system

Abstract

The system according to this embodiment aims to analyze weather data and soil data and provide optimal gardening advice. [Solution] The system according to the embodiment comprises an analysis unit, a provision unit, a diagnosis unit, and a suggestion unit. The analysis unit analyzes weather data and soil data. The provision unit provides gardening advice based on the data analyzed by the analysis unit. The diagnosis unit analyzes plant photographs to diagnose diseases and suggest modifications to cultivation methods. The suggestion unit provides suggestions to support gardening in limited spaces.

Description

Technical Field

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

Background Art

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

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a supply unit, a diagnosis unit, and a suggestion unit. The analysis unit analyzes weather data and soil data. The supply unit provides gardening advice based on the data analyzed by the analysis unit. The diagnosis unit analyzes plant photographs to diagnose diseases and suggest modifications to cultivation methods. The suggestion unit provides suggestions to support gardening in limited spaces. [Effects of the Invention]

[0007] The system according to this embodiment can analyze weather data and soil data to 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The HomeGardenHeroAI agent according to an embodiment of the present invention is a cutting-edge AI assistant tool for people who enjoy home gardening. This agent supports a variety of environments, including apartment balconies, detached house gardens, and even indoor gardening, and assists with all tasks such as planting, fertilizing, watering, harvesting schedule management, and pest and disease control. The HomeGardenHeroAI agent analyzes weather and soil data, monitors plant growth, and provides optimal gardening advice in real time. Furthermore, by taking pictures of plants, it can diagnose diseases and suggest modifications to cultivation methods, providing a home gardening experience optimized for the user's living environment. It supports gardening in limited spaces and suggests methods to avoid soiling balconies and indoors, thereby assisting efficient garden management tailored to the user's lifestyle. For example, the HomeGardenHeroAI agent can instruct watering timing based on weather data or suggest types and amounts of fertilizer based on soil data. By taking pictures of plants, it can detect changes in leaf color and shape and suggest appropriate countermeasures. It also suggests compact planters and hydroponic systems to support efficient garden management. This enables HomeGardenHeroAI agent to provide efficient home garden management tailored to the user's lifestyle. Users can enjoy home gardening with AI support, experiencing the joy of connecting with nature and harvesting. HomeGardenHeroAI agent can support all tasks in home gardening, including planting, fertilizing, watering, harvesting schedule management, and pest and disease control.

[0029] The HomeGardenHeroAI agent according to this embodiment comprises an analysis unit, a provision unit, a diagnostic unit, and a suggestion unit. The analysis unit analyzes weather data and soil data. For example, the analysis unit can analyze weather data such as temperature, precipitation, and wind speed. The analysis unit can also analyze soil data such as soil pH, nutrient content, and moisture content. Based on the weather data and soil data, the analysis unit monitors the growth status of plants. The provision unit provides gardening advice based on the data analyzed by the analysis unit. For example, the provision unit can instruct the timing of watering based on weather data. The provision unit can also suggest the type and amount of fertilizer based on soil data. The provision unit provides gardening advice in real time. The diagnostic unit analyzes photographs of plants to diagnose diseases and suggest modifications to cultivation methods. For example, the diagnostic unit can detect changes in the color and shape of plant leaves to detect the occurrence of pests and diseases early. The diagnostic unit can also analyze the health status of plants and suggest appropriate countermeasures. The proposal department offers suggestions to support gardening in limited spaces. For example, it can propose compact planters or hydroponic systems. It can also propose methods that do not soil balconies or indoor spaces. As a result, the HomeGardenHeroAI agent according to the embodiment can support all tasks in home gardening, such as planting, fertilizing, watering, managing harvest schedules, and pest and disease control.

[0030] The analysis unit analyzes weather and soil data. For example, it can analyze weather data such as temperature, precipitation, and wind speed. Specifically, it collects weather data from weather sensors and the internet, and analyzes this data using statistical methods and machine learning algorithms. It analyzes temperature fluctuation patterns, precipitation forecasts, and the effects of wind speed in detail to identify optimal environmental conditions for plant growth. The analysis unit can also analyze soil data such as soil pH, nutrient content, and moisture content. It collects data in real time using soil sensors and evaluates soil health based on this data. For example, it determines whether the soil pH is suitable for plant growth and suggests adjusting agents such as lime or sulfur as needed. Regarding nutrient content, it analyzes the balance of major nutrients such as nitrogen, phosphorus, and potassium to optimize the type and amount of fertilizer. In moisture content analysis, it detects dry or wet soil conditions and suggests an appropriate irrigation schedule. Based on this weather and soil data, the analysis unit monitors plant growth. For example, it suggests shading measures if the temperature is too high and instructs additional watering if precipitation is low. If the soil is deficient in nutrients, we advise adding appropriate fertilizers. This allows the analysis unit to perform data analysis to maintain plant health and provide an optimal growing environment.

[0031] The service provider offers gardening advice based on data analyzed by the analysis unit. For example, the service provider can instruct users on the timing of watering based on weather data. Specifically, it calculates the amount of water plants need based on temperature and precipitation forecast data and notifies the user of the optimal watering time. For example, it may instruct users to water early in the morning or in the evening on hot, dry days, and advise them to refrain from watering on days when rain is expected. The service provider can also suggest the type and amount of fertilizer based on soil data. It analyzes the nutrient content of the soil and selects the optimal fertilizer according to the growth stage and type of plant. For example, it may suggest adding nitrogen fertilizer if nitrogen is deficient, and advise adding phosphorus fertilizer if phosphorus is deficient. The service provider provides gardening advice in real time. Users can access the application via their smartphones or tablets to receive advice based on the latest weather and soil data. Furthermore, the service provider can learn the user's gardening history and preferences to provide individually customized advice. For example, based on the environmental conditions preferred by specific plants and past success stories, we can suggest more effective gardening methods. This allows the service provider to support users in managing their home gardens efficiently and effectively.

[0032] The diagnostic unit analyzes plant photos to diagnose diseases and suggest modifications to cultivation methods. For example, the diagnostic unit can detect changes in the color and shape of plant leaves, enabling early detection of pest and disease outbreaks. Specifically, when a user uploads a photo of a plant taken with their smartphone, the diagnostic unit uses image recognition technology to detect abnormalities in the color and shape of the leaves. For example, yellowing leaves may indicate nitrogen deficiency, and spots on leaves may suggest the presence of pests or diseases. If the diagnostic unit detects these abnormalities, it will suggest appropriate measures to the user. For example, if there is a nitrogen deficiency, it will advise adding nitrogen fertilizer, and if pests or diseases are suspected, it will suggest using appropriate pesticides. The diagnostic unit can also analyze the plant's health and suggest modifications to cultivation methods. For example, if a plant is absorbing too much water, it will instruct the user to reduce watering frequency, and conversely, if there is insufficient water, it will advise increasing watering frequency. Furthermore, the diagnostic unit also provides suggestions for cultivation methods tailored to the plant's growth stage. For example, it can instruct users to maintain appropriate temperature and humidity during the germination stage and advise providing sufficient sunlight and nutrients during the growth stage. In this way, the diagnostic unit can support users in succeeding in their home gardens by maintaining the health of the plants and suggesting optimal growing methods.

[0033] The proposal department offers suggestions to support gardening in limited spaces. For example, they can propose compact planters and hydroponic systems. Specifically, they propose the optimal gardening method according to the user's living environment and space constraints. For example, for limited spaces such as verandas and balconies, they propose vertically installed planters or wall-mounted planters to provide a way to make effective use of space. For indoor gardening, they propose hydroponic systems and introduce a method of growing plants without using soil. Hydroponic systems are clean, easy to maintain, and allow for efficient plant growth even in limited spaces. Furthermore, the proposal department can also propose methods to prevent soiling of verandas and indoor spaces. For example, they propose methods such as installing a tray under the planter to prevent water leakage or using waterproof sheets suitable for indoor gardening. This allows users to comfortably enjoy gardening even in limited spaces. The proposal department can also provide customized suggestions according to the user's lifestyle and preferences. For example, for busy users, they propose an automatic irrigation system, providing a way to grow plants without much effort. Furthermore, for eco-conscious users, we will propose planters made from recycled materials and organic fertilizers, and introduce environmentally friendly gardening methods. This will allow the proposal department to support users in enjoying home gardening efficiently and comfortably, even in limited spaces.

[0034] The analysis unit can instruct the timing of watering based on weather data. For example, the analysis unit analyzes weather data and instructs the timing of watering based on soil dryness and weather forecasts. For example, the analysis unit can instruct to increase the frequency of watering when temperatures are high and the soil is dry. Conversely, the analysis unit can instruct to decrease the frequency of watering when there is heavy rainfall. The analysis unit analyzes weather data in real time and instructs the optimal timing of watering. This enables appropriate watering by instructing the timing of watering based on weather data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input weather data into a generating AI, and the generating AI can instruct the timing of watering.

[0035] The analysis unit can suggest the type and amount of fertilizer based on soil data. For example, the analysis unit can analyze the soil's pH value and nutrient content and suggest the optimal type and amount of fertilizer. For example, if the soil's pH value is low, the analysis unit can suggest adding lime. It can also suggest adding appropriate fertilizer if the soil's nutrient content is insufficient. The analysis unit analyzes soil data in real time and suggests the optimal type and amount of fertilizer. This enables the appropriate use of fertilizer by suggesting the type and amount of fertilizer based on soil data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input soil data into a generating AI, which can then suggest the type and amount of fertilizer.

[0036] The diagnostic unit can analyze the health of plants and detect the occurrence of pests and diseases at an early stage. For example, the diagnostic unit can detect changes in the color and shape of plant leaves to detect the occurrence of pests and diseases at an early stage. For example, if the leaves are turning yellow, the diagnostic unit can suggest a nitrogen deficiency. The diagnostic unit can also suggest the occurrence of pests and diseases if there are spots on the leaves. The diagnostic unit analyzes the health of plants in real time and proposes appropriate countermeasures. This allows for appropriate countermeasures by analyzing the health of plants and detecting the occurrence of pests and diseases at an early stage. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input a photograph of a plant into a generating AI, which can then detect the occurrence of pests and diseases at an early stage.

[0037] The proposal unit can propose compact planters and hydroponic systems. For example, the proposal unit can propose compact planters to support gardening in limited spaces. For example, the proposal unit can propose wall-mounted planters and vertical planters. The proposal unit can also propose hydroponic systems. For example, the proposal unit can propose NFT systems and drip systems. The proposal unit can propose efficient cultivation methods to support gardening in limited spaces. This allows the proposal unit to propose compact planters and hydroponic systems to support gardening in limited spaces. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user living environment data into a generating AI, which can then propose the optimal planter or cultivation system.

[0038] The analysis unit can predict future weather by referring to past weather patterns when analyzing weather data. For example, the analysis unit analyzes seasonal weather patterns based on weather data from the past 10 years. For example, the analysis unit makes short-term weather forecasts by comparing past weather data with current weather conditions. The analysis unit predicts future weather by referring to past weather patterns. For example, the analysis unit analyzes long-term weather patterns and proposes seasonal gardening plans. This makes it possible to predict future weather more accurately by referring to past weather patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past weather data into a generating AI, which can then predict future weather.

[0039] The analysis unit can improve the accuracy of soil analysis by considering microbial activity in the soil. For example, the analysis unit can analyze the types and activity levels of microorganisms in the soil to evaluate the health of the soil. For example, the analysis unit can analyze the impact of microbial activity on plant growth and suggest the optimal type and amount of fertilizer. The analysis unit monitors microbial activity in the soil and issues an alert if an anomaly occurs. By improving the accuracy of analysis by considering microbial activity in the soil, more accurate soil analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input soil data into a generating AI, which can then improve the accuracy of the analysis by considering microbial activity.

[0040] The analysis unit can perform analyses of weather data while considering regional climate characteristics. For example, the analysis unit can analyze the climate characteristics of each region and propose an optimal gardening schedule. For example, the analysis unit can propose appropriate plant species based on regional climate characteristics. The analysis unit can adjust the timing of watering and fertilizing while considering regional climate characteristics. This makes it possible to provide more appropriate gardening advice by performing analyses while considering regional climate characteristics. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input regional climate data into a generating AI, which can then perform the analysis while considering climate characteristics.

[0041] The analysis unit can improve the accuracy of soil analysis by performing a detailed analysis of the soil's chemical components during soil data analysis. For example, the analysis unit can perform a detailed analysis of major chemical components in the soil (such as nitrogen, phosphorus, and potassium). For example, the analysis unit can measure the soil's pH value and suggest the optimal type and amount of fertilizer. The analysis unit monitors the soil's chemical components and issues an alert if an anomaly occurs. This allows for more accurate soil analysis by improving the accuracy of the analysis through a detailed analysis of the soil's chemical components. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input soil data into a generating AI, which can then perform a detailed analysis of the chemical components to improve the accuracy of the analysis.

[0042] The service provider can customize the advice given when providing gardening advice, according to the plant's growth stage. For example, during the germination stage, the service provider advises on appropriate watering and light conditions. During the growth stage, the service provider advises on the appropriate type and amount of fertilizer. During the harvest stage, the service provider advises on the timing and method of harvesting. By customizing the advice according to the plant's growth stage, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input plant growth data into a generating AI, which can then provide advice according to the growth stage.

[0043] The service provider can provide optimal gardening advice by referring to the user's past gardening history. For example, the service provider can provide optimal advice based on data of plants the user has grown in the past. For example, the service provider can provide advice by referring to successful methods from the user's past gardening history. The service provider can analyze the user's past gardening history and suggest areas for improvement. By referring to the user's past gardening history and providing optimal advice, more effective gardening becomes possible. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's gardening history data into a generating AI, which can then provide optimal advice.

[0044] The service provider can provide gardening advice while considering the characteristics of the user's residential area. For example, the service provider can suggest the most suitable plant species considering the climatic characteristics of the user's residential area. For example, the service provider can suggest the appropriate type and amount of fertilizer considering the soil characteristics of the user's residential area. The service provider can suggest the timing of watering based on the weather forecast for the user's residential area. By providing advice while considering the characteristics of the user's residential area, more appropriate gardening advice becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's residential area data into a generating AI, and the generating AI can provide advice while considering the characteristics of the residential area.

[0045] The service provider can offer gardening advice tailored to the user's lifestyle. For example, if the user is busy, the service provider can suggest low-maintenance gardening methods. If the user is a beginner, the service provider can suggest easy-to-grow plants. If the user is experienced, the service provider can suggest challenging gardening methods. By providing advice tailored to the user's lifestyle, more effective gardening becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's lifestyle data into a generating AI, which can then provide advice tailored to that lifestyle.

[0046] The diagnostic unit can improve diagnostic accuracy by referring to past pest and disease data when analyzing plant photographs. For example, the diagnostic unit can detect the occurrence of plant diseases and pests early based on past pest and disease data. For example, the diagnostic unit can improve diagnostic accuracy by comparing past pest and disease data with the current state of the plant. The diagnostic unit can propose appropriate countermeasures by referring to past pest and disease data. In this way, more accurate diagnoses become possible by improving diagnostic accuracy by referring to past pest and disease data. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input past pest and disease data into a generating AI, which can then improve diagnostic accuracy.

[0047] The diagnostic unit can perform diagnoses while considering the plant's growth history when analyzing its health status. For example, the diagnostic unit can analyze the plant's growth history and evaluate its health status. For example, the diagnostic unit can predict the occurrence of diseases and pests based on the plant's growth history. The diagnostic unit can propose appropriate countermeasures while considering the plant's growth history. This allows for a more accurate assessment of the plant's health status by considering its growth history during the diagnosis. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input plant growth history data into a generating AI, which can then perform diagnoses while considering the growth history.

[0048] The diagnostic unit can improve diagnostic accuracy by analyzing photographs of plants taken under different lighting conditions. For example, the diagnostic unit analyzes photographs of plants taken under different lighting conditions to assess their health. For example, the diagnostic unit improves diagnostic accuracy by considering the effects of differences in lighting conditions. The diagnostic unit compares photographs taken under different lighting conditions to detect the occurrence of diseases and pests at an early stage. This allows for more accurate diagnoses by improving diagnostic accuracy through the analysis of photographs taken under different lighting conditions. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input photographic data of plants taken under different lighting conditions into a generating AI, which can then improve diagnostic accuracy.

[0049] The diagnostic unit can apply different diagnostic algorithms to each plant species when analyzing the health status of plants. For example, the diagnostic unit applies the optimal diagnostic algorithm for each plant species to evaluate its health status. For example, the diagnostic unit predicts the occurrence of diseases and pests according to the plant species. The diagnostic unit proposes different countermeasures for each plant species. By applying different diagnostic algorithms to each plant species, a more accurate assessment of health status becomes possible. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input plant species data into a generating AI, which can then apply different diagnostic algorithms to each species.

[0050] The suggestion unit can provide optimal suggestions for gardening in limited spaces, based on the user's living environment. For example, the suggestion unit can suggest the most suitable planters and cultivation methods based on the user's living environment (balcony, garden, indoors, etc.). For example, the suggestion unit can suggest appropriate plant types according to the user's living environment. The suggestion unit can suggest efficient use of space based on the user's living environment. This enables more effective garden management by providing optimal suggestions based on the user's living environment. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's living environment data into a generating AI, which can then provide optimal suggestions.

[0051] The proposal unit can improve the accuracy of its proposals when proposing garden designs for limited spaces by referring to past proposal history. For example, the proposal unit makes optimal proposals based on the user's past proposal history. For example, the proposal unit makes proposals by referring to successful methods from past proposal history. The proposal unit analyzes past proposal history and suggests areas for improvement. This allows for more effective proposals by improving proposal accuracy by referring to past proposal history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input past proposal history data into a generating AI, which can then improve the accuracy of its proposals.

[0052] The suggestion function can provide suggestions tailored to the user's lifestyle when proposing a garden in a limited space. For example, if the user is busy, the suggestion function can suggest low-maintenance gardening methods. If the user is a beginner, the suggestion function can suggest easy-to-grow plants. If the user is experienced, the suggestion function can suggest challenging gardening methods. By providing suggestions tailored to the user's lifestyle, more effective garden management becomes possible. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the user's lifestyle data into a generating AI, which can then provide suggestions tailored to that lifestyle.

[0053] The proposal function can propose combinations of different cultivation methods when suggesting gardens in limited spaces. For example, the proposal function can propose a combination of hydroponics and soil cultivation. For example, the proposal function can propose a combination of vertical and horizontal cultivation. For example, the proposal function can propose the use of companion plants. By proposing combinations of different cultivation methods, more effective garden management becomes possible. Some or all of the above processing in the proposal function may be performed using AI, for example, or without AI. For example, the proposal function can input data on different cultivation methods into a generating AI, which can then make proposals that combine these cultivation methods.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The analysis unit can acquire the user's health data and provide gardening advice tailored to their health condition. For example, if a user has allergies, the analysis unit can suggest plants that will not trigger allergies. If a user is not getting enough exercise, the analysis unit can provide advice on increasing their physical activity through gardening. Furthermore, if a user is undergoing rehabilitation, the analysis unit can suggest light exercise suitable for rehabilitation. This enables gardening advice tailored to the user's health condition, supporting a healthy lifestyle.

[0056] The diagnostic unit can analyze the health of plants while taking their growth rate into consideration. For example, if a plant is growing slowly, the diagnostic unit can provide advice on fertilizer and watering to promote growth. If a plant is growing too quickly, the diagnostic unit can suggest methods to suppress its growth. Furthermore, if a plant's growth rate is uneven, the diagnostic unit can identify the cause and suggest appropriate countermeasures. This enables a diagnosis that takes into account the plant's growth rate, allowing for a more accurate assessment of its health.

[0057] The analysis unit can adjust the advice given based on the user's gardening experience. For example, beginner users can be provided with basic gardening knowledge and advice on simple tasks. Experienced users can be given advice on more advanced gardening techniques and challenging projects. Furthermore, intermediate users can be given advice on skill improvement. This allows for advice tailored to the user's gardening experience, providing more effective support.

[0058] The diagnostic unit can analyze the health of plants while considering their genetic information. For example, if a specific gene is responsible for disease resistance, the diagnostic unit can propose appropriate countermeasures based on that information. It can also predict plant growth characteristics based on genetic information and propose optimal cultivation methods. Furthermore, it can utilize genetic information to create plant breeding plans. This enables diagnosis that takes into account the plant's genetic information, allowing for a more accurate assessment of its health.

[0059] The analysis unit can consider the user's gardening history and provide advice based on past successes. For example, it can suggest cultivation methods for plants that have been successful in the past. It can also analyze past failures and provide advice to avoid repeating the same mistakes. Furthermore, it can suggest plants that suit the user's preferences based on their past gardening history. This enables advice based on the user's gardening history, providing more effective gardening support.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The analysis unit analyzes weather data and soil data. The analysis unit can analyze weather data such as temperature, precipitation, and wind speed. It can also analyze soil data such as soil pH, nutrient content, and moisture content. Based on the weather data and soil data, the analysis unit monitors the growth status of plants. Step 2: The service unit provides gardening advice based on the data analyzed by the analysis unit. For example, the service unit can instruct watering timing based on weather data. It can also suggest the type and amount of fertilizer to use based on soil data. The service unit provides gardening advice in real time. Step 3: The diagnostic unit analyzes plant photographs to diagnose diseases and suggest modifications to cultivation methods. For example, the diagnostic unit can detect changes in the color and shape of plant leaves, enabling early detection of pest and disease outbreaks. It can also analyze the plant's health and suggest appropriate countermeasures. Step 4: The proposal team will propose solutions to support gardening in limited spaces. For example, they could propose compact planters or hydroponic systems. They could also propose methods that do not soil balconies or indoor spaces.

[0062] (Example of form 2) The HomeGardenHeroAI agent according to an embodiment of the present invention is a cutting-edge AI assistant tool for people who enjoy home gardening. This agent supports a variety of environments, including apartment balconies, detached house gardens, and even indoor gardening, and assists with all tasks such as planting, fertilizing, watering, harvesting schedule management, and pest and disease control. The HomeGardenHeroAI agent analyzes weather and soil data, monitors plant growth, and provides optimal gardening advice in real time. Furthermore, by taking pictures of plants, it can diagnose diseases and suggest modifications to cultivation methods, providing a home gardening experience optimized for the user's living environment. It supports gardening in limited spaces and suggests methods to avoid soiling balconies and indoors, thereby assisting efficient garden management tailored to the user's lifestyle. For example, the HomeGardenHeroAI agent can instruct watering timing based on weather data or suggest types and amounts of fertilizer based on soil data. By taking pictures of plants, it can detect changes in leaf color and shape and suggest appropriate countermeasures. It also suggests compact planters and hydroponic systems to support efficient garden management. This enables HomeGardenHeroAI agent to provide efficient home garden management tailored to the user's lifestyle. Users can enjoy home gardening with AI support, experiencing the joy of connecting with nature and harvesting. HomeGardenHeroAI agent can support all tasks in home gardening, including planting, fertilizing, watering, harvesting schedule management, and pest and disease control.

[0063] The HomeGardenHeroAI agent according to this embodiment comprises an analysis unit, a provision unit, a diagnostic unit, and a suggestion unit. The analysis unit analyzes weather data and soil data. For example, the analysis unit can analyze weather data such as temperature, precipitation, and wind speed. The analysis unit can also analyze soil data such as soil pH, nutrient content, and moisture content. Based on the weather data and soil data, the analysis unit monitors the growth status of plants. The provision unit provides gardening advice based on the data analyzed by the analysis unit. For example, the provision unit can instruct the timing of watering based on weather data. The provision unit can also suggest the type and amount of fertilizer based on soil data. The provision unit provides gardening advice in real time. The diagnostic unit analyzes photographs of plants to diagnose diseases and suggest modifications to cultivation methods. For example, the diagnostic unit can detect changes in the color and shape of plant leaves to detect the occurrence of pests and diseases early. The diagnostic unit can also analyze the health status of plants and suggest appropriate countermeasures. The proposal department offers suggestions to support gardening in limited spaces. For example, it can propose compact planters or hydroponic systems. It can also propose methods that do not soil balconies or indoor spaces. As a result, the HomeGardenHeroAI agent according to the embodiment can support all tasks in home gardening, such as planting, fertilizing, watering, managing harvest schedules, and pest and disease control.

[0064] The analysis unit analyzes weather and soil data. For example, it can analyze weather data such as temperature, precipitation, and wind speed. Specifically, it collects weather data from weather sensors and the internet, and analyzes this data using statistical methods and machine learning algorithms. It analyzes temperature fluctuation patterns, precipitation forecasts, and the effects of wind speed in detail to identify optimal environmental conditions for plant growth. The analysis unit can also analyze soil data such as soil pH, nutrient content, and moisture content. It collects data in real time using soil sensors and evaluates soil health based on this data. For example, it determines whether the soil pH is suitable for plant growth and suggests adjusting agents such as lime or sulfur as needed. Regarding nutrient content, it analyzes the balance of major nutrients such as nitrogen, phosphorus, and potassium to optimize the type and amount of fertilizer. In moisture content analysis, it detects dry or wet soil conditions and suggests an appropriate irrigation schedule. Based on this weather and soil data, the analysis unit monitors plant growth. For example, it suggests shading measures if the temperature is too high and instructs additional watering if precipitation is low. If the soil is deficient in nutrients, we advise adding appropriate fertilizers. This allows the analysis unit to perform data analysis to maintain plant health and provide an optimal growing environment.

[0065] The service provider offers gardening advice based on data analyzed by the analysis unit. For example, the service provider can instruct users on the timing of watering based on weather data. Specifically, it calculates the amount of water plants need based on temperature and precipitation forecast data and notifies the user of the optimal watering time. For example, it may instruct users to water early in the morning or in the evening on hot, dry days, and advise them to refrain from watering on days when rain is expected. The service provider can also suggest the type and amount of fertilizer based on soil data. It analyzes the nutrient content of the soil and selects the optimal fertilizer according to the growth stage and type of plant. For example, it may suggest adding nitrogen fertilizer if nitrogen is deficient, and advise adding phosphorus fertilizer if phosphorus is deficient. The service provider provides gardening advice in real time. Users can access the application via their smartphones or tablets to receive advice based on the latest weather and soil data. Furthermore, the service provider can learn the user's gardening history and preferences to provide individually customized advice. For example, based on the environmental conditions preferred by specific plants and past success stories, we can suggest more effective gardening methods. This allows the service provider to support users in managing their home gardens efficiently and effectively.

[0066] The diagnostic unit analyzes plant photos to diagnose diseases and suggest modifications to cultivation methods. For example, the diagnostic unit can detect changes in the color and shape of plant leaves, enabling early detection of pest and disease outbreaks. Specifically, when a user uploads a photo of a plant taken with their smartphone, the diagnostic unit uses image recognition technology to detect abnormalities in the color and shape of the leaves. For example, yellowing leaves may indicate nitrogen deficiency, and spots on leaves may suggest the presence of pests or diseases. If the diagnostic unit detects these abnormalities, it will suggest appropriate measures to the user. For example, if there is a nitrogen deficiency, it will advise adding nitrogen fertilizer, and if pests or diseases are suspected, it will suggest using appropriate pesticides. The diagnostic unit can also analyze the plant's health and suggest modifications to cultivation methods. For example, if a plant is absorbing too much water, it will instruct the user to reduce watering frequency, and conversely, if there is insufficient water, it will advise increasing watering frequency. Furthermore, the diagnostic unit also provides suggestions for cultivation methods tailored to the plant's growth stage. For example, it can instruct users to maintain appropriate temperature and humidity during the germination stage and advise providing sufficient sunlight and nutrients during the growth stage. In this way, the diagnostic unit can support users in succeeding in their home gardens by maintaining the health of the plants and suggesting optimal growing methods.

[0067] The proposal department offers suggestions to support gardening in limited spaces. For example, they can propose compact planters and hydroponic systems. Specifically, they propose the optimal gardening method according to the user's living environment and space constraints. For example, for limited spaces such as verandas and balconies, they propose vertically installed planters or wall-mounted planters to provide a way to make effective use of space. For indoor gardening, they propose hydroponic systems and introduce a method of growing plants without using soil. Hydroponic systems are clean, easy to maintain, and allow for efficient plant growth even in limited spaces. Furthermore, the proposal department can also propose methods to prevent soiling of verandas and indoor spaces. For example, they propose methods such as installing a tray under the planter to prevent water leakage or using waterproof sheets suitable for indoor gardening. This allows users to comfortably enjoy gardening even in limited spaces. The proposal department can also provide customized suggestions according to the user's lifestyle and preferences. For example, for busy users, they propose an automatic irrigation system, providing a way to grow plants without much effort. Furthermore, for eco-conscious users, we will propose planters made from recycled materials and organic fertilizers, and introduce environmentally friendly gardening methods. This will allow the proposal department to support users in enjoying home gardening efficiently and comfortably, even in limited spaces.

[0068] The analysis unit can instruct the timing of watering based on weather data. For example, the analysis unit analyzes weather data and instructs the timing of watering based on soil dryness and weather forecasts. For example, the analysis unit can instruct to increase the frequency of watering when temperatures are high and the soil is dry. Conversely, the analysis unit can instruct to decrease the frequency of watering when there is heavy rainfall. The analysis unit analyzes weather data in real time and instructs the optimal timing of watering. This enables appropriate watering by instructing the timing of watering based on weather data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input weather data into a generating AI, and the generating AI can instruct the timing of watering.

[0069] The analysis unit can suggest the type and amount of fertilizer based on soil data. For example, the analysis unit can analyze the soil's pH value and nutrient content and suggest the optimal type and amount of fertilizer. For example, if the soil's pH value is low, the analysis unit can suggest adding lime. It can also suggest adding appropriate fertilizer if the soil's nutrient content is insufficient. The analysis unit analyzes soil data in real time and suggests the optimal type and amount of fertilizer. This enables the appropriate use of fertilizer by suggesting the type and amount of fertilizer based on soil data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input soil data into a generating AI, which can then suggest the type and amount of fertilizer.

[0070] The diagnostic unit can analyze the health of plants and detect the occurrence of pests and diseases at an early stage. For example, the diagnostic unit can detect changes in the color and shape of plant leaves to detect the occurrence of pests and diseases at an early stage. For example, if the leaves are turning yellow, the diagnostic unit can suggest a nitrogen deficiency. The diagnostic unit can also suggest the occurrence of pests and diseases if there are spots on the leaves. The diagnostic unit analyzes the health of plants in real time and proposes appropriate countermeasures. This allows for appropriate countermeasures by analyzing the health of plants and detecting the occurrence of pests and diseases at an early stage. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input a photograph of a plant into a generating AI, which can then detect the occurrence of pests and diseases at an early stage.

[0071] The proposal unit can propose compact planters and hydroponic systems. For example, the proposal unit can propose compact planters to support gardening in limited spaces. For example, the proposal unit can propose wall-mounted planters and vertical planters. The proposal unit can also propose hydroponic systems. For example, the proposal unit can propose NFT systems and drip systems. The proposal unit can propose efficient cultivation methods to support gardening in limited spaces. This allows the proposal unit to propose compact planters and hydroponic systems to support gardening in limited spaces. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user living environment data into a generating AI, which can then propose the optimal planter or cultivation system.

[0072] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in the user's facial expressions. The analysis unit adjusts the display method of the analysis results according to the user's emotions. For example, if the user is stressed, the analysis unit provides a simple and intuitive display method. If the user is relaxed, the analysis unit can display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit provides a concise display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user facial expression data into a generating AI, which can then estimate emotions and adjust how the analysis results are displayed.

[0073] The analysis unit can predict future weather by referring to past weather patterns when analyzing weather data. For example, the analysis unit analyzes seasonal weather patterns based on weather data from the past 10 years. For example, the analysis unit makes short-term weather forecasts by comparing past weather data with current weather conditions. The analysis unit predicts future weather by referring to past weather patterns. For example, the analysis unit analyzes long-term weather patterns and proposes seasonal gardening plans. This makes it possible to predict future weather more accurately by referring to past weather patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past weather data into a generating AI, which can then predict future weather.

[0074] The analysis unit can improve the accuracy of soil analysis by considering microbial activity in the soil. For example, the analysis unit can analyze the types and activity levels of microorganisms in the soil to evaluate the health of the soil. For example, the analysis unit can analyze the impact of microbial activity on plant growth and suggest the optimal type and amount of fertilizer. The analysis unit monitors microbial activity in the soil and issues an alert if an anomaly occurs. By improving the accuracy of analysis by considering microbial activity in the soil, more accurate soil analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input soil data into a generating AI, which can then improve the accuracy of the analysis by considering microbial activity.

[0075] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in the user's facial expressions. The analysis unit determines the priority of analysis results according to the user's emotions. For example, if the user is stressed, the analysis unit will prioritize displaying important analysis results. If the user is relaxed, the analysis unit can sequentially display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit will display the most important analysis results first. In this way, by prioritizing analysis results according to the user's emotions, it is possible to display important information preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI, which can then estimate emotions and determine the priority of the analysis results.

[0076] The analysis unit can perform analyses of weather data while considering regional climate characteristics. For example, the analysis unit can analyze the climate characteristics of each region and propose an optimal gardening schedule. For example, the analysis unit can propose appropriate plant species based on regional climate characteristics. The analysis unit can adjust the timing of watering and fertilizing while considering regional climate characteristics. This makes it possible to provide more appropriate gardening advice by performing analyses while considering regional climate characteristics. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input regional climate data into a generating AI, which can then perform the analysis while considering climate characteristics.

[0077] The analysis unit can improve the accuracy of soil analysis by performing a detailed analysis of the soil's chemical components during soil data analysis. For example, the analysis unit can perform a detailed analysis of major chemical components in the soil (such as nitrogen, phosphorus, and potassium). For example, the analysis unit can measure the soil's pH value and suggest the optimal type and amount of fertilizer. The analysis unit monitors the soil's chemical components and issues an alert if an anomaly occurs. This allows for more accurate soil analysis by improving the accuracy of the analysis through a detailed analysis of the soil's chemical components. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input soil data into a generating AI, which can then perform a detailed analysis of the chemical components to improve the accuracy of the analysis.

[0078] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on changes in the user's facial expressions. The service provider adjusts the way advice is expressed according to the user's emotions. For example, if the user is stressed, the service provider can provide simple and intuitive advice. If the user is relaxed, the service provider can provide detailed advice. Furthermore, if the user is in a hurry, the service provider can provide concise advice that gets straight to the point. By adjusting the way advice is expressed according to the user's emotions, it becomes possible to provide advice that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's facial expression data into a generating AI, which can then estimate the emotion and adjust the way the advice is expressed.

[0079] The service provider can customize the advice given when providing gardening advice, according to the plant's growth stage. For example, during the germination stage, the service provider advises on appropriate watering and light conditions. During the growth stage, the service provider advises on the appropriate type and amount of fertilizer. During the harvest stage, the service provider advises on the timing and method of harvesting. By customizing the advice according to the plant's growth stage, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input plant growth data into a generating AI, which can then provide advice according to the growth stage.

[0080] The service provider can provide optimal gardening advice by referring to the user's past gardening history. For example, the service provider can provide optimal advice based on data of plants the user has grown in the past. For example, the service provider can provide advice by referring to successful methods from the user's past gardening history. The service provider can analyze the user's past gardening history and suggest areas for improvement. By referring to the user's past gardening history and providing optimal advice, more effective gardening becomes possible. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's gardening history data into a generating AI, which can then provide optimal advice.

[0081] The service provider can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on changes in the user's facial expressions. The service provider determines the priority of advice according to the user's emotions. For example, if the user is stressed, the service provider will prioritize providing important advice. If the user is relaxed, the service provider can sequentially provide detailed advice. Furthermore, if the user is in a hurry, the service provider will provide the most important advice first. In this way, by determining the priority of advice according to the user's emotions, it is possible to provide important information preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expression data into a generating AI, which can then estimate emotions and determine the priority of advice.

[0082] The service provider can provide gardening advice while considering the characteristics of the user's residential area. For example, the service provider can suggest the most suitable plant species considering the climatic characteristics of the user's residential area. For example, the service provider can suggest the appropriate type and amount of fertilizer considering the soil characteristics of the user's residential area. The service provider can suggest the timing of watering based on the weather forecast for the user's residential area. By providing advice while considering the characteristics of the user's residential area, more appropriate gardening advice becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's residential area data into a generating AI, and the generating AI can provide advice while considering the characteristics of the residential area.

[0083] The service provider can offer gardening advice tailored to the user's lifestyle. For example, if the user is busy, the service provider can suggest low-maintenance gardening methods. If the user is a beginner, the service provider can suggest easy-to-grow plants. If the user is experienced, the service provider can suggest challenging gardening methods. By providing advice tailored to the user's lifestyle, more effective gardening becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's lifestyle data into a generating AI, which can then provide advice tailored to that lifestyle.

[0084] 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, the diagnostic unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the diagnostic unit can calculate an emotion score based on changes in the user's facial expressions. The diagnostic unit adjusts the display method of the diagnostic results according to the user's emotions. For example, if the user is stressed, the diagnostic unit displays a simple and intuitive diagnostic result. If the user is relaxed, the diagnostic unit can display a detailed diagnostic result. Furthermore, if the user is in a hurry, the diagnostic unit displays a concise diagnostic result that gets straight to the point. By adjusting the display method of the diagnostic results according to the user's emotions, it becomes possible to provide a user-friendly display. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's facial expression data into a generating AI, which can then estimate emotions and adjust how the diagnostic results are displayed.

[0085] The diagnostic unit can improve diagnostic accuracy by referring to past pest and disease data when analyzing plant photographs. For example, the diagnostic unit can detect the occurrence of plant diseases and pests early based on past pest and disease data. For example, the diagnostic unit can improve diagnostic accuracy by comparing past pest and disease data with the current state of the plant. The diagnostic unit can propose appropriate countermeasures by referring to past pest and disease data. In this way, more accurate diagnoses become possible by improving diagnostic accuracy by referring to past pest and disease data. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input past pest and disease data into a generating AI, which can then improve diagnostic accuracy.

[0086] The diagnostic unit can perform diagnoses while considering the plant's growth history when analyzing its health status. For example, the diagnostic unit can analyze the plant's growth history and evaluate its health status. For example, the diagnostic unit can predict the occurrence of diseases and pests based on the plant's growth history. The diagnostic unit can propose appropriate countermeasures while considering the plant's growth history. This allows for a more accurate assessment of the plant's health status by considering its growth history during the diagnosis. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input plant growth history data into a generating AI, which can then perform diagnoses while considering the growth history.

[0087] The diagnostic unit can estimate the user's emotions and determine the priority of diagnostic results based on the estimated emotions. For example, the diagnostic unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the diagnostic unit can calculate an emotion score based on changes in the user's facial expressions. The diagnostic unit determines the priority of diagnostic results according to the user's emotions. For example, if the user is stressed, the diagnostic unit will prioritize displaying important diagnostic results. If the user is relaxed, the diagnostic unit can sequentially display detailed diagnostic results. Furthermore, if the user is in a hurry, the diagnostic unit will display the most important diagnostic results first. In this way, by prioritizing diagnostic results according to the user's emotions, it is possible to prioritize the display of important information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's facial expression data into a generating AI, which can then estimate emotions and determine the priority of the diagnostic results.

[0088] The diagnostic unit can improve diagnostic accuracy by analyzing photographs of plants taken under different lighting conditions. For example, the diagnostic unit analyzes photographs of plants taken under different lighting conditions to assess their health. For example, the diagnostic unit improves diagnostic accuracy by considering the effects of differences in lighting conditions. The diagnostic unit compares photographs taken under different lighting conditions to detect the occurrence of diseases and pests at an early stage. This allows for more accurate diagnoses by improving diagnostic accuracy through the analysis of photographs taken under different lighting conditions. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input photographic data of plants taken under different lighting conditions into a generating AI, which can then improve diagnostic accuracy.

[0089] The diagnostic unit can apply different diagnostic algorithms to each plant species when analyzing the health status of plants. For example, the diagnostic unit applies the optimal diagnostic algorithm for each plant species to evaluate its health status. For example, the diagnostic unit predicts the occurrence of diseases and pests according to the plant species. The diagnostic unit proposes different countermeasures for each plant species. By applying different diagnostic algorithms to each plant species, a more accurate assessment of health status becomes possible. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input plant species data into a generating AI, which can then apply different diagnostic algorithms to each species.

[0090] The suggestion unit can estimate the user's emotions and adjust how the suggested content is displayed based on the estimated emotions. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in the user's facial expression. The suggestion unit adjusts how the suggested content is displayed according to the user's emotions. For example, if the user is stressed, the suggestion unit displays simple and intuitive suggestions. If the user is relaxed, the suggestion unit can display detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit displays concise suggestions that get straight to the point. By adjusting how the suggested content is displayed according to the user's emotions, it becomes possible to display information in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user facial expression data into a generating AI, which can then estimate emotions and adjust how the suggested content is displayed.

[0091] The suggestion unit can provide optimal suggestions for gardening in limited spaces, based on the user's living environment. For example, the suggestion unit can suggest the most suitable planters and cultivation methods based on the user's living environment (balcony, garden, indoors, etc.). For example, the suggestion unit can suggest appropriate plant types according to the user's living environment. The suggestion unit can suggest efficient use of space based on the user's living environment. This enables more effective garden management by providing optimal suggestions based on the user's living environment. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's living environment data into a generating AI, which can then provide optimal suggestions.

[0092] The proposal unit can improve the accuracy of its proposals when proposing garden designs for limited spaces by referring to past proposal history. For example, the proposal unit makes optimal proposals based on the user's past proposal history. For example, the proposal unit makes proposals by referring to successful methods from past proposal history. The proposal unit analyzes past proposal history and suggests areas for improvement. This allows for more effective proposals by improving proposal accuracy by referring to past proposal history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input past proposal history data into a generating AI, which can then improve the accuracy of its proposals.

[0093] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in the user's facial expressions. The suggestion unit determines the priority of suggestions according to the user's emotions. For example, if the user is stressed, the suggestion unit will prioritize displaying important suggestions. If the user is relaxed, the suggestion unit can sequentially display detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit will display the most important suggestions first. In this way, by prioritizing suggestions according to the user's emotions, it is possible to display important information preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user facial expression data into a generating AI, which can then estimate emotions and determine the priority of the proposed content.

[0094] The suggestion function can provide suggestions tailored to the user's lifestyle when proposing a garden in a limited space. For example, if the user is busy, the suggestion function can suggest low-maintenance gardening methods. If the user is a beginner, the suggestion function can suggest easy-to-grow plants. If the user is experienced, the suggestion function can suggest challenging gardening methods. By providing suggestions tailored to the user's lifestyle, more effective garden management becomes possible. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the user's lifestyle data into a generating AI, which can then provide suggestions tailored to that lifestyle.

[0095] The proposal function can propose combinations of different cultivation methods when suggesting gardens in limited spaces. For example, the proposal function can propose a combination of hydroponics and soil cultivation. For example, the proposal function can propose a combination of vertical and horizontal cultivation. For example, the proposal function can propose the use of companion plants. By proposing combinations of different cultivation methods, more effective garden management becomes possible. Some or all of the above processing in the proposal function may be performed using AI, for example, or without AI. For example, the proposal function can input data on different cultivation methods into a generating AI, which can then make proposals that combine these cultivation methods.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate the user's emotions and select plants based on those emotions. For example, if the user is feeling stressed, the analysis unit can suggest plants with relaxing effects. If the user wants to feel more energetic, the analysis unit can suggest plants with brightly colored flowers. Furthermore, if the user wants to feel calmer, the analysis unit can suggest fragrant herbs. This makes it possible to select plants that match the user's emotions, providing a more personalized gardening experience.

[0098] The analysis unit can acquire the user's health data and provide gardening advice tailored to their health condition. For example, if a user has allergies, the analysis unit can suggest plants that will not trigger allergies. If a user is not getting enough exercise, the analysis unit can provide advice on increasing their physical activity through gardening. Furthermore, if a user is undergoing rehabilitation, the analysis unit can suggest light exercise suitable for rehabilitation. This enables gardening advice tailored to the user's health condition, supporting a healthy lifestyle.

[0099] The service provider can estimate the user's emotions and adjust the timing of gardening advice based on those emotions. For example, if the user is feeling busy, the service provider can reduce the frequency of advice. If the user is relaxed, the service provider can provide detailed advice. Furthermore, if the user is in a hurry, the service provider can provide concise advice that gets straight to the point. This allows for adjusting the timing of advice according to the user's emotions, enabling more effective support.

[0100] The diagnostic unit can analyze the health of plants while taking their growth rate into consideration. For example, if a plant is growing slowly, the diagnostic unit can provide advice on fertilizer and watering to promote growth. If a plant is growing too quickly, the diagnostic unit can suggest methods to suppress its growth. Furthermore, if a plant's growth rate is uneven, the diagnostic unit can identify the cause and suggest appropriate countermeasures. This enables a diagnosis that takes into account the plant's growth rate, allowing for a more accurate assessment of its health.

[0101] The suggestion function can estimate the user's emotions and, based on those estimates, make suggestions to boost their gardening motivation. For example, if the user is feeling discouraged, the suggestion function can suggest an easy-to-succeed gardening project. If the user wants to feel entertained, the suggestion function can suggest aesthetically pleasing plants. Furthermore, if the user wants to feel a sense of accomplishment, the suggestion function can suggest plants that yield a large harvest. This allows for suggestions that boost motivation in line with the user's emotions, thereby increasing the enjoyment of gardening.

[0102] The analysis unit can adjust the advice given based on the user's gardening experience. For example, beginner users can be provided with basic gardening knowledge and advice on simple tasks. Experienced users can be given advice on more advanced gardening techniques and challenging projects. Furthermore, intermediate users can be given advice on skill improvement. This allows for advice tailored to the user's gardening experience, providing more effective support.

[0103] The service provider can estimate the user's emotions and adjust how gardening progress is reported based on those emotions. For example, if the user is stressed, the service provider can emphasize positive progress reports. If the user is relaxed, the service provider can provide detailed progress reports. Furthermore, if the user is in a hurry, the service provider can provide concise progress reports that get straight to the point. This enables progress reports that are tailored to the user's emotions, resulting in more effective communication.

[0104] The diagnostic unit can analyze the health of plants while considering their genetic information. For example, if a specific gene is responsible for disease resistance, the diagnostic unit can propose appropriate countermeasures based on that information. It can also predict plant growth characteristics based on genetic information and propose optimal cultivation methods. Furthermore, it can utilize genetic information to create plant breeding plans. This enables diagnosis that takes into account the plant's genetic information, allowing for a more accurate assessment of its health.

[0105] The suggestion function can estimate the user's emotions and support goal setting for gardening based on those estimated emotions. For example, if the user is feeling motivated, the suggestion function can set challenging goals. If the user is feeling anxious, the suggestion function can set smaller, more achievable goals. Furthermore, if the user wants to feel enjoyment, the suggestion function can set enjoyment-focused goals. This allows for goal setting that aligns with the user's emotions, helping to maintain motivation for gardening.

[0106] The analysis unit can consider the user's gardening history and provide advice based on past successes. For example, it can suggest cultivation methods for plants that have been successful in the past. It can also analyze past failures and provide advice to avoid repeating the same mistakes. Furthermore, it can suggest plants that suit the user's preferences based on their past gardening history. This enables advice based on the user's gardening history, providing more effective gardening support.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The analysis unit analyzes weather data and soil data. The analysis unit can analyze weather data such as temperature, precipitation, and wind speed. It can also analyze soil data such as soil pH, nutrient content, and moisture content. Based on the weather data and soil data, the analysis unit monitors the growth status of plants. Step 2: The service unit provides gardening advice based on the data analyzed by the analysis unit. For example, the service unit can instruct watering timing based on weather data. It can also suggest the type and amount of fertilizer to use based on soil data. The service unit provides gardening advice in real time. Step 3: The diagnostic unit analyzes plant photographs to diagnose diseases and suggest modifications to cultivation methods. For example, the diagnostic unit can detect changes in the color and shape of plant leaves, enabling early detection of pest and disease outbreaks. It can also analyze the plant's health and suggest appropriate countermeasures. Step 4: The proposal team will propose solutions to support gardening in limited spaces. For example, they could propose compact planters or hydroponic systems. They could also propose methods that do not soil balconies or indoor spaces.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the analysis unit, provision unit, diagnosis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes weather data and soil data. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides gardening advice based on the analyzed data. The diagnosis unit analyzes photographs of plants using the camera 42 of the smart device 14 and provides disease diagnoses and suggestions for correcting cultivation methods. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides suggestions to support gardening in limited spaces. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the analysis unit, provision unit, diagnosis unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes weather data and soil data. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides gardening advice based on the analyzed data. The diagnosis unit analyzes photographs of plants using the camera 42 of the smart glasses 214 and provides disease diagnoses and suggestions for correcting cultivation methods. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides suggestions to support gardening in limited spaces. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the analysis unit, provision unit, diagnosis unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes weather data and soil data. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides gardening advice based on the analyzed data. The diagnosis unit analyzes photographs of plants using the camera 42 of the headset terminal 314 and provides disease diagnoses and suggestions for correcting cultivation methods. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides suggestions to support gardening in limited spaces. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the analysis unit, provision unit, diagnosis unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes weather data and soil data. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides gardening advice based on the analyzed data. The diagnosis unit analyzes photographs of plants using the camera 42 of the robot 414 and makes disease diagnoses and suggestions for correcting cultivation methods. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes suggestions to support gardening in limited spaces. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The analysis unit analyzes weather data and soil data, A provisioning unit that provides gardening advice based on the data analyzed by the aforementioned analysis unit, The diagnostic department analyzes plant photographs to diagnose diseases and suggest improvements to cultivation methods, It includes a section for proposing solutions to support vegetable gardens in limited spaces. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The system provides instructions on when to water based on weather data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We propose the type and amount of fertilizer based on soil data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned diagnostic unit, Analyze the health of plants and detect pest and disease outbreaks early. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose compact planters and hydroponic systems. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) 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 8) The aforementioned analysis unit, When analyzing soil data, consider soil microbial activity to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) 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 11) The aforementioned analysis unit, When analyzing soil data, we improve the accuracy of the analysis by analyzing the chemical composition of the soil in detail. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned supply unit is, When providing gardening advice, customize the advice content according to the stage of plant growth. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, When providing gardening advice, we refer to the user's past gardening history to provide the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned supply unit is, When providing gardening advice, we take into account the characteristics of the user's residential area. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing gardening advice, we offer advice tailored to the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned diagnostic unit, When analyzing plant photographs, we improve diagnostic accuracy by referring to past pest and disease data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned diagnostic unit, When analyzing the health status of plants, the diagnosis is made considering the plant's growth history. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) The aforementioned diagnostic unit, When analyzing plant photographs, analyzing images taken under different lighting conditions improves diagnostic accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned diagnostic unit, When analyzing the health status of plants, different diagnostic algorithms are applied to each type of plant. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts how suggestions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When proposing a vegetable garden in a limited space, we make the best proposal based on the user's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When proposing a vegetable garden in a limited space, we improve the accuracy of the proposal by referring to past proposal history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and prioritizes suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When proposing a vegetable garden in a limited space, we offer suggestions tailored to the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When proposing a vegetable garden in a limited space, we suggest combining different cultivation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis unit analyzes weather data and soil data, A provisioning unit that provides gardening advice based on the data analyzed by the aforementioned analysis unit, The diagnostic department analyzes plant photographs to diagnose diseases and suggest improvements to cultivation methods, It includes a section for proposing solutions to support vegetable gardens in limited spaces. A system characterized by the following features.

2. The aforementioned analysis unit, The system provides instructions on when to water based on weather data. The system according to feature 1.

3. The aforementioned analysis unit, We propose the type and amount of fertilizer based on soil data. The system according to feature 1.

4. The aforementioned diagnostic unit, Analyze the health of plants and detect pest and disease outbreaks early. The system according to feature 1.

5. The aforementioned proposal section is, We propose compact planters and hydroponic systems. The system according to feature 1.

6. 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 according to feature 1.

7. The aforementioned analysis unit, When analyzing weather data, past weather patterns are referenced to predict future weather. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing soil data, consider soil microbial activity to improve the accuracy of the analysis. The system according to feature 1.

9. The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system according to feature 1.

10. The aforementioned analysis unit, When analyzing weather data, the analysis should take into account the local climate characteristics. The system according to feature 1.

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