit that supports gardening activities by integrating with other smart devices in the user's home. For example, the smart connectivity unit can work with a smart speaker to provide gardening advice via voice commands. It can also integrate with smart lighting to automatically adjust lighting conditions to optimize plant growth. Furthermore, it can integrate with a smart irrigation system to automatically water plants based on weather and soil data. This integration with other smart devices in the home provides a more efficient and convenient gardening experience.

[0105] The home gardening assistant system can also include a progress management unit that records the user's gardening activities and visualizes their progress. For example, the progress management unit can record the gardening tasks performed by the user and display them in a calendar format. It can also visualize the growth of plants with photos and graphs, allowing the user to track their progress. Furthermore, the progress management unit can display the user's level of achievement against their set goals and provide feedback to maintain motivation. This allows the system to enhance the enjoyment of gardening by recording the user's activities and visualizing their progress.

[0106] The home gardening assistant system can also include a community integration section that allows users to share their gardening activities with other users and form a community. This community integration section can, for example, provide a platform for users to share photos and growth records of plants they have grown with other users. It can also provide a forum for users to exchange information and advice about gardening. Furthermore, it can provide information on gardening events and workshops that users can participate in. This allows users to interact with other gardening enthusiasts, share information, and further expand the enjoyment of gardening.

[0107] The home gardening assistant system can further incorporate a gamification section to enhance the enjoyment of users' gardening activities. This gamification section could, for example, provide a system where users earn points and receive rewards for their gardening activities. It could also allow users to earn badges and titles by achieving goals they set. Furthermore, the gamification section could provide a ranking function for users to compete with others. In this way, gamifying gardening activities can increase user motivation and enhance the enjoyment.

[0108] The home gardening assistant system can also include an education section that provides educational content to support users' gardening activities. For example, the education section could offer online courses to learn basic gardening knowledge and techniques. It could also provide detailed guides and tutorials on plants that users wish to grow. Furthermore, the education section could provide the latest research findings and trend information related to gardening. This would allow users to deepen their gardening knowledge and engage in gardening activities more effectively.

[0109] The home gardening assistant system can also include a reminder unit that provides reminder functions to support the user's gardening activities. The reminder unit notifies the user of important tasks such as planting, watering, and fertilizing. It can also send reminders at appropriate times based on a schedule set by the user. Furthermore, the reminder unit can provide a function to automatically remind users of tasks they tend to forget. This ensures that users remember to perform important gardening tasks and maintain the health of their plants.

[0110] The home gardening assistant system can also include a customization section that provides customization features to further support the user's gardening activities. For example, the customization section allows users to customize system settings according to the plants they want to grow and their environment. Furthermore, the customization section can adjust the interface design and display content according to the user's preferences. In addition, the customization section can provide a function for users to set priorities for specific gardening tasks. This allows users to customize the system according to their needs and preferences, enabling them to engage in gardening activities more effectively.

[0111] The home gardening assistant system can also include an eco-suggestion section that offers eco-friendly suggestions to support the user's gardening activities. For example, the eco-suggestion section might suggest the use of environmentally friendly fertilizers and pesticides. It could also recommend the use of recyclable gardening materials. Furthermore, the eco-suggestion section could provide advice to users on adopting energy-efficient gardening methods. This allows users to engage in environmentally conscious gardening activities and achieve sustainable home gardening.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk inputs information about the home garden. This information includes, for example, the type of plant, cultivation method, and soil condition. Users can input information about the plants they want to grow and the environment they want to cultivate. Step 2: The analysis unit analyzes weather and soil data based on the information entered by the reception unit. Weather data includes temperature, precipitation, and humidity, while soil data includes pH value, nutrient content, and moisture content. The analysis unit analyzes the weather data to suggest the optimal planting time and watering schedule. It also analyzes the soil data to suggest the optimal amount and type of fertilizer. Step 3: The service department provides optimal gardening advice based on the results analyzed by the analysis department. This advice includes planting time, watering frequency, and fertilizer type. The service department suggests the optimal planting time and watering timing based on weather data, and the optimal amount and type of fertilizer based on soil data. Step 4: The diagnostic department analyzes plant photographs to diagnose diseases and suggest improvements to cultivation methods. The diagnostic department analyzes plant photographs to detect signs of disease and proposes appropriate countermeasures. If there are problems with cultivation methods, it will also suggest improvements and propose the optimal cultivation method.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and diagnostic unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, and allows the user to input information about the plants they want to grow and the environment. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and analyzes weather data and soil data. The provision unit is implemented by, for example, the control unit 46A of the smart device 14, and provides optimal gardening advice based on the analysis results. The diagnostic unit takes a picture of the plant using the camera 42 of the smart device 14, and the identification processing unit 290 of the data processing unit 12 diagnoses diseases and suggests modifications to cultivation methods. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and diagnostic unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, and allows the user to input information about the plants they want to grow and the environment. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes weather data and soil data. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides optimal gardening advice based on the analysis results. The diagnostic unit takes a picture of the plant using the camera 42 of the smart glasses 214, and the identification processing unit 290 of the data processing unit 12 diagnoses diseases and suggests modifications to cultivation methods. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and diagnostic unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to input information about the plants they want to grow and their environment. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes weather data and soil data. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides optimal gardening advice based on the analysis results. The diagnostic unit takes pictures of plants using the camera 42 of the headset terminal 314, and the identification processing unit 290 of the data processing unit 12 diagnoses diseases and suggests modifications to cultivation methods. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and diagnostic unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, and can receive input from the user regarding the plants they wish to grow and the environment. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and analyzes weather data and soil data. The provision unit is implemented by, for example, the control unit 46A of the robot 414, and provides optimal gardening advice based on the analysis results. The diagnostic unit takes pictures of plants using the camera 42 of the robot 414, and the identification processing unit 290 of the data processing unit 12 diagnoses diseases and suggests modifications to cultivation methods. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A reception area for entering information about home gardening, An analysis unit analyzes weather data and soil data based on the information entered by the reception unit, A provisioning unit provides optimal gardening advice based on the results analyzed by the aforementioned analysis unit, It includes a diagnostic unit that analyzes plant photographs to diagnose diseases and suggest modifications to cultivation methods. A system characterized by the following features. (Note 2) The aforementioned reception unit is Users enter information about the plants they want to grow and the environment they want to create. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze weather data to suggest the optimal planting time and watering schedule. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We analyze soil data and propose the optimal amount and type of fertilizer. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned diagnostic unit, We analyze plant photos, detect signs of disease, and suggest appropriate countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned diagnostic unit, If there are problems with the way you are raising your pet, we will suggest corrections and propose the optimal way to raise them. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, We propose solutions to support home gardening in limited spaces. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Add functionality to support voice input and gesture input when users are inputting data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is The system prioritizes input of region-specific plant and environmental information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is Analyzes users' social media activity and automatically inputs relevant plant and environmental information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing weather data, past weather patterns are referenced to predict future weather. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing soil data, we propose the optimal type of fertilizer considering the microbial activity in the soil. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing weather data, the analysis should take into account the local climate characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, When analyzing soil data, the system improves the accuracy of the analysis by referencing the user's past cultivation history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, 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 21) The aforementioned supply unit is, The level of detail in the advice provided will be adjusted according to the user's experience level. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, We will add seasonal gardening information to the advice we provide. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, 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 24) The aforementioned supply unit is, The advice provided will include region-specific information that takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We will analyze users' social media activity and add relevant information to the advice we provide. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned diagnostic unit, It estimates the user's emotions and adjusts how the diagnostic results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned diagnostic unit, When analyzing plant photographs, we improve the accuracy of diagnoses by referring to a database of past diseases. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned diagnostic unit, When analyzing plant photographs, the diagnosis should take into account the plant's growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned diagnostic unit, It estimates the user's emotions and prioritizes the diagnostic results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned diagnostic unit, When analyzing plant photographs, the diagnosis should take into account the local climate characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned diagnostic unit, When analyzing plant photos, the system improves diagnostic accuracy by referencing the user's past cultivation history. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0186] 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 reception area for entering information about home gardening, An analysis unit analyzes weather data and soil data based on the information entered by the reception unit, A provisioning unit provides optimal gardening advice based on the results analyzed by the aforementioned analysis unit, It includes a diagnostic unit that analyzes plant photographs to diagnose diseases and suggest modifications to cultivation methods. A system characterized by the following features.

2. The aforementioned reception unit is Users enter information about the plants they want to grow and the environment they want to create. The system according to feature 1.

3. The aforementioned analysis unit, We analyze weather data to suggest the optimal planting time and watering schedule. The system according to feature 1.

4. The aforementioned analysis unit, We analyze soil data and propose the optimal amount and type of fertilizer. The system according to feature 1.

5. The aforementioned diagnostic unit, We analyze plant photos, detect signs of disease, and suggest appropriate countermeasures. The system according to feature 1.

6. The aforementioned diagnostic unit, If there are problems with the way you are raising your pet, we will suggest corrections and propose the optimal way to raise them. The system according to feature 1.

7. The aforementioned supply unit is, We propose solutions to support home gardening in limited spaces. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system according to feature 1.

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